The World Health Organization recommends that adults engage in at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity physical activity (PA; or an equivalent combination of both) per week and limit the amount of time spent engaging in sedentary behavior (SB) [1]. Despite these recommendations, approximately 28% of adults do not meet PA guidelines [2]. Furthermore, on average, adults engage in 8.2 hours per day of SB [3]. This represents a considerable health burden as individuals who are physically inactive and do not meet PA guidelines have a 20% to 30% increased chance of premature death versus individuals who are active and meet PA guidelines [2,4]. Physical inactivity and SB can also lead to chronic diseases and are a contributing factor to 35 pathological and clinical conditions (eg, obesity, type 2 diabetes, and cardiovascular diseases) [5]. Considering the risks associated with physical inactivity and SB, understanding the factors that influence movement-related behaviors such as PA and SB is paramount.

There are a number of different theoretical approaches to understand and explain PA and SB engagement (eg, the humanistic framework, the dual process framework, and ecological models) [6]. Social cognitive framework represents one of the most used and long-standing theoretical frameworks within the movement-related behavior literature to explain and predict behavioral engagement [6]. Social cognitive framework emphasizes key determinants of behavior as individuals’ cognitions about the anticipated outcomes of a behavior and their ability to engage in a behavior [7,8]. In line with these theories, individuals will focus their efforts toward (eg, intentions) and subsequently engage in a behavior if their beliefs about the behavior are positive (eg, outcome expectations) and they are confident in their ability to engage in the behavior (eg, self-efficacy) [9-13]. There are 2 prominent theories that are considered part of the social cognitive framework are Social Cognitive Theory and the Theory of Planned Behavior [9,10].

Specifically, Social Cognitive Theory identifies self-efficacy—​an individual’s belief in their ability to engage in a behavior—and outcome expectations—the perception of consequences (positive or negative) of an individual’s action—as key determinants of behavior [8,10]. The Theory of Planned Behavior posits that intention, or one’s willingness to try to engage in a behavior, is the main antecedent of behavior [7,9]. The Theory of Planned Behavior also proposes that intention formation is predicted by three factors: (1) attitudes or the extent to which one has positive or negative evaluations of a behavior, (2) subjective norms or the perceived social pressure to engage in a behavior, and (3) perceived behavioral control or the extent to which a person has the capacity and free will to engage in a behavior [7,9]. Furthermore, both theories acknowledge that facilitators and barriers can help or hinder engagement in a behavior, respectively [7-10]. Therefore, these theories outline social cognitive determinants hypothesized to influence movement-related behaviors.

Several reviews have indicated that social cognitive determinants predict PA behavior in observational studies. However, there is experimental evidence indicating that the extent to which these determinants predict changes in behavior is generally much weaker [14-16]. Within the last decade, evidence has accumulated suggesting that PA and SB are independent health behaviors [17,18], and studies have applied social cognitive frameworks to explain and predict SB. Findings across SB studies appear to mirror those of PA studies, with evidence for relationships between social cognitive determinants and behavior indicating stronger relationships in observational studies than in experimental studies [19-21].

A potential reason for the limited effectiveness of social cognitive determinants in explaining and predicting PA and SB is that the timescale in which these relationships are assessed is not the timescale in which social cognitive determinants influence decisions to engage in a behavior [10,22]. Most of the research investigating associations between social cognitive determinants and PA or SB tends to assess these constructs infrequently and ask participants to report their usual level of social cognitive determinants or behavior over macrotimescales (eg, weeks and months) [21,23]. However, PA and SB are repeat-occurrence behaviors, meaning that these behaviors are typically engaged in multiple times per week or even multiple times per day [24]. Furthermore, the day is an elemental structure in human life. Days are easily defined and universally experienced because of the light-dark cycles of the sun and associated sleep-wake cycles. Moreover, people self-regulate and restore self-regulatory resources throughout these cycles [25]. The changing contexts of people’s daily lives could also influence daily and within-day motivation and movement-related behaviors. Therefore, methods that capture typical levels of movement-related behaviors and social cognitive determinants on a macrotimescale may overlook important information regarding decisions to engage in a bout of PA or SB across microtimescales (eg, hours and days). Previous research has documented that social cognitive determinants, PA, and SB are dynamic, varying within individuals across time and space [26-28]. Investigating associations between social cognitive determinants and movement-related behaviors in the content of daily life across microtimescales can elucidate the motivational determinants of movement-related behaviors and potentially enhance intervention efforts.

