Regular engagement in physical activity confers physical and mental health benefits in both youth (5–18 years old) and adult populations, including favourable cardiometabolic biomarkers, improved cognition and well-being [1, 2], and among adults, lower risk of all-cause mortality [3, 4]. Conversely, higher levels of sedentary behaviours such as screen time are associated with negative physical and mental health outcomes in youth [5], as well as cardiometabolic diseases, cancer incidence, and depression in adults [6, 7]. Globally, 75% of countries participating in the Active Healthy Kids Global Alliance on physical activity for children and youth (n = 49) reported that over 80% of children did not meet the daily moderate- to vigorous-intensity physical activity (MVPA) guidelines of 60 min per day [8]. Moreover, a pooled analysis of 1.6 million adolescents and of 1.9 million adults found 81% [9] and 28% [10], respectively, failed to meet their specific physical activity guidelines [11]. Significant efforts have focused on increasing physical activity and reducing sedentary behaviour across all age groups and in a range of settings (e.g., schools, work places, community, and home) [1215], yet interventions have had varied efficacy and have typically failed to sustain changes in behaviours over time [13, 1618].

One potential explanation for such varied intervention efficacy is activity compensation. It has been hypothesised that activity levels may be under some degree of biological control (an ‘activitystat’), which operates in the same way as the homeostatic mechanisms that regulate body temperature, blood pH, and fluid balance within the body [19]. Specifically, the activitystat hypothesis posits that physical activity levels are kept within tolerable activity levels or energy expenditure ranges (activity set-points), meaning that intensity, frequency, duration and/or load of activity may increase or decrease in response to a perturbation (e.g., an activity intervention) to compensate for the additional (or lack thereof) activity [20]. It is crucial to highlight the importance of such changes occurring in response to a perturbation, as this is what sets compensatory responses apart from habitual activity. In addition, as all activity intensities would contribute to the total activity set-point, the compensatory responses would be expected to occur across the activity spectrum (i.e. sedentary behaviour [SED], light physical activity [LPA], and MVPA) [21]. Upon removal of the perturbation, activity levels are hypothesised to return their original levels [22]. This may explain why interventions have limited efficacy for sustained change in activity levels. Despite this, past behavioural activity research has mostly focused on the impact of social and environmental variables on behaviours, largely neglecting the potential biological basis for activity [19, 23].

In a 2013 review of studies examining activity compensation, Gomersall and colleagues [24] reported that 63% (5/8) of child studies, 40% (6/15) of adult studies and 80% (4/5) of elderly studies indicated compensation had occurred [24]. Whilst Gomersall and colleagues [24] focused on experimental and intervention studies, which enables changes in activity levels to be examined under controlled conditions [24], observational studies that can provide insights into individual day-to-day variability in activity were excluded [25]. Further, though compensation is hypothesised to be a biological response, the way in which any responses are observed or potential reasons for occurring has not been reviewed to date. Specifically, it is unknown what behaviours may change and the potential mechanisms underlying such changes. Consequently, there is a need to synthesise activity compensation evidence with methodological considerations and examine any potential reasons as to why compensation may occur (if at all).

The primary aim of this systematic review was to synthesise research that has investigated activity compensation in youth and adults. The secondary aim was to identify and examine any reasons for and/or awareness of compensatory changes that may have occurred.


Protocol and registration

The systematic review was registered with PROSPERO (CRD42019133914). The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [26]. The PRISMA Checklist is provided in Supplementary Information 1.

Search strategy

An electronic search of the EBSCOhost (via Academic Search Complete, CINAHL Complete, Education Source, Health Source: Nursing/Academic Edition, PsycINFO, SPORTdiscus with Full Text), MEDLINE Complete, Global Health, EMBASE, Scopus and Web of Science databases up to May 2021 was conducted. The search strategy was developed in conjunction with a research librarian with key words in the following areas: activity compensation ([compensation and physical activity or sedentary or exercise or energy expenditure or energy balance] or [ActivityStat or EnergyStat or energy displacement]) and age ([child or youth or adolescent] or [adult]). The full search strategy, including proximity search strategy functions and truncations, for the different databases can be found in Supplementary Information 2. All titles and abstracts were screened in full and independently by two reviewers (B.S., and S.V. or N.R) using the Cochrane review production platform Covidence (Veritas Health Innovation; Melbourne, Australia). Discrepancies were recorded through Covidence and reviewed by three researchers (B.S., S.V. and N.R.) until a consensus was reached. In the case that a consensus could not be reached, discrepancies were discussed with the research team. Agreement between reviewers in the title/abstract stage was 91%. Full text articles that met the initial screening criteria were then independently screened for eligibility to be included in the review by two researchers (B.S. and S.V.), and inconsistencies were again discussed and resolved with the research team where required. Agreement between reviewers was 72%. The reference lists of studies deemed eligible for inclusion were searched for additional relevant studies for potential inclusion [27].

