Associations of habitual sedentary time with executive functioning and short-term memory in 7th and 8th grade adolescents.

BACKGROUND: While there is increasing evidence for negative physical health consequences of high volumes of sedentary time and prolonged sedentary time in adolescents, the association with cognition is less clear. This study investigated the association of volumes of habitual sedentary time and prolonged sedentary time with executive functions and short-term memory in adolescents. METHODS: This study has a cross-sectional observational study design. Volumes of sedentary time and prolonged sedentary time (accumulated sedentary time spent in bouts of  ≥ 30 min) were measured using the Axivity AX3 accelerometer. Six cognitive functions (spatial and verbal short-term memory; and working memory, visuospatial working memory, response inhibition and planning as executive functions) were measured using six validated cognitive assessments. Data were analysed using generalised linear models. RESULTS: Data of 119 adolescents were analysed (49% boys, 13.4 ± 0.6 year). No evidence for an association of volumes of sedentary time and prolonged sedentary time with spatial and verbal short-term memory, working memory, and visuospatial working memory was found. Volumes of sedentary time and prolonged sedentary time were significantly related to planning. One hour more sedentary time or prolonged sedentary time per day was associated with respectively on average 17.7% (95% C.I.: 3.5-29.7%) and 12.1% (95% C.I.: 3.9-19.6%) lower scores on the planning task. CONCLUSIONS: No evidence was found for an association of volumes of habitual sedentary time and prolonged sedentary time with short-term memory and executive functions, except for planning. Furthermore, the context of sedentary activities could be an important confounder in the association of sedentary time and prolonged sedentary time with cognition among adolescents. Future research should therefore collect data on the context of sedentary activities. TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov in January 2020 (NCT04327414; released on March 11, 2020).

Adolescents' sleep quality in relation to peer, family and school factors: findings from the 2017/2018 HBSC study in Flanders.

PURPOSE: While a multitude of studies focused on biological and behavioral determinants of adolescents' sleep quality, a paucity of research examined the impact of social factors. The current study therefore examines the relationship between adolescents' sleep quality and peer, family and school factors. METHODS: Data of Flemish participants in the 2017/2018 Health Behavior in School-aged Children survey (HBSC) were used, which is representative of the adolescent population (11-18 years) in Flanders. Adolescents' sleep quality was linked to individual-level data on peer relationships (i.e., peer support), family relationships (i.e., family support, perceived family wealth, caregiving responsibilities) and school relationships (i.e., teacher support, school support, school pressure) and contextual-level data on the school culture. Three-level multilevel models were fitted to account for the clustering of individuals (N = 8153) within classes (N = 769) and classes within schools (N = 177). RESULTS: The individual-level results indicated that adolescents' sleep quality was positively related to family support, teacher support, student support and perceived family wealth. In contrast, adolescents' sleep quality was negatively related to caregiving responsibilities and school pressure. In addition, the contextual-level results pointed out that adolescents tended to report better sleep quality in less-demanding schools. CONCLUSION: These findings highlight the need to consider social factors in promoting better sleep in adolescence.

Bidirectional associations between sedentary time and sleep duration among 12- to 14-year-old adolescents.

