IntroductionBackground

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) []. Despite these recommendations, approximately 28% of adults do not meet PA guidelines []. Furthermore, on average, adults engage in 8.2 hours per day of SB []. 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 [,]. 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) []. 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) []. 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 []. 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 [,]. 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) [-]. There are 2 prominent theories that are considered part of the social cognitive framework are Social Cognitive Theory and the Theory of Planned Behavior [,].

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 [,]. 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 [,]. 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 [,]. Furthermore, both theories acknowledge that facilitators and barriers can help or hinder engagement in a behavior, respectively [-]. 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 [-]. Within the last decade, evidence has accumulated suggesting that PA and SB are independent health behaviors [,], 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 [-].

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 [,]. 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) [,]. 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 []. 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 []. 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 [-]. 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 [,]. 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) []. 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 []. 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 [,-].

Objectives

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 [] and environmental contexts []. 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.

Methods

This systematic review was conducted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [] and was registered in PROSPERO (CRD42022328500).

Inclusion and Exclusion Criteria

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 [-,,]. 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.

Search Process

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 . 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 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 []. 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 [].

‎Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram for the literature search on social cognitive determinants, movement-related behaviors, and ecological momentary assessment (EMA) methods. Article Screening and Coding

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 () [].

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) [] 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) [] 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.

Analysis

The associations between social cognitive determinants and movement-related behaviors were assessed using the guidelines developed by Sallis et al []. 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).

ResultsOverview

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 (). The publication year, sample and study characteristics, and methodological quality scores of each article are presented in .