Eligibility criteria

All original study designs were considered for inclusion. Studies were eligible if they met the following criteria: (a) participant’s mean age was 5–65 years; (b) focused on the general population, i.e., the target population did not solely focus on participants with chronic conditions, athletes, or overweight/obesity (as they may have different compensation ‘drivers’ such as chronic pain, muscular atrophy, etc.); (c) the study explicitly undertook analyses designed to examine activity compensation or compensatory responses, or explored compensatory responses as part of their methods (i.e., study was designed to examine changes in activity across the activity spectrum, or between settings, and used compensation when describing their results); (d) was published in English; and (e) was published between January 1999 to May 2021. The start date was selected to align with the first publication outlining the activitystat hypothesis (1999) [19]. Quantitative studies that, for example, were not designed to look at similarities or differences in activity between settings or time periods, but rather used compensation as a discussion point were not included. Quantitative and qualitative studies were included if they discussed potential mechanisms, reasons, or insights into activity compensation. Articles that were published ahead of print and had a DOI were also eligible for inclusion. Abstracts, conferences, reviews, study protocols, and dissertations were not eligible for inclusion.

Data extraction

For this review, studies were classified into four categories: 1) Quantitative only (i.e. measuring compensation quantitatively); 2) Quantitative and behavioural (i.e. quantitative compensation studies that also recorded behaviours, this included studies measuring mechanisms/perceptions of compensation); 3) Behavioural only (i.e. a non-qualitative assessment of behaviours, perceptions of compensation, or mechanisms); and 4) Qualitative only. This approach was used to distinguish between studies that were eligible for inclusion in this review but examined different aspects of activity compensation. Quantitative data were extracted by one reviewer (B.S). For consistency purposes, 15% of articles were extracted and reviewed by another reviewer (S.V.). Data were extracted using a standardised form and included: study/participant characteristics (e.g. mean age, study design, % male/female, % overweight/obesity, etc.), outcomes examined (e.g. sedentary time), activity assessment method (e.g. pedometer, accelerometer), study design (e.g. cross-sectional), activity compensation methodological considerations (e.g. timeframe examined, analytical approach), reported results (e.g. compensation reported), and behavioural assessments (if any; e.g. sitting time in different locations, active transport, etc.). The authors then reviewed the information extracted and clarified where any differences in information were identified. Support was provided via discussion with the remaining authors if clarifications were required (e.g., what analytical approaches were used). The remaining data were re-checked and verified by one reviewer (B.S.). Qualitative data were extracted by one reviewer using thematic synthesis (B.S.) [28].

Quality assessment

A quality assessment tool, derived from the McMaster Quality Assessment Tool [29] and compensation specific criteria as defined by Rowlands [30], was developed by the research team. The tool was used to assess the included quantitative studies only (categories 1, 2, and 3). Nine compensation specific criteria were developed [30] and included: study design (i.e. experimental design as the ‘gold standard’), implementing activity during inactive times and/or restricting activity during inactive times (i.e. when perturbation occurred), measuring activity across settings, sensitivity of measurement tools, analytical approach (e.g. within-group), and assessed the whole activity spectrum (i.e. SED to MVPA) [21]. In total, 16 criteria, including general and compensation-specific items, were used to assess quality across six overarching categories of (a) selection bias (e.g. is the sample representative); (b) study design; (c) data collection (e.g. is the measurement tool objective, valid and reliable); (d) withdrawals and dropouts (e.g. % of dropouts reported); (e) exposure integrity (e.g. % of participants receiving allocated exposure/intervention); and (f) analyses (e.g. within/between-person analyses). The compensation specific criteria were included across all categories except the withdrawals/dropouts. For category 1, 2, and 3 studies, a quality rating of strong, moderate, or weak was given to each component, except for dichotomous variables that were rated strong or weak. In the event a component could not be clearly determined from the paper, a weak rating was given. No overall study quality score was given in line with current recommendations [31]. Category 4 papers were assessed using the McMaster Qualitative Review Form [32]. The category 4 paper was not given a rating according to the review form guidelines [32]. The full quality assessment tools can be found in the Supplementary Information 3 and 4.