Recent evidence of the dynamic and time-varying nature of PA and SB has increased in part from advances in methodology to assess behavior and its determinants. Ecological momentary assessment (EMA) has gained popularity over the last decade in the movement-related behavior literature as a methodology to capture fluctuations in behavior and social cognitive determinants across microtimescales in naturalistic settings [29,30]. EMA is a real-time data capture methodology that repeatedly assesses individuals on a phenomenon of interest in their natural environment (eg, motivation and behavior) [31]. EMA is useful for assessing phenomena that change across time and space, such as movement-related behaviors and their determinants. For instance, an individual’s engagement in PA behaviors may change over the course of the day, as may their feelings of confidence (ie, self-efficacy) in engaging in PA. To study these fluctuations in behavior as well as how these 2 constructs might covary, a smartphone-based EMA protocol could assess self-efficacy at predetermined (eg, every 2 hours) or randomly occurring (eg, anytime between 8 AM and 8 PM) times throughout the day. In addition, monitoring via an accelerometer could be conducted. In an EMA protocol, when participants receive a notification to complete a questionnaire through an app or website on their smartphone that measures self-efficacy to engage in PA over the following 2 hours, they are asked to briefly stop what they are doing to complete the questionnaire. Responses on the smartphone are date- and time-stamped to facilitate easy pairing of the smartphone questionnaire and accelerometer data in the 2-hour window (referenced in the self-efficacy assessment) after the EMA prompt. As these notifications happen repeatedly, researchers are able to capture these constructs and their associations as individuals go about their day-to-day activities, allowing them to examine these associations across the changing contexts of everyday life.

Therefore, EMA methodology can reduce recall biases and enhance ecological validity by evaluating a phenomenon of interest close in time to when it occurs in real-world settings as individuals go about their normal day-to-day lives [29]. Today, EMA protocols can be delivered through various media (ie, apps on mobile devices, internet-based questionnaires, and SMS text messages) that can provide time stamps of participant responses. As noted, this can facilitate the pairing of EMA responses with other time-stamped data sources such as accelerometers and allows for the investigation of the temporal sequence of relationships between key constructs. Previous research has established the feasibility and validity of using smartphone-based EMA to assess PA and SB as well as their determinants in diverse populations across their life span [28,32-35].

The use of EMA to capture and understand PA and SB has increased over the past decade. As a result, recent reviews have summarized EMA findings regarding various determinants of PA and SB, including affective states [36] and environmental contexts [37]. However, there is yet to be a review summarizing the associations between social cognitive determinants and movement-related behaviors from studies using EMA methodologies. This systematic review aimed to summarize the literature regarding within-day and day-level associations between social cognitive determinants and movement-related behaviors using within-day or daily EMA methodologies. The decision to focus exclusively on within-day and day-level associations was based on the repeat-occurrence nature of movement-related behaviors and the fact that assessment schedules occurring less frequently than the day level may not be sensitive to the changing contexts in everyday life and the factors driving decisions to engage in occasions of PA or SB. This rationale is bolstered by the fact that the day represents a natural and fundamental reoccurring event in human life.

Articles that met the following criteria were included in the review: (1) human-participant research, (2) available in English, (3) quantitative data available for at least one association between a social cognitive determinant (ie, the independent variable) and movement-related behavior (ie, the dependent variable), and (4) within-day or daily EMA study design. Social cognitive determinants were defined as constructs specified within Social Cognitive Theory and the Theory of Planned Behavior, 2 popular social cognitive frameworks in the movement-related behavior literature [7-9,22,39]. Therefore, social cognitive determinants of interest for this systematic review included intentions, attitudes, subjective norms, perceived behavioral control, self-efficacy, outcome expectations, risk perceptions, barriers, facilitators, goals, and plans regarding movement-related behaviors. To focus on naturally occurring associations between social cognitive determinants and movement-related behaviors, articles were excluded if they used an active experimental design. In addition, articles were excluded if they were not published in a peer-reviewed scholarly journal.