Table 1. Summary of article characteristics (N=24).StudySample size, NAge (years), mean (SD)Sex or gender (% of female and women participants)Race and ethnicity (% of White or non-Hispanic White participants)PopulationEMAa delivery mediumEMA protocol designSocial cognitive determinants of interestBehavioral constructs of interestBehavioral assessment methodCREMASb ratingMQQc ratingArigo et al []7551.61 (5.43)10073Adult womenWebsite5 prompts per day for 10 days; single wave; signal contingentIntentionsPAdAccelerometer1021Cook et al []5520-69e1558Adults with HIVWebsite1 prompt per day for 30 days; single wave; signal contingentSelf-efficacyPAAccelerometer916Maher et alf []11640. 3 (9.6)74.246.6AdultsSmartphone8 prompts per day for 4 days; multiple waves; signal contingentIntentions, self-efficacy, and outcome expectationsPAAccelerometer1522Maher et alf []11640.3 (9.6)74.246.6AdultsSmartphone8 prompts per day for 4 days; multiple waves; signal contingentIntentionsPAAccelerometer1527Pickering et alf []11640.3 (9.6)74.233AdultsSmartphone8 prompts per day for 4 days; multiple waves; signal contingentIntentions, self-efficacy, and outcome expectationsPAAccelerometer1325Maher and Duntong []10372 (7)6274Older adultsSmartphone6 prompts per day for 10 days; single wave; signal contingentIntentions and self-efficacySBhAccelerometer1527Maher and Duntong []10372.4 (7.44)62.567.3Older adultsSmartphone6 prompts per day for 10 days; single wave; signal contingentIntentions and self-efficacyPA and SBAccelerometer1527Reifsteck et al []1721.82 (0.64)47.0682.35College student athletesSmartphone4 prompts per day for 7 days; single wave; signal contingentIntentions, self-efficacy, and outcome expectationsPAAccelerometer1418Zenk et al []9725-65e1000Adult womenWebsite5 prompts per dayi for 7 days; single wave; signal contingentBarriersPA and SBAccelerometer1019Anderson []7640.29 (13.69)57.985.5AdultsWebsite1 prompt per day for 14 days; single wave; interval contingentPlanningPASelf-report1127Carraro and Gaudreau []9720.45 (4.61)6871College studentsWebsite1 prompt per day for 6 days; single wave; interval contingentAction planning and coping planningPASelf-report1325Dunton et al []2360.65 (8.22)7091Older adultsPDA4 prompts per day for 14 days; single wave; interval contingentSelf-efficacyPASelf-report1414McDonald et al []762.7171.4Not collectedjOlder adultsPDA2 prompts per day for a minimum of 2 months (maximum of 7 months); single wave; interval contingentIntentions and perceived behavioral controlPAAccelerometer1114Bermudez et al []11162.32 (10.85)14.4Not collectedAdults with cardiac diseasePDA1 prompt per day for 21 days; single wave; event contingentIntentions, perceived behavioral control, subjective norms, and explicit attitudesPAAccelerometer816Berli et al []120Women: 43.39 (12.67); men: 45.07 (13.92)50Not collectedAdult dual-smoker couplesSmartphone1 prompt per day for 28 days; single wave; event contingentIntentions, self-efficacy, and action planningPAAccelerometer1223Bond et al []2148.5 (2.8)8171.4Adults who had undergone bariatric surgeryPDA1 prompt per day for 6 days; single wave; event contingentIntentionsPASelf-report1124Borowski et al []6141.4 (9.9)88.590.2AdultsWebsite1 prompt per day for 7 days; single wave; event contingentBarriersPASelf-report1021Conroy et al []63Not collected58.787College studentsWebsite1 prompt per day for 14 days; single wave; event contingentIntentionsPASelf-report1423Conroy et al []12821.3 (1.1)58.689College studentsWebsite1 prompt per day for 14 days; single wave; event contingentIntentionsSBAccelerometer1223Curtis et al []18561.7 (5.9)55.1Not collectedOlder adultsWebsite1 prompt per day for 7 days; single wave; event contingentSelf-efficacyPASelf-report1018Maher and Conroy []10074.2 (8.2)6799Older adultsPDA2 prompts per day for 14 days; single wave; event contingentIntentions, self-efficacy, action planning, and coping planningSBAccelerometer and self-report1223Rebar et al []10321.57 (2.97)52Not collectedCollege studentsWebsite1 prompt per day for 7 days; single wave; event contingentIntentionsPASelf-report1121Schwaninger et al []198Women: 45.31 (13.51); men: 47.29 (13.94)50Not collectedAdult couplesSmartphone1 prompt per day for 14 days; single wave; event contingentSelf-efficacyPAAccelerometer815Zhaoyang et al []13565.71 (9.83)56.386.67Older adult couples with knee OAkPDA2 prompts per day for 22 days; single wave; event contingentSelf-efficacyPAAccelerometer1325

aEMA: ecological momentary assessment.

bCREMAS: Checklist for Reporting Ecological Momentary Assessment Studies. Lowest score=0; highest score=16.

cMQQ: Methodological Quality Questionnaire. Lowest score=0; highest score=27.

dPA: physical activity.

eOnly age range was reported.

fArticles used data from the same study.

gArticles used data from the same study.

hSB: sedentary behavior.

iAlthough the study assessed the constructs of interest within days, the data were aggregated to the day level for analysis.

jParticipants were not asked about this variable.

kOA: osteoarthritis.

Study Sample Characteristics

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 [] (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 [,,,,]. The samples reported in 50% (12/24) of the articles focused on adults [-,,,-,]. The samples reported in 29% (7/24) of the articles were older adults (aged ≥50 years) [,,,,,,]. 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) [-] indicated that participants identifying as White or non-Hispanic White did not make up most of the sample [-,].

EMA Protocol

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 [-]. 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 []. Regarding EMA prompting design, of the 24 articles, 12 (50%) reported on studies that had 1 prompt per day [,,,-,,], 3 (12%) reported on studies that had 2 prompts per day (ie, one in the morning and one in the evening) [,,], and the remaining articles (9/24, 38%) reported on studies that delivered between 4 and 8 prompts per day [,-,].

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

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 [-,]. 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 [,], whereas another study had participants select their own start day to fit their work schedule [].