Description of included studies

Extracted data were analysed between May 2021–July 2021. Of the studies initially identified, 109 full-text studies were screened, and 44 studies were included in the review. Of these, 31 were classified as quantitative only (category 1) [20, 21, 3361], 11 assessed quantitative outcomes but included subjective behavioural components (category 2) [6272], one examined self-reported behaviours only (category 3) [73], and one qualitative study examined mechanisms and perceptions of compensation (category 4) [74]. The PRISMA flowchart can be found in Fig. 1.

Fig. 1
figure 1

PRISMA Flow Diagram. 2020 PRISMA flow diagram [26] of studies assessed for eligibility and included in review

The characteristics of the included studies are found in Table 1. Studies were conducted in 10 different countries (see Table 1), with the majority occurring in the USA (n = 13), the UK (n = 13) and Australia (n = 9). The age of participants ranged between 5 [72] to 63 [67] years, with 50% studies specifically focusing on children and/or adolescents (n = 22; [20, 21, 33, 36, 39, 4144, 4752, 55, 58, 6365, 71, 72]) and 50% focusing on adults (n = 22; [34, 35, 37, 38, 40, 45, 46, 53, 54, 56, 57, 5962, 6670, 73, 74]). Study sample sizes ranged from 16 participants [35, 45] to 12,969 [69] participants. Of the 44 studies included, the primary or secondary aim of 30 [20, 21, 33, 3539, 4144, 48, 5056, 59, 64, 66, 6874] and eight studies [34, 38, 40, 47, 57, 58, 62, 63], respectively, was to examine activity compensation. The remaining six were ‘unspecified’ (e.g., results included compensation analyses but this was not a specified aim) [45, 46, 49, 60, 61, 67]. Studies were primarily cross-sectional (52%), followed by experimental (randomised crossover n = 7; randomised experiment n = 2; pre-post n = 1; two-phase single case n = 1) (25%), and randomised controlled trials or intervention studies (18%). There was one longitudinal [69] and one qualitative study [74] included in the review.

Table 1 Description of reviewed studies

Quantitative study overview (categories 1 & 2)

Of the 31 quantitative studies and 11 quantitative/behavioural studies, 11 studies reported evidence of compensation [21, 37, 46, 50, 52, 56, 57, 64, 69, 71, 72], 29 studies reported no evidence of compensation [20, 3436, 3845, 4749, 51, 5355, 5863, 65, 66, 68, 70] and two studies had mixed [33] or unclear results [67] (see Table 2).

Table 2 Quantitative study description

Evidence of compensation

Of the 11 studies reporting evidence of compensation, six were in youth [21, 50, 52, 64, 71, 72] and five were in adult [37, 46, 56, 57, 69] populations. The time frame of compensation included within-day (n = 4; [33, 52, 56, 71]) to between-day (n = 5; [21, 50, 52, 57, 72]), to between-weeks (e.g. baseline to end of intervention) (n = 2; [37, 46]) to between-seasons [64]. All studies used accelerometers, except for one longitudinal study in adults, which assessed compensation within-day at two timepoints (4 years apart) and used the Physical Activity Questionnaire (PAQ) [69]. Outcome variables included energy expenditure [37], steps [57], counts per minute [64] and counts per day [46], LPA [71] and MVPA [64, 71] (Table 2). Only two studies, both conducted with youth, examined compensatory changes across the full waking activity spectrum (SED, LPA, MVPA) [21, 50]. Six studies used a within-person design [21, 33, 37, 50, 52, 71], whilst three studies used between group analyses [64, 69, 72]. Three studies used both within-person and between-person or between- group analyses [46, 56, 57]. One study (adolescent population) [33], reported that compensation only occurred ‘between locations’ (Table 2).