Literature searches were conducted on May 25, 2022, in the PubMed, SPORTDiscus, and PsycINFO databases to identify relevant articles that used EMA methods to examine associations between social cognitive determinants and movement-related behaviors. No date restrictions were applied in the searches. This process is shown in the PRISMA flow diagram in Figure 1. In total, 3 sets of search terms were used to identify potentially relevant articles. The first set of search terms used general terms, including the following: (“physical activity” OR “exercise” OR “sedentary behavior” OR “movement behavior” OR “physical exercise” OR “sitting”) AND (“ecological momentary assessment” OR “EMA” OR “daily diary” OR “experience sampling”). The second set of search terms included the general terms (from the first search) along with specific terms related to psychological determinants: (“social cognitive” OR “motivation” OR “psychosocial” OR “behavioral cognitions”). The third set of search terms included the general terms from the first search along with specific terms related to social cognitive determinants: (“self-efficacy” OR “outcome expectation” OR “intention” OR “attitude” OR “subjective norm” OR “control” OR “risk perception” OR “barriers” OR “facilitators” OR “goal” OR “plan”). See Multimedia Appendix 1 for the complete search term queries in each database. These specific social cognitive terms were selected by identifying constructs outlined within the Theory of Planned Behavior and Social Cognitive Theory, 2 of the most prominent social cognitive frameworks [6]. The first search using general terms was completed so as to not miss any EMA movement-related behavior articles that may have assessed social cognitive determinants but did not have them as their focus. All articles were collected in Zotero (Corporation for Digital Scholarship) and uploaded to Rayyan (Rayyan Systems Inc), a web tool used to provide support for systematic reviews [40].

A total of 9 reviewers were involved in the process of screening and extracting information from the eligible articles. All reviewers received training on EMA studies of movement-​related behaviors and social cognitive determinants from an expert in this content area (JPM). Following training, all reviewers screened the same 50 abstracts to determine whether the full text should be examined for eligibility. All reviewers then met with the content area expert to discuss and resolve discrepancies. Following this, reviewers independently screened assigned abstracts. One reviewer (KMB) screened all abstracts, and one additional reviewer (KYO, RMR, LB, CM, JY, JMS, BLH, or DJH) was assigned to each abstract. Following screening of all abstracts, discrepancies were discussed and resolved between each pair of reviewers with the content area expert.

The articles retained after title and abstract screening were gathered as full texts. In total, 2 reviewers (KMB and JPM) reviewed the full articles independently to determine eligibility and came together to discuss and resolve any discrepancies. A flowchart using the PRISMA 2020 guidelines shows the screening process for eligible articles (Figure 1) [41].

After identifying the articles to be included in this systematic review, 4 reviewers (KMB, RMR, KYO, and BLH) independently extracted relevant information from 8 articles, met with the content area expert (JPM) to discuss and resolve any discrepancies, and then independently coded the remaining articles. One reviewer (KMB) extracted relevant information from all the articles, and one additional reviewer (RMR, KYO, or BLH) was assigned to each article. Finally, the reviewers and content area expert met to discuss and resolve any discrepancies regarding article information extraction.

For each eligible article, the extracted information focused on study participant characteristics, study design characteristics, study main findings, and methodological quality assessments according to 2 established instruments. First, the Methodological Quality Questionnaire (MQQ) [42] assessed overall study quality based on 9 dimensions (ie, theoretical or conceptual definition, research design, sampling design, sample, evidence of reliability and validity, data analysis, implications for practice, and implications for policy). MQQ scores can range from 0 to 27. Second, the Checklist for Reporting Ecological Momentary Assessment Studies (CREMAS) [43] assessed quality with regard to reporting EMA methodology on 5 dimensions that outline specific criteria that have to be included in the title, introduction, methods, results, and discussion. Across these 5 dimensions, there were 16 items (ie, title and keywords, rationale, training, technology, wave duration, monitoring period, prompting design, prompt frequency, design features, attrition, prompt delivery, latency, compliance rate, missing data, limitations, and conclusions).

CREMAS scores can range from 0 to 16, with 1 point per item addressed. For both the MQQ and CREMAS, higher scores indicate better methodological quality.