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%) [], movisensXS (2/24, 8%) [,], and MyExperience (3/24, 12%) [-] 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 [,]. PDA devices comprised handheld computers (3/24, 12%) [,,], tablets (2/24, 8%) [,], and a wrist-worn Patient-Reported Outcomes Diary device (1/24, 4%) []. Articles on studies that used websites either did not specify the website [,,,,-] or did specify it using Prolific [], Qualtrics [], or REDCap (Research Electronic Data Capture; Vanderbilt University) [].

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.

Social Cognitive Determinants

Intentions (15/24, 62%) [,-,,-,,,,] and self-efficacy (14/24, 58%) [,,-,-,-] were the most frequently assessed social cognitive determinants. Of the 14 articles categorized as reporting on studies that assessed self-efficacy [,], 2 (14%) operationalized their construct of interest as perceived behavioral control; however, perceived behavioral control is generally considered synonymous with self-efficacy [,], so the findings were combined into 1 category for this review. Other social cognitive determinants assessed included planning (4/24, 17%) [,,,], outcome expectations (3/24, 12%) [,,], barriers (2/24, 8%) [,], and explicit attitudes (1/24, 4%) [].

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 []. The studies reported in 29% (7/24) of the articles adapted social cognitive determinant items from validated scales [,,,,,,], 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 [,-,-,,-], engaging in a specified duration of PA [,-,], or limiting SB to a specified total amount of time [,,,].

Movement-Related Behaviors

Methods for assessing movement-related behaviors included device-based (15/24, 62%) and self-reported (8/24, 33%) assessments or both (1/24, 4%) []. Common devices used included the ActiGraph GT3x or GT3x+ (7/24, 29%) [,,,,,,], ActiGraph GT2M (3/24, 12%) [-], ActiGraph GT1M (1/24, 4%) [], Fitbit Alta HR (1/24, 4%) [], and activPAL 3 (3/24, 12%) [,,]. A total of 4% (1/24) of the articles reported on studies that provided participants with either the ActiGraph GT1M or GT3x+ device [], and 4% (1/24) used the Patient-Reported Outcomes Diary device []. 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 [-,,,​,,,-]. Minutes of moderate- to vigorous-intensity PA (MVPA) were the most common operationalization of PA (12/24, 50%) [,-,,,,,,,,], and minutes of sedentary time were the most common operationalization of SB (5/24, 21%) [,,,,] 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 [,], Godin Leisure-Time Exercise Questionnaire by Godin and Shephard [,,], and a measure of sedentary time in older adults developed by Gardiner et al []. 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 [,]. Bond et al [] and Rebar et al [] assessed the daily duration of MVPA using items created for the study.

Methodological Quality

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 []. 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%) [,,,-,-,,] followed by implications for policy (11/24, 46%) [,,,-,,,].

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 [,,-,-,-]. In total, 50% (12/24) of the articles did not report any training to familiarize participants with the EMA protocol [,,-,,,,-]. Except for the studies reported in 12% (3/24) of the articles [-], 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.

PA and Social Cognitive Determinants

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.

IntentionsMomentary Associations

The studies reported in 25% (6/24) of the articles assessed associations between intentions and PA at the momentary level, which is shown in . Of those 6 articles, 3 (50%) focused on direct relationships between momentary intentions and subsequent PA. Among college student athletes, Reifsteck et al [] documented a positive association between intentions and behavior such that, on occasions when participants reported stronger-than-usual intentions, they engaged in more device-based MVPA over the following 3 hours. Similarly, Arigo et al [] found a weak positive association between the number of intended minutes and minutes of device-based MVPA over the following 3 hours among adults, although the results were not significant. However, this positive association between intentions and behavior became stronger and more significant on occasions when participants experienced less contentment or body satisfaction than usual. Conversely, Pickering et al [] found a null association between momentary intentions and subsequent device-based MVPA among adults. However, on occasions when adults had higher self-efficacy to engage in PA than was typical for them, momentary intentions were positively associated with subsequent MVPA. As only 12% (3/24) of the articles reported on studies that investigated momentary intention–PA relationships and we determined a priori that there must be at least 4 articles present to draw a conclusion regarding an overall association, this cannot be made at this time.