No evidence of compensation

Of the 29 studies reporting no evidence of compensation, 15 were conducted in youth populations [20, 36, 39, 4144, 4749, 51, 55, 58, 63, 65] whilst 14 were conducted in adult populations [34, 35, 38, 40, 45, 53, 54, 5962, 66, 68, 70]. The time frame examined varied from within-day (n = 9; [34, 36, 39, 41, 44, 58, 62, 63, 66]) and (n = 6; [20, 40, 48, 51, 53, 54])/or between-day (n = 5; [42, 47, 49, 59, 65]) to between-weeks (e.g. pre, mid, and post intervention (n = 3; [38, 43, 70]). The majority (90%) of studies used device-based measures of activity, primarily accelerometers (n = 23; [20, 3436, 3845, 48, 49, 51, 5355, 59, 60, 63, 65, 70]) and pedometers (n = 3; [47, 58, 62]). Two studies subjectively measured adults’ physical activity using surveys [67, 68]. Five studies (three in youth, two in adults) examined the whole activity spectrum [34, 35, 42, 51, 55]. One study, conducted with adolescents, examined the activity spectrum where LPA was classified as non-exercise activity thermogenesis (NEAT) [44]. Five studies examined changes in MVPA [58, 65], moderate-intensity physical activity (MPA), and/or vigorous-intensity physical activity (VPA) only [40, 41, 63], while another assessed both MVPA and energy expenditure [38]. Other outcome variables included energy expenditure (e.g. activity energy expenditure) [43, 45, 48] and time use variables (e.g. sitting time) [59, 61, 62, 66]. The analytical approach for studies that reported no evidence of compensation included 11 within-person analyses [20, 36, 41, 42, 48, 49, 51, 58, 61, 63, 68] and 12 between-group analyses [38, 39, 43, 45, 47, 5355, 59, 60, 62, 66], whilst six used both analytical approaches [34, 35, 40, 44, 65, 70]. One study with mixed results (adolescent population) [33], reported that no compensation occurred in the location-based MVPA or overall MVPA component of their data (see Table 2).

Behavioural studies (categories 2, 3 & 4)

Thirteen studies measured specific behaviours [62, 6472], perceptions of compensation [63, 74], and/or mechanisms of compensation [73, 74] (see Table 3). Five studies were conducted with youth populations [6365, 71, 72] and eight with adults [62, 6670, 73, 74]. Ten quantitative studies contained a behavioural component recorded via a survey [63, 6569, 71, 73] or activity diary [62, 64]. Two studies examined perceptions of compensation [63, 74], and two assessed potential mechanisms of compensation [73, 74].

Table 3 Potential behaviours and mechanisms of compensation


The numbers of behaviours assessed ranged from 1 [64]-26 [65] and included passive and active travel [62, 6668, 72], out-of-school activities [64, 65], leisure-time or personal activities [62, 6467, 69, 70], occupational activity [62, 66, 67, 69], recreational walking [68], and screen time [7072]. Behaviours were typically assessed using activity diaries and surveys, though one study combined a survey that was cross-checked with MVPA data collected using an accelerometer in settings [65] (see Table 3). In one study, it was unclear how behaviours were measured [72].

Of the 10 quantitative studies that included a behavioural component, four reported evidence of compensation [64, 69, 71, 72]. However, in three of these studies it was not clear whether compensation occurred in specific behaviours (i.e. data only reported the quantitative activity measures) [64, 71, 72]. In the remaining study, Nooijen and colleagues reported that adults who moved to a higher activity occupation compensated by decreasing their leisure-time exercise [69]. No evidence of compensation was reported in six studies (one in youth and five in adults) [62, 6568, 70]. Based on time-use assessment, Jans et al. reported that those who had highly sedentary occupations did not compensate by decreasing leisure-time sedentary behaviour [66]. Further, Goodman et al. reported that there was no evidence of compensation in children aged 8–13 in any of the 26 MVPA behaviours assessed (e.g. MVPA in school lessons, P.E./games, active travel, etc. [65]) (see Table 3).