The associations between social cognitive determinants and movement-related behaviors were assessed using the guidelines developed by Sallis et al [44]. An association was supported if 60% to 100% of the articles reported such an association. No association was supported if 0% to 33% of the articles reported an association. An indeterminate or inconclusive association was supported if 34% to 59% of the articles reported an association. Statistical significance (P<.05) and parameter estimates (and 95% CIs, if reported) were used to determine whether any association between a social cognitive determinant and movement-related behaviors existed and the direction of the association (ie, positive or negative), respectively. All findings from individual articles were presented; however, at least 4 articles were needed to make an assessment regarding an overall association between a given social cognitive determinant and movement-related behavior in this systematic review. The choice to conduct a systematic review of results was based on the substantial diversity of study designs, operationalizations of social cognitive determinants and movement-related behaviors, and analyses to test associations (eg, multilevel linear regression, multilevel logistic regression, multilevel negative binomial model, and time-varying effect modeling).

A total of 3510 articles were identified across all the database searches. After duplicates were removed (781/3510, 22.25%), the remaining articles were screened by title and abstract (2729/3510, 77.75%). After screening, 2.02% (55/2729) of the articles were identified for full-text retrieval. Of those 55 articles, 31 (56%) were excluded, leaving 24 (44%) articles from 21 unique studies to be included in this systematic review (Figure 1). The publication year, sample and study characteristics, and methodological quality scores of each article are presented in Table 1.

The analytic sample size of each study ranged from 7 to 198 participants, with a mean sample size of 90.05 (SD 50.70). All articles except for the one by Reifsteck et al [52] (23/24, 96%) reported on studies with samples in which at least 50% of the participants identified as women. The samples reported in 21% (5/24) of the articles were college or university students [52,55,62,63,66]. The samples reported in 50% (12/24) of the articles focused on adults [45-49,53,54,58-61,67]. The samples reported in 29% (7/24) of the articles were older adults (aged ≥50 years) [50,51,56,57,64,65,68]. The mean age of the participants from the 21 unique studies was 47.65 (SD 17.84) years. None of the studies included samples of children or adolescents. Of the 18 articles that reported on participants’ racial or ethnic identities, 4 (22%; n=3, 75% using data from the same study) [47-49] indicated that participants identifying as White or non-Hispanic White did not make up most of the sample [47-49,53].

Of the 24 articles, 21 (88%) reported on studies that collected data over 1 wave. A total of 12% (3/24) of the articles (using data from the same study) reported that data were collected across 3 measurement waves spaced over 1 year [47-49]. Regardless of the number of waves, the monitoring period in each study ranged from 4 to 30 days, except for 4% (1/24) of the articles, which sampled participants daily for 2 to 7 months [57]. Regarding EMA prompting design, of the 24 articles, 12 (50%) reported on studies that had 1 prompt per day [46,54,55,58-64,66,67], 3 (12%) reported on studies that had 2 prompts per day (ie, one in the morning and one in the evening) [57,65,68], and the remaining articles (9/24, 38%) reported on studies that delivered between 4 and 8 prompts per day [45,47-53,56].

In total, 38% (9/24) of the articles indicated that EMA prompting was a signal-contingent design (ie, occurring at randomly prompted times) [45-53]. A total of 17% (4/24) of the articles reported on studies that used interval-contingent design (ie, occurring at fixed times) [52,54,56,57], whereas 46% (11/24) of the articles reported on studies that used event-contingent design (eg, self-initiated EMA questionnaire after a specific event) [55,58-65,67,68].

Regarding design considerations to reduce participant burden, the studies reported in 17% (4/24) of the articles did not ask about social cognitive determinants at every EMA prompt to limit the number of items in each prompt [47-49,56]. The studies reported in 8% (2/24) of the articles customized the time of the prompts based on each participant’s wake and sleep schedules [45,57], whereas another study had participants select their own start day to fit their work schedule [61].

