physical exercise literature review

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• Published: 28 August 2024

A systematic review and Bayesian meta-analysis provide evidence for an effect of acute physical activity on cognition in young adults

• Jordan Garrett   ORCID: orcid.org/0000-0002-5893-5904 1 , 2 ,
• Carly Chak 1 , 2 ,
• Tom Bullock 1 , 2 &
• Barry Giesbrecht   ORCID: orcid.org/0000-0002-1976-1251 1 , 2  

Communications Psychology volume  2 , Article number:  82 ( 2024 ) Cite this article

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• Cognitive neuroscience
• Human behaviour

Physical exercise is a potential intervention for enhancing cognitive function across the lifespan. However, while studies employing long-term exercise interventions consistently show positive effects on cognition, studies using single acute bouts have produced mixed results. Here, a systematic review and meta-analysis was conducted to determine the impact of acute exercise on cognitive task performance in healthy young adults. A Bayesian hierarchical model quantified probabilistic evidence for a modulatory relationship by synthesizing 651 effect sizes from 113 studies from PsychInfo and Google Scholar representing 4,390 participants. Publication bias was mitigated using the trim-and-fill method. Acute exercise was found to have a small beneficial effect on cognition ( g  = 0.13 ± 0.04; BF = 3.67) and decrease reaction time. A meta-analysis restricted to executive function tasks revealed improvements in working memory and inhibition. Meta-analytic estimates were consistent across multiple priors and likelihood functions. Physical activities were categorized based on exercise type (e.g., cycling) because many activities have aerobic and anaerobic components, but this approach may limit comparison to studies that categorize activities based on metabolic demands. The current study provides an updated synthesis of the existing literature and insights into the robustness of acute exercise-induced effects on cognition. Funding provided by the United States Army Research Office.

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Introduction.

A single bout of exercise induces a cascade of neuromodulatory changes that influence multiple brain systems 1 , 2 . This includes an increase in the synthesis of neurotransmitters (e.g., acetylcholine, dopamine, GABA, glutamate) and neurotrophic factors (e.g., BDNF), which can occur in a brain-region-specific manner (see ref. 1 for review). Given these impacts on the brain, it would be reasonable to hypothesize that single brief bouts of exercise are associated with changes in performance across a range of cognitive domains. Consistent with this hypothesis, there is abundant evidence that attention 3 , 4 , 5 , 6 , working memory 7 , 8 , 9 , 10 , 11 , decision making 12 , 13 , and cognitive control 14 , 15 are facilitated by brief bouts of physical exercise. However, there is also evidence suggesting that exercise has little or no effect on cognitive task performance. For instance, Komiyama et al. 16 observed no difference in accuracy on a spatial delayed response task between exercise and rest conditions. Further, working memory performance has been shown to remain unchanged either during or after a single bout of exercise 6 . The discrepant pattern of results in the literature investigating the link between exercise and performance on cognitive tasks is surprising given the consistent and robust physiological effects of even brief bouts of physical activity. However, it is unclear whether this limited impact of exercise on performance reflects the true state of affairs or whether the apparent lack of robust influence is due to vast empirical discrepancies across studies in the literature. Studying the impact of single exercise sessions on cognition can provide insight into how changes in our body’s physiological state impact behavior. This understanding can then guide the creation of more effective longer-term exercise interventions, which essentially involve regularly repeating brief exercise sessions over an extended period.

Meta-analytic techniques are a set of powerful tools that can expose dominant trends within a methodologically heterogeneous literature. There is a consensus amongst narrative reviews and previous meta-analyses that an acute bout of exercise has a small positive influence on behavioral performance 1 , 17 , 18 , 19 , 20 , 21 , 22 , 23 . The nature of this effect is moderated by exercise protocol, cognitive tasks, and participant characteristics. For instance, Lambourne & Tomporowski 20 observed that task performance during exercise was dependent on exercise modality, the type of cognitive task, and when it was completed relative to exercise onset. Similarly, post-exercise performance was moderated by exercise modality and the type of cognitive task. Chang et al. 18 reported that post-exercise cognitive performance was influenced by exercise intensity, duration, and the time of cognitive test relative to exercise cessation. Interestingly, the authors found that study sample age was a significant moderator, where larger positive effects were found for high school (14–17 years), adult (31–60 years), and older adult (>60 years) samples compared to elementary (6–13 years) and young adult (18–30 years) samples. Multiple meta-analyses have observed that the effect of exercise is dependent on cognitive domain, with measures of executive function, attention, crystallized intelligence, and information processing speed showing the largest gains 18 , 19 , 24 , 25 , 26 . Further, there is evidence that exercise has a differential influence on the speed and accuracy of cognitive processes. McMorris et al. 21 observed that acute, intermediate exercise facilitated response times on working memory tasks, while accuracy was compromised. In contrast, exercise has been shown to boost both the accuracy and speed of cognitive control 23 . Altogether, it is important to consider cognitive task, participant, and physical activity characteristics to develop a holistic model of the relationship between exercise and cognition.

While these earlier meta-analyses have provided unique insights into understanding the relationship between acute exercise and cognition, they have two major limitations. First, the most recent holistic quantitative synthesis of the extant literature was published over a decade ago 18 . Meanwhile, the exercise and cognition literature has grown drastically. According to the electronic database Web of Science, almost 6,000 articles associated with the search term “exercise and cognition” have been published since this last holistic meta-analysis. In addition, more recent meta-analyses have primarily focused on executive processes 19 , 22 , 26 , 27 . Thus, previous models may provide an outdated and limited account of exercise-induced influences on other aspects of cognition, such as perception, long-term memory, and learning. Second, previous meta-analytic approaches employed frequentist statistical methods, which are based on a decision threshold rather than a characterization of the relevant evidence. As a result, it is possible that acute exercise and moderator variables are deemed to have a significant influence on task performance despite the fact that there may only be a small degree of probabilistic evidence in favor of this notion. In addition, relying on a decision threshold prevents these models from conveying the likelihood that an exercise protocol elicits a change in cognitive task performance. Past frequentist meta-analytic models also treated heterogeneity parameters as a fixed quantity and utilize only a point estimate, which can lead to an underestimation of the variability either between or within studies 28 , 29 , 30 , 31 . This is especially true when the number of modeled studies is low 32 , 33 , 34 . When considered together, there is a clear need for an updated meta-analysis using an approach that addresses these limitations.

The current study addressed these limitations in two ways. First, a comprehensive literature search was conducted spanning the years 1995–2023. To quantify the influence of exercise on cognition in young healthy adults, the search was limited to non-clinical studies whose subjects were between 18–45 years old. The analysis focused on subjects within this age range since exercise research has predominantly been dedicated toward studying the effects in children and older adults 35 , 36 . Studies were required to be experimental in nature, and consist of both an acute exercise manipulation and cognitive task measurements. A broad range of cognitive domains encompassing tasks probing perception to executive function were included in the meta-analysis. Similarly, a wide range of exercise types and testing contexts were included. For example, traditional laboratory exposures to exercise (e.g., cycling, running) and sport activities in real-world settings were viable candidates for analysis. By casting a wide net, the current study provides a large scope and updated summary of the current state of the exercise and cognition literature.

Second, the current study uses a Bayesian meta-analytic approach to synthesize studies across the acute exercise and cognition literature. The Bayesian approach affords a flexible modeling framework that uses reported effect sizes to characterize the relative evidence in favor of a modulatory account. Inherently, a random effects meta-analytic model is hierarchical in nature, making it well suited for Bayesian methods. When utilized within this statistical framework, priors are placed on parameters at the highest level of the model such as the estimated pooled effect size and measures of heterogeneity. This approach has several advantages compared to its frequentist counterpart. First, the use of priors on heterogeneity parameters can attenuate the underestimation of variation both between and within studies 37 , 38 , leading to a clearer understanding of sources of heterogeneity and an increased precision when estimating the pooled effect size 39 . Furthermore, priors provide additional constraints on low-level parameter estimates and a greater degree of “shrinkage” of outliers towards the overall pooled effect size or mode(s) of grouping variables 39 , 40 . Therefore, a Bayesian meta-analysis is more robust to outliers and can be more conservative when proper priors are employed. Second, the method yields a posterior distribution for all parameter estimates. This grants the capability of directly modeling the degree of uncertainty in heterogeneity estimates 37 . Posterior distributions can be used to compute the probability that an exercise protocol elicits a change in task performance of a given magnitude (e.g., large effect size). Compared to the approximation of p -values and confidence intervals, which require additional assumptions for hierarchical models, calculating the high-density interval (HDI), which indicates the most credible outcomes in the posterior distribution, for complex hierarchical models is seamless 39 . Third, it is possible to incorporate knowledge from previous meta-analyses when constructing prior distributions. This affords the ability to quantitatively compare the observed data to the predictions of previous models.

Considering the results of past meta-analyses, exercise was expected to have a small positive influence on cognition. Cognitive task and exercise characteristics were anticipated to moderate this relationship, as evidenced by nonzero parameter estimates, reflecting the selective nature of exercise-induced effects. Model comparisons were conducted to evaluate how moderator inclusion improved predictive performance, and robustness of parameter estimates were determined by employing multiple priors and likelihood functions.

Literature search

Studies investigating the impact of an acute bout of exercise on cognition were obtained through searches of the electronic databases PsychInfo and Google Scholar according to the PRISMA guidelines 41 . On 09 September 2023, databases were queried using a search string that combined the following physical activity and cognitive domain keywords: [“exercise” OR “physical activity” OR “physical exertion” OR “physical fatigue”] AND [“perception” OR “attention” OR “working memory” OR “executive function” OR “memory” OR “decision making” OR “motor skill” OR “skill acquisition” OR “language” OR “reasoning”]. For the PsychInfo search, the filters “journal article”, “English”, “empirical study”, “human”, and “peer reviewed” were applied. Search results were limited to studies published between 1995 and 2023 and whose subjects were between 18 and 45 years of age. Note, this literature search and analysis were not preregistered, nor was a review protocol prepared prior to the literature search.

Eligibility criteria

Studies were deemed eligible for inclusion in the meta-analysis if they met all of the following criteria: assessed the influence of an acute bout of exercise on cognition, compared the effects of exercise with an active and/or passive control group(s), utilized cognitive tasks that measured reaction time (RT) and/or accuracy, tested cognition either during, pre-, or post-exercise and consisted of cognitively normal subjects. Note, an acute bout was defined as an instance of physical activity that occurred within a single 24-hour period 18 . Two researchers independently screened records based on their title, abstract, and full text. In the case of discrepancies, a third researcher resolved them by reading the full-text.

Data extraction and coding

Information concerning experimental design and procedures, exercise details (i.e., type, intensity, duration), and sample characteristics were extracted from the final list of studies by a single researcher. Means and standard deviations of accuracy and/or RT measures on all cognitive tasks were inserted into an electronic spreadsheet for the calculation of effect sizes. The primary outcome measures for each domain were inserted separately if a task assessed multiple cognitive domains. Regarding studies that probed cognition at multiple time points during or post-exercise, measures for each time point were also recorded separately. If the statistics necessary for calculating effect sizes were not reported in the full-text of the article, the authors were contacted and asked to provide them.

All effect sizes were categorized into one of seven cognitive domains that were generally based on the DSM-5 42 : executive function, information processing, perception, attention, learning, motor skills, and memory. The classification criteria used for categorizing a cognitive task into a domain is provided in the Supplementary Table  1 . To account for variability in the metric used to measure exercise intensity across studies (e.g., ventilatory threshold, heart rate), each intensity was labeled as either light, moderate, or vigorous according to the American College of Sports Medicine guidelines 43 . Exercise durations were grouped into one of five time bins: ≤16 minutes, 20–27 minutes, 30–35 minutes, 40–45 minutes, ≥60 minutes. In the event that a study did not provide the exercise duration, its time bin was labeled as “not provided”. Exercise types were based on the modality reported in each study, yielding the following categorizations: cycling, high intensity interval training (HIIT), running, walking, circuit training, resistance exercise, and sports activity. The latter category encompassed studies that used sports-related exercises that did not fit into the other labels, such as rock climbing or soccer. The time at which cognitive task performance was evaluated relative to exercise was categorized as either during exercise or 0, 15, 20–75, and ≥180 minutes after cessation. Lastly, effect sizes were also coded according to task performance dependent measures (i.e., RT vs accuracy). Note, the levels of each categorical moderator were chosen with the intention of achieving a balance between specificity and statistical power to yield reliable estimates that can inform the design of future exercise studies.

Calculating effect sizes

Cohen’s d effect sizes were calculated for studies that tested cognition pre-/post-exercise without a control condition by dividing the mean change in performance by the standard deviation of the pre-test. If the study included a control group (e.g., rest), the mean change of the control condition was subtracted from the mean change of the exercise condition and divided by the pooled standard deviation of pretest scores 20 , 44 . For studies that tested cognition during, or only after exercise, the mean of the control condition was subtracted from the mean of the exercise condition and divided by the standard deviation of the control condition 21 . All effect sizes were converted into the bias-corrected standardized mean difference, Hedge’s g , by multiplying them by the correction factor \({{\rm{J}}}=1-\frac{3}{4{df}-1}\) where df is the degrees of freedom 45 . The sign of effect sizes for RT and error were reversed to reflect a positive influence of exercise on cognitive task performance. Once effect sizes were extracted from each study, inspection of a funnel plot and Egger’s regression test were conducted to assess the risk of publication bias.

Bayesian hierarchical modeling

The overall effect of exercise on cognition was assessed using a Bayesian hierarchical model 46 , 47 , which was implemented through the R package brms 48 . In the first level of the model, a study’s observed effect size(s) \({\hat{{{\rm{\theta }}}}}_{{ik}}\) was assumed to be an estimate of the true effect size \({{{\rm{\theta }}}}_{k}\) . The observed effect(s) \({\hat{\theta }}_{{ik}}\) were modeled as being sampled from a normally distributed population underlying study k with a mean equivalent to the true effect and a variance of \({{{\rm{\sigma }}}}_{k}^{2}\) . In the second level of the model, the true effect size \({\theta }_{k}\) was assumed to have been drawn from an overarching distribution whose mean represented the overall pooled effect \({{\rm{\mu }}}\) , and whose variance depicted the degree of between-study heterogeneity \({\tau }^{2}\) . The final level of the model contained weakly informative priors. A standard normal prior was used for the pooled effect, while the prior for \({\tau }^{2}\) was a Half-Cauchy distribution with location and scale parameters set to 0 and 0.5, respectively.

Following the main meta-analysis, subgroup analyses were conducted to determine potential moderators of the relationship between exercise and cognitive task performance. More specifically, we analyzed the influence of the following primary moderators: cognitive domain, time of cognitive test relative to exercise, task outcome measure, exercise intensity, duration, and type. The following secondary moderators were also analyzed to determine the influence of study and participant characteristics on the overall pooled effect size: average sample age, body mass index (BMI kg/m 2 ), height (cm), weight (kg), VO2 max (ml/kg/min), percentage of female participants, within- vs between-study design, and publication year. With the exception of publication year and the percentage of female participants, all secondary moderators were mean centered for interpretability. A standard normal distribution was used as a weakly informative prior for the difference in effect sizes between subgroups. When reporting model parameter estimates, we use the [mode ± standard deviation] and the 89% HDI of posterior distribution.

Statistical inference

For all estimated effect sizes, Bayes Factors (BFs) were used to determine the degree of evidence in favor of a difference from zero. BFs were approximated using the reciprocal of the Savage-Dickey density ratio, which was implemented using the function bayesfactor_parameters from the bayestestR package 49 . This method involves dividing the height of the prior distribution for the null value by the height of the posterior distribution at the same value, and represents the credibility of the null value for a parameter once the data has been taken into consideration. BFs were also used to ascertain the predictive performance of subgroup models. After each model was compared to a null counterpart (i.e., moderator excluded) using the function bayesfactor_models , an inclusion BF ( bayesfactor_inclusion ) was estimated to determine if including a moderator improved predictive power 50 . To estimate stable BFs, a large number of sampling iterations (10,000) and warmup samples (2000) were used for each of four chains when estimating model parameters 51 . BFs were interpreted following the guidelines proposed by Jeffreys 52 . A BF between 1 and 3 indicates “anecdotal” evidence for the alternative hypothesis, between 3 and 10 indicates “moderate” evidence, between 10 and 30 indicates “strong” evidence, and greater than 30 indicates “very strong” evidence 39 , 53 , 54 , 55 . The reciprocal of these ranges signifies evidence in favor of the null hypothesis (e.g., 0.33-1 = anecdotal evidence). When conducting subgroup analyses with more than two factors, orthonormal coding was employed to ensure that an identical prior was used for each factor level and that estimated BFs were accurate 56 . Parameter estimates were extracted from all models using the R package emmeans .

Sensitivity analysis

A popular criticism of the Bayesian approach is that priors are chosen subjectively, which in turn can bias parameter estimates and their corresponding BFs 40 , 57 . Although utilizing weakly informative priors mitigates bias, a sensitivity analysis that evaluates the contribution of both priors and the likelihood function must be conducted to determine if the model results are robust 38 , 58 , 59 , 60 . Thus, we replicated the previously described modeling approach with the exception of using two different priors for the overall pooled effect size. The first was a normal distribution with a mean of zero and standard deviation of ½. Since this prior adds greater weight to the probability that exercise has no influence on task performance, we denoted it as the no effect (NE) prior. The second prior was constructed by synthesizing estimates from previous meta-analyses on acute exercise and cognition 18 , 20 , 21 , 22 , 23 , resulting in a normal distribution with a mean of 0.24 and standard deviation of 0.57. This prior was denoted as the positive effect (PE) prior.

The influence of the likelihood function was assessed by modeling study effect sizes as being sampled from a t -distribution. An advantage of using this likelihood function, compared to a normal distribution, is that model parameter estimates are influenced less  by outliers 40 . The Half-Cauchy prior was used for the scale of the distribution, while a standard normal prior was used for its mean. For its shape (i.e., degree of freedom) an exponential distribution with a rate equal to 1/29 served as a prior. To determine if meta-analytic estimates were robust across the alternative priors and likelihood function, we visually compared the posterior distributions across models for large deviations 58 .

Description of studies

The literature search yielded 15,900 peer reviewed journal articles, and after removing duplicates 8295 remained. Subsequent an initial screening based off the titles and abstracts, 805 studies were identified as potential candidates for modeling. 113 of these studies were deemed eligible for inclusion in the meta-analysis according to their full-text contents (Fig.  1 ). In total, 642 effect sizes were extracted from these studies, representing data from 4390 subjects. A majority of the effects measured the influence of exercise on executive function ( k  = 434) and attention ( k  = 109). Fewer effects were measured during exercise ( k  = 82) relative to after the cessation of exercise ( k  = 560). Visual inspection of a funnel plot suggested that the effect sizes were distributed symmetrically (Fig.  2a ), however there was very strong evidence for asymmetry according to Egger’s regression intercept ( \({{\rm{\beta }}}\)  = 1.18 ± 0.25; HDI = [0.78, 1.58]; BF = 253.24) suggesting the presence of publication bias. This was addressed by employing the trim and fill approach, which imputes low-precision effect sizes until the funnel plot is symmetrical 61 .

A total of 113 studies were deemed eligible for meta-analytic modeling.

a Funnel plot of 642 study effect sizes (black circles). Imputed effect sizes after using the trim and fill method are represented by the unfilled circles ( \(n=9\) ). Vertical blue line indicates the estimated pooled effect sizes, while dashed black lines represent a pseudo 95% confidence limits. b Posterior distribution of estimated pooled effect. Horizontal black line indicates bounds of 89% HDI derived using \(n=651\) effect sizes. c Empirical cumulative density function of distribution in b , where the dashed black line indicates the pooled effect. d Representation of using the Savage-Dickey ratio to calculate BFs. The density of the null value in the prior distribution (red) is divided by its density in the posterior distribution (blue) to yield probabilistic evidence in favor of the alternative hypothesis. e Posterior distributions of between and within study heterogeneity.

Overall effect

The meta-analysis indicated that there was moderate evidence for an acute bout of exercise to have a small positive influence on overall performance across cognitive domains ( g  = 0.13 ± 0.04; HDI = [0.06, 0.20]; BF = 3.67) (Fig.  2 b, d ) . According to the posterior distribution, there was a low probability that the estimated pooled effect was less than or equal to zero ( p  = 0.01) and an 80% chance that the effect size fell between the range of 0 to 0.2 (Fig.  2c ). There was a large amount of heterogeneity within ( \({{{\rm{\tau }}}}_{{within}}\,\) = 0.65 ± 0.03; HDI = [0.60, 0.70]; \({I}_{{within}}^{2}\,\) = 81.19%) and moderate amount between ( \({{{\rm{\tau }}}}_{{between}}\,\) = 0.29 ± 0.05; HDI = [0.20, 0.38]; \({I}_{{between}}^{2}\)  = 15.9%) studies (Fig.  2e ). Effect size estimates for each individual study are presented in Table  1 .

Subgroup analyses

Primary subgroup analyses revealed that acute exercise reduced RT on cognitive tasks ( g  = 0.27; HDI = [0.18, 0.36]; BF \(=6.71\times {10}^{3}\) ), but had no impact on accuracy ( g  = 0.04; HDI = [−0.04, 0.12]; BF \(=6.15\times {10}^{-2}\) ) (Table  2 ) (Fig.  3a ). Engaging in either cycling ( g  = 0.21; HDI = [0.11, 0.32]; BF =  \(14.74\) ) or HIIT ( g  = 0.73; HDI = [0.40, 1.09]; BF \(=26.05\) ) was found to have an enhancing effect on performance in cognitive tasks (Fig.  3b ). In regard to cognitive domain, there was evidence that acute exercise has a positive influence on executive processes ( g  = 0.18; HDI = [0.10, 0.27]; BF \(=36.97\) ). Furthermore, behavioral performance was found to improve immediately after exercise cessation ( g  = 0.16; HDI = [0.11, 0.30]; BF \(=4.03\) ) and in response to vigorous intensity exercises ( g  = 0.19; HDI = [0.09, 0.28]; BF \(=5.03\) ). Lastly, at least moderate evidence in favor of non-zero parameter estimates were observed for the secondary moderators publication year, within-subjects design, age, percentage of female participants, and weight (Table  3 ).

Posterior distributions of a cognitive and b exercise moderators. Horizontal black line indicates the 89% HDI interval, while the black dot represents the mode of the posterior distribution. Intervals derived using \({{\rm{n}}}=651\) effect sizes.

To test for the possible contribution of a learning effect to the estimated overall pooled effect size, a separate meta-analysis was conducted on effects from studies employing a pre-/post-test design ( N effect sizes = 298). Despite the estimated pooled effect size for this subset of data being nominally similar to the estimate for the entire dataset, there was anecdotal evidence in favor of the null hypothesis ( g  = 0.15 ± 0.06; HDI = [0.04, 0.24]; BF \(=0.95\) ). Moderator analyses indicated that there was no credible evidence for a difference in this estimated pooled effect size as a function of whether or not a control group was included in the study \(({{{\rm{BF}}}}_{{{\rm{Inclusion}}}}=0.12;\,{\mbox{w}}/{\mbox{control}}:g\,=0.18\,\pm \,0.10;\,{\mbox{HDI}}=[0.03,\,0.33];{\mbox{BF}}=0.51;{\mbox{w}}/{{\rm{o}}} {{\rm{control}}}:g=0.11\pm 0.13;{\mbox{HDI}}=\left[-0.03,\,0.26\right]{\mbox{;BF}}=0.18)\) , suggesting that the estimated influence of exercise on general cognitive performance is not driven by a learning effect.

Model comparisons

Model comparisons were performed to determine if including a moderator improved predictive performance. Only a model that included task performance measure as a moderator was more likely when compared to a null counterpart (BF Inclusion \(=357.10\) ) (Table  4 ). This is likely due to a number of factors. First, acute exercise had a negligible impact on a majority of the levels in each subgroup. Second, there was a high degree of uncertainty in estimated model coefficients, as evidenced by their wide HDI intervals. Third, Bayesian inference automatically penalizes model complexity and favors more parsimonious models. If a model has many parameters, but a majority of them are nonzero, then a simpler counterpart will be favored.