Mechanisms of compensation

Two studies examined potential mechanisms of compensation [73, 74]. In a sample of purposely selected participants who were identified as compensating their non-exercise physical activity during a 4-week structured activity intervention, reasons for activity compensation included fatigue, time constraints, lack of motivation, drive to be inactive (i.e. more activity means you can do less activity later), and fear of overexertion [74]. The second study, which examined the association between physical inactivity and compensatory health behaviours in young adults, reported that young adults with strong autonomous motivation believed that they could compensate their sedentary time by using the stairs later [73].

Perceptions of compensation

Two studies examined perceptions of compensation. Costigan and colleagues reported that compensation had not occurred when assessed using accelerometers, yet 13% of participants self-reported that their participation in the high intensity interval training (HIIT) sessions had made them less active during school breaks, and 19.4% thought they were less active after school [63]. In a qualitative study, Gray and colleagues reported that 56% of participants were unaware that they had compensated their activity [74].

Quality assessment

The quality assessment for each study is shown in Table 4. The majority of studies (80%; n = 35) used device-based assessments, of which 24 studies included devices that were considered valid and reliable (54%). Examining activity across settings was evident in 72% of studies (n = 32). However, 86% of studies did not include an exposure (e.g. perturbation) as part of their design or did not deliver > 60% [29] of the exposure as intended (n = 13). Only two studies restricted activity during a time that would normally be active [36, 51], with one imposing activity during a time where children are normally inactive (i.e. timing of perturbation) [51]. Only 9 (20%) studies examined compensation across the activity spectrum [20, 21, 34, 35, 42, 45, 50, 51, 55].

Table 4 Modified McMaster for quality assessment of compensation studies


This systematic review aimed to synthesise research that has investigated activity compensation in youth and adults and identify reasons for and/or awareness of compensatory changes that may have occurred. In general, this review did not find clear evidence that activity compensation occurs in either youth or adults. This may be due to the diverse approaches used to assess activity compensation, including different timeframes and study designs. However, 91% of the studies that reported evidence of compensation (n = 11), included assessing compensation as a primary (n = 9) or secondary (n = 1) aim, suggesting that purpose-designed studies are required to examine compensatory responses. Few studies examined perceptions and mechanisms of compensation, however; the results also suggested that while compensatory changes may occur, there was a lack of awareness of such responses in youth and adults.

This review builds on a previous review [24] through the inclusion of observational, experimental and intervention study designs. Interestingly, regardless of the study design utilised, no clear evidence of compensatory responses were observed, similar to a previous review, where mixed evidence of compensation was reported in children and adults [24]. It is worth noting that whilst 29 studies reported no evidence of compensation, 21% (n = 6) [35, 43, 5355, 59] included a dietary compensation component, of which 67% (n = 4) [35, 53, 54, 59] reported some level of dietary compensation. As such, it could be that compensatory responses occur through the energy intake rather than energy expenditure. Further research is needed to examine the potential relationship between dietary and activity compensation. Another potential reason for the inconsistent results could be due to the way that compensatory changes were analysed. A range of analytical approaches were used by included studies to examine whether compensation occurs, including within-person and/or between person/group analyses. At least one-quarter of the observational studies [39, 47, 53, 54, 59, 62, 64, 66, 69, 72], interventions [38, 43, 60] and experimental studies [45, 55] only utilised between-person/group analyses, despite the activitystat hypothesis being a within-person hypothesis [19]. As such, this may impact the interpretation of findings. Studies should consider a within-person rather than between-group analytic approach to assess activity compensation given this is an individual response [19, 30]. Interestingly, of the studies that used between-group analyses, 25% reported evidence of compensation, whilst 35% of studies using within-person analyses reported evidence of compensation, indicating that when a purpose-driven methodological design is utilised, higher evidence of compensation is reported.

The time frame within which compensation would be expected to occur has been debated, with some suggesting that compensation would be unlikely to occur within-days [24], whilst others reporting that within-day compensatory changes were observed [52]. In this review, there was no clear evidence of a compensation time frame. Some studies reported evidence of compensation within [71] and/or between-days [52, 57], whilst others reported that compensation was evident over a longer period of time, such as between-seasons [37, 64, 69]. In contrast, some studies found no compensation within-day [36] and/or between-days [20, 42] or over longer periods of time [38, 43]. For intervention and experimental studies, when analysing two time points for compensation the days should be ‘comparable’ (i.e. structured similarly) to determine whether the changes observed may be attributed to compensatory responses [75] or variations driven by other factors (e.g. timetabling). However, few included studies reported considering the temporal nature of activity data in this way, and of those that did, only short time frames (e.g. < 24 h) were examined [51]. A previous review [24] suggested that compensation duration was synonymous with intervention duration, ranging from within-day to 4 years. However, it is unclear whether this reflects maintenance or changes in activity behaviours rather than compensation, as from a biological perspective, homeostatic processes could be expected to occur acutely. Future research assessing the time frame of compensation should initially examine acute responses before assessing changes over longer time periods.