In total, 12% (3/24) of the articles focused specifically on time-varying moderators of intention-PA relationships, with all articles (3/3, 100%) reporting on studies that used device-based measures of PA. Time of day and day of the week were moderators investigated by Maher et al [,]. Time-varying effect models applied by Maher et al [] revealed that intentions to be physically active positively predicted subsequent MVPA in the mornings and evenings but not in the afternoons. On weekdays, intentions were unrelated to subsequent PA on weekends. Using a similar approach, Maher and Dunton [] found that, among older adults, intentions to be active were positively associated with subsequent time spent upright (ie, standing or stepping) during the morning, afternoon, and evening on both weekdays and weekends, although the magnitude of the associations changed throughout the day. Finally, Maher et al [] investigated affect states and physical context as moderators of the intention-PA coupling and found that individuals were more likely to follow through with their intentions to be physically active on occasions when they reported greater positive affect than was typical for them (at the same time that they reported their intentions). Owing to the range of time-varying moderators investigated, conclusions regarding consistent moderators of intention-PA relationships at the momentary level cannot be drawn.

Table 2. Associations between social cognitive determinants and physical activity and sedentary behavior.aSocial cognitive determinant and timescale of associationsPositive associationNegative associationNull associationModeratorsOverall associationPhysical activity
Intentions predicting physical activity

Momentary associationsReifsteck et al []
‎ Arigo et al []
‎ No articles reported a negative association
‎ Self-efficacy: Pickering et al []
‎ Time of day: Maher et al [] and Maher and Dunton []
‎ Day of the week: Maher et al [] and Maher and Dunton []
‎ Positive affect: Maher et al []
‎ N/Ab

Daily associationsConroy et al []
‎ Berli et al []
‎ Bermudez et al []
‎ Bond et al []
‎ McDonald et al []
‎ Rebar et al []
‎ McDonald et al []
‎ Ego depletion: Rebar et al []
‎ +c
Self-efficacy predicting physical activity

Momentary associationsCook et al []
‎ Dunton et al []
‎ No articles reported a negative association
‎ Reifsteck et al []
‎ Cook et al []
‎ Pickering et al []
‎ Intentions: Pickering et al []
‎ Time of day: Maher et al [] and Maher and Dunton []
‎ Day of the week: Maher et al [] and Maher and Dunton []
‎ ?d

Daily associationsBerli et al []
‎ Schwaninger et al []
‎ Curtis et al []
‎ McDonald et al []
‎ Zhaoyang et al []
‎ Bermudez et al []
‎ McDonald et al []
‎ Bermudez et al []
‎ McDonald et al []
‎ +
Outcome expectations predicting physical activity

Momentary associationsNo articles reported a negative association
‎ Maher et al []
‎ Pickering et al []
‎ No articles assessed moderators
‎ N/A
Planning predicting physical activity

Daily associationsCarraro and Gaudreau []
‎ Anderson []
‎ Berli et al []
‎ No articles reported a negative association
‎ Carraro and Gaudreau []
‎ Typical plans: Anderson []
‎ Goal conflict: Carraro and Gaudreau []
‎ N/A
Barriers predicting physical activity

Daily associationsNo articles reported a positive association
‎ Borowski et al []
‎ Zenk et al []
‎ No articles assessed moderators
‎ N/A
Attitudes predicting physical activity

Daily associationsNo articles reported a positive association
‎ No articles reported a negative association
‎ No articles assessed moderators
‎ N/ASedentary behavior
Intentions predicting sedentary behavior

Momentary associationsNo articles reported a positive association
‎ No articles reported a null association
‎ Time of day: Maher and Dunton []
‎ Day of the week: Maher and Dunton []
‎ N/A