Interactions between moderators

An exploratory analysis was conducted to determine if the influence of moderator variables was contingent on one another. Due to the computationally intensive nature of Bayesian modeling, analyses were limited to the following pairs of moderators: (1) exercise intensity and type, (2) exercise intensity and duration, (3) exercise type and duration, (4) cognitive domain and exercise type, (5) cognitive domain and exercise intensity, (6) cognitive domain and task performance measure, (7) exercise type and task performance measure. Although none of the pairs of interaction models had more predictive power compared to a null counterpart ( \({{{\rm{BF}}}}_{{{\rm{Inclusion}}}}\) : Model 1 =  \(3.86\times {10}^{-6}\) ; Model \(2=1.66\times {10}^{-8}\) ; Model \(3=1.84\times {10}^{-3}\) ; Model 4 = 1.31 × 10 −4 ; Model 5 =  \(3.91\times {10}^{-5}\) ; Model 6 =  \(7.1\times {10}^{-3}\) ; Model 7 =  \(7.05\times {10}^{-4}\) ), there were two that had nonzero parameter estimates.

The first model included an interaction between cognitive domain and exercise type. There was evidence in favor of cycling improving performance on tasks that probed attention ( g  = 0.34; HDI = [0.14, 0.56]; BF \(=3.05\) ) and executive function ( g  = 0.28; HDI = [0.14, 0.40]; BF =  \(17.83\) ). HIIT exercises were found to bolster executive function ( g  = 1.01; HDI = [0.61, 1.43]; BF \(=155.33\) ), while resistance exercises had an aversive impact on attentional performance ( g  = −0.76; HDI = [−1.20, −0.38]; BF \(=18.07\) ) (Fig.  4a ). The second model included an interaction between cognitive domain and task performance measure and indicated that time-dependent measures of executive function are improved ( g  = 0.30; HDI = [0.19, 0.39]; BF \(=1.10\times {10}^{3}\) ) (Fig.  4b ).

Posterior mode estimates of models including interactions between cognitive domain and a exercise type and b task outcome measure. Width of line represents 89% HDI derived using \({{\rm{n}}}=651\) effect sizes.

Sensitivity analyses

The estimated overall effect of acute exercise on cognition was consistent across the NE prior ( g  = 0.13 ± 0.04; HDI = [0.06, 0.20]; BF = 6.52), PE prior ( g  = 0.12 ± 0.04; HDI = [0.06, 0.19]; BF \(=6.51\) ), and t likelihood function ( g  = 0.12 ± 0.04; HDI = [0.06, 0.18]; BF \(=8.77\) ) (Fig.  5a ). Interestingly, there was anecdotal-to-moderate evidence in favor of the synthesized estimate from previous meta-analyses (i.e., g  = 0.24) across the PE (BF \(=3.19\) ), NE (BF \(=2.78\) ), and standard normal (BF \(=5.27\) ) priors. Estimates of between-study heterogeneity were also robust across the NE prior ( \({{{\rm{\tau }}}}_{{between}}\,\) = 0.29 ± 0.05; HDI = [0.20, 0.37]), the PE prior ( \({{{\rm{\tau }}}}_{{between}}\,\) = 0.29 ± 0.05; HDI = [0.21, 0.37]), and t likelihood function ( \({{{\rm{\tau }}}}_{{between}}\,\) = 0.31 ± 0.03; HDI = [0.26, 0.38]) (Fig.  5b ). In contrast, within study heterogeneity was estimated to be lower when using the t likelihood function ( \({{{\rm{\tau }}}}_{{within}}\,\) = 0.17 ± 0.02; HDI = [0.13, 0.19]) relative to the NE ( \({{{\rm{\tau }}}}_{{within}}\,\) = 0.65 ± 0.03; HDI = [0.61, 0.70]) and PE ( \({{{\rm{\tau }}}}_{{within}}\,\) = 0.65 ± 0.02; HDI = [0.60, 0.70]) priors (Fig.  5c ). Note, this reduction reflects the diminished influence of outliers on variance estimates by the inclusion of the shape parameter for the t distribution ( v  = 1.52 ± 0.14; HDI = [1.30; 1.73]). In addition to testing the robustness of parameter estimates, a model comparison was conducted to determine if either the null or positive effect prior was more probable given the data. The t -likelihood function was not included in this comparison since it would only indicate if effect sizes were more likely to have been drawn from either a normal or t -distribution. When compared to a standard normal prior, there was anecdotal evidence in favor of both the PE (BF \(=2.56\) ) and NE (BF \(=1.48\) ) priors. Relative to the PE prior, there was anecdotal evidence against the NE prior (BF \(=0.73\) ). Altogether, parameter estimates were not biased by the prior or likelihood function.

Estimates for the a overall pooled effect size, b between- and c within-study heterogeneity parameters across the t-likelihood function (TL), weakly informed, null effect (NE), and positive effect (PE) priors. Color dots represent mode of posterior distributions, while color horizontal line depicts the 89% HDI derived using \(n=651\) effect sizes.

Executive function meta-analysis

Considering that the majority of the effect sizes were from tasks that probed executive function, and that this cognitive domain encompasses multiple sub-domains, a separate meta- analysis and set of meta-regressions were conducted on this subset of data. Categorization criteria from previous meta-analyses and systematic reviews 17 , 24 , 26 were used to classify effect sizes into the following sub-domains of executive function: working memory, cognitive control, decision making, planning, and inhibition. For completeness, the primary moderators used in the main meta-analysis were also tested.

The results were similar to the main meta-analysis. There was very strong evidence in favor of exercise having a small positive influence on overall task performance ( \(g\)  = 0.20 ± 0.06; HDI = [0.12, 0.30]; BF \(=29.57\) ), and a moderate degree of heterogeneity both within ( \({{{\rm{\tau }}}}_{{within}}\,\) = 0.51 ± 0.03; HDI = [0.47, 0.57]) and between studies ( \({{{\rm{\tau }}}}_{{between}}\,\) = 0.40 ± 0.06; HDI = [0.30, 0.48]). Subgroup analyses indicated that a model including the moderator task outcome measure had more predictive power relative to a null counterpart (BF Inclusion \(=48.43\) ). Paralleling the main meta-analysis, there was very strong evidence that acute exercise improved RT on executive function tasks ( g  = 0.32; HDI = [0.21, 0.42]; BF \(=748.18\) ), but no credible evidence was observed for an effect on accuracy ( g  = 0.13; HDI = [0.04, 0.23]; BF \(=0.63\) ) (Table  5 ). Furthermore, there was moderate evidence in favor of a positive impact of exercise on inhibition ( g  = 0.21; HDI = [0.09, 0.33]; BF \(=3.14\) ) and working memory ( g  = 0.22; HDI = [0.11, 0.34]; BF \(=6.89\) ) (Fig.  6 ). Yet, a model including executive function sub-domain as a moderator did not improve model performance (BF Inclusion \(=7.52\times {10}^{-4}\) ), nor did models including interactions between moderators.

Posterior distributions for executive function sub-domain. Horizontal black line indicates the 89% HDI interval, while the black dot represents the mode of the posterior distribution, which was derived using \(n=433\) effect sizes.

A large corpus of empirical work has examined how a single bout of acute exercise modulates activity within multiple brain systems that underly cognition. Despite inconsistencies in results across empirical studies, there is consensus amongst previous reviews and meta-analyses that acute exercise impacts behavioral performance 18 , 20 , 22 and that this relationship is moderated by both exercise protocol and behavioral task characteristics. The goal of the present work was to address two key limitations of previous meta-analyses. First, recent meta-analyses have a narrower focus, often limited to a single cognitive domain or a specific subset of domains. In contrast, the current meta-analysis presents an updated synthesis of the literature spanning a much wider range of cognitive domains. Second, in contrast to previous frequentist approaches, a Bayesian framework was adopted allowing for the quantification of the degree of evidence in favor of the hypothesis that acute exercise influences cognition in young healthy adults. The current meta-analysis observed that acute exercise has a small positive influence on overall cognitive task performance, and sensitivity analyses indicated that the alternative hypothesis was 6.51–8.77 times more likely than the null across multiple priors and likelihood functions. The magnitude and directionality of this effect were consistent with the results of previous meta-analyses on acute exercise and cognition 18 , 21 , 22 , 62 . Subgroup analyses suggested that this relationship is moderated by task performance measure, cognitive domain, exercise type and intensity, and the time of task completion relative to exercise cessation. Model comparison results indicated that accounting for variations amongst moderator levels did not improve predictive performance. Given our eligibility criteria, these results are limited to healthy individuals between the ages of 18–45 years old.

Similar to McMorris et al. 63 , acute exercise was found to improve RT but no credible evidence was observed for an influence on accuracy. A possible explanation for this differential impact on task outcome measures is that exercise modulates primary motor cortex (M1) excitability 64 . There is accumulating evidence that acute exercise increases M1 intracortical facilitation 65 , 66 , 67 , 68 and inhibition 69 , 70 . Yamazaki et al. 68 observed that the intracortical circuits of both exercised (i.e., legs) and non-exercised (i.e., hand) effectors are modulated by an acute bout of low intensity pedaling. Thus, alterations in the activity of excitatory or inhibitory circuits of non-exercised cortical representations may promote faster RT on cognitive tasks. However, the lack of concurrent changes in corticospinal excitability or motor-evoked potentials suggests that this explanation is not a viable account of a mechanism that engenders faster RTs. An alternative explanation is that exercise increases peripheral and central concentrations of catecholamines, such as norepinephrine, epinephrine, and dopamine, which in turn improves the speed of cognition 1 , 71 , 72 . Indeed, acute exercise has been found to improve response time on choice RT, decision-making, and interference tasks 18 , 73 , 74 . Yet, it is unclear as to why changes in neurochemical levels would facilitate RT but have no impact on accuracy. Considering that physical activity modulates population-level tuning in the sensory areas of nonhuman animals and invertebrates 75 , 76 , 77 , 78 , 79 , 80 , 81 , along with sensory responses in humans 82 , 83 , 84 , it stands to reason that the fidelity of stimulus representations would also be impacted, resulting in changes in accuracy. Changes in the fidelity of feature selective stimulus representations can be determined by applying encoding models to recorded neural activity 83 , 85 , 86 , 87 , 88 , 89 , 90 . For instance, Garrett et al. 91 applied an inverted encoding model to topographical patterns of alpha band activity, recorded at the scalp, while subjects completed a spatial working memory task both at rest and during a bout of moderate intensity cycling. Notably, it was possible to reconstruct spatially selective responses during exercise, and the selectivity of these responses decreased during exercise relative to rest. Therefore, encoding models can be a powerful tool for future research to demystify how the precision of task-relevant representations is influenced by exercise. It is also important to keep in mind that many psychological tasks are relatively simple to do, which can lead to ceiling effects that may mask the influence of exercise on accuracy measures. Lastly, the differential impact of exercise on accuracy and RT may be due to the relative sensitivities of these dependent measures to modulations of different stages of information processing. For example, there is evidence that in near-threshold tasks accuracy is sensitive to perceptual manipulations, whereas in supra-threshold (i.e., perceptually easy tasks, including many of those used in the studies in this meta-analysis) RT is sensitive to modulations in both perceptual and post-perceptual processes 92 , 93 . Indeed, Davranche et al. 73 utilized a drift diffusion model to determine which aspects of decision-making are modulated by HIIT. Importantly, drift rate and decision response boundary size increased significantly after exercise relative to before, while non-decision time decreased. This suggests there was an improvement in perceptual discrimination, the efficiency of non-decisional processes (e.g., motor execution), and the adoption of a more conservative criterion. Future research employing computational models of response time and representational fidelity is needed to develop a comprehensive understanding of the selective influence exercise on information processing speed and accuracy.

Parameter estimates of a model including exercise modality as a moderator suggested that engaging in cycling or HIIT may beneficially impact cognition, especially on attentional and executive processes. Cycling is a commonly used modality in exercise and cognition research. Numerous empirical studies have found that a bout of cycling benefits inhibition, as measured using either the Stroop or Eriksen Flanker task 15 , 94 , 95 , 96 , 97 , 98 , 99 . Improvements in planning 94 , 100 , task-switching 27 , 101 , 102 , and the speed of decision making 103 have also been reported. In contrast to the ubiquity of cycling, the use of HIIT workouts in exercise and cognition research is a relatively recent practice, hence the small number of effect sizes from studies using this modality compared to other types of exercise. The number of effect sizes is important because low-level parameters in a hierarchical model are influenced both by the subset of data directly dependent on the low-level parameter, and by high-level parameter estimates that rely on all of the data. This makes low-level parameter estimates indirectly dependent on the entire dataset, and causes shrinkage in estimates at all levels of the model. In other words, the estimated relationship between HIIT and behavioral performance is derived directly from the few representative effect sizes and indirectly from the rest of the data. The observed positive effect of HIIT on cognition corroborates previous findings. For example, Alves et al. 3 observed that the time to complete a Stroop Task decreased after ten 1-minute bouts of exercising at 80% heart rate reserve relative to a control condition. Improvements in time-dependent measures on interference tasks (i.e., Stroop and flanker) have been correlated with an increase in left dorsolateral prefrontal cortex activity, as measured with functional near infrared spectroscopy (fNIRS), and a decrease in P3 latency measured with EEG 104 . Furthermore, enhancements have also been shown to coincide with an increase in peripheral levels of neural growth factors and lactate 105 . Lastly, a recent meta-analysis on elite athletes observed that HIIT team-based sports had a positive impact on cognitive task performance 25 . Interestingly, because of the small number of published studies in the literature, it is currently unclear if the type of exercise modality used for HIIT workouts (e.g., cycling, sprinting, resistance) differentially impacts cognition.

Behavioral task performance was found to be improved by engaging in vigorous intensity exercise. These results are surprising, considering that exercise intensity is believed to have an inverted-U relationship with performance; where moderate intensity exercise elicits the greatest enhancements while more intense, fatiguing exercise imposes decrements 18 , 62 , 63 , 71 , 106 , 107 . This effect could be driven by HIIT workouts, but may also depend on multiple cognitive task and exercise protocol characteristics. For instance, Chang et al. 18 observed that exercise intensity was only a significant moderator when cognition was tested post-exercise. Similarly, Oberste et al. 23 found that exercise intensity influenced time-dependent measures of interference control but not accuracy. When considering these results, one must also consider that both aforementioned meta-analyses included studies whose subjects were children, adolescents, and older adults. In contrast, the current study was limited to young adults, and there is evidence that the effect of exercise on cognition is comparatively smaller in this age group 18 , 23 . Thus, a model containing an interaction between cognitive domain, task outcome measure, and age groups across the lifespan may be required to observe evidence for an effect of intensity. In addition, there was evidence for the enhancing effects of exercise post-cessation, corroborating previous research 1 , 18 , 94 . Interestingly, in the current meta-analysis cognition was not found to be impacted during exercise. Prior meta-analytic findings on cognition during exercise are mixed, with some reporting that it is exacerbated 20 , while others that find evidence for an enhancement 18 .

Given that the majority of the effect sizes were from tasks that probed executive function, a separate meta-analysis was conducted on this subset of data. This analysis revealed that exercise has a small positive impact on RT measures of executive processes. When looking at model parameters, there was evidence in favor of exercise-enhancing inhibition and working memory. Behavioral research has shown that both the accuracy 9 and speed of working memory 108 , 109 are facilitated by an instance of physical activity. What remains to be determined is the neural mechanisms that engender these behavioral effects. Kao et al. 108 observed that a reduction in RT on the Sternberg task post-HIIT corresponded to an increase in frontal alpha desynchronization during encoding, maintenance, and retrieval periods when working memory load is high. Neuroimaging studies have also found evidence for changes in the activation levels of frontal areas 110 and their connectivity with the intraparietal sulcus post-exercise 111 . These changes in neural activity were not accompanied by a change in behavior, suggesting that more research is needed to demystify the neuromodulatory effect of acute exercise on working memory.

Engaging in repeated bouts of acute exercise over a long period of time can have lasting changes on baseline neurochemical levels, cortical volume, and structural/functional connectivity, which can alter cognitive task performance 1 , 112 , 113 , 114 , 115 . Research investigating the influence of these long-term interventions on cognition has primarily focused on children or older adults. Systematic reviews and meta-analyses suggest that exercise has a small to moderate beneficial impact on general task performance for both of these age groups, with the largest effect sizes observed for measures of executive function, attention, and academic performance 35 . Despite the relative paucity of meta-analyses on how exercise interventions impact cognition in healthy young adults, recent work suggests that it may have a similar beneficial effect. Indeed, a recent meta-analysis, conducted by Ludyga et al. 116 , indicated that long-term exercise interventions have a small positive influence on general cognition regardless of age. The magnitude of this effect was dependent on the interaction between intervention length and exercise duration, with longer interventions and sessions producing greater benefits. Integrating these findings with the current meta-analysis, there is support for the notion that the beneficial impact of long-term interventions on cognition may be a product of repeated exposure to acute exercise induced effects.

There are a number of possible explanations as to why exercise induced effects are small. One possibility is that cognitive function is at its peak during young adulthood, leaving little room for improvements in task performance. Indeed, previous reviews and meta-analyses have observed that the effect of exercise is moderated by age 35 , with the greatest benefits observed for preadolescent children and older adults 18 , 23 , 26 . Contrary to this account, though, the largest exercise induced effects were observed for executive processes, which are believed to be at peak efficiency during this period in the lifespan 117 , 118 . Furthermore, there was moderate evidence that the impact of exercise increased as the average age of sampled young adults also increased. Another explanation may be that cognition is resilient to slight or modest perturbations in overall global state. For example, Bullock et al. 119 demonstrated there was no change in accuracy or RT on a target detection task during experimentally induced hypoxia, hypercapnia, hypocapnia, and normoxia. Meta-analytic modeling of the influence of acute stress on executive function revealed that stress has a small negative impact on working memory and cognitive flexibility, but no impact on inhibition 120 . This suggests that cognition is able to selectively adapt to changes in physiological state caused by various types of stressors, including exercise. A final more intriguing and functional explanation for exercise having a small impact on cognition is that experimental protocols do not typically require the engagement of the body to execute the cognitive task, but rather have people engage in a cognitive task while exercising (or shortly thereafter). This experimental design contrasts real-world tasks that require engagement of the body in the service of the cognitive task. When components of the exercise are incorporated into task goals, then larger changes in performance may be observed. Empirical research investigating how exercise influences task performance in embodied settings versus classic laboratory settings (see 121 for review) is necessary to test the plausibility of this explanation. In addition, the notion that the integrated action of the body and the mind are required to produce the largest effects of exercise on cognition is consistent with a recent evolutionary account of the link between cognition and exercise 122 .

The discrepancy in moderator results between the current meta-analysis and previous meta-analyses could be due to differences in the statistical approach. Frequentist methods typically conduct an omnibus test to determine if levels of a moderator are significantly different from one another and as a measure of a model’s goodness of fit. In contrast, the Bayesian approach determines how likely the observed effect sizes are under a model that includes a moderator and if predictive power is increased. There are a few key advantages to using the Bayesian approach compared to classical frequentist methods. First, it models the uncertainty involved in estimates of between- and within-study heterogeneity and returns a full posterior distribution for both parameters 123 . With these posterior distributions, one can simulate possible pooled effect sizes across credible levels of heterogeneity and develop an informed hypothesis for a subsequent meta-analysis. Similarly, the posterior distributions of effect size estimates can be used as well-informed prior distributions for new data. Importantly, this facilitates the updating of meta-analyses as new research is published. It should be mentioned that the degree of between-study heterogeneity was numerically similar to previous meta-analyses 18 , 22 , implying that they did not suffer from an issue of underestimation by assuming heterogeneity to be a fixed quantity. Second, the Bayesian approach permits the inclusion of prior knowledge. Across all tested priors, there was evidence in favor of a pooled effect derived from averaging the reported estimates of previous meta-analyses. When comparing a prior distribution based on this knowledge to a null effect prior, the former was found to be more probable. Lastly, the posterior distribution of parameter estimates can be used to ascertain the likelihood that one will observe an effect size of a given magnitude for an exercise protocol and cognitive task combination. For example, a researcher could compute the probability that the influence of a bout of cycling on cognitive control will fall within the range of large effect sizes, even if that range does not encompass the maximum a posteriori probability estimate. In contrast, the frequentist approach only produces the maximum likelihood estimate and an interval around it based on fictitious repeats of the meta-analysis. Therefore, the Bayesian approach provides more information for designing future exercise and cognition studies.

Limitations

A potential limitation in the current meta-analysis is the categorization of exercise type using the activity reported in each study. An alternative approach is to categorize exercise based on the theoretical and physiological distinctions between aerobic and anaerobic exercise. We did not adopt this approach here because many activities used in the literature typically include aerobic and anaerobic components, and basing their classification on what authors reported provides insights into the exercise modalities that have been predominantly used in the literature. Another limitation is the schema used to categorize exercise duration. In the event that a study did not report how long participants engaged in exercise, these effects were classified as “not provided”, rendering them as uninterpretable. Lastly, sensitivity analyses were not conducted for moderator parameter estimates due to the high degree of computational demands. However, considering that the pooled effect size estimate was robust across multiple priors and likelihood functions, it is likely that moderator parameter estimates are also consistent.

Conclusions

In summary, the current meta-analytic examination has shown that there is moderate evidence for an acute bout of aerobic exercise inducing a small enhancement in overall performance on cognitive tasks, especially on those that probe executive function and measure response time. Incorporating computational models of decision-making processes, such as drift-diffusion or signal detection models, into exercise research may provide useful insights into the nature of speeded executive processes. Furthermore, testing performance in a real-world setting where individuals typically engage in physical activity may amplify exercise-induced effects.

Data availability

Data have been made publicly available at https://github.com/jggarrett23/PACMAn .

Code availability

Code has been made publicly available at https://github.com/jggarrett23/PACMAn .

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Acknowledgements

This work was funded by the U.S. Army Combat Capabilities Development Command Soldier Center Measuring and Advancing Soldier Tactical Readiness and Effectiveness (MASTR-E) program through award W911NF-19F-0018 under US Army Research Office contract W911NF-19-D-0001 for the Institute for Collaborative Biotechnologies. The views expressed in this article are solely those of the authors and do not reflect the official policies or positions of the Department of Army, the Department of Defense, or any other department or agency of the U.S. government. The funders had no role in study design, data collection, and analysis, the decision to publish, or preparation of the manuscript. Thank you to Grace Giles, Ph.D., Julie Cantelon, Ph.D., and Neil Dundon, Ph.D., for providing guidance in conducting analyses and interpreting results. Lastly, thank you to Emily Machniak and Riddhima Chandra for assisting in data collection efforts.

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J.G., C.C., T.B., and B.G., designed the study, created the literature search criterion, and performed initial article screenings. J.G., C.C., and B.G. performed screenings of articles based on their full text. J.G., extracted effect sizes, conducted statistical analyses, and wrote the initial draft of the manuscript. B.G., T.B., and C.C. provided edits to the manuscript.

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Garrett, J., Chak, C., Bullock, T. et al. A systematic review and Bayesian meta-analysis provide evidence for an effect of acute physical activity on cognition in young adults. Commun Psychol 2 , 82 (2024). https://doi.org/10.1038/s44271-024-00124-2

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• Published: 08 February 2018

A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study

• Kieran P. Dowd 1 ,
• Robert Szeklicki 2 ,
• Marco Alessandro Minetto 3 ,
• Marie H. Murphy 4 ,
• Angela Polito 5 ,
• Ezio Ghigo 3 ,
• Hidde van der Ploeg 6 , 7 ,
• Ulf Ekelund 8 , 9 ,
• Janusz Maciaszek 2 ,
• Rafal Stemplewski 2 ,
• Maciej Tomczak 2 &
• Alan E. Donnelly   ORCID: orcid.org/0000-0002-6874-0991 10  

International Journal of Behavioral Nutrition and Physical Activity volume  15 , Article number:  15 ( 2018 ) Cite this article

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The links between increased participation in Physical Activity (PA) and improvements in health are well established. As this body of evidence has grown, so too has the search for measures of PA with high levels of methodological effectiveness (i.e. validity, reliability and responsiveness to change). The aim of this “review of reviews” was to provide a comprehensive overview of the methodological effectiveness of currently employed measures of PA, to aid researchers in their selection of an appropriate tool. A total of 63 review articles were included in this review, and the original articles cited by these reviews were included in order to extract detailed information on methodological effectiveness.

Self-report measures of PA have been most frequently examined for methodological effectiveness, with highly variable findings identified across a broad range of behaviours. The evidence-base for the methodological effectiveness of objective monitors, particularly accelerometers/activity monitors, is increasing, with lower levels of variability observed for validity and reliability when compared to subjective measures. Unfortunately, responsiveness to change across all measures and behaviours remains under-researched, with limited information available.

Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be aware of the methodological effectiveness of any measure selected for use when examining PA. Although no “perfect” tool for the examination of PA in adults exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in free-living environments.