The study design and compensation timeframe period are important when considering the perturbation of activity. Whilst a few studies examined the effect of a stimulus on participants’ activity [49, 55], the dose was not always reported. Few studies reported whether the stimulus occurred during a time when children were already active (e.g. during recess), making it difficult to determine whether the stimulus is eliciting a compensatory response or displacing usual activity [22, 30]. Only two studies restricted activity during normally active times (e.g. recess) [36, 51], despite compensatory responses being hypothesised to occur under such conditions [30]. A third study, which imposed sedentary time on children for an 8-h period, will have imposed inactivity on active periods of a child’s day. However, the amount of usual activity that was restricted during the imposed 8-h sedentary time period was not reported [55]. Lastly, 55% of the included quantitative studies were observational. Whilst observational studies may provide insights into intra-individual variability, the type and dose of perturbation were not described. As such, it is difficult to determine whether the dose of imposed activity and/or inactivity was outside the normal day-to-day variability (i.e. habitual activity patterns), to illicit a compensatory response [30]. In addition, it limits conclusions that any behaviour compensation was purely a biological response, or conversely a response influenced by the environments in which a person lives (e.g. structure of the day) [72]. Overall, future research should aim to report intra-individual variability to determine whether the perturbation exceeds such variability [76], and report the duration and activity intensity of the perturbation during the day.

Few studies (26%) considered changes in activity across the whole activity spectrum [20, 21, 34, 35, 42, 44, 45, 50, 51, 55, 67], despite the co-dependency of activity intensities occurring within a finite period (e.g. 24 h) [77]. The main activity intensity examined in both youth and adult populations was MVPA, which enables the assessment of changes in this intensity only. Arguably, responses to perturbation across activity intensities would be expected to occur across the whole activity spectrum, as all intensities would contribute to a daily set-point [21]. Given MVPA only constitutes 5% of a child’s waking hours [5] and 3% of an adult’s total day [78], if compensation were to occur, it is very likely to occur in lower intensities of the activity spectrum (LPA and SED) and not just in the intensity measured. Furthermore, it is possible that the findings generalised to other/daily behaviours, nor other population sub-groups. For example, some studies examined specific population groups (e.g. army cadets [40], office workers [66]), and outcomes reported were specific to those target groups (e.g. impact on MVPA, sitting time, etc.). Such findings are therefore specific to that population group and behaviour/intensity. Future studies should focus on assessment of compensation across the entire activity spectrum, and use statistical analyses that appropriately deal with co-dependency between these behaviours, such as compositional data analysis [77], to explore whether compensations may occur across the activity spectrum rather than within a single intensity. Further, future studies could consider sub-group analyses to see how compensation may occur across population groups.

Given the mixed findings and variability in methods and approaches it is difficult to draw conclusions concerning the existence of an activitystat and whether compensation occurs. While the one study [51] that scored ‘moderate’ or ‘strong’ across all compensation specific criteria of the quality assessment reported that compensation had occurred, this study was limited as participants did not participate in all three experimental conditions (imposed moderate- to vigorous physical activity, imposed light physical activity, and restricted physical activity) that is arguably needed to fully test the activitystat hypothesis. As some findings did report compensatory changes, this indicated that such responses do need to be considered in intervention designs moving forward. While compensation may not necessarily be harmful, it may depend on the response to a perturbation. Past literature has suggested that a new equilibrium around activity would indicate that individuals were able to modulate physical activity upwards and subsequently adjust the setpoint for physical activity [22, 72]. However, the issue therein, is that once a perturbation has been removed, there is little evidence to suggest that the modulated physical activity continues at that higher level [22]. These questions are important, yet complex to answer, without a clear understanding of whether compensation occurs (or not). As such, experimental studies are needed to determine what the impact of compensation is on health and whether different types of compensation have different health effects.