BACKGROUND: The aim of this study was to investigate bidirectional associations between (prolonged) sitting time and sleep duration in 12- to 14-year-old adolescents using a between-subjects and within-subjects analyses approach. METHODS: Observational data were used from 108 adolescents (53% girls; mean age 12.9 (SD 0.7) years) from six schools in Flanders, Belgium. The Axivity AX3 triaxial accelerometer, worn on the thigh, was used to assess daily total sitting time and daily time spent in sedentary bouts of ≥30 min (as a proxy for prolonged sitting time). The Fitbit Charge 3 was used to assess nightly sleep duration. Both monitors were worn on schooldays only (ranging from 4 to 5 days). Linear mixed models were conducted to analyse the associations, resulting in four models. In each model, the independent variable (sleep duration, sitting time or prolonged sitting time) was included as within- as well as between-subjects factor. RESULTS: Within-subjects analyses showed that when the adolescents sat more and when the adolescents spent more time sitting in bouts of ≥30 min than they usually did on a given day, they slept less during the following night (p = 0.01 and p = 0.05 (borderline significant), respectively). These associations were not significant in the other direction. Between-subjects analyses showed that adolescents who slept more on average, spent less time sitting (p = 0.006) and less time sitting in bouts of ≥30 min (p = 0.004) compared with adolescents who slept less on average. Conversely, adolescents who spent more time sitting on average and adolescents who spent more time sitting in bouts of ≥30 min on average, slept less (p = 0.02 and p = 0.003, respectively). CONCLUSIONS: Based on the between-subjects analyses, interventions focusing on reducing or regularly breaking up sitting time could improve adolescents' sleep duration on a population level, and vice versa. However, the within-subjects association was only found in one direction and suggests that to sleep sufficiently during the night, adolescents might limit and regularly break up their sitting time the preceding day. TRIAL REGISTRATION: Data have been used from our trial registered at ClinicalTrials.gov ( NCT04327414 ; registered on March 11, 2020).

Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention.

BACKGROUND: E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it's not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs. METHODS: In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention 'MyPlan2.0' for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. RESULTS: First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = - 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively. CONCLUSIONS: This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future. TRIAL REGISTRATION: This study was preregistered as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.

Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies.

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.

Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis.

BACKGROUND: Sedentary behavior occurs largely subconsciously, and thus specific behavior change techniques are needed to increase conscious awareness of sedentary behavior. Chief amongst these behavior change techniques is self-monitoring of sedentary behavior. The aim of this systematic review and meta-analysis was to evaluate the short-term effectiveness of existing interventions using self-monitoring to reduce sedentary behavior in adults. METHODS: Four electronic databases (PubMed, Embase, Web of Science, and The Cochrane Library) and grey literature (Google Scholar and the International Clinical Trials Registry Platform) were searched to identify appropriate intervention studies. Only (cluster-)randomized controlled trials that 1) assessed the short-term effectiveness of an intervention aimed at the reduction of sedentary behavior, 2) used self-monitoring as a behavior change technique, and 3) were conducted in a sample of adults with an average age ≥ 18 years, were eligible for inclusion. Relevant data were extracted, and Hedge's g was used as the measure of effect sizes. Random effects models were performed to conduct the meta-analysis. RESULTS: Nineteen intervention studies with a total of 2800 participants met the inclusion criteria. Results of the meta-analyses showed that interventions using self-monitoring significantly reduced total sedentary time (Hedges g = 0,32; 95% CI = 0,14 - 0,50; p = 0,001) and occupational sedentary time (Hedge's g = 0,56; 95% CI = 0,07 - 0,90; p = 0,02) on the short term. Subgroup analyses showed that significant intervention effects were only found if objective self-monitoring tools were used (g = 0,40; 95% CI = 0,19 - 0,60; p < 0,001), and if the intervention only targeted sedentary behavior (g = 0,45; 95% CI = 0,15-0,75; p = 0,004). No significant intervention effects were found on the number of breaks in sedentary behavior. CONCLUSIONS: Despite the small sample sizes, and the large heterogeneity, results of the current meta-analysis suggested that interventions using self-monitoring as a behavior change technique have the potential to reduce sedentary behavior in adults. If future - preferably large-scale studies - can prove that the reductions in sedentary behavior are attributable to self-monitoring and can confirm the sustainability of this behavior change, multi-level interventions including self-monitoring may impact public health by reducing sedentary behavior.

Results of MyPlan 2.0 on Physical Activity in Older Belgian Adults: Randomized Controlled Trial.