Daily associationsNo articles reported a positive association
‎ No articles reported a null association
‎ No articles assessed moderators
‎ N/A
Self-efficacy predicting sedentary behavior

Momentary associationsNo articles reported a positive association
‎ No articles reported a null association
‎ Time of day: Maher and Dunton []
‎ Day of the week: Maher and Dunton []
‎ N/A
Planning predicting sedentary behavior

Daily associationsNo articles reported a positive association
‎ No articles reported a null association
‎ No articles assessed moderators
‎ N/A
Barriers predicting sedentary behavior

Daily associationsNo articles reported a positive association
‎ No articles reported a negative association
‎ No articles assessed moderators
‎ N/A

aA total of 4 articles were needed to make an overall association.

bN/A: not applicable; <4 articles on the social cognitive determinant, so an overall association cannot be determined.

c+: Positive association (≥60% of the studies showing an association).

d?: inconclusive (34%-59% of the studies showing an association).

Daily Associations

The studies reported in 25% (6/24) of the articles assessed the association between intentions and PA at the day level. Of these 6 articles, 4 (67%) documented positive associations between daily intentions and behavior regardless of whether intentions were assessed upon waking [] or the previous evening []. Furthermore, these associations were consistent across different operationalizations of PA, including self-reported MVPA [,] and device-based MVPA [-]. Conversely, Rebar et al [] found a null association between intentions to exercise the following day and self-reported exercise; however, on nights when university students experienced more ego depletion (limited cognitive and physical capabilities), they were more likely to successfully enact those exercise intentions the following day. McDonald et al [] observed older adults in the months leading up to and following retirement and found that intentions did not consistently predict PA in all participants. The findings indicate that intentions to be active were positively associated with (1/24, 4%), negatively associated with (1/24, 4%), or not related to (5/24, 21%) likelihood of engaging in a PA bout depending on the participant. On the basis of the criteria by Sallis et al [], the findings indicate an overall positive association between intentions and subsequent PA at the day level (ie, ≥60 of the articles; 4/6, 67% reported a consistent positive association); however, further investigation of moderators of these daily associations is warranted.

Self-efficacyMomentary Associations

The studies reported in 25% (6/24) of the articles assessed associations between self-efficacy and PA at the momentary level, which is shown in . Of these 6 articles, 4 (67%) focused on direct relationships between momentary self-efficacy and subsequent PA. Among older adults, Dunton et al [] found a positive association between momentary self-efficacy and subsequent self-reported MVPA. Similarly, Cook et al [] found a positive association between momentary self-efficacy and device-based MVPA, although a null association was found between momentary self-efficacy and total steps.

Conversely, both Pickering et al [] and Reifsteck et al [] found null associations between momentary self-efficacy and subsequent device-based MVPA among adults and college student athletes, respectively. Pickering et al [] did find that momentary intentions moderated momentary self-efficacy–​behavior relationships such that, on occasions when adults had stronger intentions to engage in PA than usual, momentary self-efficacy was positively associated with subsequent MVPA. On the basis of the criteria by Sallis et al [] and because of mixed findings across these articles, an inconclusive overall association was found between momentary self-efficacy and PA (ie, 12/24, 50% of the articles reported a consistent positive association).

The studies reported in 8% (2/24) of the articles specifically explored moderators of self-efficacy–PA relationships at the momentary level. Maher et al [,] investigated differences in self-efficacy and PA associations by time of day and day of the week. Among adults, self-efficacy was positively associated with device-based MVPA on weekday evenings []. At no other time on weekdays or weekends was self-efficacy associated with subsequent PA. Among older adults, self-efficacy was positively associated with device-based time spent upright (standing or stepping) all day on weekdays and during the mornings and afternoons but not in the evenings on weekends []. These findings point to potential temporal moderators of self-efficacy–PA relationships at the momentary level; however, as only 8% (2/24) of the articles reported on studies that investigated these temporal processes, firm conclusio