This systematic review was the first to examine mechanisms of or potential reasons for compensatory responses. Understanding how compensation may manifest behaviourally may enable researchers to specifically target behaviours at risk of compensatory changes. Ten studies examined potential compensatory changes in ~ 35 behaviours, yet few behaviours were consistently studied or clearly included in the compensatory analysis. Indeed, studies used different methods, such as temporal associations [65] and time use [66, 67], and MVPA in-school/out-of-school [65] and in different locations [64]. The one study that focused on a specific behaviour reported that adults who moved to a higher activity occupation compensated by decreasing their leisure-time exercise [69]. However, while two within-day measurements were analysed, the measurement time points were 4 years apart, making it difficult to understand whether this is truly a compensatory response, or if other factors (e.g., the environment) may also explain the results [69]. Overall, it is challenging to understand whether compensatory changes to behaviours occur, and if they do occur, how these may manifest between (e.g., walking to school, then public transportation home) or within behaviours (e.g., less active during a sports session). Future research should consider the use of purpose-designed surveys to examine time-use in different behaviours across settings, in conjunction with device-based assessments measurements.

Few studies examined potential mechanisms or reasons for compensatory behaviours. Fatigue, time constraints, lack of motivation, drive to be inactive, fear of overexertion, and perceived effort were identified as potential reasons or mechanisms of compensation in older adults [74]. Similarly, perceived effort to compensate combined with a drive to be inactive seemed prevalent in a study in young adults who reported that SED time could be compensated by a healthy behaviour such as taking the stairs [73]. To date, no studies have examined potential mechanisms (e.g., behavioural, psychological, or physiological mechanisms) of compensation in children. Despite this, results indicate that compensation may manifest in different ways within different population groups. Whilst qualitative research, for example, cannot determine whether compensatory changes occurred, it provides unique insights into potential mechanisms that could then be targeted by future interventions that aim to minimise such responses.

Lastly, few studies examined perceptions or awareness of any potential compensatory responses. In the qualitative study by Gray et al. [74], over half (56%) of participants (older adults) were unaware that they had compensated. Only one study measured self-reported perceived compensation [63]. Whilst most adolescent participants did not believe they compensated their activity because of the HIIT sessions, some thought they did compensate during (13%) or after school (19%) [63]. However, no further analyses were performed to see if their subjective experience matched the objective measurements or what traits, if any, these participants shared. It is unknown whether those that thought they compensated their activity actually did so, though it appears that, to some degree, people are aware that compensation may occur after activity. Future research should assess perceptions of activity compensation and examine differences across age groups (for example) and behaviour intensities. Understanding individual awareness of compensation, and any potential reasons for it, may identify why past activity interventions have had limited effectiveness, and inform the development of targeted interventions in the future.

Strengths and limitations

This systematic review was the first to consider potential reasons for any compensatory changes observed. This review included all study designs, as well as behavioural studies, and was able to highlight a number of gaps in activitystat/activity compensation research. However, a few limitations must be acknowledged. Whilst the inclusion criteria were broad to reflect the way in which compensation has been examined to date, it was difficult to compare studies given the diverse range of approaches used and lack of standardised approaches (e.g., different statistical methods [within/between subjects], study designs [experimental, observational], etc.). This review aimed to synthesise all available activity compensation research; however, it was unable to draw firm conclusions as to the existence of activity compensation, and how it may manifest, given the variability in the methodology of studies that have examined this research area.


Overall, this review found that compensation was observed in approximately one-third (32%) of youth and one-quarter (23%) of adult studies that utilised quantitative methods to examine the activitystat hypothesis. There was some evidence of compensation reported in studies where behaviours were assessed. However, there was substantial variability in study designs, time frames assessed, analytical approaches used, and behaviours examined in both the youth and adult studies, making it difficult to draw firm conclusions to the existence of the activitystat. Future research should consider focusing on experimental designs (with the type, timing and dose of perturbation reported), examining the whole activity spectrum, utilising a within-person analysis design across short and acute timeframes to assess whether compensation responses have occurred. Additionally, potential mechanisms of compensatory changes, and whether participants are aware of their compensation, should be assessed. This would provide valuable insights into what behaviours may be targeted in future interventions to negate compensatory changes.