BACKGROUND: The beneficial effects of physical activity (PA) for older adults are well known. However, few older adults reach the health guideline of 150 min per week of moderate-to-vigorous PA (MVPA). Electronic health (eHealth) interventions are effective in increasing PA levels in older adults in the short term but, rarely, intermediate-term effects after a period without the support of a website or an app have been examined. Furthermore, current theory-based interventions focus mainly on preintentional determinants, although postintentional determinants should also be included to increase the likelihood of successful behavior change. OBJECTIVE: This study aimed to investigate the effect of the theory-based eHealth intervention, MyPlan 2.0, focusing on pre- and postintentional determinants on both accelerometer-based and self-reported PA levels in older Belgian adults in the short and intermediate term. METHODS: This study was a randomized controlled trial with three data collection points: baseline (N=72), post (five weeks after baseline; N=65), and follow-up (three months after baseline; N=65). The study took place in Ghent, and older adults (aged ≥65 years) were recruited through a combination of random and convenience sampling. At all the time points, participants were visited by the research team. Self-reported domain-specific PA was assessed using the International Physical Activity Questionnaire, and accelerometers were used to objectively assess PA. Participants in the intervention group got access to the eHealth intervention, MyPlan 2.0, and used it independently for five consecutive weeks after baseline. MyPlan 2.0 was based on the self-regulatory theory and focused on both pre- and postintentional processes to increase PA. Multilevel mixed-models repeated measures analyses were performed in R (R Foundation for Statistical Computing). RESULTS: Significant (borderline) positive intervention effects were found for accelerometer-based MVPA (baseline-follow-up: intervention group +5 min per day and control group -5 min per day; P=.07) and for accelerometer-based total PA (baseline-post: intervention group +20 min per day and control group -24 min per day; P=.05). MyPlan 2.0 was also effective in increasing self-reported PA, mainly in the intermediate term. A positive intermediate-term intervention effect was found for leisure-time vigorous PA (P=.02), moderate household-related PA (P=.01), and moderate PA in the garden (P=.04). Negative intermediate-term intervention effects were found for leisure-time moderate PA (P=.01) and cycling for transport (P=.07). CONCLUSIONS: The findings suggest that theory-based eHealth interventions focusing on pre- and postintentional determinants have the potential for behavior change in older adults. If future studies including larger samples and long-term follow-up can confirm and clarify these findings, researchers and practitioners should be encouraged to use a self-regulation perspective for eHealth intervention development. TRIAL REGISTRATION: Clinicaltrials.gov NCT03194334; https://clinicaltrials.gov/ct2/show/NCT03783611.

Efficacy of a Self-Regulation-Based Electronic and Mobile Health Intervention Targeting an Active Lifestyle in Adults Having Type 2 Diabetes and in Adults Aged 50 Years or Older: Two Randomized Controlled Trials.

BACKGROUND: Adopting an active lifestyle plays a key role in the prevention and management of chronic diseases such as type 2 diabetes mellitus (T2DM). Web-based interventions are able to alter health behaviors and show stronger effects when they are informed by a behavior change theory. MyPlan 2.0 is a fully automated electronic health (eHealth) and mobile health (mHealth) intervention targeting physical activity (PA) and sedentary behavior (SB) based on the Health Action Process Approach (HAPA). OBJECTIVE: This study aimed to test the short-term effect of MyPlan 2.0 in altering levels of PA and SB and in changing personal determinants of behavior in adults with T2DM and in adults aged ≥50 years. METHODS: The study comprised two randomized controlled trials (RCTs) with an identical design. RCT 1 was conducted with adults with T2DM. RCT 2 was performed in adults aged ≥50 years. Data were collected via face-to-face assessments. The participants decided either to increase their level of PA or to decrease their level of SB. The participants were randomly allocated with a 2:1 ratio to the intervention group or the waiting-list control group. They were not blinded for their group allocation. The participants in the intervention group were instructed to go through MyPlan 2.0, comprising 5 sessions with an interval of 1 week between each session. The primary outcomes were objectively measured and self-reported PA (ie, light PA, moderate-to-vigorous PA, total PA, number of steps, and domain-specific [eg, transport-related] PA) and SB (ie, sitting time, number of breaks from sitting time, and length of sitting bouts). Secondary outcomes were self-reported behavioral determinants for PA and SB (eg, self-efficacy). Separate linear mixed models were performed to analyze the effects of MyPlan 2.0 in the two samples. RESULTS: In RCT 1 (n=54), the PA intervention group showed, in contrast to the control group, a decrease in self-reported time spent sitting (P=.09) and an increase in accelerometer-measured moderate (P=.05) and moderate-to-vigorous PA (P=.049). The SB intervention group displayed an increase in accelerometer-assessed breaks from sedentary time in comparison with the control group (P=.005). A total of 14 participants of RCT 1 dropped out. In RCT 2 (n=63), the PA intervention group showed an increase for self-reported total PA in comparison with the control group (P=.003). Furthermore, in contrast to the control group, the SB intervention group decreased their self-reported time spent sitting (P=.08) and increased their accelerometer-assessed moderate (P=.06) and moderate-to-vigorous PA (P=.07). A total of 8 participants of RCT 2 dropped out. CONCLUSIONS: For both the samples, the HAPA-based eHealth and mHealth intervention, MyPlan 2.0, was able to improve only some of the primary outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03291171; http://clinicaltrials.gov/ct2/show/NCT03291171. ClinicalTrials.gov NCT03799146; http://clinicaltrials.gov/ct2/show/NCT03799146. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12413.