Physical inactivity is the fourth leading cause of death worldwide [ 1 ]. Despite this, PA levels of adults across developed nations remain low and the promotion of regular participation in PA is a key public health priority [ 2 ]. Population level PA surveillance relies upon having tools to accurately measure activity across all population sub-groups. In addition to surveillance, it is essential that valid, reliable and sensitive measures of PA are available to practitioners, researchers and clinicians in order to examine the effectiveness of interventions and public health initiatives. The accurate measurement of PA in adults has relevance not only for refining our understanding of PA-related disorders [ 3 ], but also for defining the dose-response relationship between the volume, duration, intensity and pattern of PA and the associated health benefits.

A number of methods are available for the assessment of PA [ 4 ]. When selecting a measurement technique, researchers and practitioners need to consider not only feasibility and practicality of the measure, but also the methodological effectiveness, such as the validity, reliability and sensitivity. Validity refers to the degree to which a test measures what it is intended to measure, and is most often investigated by comparing the observed PA variables determined by the proposed measure with another comparable measure [ 5 ]. Criterion validity is when a measure is validated against the ‘gold standard’ measure. Good agreement between the proposed method and the gold standard provides some assurance that the results are an accurate reflection of PA behaviour. Other frequently examined forms of validity are concurrent validity (when two measures that give a result that is supposed to be equal are compared) and construct validity (when two measures that are in the same construct are compared). Reliability refers to the degree to which a test can produce consistent results on different occasions, when there is no evidence of change, while sensitivity is the ability of the test to detect changes over time [ 5 ].

In addition to methodological effectiveness, other factors need to be considered when selecting a method for assessing PA and interpreting the findings derived from these methods. Feasibility often drives the selection of the study measures. Some measures are more feasible than others depending on the setting, number of participants and cost. For example, the use of activity monitors to estimate PA may be less feasible in epidemiological studies where large numbers of individuals are being assessed. Reactivity may mean that the act of measuring PA may change a person’s behaviour: for example, being observed for direct observation [ 6 ] or wearing an activity monitor may cause the participant to alter their habitual PA behaviour [ 7 ]. When using self-report measures, social desirability may result in over-reporting of PA among participants keen to comply with the intervention aims [ 8 ]. These factors require careful consideration when selecting methods for assessing PA.

Although methods for the measurement of PA have been extensively examined, reviews to date have focused on specific categories of methods (i.e. self-report questionnaires [ 9 , 10 , 11 ], specific techniques i.e. Doubly Labelled Water (DLW) [ 12 ], smart phone technology [ 13 ], motion sensors and heart rate monitors (HRM) [ 14 ], pedometers [ 15 ] or a comparison of two or more methods [ 16 ]). Some reviews looked exclusively at specific PA behaviours (e.g. walking) [ 17 ] or focused solely on validity and/or reliability issues [ 18 , 19 , 20 ]. Other reviews have concentrated on methods for assessing PA in population subgroups (e.g. individuals with obesity [ 21 ] or older adults) [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Due to the level of variability in how information on measurement properties has been presented, and due to the wide range of different measures examined in existing reviews, it is extraordinarily difficult for researchers to compare and contrast measures of PA in adult populations.

The purpose of this article is to review existing reviews (a review of reviews ) that have examined the methodological effectiveness of measures of PA. To aid in the comparison of measurement properties between different PA measures, original papers referred to within each review article were sourced, and additional analysis of these references was completed to enable better comparison and interpretation of findings. This review of reviews (as it will be referred to for the remainder of this article) is intended to provide a concise summary of PA measurement in adults. This work was completed as a component of the European DEDIPAC (DEterminants of DIet and Physical ACtivity) collaboration.

Literature search and search strategy

A systematic search of the electronic databases PubMed, ISI Web of Science, CINAHL, PsycINFO, SPORTDiscus and EMBASE took place in April 2014. The search strategy was developed by two of the authors from examining existing literature reviews, whereby common terminology utilised by published systematic reviews of specific methodologies or narrative reviews of all methodologies were included [ 4 , 5 , 31 , 32 , 33 , 34 , 35 ]. The developed search strategy was reviewed and agreed on by all members of the review team. The electronic databases were searched for the terms “Physical Activity” AND “Review OR Meta-Analysis” AND “Self-report” OR “Logs” OR “Diaries” OR “Questionnaire” OR “Recall” OR “Objective” OR “Acceleromet * ” OR “Activity Monitor * ” OR “Motion Sensor * ” OR “Pedom * ” OR “Heart Rate Monitor*” or “Direct Observation” AND “Valid * ” OR “Reliab * ” OR “Reproducib * ” OR “Sensitiv * ” OR “Responsiv * ”. The search terms and criteria were tailored for each specific electronic database to ensure consistency of systematic searching. Only articles that were published in peer reviewed journals in the English language and were included in this review.

Eligibility for inclusion

Although DLW is suggested as the gold standard measure of energy expenditure [ 36 ], it has not been included in the search strategy, as its feasibility for use in population surveillance research is limited due to its high cost and participant invasiveness [ 34 ]. Due to similar limitations, indirect calorimetry has also not been included in this search strategy. However, reviews that discuss studies which have examined the validity of PA measures against DLW and indirect calorimetry were included. The term Global Positioning System (GPS) was not included as it was felt that the limitations associated with GPS used alone [ 37 ] deemed it an inappropriate measure of PA for population surveillance in its current form.

Review articles that focused solely on the methodological effectiveness of measures of PA in clinical populations and in children/adolescents were not included in this review. Reviews identified in this study which described the methodological effectiveness of measures of PA in both adult and youths were included, but only the adult data extracted and included.

Article selection

A single reviewer screened all article titles, with only articles that were clearly unrelated to the review of reviews removed at this level. Two independent reviewers examined the article abstracts. Results were collated and reported to a third reviewer, who made the final decision in the case of conflicting results. The full texts of included articles were reviewed by two reviewers using the same protocol for handling conflicting results. Reference lists of identified articles were reviewed to ensure that no relevant articles were overlooked. The collated list of accepted reviews was examined by three leading PA measurement experts, who identified key reviews they felt were not included. The full screening protocol was repeated for all supplementary articles identified (Fig. 1 ).

PRISMA flow diagram for search and inclusion process for identification of review articles

Quality assessment

The methodological quality of the systematic reviews was evaluated using the Assessment of Multiple Systematic Reviews (AMSTAR) quality assessment tool [ 38 ]. No similar quality assessment tool exists for narrative reviews. The AMSTAR protocol was applied to each article by two researchers with any conflicting results resolved by a third reviewer.

Data extraction

Initially, the full text and the reference list of each review article meeting the inclusion criteria was screened by a single reviewer for all references to methodological effectiveness, and each methods paper was sourced, screened and all relevant data extracted. The extracted data included general information about the article, the specific measure of PA examined and the demographic characteristics, including the sample population age, size and gender.

Finally, all relevant information relating to properties of methodological effectiveness (i.e. reliability, validity and sensitivity) was recorded. This included the key methodological details of the study and all relevant statistics used to examine measures of methodological effectiveness.

Data synthesis

Data synthesis was conducted separately for each of the PA measurement methods, including general recommendations of the method and its effectiveness indicators. The results extracted from the methods papers were presented in the following order: Validity data is presented as mean percentage difference (MPD) in modified forest plots. Similar to Prince and colleagues (2008), where possible, the MPD was extracted or calculated from the original articles as (((Comparison Measure – Criterion Measure)/Criterion Measure)*100) [ 16 ]. Data points positioned around the 0 mark suggest high levels of validity compared to the reference measure. Data points positioned to the left of the 0 mark suggest an underestimation of the variable in comparison to the reference measure. Data points positioned to the right of the 0 mark suggest an overestimation of the variable in comparison to the reference measure. The further away from the 0 mark the point is positioned, the greater the under/overestimation. Data points 250% greater than or less than the reference measure were capped at 250%, and are marked with an asterisk. Due to the lack of reporting of variance results, and the use of differing and incompatible measurement units, confidence intervals are not reported.

Study selection

The literature search produced 260 potentially relevant abstracts for screening, of which 58 were included in the review following abstract and full text review. After consultation from three international PA experts, and from bibliography review, a further 5 articles were identified for inclusion, providing a total of 63 articles for data extraction (Fig. 1 ) [ 4 , 5 , 6 , 7 , 9 , 10 , 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ].

For this article, reviews were categorised as either “Narrative Reviews” or “Systematic Reviews”. A systematic review was defined as a review which described a search strategy for identification of relevant literature. Of the 63 articles, 41 were categorised as narrative reviews, while 22 were identified as systematic reviews. Findings of the AMSTAR quality assessment and review are described in Table 1 . The mean AMSTAR score across the 22 articles was 5.4 (out of a possible score of 11), with three articles achieving a score of 3, four articles scoring 4, six articles scoring 5, four articles scoring 6, two articles scoring 7, two articles scoring 8 and one article achieving a score of 9 (Table 1 ). Based on AMSTAR categorisation, three reviews were considered low quality, 16 reviews were of medium quality and three reviews were considered high quality. The predominant measures examined/discussed in the identified review articles were activity monitors (n=44; 70%), self-report measures (n=28; 44%), pedometers (n=23; 37%) and HRM (n=18; 29%). Other measures included combined physiologic and motion sensors, multi-physiologic measures, multiphasic devices and foot pressure sensors. These measures were incorporated under the combined sensors heading.

Self-report measures

Criterion validity : A total of 35 articles examined the criterion validity of self-reported measures by comparison to DLW determined energy expenditure [ 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 ]. Self-reported measures of PA included 7 day recall questionnaires, past year recall questionnaires, typical week questionnaires and PA logs/diaries were validated against 8-15 days of DLW measurement (Additional file 1 : Table S1).

The mean values for self-reported and criterion determined PA energy expenditure were available for the calculation of MPD in 27 articles [ 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 91 , 92 , 93 , 95 , 97 , 99 , 100 , 102 , 104 , 105 , 106 , 107 ]. Energy expenditure was calculated from a range of behaviours, including leisure time PA, work based PA and PA frequency. The MPD between self-reported PA energy expenditure (time spent in PA normally converted to energy expenditure using a compendium of PA) is presented in Fig. 2 . The MPDs observed in studies that examined the validity of PA diaries ranged from -12.9% to 20.8%. MPD for self-reported PA energy expenditure recalled from the previous 7 days (or typical week) were larger, ranging from -59.5% to 62.1%. MPDs from self-reported PA energy expenditure for the previous month compared to DLW determined energy expenditure ranged from -13.3% to 11.4%, while the difference between self-reported PA from the previous twelve months and DLW determined energy expenditure ranged from -77.6% to 112.5%.

Forest plot of percentage mean difference between self-reported energy expenditure (TEE, PAEE, PAL) compared to criterion measure of energy expenditure (doubly labelled water)

Concurrent validity: A total of 89 articles reported on concurrent validity of self-reported measures [ 75 , 80 , 83 , 84 , 97 , 102 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 ]. Articles were collated based on the types of referent measures used (Additional file 1 : Table S2). The MPD between self-reported energy expenditure and energy expenditure from PA log/diaries for 12 studies ranged from -67.6% to 23.8% (Additional file 1 : figure S2a) [ 80 , 108 , 110 , 111 , 128 , 145 , 152 , 157 , 159 , 160 , 169 , 175 ]. These findings suggest that self-report underestimates energy expenditure compared to activity logs/diaries. Seven studies compared self-reported time spent in specific activity intensities with PA intensities from logs/diaries (Additional file 1 : figure S2a) [ 109 , 120 , 121 , 146 , 152 , 182 , 187 ]. A wider MPD range (-69.0% to 438.5%) was evident, with the greatest differences occurring for moderate intensity physical activity (MPA) and vigorous intensity physical activity (VPA) [ 109 , 120 , 121 ].

Eight studies compared two different self-reported measures of PA energy expenditure [ 80 , 83 , 97 , 152 , 158 , 162 , 175 , 190 ], and and 6 studies compared two different self-reported measures of time spent in PA [ 112 , 135 , 136 , 146 , 152 , 153 , 158 ] (Additional file 1 : figure S2b). Additional file 1 : figure S2c presents 15 studies that compared self-reported PA energy expenditure with PA energy expenditure from activity monitors [ 80 , 132 , 142 , 143 , 144 , 150 , 159 , 168 , 170 , 172 , 174 , 178 , 183 , 185 , 191 ]. The MPD ranged from -74.7% to 82.8%, with self-reported measures tending to overestimate energy expenditure.

Self-reported time spent in light intensity physical activity (LIPA) (n=6) [ 75 , 119 , 131 , 146 , 179 , 189 ], MPA (n=17) [ 75 , 115 , 119 , 130 , 131 , 133 , 134 , 139 , 140 , 141 , 146 , 147 , 161 , 163 , 176 , 177 , 187 , 189 ] and moderate-to-vigorous intensity physical activity (MVPA) (n=7) [ 115 , 116 , 127 , 145 , 149 , 153 , 179 , 192 ] was validated against activity monitors that mainly employed count-to-activity thresholds to determine PA intensity (Additional file 1 : figure S2d), with the MPD for LIPA ranging from -70.1% to 129.2%, MPA ranging from -78.9% to 1007.6% and MVPA ranging from -34.9% to 217.1%. The MPD for VPA was also validated against activity monitors (Additional file 1 : figure S2e) [ 75 , 115 , 119 , 130 , 131 , 133 , 134 , 140 , 141 , 146 , 147 , 161 , 163 , 177 , 187 , 189 ], with all studies identifying an overestimation of self-reported VPA (Additional file 1 : figure S2e).

The concurrent validity of additional self-reported variables, including total PA [ 163 , 181 , 184 , 193 ], frequency of MVPA [ 149 ], active time [ 151 , 161 ], time standing [ 192 ] and time stepping [ 192 ] were also compared to activity monitor determined variables (Additional file 1 : figure S2e).

The MPD between self reported energy expenditure and both pedometer and HRM determined energy expenditure [ 80 , 102 , 123 , 142 , 194 ]; and self-reported time spent in PA intensities and HRM determined time spent in PA intensities [ 118 , 129 , 146 , 154 , 174 , 195 ] are presented in Additional file 1 : figure S2f. Self-reported energy expenditure overestimated pedometer determined energy expenditure (range=17.1% to 86.5%). Self-reported measures notably overestimated time spent in PA intensities when compared to HRM. Although self-reported energy expenditure underestimated HRM determined energy expenditure, this underestimation was small compared to other measures (-17.7% to -1.3%).

Reliability

Intra-instrument reliability: One article examined the intra-instrument reliability of a self-reported measure of PA [ 196 ]. A self-reported instrument examining the previous 14 days of PA was administered [ 196 ]. After 3 days, the instrument examined the PA of the same 14 day period. The findings identified high levels of intra-instrument reliability for total activity (ICC=0.90; 95% CI=0.86-0.93), MPA (ICC=0.77; 95% CI=0.69-0.84), VPA (ICC=0.90; 95% CI=0.86-0.93), walking (ICC=0.89; 95% CI=0.85-0.93) and energy expenditure (ICC=0.86; 95% CI=0.80-0.90) (Additional file 1 : Table S3).

Test-retest reliability: The test-retest reliability of self-reported measures was examined in 116 studies [ 75 , 77 , 83 , 110 , 116 , 117 , 122 , 125 , 126 , 127 , 129 , 131 , 132 , 135 , 137 , 140 , 144 , 145 , 147 , 149 , 152 , 153 , 155 , 157 , 159 , 161 , 162 , 167 , 168 , 169 , 172 , 175 , 176 , 178 , 179 , 180 , 181 , 184 , 187 , 188 , 190 , 191 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 ]. Due to the wide test-retest periods, articles were allocated to one of 5 periods, ≤1 week (Additional file 1 : Table S4a), >1 - <4 weeks (Additional file 1 : Table S4b), >4 - <8 weeks (Additional file 1 : Table S4c), >8 weeks - <1 year (Additional file 1 : Table S4d) and >1 year (Additional file 1 : Table S4e). Test-retest statistics employed were extracted and are presented in Table 2 . An overview of all identified studies examining the test-retest reliability of PA/energy expenditure measured by self-report, along with all test-retest statistics is provided in Additional file 1 : Table S4a-e.

Sensitivity : Two studies examined the sensitivity of self-reported measures to detect change in PA behaviours over time [ 256 , 270 ]. Both studies identified small to moderate effect sizes for specific PA behaviours over a six month period in older adults (Additional file 1 : Table S5).

Activity monitors

Criterion validity : Fifty-eight articles examined the criterion validity of activity monitor determined PA variables [ 73 , 77 , 80 , 96 , 105 , 119 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 , 300 , 301 , 302 , 303 , 304 , 305 , 306 , 307 , 308 , 309 , 310 , 311 , 312 , 313 , 314 , 315 , 316 , 317 , 318 , 319 , 320 , 321 , 322 , 323 ]. The majority of articles compared activity monitor determined energy expenditure with DLW [ 73 , 77 , 80 , 96 , 105 , 274 , 275 , 277 , 278 , 280 , 281 , 285 , 292 , 293 , 295 , 296 , 300 , 303 , 304 , 305 , 311 , 313 , 317 , 323 ], while activity monitor determined steps [ 119 , 271 , 283 , 287 , 289 , 298 , 299 , 306 , 307 , 314 , 315 , 318 ], distance travelled [ 282 ] and activity type [ 272 , 273 , 276 , 279 , 284 , 286 , 288 , 290 , 291 , 294 , 297 , 301 , 302 , 308 , 309 , 310 , 312 , 316 , 319 , 320 , 321 , 322 ] were also compared to direct observation (Additional file 1 : Table S6).

The range of MPD observed in studies that examined the criterion validity of activity monitor determined energy expenditre ranged from -56.59% to 96.84% (Fig. 3a ). However, a trend was apparent for activity monitor determined energy expenditure to underestimate the criterion measure. The range of MPD between activity monitor and direct observation determined steps was smaller, with values ranging from -48.52% to 7.47%, with 96% of studies having a MPD between -10% to 10% (Fig. 3b ). Activity monitors overestimated distance walked/run (0.88% to 27.5%). Activity monitors also tended to underestimate activity classification, with MPD varying between -36.67% to 2.00%.

a Forest plot of percentage mean difference between accelerometer determined energy expenditure (TEE, PAEE, PAL) compared to criterion measure of energy expenditure (doubly labelled water). b Forest plot of percentage mean difference between accelerometer determined steps, distance walked and activity type compared to criterion measure of direct observation

Concurrent validity: A total of 103 articles examined the concurrent validity of activity monitor measures of PA [ 73 , 77 , 80 , 119 , 146 , 151 , 174 , 192 , 194 , 195 , 262 , 271 , 282 , 295 , 305 , 316 , 324 , 325 , 326 , 327 , 328 , 329 , 330 , 331 , 332 , 333 , 334 , 335 , 336 , 337 , 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , 365 , 366 , 367 , 368 , 369 , 370 , 371 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 , 380 , 381 , 382 , 383 , 384 , 385 , 386 , 387 , 388 , 389 , 390 , 391 , 392 , 393 , 394 , 395 , 396 , 397 , 398 , 399 , 400 , 401 , 402 , 403 , 404 , 405 , 406 , 407 , 408 , 409 ]. Data extractions were grouped by the types of measures used (Additional file 1 : Table S7).

The MPD of activity counts from two different activity monitors ranged from -40.6% to 13.2% [ 262 , 327 , 351 , 389 , 392 , 405 ]. The MPD for a wide range of activity behaviours from two different activity monitors were examined; LIPA (-12.5% - 13.7%) [ 146 , 340 , 392 , 405 ], MPA (-10.9% - 3.1%) [ 146 , 340 ], VPA (-9.7% - 20.3%) [ 146 , 352 ], MVPA (-57.5% - 3.3%) [ 344 , 392 , 405 ], total PA (1.1%) [ 146 ]. Stepping [ 151 , 192 ] and step counts [ 77 , 119 , 340 , 405 ] were compared between 2 activity monitor devices (MPD ranged from -21.7% - 0% for step counts and -57.1% - 56% for stepping). Energy expenditure estimated by two activity monitors were compared [ 372 , 404 , 408 ], with MPD ranging from -21.1% - 61% (Additional file 1 : figure S3c).

Energy expenditure at different PA intensities from activity monitors were compared against estimates from indirect calorimetry and whole room calorimetry. For LIPA, the MPD ranged from -79.8% - 429.1% [ 349 , 394 ]. For MPA, MPD ranged from -50.4% - 454.1% [ 349 , 395 ], while estimates for VPA ranged from -100% - 163.6%. Energy expenditure estimates from activity monitoring devices for total PA were compared against indirect calorimetry estimates [ 368 , 394 , 396 , 398 , 404 ], where MPD ranged from -41.4% to 115.7%. The MPD range for activity monitor determined total energy expenditure compared with whole-room calorimetry were narrower (-16.7% to -15.7%) [ 343 , 364 ] (Additional file 1 : figure S3d).

Activity monitor estimates of energy expendture were compared to HRM estimates of energy expenditure for total PA (-10.4% - 22.2%) [ 80 , 402 ], for LIPA (-75.4% - 72.8%) [ 146 ], for MPA (49.2% - 677.7%), VPA (-46.2% - 46.2%) [ 146 , 361 ] and for total time spent in PA (-16.1% - 34.9%) [ 146 , 174 ]. Self-reported measures were used to examine the concurrent validity of activity monitors for energy expenditure [ 80 ] and total time spent in PA [ 174 ], with MPD ranging from -6.0% - 32.1% (Additional file 1 : figure S3e).

Estimated energy expenditure was compared between activity monitors and indirect calorimetry (kcal over specified durations; Additional file 1 : figure S3f (-68.5% - 81.1%)) [ 282 , 328 , 341 , 358 , 367 , 369 , 370 , 375 , 376 , 380 , 382 , 383 , 385 , 387 ]; (METs over specified durations; Additional file 1 : figure S3g (-67.3% -- 48.4%)) [ 195 , 325 , 345 , 346 , 347 , 349 , 350 , 353 , 357 , 362 , 384 , 397 , 400 , 407 , 409 ]. A single study compared the estimated energy expenditure from 5 different activity monitors and indirect calorimetry at incremental speeds (54, 80, 107, 134, 161, 188 and 214 m.min -1 ) in both men and women (MPD ranged from -60.4% - 90.8%) (Additional file 1 : figure S3h) [ 374 ].

Inter-instrument reliability : The inter-instrument reliability of activity monitoring devices (e.g. the reliability of the same device worn by the same participant over the same time period) was examined in 18 studies [ 301 , 315 , 337 , 344 , 370 , 385 , 387 , 406 , 409 , 410 , 411 , 412 , 413 , 414 , 415 , 416 , 417 , 418 ]. Study methodologies included the wearing of devices over the left and right hip [ 337 , 370 , 385 , 387 , 406 , 413 , 415 , 417 ], over the hip and lower back [ 409 ], the wearing of devices side by side at the same location on the hip [ 301 , 344 , 411 , 414 , 416 , 417 , 418 ], devices worn at 3 rd intercostal space and just below the apex of the sternum [ 410 ], device worn on both wrists [ 412 ], worn on both legs [ 315 ] and worn side by side on the same leg [ 315 ]. Coefficients of variations ranged from 3% to 10.5% for the ActiGraph device [ 418 ] and from <6% to 35% for the RT3 accelerometer [ 387 , 416 ]. All reported correlation coefficients were significant and greater than 0.56 [ 370 , 385 , 387 , 406 , 409 , 412 , 415 , 417 ]. ICC values for the majority of devices were >0.90 [ 301 , 315 , 337 , 344 , 411 , 413 ], excluding those observed for the RT3 accelerometer (0.72-0.95) [ 417 ], Actitrac (0.40 -0.87) and Biotrainer devices (0.60–0.71) [ 406 ] (Additional file 1 : Table S8).

Test-retest reliability: Test-retest reliability of activity monitoring devices was examined in 26 studies [ 153 , 155 , 228 , 234 , 262 , 282 , 297 , 314 , 358 , 385 , 407 , 411 , 414 , 416 , 419 , 420 , 421 , 422 , 423 , 424 , 425 , 426 , 427 , 428 , 430 ]. For the laboratory-based studies, variables examined included distance walked [ 282 ], steps at different speeds [ 314 , 420 ], resting periods [ 358 ], accelerometer counts [ 385 , 407 , 411 , 414 , 416 , 425 , 429 , 430 ], energy expenditure [ 426 ] and postural position [ 297 , 429 ]. For the free-living analyses, behaviours examined included activity behaviours [ 155 , 419 ], accelerometer counts [ 262 , 421 , 422 ], step count [ 422 ], energy expenditure [ 228 , 234 ] and the number of people achieving the recommended amount of PA [ 153 ] (Additional file 1 : Table S9).