EMA protocols were delivered via smartphone (8/24, 33%), PDA (6/24, 25%), or website (10/24, 42%). Among the articles that reported on studies that used a smartphone to deliver EMA protocols, commercially available apps including Personal Analytics Companion (1/24, 4%) [66], movisensXS (2/24, 8%) [50,51], and MyExperience (3/24, 12%) [47-49] were used. A total of 8% (2/24) of the articles reported on studies that had participants complete the study protocol on a smartphone but did not specify the platform used to deliver the EMA prompts [59,67]. PDA devices comprised handheld computers (3/24, 12%) [56,60,68], tablets (2/24, 8%) [58,65], and a wrist-worn Patient-Reported Outcomes Diary device (1/24, 4%) [57]. Articles on studies that used websites either did not specify the website [45,52,53,55,62-64] or did specify it using Prolific [54], Qualtrics [61], or REDCap (Research Electronic Data Capture; Vanderbilt University) [46].

All articles (24/24, 100%) reported participants’ compliance or response rates within the EMA protocol. These rates ranged from 56.9% to 95%, indicating general compliance with the EMA methodology.

Intentions (15/24, 62%) [45,47-51,55,57-60,62,63,65,66] and self-efficacy (14/24, 58%) [46,47,49-51,56-59,64-68] were the most frequently assessed social cognitive determinants. Of the 14 articles categorized as reporting on studies that assessed self-efficacy [57,58], 2 (14%) operationalized their construct of interest as perceived behavioral control; however, perceived behavioral control is generally considered synonymous with self-efficacy [9,69], so the findings were combined into 1 category for this review. Other social cognitive determinants assessed included planning (4/24, 17%) [52,54,59,65], outcome expectations (3/24, 12%) [47,49,66], barriers (2/24, 8%) [53,61], and explicit attitudes (1/24, 4%) [58].

Intentions and self-efficacy were primarily assessed using 1 to 2 items, but 4% (1/24) of the articles reported on studies that used 4 items to assess self-efficacy [46]. The studies reported in 29% (7/24) of the articles adapted social cognitive determinant items from validated scales [46,54,55,58,59,61,65], but almost all (5/7, 71%) reduced the number of items or adapted the time frame of the items for delivery as part of their EMA protocol. Common behavioral targets assessed with social cognitive determinant items included engaging in PA [46,53-56,58-62,64,66-68], engaging in a specified duration of PA [45,47-52,57], or limiting SB to a specified total amount of time [53,63,65,66].

Methods for assessing movement-related behaviors included device-based (15/24, 62%) and self-reported (8/24, 33%) assessments or both (1/24, 4%) [65]. Common devices used included the ActiGraph GT3x or GT3x+ (7/24, 29%) [45,55,58,59,63,66,67], ActiGraph GT2M (3/24, 12%) [47-49], ActiGraph GT1M (1/24, 4%) [53], Fitbit Alta HR (1/24, 4%) [46], and activPAL 3 (3/24, 12%) [50,51,65]. A total of 4% (1/24) of the articles reported on studies that provided participants with either the ActiGraph GT1M or GT3x+ device [68], and 4% (1/24) used the Patient-Reported Outcomes Diary device [57]. In total, 75% (18/24) of the articles reported on studies that required a minimum threshold of valid wear time for data to be included in the analysis [45-53,56,58,​59,61,63,65-68]. Minutes of moderate- to vigorous-intensity PA (MVPA) were the most common operationalization of PA (12/24, 50%) [45,47-49,53,55,56,58,59,63,66,67], and minutes of sedentary time were the most common operationalization of SB (5/24, 21%) [50,51,53,55,65] using device-based measures.

Among the self-reported measures of movement-related behaviors, the studies reported in 25% (6/24) of the articles adapted items from existing validated measures, including the International Physical Activity Questionnaire–Short Form by Sjöström et al [63,65], Godin Leisure-Time Exercise Questionnaire by Godin and Shephard [52,54,61], and a measure of sedentary time in older adults developed by Gardiner et al [65]. Other assessments of daily movement-related behaviors included checklists in which participants indicated the activities they had participated in that day and for how long [56,64]. Bond et al [60] and Rebar et al [66] assessed the daily duration of MVPA using items created for the study.

The average MQQ score was 21.38 (SD 4.31; range 14-27), with 21% (5/24) of the articles scoring 27 (the highest possible score). The interrater reliability for the MQQ scores was 98.3%, which is an acceptable level of agreement [55]. On the basis of the 9 criteria of the MQQ, the most frequently omitted information pertained to evidence of reliability and validity provided for the data collected (12/24, 50%) [45,46,53,56-58,60-63,66,67] followed by implications for policy (11/24, 46%) [45,46,53,55-59,61,64,67].