Physical activity to improve cognition in older adults: can physical activity programs enriched with cognitive challenges enhance the effects? A systematic review and meta-analysis.

BACKGROUND: Aging-related cognitive decline and cognitive impairment greatly impacts older adults' daily life. The worldwide ageing of the population and associated wave of dementia urgently calls for prevention strategies to reduce the risk of cognitive decline. Physical activity (PA) is known to improve cognitive function at older age through processes of neuroplasticity. Yet, emerging studies suggest that larger cognitive gains may be induced when PA interventions are combined with cognitive activity (CA). This meta-analysis evaluates these potential synergistic effects by comparing cognitive effects following combined PA + CA interventions to PA interventions (PA only), CA interventions (CA only) and control groups. METHODS: Pubmed, Embase, PsycInfo, CINAHL and Sportdiscus were searched for English peer-reviewed papers until April 2018. Data were extracted on cognition and factors potentially influencing the cognitive effects: mode of PA + CA combination (sequential or simultaneous), session frequency and duration, intervention length and study quality. Differences between older adults with and without mild cognitive impairments were also explored. RESULTS: Forty-one studies were included. Relative to the control group, combined PA + CA intervention showed significantly larger gains in cognition (g = 0.316; 95% CI 0.188-0.443; p < .001). Studies that compared combined PA + CA with PA only, showed small but significantly greater cognitive improvement in favor of combined interventions (g = 0.160; 95% CI 0.041-0.279; p = .008). No significant difference was found between combined PA + CA and CA only interventions. Furthermore, cognitive effects tended to be more pronounced for studies using simultaneous designs (g = 0.385; 95%CI 0.214-0.555; p < .001) versus sequential designs (g = 0.114; 95%CI -0.102- 0.331, p = .301). Effects were not moderated by session frequency, session duration, intervention length or study quality. Also, no differences in effects were found between older adults with and without mild cognitive impairments. CONCLUSION: Findings of the current meta-analysis suggest that PA programs for older adults could integrate challenging cognitive exercises to improve cognitive health. Combined PA + CA programs should be promoted as a modality for preventing as well as treating cognitive decline in older adults. Sufficient cognitive challenge seems more important to obtain cognitive effects than high doses of intervention sessions.

How Users Experience and Use an eHealth Intervention Based on Self-Regulation: Mixed-Methods Study.