As the examination of PA over a number of days can be considered a measure of test-retest reliability, researchers have used statistical processes (i.e. generalizability theory or the Spearman Brown Prophecy formula) to determine the minimum number of days required to provide a reliability estimate of PA behaviours [ 431 ]. Studies reported that a minimum of three days of ActiGraph data are required to provide a reliable estimate of total PA [ 423 ] and time spent in MVPA [ 424 ], while a minimum of 2 days is required to provide a reliable estimate of ActiGraph determined steps per day, accelerometer counts per day and intermittent MVPA per day [ 427 ]. However, for the examination of continuous 10 minute bouts of MVPA (as suggested in the majority of international PA recommendations), a minimum of 6 days of measurement is required [ 427 ].

Sensitivity : The only study of responsiveness to change in activity monitors, using the ActiWatch, identified that this device was able to detect significant differences in activity counts accumulated between young adults and sedentary older adults and between active older adults and sedentary older adults [ 421 ]. However, no differences could be detected between the young adults and active older adults (Additional file 1 : Table S10).

Criterion validity: A total of 30 studies were sourced that examined the criterion validity of step count in pedometer devices [ 283 , 289 , 298 , 306 , 307 , 314 , 318 , 365 , 391 , 432 , 433 , 434 , 435 , 436 , 437 , 438 , 439 , 440 , 441 , 442 , 443 , 444 , 445 , 446 , 447 , 448 , 449 , 450 , 451 , 452 ], while 3 studies examined the criterion validity of pedometer determined energy expenditure compared to DLW [ 93 , 453 , 454 ]. Of the laboratory based studies assessing criterion validity, 30% used over ground walking protocols [ 307 , 318 , 365 , 391 , 442 , 445 , 446 , 447 , 450 , 451 ] and the remaining treadmill-based protocols [ 283 , 289 , 298 , 306 , 314 , 432 , 433 , 434 , 435 , 436 , 437 , 438 , 439 , 440 , 441 , 443 , 444 , 448 , 449 ] or a combination of the two [ 452 ]. In free-living studies which examined the criterion validity of pedometer determined energy expenditure, pedometers were worn for 2 [ 454 ], 7 [ 93 ] and 8 days [ 453 ] (Fig. 4 ; (-62.3% - 0.8%)). Pedometer determined step count was generally lower when compared to direct observation (-58.4% - 6.9%). Some studies also examined the effect of speed on pedometer output. Pedometers had relatively high levels of accuracy across all speeds, but appear to be more accurate at determining step-count at higher walking speeds compared to lower walking speeds (Additional file 1 : Table S11) [ 306 , 436 , 438 , 439 ].

Forest plot of mean percentage difference between pedometer determined step count/energy expenditure compared to criterion measure (direct observation/doubly labelled water respectively). * denotes multiple devices compared in the same study

Concurrent validity: The concurrent validity of pedometers was examined in a total of 22 articles [ 77 , 194 , 298 , 376 , 391 , 399 , 404 , 422 , 432 , 433 , 434 , 441 , 444 , 448 , 449 , 451 , 452 , 455 , 456 , 457 , 458 , 459 ]. Various approaches were used to examine the concurrent validity of pedometers, with 14 studies comparing pedometer step count with steps determined from other pedometers [ 432 , 451 , 458 ] and activity monitors [ 77 , 298 , 391 , 422 , 433 , 434 , 444 , 455 , 456 , 457 , 459 ] and 4 studies comparing pedometer determined energy expenditure with energy expenditure determined from indirect calorimetry [ 376 , 399 , 404 , 441 , 448 , 451 ] and/or energy expenditure determined from other activity monitors [ 451 ]. One study compared pedometer determined distance travelled with treadmill determined distance travelled [ 449 ], while one study compared pedometer determined MVPA with activity monitors determined MVPA [ 452 ] (Additional file 1 : figure S4a). Pedometers appear to underestimate time spent in MVPA and estimated energy expenditure when compared to other measures. The findings are less clear for step count determined from pedometers when compared to other pedometers or activity monitors, with devices appearing to both over and underestimate step count (Additional file 1 : Table S12).

Inter-instrument reliability: A total of 6 articles examined the inter-instrument reliability of pedometer output obtained from 18 different devices [ 314 , 315 , 447 , 449 , 451 , 457 ]. Many included articles examined the inter-instrument reliability of multiple devices in the same study (e.g. 2 pedometers [ 315 ], 5 pedometers [ 451 ], 10 pedometers [ 446 , 449 ]). Inter-instrument reliability was examined by comparing pedometer outputs from two of the same model devices worn on the left and right hip [ 315 , 449 , 451 , 457 ], on the left hip, right hip and middle back [ 447 ] and on the left and right hip and repeated with two further devices of the same model [ 446 ].

Three studies (1 examining the inter-instrument reliability of a single pedometer and 2 examining the inter-instrument reliability of multiple pedometers), identified that the majority of devices had acceptable levels of inter-instrument reliability (ICC ≥ 080) [ 446 , 449 , 457 ]. In the studies which examined the inter-instrument reliability of multiple devices, 8/10 pedometers [ 449 ] and 9/10 pedometers [ 446 ] achieved ICC ≥ 0.80. Using planned contrasts, Bassett and colleagues highlight that no significant differences were observed between devices worn on the left and right hip [ 451 ]. Two studies investigated the effect of walking speed on inter-instrument reliability, highlighting that ICC values increased as speed increased [ 315 , 447 ] (Additional file 1 : Table S13).

Test-retest reliability: A single laboratory-based test-retest reliability study in a laboratory-based treadmill protocol identified that the Yamax Digiwalker SW-200 (Tokyo, Japan) had appropriate test-retest reliability (ICC > 0.80 and significant) at 7 out of 11 treadmill speeds (non-significant speeds = 4, 20, 22 and 26 km.h -1 ) [ 314 ].

A total of 6 articles examined the reliability of pedometer steps obtained over a specified measurement period [ 423 , 427 , 460 , 461 , 462 , 463 ], presenting the minimum number of days of pedometer measurement to reliably estimate PA behaviours. The minimum number of days of measurement required for a reliable estimate (i.e. ICC >0.8) of pedometer steps was 2-4 days (Additional file 1 : Table S14) [ 423 , 427 , 460 , 461 , 462 , 463 ].

Sensitivity : In the only study of pedometer responsiveness to change, effect size was used to examine the meaningfulness of difference between means [ 464 ]. A large effect size (>0.8) was observed, suggesting that pedometers, in this study, were sensitive to change (Additional file 1 : Table S15).

• Heart rate monitors

Criterion validity: All 12 studies that examined the criterion validity of HRMs were unstructured, free-living protocols [ 80 , 85 , 87 , 96 , 99 , 100 , 102 , 123 , 304 , 371 , 465 , 466 ]. The duration of monitoring for HRM ranged from 24 hours [ 102 , 465 ] to 14 days [ 96 , 371 ]. Two studies examined the validity of HRM determined physical activity levels (PAL) compared to DLW determined PAL. All remaining articles compared estimates of energy expenditure determined by HRM techniques with DLW determined energy expenditure. The flex heart rate methodology, which distinguishes between activity intensities based on heart rate versus VO 2 calibration curves, were utilised in all studies using individual calibration curves. MPDs between HRM determined energy expenditure and DLW determined energy expenditure ranged from -60.8% - 19.7% across identified studies (Fig. 5 ). No clear trend for over/under estimation was apparent (MPDs for energy expenditure ranging between -60.8% - 19.7%). For PAL, a slight trend in underestimation was apparent (-11.1 to -7.6) (Additional file 1 : Table S16).

Forest plot of percentage mean difference between heart rate monitor determined energy expenditure/physical activity level compared to criterion measure (doubly labelled water)

Concurrent validity: The concurrent validity of HRM determined energy expenditure [ 80 , 467 , 468 , 469 , 470 ], PAL [ 80 ] and PA intensity [ 146 , 174 ] was examined using a range of measures, including direct/indirect calorimetry [ 467 , 469 , 470 ], activity monitoring [ 80 , 146 , 174 , 401 ] and measures of self-reported PA [ 80 , 174 , 468 ] (Additional file 1 : Table S17). A slight trend in overestimation of energy expenditure and PAL was observed (Additional file 1 : figure S5a). For PA intensities, MPDs were larger and more variable, with MPA underestimated and VPA overestimated. The MPD between HRM determined LIPA and LIPA determined by the Tritrac and MTI activity monitors fell outside the range for the presented forest plot, with values of +306.4% and +367.2%, respectively [ 146 ] (Additional file 1 : figure S5a). No articles sourced through the data extraction reported on the reliability or responsiveness to change of HRM.

Combined sensors

Criterion validity: A total of 8 articles were identified that examined the criterion validity of multiple accelerometers [ 471 , 472 , 473 , 474 ] or accelerometers combined with gyroscopes [ 475 ] or HRMs [ 371 , 476 , 477 ]. The included studies had relatively small sample sizes, ranging from 3-31 participants. Studies primarily examined the effectiveness of data synthesis methodologies (i.e. Decision Tree Classification, Artificial Neural Networks, Support Vector Machine learning etc.) to identify specific postures/activities [ 471 , 472 , 473 , 474 , 475 , 476 , 477 ] or energy expenditure [ 371 , 477 ]. Time spent in specific body postures/activity types tended to be underestimated from combined sensors when compared to direct observation (-33.3% to -3.2%; Fig. 6 ). In contrast, energy expenditure was overestimated by combined sensors when compared to DLW in free-living settings (13.0% to 26.8%) (Additional file 1 : Table S18) [ 371 ].

Forest plot of percentage mean difference between energy expenditure/body posture determined by combined sensors compared to criterion measure (doubly labelled water/direct observation)

Concurrent validity: Eleven studies examined the validity of combined accelerometry and HRM determined energy expenditure compared to whole room calorimetry [ 478 , 479 , 480 ] or indirect calorimetry [ 400 , 477 , 481 , 482 , 483 , 484 , 485 , 486 ] determined energy expenditure. No clear trend for under/overestimation was apparent, with combined sensors appearing to be relatively accurate in estimating energy expenditure when compared to indirect calorimetry in both a structured (-13.8% - 31.1%) and unstructured (0.13%) [ 485 ] settings (Additional file 1 : Table S19). No articles sourced through the data extraction reported on the reliability or responsiveness to change of combined sensors.

To the authors’ knowledge, this is the first systematic literature review of reviews to simultaneously examine the methodological effectiveness of the majority of PA measures. The greatest quantity of information was available for self-reported measures of PA (198 data extraction points), followed by activity monitors (179 data extraction points), pedometers (52 data extraction points), HRMs (19 data extraction points) and combined sensors (18 data extraction points).

The criterion validity of measures was determined through the examination of energy expenditure via DLW and by direct observation of steps and PA behaviours. For accelerometry, although variability was lower, a substantial proportion of studies (44/54) underestimated energy expenditure compared to DLW when proprietary algorithms or count-to-activity thresholds were employed. Based on the amended forest plots for the criterion validity of measures of PA, a greater level of variability was apparent for self-reported measures compared to objective measures (Figs. 2 – 6 ). Limited data on the criterion validity of HRM and combined sensors determined energy expenditure was available. HRMs tended to underestimate DLW determined energy expenditure, while combined sensors often overestimated energy expenditure. Unfortunately, due to the lack of measures of variability, resulting in the absence of meta-analysis, it was not possible to describe the extent of differences between measures statistically. For step counts, both activity monitors and pedometers achieved high levels of criterion validity. When comparing the two, pedometers appeared to be less accurate than activity monitors at estimating step count, tending to underestimate steps when compared to direct observation. Activity monitors tended to slightly overestimate distance travelled, while time spent in each activity type (or posture) determined by both activity monitors and combined sensors was slightly underestimated when compared to direct observation (Fig. 3a and Fig. 6 ). For concurrent validity of all measure of PA, high levels of variability were observed across a wide range of activity behaviours. In particular, high levels of variability were apparent in the estimation of PA intensities, with VPA substantially overestimated in the majority of concurrent validations across all measures. In summary, objective measures are less variable than recall based measures across all behaviours, but high levels of variability across behaviours are still apparent.

For activity monitors and pedometers, acceptable inter-instrument reliability was observed in the majority of studies. Variability for inter-instrument reliability across different activity monitors and pedometers was apparent, with some instruments demonstrating better reliability compared to others. However, a detailed examination of study methodology, device wear locations and activities performed is necessary when interpreting the inter-instrument reliability of pedometers and activity monitors.

A wide range of values were reported for the test-retest reliability of self-reported measures, with apparent trends for reduced levels of test-retest reliability as the duration of recall increased. Researchers must be cognisant of potential differences in test-retest reliability due to duration between administrations and between PA behaviours assessed within each tool when selecting a self-reported measure of PA. Moderate to strong test-retest reliability was observed for activity monitors in free-living environments. However, the reliability of activity monitors attenuated as the duration between measurements increased. As expected, the test-retest reliability of different devices varied, while intensity of activity often had a significant effect. The test-retest reliability of pedometer determined steps in a laboratory setting was high across the majority of speeds, but the reliability appeared to weaken at higher speeds (e.g. 20, 22 and 26 km·h -1 ). Although moderate to strong test-retest reliability of both pedometers and activity monitors were apparent, researchers should be aware of differences between models and devices when selecting a measure for use. Furthermore consideration should be given to the duration between test and retest and the behaviour being assessed when considering test-retest reliability, as although a measure may be reliable for one output, it may not be reliable for all outcomes.

When examining PA in free-living environments, it is essential that sufficient data is gathered to ensure a reliable estimate is obtained [ 7 , 431 ]. By determining the inter- and intra-individual variability across days of measurement, researchers can define the number of days of monitoring required to reliably estimate such behaviours. For activity monitors and pedometers, analysis has been conducted to estimate the minimum number of days of measurement required to provide a reliable estimate of PA behaviors. For activity monitors, two days of measurement are recommended for a reliable estimate of steps per day, accelerometer counts per day and intermittent MVPA per day measured, 3 days for a reliable estimate of total PA and time spent in MVPA and 6 days are required for a reliable estimate of continuous 10 minute bouts of MVPA. For pedometers, a minimum of 2-4 days of measurement was required to provide a reliable estimate of steps in older adults, while 2-5 days of measurement was required in adults. These findings highlight the importance of knowing what behaviours are to be examined prior to collecting objective data from free-living environments, to ensure that sufficient information is recorded to provide reliable estimates of the behaviours of interest.

The responsiveness of measures to detect change over time was the least reported property of measures of PA. When evaluating interventions, or indeed evaluating changes in PA behaviours in longitudinal research, it is critical to utilise measures that can detect such changes. Although validity and reliability are requirements for sensitivity/responsiveness to change [ 5 ], this does not imply that a measure is responsive to change simply because it is valid and reliable. Responsiveness to change must be evaluated, and not assumed. Currently, the research on the responsiveness to change for all types of PA measurement is at best limited. Substantial investigation into the responsiveness of PA measures to detect change is required to ensure that measures employed in future intervention and longitudinal research can detect meaningful change.

Although the validity, reliability and responsiveness to change are key when selecting a measure of PA and energy expenditure, other factors including feasibility and cost should be considered. For example, wearing several sensors around the body for a short period in a laboratory setting is often quite feasible, but prolonging the wear period for several days may be uncomfortable for participants, while reattachment of sensors may require specific and detailed training. The appropriateness of the measure for use in specific populations is critical. Activity monitors or HRMs may need to be attached to body locations that are visible and may be considered “embarrassing” for certain populations in free-living environments, likely resulting in lower compliance to wear protocols. Finally, while the cost of objective measures have reduced significantly and are now feasible for inclusion in large scale data collections (i.e. UK Biobank study, HELENA study), worn devices can be expensive to use in large populations, especially if recording needs to be concurrent, requiring 100’s or 1000’s of devices. Although these issues are often the dominant determinant for researchers when selecting a measure of PA, it is critical that researchers consider selecting the measure with the best validity, reliability and responsiveness to change available to them; a larger dataset with less valid measures may not always be superior to a smaller dataset.

The findings of this review have highlighted the substantial quantity of research which has focused on the validity, reliability and responsiveness to change of measures of PA. A substantial number of review articles have been conducted on the measurement of PA in adult populations. The majority of such reviews were not systematic in nature. Of the systematic reviews articles identified, the methodological quality (as assessed by the AMSTAR quality assessment tool) was relatively poor, with 3 reviews considered low quality, 16 articles considered medium quality and 3 articles considered high quality. An obvious increase in the quantity of research using objective measures of PA over the past number of decades is apparent. Unfortunately, with the enormous quantity of research on the methodological effectiveness of PA measures comes extreme variability in study design, data processing and statistical analysis conducted. Such variability makes comparison between measurement type and specific measurement devices/tools extremely difficult. The sometimes questionable study designs and research questions in some of the existing published literature is a reanalysis of “suitable” data, rather than from a study designed to collect data to answer a specific research questions. The authors propose that to aid researchers in making informed decisions on the best available measure of PA, the development of “best practise” protocols for study design and data collection, analysis and synthesis are required, which can be employed across all measures, providing comparable information that is easy for researchers from outside of the field to digest. The authors also propose that any future undertaking of reviews on the measurement of PA follow best practise, and ensure that the reviews conducted are of the highest possible quality. Such improvements will provide researchers with the best available evidence for making a decision on which measure of PA to employ.

Strengths and limitations

This review of reviews had limitations that should be taken into account when considering the findings presented here. As this article reviewed existing literature reviews, and due to potential methodological errors within these reviews, it is likely that some relevant literature on the methodological effectiveness for measures of PA has been overlooked. Additionally, articles that have been published since the publication of each review will also have been overlooked. Due to the quantity of identified articles, and difficulties in contacting primary authors regarding articles published over the last 60 years, the primary data from these articles was not sourced. Although prior research has systematically reviewed the literature for accuracy of measures of PA, and some narrative reviews have compared the methodological effectiveness of different measures of PA, this is the first study to comprehensively examine and collate details on the validity, reliability and responsiveness to change of a range of measures of PA in adult populations. For researchers that are selecting a measure of PA, this will enable the comparison between different measures of PA within one article, rather than having to refer to a wide range of available literature that examines each single measure. Additionally, rather than focusing solely on information presented within each existing review of the literature, the original articles referred to within each review were sought and data was extracted independently.

In general, objective measures of PA demonstrate less variability in properties of methodological effectiveness than self-report measures. Although no “perfect” tool for the examination of PA exists, it is suggested that researchers aim to incorporate appropriate objective measures, specific to the behaviours of interests, when examining PA in adults in free-living environments. Other criteria beyond methodological effectiveness often influence tool selection, including cost and feasibility. However, researchers must be cognisant of the value of increased methodological effectiveness of any measurement method for PA in adults. Additionally, although a wealth of research exists in relation to the methodological effectiveness of PA measures, it is clear that the development of an appropriate and consistent approach to conducting research and reporting findings in this domain is necessary to enable researchers to easily compare findings across instruments.

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Acknowledgements

The authors would like to acknowledge the contribution of all members of the DEDIPAC Knowledge Hub consortium for their help and assistance in the completion of this work.

The preparation of this paper was supported by the DEterminants of DIet and Physical ACtivity (DEDIPAC) knowledge hub. This work is supported by the Joint Programming Initiative ‘Healthy Diet for a Healthy Life’. This work was funded by the Health Research Board (HRB) of Ireland. Additional funding agencies supporting this work were (in alphabetical order of collaborative Member State): Germany: Project Management Agency in the German Aerospace Centre (PT-DLR); Italy: Ministry of Education, University and Research/ Ministry of Agriculture Food and Forestry Policies; The Netherlands: The Netherlands Organisation for Health Research and Development (ZonMw); Norway: The Research Council of Norway, Division for Society and Health; Poland: The National Centre for Research and Development; The United Kingdom: The Medical Research Council (MRC).

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HvdP, UE and AD were involved in the conception of the review title. KD, RSz, SC, AP, HvdP, UE and AD contributed to the design of the review protocol. KD conducted the database search. Several reviewers independently performed the selection of articles (KD, RSz, JM, RSt, MAM and MT) and examined the titles and abstracts of the identified references to exclude articles out of scope. Any disagreements on study inclusions were resolved through discussions with another reviewer (AD) and a consensus reached. KD, RSz JM, RSt, MAM and MT assessed the eligible papers, extracted the data, and discussed the findings. KD, RSz, MAM, MM, AP, HvdP, UE and AD drafted the paper and all authors listed reviewed the manuscript and contributed to subsequent drafts. All authors read and approved the final document.

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Additional file

Additional file 1: table s1..

Criterion validity of self-reported measures of physical activity/energy expenditure. Table S2 . Concurrent validity of self-reported measures of physical activity/energy expenditure. Table S3 . Intra-instrument Reliability of self-reported measures of physical activity. Table S4a . Test-retest reliability of self-reported measures of physical activity/energy expenditure within a duration of less than or equal to one week. Table S4b . Test-retest reliability of self-reported measures of physical activity/energy expenditure within a duration of between 1 week and 4 weeks. Table S4c . Test-retest reliability of self-reported measures of physical activity/energy expenditure within a duration of between 4 weeks and 8 weeks. Table S4d . Test-retest reliability of self-reported measures of physical activity/energy expenditure within a duration of between 8 weeks and 1 year. Table S4e . Test-retest reliability of self-reported measures of physical activity/energy expenditure within a duration of greater than 1 year. Table S5 . Sensitivity to change over time of self-reported measures of physical activity/energy expenditure. Table S6 . Criterion validity of accelerometer activity monitor determined physical activity/energy expenditure. Table S7 . Concurrent validity of accelerometer/activity monitor determined physical activity/energy expenditure. Table S8 . Inter-instrument reliability of accelerometer/activity monitor determined physical activity/energy expenditure. Table S9 . Test-retest reliability of accelerometer/activity monitor determined physical activity/energy expenditure. Table S10 . Sensitivity to change over time of accelerometer devices. Table S11 . Details of studies that examined the Criterion Validity of Pedometers. Table S12 . Details of studies examining the concurrent validity of pedometers. Table S13 . Details of studies examining inter-instrument reliability in pedometer devices. Table S14 . Details of studies examining the test-retest reliability of pedometers. Table S15 . Details of studies examining the sensitivity to change of pedometers. Table S16 . Details of studies examining the criterion validity of heart rate monitoring devices. Table S17 . Details of studies examining the concurrent validity of heart rate monitoring devices. Table S18 . Details of studies examining the criterion validity of combined sensors. Table S19 . Details of studies examining the concurrent validity of combined sensors. Figure S1 . PRISMA Checklist. Figure S2a . Forest plot of percentage mean difference between self-report measures of energy expenditure compared to energy expenditure from activity logs/diaries. Figure S2b . Forest plot of percentage mean difference between self-report measures of energy expenditure and time spent in physical activity compared to other self-report measures of energy expenditure or time spent in physical activity. Figure S2c . Forest plot of percentage mean difference between self-report measures of energy expenditure compared to energy expenditure determined from activity monitors. Figure S2d . Forest plot of percentage mean difference between self-report measures of time spent in physical activity intensities (Light, Moderate and Moderate-to-Vigorous intensity physical activity) compared to time spent in physical activity intensities determined from activity monitors. Figure S2e . Forest plot of percentage mean difference between self-report measures of time spent in physical activity intensities (Vigorous physical activity, Total physical activity, times active, time standing, time stepping) compared to time spent in physical activity intensities determined from activity monitors. Figure S2f . Forest plot of percentage mean difference between self-report measures of energy expenditure and time spent in physical activity intensities (Vigorous physical activity, Total physical activity, times active, time standing, time stepping) compared to energy expenditure time spent in physical activity intensities determined from pedometers and heart rate monitors. Figure S3c . Forest plot of percentage mean difference between accelerometer/activity monitor determined variables (activity counts, time spent in light intensity physical activity, time spent in moderate intensity physical activity, time spent in moderate-to-vigorous intensity physical activity, time spent in vigorous intensity physical activity, total physical activity, stepping and energy expenditure) compared to an alternative accelerometer/activity monitor. Figure S3d . Forest plot of percentage mean difference between accelerometer/activity monitor determined energy expenditure (METs) in light intensity physical activity, moderate intensity physical activity, vigorous intensity physical activity and total physical activity (METs, MJ.d, KJ.h, KJ.kg.min -1 ) compared to estimates from indirect (IC) and whole room calorimetry (WRC). Figure S3e . Forest plot of percentage mean difference between accelerometer/activity monitor determined energy expenditure, energy expenditure from light intensity physical activity, moderate intensity physical activity, vigorous intensity physical activity, total physical activity compared to estimates from Heart Rate Monitoring (HRM) and Self-Report (SR) measures. Figure S3f . Forest plot of percentage mean difference between accelerometer/activity monitor determined energy expenditure (kcal.min -1 , kcal.kg.hr -1 ) compared to indirect calorimetry determined energy expenditure (kcal.min -1 , kcal.kg.hr -1 ). Figure S3g . Forest plot of percentage mean difference between accelerometer/activity monitor determined energy expenditure (METs.min -1 , METs.hr -1 ) compared to indirect calorimetry determined energy expenditure (METs.min -1 , METs.hr -1 ). Figure S3h . Forest plot of percentage mean difference between accelerometer/activity monitor determined total energy expenditure compared to indirect calorimetry determined total energy expenditure. Figure S3h (cont). Forest plot of percentage mean difference between accelerometer/activity monitor determined energy expenditure (kcal.min -1 , kcal.kg.hr -1 ) compared to indirect calorimetry determined energy expenditure (kcal.min -1 , kcal.kg.hr -1 ). Figure S4a . Forest plot of percentage mean difference between pedometer determined step count/energy expenditure/MVPA compared to concurrent measures (i.e. accelerometry, indirect calorimetry, pedometers). Figure S5a . Forest plot of percentage mean difference between heart rate monitor determined energy expenditure/physical activity level/physical activity intensity compared to concurrent measures (accelerometers, self-report, indirect calorimetry) Figure S6 . Forest plot of percentage mean difference between energy expenditure determined by combined sensors compared to concurrent measure (indirect calorimetry). (DOCX 1304 kb)

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Dowd, K.P., Szeklicki, R., Minetto, M.A. et al. A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act 15 , 15 (2018). https://doi.org/10.1186/s12966-017-0636-2

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The association between physical activity and mental health during the first year of the COVID-19 pandemic: a systematic review

• Priscila Marconcin 1 , 2 ,
• André O. Werneck 3 ,
• Miguel Peralta 2 , 4 ,
• Andreas Ihle 5 , 6 , 7 ,
• Élvio R. Gouveia 8 , 9 ,
• Gerson Ferrari 10 ,
• Hugo Sarmento 11 &
• Adilson Marques 2 , 3  

BMC Public Health volume  22 , Article number:  209 ( 2022 ) Cite this article

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Introduction

The Coronavirus disease-19 (COVID-19) pandemic affected countries worldwide and has changed peoples’ lives. A reduction in physical activity and increased mental health problems were observed, mainly in the first year of the COVID-19 pandemic. Thus, this systematic review aims to examine the association between physical activity and mental health during the first year of the COVID-19 pandemic.