The average CREMAS score was 11.92 (SD 2.21; range 8-15). The interrater reliability for the CREMAS scores was 93.75%. Commonly omitted elements of the CREMAS among applicable articles included participant training procedures, latency, and missing data analysis. Of the 20 articles in which latency was applicable (eg, interval- and signal-contingent designs), none reported latency. A total of 54% (13/24) of the articles did not report any missing data analyses [45,46,52-54,58-61,64-67]. In total, 50% (12/24) of the articles did not report any training to familiarize participants with the EMA protocol [46,49,53-56,58,61,64,66-68]. Except for the studies reported in 12% (3/24) of the articles [47-49], wave duration (ie, the number of data collection waves in a study) was not applicable as all other studies collected data over a single wave.

This systematic review identified studies examining associations between specific social cognitive determinants (ie, intentions, self-efficacy, outcome expectations, planning, perceived barriers, and attitudes) and PA; however, the availability of data regarding these associations differed at the momentary and daily levels. The findings are summarized in the following sections.

This review is the first to summarize the available EMA evidence of associations between social cognitive determinants and movement-related behaviors at the momentary and daily levels. Although this review included 24 articles comprising 21 unique studies, there were limited studies investigating each individual social cognitive determinant’s relationship with subsequent behavior, especially after accounting for the timescale of assessment (ie, momentary level vs day level). The largest evidence base and, therefore, the strongest conclusions in the systematic review pertain to relationships between intentions and PA and between self-efficacy and PA. Overall, synthesizing the available evidence contributes to our preliminary understanding of the impact of social cognitive determinants on subsequent movement-related behaviors in real-world environments, identifies gaps in the literature to direct future movement-related behavior EMA research, and can begin to inform intervention efforts that are designed to deliver contextually relevant motivation content during periods of opportunity and vulnerability.

This systematic review suggests that positive associations exist among intentions, self-efficacy, and PA at the day level. It is not surprising that intentions and self-efficacy, which are prominent constructs posited to directly influence behavior within social cognitive frameworks [70,71], emerged as consistent and positive predictors of PA at the day level in this systematic review. However, articles that reported on studies investigating associations among intentions, self-efficacy, and PA at the momentary level (6/24, 25% and 6/24, 25%, respectively) revealed mixed findings. For instance, although a sufficient number of studies investigated associations between momentary self-efficacy and subsequent PA to determine an overall association, conflicting findings across the studies resulted in an inconclusive overall association. Several time-varying moderators were documented across the articles at the momentary level. It is possible that associations between momentary social cognitive determinants and subsequent PA may be affected by contextual factors that change across time and space as individuals navigate their daily lives, whereas day-level associations may be less affected by immediate contextual features of one’s current environment.

This systematic review suggests that more research is needed to better understand associations between social cognitive determinants and subsequent movement-related behaviors in the context of everyday life. For instance, results from this systematic review suggest that no studies used daily or within-day EMA methodology to examine relationships between PA facilitators (eg, optimal weather) and subsequent PA behavior or between outcome expectations and attitudes and subsequent SB. In addition, there were not enough eligible articles that reported on studies investigating relationships between any social cognitive determinant and SB to draw conclusions. In addition, although some social cognitive determinants such as daily planning appeared to indicate consistent associations with PA across the studies, <4 studies investigated this association, which prevented a conclusion from being drawn. Further complicating the state of the literature in this area is that a variety of measures were used to assess both social cognitive determinants and behavior. Such diversity may have contributed to the discrepant findings across the studies. For instance, among the studies investigating associations between momentary self-efficacy and PA (where an inconclusive overall association was determined; only 2/4, 50% indicated a positive association), Dunton et al [56] assessed participants’ confidence in engaging in PA and found a positive association with PA, whereas Pickering et al [49] assessed participants’ confidence in engaging in PA despite possible barriers and found a null association. However, even across the diverse assessments used, we were able to find consistent trends in relationships between some social cognitive determinants and behaviors (eg, daily associations between intentions and PA), increasing the confidence in our conclusions on those relationships as the diversity of measures reduces the likelihood that the effect is the result of measurement bias. Greater consistency across the studies in the assessment of social cognitive determinants and behavior would allow for more precise conclusions on associations between social cognitive determinants and movement-related behavior and would be more appropriate to quantitatively estimate associations in a meta-analysis.