BACKGROUND: eHealth interventions show stronger effects when informed by solid behavioral change theories; for example, self-regulation models supporting people in translating vague intentions to specific actions have shown to be effective in altering health behaviors. Although these theories inform developers about which behavioral change techniques should be included, they provide limited information about how these techniques can be engagingly implemented in Web-based interventions. Considering the high levels of attrition in eHealth, investigating users' experience about the implementation of behavior change techniques might be a fruitful avenue. OBJECTIVE: The objective of our study was to investigate how users experience the implementation of self-regulation techniques in a Web-based intervention targeting physical activity and sedentary behavior in the general population. METHODS: In this study, 20 adults from the general population used the intervention for 5 weeks. Users' website data were explored, and semistructured interviews with each of the users were performed. A directed content analysis was performed using NVivo Software. RESULTS: The techniques "providing feedback on performance," "action planning," and "prompting review of behavioral goals" were appreciated by users. However, the implementation of "barrier identification/problem solving" appeared to frustrate users; this was also reflected by the users' website data-many coping plans were of poor quality. Most users were well aware of the benefits of adopting a more active way of living and stated not to have learned novel information. However, they appreciated the provided information because it reminded them about the importance of having an active lifestyle. Furthermore, prompting users to self-monitor their behavioral change was not sufficiently stimulating to make users actually monitor their behavior. CONCLUSIONS: Iteratively involving potential end users offers guidance to optimally adapt the implementation of various behavior change techniques to the target population. We recommend creating short interventions with a straightforward layout that support users in creating and evaluating specific plans for action.

Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies.

Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

Physical Activity, Sedentary Behavior, and Diet-Related eHealth and mHealth Research: Bibliometric Analysis.

BACKGROUND: Electronic health (eHealth) and mobile health (mHealth) approaches to address low physical activity levels, sedentary behavior, and unhealthy diets have received significant research attention. However, attempts to systematically map the entirety of the research field are lacking. This gap can be filled with a bibliometric study, where publication-specific data such as citations, journals, authors, and keywords are used to provide a systematic overview of a specific field. Such analyses will help researchers better position their work. OBJECTIVE: The objective of this review was to use bibliometric data to provide an overview of the eHealth and mHealth research field related to physical activity, sedentary behavior, and diet. METHODS: The Web of Science (WoS) Core Collection was searched to retrieve all existing and highly cited (as defined by WoS) physical activity, sedentary behavior, and diet related eHealth and mHealth research papers published in English between January 1, 2000 and December 31, 2016. Retrieved titles were screened for eligibility, using the abstract and full-text where needed. We described publication trends over time, which included journals, authors, and countries of eligible papers, as well as their keywords and subject categories. Citations of eligible papers were compared with those expected based on published data. Additionally, we described highly-cited papers of the field (ie, top ranked 1%). RESULTS: The search identified 4805 hits, of which 1712 (including 42 highly-cited papers) were included in the analyses. Publication output increased on an average of 26% per year since 2000, with 49.00% (839/1712) of papers being published between 2014 and 2016. Overall and throughout the years, eHealth and mHealth papers related to physical activity, sedentary behavior, and diet received more citations than expected compared with papers in the same WoS subject categories. The Journal of Medical Internet Research published most papers in the field (9.58%, 164/1712). Most papers originated from high-income countries (96.90%, 1659/1717), in particular the United States (48.83%, 836/1712). Most papers were trials and studied physical activity. Beginning in 2013, research on Generation 2 technologies (eg, smartphones, wearables) sharply increased, while research on Generation 1 (eg, text messages) technologies increased at a reduced pace. Reviews accounted for 20 of the 42 highly-cited papers (n=19 systematic reviews). Social media, smartphone apps, and wearable activity trackers used to encourage physical activity, less sedentary behavior, and/or healthy eating were the focus of 14 highly-cited papers. CONCLUSIONS: This study highlighted the rapid growth of the eHealth and mHealth physical activity, sedentary behavior, and diet research field, emphasized the sizeable contribution of research from high-income countries, and pointed to the increased research interest in Generation 2 technologies. It is expected that the field will grow and diversify further and that reviews and research on most recent technologies will continue to strongly impact the field.

A Self-Regulation-Based eHealth Intervention to Promote a Healthy Lifestyle: Investigating User and Website Characteristics Related to Attrition.