In July 2021, a search was applied to PubMed, Scopus, and Web of Science. Eligibility criteria included cross-sectional, prospective, and longitudinal study designs and studies published in English; outcomes included physical activity and mental health (e.g., depressive symptoms, anxiety, positive and negative effects, well-being).

Thirty-one studies were included in this review. Overall, the studies suggested that higher physical activity is associated with higher well-being, quality of life as well as lower depressive symptoms, anxiety, and stress, independently of age. There was no consensus for the optimal physical activity level for mitigating negative mental symptoms, neither for the frequency nor for the type of physical activity. Women were more vulnerable to mental health changes and men were more susceptive to physical activity changes.

Physical activity has been a good and effective choice to mitigate the negative effects of the COVID-19 pandemic on mental health during the first year of the COVID-19 pandemic. Public health policies should alert for possibilities to increase physical activity during the stay-at-home order in many countries worldwide.

Peer Review reports

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a highly contagious virus that infects humans and causes coronavirus disease-19 (COVID-19), which is currently having a damaging impact on almost all countries worldwide [ 1 ]. To bring this pandemic to an end, a large share of the world needs to be immune to the virus, and the safest way to achieve this is with a vaccine. Fortunately, in December 2020 the vaccination started in the United Kingdom [ 2 ] and is currently pursued in different countries [ 3 ]. Until November 2021, 53.3% of the world population has received at least one dose of a COVID-19 vaccine [ 4 ]. However, the number of infected people and deaths continues to grow [ 5 ]. The World Health Organization (WHO) published a weekly report and on 16th of November 2021 it was observed a increasing trend in new global weekly cases [ 6 ]. From the beginning of the pandemic, as a community mitigation strategy used to reduce the spread of COVID-19, most countries adopted the stay-at-home order as well as the stimulation of facemask wearing and hygiene habits [ 7 , 8 ].

As a consequence of the stay-at-home strategies, mainly during the first year of the COVID-19 pandemic, studies had reported multiple behaviour changes. Some common impacts include disturbed eating behaviours [ 9 ], changes in alcohol consumption [ 10 ], and substance use [ 11 ]. A wide range of psychological outcomes has been observed during the virus outbreak, including a reduction in well-being as well as increases in depressive and anxiety symptoms [ 12 , 13 ]. Considering the need for social distancing measures, the investigation of possible factors that can mitigate the negative effects of social distancing on mental health should help the promotion of intervention strategies.

Physical activity (PA) is well recognised as a key factor for the prevention and management of mental illness, including mental disorders such as depression and anxiety as well as the promotion of mental health such as well-being [ 14 , 15 ]. Nevertheless, globally, approximately 23% of adults and 81% of adolescents do not meet the WHO recommendations regarding PA for maintaining health [ 16 , 17 , 18 ]. This situation even worsened with the COVID-19 pandemic. Studies have demonstrated that PA declined and sedentary behaviour increased during the COVID-19 pandemic stay at home order in many countries, regardless of the subpopulation [ 19 ]. Different studies sought to investigate whether these changes in PA were associated with mental health indicators during the COVID-19 pandemic and a previous systematic review synthetised that PA is an effective strategy to face the psychological effects of the COVID-19 pandemic [ 20 ]. However, the previous review included articles published between 1 January 2019, and 15 July 2020, before the second wave of the COVID-19 pandemic. Therefore, our systematic review aimed to update those findings and clarify if PA is associated with mental health during the first full year of the COVID-19 pandemic and to analyse if PA mitigates the effects of the stay-at-home order on mental health. We aimed to explore the first year of the COVID-19 pandemic because it was the period when restrictive orders were strictest when people were strongly encouraged to comply with the stay-at-home order.

This systematic review focuses on peer-reviewed journal articles on the relationship of PA to mental health during the COVID-19 pandemic published until 30 July 2021.

Data sources and searches

A systematic review protocol was registered with the PROSPERO database on the 29th of January 2021 (IDCRD42021233921). A broad search strategy was employed. Searches were conducted on the 30th of January 2021, in the following electronic databases: PubMed, Scopus, and Web of Science. The search was performed in the three databases using the terms: (physical activity OR physical inactivity OR exercise OR training OR sport* OR fitness OR physical function* OR movement behavio* OR sedentary behavio*) AND (mental health OR psychological health OR depress* OR anxiety OR psychological function* OR mental function* OR wellbeing OR well-being OR burnout OR burn-out OR fear OR fears OR worries OR worry) AND (coronavirus disease OR COVID-19 OR SARS-CoV-2 OR lockdown OR shutdown OR quarantine OR confinement OR social isolation). These terms were searched in title and abstract of scientific articles. Additionally, cross-referencing search was performed in the full-text read of potentially included articles.

Study selection

Observational studies (cross-sectional, prospective, or longitudinal) were eligible for this review. Furthermore, studies were also required to meet the following criteria: (1) assessing PA by a validated instrument, (2) assessing mental health by a validated instrument, (3) presenting an analysis on the association between PA and mental health. Studies with samples including pregnant women, chronic disease patients, athletes, COVID-19 survivors, and frail older adults were excluded. Besides, studies reporting PA as a moderate or mediated variable were also excluded. Two co-authors screened titles and abstracts to identify articles that met the inclusion criteria. Two co-authors read the articles and decided whether they should be included in the analysis or excluded. The inclusion decision was consensual and in cases of disagreement, the decision was made by mutual agreement.

Data extraction and synthesis of results

Data extraction was completed independently by one co-author. Data extracted from all studies included study details (author, year of publication, study design, recruitment processes, and date and location of the study); participant characteristics (sex, mean age); outcome and instruments, and main findings. A table was made for articles that analysed the association between PA and mental health among adults, and another table for the analyses of the association between PA and mental health among children and adolescents.

Quality assessment

The risk of bias was assessed by two independent reviewers, using the Newcastle-Ottawa Scale (NOS) [ 21 ] which was also adapted for cross-sectional studies [ 22 ]. Therefore, we used the original scale for cohort studies and the adapted scale for the cross-sectional studies. The original scale varies between 0 and 9, while the adapted scale for cross-sectional studies varies between 0 and 10, with higher scores indicating research of better quality.

Narrative synthesis

Considering the heterogeneity of methods used for the estimation of the exposures and outcomes, it was not possible to conduct a meta-analysis. Therefore, we compared the findings across the included articles according to each outcome.

Results of the search

From the database search, 734 records were identified. After removing duplicates, the titles and abstracts of 328 articles were screened concerning the eligibility criteria, and 205 were excluded. The full texts of the remaining 1237 articles were evaluated and 92 were excluded for the following reasons: sample characteristics ( n  = 24), data were not analysed regarding the association between PA and mental health variables ( n  = 23), review studies ( n  = 4), no valid instruments to assess PA ( n  = 31) and mental health ( n  = 6), the study was not in the period of the COVID-19 pandemic ( n  = 4). Thirty-one studies were included in this review, 27 about adults and old adults and 4 about children and adolescents. The flow diagram of study search and selection was created according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 23 ] and is presented in Fig.  1 . The mean score of quality was 5.7 ± 1.5. More details are presented in Tables 1 and 2 .

Flow diagram of study selection

The association between physical activity and mental health among adults and old adults

The details of the association between PA and mental health among adults and old adults are summarised in Table  1 , 27 studies were included [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 51 ].

Participants characteristics and date of filling the questionnaires

The number of participants in the 27 included studies varied between 66 [ 50 ] and 14,715 [ 40 ] participants. Regarding sex, with exception of four studies [ 24 , 31 , 40 , 48 ] the majority included more women than men. Concerning age, most articles presented the mean age range between 20 and 30 years [ 24 , 25 , 36 , 37 , 44 , 48 , 49 , 50 , 56 , 57 ], two article presented mean age between 30 and 40 years [ 33 , 43 ]; five articles presented mean age between 40 and 50 years [ 28 , 32 , 35 , 41 , 42 ]; and seven articles presented mean age above 50 years [ 26 , 27 , 34 , 45 , 46 , 47 , 51 ]. Four articles did not present mean age [ 31 , 38 , 39 , 40 ], for one article the age ranged between 21 and 35 years [ 31 ], for another, the age ranged between 21 and 40 years [ 38 ], for another, the age ranged between 17 and 69 years [ 40 ], and in one the age ranged between 27 and 53 years [ 39 ]. The majority of studies reported, the sample filled out online questionnaires. One study used interviewed by telephone call [ 34 ]. Twenty four studies conducted a cross-sectional analysis and four a longitudinal analysis.

Study location

The studies were carried out on five different continents. Ten studies from Europe [ 24 , 25 , 26 , 27 , 28 , 38 , 39 , 43 , 44 , 49 ], foure studies from Asia [ 36 , 40 , 46 , 48 , 50 ], seven studies from America [ 33 , 34 , 35 , 37 , 41 , 51 , 57 ] two studies from Africa [ 31 , 56 ], two study from Oceania [ 42 , 45 ], and two multi-centre study [ 32 , 47 ].

Outcomes and instruments

Concerning outcomes and instruments, 12 articles used the International Physical Activity Questionnaire (IPAQ) to assess PA and one article calculated an estimate of cardiorespiratory fitness (algorithm includes age, body composition, resting heart rate and PA) [ 57 ]. The others articles assessed PA with different instruments. Mental health included analyses of subjective well-being, sleep quality, depressive symptoms, anxiety, quality of life, psychological distress, motivation, resilience, affects (positive and negative), and health-related quality of life.

Main findings

Overall, all articles found a positive association between PA and better outcomes of mental health (e. g., depression, anxiety, well-being). Physical activity was explanatory variable for mental health [ 25 ]. Physical activity was positive associated with mental health [ 42 , 49 ]. Articles that observed a decrease in PA during the stay-at-home order also observed a decrease in well-being [ 47 , 56 ], negative changes in depressive symptoms [ 57 ], and negative changes in anxiety and stress symptoms [ 45 ]. This relationship seems to be bidirectional, since participants who decrease in mental health had greater reduction in physical activity [ 37 ]. Inactive people had worse well-being, highest depression and enxiety compared with moderately active and very active participants [ 24 , 51 ]. Also, inactive old adults had more depressive symptoms [ 26 ]. On the other hand, participants sufficiently active reported significantly lower depression and anxiety and higher life satisfaction. Moreover, it was found that exercise intensity seems to be important. Two studies founded that depression was significantly negatively correlated with moderate-intensity PA but not vigorous and walking/light exeeercuse [ 36 , 40 ]. Another one found that vigorous PA better predicted depressive symptoms than moderate PA [ 27 ]. Three studies suggested that the threshold of PA should be done to fells the benefits on mental health [ 28 , 39 , 50 ]. At least 4 h of MVPA reduced by 49% odds of depressive symptoms [ 28 ], and at leas 477 METs-min/week was associated with a 33% decrease in the probability of depressive symptoms [ 39 ]. On the other hand, a non-significant association was found between PA and anxiety [ 35 ], and between PA and health-related quality of life [ 46 ]. One study found that the decrease in mental wellbeing and increase in perceived stress was not related to changes in PA [ 44 ].

The association between physical activity and mental health among children and adolescents

The details of the association between PA and mental health among children and adolescents are shown in Table 2 , four studies were included [ 52 , 53 , 54 , 55 ].

The number of participants ranged between 64 and 4898 children and adolescents. More girls than boys participated in the studies. The mean ages were 11, 14, 15, and 16 years old. One study was longitudinal and presents two moments of assessment, also opting for phone or video calls to collect the data [ 52 ]. The other three studies were cross-sectional, and collected data by online surveys.

One study was from the USA [ 52 ], one from Greece [ 54 ] and the other two were from China [ 53 , 55 ].

Three studies assessed PA by the International physical activity questionnaire (IPAQ) questionnaire [ 53 , 54 , 55 ], and another one used a 24-h physical activity recall [ 52 ]. Regarding mental health, different outcomes were assessed, such as anxiety, positive and negative affect, insomnia, depressive symptoms, psychological well-being and mood states.

Moderate physical activity was associated with less state anxiety [ 52 , 53 ]. Positive affect was not related to physical activity [ 52 ]. Higher levels of physical activity were also significantly associated with lower levels of total mood disturbance [ 55 ]. Regarding the dose of physical activity, days of physical activity per week was stronger predictor of well-being than minutes of physical activity per week [ 54 ].

This systematic review focuses on the association between PA and mental health during the first year of the COVID-19 pandemic. In particular, we sought to answer if PA mitigates the effects of the stay-at-home order on mental health. The COVID-19 pandemic generated numerous challenges for public health, particularly the significant burden of mental health in the population [ 58 , 59 ]. In addition, PA has been recognized as an effective mitigation strategy for improving mental health [ 60 ]. The COVID-19 pandemic has been affecting all continents in the world, at different scales. This study analysed 31 research articles, 27 about adults and old adults and 4 about children and adolescents. The articles are mainly based on cross-sectional studies and five are longitudinal studies. In nearly all of the studies comprised in the present systematic review, investigators used online surveys as the main procedure to collect data. Overall, the studies suggested that higher PA is associated with less negative mental health symptoms, such as depression, anxiety, well-being, and fear, independently of age.

The studies observed that women showed more depressive symptoms than men [ 28 , 39 , 50 , 53 , 55 ], and this trend increases with age [ 24 ] Furthermore, individuals with a lower level of masculinity traits (not specifically females) increased risk of developing depression [ 36 ], and women experienced more generalised anxiety [ 41 ]. The reduction of PA levels may mostly influence the mental well-being of females [ 38 , 49 ]. Those findings are expected since the literature is consistent in signalising sex differences in most mental disorders [ 61 ]. On the whole, the prevalence rates of anxiety and depression were both higher than the rates found in previous studies before the COVID-19 pandemic [ 36 , 37 , 43 , 45 , 48 , 57 ], which highlights a worsening in mental health during the first year of the COVID-19 pandemic. Regarding age, younger individuals experienced significantly higher anxiety and depression, also income influenced mental health, lower-income participants present worse mental health [ 37 ].

Five articles conducted a longitudinal study. Among those studies, four enrolled adults and old adults [ 28 , 44 , 46 , 50 , 51 ] and in one the sample comprised children [ 52 ]. Among the studies that have collected measures before and after the stay-at-home order, both observed a significant reduction in PA [ 44 , 46 ]. Two study collected measures after the stay-at-home order and during this period physical activity mean score decreased minimally [ 51 ], and depressive symptoms increase as the weeks of isolation go [ 28 ]. Other studies also reported a reduction in self-reported PA [ 26 , 33 , 34 , 37 , 38 , 42 , 43 , 45 , 56 , 57 ] and increase in sedentary behaviour [ 25 , 26 , 34 ]. Individuals who reported larger decrease in MVPA pre to during COVID-19 reported relatively poorer mental health [ 33 ]. Participants whose mental health got “worse” or “much worse” had greater reductions in physical activity [ 37 ]. Also, increased levels of physical activity were associated with stronger effects on wellbeing [ 42 ]. The reduction was more pronounced in men than women [ 32 , 38 , 44 ], in vigorous PA [ 38 ], and between those with lower health-related quality of life scores before the COVID-19 pandemic [ 46 ]. The possible explanation for this sex-difference is that men are more engaged in group/community PA and sports in clubs or gyms, and those were more impacted by the COVID-19 restrictions. Also, women are more engaged in low and moderate physical activities, which can be done at home. Besides, women spent more time in housework activities. Women without changes in childcare provision reported more opportunities to be physically active [ 41 ]. The same sex-differences were observed in an Italian study [ 62 ]. Increases in PA were observed for a minority, but the majority of the respective study samples demonstrating a positive change were individuals who did not meet recommended PA guidelines before the COVID-19 pandemic [ 32 , 38 ]. Additional reasons could be an increase in awareness for health issues and more time to pursue PA during the stay-at-home order [ 32 ]. These behaviour changes can help to maintain a more active lifestyle during the pandemic. Another study found an increase of 40% in PA in a sample that was already active before the COVID-19 pandemic [ 35 ]. PA could be used as a coping strategy to deal with the consequences of the pandemic. The place where individuals prefer to practice PA seems to be important, since active participants reported greater connectedness to nature and nature relatedness than the inactive population [ 35 ].

There was no consensus across studies for the optimal PA levels for mitigating negative mental symptoms. The more the physical activity is frequent and vigorous, the best people feel themselves [ 24 ]. Among Chinese students, 2500 METs minute/week of PA every week was the optimal dose to alleviate negative emotion [ 50 ]. On the other hand, a Spanish community sample study showed that 477 METs-minute/week was associated with a 33% decrease in the probabilities of notable depressive symptoms [ 39 ]. The difference between the values must be relativised considering the samples’ characteristics. The first one concerns students with a mean age of 20 years [ 50 ], and the second one concerns a community sample with a mean age of 43.2 years for women and 40.5 years for men [ 39 ]. In addition, it is claimed that at least 3000 METs-minute/week reduce the odds of depressive symptoms by 47% [ 39 ]. These studies used the IPAQ to assess PA, and according to IPAQ, to reach a minimally active category at least 600 METs minute/week is needed [ 63 ]. The American College of Sports Medicine also recommends for healthy adults aged 18–65 years at least 600 METs minute/week but did not specify the minimum dose to prevent depressive symptoms [ 14 , 64 ].

Studies also examined the association between PA and mental health according to PA intensity. Moderate-intensity PA (e.g., walking or jogging on a treadmill, using an elliptical trainer, cleaning house) is associated with better mental health outcomes than vigorous-intensity PA [ 36 , 40 ], and light-intensity PA [ 40 ]. On the other hand, vigorous-intensity PA better predicted depressive symptoms than moderate-intensity PA; also the effect size was higher for the association between vigorous-intensity PA and level of resilience compared with moderate-intensity PA [ 27 ]. One study found that performing high PA levels has no positive effect on depressive symptoms [ 39 ]. Another study explored the type of PA and showed that stretching and resistance training were associated with lower anxiety, and three types of PA (household chores, stretching, and resistance training) were associated with lower depression symptoms [ 48 ].

One study explored the association of specific types of physical exercise and mental health, and founded that home-based group entertainment exercise, rope skipping and badminton, Chinese traditional sports, video dancing and sensory-motor games present a greater reduction in mental health than others types [ 40 ].

Sedentary behaviour was observed in few studies and contradictory findings were observed. No association between sedentary time and depressive symptoms was observed [ 26 , 36 ]. However, other studies have shown that sedentary behaviour was associated with poorer mental health [ 25 ], well-being [ 32 ] and perceived stress [ 44 ].

Concerning the association between PA and mental health outcomes among children and adolescents, only four articles were selected. Some issues must be highlighted. This population had to face, beyond the reality that changed from the COVID-19 pandemic, the changes in the education system such as online learning became the main learning method for students and uncertainty of academic development, which probably caused more anxiety level [ 52 , 53 ]. Both moderate and highly active groups were significantly associated with less depressive symptoms [ 53 ] and anxiety [ 52 , 53 ], and only the most active adolescents reported significantly lower insomnia symptoms [ 53 ] and better mood states [ 55 ]. Days of physical activity per week was stronger predictor of well-being than minutes of physical activity per week [ 54 ].

Regarding old-age samples, the studies mentioned the particular vulnerability to changes in social circumstances, and highlight the importance of health-related quality of life [ 46 ] and levels of resilience [ 27 ] to deal with the consequences of the COVID-19 pandemic on the PA level. The stay-at-home order can cause greater distress and feelings of sadness, considered specific risk factor for depressive symptoms [ 34 ]. Mental health and physical activity decrease pre to during stay-at-home order [ 47 ]. In a group that were previously regular participants of a formal exercise program, MVPA was significantly higher within the non-depressed group compared with depressed group [ 26 ]. Being active previously of COVID-19 confinement did not prevent 30.4% of Brazilian older adults from having depressive symptoms, but these results is much lower than prevalence of depression in Brazilian general population, which is 68% [ 34 ].

The present systematic review had some limitations that must be mentioned. First, the studies present correlative analyses, not causal ones, thus randomised controlled trials must be conducted in the context of the COVID-19 pandemic and the stay-at-home order to clarify the direction of the association. However, beyond the COVID-19 context, randomised controlled trials showed that PA interventions show beneficial effects on mental health outcomes such as depression and anxiety [ 65 ]. Thus, a nuanced perspective particularly during the COVID-19 context in future research is needed. Moreover, the included studies with community samples were limited, and the analyses were mostly based on convenience samples with college students, which had specific characteristics and low mean age. Thus, future research needs to focus on representative study samples.

This review helps to clarify the positive association between PA and mental health during the first year of the COVID-19 pandemic, especially considering the effects of the stay-at-home order worldwide. Although there is an association between increased PA and improved mental health, further studies are needed, specifically randomised clinical trials, to identify the direction of this relationship, and what kind of PA, intensity, and frequency are most indicated to maximise the effects. Also, an investigation to examine the association during the second year of the COVID-19 pandemic is needed. The impact of the COVID-19 pandemic on mental health may be continuous and long-term [ 66 , 67 ]. Thus, public health agencies must provide timely and effective interventions, in which PA and exercise should be a priority.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Coronavirus disease-19

Severe acute respiratory syndrome coronavirus-2

Physical activity

World Health Organization

International physical activity questionnaire

Metabolic equivalent

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Acknowledgements

AI acknowledges support from the Swiss National Centre of Competence in Research LIVES – Overcoming vulnerability: life course perspectives, granted by the Swiss National Science Foundation (grant number: 51NF40-185901). AI acknowledges support from the Swiss National Science Foundation (grant number: 10001C_189407). André Werneck is supported by the São Paulo Research Foundation (FAPESP) with a PhD scholarship (FAPESP process: 2019/24124-7). This paper presents independent research. The views expressed in this publication are those of the authors and not necessarily those of the acknowledged institutions.