Although studies using EMA methods can uncover more nuanced associations between social cognitive determinants and movement-related behaviors by collecting ecologically valid and intensive longitudinal data, the limitations of the studies included in this review should be noted. First, the findings synthesized in this review may not apply to all developmental periods across the life span or individuals of racially or ethnically minoritized backgrounds. No studies included in this review focused on child or adolescent samples, and almost all studies (12/14, 86% that reported race and ethnicity) featured samples in which most participants identified as White or non-Hispanic White. Given that PA levels decline rapidly throughout late childhood and adolescence and racial and ethnic minorities experience physical inactivity–related health disparities [72], EMA may be a critical methodological tool to understand relationships between motivation and behavior in these vulnerable populations. In addition, the studies featured in this review comprised insufficiently active or sedentary individuals as well as those who were sufficiently active; therefore, it is likely that the results are aggregated across individuals at various stages of the behavior change continuum. There is evidence suggesting that social cognitive determinants may differentially regulate behavior across the behavior change continuum, which is an important direction to explore in EMA work to understand movement-related behaviors [24,73]. Further influencing the generalizability of the findings is the lack of missing data analysis in several studies included in this review (12/24, 50%). Such an analysis is essential in EMA studies to determine if data are missing at random or in systematic patterns, which may influence the extent to which documented associations translate to all occasions or all people [43,74].

Most EMA studies included in this review (16/24, 67%) created measures to assess social cognitive determinants or behavior. Although such an approach is common in EMA research as fewer items are used to limit participant burden and fatigue that may result from repeated, intensive assessments [29], the items used in the studies in this review rarely presented psychometric data to establish the validity or reliability of the measures, and the studies often appeared to create items based on face validity. More rigorous and systematic approaches are necessary to develop EMA items to assess social cognitive determinants and movement-related behaviors (for a more in-depth discussion, see the study by Reichert et al [75]).

Furthermore, the limitations of the systematic review itself should be addressed. Although this review assessed the quality of the studies and study reporting through the MQQ and CREMAS, respectively, the information was not used in the interpretation of the results. Neither quality assessment tool specifies thresholds for low-, medium-, or high-quality articles. Therefore, we chose to present the raw scores on each assessment tool. On the basis of those scores, of the 24 articles included in the review, only 5 (21%) scored <18 on the MQQ, and 7 (29%) scored <11 on the CREMAS, which would indicate that those articles satisfied less than two-thirds of the criteria specified within the respective quality assessment tools.

In addition, the inclusion and exclusion criteria of this review did not place any restrictions on the participant sample size necessary for inclusion. A small participant sample size can affect the likelihood of obtaining significant results as well as the magnitude of the association documented. Only 17% (4/24) of the articles included in this review reported on studies that had <50 participants—a threshold determined through simulation to provide adequate power for variance, SE, and fixed-effects estimates at both the between- and within-person levels [76,77]. However, the findings presented in this review primarily focus on within-person findings (or the extent to which social cognitive determinants predict subsequent behavior on a given day or moment). Therefore, power for the analysis is derived from the number of occasions in the analytic sample, and because of the intensive assessments that are a hallmark of EMA, studies typically generate many observations per person. For instance, the study by McDonald et al [57] had the smallest participant sample size of the studies included in the review (N=7) but collected daily assessments from 2 to 7 months, resulting in between 87 and 196 observations per participant. Furthermore, the size of the associations was not considered to fully interpret the relationships documented across the studies as few (7/24, 29%) reported effect sizes. As the volume of studies in this area increases, a meta-analysis should be conducted that accounts for the sample size and effect sizes of relevant studies to further characterize momentary and daily associations between social cognitive determinants and movement-related behaviors.

PA and SB are considered independent health behaviors with different processes regulating each behavior and different health consequences associated with each [78]; however, most of the research investigating relationships between social cognitive determinants and movement-related behaviors focuses on PA. Given the prevalence of excessive SB as well as recent calls to design movement-related behavior interventions to initially focus on reducing SB and over time build to engaging in MVPA [79,80], EMA studies specifically addressing social cognitive determinant–SB relationships are necessary to develop and refine theoretical frameworks to explain and predict SB.