BACKGROUND: eHealth interventions can reach large populations and are effective in increasing physical activity (PA) and fruit and vegetable intake. Nevertheless, the effects of eHealth interventions are overshadowed by high attrition rates. Examining more closely when users decide to leave the intervention can help eHealth developers to make informed decisions about which intervention components should be reshaped or simply removed. Investigating which users are more likely to quit an intervention can inform developers about whether and how their intervention should be adapted to specific subgroups of users. OBJECTIVE: This study investigated the pattern of attrition in a Web-based intervention to increase PA, fruit, and vegetable intake. The first aim was to describe attrition rates according to different self-regulation components. A second aim was to investigate whether certain user characteristics are predictors for start session completion, returning to a follow-up session and intervention completion. METHODS: The sample consisted of 549 adults who participated in an online intervention, based on self-regulation theory, to promote PA and fruit and vegetable intake, called "MyPlan 1.0." Using descriptive analysis, attrition was explored per self-regulation component (eg, action planning and coping planning). To identify which user characteristics predict completion, logistic regression analyses were conducted. RESULTS: At the end of the intervention program, there was an attrition rate of 78.2% (330/422). Attrition rates were very similar for the different self-regulation components. However, attrition levels were higher for the fulfillment of questionnaires (eg, to generate tailored feedback) than for the more interactive components. The highest amount of attrition could be observed when people were asked to make their own action plan. There were no significant predictors for first session completion. Yet, two subgroups had a lower chance to complete the intervention, namely male users (OR: 2.24, 95% CI=1.23-4.08) and younger adults (OR: 1.02, 95% CI=1.00-1.04). Furthermore, younger adults were less likely to return to the website for the first follow-up after one week (OR: 1.03, 95% CI=1.01-1.04). CONCLUSIONS: This study informs us that eHealth interventions should avoid the use of extensive questionnaires and that users should be provided with a rationale for several components (eg, making an action plan and completing questions). Furthermore, future interventions should focus first on motivating users for the behavior change before guiding them through action planning. Though, this study provides no evidence for removal of one of the self-regulation techniques based on attrition rates. Finally, strong efforts are needed to motivate male users and younger adults to complete eHealth interventions.

Within-Person Associations of Accelerometer-Assessed Physical Activity With Time-Varying Determinants in Older Adults: Time-Based Ecological Momentary Assessment Study.

BACKGROUND: Despite the availability of physical activity (PA) interventions, many older adults are still not active enough. This might be partially explained by the often-limited effects of PA interventions. In general, health behavior change interventions often do not focus on contextual and time-varying determinants, which may limit their effectiveness. However, before the dynamic tailoring of interventions can be developed, one should know which time-dependent determinants are associated with PA and how strong these associations are. OBJECTIVE: The aim of this study was to examine within-person associations between multiple determinants of the capability, opportunity, motivation, and behavior framework assessed using Ecological Momentary Assessment (EMA) and accelerometer-assessed light PA, moderate to vigorous PA, and total PA performed at 15, 30, 60, and 120 minutes after the EMA trigger. METHODS: Observational data were collected from 64 healthy older adults (36/64, 56% men; mean age 72.1, SD 5.6 y). Participants were asked to answer a time-based EMA questionnaire 6 times per day that assessed emotions (ie, relaxation, satisfaction, irritation, and feeling down), the physical complaint fatigue, intention, intention, and self-efficacy. An Axivity AX3 was wrist worn to capture the participants' PA. Multilevel regression analyses in R were performed to examine these within-person associations. RESULTS: Irritation, feeling down, intention, and self-efficacy were positively associated with subsequent light PA or moderate to vigorous PA at 15, 30, 60, or 120 minutes after the trigger, whereas relaxation, satisfaction, and fatigue were negatively associated. CONCLUSIONS: Multiple associations were observed in this study. This knowledge in combination with the time dependency of the determinants is valuable information for future interventions so that suggestions to be active can be provided when the older adult is most receptive.

The variability of emotions, physical complaints, intention, and self-efficacy: an ecological momentary assessment study in older adults.