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Marconcin, P., Werneck, A.O., Peralta, M. et al. The association between physical activity and mental health during the first year of the COVID-19 pandemic: a systematic review. BMC Public Health 22 , 209 (2022). https://doi.org/10.1186/s12889-022-12590-6

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The relationship between physical exercise and brain health is a burgeoning field of research in neuroscience, with a pivotal impact on our understanding of cognitive well-being, mental health, and aging. Existing studies evidence the positive influences of regular physical activity on brain health, suggesting its implications on learning, memory, and mood. Despite significant advancements, comprehensive analysis incorporating broader perspectives and deeper explorations remain scarce. The objective of this Research Topic is to create an enriching platform for focused discourse on the interconnection between physical exercise and brain health. The goal is to bring together theoretical and experimental research papers that depict a comprehensive overview of recent developments, examine the mechanistic underpinnings of the exercise-brain interaction, and delve into the future potential of this promising area. We welcome contributions that explore, but are not limited to, the following themes: • Impact of various types of exercises on mental health and cognitive functions. • Role of physical activity in stress, anxiety, and mood disorders management. • The molecular and neurochemical effects of exercise on the brain. • Exercise mitigating neurodegenerative disorders and age-related cognitive decline. • Effects of physical exercise on brain development and neuroplasticity. Manuscript types desired for this topic are Original Research, Review, Systematic Review, Mini Review, Perspective, and Opinion articles. Emphasis is on rigorous and high-quality methodology, analysis, and data presentation. The Research Topic places a high priority on interdisciplinary approaches and the potential practical implications of research findings.

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Nutritional Considerations in Exercise-Based Heat Acclimation: A Narrative Review

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• Paul Rosbrook   ORCID: orcid.org/0000-0002-1767-1615 1 ,
• Lee M. Margolis 2 &
• J. Luke Pryor 1  

In addition to its established thermoregulatory and cardiovascular effects, heat stress provokes alterations in macronutrient metabolism, gastrointestinal integrity, and appetite. Inadequate energy, carbohydrate, and protein intake have been implicated in reduced exercise and heat tolerance. Classic exercise heat acclimation (HA) protocols employ low-to-moderate–intensity exercise for 5–14 days, while recent studies have evolved the practice by implementing high-intensity and task-specific exercise during HA, which potentially results in impaired post-HA physical performance despite adequate heat adaptations. While there is robust literature demonstrating the performance benefit of various nutritional interventions during intensive training and competition, most HA studies implement few nutritional controls. This review summarizes the relationships between heat stress, HA, and intense exercise in connection with substrate metabolism, gastrointestinal function, and the potential consequences of reduced energy availability. We discuss the potential influence of macronutrient manipulations on HA study outcomes and suggest best practices to implement nutritional controls.

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Rosbrook, P., Margolis, L.M. & Pryor, J.L. Nutritional Considerations in Exercise-Based Heat Acclimation: A Narrative Review. Sports Med (2024). https://doi.org/10.1007/s40279-024-02109-x

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Optimizing labor duration with pilates: evidence from a systematic review and meta-analysis of randomized controlled trials

• Arezoo Haseli   ORCID: orcid.org/0000-0003-1487-633X 1 ,
• Farideh Eghdampour 2 ,
• Hosna Zarei   ORCID: orcid.org/0000-0002-3192-624X 3 ,
• Zahra Karimian 4 &
• Dara Rasoal   ORCID: orcid.org/0000-0002-0068-943X 5  

BMC Pregnancy and Childbirth volume  24 , Article number:  573 ( 2024 ) Cite this article

Metrics details

Pilates has captured interest due to its possible advantages during pregnancy and childbirth. Although research indicates that Pilates may reduce labor duration, alleviate pain, and improve satisfaction with the childbirth experience, consensus on these outcomes remains elusive, underscoring the necessity for additional studies.

This systematic review and meta-analysis aimed to assess the impact of Pilates exercises on labor duration among pregnant women.

The online database was searched to yield the literature using the terms of ‘Pilates’, ‘childbirth’, and ‘labor duration’, and similar terms including PubMed, Clinical Key, Scopus, Web of Science, Embase, and Cochrane Database of Systematic Reviews up to June 25, 2023. Studies were considered eligible if they were randomized or clinical controlled trials (RCTs/CCTs) published in English, focusing on healthy pregnant women without exercise contraindications. The studies needed to include interventions involving Pilates or exercise movement techniques, a comparison group with no exercise, and outcomes related to labor duration, the period of the active phase, and the second stage of delivery.

Eleven studies, totalling 1239 participants, were included in the analysis. These studies provided high-quality evidence from exercise only RCTs/CCTs. The findings indicated a significant reduction in the active phase of labor (8 RCTs, n  = 1195; Mean Difference [MD] = -56.35, 95% Confidence Interval [CI] [-89.46 to -23.25]) and overall labor duration (8 RCTs, n  = 898; MD = -93.93, 95% CI [-138.34 to -49.51]) in pregnant women who engaged in Pilates exercises compared to those who did not but doesn’t affect on the duration of the second stage of labor (7 RCTs, n  = 1135; MD = -0.11, 95% CI [-7.21 to 6.99]).

Conclusions

While this review primarily addresses the effects of Pilates on healthy and low-risk pregnant women, the findings suggest a potential role for Pilates in shortening labor duration. Therefore, engaging in Pilates or similar physical activities is recommended for pregnant women to potentially facilitate a more efficient labor process.

Peer Review reports

Introduction

Pilates is characterized as a distinct form of resistance and strength training [ 1 ], and has been described as a “healing exercise” [ 2 ]. The primary objective of Pilates is to establish muscular strength and flexibility by fortifying weaker muscles and augmenting the elasticity, and preparing the body of a pregnant woman for childbirth and postnatal recovery [ 3 , 4 , 5 ]. Pilates, a form of exercise increasingly popular in health promotion programs worldwide, especially during pregnancy, emerges as a promising activity in this context [ 6 ]. Several studies have investigated the impact of Pilates on various aspects of pregnancy and childbirth [ 3 , 7 ]. Some studies suggest that Pilates may reduce perceived stress during pregnancy, potentially creating mental space for bonding with the unborn child and enhance maternal satisfaction [ 7 , 8 , 9 ]. This exercise method is recommended as a particularly relevant physical activity for healthy pregnant women, owing to its capacity to enhance postural coordination, flexibility, balance, and overall quality of life [ 8 , 10 ]. Moreover, the Pilates method has been used by pregnant women to improve the physical and psychological outcomes of pregnancy, potentially reducing pain such as back pain and pain during labor [ 11 , 12 ]. Additionally, Pilates has been reported to have no negative effects on gestational age, birth weight of infants, and APGAR scores [ 13 ]. However, the comparison of the effects of clinical Pilates exercises with and without childbirth training on pregnancy and birth results did not show any negative impact on the APGAR score [ 14 ].

Contemporary research posits that sedentary women should commence physical activity during pregnancy to mitigate these risks [ 15 ]. At the same time, there has been an increasing focus in recent years on enhancing the childbirth experience and employing techniques to reduce labor duration [ 16 ]. Another aspect that is described from previous studies is the established correlation between protracted labor and heightened risks of mortality, as well as maternal and perinatal complications [ 17 , 18 , 19 , 20 ]. These complications encompass a range of issues, including increased maternal fatigue, the necessity for induction and cesarean section, instrumental delivery, uterine atony, maternal mortality, elevated fetal distress, hypoxia, low apgar scores, and ultimately, fetal demise [ 21 ].

However, the effects of Pilates on childbirth outcomes are still a topic of debate. Some studies have shown that Pilates exercise alone had no significant effect on increasing the rate of natural childbirth in primiparous women [ 9 ]. Similarly, there is no consensus on the effects of Pilates methods with voluntary pelvic floor muscle contractions in pregnant women [ 22 ].

Research has also demonstrated that Pilates exercises applied during pregnancy could improve women’s core stability and balance levels and reduce their fear of childbirth [ 23 ]. While some studies suggest the potential benefits of Pilates on labor duration, there is still a lack of consensus on its overall impact. This systematic review and meta-analysis have been conducted with the objective of elucidating the impact of Pilates exercise on the duration of labor.

This systematic review has been meticulously conducted in alignment with the established protocols and recommendations outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 24 ].

Literature search and data collection

This research was conducted through June 2023 using the following search terms: (childbirth OR pregnancy OR pregnant OR labor OR obstetric) AND (Pilates OR physical activity OR exercise OR movement techniques) AND (duration of labor OR length of labor OR length of delivery). The research was carried out using the following databases: PubMed, Clinical Key, Scopus, Web of Science, Embase, and the Cochrane Database of Systematic Reviews. See the supplementary file for detailed search strategies specific to each database.

Studies selection and eligibility criteria

The main inclusion criteria for this systematic review, structured according to the PICOS framework, are as follows:

Population (P): Pregnant women. Intervention (I): Pilates exercises and physical activity. Comparison (C): No activity. Outcomes (O): Duration of labor, active phase duration, second stage duration. Study Design (S): Randomized Controlled Trials (RCT)/Clinical Controlled Trials (CCT).

Additionally, the studies must be; published in a peer-reviewed journal up to June 2023, and written in the English language.

Two authors (A.H. and H.Z.) initially screened the titles and abstracts of all retrieved records, and subsequently, reviewed the full texts independently. In instances of uncertainty regarding whether studies met the inclusion criteria, a third researcher (F.E.) was consulted, and decisions were made by consensus. We excluded articles not published in peer-reviewed journals, encompassing conference papers, theses, dissertations, books, book chapters, and reports from non-peer-reviewed sources, as well as those in languages other than English. Furthermore, we excluded studies that did not utilize a randomized controlled trial (RCT) or clinical controlled trial design. We included all studies published from the earliest date of consideration up to June 25, 2023, to ensure the relevance and timeliness of the evidence gathered. In cases where multiple reports originated from the same study, the most comprehensive report was selected. If the full text of an article was unavailable, the information provided in the abstract was utilized. However, if the abstract did not furnish sufficient information, the article was excluded from the study. Upon completion of the search, the EndNote program was employed to eliminate duplicates. Relevance checks were performed based on both the titles and abstracts, as well as the full texts. Additionally, the references of the included studies were reviewed to identify any potentially missing relevant papers.

Quality assessment

RCTs were included in the evaluation using the Cochrane Risk of Bias tool (version 1). This tool comprises several domains: selection, performance, detection, attrition, reporting, and other biases. The potential for bias in these assessments can be categorized as high, low, or unclear risk of bias. The quality of all included studies was independently assessed by two investigators, AH and HZ. In cases of disagreement, resolutions were sought through consultation with colleagues FA and DR.

Data extraction

The data were extracted into a sheet (Tables  1 and 2 ). The extracted data included the following items: summary characteristics such as author (s), year, sample size, study design, age, body weight, BMI, height, education, gestational age at study entry, session characteristics, and duration of labor. Overall labor duration is defined length of the first and second stages of labor. The first stage of labor is the time elapsed from the beginning of contractions to 10 cm of dilation; which includes the latent phase (the beginning of contractions up to 4 cm of dilation) and the active phase (the time elapsed from 4 to 10 cm. of dilation); the second stage is the time elapsed from full dilation (10 cm of dilation) until fetal expulsion [ 25 ].

Data analysis

Statistical analysis was conducted using Review Manager (RevMan) version 5.4 and STATA version 17. A significance level was set at a P value of less than 0.05. Overall effects were synthesized through the mean and standard deviation of labor length. Heterogeneity among studies was assessed using the I-squared test (I²). If the I² value exceeded 50%, outlier studies were first removed to homogenize the study sample. When heterogeneity persisted, random-effects models were utilized for the analysis instead of fixed-effect models. Publication bias was assessed using Egger’s test.

Equity, diversity, and inclusion statement

Our research team is dedicated to promoting diversity, equity, and inclusion in clinical practice, research, and training programs. Accordingly, the processes of data extraction, study selection, quality assessment, analysis, and interpretation were conducted independently to ensure results that accurately reflect the most diverse and realistic picture possible. We incorporated data from all primary research worldwide that met the inclusion criteria, without bias towards race, ethnicity, culture, socioeconomic status, or geographical regions.

Figure  1 presents a chart detailing the flow of studies through this meta-analysis. In total, 330 records were screened, comprising 316 studies identified through database searches and 14 studies identified through other sources. After the removal of duplicates (24 studies), 290 studies did not meet the inclusion criteria and were subsequently excluded. The remaining 16 articles were retrieved in full for further examination. Of these, eleven studies met the inclusion criteria. The remaining five studies were excluded for the following reasons: two were cohort studies, one was a secondary publication, and two studies did not report the effect size.

PRISMA flow diagram of the screening procedure

The quality assessment (risk of bias)

The authors have meticulously noted and classified the biases associated with the research methodologies into three distinct categories: low risk, high risk, and unclear risk. High Risk : Aktan [ 14 ], Ferreira [ 26 ], Gehan [ 27 ], Perales [ 28 ], and Mazzarino [ 29 ] were categorized as high risk due to methodological limitations, potential biases, and data reliability issues.

Barakat [ 30 ], Bolanthakodi [ 31 ] and Haakstad [ 32 ] exhibited robust methodology, adequate sample sizes, and clear results, justifying their low-risk categorization.

Unclear risk

Ghandali [ 9 ], Rodriguez-Blanque [ 33 ] and Price [ 34 ] was categorized as uncertain due to ambiguities in its methodology and potential unaddressed confounding factors. This classification adheres to the criteria set forth by the Cochrane Collaboration tool. Detailed insights into this categorization, along with an exhaustive breakdown of the types and instances of bias present in the study methodologies, are comprehensively illustrated in Fig.  2 .

Risk of bias graph for studies

Study characteristics

Relevant information from the 11 selected samples is concisely summarized in Table  2 . The meta-analytic sample comprised a total of 1239 participants, with 602 in the intervention group and 637 in the control group. The mean age of the studied participants ranged from 23.4 years [ 31 ] to 36.8 years [ 35 ]. Three studies were conducted in Spain [ 28 , 30 , 33 ], while the remaining were carried out in Turkey [ 14 ], Australia [ 29 ], Norway [ 32 ], Iran [ 9 ], Brazil [ 26 ], the USA [ 34 ], India [ 31 ], and Egypt [ 35 ]. All study designs were Randomized Control Trials, except for two studies which were Clinical Control Trials [ 26 , 35 ]. The number of exercise sessions in the studies varied from 7 [ 31 ] to 96 [ 34 ]. Some studies reported only weight [ 32 ], or body mass index (BMI) [ 9 , 26 , 28 , 30 , 34 , 35 ], while others included all of metrics (weight, height, and BMI) [ 14 , 29 , 31 , 33 ]. Three studies have presented only the duration of labor [ 14 , 29 , 33 ], while other studies have reported the duration of the active phase, the second stage, and/ or the duration of labor separately [ 9 , 26 , 28 , 30 , 31 , 32 , 34 , 35 ] (Tables  1 and 2 ). No studies reported an increased risk of adverse birth outcomes from Pilates exercises among previously inactive, healthy women.

Quantitative data synthesis

The pooled effect size of Pilates exercises indicates a significant reduction in duration of the active phase (Mean Difference [MD] = -56.35, 95% Confidence Interval [CI] [-89.46 to -23.25], p  < 0.001) with a sample size of 1195.

The mean difference in active phase of labour (− 56.35) means that Pilates reduces the duration of active phase of labor by 56.35 min in the intervention group compared to the control group. The analysed data were heterogeneous (I² = 60.96%), hence random-effects models were utilized for the analysis, but the heterogeneity could not be fully resolved (see Fig.  3 A).

A ; Forest plot of duration of the active phase of labor, B ; Forest plot of duration of the second stage of labor, C ; Forest plot of duration of labor (sum of active phase and second stage)

The Pilates exercises didn’t decrease the duration of the second stage of labor (MD = -0.11, 95% CI [-7.21 to 6.99], P value = 0.98), in a sample of 1135 pregnant women. The pooled estimate was homogeneous (I² = 71.75%, using a random-effect model) (refer to Fig.  3 B).

Eight articles investigated the overall duration of labor (sample size = 898 pregnant women). The data indicated that the Pilates group experienced a shorter duration of labor compared to the control group (MD = -93.93, 95% CI [-138.34 to -49.51], P value = 0.001). The results showed heterogeneity (I² = 61.97%), and this heterogeneity could not be resolved (refer to Fig.  3 C).

Assessment of publication bias

We did not observe publication bias for the study in any of the variables studied. The estimated bias coefficient of labor duration was − 0.29 (Egger bias B = − 0.30 (95% CI: − 2.66–2.08) with a standard error of 0.97, giving a p -value of 0.77. Thus, the test provides no evidence for the presence of small-study effect. Thus, the test provides no evidence for the presence of small-study effect. Figure  4 presents the funnel plot result with the 95% confidence limit. The estimated bias coefficient of active phase duration was − 0.85 (Egger bias B = − 0.88 (95% CI: − 3.19–1.50) with a standard error of 0.96, giving a p -value of 0.41 and it was 1.17 (Egger bias B = 0.50 (95% CI: − 4.82–7.16) with a standard error of 2.33 ( p -value = 0.64) for second stage of labor.

Presentation of funnel plot

The present study aimed to systematically review the literature regarding the efficacy of Pilates in reducing labor length. This meta-analysis was conducted using optimal methods for secondary analysis and included 11 primary studies, all of which were clinical trials. The analytical results indicate that Pilates exercises can significantly shorten the overall duration of labor, including both the active phase and the second stage.

The meta-analysis revealed a notable difference between the Pilates exercise group and control group in the duration of the first stage of labor, despite contradictory findings in the primary studies. The disparity in results from these primary studies could be attributed to variations in sample sizes (ranging from 60 to 325) and the timing of intervention initiation (between 12 and 39 weeks’ gestation). A detailed examination of the primary studies revealed that Pilates exercises were generally more effective in studies characterized by longer durations, higher frequency, and earlier commencement of the intervention [ 28 ]. Therefore, under these specific conditions, Pilates appears to strengthen pelvic muscles, increase pelvic diameter, relax muscles, and consequently enhance the condition of the birth canal parts. This, in turn, contributes to the shortening of the active phase of labor [ 29].

The findings of this study demonstrate that Pilates doesn’t affect the duration of the second stage of labor. This conclusion confirm with some earlier studies which indicated no significant difference in second stage’s length between women who practiced Pilates and those who did not [ 14 ]. It seems that the length of the second stage of labor, which is defined from the dilatation of the cervix to the expulsion of the fetus, is more influenced by the cephalo-pelvic proportion, birth weight, the strength of the mother’s perineal tissue, and station at complete dilatation [ 36 ] as well as differences in obstetric population characteristics and variation in clinical practice [ 37 ].

In this study, Pilates was observed to reduce the overall length of labor. Supporting this finding, the outcomes of several meta-analyses have underscored the positive impact of exercise programs on facilitating the childbirth process, particularly in shortening the duration of delivery [ 38 ]. However, there exists a divergence of opinion among researchers on this matter. For instance, the findings of two separate meta-analyses suggest no significant association between the regularity, intensity, and duration of exercise and the length of childbirth [ 39 , 40 ]. This discrepancy may be attributable to the type of exercise. Veisy et al., for example, focused on the influence of aerobic exercise on labor duration [ 39 ]. Contrastingly, Pilates, a non-aerobic form of exercise, is primarily recognized for its capacity to enhance physical, mental, and motor functions [ 15 ].

This form of exercise encompasses a series of low-impact routines designed to enhance strength and flexibility across the body. Regular participation in these exercises has been demonstrated to fortify the pelvic floor muscles and improve their structural functionality [ 41 ]. The enhancement of central and pelvic floor muscle strength, flexibility, and the adoption of proper breathing techniques through Pilates are known to facilitate the birthing process [ 9 , 42 ]. Additionally, Pilates, initially conceptualized as a body conditioning method termed ‘Contrology’, is founded on six key principles: breath, centering, concentration, control, precision, and flow [ 43 ]. Individual investigations into each of these elements have shown that they collectively contribute to a reduction in the duration of childbirth [ 44 , 45 ].

Notably, the impact of Pilates exercise on labor duration has not been previously analyzed in a systematic review or meta-analysis with a sample size of this scale. The breadth of studies and the substantial number of participants involved in this analysis afford some of the most valid and reliable conclusions that can be drawn on this subject.

Clinical implications

Considering that prolonged labor presents a significant clinical challenge in contemporary midwifery practice, leading to various complications for both mother and child, one effective strategy to enhance labor comfort is the reduction of labor duration, specifically through methods that alleviate the duration of pain. The findings of this review indicate that practicing Pilates during pregnancy serves as a viable approach to decreasing labor length. Pilates, characterized as a discipline that bolsters physical, mental, and motor capacities, comprises a series of low-impact exercises aimed at strengthening and increasing flexibility throughout the body [ 15 ]. Analysis of primary studies reveals that Pilates can be reduced labor duration.

To date, there have been no reports of risks associated with moderate-intensity exercise for either the mother or the infant [ 46 ]. In light of this, low-risk pregnant women are encouraged to engage in progressive aerobic and resistance exercises before, during, and after childbirth. Nevertheless, it is advisable for pregnant women to undergo clinical evaluation before initiating any exercise program to ensure there are no medical contraindications to continuing with the exercise [ 47 ]. Engaging in Pilates exercises of moderate intensity during pregnancy has been shown to facilitate the delivery process.

Our findings endorse the capability of Pilates exercise to reduce the duration of labor in pregnant women. Therefore, Pilates could potentially be incorporated into childbirth preparation classes as a beneficial practice.

Limitations

This study encountered several limitations, both at the level of the individual studies and the review itself. A notable limitation within the primary studies was the lack of reported data on the intensity of the Pilates exercises. Additionally, variations in the initiation timing and duration of exercise sessions may have influenced the results of the analysis. Another limitation at the study level was the heterogeneity of the samples. For instance, while some studies had exercises performed under professional supervision, others involved participants practicing at home without such oversight. Moreover, the participant demographics varied across studies, with many focusing on primiparous women, while others included both multiparous and primiparous participants. This variability, particularly in terms of parity, was not accounted for as a potential confounding factor, potentially contributing to the high heterogeneity observed in the meta-analyses. Consequently, the results of this study should be interpreted with caution.

At the review level, the mean effect size of the active phase period and labour duration was analysed using a random-effects model due to the heterogeneity of the studies. Furthermore, this review was limited to scientific papers published in English, representing another constraint on its scope.

The evidence presented herein underscores that engaging in Pilates exercises during pregnancy is not only safe but also beneficial in optimizing the delivery process and shortening labor duration. Consistent with the guidelines of the American College of Obstetricians and Gynecologists (ACOG), this study reinforces the recommendation that, despite physiological and anatomical changes during pregnancy, women should be encouraged to maintain physical activity. Pilates, as demonstrated, can shorten length of labor. It is recommended that midwives emphasize the use of this exercise in childbirth preparation classes to reduce the duration of childbirth.

Data availability

Data is provided within the manuscript or supplementary information files.

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Acknowledgements

We acknowledge all authors who contributed to the primary studies. This study is the result of research project No. 4020044, approved by the Student Research Committee at Kermanshah University of Medical Sciences, Kermanshah, Iran. Our gratitude extends to the officials of the Clinical Research Development Center at Motazedi Hospital, Kermanshah University of Medical Sciences, the Department of Midwifery at Kashan University of Medical Sciences, and the School of Health and Welfare at Dalarna University, Sweden, for their invaluable support and contributions.

The present study was supported by the Student Research Committee, Kermanshah University of Medical Sciences. Kermanshah, Iran (Grant number: 4020044).

Open access funding provided by Dalarna University.

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Arezoo Haseli

Department of Midwifery, Islamic Azad University, Marand, Iran

Farideh Eghdampour

Student Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran

Hosna Zarei

Department of Midwifery, Nursing and Midwifery Faculty, Kashan University of Medical Sciences, Kashan, Iran

Zahra Karimian

School of Health and Welfare, Dalarna University, Högskolegatan 2, Falun, 79188, Sweden

Dara Rasoal

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AH and HZ designed the search strategy and selection criteria. They screened the eligibility of the articles, reviewed them independently, assessed the quality of the articles, and constructed the tables and figures. FE and DR, as the third and fifth co-authors, checked for contradictions and drafted the results and discussion sections. ZK conducted new and updated statistical analyses during the revisions to the journal. AH wrote the first draft of the manuscript, while DR edited and proofread the entire manuscript. Finally, FE also proofread the manuscript and provided comments. All authors read and approved the final version of the manuscript.