Across both behaviors, intentions and self-efficacy were the most investigated social cognitive determinants. This is not surprising as the Theory of Planned Behavior and Social Cognitive Theory posit that intentions and self-efficacy are proximal determinants of behavior, respectively [7,9,10,39]. However, based on preliminary evidence from this review, momentary self-efficacy and daily planning may also be important motivational determinants of behavior, but this is based on a limited number of studies. Interestingly, the relationships between planning and PA seemed to depend on the type of planning used (eg, action planning vs coping planning), but it is unclear why these differences might exist across microtimescales given the consistent finding across macrotimescales [81]. Perhaps associations between coping planning and movement-related behavior on microtimescales depend on whether an anticipated barrier is encountered in one’s daily experiences. Similarly, although many studies (12/24, 50%) indicated that self-efficacy was assessed, further inspection of items revealed subtle differences in the type of self-efficacy assessed across the studies (eg, task self-efficacy and barrier self-efficacy), which may be differentially related to behavior and are likely important to consider in future EMA research [82,83].

Findings from this review provide evidence that both time-varying and time-invariant factors can moderate associations between social cognitive determinants and subsequent movement-related behaviors at the momentary and daily levels. A recent systematic review of studies investigating the PA intention-behavior coupling across macrotimescales (eg, weeks and months) found that consistent moderators of the intention-behavior coupling included motivational factors such as intention stability, intention commitment, low goal conflict, affective attitude, anticipated regret, perceived behavioral control or self-efficacy, and exercise identity [69]. Although some EMA studies have investigated some of these motivational factors as moderators [49,55], more work is needed to establish whether between-person moderators across macrotimescales also serve as within-person moderators across microtimescales. In addition, to date, only a handful of studies have investigated the moderating role of affective states and physical and social contexts on social cognitive determinant–behavior relationships [48]. There is recent evidence suggesting that these time-varying contextual factors can influence behavior, and it may be that this influence on behavior is through motivational processes [36,37]. Future research should continue to investigate context-sensitive moderators as this work is an essential first step in theory refinement that is sensitive to the contexts that people encounter in their daily lived experiences [84].

This systematic review focused on observational EMA studies to better understand the naturally occurring relationships between social cognitive determinants and subsequent behavior. However, using intensive assessment methods such as EMA and accelerometry to understand how social cognitive determinant–movement-related behavior relationships change over the course of an intervention may be important for developing more effective behavioral interventions. For instance, Basen-Engquist et al [85] had endometrial cancer survivors complete 3- , 10- , and 12-day EMA protocols spaced over the course of a 6-month PA intervention. Although the authors aggregated data across these 3 time points to determine momentary associations among self-efficacy, outcome expectations, and PA, such data could reveal the extent to which associations between social cognitive determinants and subsequent behavior change over the course of an intervention. Such data could also reveal the extent to which behavior change content delivered at a specific point in the intervention is able to affect social cognitive determinant–behavior relationships. Collecting EMA data regarding social cognitive determinant–behavior relationships may help identify potent intervention content in the context of everyday life.

This systematic review synthesized EMA-derived associations between social cognitive determinants and subsequent movement-related behavior over microtimescales. Overall, based on the available evidence, social cognitive determinants do regulate movement-related behaviors in the context of everyday life. Specifically, daily intentions and self-efficacy appeared to have a consistent and positive link with PA behavior across the studies using self-reported and device-based measures of behavior. Future research is necessary to determine whether these associations extend to the momentary level, investigate associations among a broader range of social cognitive determinants and movement-related behaviors in diverse populations across the life span, and explore time-varying and time-invariant moderators of these associations. In addition, efforts should be devoted to developing more rigorous study designs, including validating EMA social cognitive determinant assessments and conducting missing data analyses. Ultimately, this systematic review provides foundational knowledge for understanding the motivational determinants of movement-related behaviors within people and is essential for directing future research regarding movement-related behaviors in the context of daily life. In addition, such inquiries are necessary to inform the development of interventions to promote active lifestyles in the context of everyday life.