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Haseli, A., Eghdampour, F., Zarei, H. et al. Optimizing labor duration with pilates: evidence from a systematic review and meta-analysis of randomized controlled trials. BMC Pregnancy Childbirth 24 , 573 (2024). https://doi.org/10.1186/s12884-024-06785-5

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DOI : https://doi.org/10.1186/s12884-024-06785-5

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Physical exercise and quality of life following cancer diagnosis: a literature review

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• 1 Faculty of Physical Education, University of Alberta, Edmonton, Canada.
• PMID: 10499138
• DOI: 10.1007/BF02908298

With almost 8 million Americans alive today who have been through the cancer experience, it is important to develop interventions to maintain quality of life (QOL) following cancer diagnosis. Physical exercise is an intervention that may address the broad range of QOL issues following cancer diagnosis including physical, functional, psychological, emotional, and social well-being. The purpose of the present article was to provide a comprehensive and critical review of the topic and to offer suggestions for future research. The review located 24 empirical studies published between 1980 and 1997. Eighteen of the studies were interventions (i.e. quasi-experimental or experimental) but most of these were preliminary efficacy studies that suffered from the common limitations of such designs. Overall, however, the studies have consistently demonstrated that physical exercise has a positive effect on QOL following cancer diagnosis, including physical and functional well-being (e.g. functional capacity, muscular strength, body composition, nausea, fatigue) and psychological and emotional well-being (e.g. personality functioning, mood states, self-esteem, and QOL). Besides overcoming the limitations of past research, recommendations for future research included: (a) extending the research beyond breast and early-stage cancers; (b) comparing and integrating physical exercise with other QOL interventions; (c) examining resistance exercises, the timing of the intervention, and contextual factors; (d) expanding the breadth of the QOL indicators examined; and (e) investigating the rates and determinants of recruitment and adherence to an exercise program following cancer diagnosis.

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Fortifying industry 4.0: internet of things security in cloud manufacturing through artificial intelligence and provenance blockchain—a thematic literature review.

1. Introduction

1.1. rationale, 1.2. objectives.

• Deeper analysis and synthesis: To perform an in-depth analysis and synthesis of existing studies, identify gaps and areas requiring further research, and assess the significance of the existing research in addressing these gaps;
• Integration of findings: To present an informative framework by integrating the findings, providing valuable insights;
• Discussion on challenges and limitations: To justify the feasibility and challenges of the integrated framework and provide valuable insights for future research and implementation by discussing the limitations and potential challenges associated with the framework.

1.3. Research Questions

2. methodology, 2.1. search strategy.

• Category 1: Keywords related to cloud manufacturing, including “Industry 4.0”, “security risks”, “cloud manufacturing”, “design approaches”, and “design vulnerabilities”;
• Category 2: Keywords related to cybersecurity risks to the IoT, including “cybersecurity vulnerabilities”, “Internet of Things”, “cloud-based manufacturing”, “IoT security threats”, and “cloud manufacturing”;
• Category 3: Keywords related to provenance blockchain, including “blockchain technology”, “IoT security”, “data provenance”, “authentication”, and “threat mitigation”;
• Category 4: Keywords related to AI, including “Internet of Things”, “IoT security”, “predictive analytics”, “artificial intelligence”, “security measures”, and “future directions”.

2.2. Study Selection Process

• The cloud manufacturing design and specific aspects causing exposure to cybersecurity should be covered;
• Articles should comprehensively address the cybersecurity risks associated with IoT devices in cloud manufacturing;
• Blockchain security for IoT devices should be covered with some proposed models, at least theoretically;
• Articles should be related to IoT security in the context of provenance, blockchain, and AI, even if not integrated as a solution;
• AI for IoT security should be comprehensively covered.
• Articles that cover cloud manufacturing design in general terms were excluded;
• Articles that did not cover IoT security risks in the context of cloud manufacturing were excluded;
• Articles that did not discuss models of provenance and blockchain and provided only generic discussion were excluded;
• Articles that did not provide technical details of the role of AI in IoT security were excluded;
• Articles that did not provide technical details of the role of AI in IoT security, albeit providing generic discussion, were excluded.

2.3. Analysis of Data

• Cloud Manufacturing Design: The first theme focuses on the design of cloud manufacturing to establish the research context and identify the problem area;
• Cybersecurity Risks to IoT Devices: The second theme examines cybersecurity risks associated with IoT devices in a cloud manufacturing setting, aiming to establish a comprehensive problem description;
• Provenance and Provenance Blockchains: The third theme explores the concept of provenance and the use of provenance blockchains to enhance security and traceability;
• Artificial Intelligence in IoT Security: The fourth theme investigates the role of artificial intelligence in IoT security.

3.1. Theme 1: Cloud Manufacturing Design

3.2. theme 2: cybersecurity risks to iot used in cloud manufacturing processes, 3.3. theme 3: provenance blockchains for iot security, 3.4. theme 4: artificial intelligence for iot security, 3.5. review of blockchain frameworks.

• R0, R1, R2, and R3 = Organizations collaborating to build the blockchain network for provenance called BNP; R0 is the contracting authority, and others are suppliers;
• R1 has access only to the network configuration CC1; R2 and R3 have access to network configurations CC2 and CC3.
• P1, P2, and P3 = Blockchain peers for the suppliers R1, R2, and R3;
• O = Blockchain contracting authority (owner of the smart contracts);
• C1 and C2: Network channels for network configurations CC1 and CC2, respectively;
• CA0, CA1, CA2, and CA3: Certification authorities of the organisations R0, R1, R2, and R3, respectively;
• S1 and S2: State databases of network configurations CC1 and CC2;
• L1 and L2: Ledgers of network configurations CC1 and CC2;
• A1, A2, and A3: Off-chain cloud manufacturing applications maintained by R1, R2, and R3;
• ProvDB: Provenance database interfacing A, A2, and A3;
• AI: Artificial intelligence.

4. Discussion

4.1. design analysis, 4.2. challenges and limitations associated with iot security in cloud manufacturing, 5. conclusions and recommendations, 5.1. conclusions, 5.2. recommendations, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Umer, M.A.; Belay, E.G.; Gouveia, L.B. Fortifying Industry 4.0: Internet of Things Security in Cloud Manufacturing through Artificial Intelligence and Provenance Blockchain—A Thematic Literature Review. Sci 2024 , 6 , 51. https://doi.org/10.3390/sci6030051

Umer MA, Belay EG, Gouveia LB. Fortifying Industry 4.0: Internet of Things Security in Cloud Manufacturing through Artificial Intelligence and Provenance Blockchain—A Thematic Literature Review. Sci . 2024; 6(3):51. https://doi.org/10.3390/sci6030051

Umer, Mifta Ahmed, Elefelious Getachew Belay, and Luis Borges Gouveia. 2024. "Fortifying Industry 4.0: Internet of Things Security in Cloud Manufacturing through Artificial Intelligence and Provenance Blockchain—A Thematic Literature Review" Sci 6, no. 3: 51. https://doi.org/10.3390/sci6030051

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Physical exercise as a tool to help the immune system against COVID-19: an integrative review of the current literature

Matheus pelinski da silveira.

1 Federal University of Fronteira Sul, Campus Chapecó, SC 484 - KM 02, 89802-000 Chapecó, SC Brazil

Kimberly Kamila da Silva Fagundes

Matheus ribeiro bizuti, Édina starck, renata calciolari rossi.

2 University of Oeste Paulista (UNOESTE), Presidente Prudente, Brazil

Débora Tavares de Resende e Silva

Acute viral respiratory infections are the main infectious disease in the world. In 2020, a new disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19), became a global pandemic. The immune response to the virus depends on factors such as genetics, age and physical state, and its main input receptor is the angiotensin-converting enzyme 2. The practice of physical exercises acts as a modulator of the immune system. During and after physical exercise, pro- and anti-inflammatory cytokines are released, lymphocyte circulation increases, as well as cell recruitment. Such practice has an effect on the lower incidence, intensity of symptoms and mortality in viral infections observed in people who practice physical activity regularly, and its correct execution must be considered to avoid damage. The initial response is given mainly by type I interferons (IFN-I), which drive the action macrophages and lymphocytes, followed by lymphocyte action. A suppression of the IFN-I response has been noted in COVID-19. Severe conditions have been associated with storms of pro-inflammatory cytokines and lymphopenia, as well as circulatory changes and virus dispersion to other organs. The practice of physical activities strengthens the immune system, suggesting a benefit in the response to viral communicable diseases. Thus, regular practice of adequate intensity is suggested as an auxiliary tool in strengthening and preparing the immune system for COVID-19. Further studies are needed to associate physical exercise with SARS-CoV-2 infection.

Introduction

Acute respiratory infections (ARIs) are caused by respiratory viruses and bacteria, being the most infectious disease in humans [ 1 , 2 ]. These can be caused by more than 200 different viruses, with rhinovirus being the most common etiological agent [ 3 – 5 ]. In December 2019, a new coronavirus outbreak was reported in China, being called the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), spreading rapidly and infecting more than 14 million people, being declared a Health Emergency International Public Service on January 30, 2020 [ 6 , 7 ].

The main mode of transmission is contact with droplets containing viral particles eliminated through the cough or sneeze of an infected person, and the incubation period usually varies from 2 to 14 days. Approximately 80% of the cases are asymptomatic or with mild symptoms, and the others can be severe or critical and can lead to death [ 8 ]. The development of Coronavirus Disease 2019 (COVID-19) is dependent on the interaction between SARS-CoV-2 and the host’s immune system, the immune response being influenced by genetics (HLA genes), age, sex, nutritional status and status physical [ 9 ].

The immune response includes two stages, innate immunity and adaptive immunity. The first one comprises physical and chemical barriers and the action of cells such as macrophages, dendritic cells (DCs), natural killer cells (NKs), neutrophils and molecules such as cytokines, interleukins (ILs), nitric oxide (NO) and superoxide anion (O2–). The second one has as mechanism of action the T lymphocytes (TCD4 + and TCD8 +) and B lymphocytes and their products, such as antibodies and cytokines. Furthermore, the adaptive immune response can be subdivided into cellular immunity (mediate by cells as macrophages and lymphocytes) and humoral immunity (mediates by cells as macrophages and lymphocytes) and humoral immunity (mediated by antibodies) [ 10 , 11 ]. The regular practice of physical exercises promotes improvements in quality of life and can act in the immune response, reducing the risk of developing systemic inflammatory processes and stimulating cellular immunity [ 12 ].

Therefore, the present article aims to perform an integrative review of the literature relating the role of physical exercise on the immune system in the fight against COVID-19. For this purpose, the bibliographic study included knowledge about respiratory infections, influences of physical exercise on the immune system and proposed the comprehension of the most recent information about the immunopathogenesis of SARS-CoV-2 infection, also comprising its relationship with the host’s physical and health conditions.

Influence of physical exercise on the immunological response

Physical activity is considered one of the main components of healthy living. In addition to the functions related to the prevention of excess body weight, systemic inflammation and chronic non-communicable diseases, a potential benefit of physical exercise in reducing communicable diseases, including viral pathologies, is suggested [ 13 ].

The practice of physical exercise, both in its acute form and in its chronic form, significantly alters the immune system [ 14 , 15 ]. Studies indicate that the modulation of the immune response related to exercise depends on factors such as regularity, intensity, duration and type of effort applied [ 13 , 16 ].

Moderate-intensity physical exercises stimulate cellular immunity, while prolonged or high-intensity practices without appropriate rest can trigger decreased cellular immunity, increasing the propensity for infectious diseases [ 14 , 15 ]. According to the International Society for Exercise and Immunology (ISEI), the immunological decrease occurs after the practice of prolonged physical exercise, that is, after 90 min of moderate- to high-intensity physical activity [ 17 ]. Cellular changes due to physical activity are illustrated in Fig.  1 .

Effect of physical activity on the immune system.

Source : The Authors (2020)

Effects of physical exercise on immune system components

Cytokines are classified as anti-inflammatory and pro-inflammatory according to their functions. Among the anti-inflammatory cytokines, we highlight IL-10 and transforming growth factor-beta (TGF-β), responsible for inhibiting the production of pro-inflammatory cytokines [ 18 ]. Among the pro-inflammatory cytokines, we highlight IL-1, IL-2, IL-12, IL-18, interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) [ 19 ]. Cytokine production can be modified due to hormonal or oxidative stress and physical exercise. The muscle contraction has the effect of increasing the release of antinflammatory and pro-inflammatory cytokines at levels that vary according to the volume of contractile mass involved, duration and intensity of exercise [ 20 ].

Neutrophils

During the practice of physical exercise, the activation of the muscle fiber is responsible for increasing the release of calcium (Ca2 +) and, therefore, promoting the synthesis of pro-inflammatory cytokines, namely TNF-α and IL-1β, which act in the regulation of selectins, which, in turn, attract neutrophils to the site [ 21 ]. The neutrophilia induced by physical activity is due to the release of neutrophils from the bone marrow due to the influence of cortisol [ 22 ].

After aerobic physical exercise (approximately 24 h), there is a significant reduction in neutrophil chemotaxis, however, without compromising bactericidal activity. The reduction in neutrophil chemotaxis is reversed within 48 h after physical activity, during which the opportunistic activity of infectious microorganisms can occur [ 23 ].

Physical activity is also responsible for increasing the concentration of circulating leukocytes [ 24 ]. This is due to shearing of immune cells in blood vessels, especially secondary lymphoid tissues such as liver, spleen and lung [ 25 ]. The leukocyte concentration remains high with a peak of 30–120 min after constant physical activity, which may persist for up to 24 h after [ 24 ].

Antigen-presenting cells (APCs)

The practice of aerobic physical exercise in an exacerbated manner is responsible for decreasing the expression of Toll-like receptors (TLRs) in macrophages, considerably reducing the presentation of antigens to T lymphocytes, thus causing the suppression of the inflammatory T helper type 1 (Th1) response. Thus, the failure to develop an inflammatory activity precludes possible tissue damage resulting from inflammatory mediators and, consequently, the risk of chronic inflammatory processes. However, the susceptibility to infections due to intracellular microorganisms increases [ 26 ].

Natural killer cells (NKs)

During physical activity, blood flow increases in order to supply the metabolic demands of the human body. The recruitment of NK cells occurs through cellular stress promoted by exercise and a consequent decrease in adhesion molecules induced by catecholamines [ 27 ]. However, physical activity lasting more than three hours causes the concentration of NK cells to return to the pre-exercise state or even lower than this. This is because the NK cells migrate to the muscle injury site [ 28 ].

Lymphocytes

During moderate physical exercise, the concentration of lymphocytes increases in the vascular bed and, after strenuous exercise, decreases to levels below the pre-exercise period [ 29 , 30 ]. The CD4 +:CD8 + ratio decreases as TCD8 + cells increase [ 14 ].

TCD4 + cells decrease due to the increase in NK cells [ 14 , 31 ]. After physical activity, the lymphocyte concentration decreases due to the apoptosis mechanism [ 32 ]. Thus, the increase in lymphocyte concentrations favors the Th1-mediated immune response, preventing infections by intracellular microorganisms [ 24 ] (Fig.  2 ).

Innate immune response to the presence of viruses.

SARS-COV-2 virology and the role of the immune system in COVID-19 infection

SARS-CoV-2 is constituted by single-strand positive-sense RNA and belongs to the genus Betacoronavirus, lineage B and subgenus Sarbecovirus. Viral genome studies have identified similarity of SARS-CoV-2 with bat coronaviruses, as well as coronaviruses responsible for two previous pandemics: the Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV) and the Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV) [ 33 , 34 ].

Similar to SARS-CoV, the novel coronavirus uses a structural glycoprotein to infect cells: the envelope Spike protein (S), using the Angiotensin-Converting Enzyme-2 (ACE2) as entry receptor [ 33 , 34 ]. ACE2 consists of a cell membrane protein abundantly expressed in the organism, which is present in cardiac, pulmonary, renal, intestinal and vascular cells, and the binding of SARS-CoV-2 with this enzyme has a strong affinity, which may explain the high transmissibility of the virus [ 8 , 9 , 34 ].

Due to mainly respiratory symptoms, it is believed that the target cells of SARS-CoV-2 are in lower airways [ 35 ], as is the case of type 2 pneumocytes or alveolar cells, the main site of expression of ACE2 receptors [ 8 ]. The result of the interaction between the S receptor binding domain (RBD) and ACE2 is the fusion of the viral and host membranes, which proceeds for viral replication and dissemination and may reach the other cells with ACE2 expression in the organism [ 8 ].

With the purpose of containing the infection, the innate and adaptive immune system is activated by mechanisms that are not yet completely elucidated. In spite of this, it is known that effective immunological actions are essential to control viral replication and dissemination, cellular inflammation and tissue injury, and many studies have reported that the immune response of the host influences the severity of COVID-19 [ 9 , 36 – 38 ].

Immune response to coronavirus infections

To initiate the antiviral response, cells of the innate immune system need to recognize the infection, a process performed through pattern-recognition receptors (PRRs) such as TLR, NOD-like receptor (NLR), C-type lectin-like receptor (CLR), RIG-I-like receptor (RLR) and free-molecule receptors in the cytoplasm, which detect pathogen-associated molecular patterns (PAMPs). Once viral nucleic acids are recognized as PAMPs, PRRs activate molecular pathways of inflammatory response, stimulating chemotaxis, maturation of immune cells, phagocytosis and expression of inflammatory factors [ 39 ].

Viral recognition by TLR3, TLR7 and RIG-I receptors leads to activation of the nuclear factor-κB (NF-κB) and IRF3 signaling cascade, with nuclear transcription and expression of type I interferons (IFNs-I) and pro-inflammatory cytokines, creating the first line of defense against viral infections. The IFNs-I (IFN-α and IFN-β) are the most important antiviral cytokines, and they act as immunomodulators influencing the activities of macrophages and lymphocytes, performing actions such as protection of non-infected cells, containment of viral replication and effective activation of the adaptive immune system [ 8 , 9 , 39 ].

A suppression or delay of IFNs-I response—due to viral evasion mechanisms—results in impairment of early infection control, hyperinflammatory infiltrate of neutrophils, macrophages and monocytes into the lungs, production of cytokines by these cells and lung tissue damage. This process, described in SARS-CoV and MERS-CoV, has been suggested as a possible strategy to trigger or collaborate to the pathology of COVID-19 [ 8 , 9 ].

Macrophages and DCs act as APCs for lymphocytes via MHC and produce a microenvironment of signaling cytokines, activating the adaptive immune system. T lymphocytes perform important functions against viral microorganisms, since TCD8 + can cause direct cytotoxicity against infected cells and TCD4 + stimulate B lymphocytes to produce neutralizing antibodies. In turn, T helper lymphocytes (Th)—predominantly Th1—contribute to the organization of the adaptive response and release cytokines that are able to recruit monocytes and neutrophils and to promote other cascades of pro-inflammatory molecules, amplifying the immune response [ 8 , 39 ] (Fig.  3 ).

Adaptive immune response to the presence of viruses.

The complement system can also be activated and has an important role in coronavirus infections, because it helps the innate immune system to identify antigens. However, its activation can contribute to the disease due to its potent capacity to stimulate neutrophils and to recruit inflammatory cells, which can trigger tissue damage [ 39 ].

Unbalanced immunological responses at COVID-19

Studies suggest that in mild cases, pulmonary tissue macrophages are capable of containing SARS-CoV-2 and innate and adaptive immune responses are efficiently activated against viral replication. However, severe cases of COVID-19 are associated with an imbalance in antiviral immunity, characterized by two main situations: a pro-inflammatory cytokine storm and a lymphopenia state [ 37 , 38 ].

The degree of lymphopenia and cytokine storm was related to the severity of COVID-19 [ 7 ], and similar situations had already been reported in other respiratory viral infections, including influenza, SARS-CoV and MERS-CoV. Activation of the complement system and abnormalities in coagulation were also observed in severe patients with markers such as C-reactive protein (CRP), dimero-D and fibrin degradation products, which are usually elevated in advanced stages of the disease [ 37 , 38 ].

The mechanism of cytokine storm and lymphopenia associated with circulatory alterations and viral dissemination to several organs was proposed as being responsible for viral sepsis. Among the complications of this condition are acute respiratory distress syndrome (ARDS), septic shock, multiple organ failure (MOF) and death [ 8 , 38 ].

Cytokine storm

In patients with severe COVID-19, increased levels of cytokines were observed, including IL-1β, IL-2, IL-6, IL-8, IL-10, IL-17, interferon-gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), granulocyte-colony stimulating factor (G-CSF), gamma-induced protein 10 (IP10), monocyte chemoattractant protein 1 (MCP1), macrophage inflammatory protein 1 alpha (MIP1-α) and other molecules, characterizing the cytokine storm [ 37 , 38 ]. Additionally, elevations of IL-1β, IFN-γ, IP10 and MCP1 in infections by the novel coronavirus were associated with the Th1 response; however, an increase in interleukins of the T helper type 2 (Th2) profile, such as IL-4, IL-5, IL10, which suppress the inflammation, was also associated with a greater severity of COVID-19, which may demonstrate an imbalance in immune regulation and an attempt to minimize tissue inflammatory damage [ 35 , 40 ].

The cytokine storm generates an immunological system attack against the organism, which can cause substantial lesions in organs such as the lung, heart, brain, kidneys, spleen, liver and lymph nodes. The increase of neutrophils, macrophages and monocytes in association with a dysfunction of the IFN-I response has been reported as the main cause of lethality in SARS-CoV and MERS-CoV pneumonia, and a similar conclusion has been suggested for SARS-CoV-2 [ 9 , 37 , 38 ].

In the lungs, alveolar macrophages and epithelial cells are the most responsible for the production of cytokines and chemokines. During infection by the novel coronavirus, excessive secretion of these molecules by the immune cells mediates a massive pulmonary infiltrate of neutrophils, monocytes and macrophages, which results in alveolar damage due to wall thickening and formation of hyaline membranes. In addition, the accumulation of neutrophils in the lungs increases the production of reactive oxygen species (ROS) and pro-inflammatory molecules, predisposing to injury [ 41 ]. Therefore, high levels of pro-inflammatory cytokines are associated with respiratory insufficiency, ARDS and may lead to shock, MOF and death in the COVID-19 [ 37 , 38 ].

In comparison, pulmonary disease caused by SARS-CoV also presents with formation of hyaline membranes, desquamation of alveolar space pneumocytes and interstitial infiltration with lymphocytes and mononuclear cells. In the serum of patients who develop SARS, there are high levels of cytokines and pro-inflammatory chemokines [ 42 ].

Additionally, it has been observed that during viral infections, pro-inflammatory cytokines can stimulate an increase in the levels of ACE2 protein, the receptor for SARS-CoV-2. A larger quantity of this protein may accelerate the entry of the novel coronavirus into the host cells and contribute to its dissemination in the organism, negatively influencing the antiviral response [ 34 ].

Changes in cellular counts

In critically ill patients, a state of immune suppression is also described, with a significant and sustained decrease in the absolute number of TCD4 + and TCD8 + lymphocytes, possible reduction of B lymphocytes, NK cells, monocytes, eosinophils and basophils [ 37 , 38 ]. The study conducted by Liu et al. [ 7 ] did not observe significant changes in the total counts of B lymphocytes, NK cells and monocytes; however, there was a considerable reduction in the lymphocyte count in peripheral blood of patients with severe COVID-19 at the beginning of the disease, especially cytotoxic TCD8 + cells [ 7 ].

Activation markers for TCD4 + and TCD8 + lymphocytes showed excessive stimulation and exhaustion markers for TCD8 + lymphocytes were elevated in the disease, suggesting lymphocyte dysfunction [ 9 , 37 ]. Additionally, an increase in the number of neutrophils and a greater neutrophil-to-CD8 + T cell ratio (N8R) was associated with severe COVID-19 and proposed as the most significant predictor of poor prognosis [ 7 , 37 ].

Cell death induced by interaction between Fas and Fas ligand by activation of the TNF-related apoptosis inducing ligand axis and by direct infection of T lymphocytes by SARS-CoV-2 may be responsible for the origin of lymphopenia in COVID-19 [ 38 ]. The cytokine storm can also influence the lymphopenia, since in the study of Liu et al. [ 7 ], the peaks in cytokine levels IL-2, IL-4, IL-10, TNF-α and IFN-γ coincided with the lowest T lymphocyte counts, about 4–6 days after the onset of severe COVID-19; therefore, the restoration of T cell numbers was associated with reductions in circulating cytokines [ 7 ].

The fact that T cells are important regulators of the activation of the immune system during a viral infection may explain how lymphopenia is related to the worsening of inflammatory responses [ 7 ]. In consequence of lymphopenia and lymphocyte dysfunction, the adaptive immune response is ineffective and the infection is not adequately controlled, further increasing the stimulation of cytokines and cellular infiltrations [ 38 ].

Coagulation disorders

It was observed that patients with SARS-CoV-2 infection present increased risk of venous thromboembolism (VTE) and disseminated intravascular coagulation (DIC). Coagulopathy associated with COVID-19, as it may be named, is characterized by hypercoagulability and thrombosis and is associated with worse prognosis in infection. Among the altered coagulation parameters in patients with severe COVID-19 are exacerbated coagulation activation, coagulation factor consumption, prolongation of prothrombin time (PT) and activated partial thromboplastin time (aPPT), moderate to severe thrombocytopenia, increased D-dimer and reduction of fibrinogen [ 43 ].

Through a retrospective analysis of 183 patients with coronavirus pneumonia, high levels of the D-dimer and fibrin degradation product have been identified, in addition to prolonged PT, as well as aPPT in patients who had deceased. Tang et al. [ 44 ] considered the D-dimer as an important coagulopathy marker in cases of SARS-COV-2 infection. The same findings were found in the studies by Han et al. [ 45 ] who see the use of hemostasis tests as tools to be used in early diagnosis and in monitoring disease progression [ 46 ].

Blood coagulation is the fastest mechanism in the confinement and inactivation of infections, being the first and the last defense line of the innate immune system to take place in tissues and blood circulation. The sore promotes the activation of the endothelial cells and the dysfunction of the endothelium, thus generating a pro-thrombotic state [ 47 ]. In cases hyperactivation of the immune system, the coagulation may become intravascular and disseminated, therefore causing multiple organ failure. After all, the amount of vascular endothelial lesions of organs and tissues is, due to their activation intent, inversely proportional to the amount of existing coagulation factors [ 48 ].

Thereupon, the first clinical studies carried out in patients with pneumonia caused by the Coronavirus confirmed the occurrence of organ dysfunction and coagulopathy as possible causes of the negative outcomes of the disease [ 44 ]. The activation of the immune system, in response to the infection, leads the production of cytokines and tissue factor expression. The cytokines, in large amounts, harm the gas exchange and lead not only to inflammation but fibrinolysis, thus increasing D-dimer concentration [ 49 ].

The tissue factor is related to an increase in thrombin generation and fibrin deposition, leading to hypercoagulability and CIVD and, thus, a worse prognosis [ 46 , 49 ]. As a contributor to the coagulation process, there is the presence of polymorphonuclear leukocytes (PMN) that are activated during the inflammatory process, releasing extracellular neutrophil traps (NETs) which contain proteases that generate the inactivation of endogenous anticoagulants and the propagation of a procoagulant state. The interaction of activated platelets with PMN can form vaso-occlusive thrombotic complexes [ 47 ].

Some authors also elucidate the virus’s relationship with the ECA2 functional receptor, present in the arterial and venous endothelial cells of most human organs and part of both axes of the renin–angiotensin system, the vasoconstrictor ECA/Ang/AT1R and the vasodilator ECA2/Ang-(1-7)/MAS [ 50 , 51 ]. Dalan et al. [ 52 ] cite that both aging and metabolic disorders positively regulate the ECA/Ang/AT1R axis, leading to inflammatory, oxidative, vasoconstrictor and fibrotic effects. Therefore, the ECA2/Ang-(1-7)/MAS axis is negatively regulated, resulting in a decrease in the anti-inflammatory and anti-fibrotic effects [ 52 ]. The connection of SARS-COV-2 to the ECA2 receiver affects the balance between ECA/Ang/AT1R and ECA2/Ang-(1-7)/MAS, making the effects of the ECA/Ang/AT1R axis even more prominent [ 53 ].

Host condition and COVID-19

The different immune responses of the host to the SARS-CoV-2 infection may explain the reason why men and women, young and old, infected by the virus can suffer a different severity of the disease [ 54 ]. Therefore, a considerably higher mortality rate was observed in patients with advanced chronological age [ 55 ].

Immune aging is related to an increase in individuals’ susceptibility to infections, due to the decline in immune function, which can occur at any stage of the immune response. Such changes can be seen, especially when associated with emotional stress [ 56 ]. Immune senescence is associated with the suppression of the activation and presentation of antigens by macrophages, which consequently prevent the migration of dendritic cells and the activation made by Toll receptors with less effect [ 56 , 57 ]. Ewers et al. [ 58 ] mention the decline and proliferation of T cells, in addition to the increased production of pro-inflammatory cytokines IL-1, IL-6 and TNF-α in the elderly.

Another point is the imbalance between Th1 and Th2 cytokines, generating an increase in the susceptibility of these individuals as infections by viruses and extracellular bacteria [ 58 ]. In view of this, aging is associated with a constitutive pro-inflammatory environment due to persistent and low-grade immune activation, which can lead to increased tissue damage caused by infections [ 57 ].

Also, there should be taken into account the positive regulation of the ACE-Ang-II-AT axis that leads to pro-inflammatory and pro-fibrotic effects. Although not very detailed, it is still suggested the occurrence of a greater number of ECA2 receptors associated with aging, which would increase this imbalance. After all, it is through this receptor that SARS-COV-2 infects humans and thus contributes to the development of COVID-19 in older people [ 52 ].

Obesity, type 2 diabetes mellitus (T2DM) and metabolic syndrome (MS)

The precarious metabolic health is considered the main risk factor for the development of severe forms of COVID-19. This may occur in T2DM, obesity and MS, possibly due to immune dysfunction in synergism with pathophysiological complications of these comorbidities [ 59 ].

Increased ACE2 expression is a protective adaptive mechanism in T2DM; however, it may facilitate the viral entry and spread of SARS-CoV-2 in the body [ 59 ]. Adipose tissue also exhibits high expression of ACE2, so the population with obesity may present greater vulnerability to COVID-19 [ 43 ].

It was observed that the expression of ACE2 in the adipose tissue of obese patients allows viral entry in adipocytes and makes this tissue a reservoir for the viral dissemination of SARS-CoV-2, since it is viscerally distributed [ 43 , 51 ]. In addition, obesity is an important factor for the development of T2DM—especially when associated with low levels of physical activity and poor physical conditioning—and as mentioned, both diseases are related to higher expression of ACE2, increasing the risk of advanced infection by SARS-CoV-2 [ 43 ].

Metabolic disorders lead to immune activation of tissues such as the adipose, increasing the concentration of low-grade chronic inflammation plasma markers, called metabolic inflammation or meta-inflammation [ 61 ]. In this sense, the release of pro-inflammatory adipokines such as leptin, TNF-α, IL-6 and IL-1b is observed, with a reduction in anti-inflammatory action through the suppression of adiponectins [ 62 , 63 ]. The presented relationship is directly proportional to the presence of adipose tissue, the same that can be regulated through the practice of physical activity [ 64 ].

The pro-inflammatory state found in metabolic syndrome and T2DM may increase the probability of an unbalanced inflammatory response in COVID-19, like the cytokine storm described in patients with severe disease [ 59 ]. Similarly, as obesity is a state of low-grade chronic inflammation, it shows a potential for immune amplification of pathogens, as the regulatory elements of the immune response are absent or dysfunctional and this may contribute to the cytokine storm, which is already in greater concentration in obese individuals and which sustains and activates multiple cytokine pathways for a long time after the viral insult [ 65 , 66 ].

Damage to blood vessels caused by chronic diseases, such as T2DM, associated with hypercoagulability present in COVID-19 may intensify the risk of infection complications [ 59 ]. Obesity can also aggravate endothelial dysfunction present in COVID-19 due to inflammation triggered by perivascular and vascular adipose tissue, combined with changes in the synthesis of endogenous vasoactive agents, leading to platelet hyperactivation, leukocyte adhesion and other modifications related to endothelial inflammation, prothrombosis and proatherogenesis [ 43 ].

Hypercoagulability is directly proportional to the severity of overweight in obese patients. Among the pathophysiological mechanisms are the action of adipocytokinins, with leptin and adiponectin, overactivity of coagulation factors, reduction of fibrinolytic function and, once again, increased inflammation (TNF and IL-6). Other contributors include elevated oxidative stress, lipid and glucose tolerance disorders, MS and venous stasis. Thus, it is considered a synergic effect of obesity and MS on the state of hypercoagulability in COVID-19, aggravating the risk of VTE and DIC even more [ 43 ].

There is also a greater amount of macrophages in the adipose tissue of obese individuals due to areas of microhypoxia, which lead to the nuclear factor-κB (NF-κB) pathway activation, thus increasing the expression of genes involved in inflammation [ 61 ]. This condition is the result of the attraction of monocytes in the circulation made by chemokines; when they infiltrate the adipose tissue, they transform into macrophages, which, in turn, release TNF-α and IL-6 which induce the tissue’s resistance to insulin [ 63 ]. Insulin resistance is also related to the host’s immune response, as it can inhibit the resolution of T cell-mediated inflammation [ 61 ].

The adipose tissue is not the only one affected by the deposition of fats, because of that, bone marrow and thymus also present significant changes to the immune system of obese individuals and those with MS [ 67 ]. Thus, there is a marked deregulation of immune responses, which leads to a lower presence of circulating T cells, reducing the response to pathogens [ 60 , 61 , 65 ].

Physical exercise and COVID-19

Despite the lack of accurate data on how physical activity improves the immune response against the new coronavirus, there is evidence of lower rates of ARI incidence, duration and intensity of symptoms and risk of mortality from infectious respiratory diseases in individuals who exercise at high levels appropriate. Furthermore, different studies suggest that regular physical exercise is directly related to decreased mortality from pneumonia and influenza, improvements in cardiorespiratory function, vaccine response, metabolism of glucose, lipids and insulin [ 13 , 16 ].

Increased immune surveillance against infections has been proposed as a mechanism responsible for improving the immune response related to physical exercise. Moderate-intensity physical activity is responsible for providing an increase in the anti-pathogenic activity of macrophages, at the same time as elevations in the circulation of immune cells, immunoglobulins and anti-inflammatory cytokines occur, thereby reducing the burden of pathogen on organs such as the lung and the risk of lung damage due to the influx of inflammatory cells [ 12 ].

During regular physical exercise practices, inflammatory responses and stress hormones are decreased; in contrast, lymphocytes, NK cells, immature B cells and monocytes are at high levels. Thus, there is an improvement in immunovigilance, as well as a reduction in the systemic inflammatory process, factors that corroborate that regular physical activity helps to improve the immune system, while helping to prevent respiratory diseases and thus protect against infections such as COVID-19 [ 68 ].

Dynamic exercises that generate cardiorespiratory overload promote the mobilization and redistribution of effector lymphocytes, mediated by catecholamines. This action primarily stimulates subtypes of lymphocytes capable of migrating from reservoirs—such as blood vessels, spleen and bone marrow—to lymphoid tissues and organs—such as the upper respiratory tract, lungs and intestines, aiming at recognizing and fighting pathogens and, thus, increasing immune surveillance and improving the antiviral response [ 16 ].

Similarly, regular exercise practices at moderate levels favor the function of the human body’s immune surveillance against pathogens, as they stimulate an exchange of white blood cells between the circulatory system and tissues, a fact that reduces morbidity and mortality from acute respiratory disease and infections viral. They are also capable of promoting protection against infections triggered by intracellular microorganisms, as viral agents, given that the predominant immune response is mediated by Th1 cells [ 68 ].

Regular exercise of moderate intensity has already been associated with a reduction in respiratory infections compared to sedentariness. However, exhaustive physical practices before or during an infectious condition, such as influenza or COVID-19, can trigger severe illness due to changes in the immune system [ 40 , 68 ]. This occurs due to the production of Th2 anti-inflammatory cytokines in order to reduce muscle tissue damage, but in strenuous activities this effect can reach immunosuppression levels, thus providing the opportunity for infections [ 12 , 40 ]. Therefore, attention should be paid to the importance of developing physical training at appropriate levels of execution.

To the detriment of the world demographic change and the habits arising from the technological revolution, the population is aging more, becoming more obese and, consequently, less active when it comes to physical exercise. In this way, the immune system undergoes negative changes; that is, there is a functional impairment of innate immunity and adaptive immunity called immunosenescence, which results in greater susceptibility to infectious diseases and systemic inflammatory processes, decreased response to antibodies and, therefore, compromised immunological surveillance [ 68 ].

Therefore, for the elderly population, physical activity is even more essential, as these individuals generally have greater comorbidities and, in relation to the new coronavirus, are more vulnerable to contracting the disease [ 69 ]. Damiot et al. [ 70 ] suggested that individuals who have remained active throughout their lives have less pronounced immunosenescence characteristics, which may be a possible protective factor against the development of complications caused by COVID-19.

In this sense, beneficial effects of regular physical exercise have been reported in the elderly population, including reduction in oxidative stress, improvement in immune competence and reduction in cellular changes related to immunosenescence [ 13 , 16 ]. Elderly individuals who maintain continued physical activity have levels of TCD4 + and TCD8 + lymphocytes similar to younger individuals, in addition to not having harmful defects in the recruitment of lymphocytes during the infectious process [ 58 ].

According to the study by Ferrer et al. [ 71 ] with 116 elderly volunteers, through physical activity there is a decrease in the current levels of IL-6, as well as an increase in the expression of IL-10 in active individuals [ 71 ]. A low presence of circulating pro-inflammatory cytokines is observed in contrast to the increase in anti-inflammatory cytokines. Thus, there are positive changes in the immune system of these individuals, including enhancements in host response and vaccine immunoprotection [ 70 ].

Similarly, while prolonged maintenance or worsening of obesity and MS perpetuates deregulation of immune responses, promoting greater risks of the individual developing diseases and increasing their vulnerability to infection by the novel coronavirus [ 43 , 59 , 61 ]. An association between physical activity and reduction of inflammatory markers in obese and overweight patients is suggested [ 13 ].

Luzi and Radaelli [ 72 ] add the lack of physical activity as an important factor among obese patients, as it impairs the immune response against microbial agents, from the activation of macrophages to the inhibition of pro-inflammatory cytokines. On the other hand, both metabolic health and immune health benefit from the practice of physical activity, which reduces the risk of infectious complications. Thus, regular physical exercise appears as a preventive measure in the defense of the host against viral infection [ 72 ].

Muscle contraction is responsible for the transient increase in circulating levels of IL-6 cytokine, in proportion to the duration of physical activity and the amount of muscle mass recruited. The elevation of this interleukin seems to be followed by increases in antinflammatory cytokines, such as IL-10, released by cells of innate immunity and responsible for promoting an antinflammatory environment, inhibiting inflammatory mediators to limit tissue damage. This effect may be beneficial in cases of chronic inflammation, such as obesity, T2DM and MS, and may reduce the risk of a pathogenic inflammatory response such as the cytokine storm present in severe COVID-19 [ 40 , 59 ].

In addition, IL-10 is associated with enhanced insulin sensibility and glycemic metabolism [ 40 ]. Physical practice is able to reduce the excessive concentration of pro-inflammatory adipocin leptin and improve sensitivity to leptin and insulin [ 72 , 73 ]. In patients with T2DM and new coronavirus infection, good glycemic control has been associated with better prognosis in COVID-19 [ 59 ].

Thus, physical exercise is shown to be an immunomodulatory and non-pharmacological intervention, achieving positive immunomodulation through exercises of light to moderate intensity [ 72 ]. Through exercise, there is an improvement in the response to infection in obese individuals, due to immune and cellular restoration [ 74 ]. Although COVID-19 is not primarily a metabolic disease, there is a need to maintain metabolic control of glucose, lipid levels and blood pressure in order to prevent metabolic and cardiovascular complications, as well as to reduce the local inflammatory response and block the virus entering the cells [ 75 ].

As seen, innate immunity has an important role in the pathogenesis of COVID-19 and ARDS, due to inflammatory cascades, recruitment of neutrophils, macrophages and DC cells and increased production of ROSs. In turn, by the modulation mechanism of chemokine production, physical training and therapeutic exercises can attenuate alveolar neutrophilia in the face of lung injury [ 41 ].

Furthermore, the expression of the extracellular superoxide dismutase enzyme (EcSOD), an important antioxidant in the body and highly present in the lungs, is enhanced by resistance physical activity and was associated with inhibition of endothelial activation and inflammatory adhesion, with potential benefit to reduce oxidative stress and tissue damage in COVID-19 [ 41 ]. The amplification of the antioxidant defense generated by routine physical activity also contributes to immunological surveillance [ 13 ].

Moreover, Womack, Nagelkirk and Coughlin [ 76 ] point out that through the intensity of physical exercise, a change in the potential for coagulation, platelet aggregation and fibrinolysis can be seen. As an example, there is long-term training through aerobic exercises where a decrease in the clotting potential is observed in healthy individuals. Thus, it is suggested that the practice of physical exercises contributes to reducing the risk of ischemic events depending on their intensity and duration [ 76 ] and may contribute to attenuating coagulation disorders associated with SARS-CoV-2 infection.

The anti-inflammatory, antioxidant and endothelial activation inhibitor benefits may also be linked to the reduction in hypercoagulability related to COVID-19. This is because, as previously mentioned, the exacerbated activation of the immune system increases the expression of the tissue factor and, consequently, the predisposition to the formation of thrombi; in addition, metabolic disorders, oxidant stress and changes in senescence positively stimulate the vasoconstrictor axis ECA/Ang/AT1R, contributing to endothelial imbalances [ 46 , 47 , 49 – 53 , 69 ]. Therefore, the immunometabolic improvements promoted by physical exercise may help in the control of coagulation disorders in the COVID-19 (Fig.  4 ).

Benefits of regular moderate-intensity physical activity on factors that influence the response against to COVID-19.

Finally, in view of the quarantine status adopted in several countries as a measure to prevent and control the spread of SARS-CoV-2 during the COVID-19 pandemic, social isolation and restrictions on the movement of people reduced the practice of physical activity, predisposing the population to adopt sedentary behavior [ 77 ]. Social distancing is a sine qua non in reducing the speed of contagion of COVID-19 and associated deaths. However, due to these measures, sports clubs, gyms and fitness spaces have suspended their activities in order to reduce agglomerations; thus, difficulties regarding physical exercise were imposed [ 78 ].

Therefore, despite being one of the main strategies against COVID-19, social isolation has been related to behavioral and physiological changes, including the increased prevalence of sedentarism and eating disorders (food compulsion, hyperphagia), resulting in negative consequences for metabolic health, such as weight gain, growth of fat tissue, hyperglycemia and insulin resistance and loss of muscle tissue [ 59 ]. Since this condition can harm the body’s defenses and contribute significantly to the reduction in individuals’ physical condition, functional and health loss, the adoption of healthy habits and an exercise routine can help in maintaining health [ 59 , 69 ].

It is significant to consider that contexts like this increase the susceptibility to stressful events and elevations of glucocorticoids (cortisol), with consequent inhibition of the functions of NK cells and TCD8 + lymphocytes in the antiviral response. However, good physical conditioning was associated with lower risks of reactivation of latent viral infections in situations of isolation and confinement, indicating a favored immune system compared to individuals with less physical fitness [ 16 ].

Physical activity is considered a non-medication practice for the prevention and treatment of diseases of psychological, physical and/or metabolic origin [ 78 ]. Regular physical exercise should be encouraged during social isolation as a preventive measure for health, given that exercise is essential during the period of fight against the spread of coronavirus [ 69 ].

The American College of Sports Medicine (ACSM) recommends that the practice of moderate physical exercise should be maintained during the quarantine period, since it helps in the immune reinforcement against SARS-CoV-2. The WHO recommends that asymptomatic and healthy individuals should exercise at least 150 min per week for adults and 300 min per week for children and adolescents. These times can be distributed during the days of the week and according to the person’s routine [ 69 ]. It is important to emphasize that physical activity should be interrupted and a health professional should be consulted in case symptoms such as fever, dyspnea at rest and dry cough are manifested, because these symptoms can be related to COVID-19 [ 69 , 79 ].

In social isolation, the home environment has become the ideal and necessary place for physical activity. Activities that are satisfactory and that allow better exploring the home space should be sought. Activities of daily living such as organization of spaces, cleaning and maintenance also help in coping with COVID-19. In environments with children, playing and exercising with them is a great way to promote energy expenditure, thus leaving the beginning of sedentary rest. Meditation, stretching and relaxation are allies in combating a sedentary lifestyle. It is important to avoid long rest periods, which should be intercalated with active practices [ 69 ].

The ACSM has published guidelines for moderate-intensity activities that can be practiced during the pandemic period, including aerobic exercises and strength training, indoors, like at home, or outdoors, when permitted by government authorities. Options for aerobic activities to be performed at home include walk briskly around the house, up- and downstairs, dancing and jumping rope. When possible, walking or running outdoors, cycling, gardening work and family games are interesting alternatives, as long as infection prevention measures are maintained [ 79 ].

Among the strength activities, ACSM indicates downloading a strength workout app that does not require any equipment and suggests exercises such as squat, sit-ups, push-ups, lunges and yoga practice, which can also help in anxious states [ 79 ]. Oliveira Neto et al. [ 80 ] suggest resistance exercises based on ACSM recommendations to be performed at home, including exercises involving the muscles of the lower body, upper body and limbs, and lower limbs, which can be adapted for beginners in physical practice or experienced people.

Activities that make use of the individual’s own body weight, associated with resistance training as well as the use of elastic bands, provide excellent health results, results similar to those achieved by traditional gyms. Thus, objects such as backpacks, books, market bags and water bottles can be used as an auxiliary tool in resistance physical activity. Exercises such as squats, jumping jacks and going up and down steps can be effective in physical training [ 78 ].

As for the intensity and volume of physical exercise practices, these must be moderate, as they exceed both the volume and the intensity, effects such as momentary immunosuppression are achieved, thus providing greater vulnerability as to the contagion of the novel coronavirus. If individuals want to practice high-intensity exercises, a reduction in exercise volume should be adopted as a preventive measure, in order to avoid strenuous exercises [ 78 ].

In addition, technological tools can contribute to the better performance of these activities in the home environment, as video calls with a physical education professional facilitate the orientation of the exercises to be performed, providing support that has, as a consequence, better results and greater safety in the execution of the exercises. Regardless of whether or not you are in the risk group for COVID-19, regular exercise, according to the ACSM, should be regularly performed, given that it aims to improve the immune system, reduce stress perceived and decrease anxiety disorders [ 78 ].

There are still gaps in the knowledge regarding the pathogenic mechanisms involved in SARS-CoV-2 infection. However, there is consensus in the scientific literature about the important involvement of the immune system in the susceptibility, progression and outcome of COVID-19. The imbalance in innate and adaptive immune responses, characterized mainly by changes such as cytokine storm and lymphopenia, in addition to the disorders in coagulation- and host-related conditions, including obesity, metabolic syndrome and aging (immunosenescence), is among the factors notoriously associated with a worse prognosis of infection.

The benefits of exercise—regular and at appropriate intensity levels—for the immune system in respiratory infections such as COVID-19 include increased immunovigilance and improved immune competence, which help in the control of pathogens, a fact that becomes more important considering the immunosenescence and susceptibility of the elderly population to severe infection. Other favorable effects in relation to host factors, such as prevention or reduction of overweight, increased physical and cardiopulmonary conditioning, attenuation of the systemic pro-inflammatory and pro-thrombotic states, decrease in oxidative stress, improvements in glycemic, insulinic and lipidic metabolisms, besides the enhancement of the vaccination response, also indicate how adequately physical activity can help the organism’s immune response against COVID-19.

In the COVID-19 pandemic situation, adopting mitigation practices is an essential strategy to reduce the risks related to the novel coronavirus infection. These interventions include the use of personal protective equipment (PPE), adherence to hygiene procedures and social isolation measures, as well as actions that lead to a healthier lifestyle, minimize stress factors and strengthen the immune system, such as regular physical activity.

However, remaining active at appropriate levels seems to be a challenge in a context of confinement and social isolation, which emphasizes the importance of developing training with recommendations adapted to the new routine of the population. Fortunately, there are viable alternatives for performing physical exercises in restricted environments, enabling the population to enjoy the advantages of physical training for health in the context of OVID-19.

Finally, future studies that deepen the relationship between physical activity and infection by SARS-CoV-2, including the influences of exercise on metabolic and immunological disorders present in COVID-19, will certainly be relevant in view of the probable benefits already mentioned and considering the impacts of infection by the novel coronavirus in the global context. Faced with the possibility of new pandemics by previously unknown microorganisms, without totally effective prevention measures, vaccines or specific treatments of proven efficacy, the host organism’s capacity against infections becomes the most important line of defense, thus emphasizing the importance of investing in lifestyle habits that promote health and well-being, such as the practice of physical activity.

Authors’ contributions

DTRS had the idea for the article. ES, KKSF e MRB performed the literature search, data analysis and translation. MPS drafted and revised the work.

Not applicable.

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The authors declare that they have no conflict of interest.

This article does not contain any studies with human participants or animals performed by any of the authors, and informed consent is not a standard required.

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