Background: Many theoretical frameworks have been used in order to understand health behaviors such as physical activity, sufficient sleep, healthy eating habits, etc. In most research studies, determinants within these frameworks are assessed only once and thus are considered as stable over time, which leads to rather 'static' health behavior change interventions. However, in real-life, individual-level determinants probably vary over time (within days and from day to day), but currently, not much is known about these time-dependent fluctuations in determinants. In order to personalize health behavior change interventions in a more dynamic manner, such information is urgently needed. Objective: The purpose of this study was to explore the time-dependent variability of emotions, physical complaints, intention, and self-efficacy in older adults (65+) using Ecological Momentary Assessment (EMA). Methods: Observational data were collected in 64 healthy older adults (56.3% men; mean age 72.1 ± 5.6 years) using EMA. Participants answered questions regarding emotions (i.e., cheerfulness, relaxation, enthusiasm, satisfaction, insecurity, anxiousness, irritation, feeling down), physical complaints (i.e., fatigue, pain, dizziness, stiffness, shortness of breath), intention, and self-efficacy six times a day for seven consecutive days using a smartphone-based questionnaire. Generalized linear mixed models were used to assess the fluctuations of individual determinants within subjects and over days. Results: A low variability is present for the negative emotions (i.e., insecurity, anxiousness, irritation, feeling down) and physical complaints of dizziness and shortness of breath. The majority of the variance for relaxation, satisfaction, insecurity, anxiousness, irritation, feeling down, fatigue, dizziness, intention, and self-efficacy is explained by the within subjects and within days variance (42.9% to 65.8%). Hence, these determinants mainly differed within the same subject and within the same day. The between subjects variance explained the majority of the variance for cheerfulness, enthusiasm, pain, stiffness, and shortness of breath (50.2% to 67.3%). Hence, these determinants mainly differed between different subjects. Conclusions: This study reveals that multiple individual-level determinants are time-dependent, and are better considered as 'dynamic' or unstable behavior determinants. This study provides us with important insights concerning the development of dynamic health behavior change interventions, anticipating real-time dynamics of determinants instead of considering determinants as stable within individuals.

The impact of mental and somatic stressors on physical activity and sedentary behaviour in adults with type 2 diabetes mellitus: a diary study.

BACKGROUND: Adopting an active lifestyle is key in the management of type 2 diabetes mellitus (T2DM). Nevertheless, the majority of individuals with T2DM fails to do so. Additionally, individuals with T2DM are likely to experience mental (e.g., stress) and somatic (e.g., pain) stressors. Research investigating the link between these stressors and activity levels within this group is largely lacking. Therefore, current research aimed to investigate how daily fluctuations in mental and somatic stressors predict daily levels of physical activity (PA) and sedentary behaviour among adults with T2DM. METHODS: Individuals with T2DM (N = 54) were instructed to complete a morning diary assessing mental and somatic stressors and to wear an accelerometer for 10 consecutive days. The associations between the mental and somatic stressors and participants' levels of PA and sedentary behaviour were examined using (generalized) linear mixed effect models. RESULTS: Valid data were provided by 38 participants. We found no evidence that intra-individual increases in mental and somatic stressors detrimentally affected participants' activity levels. Similarly, levels of sedentary behaviour nor levels of PA were predicted by inter-individual differences in the mental and somatic stressors.

When, why, and how do People Deviate from Physical Distancing Measures During the COVID-19 Pandemic: A Mixed-Methods Study.

INTRODUCTION: To limit the spread of COVID-19, many countries, including Belgium, have installed physical distancing measures. Yet, adherence to these newly installed behavioral measures has been described as challenging and effortful. Based on the Health Action Process Approach (HAPA) model, this study performed an in-depth evaluation of when, why, and how people deviated from the physical distancing measures. METHODS: An online mixed-method study was conducted among Belgian adults (N = 2055) in the beginning of May 2020. Participants were recruited via an open call through email and social media platforms, using snowball sampling. Conditions wherein people deviated from the physical distancing measures were assessed by means of an open-ended question. HAPA determinants were assessed in a quantitative way. RESULTS: Half of the sample reported to deviate from the measures. Further, deviation from the measures was associated with each determinant outlined by the HAPA. Findings highlight that many people deviated from the measures because of their need for social contact. The majority of the people who deviated from the measures stated that they carefully weighed the risks of their behavior. CONCLUSIONS: Need for social contact pushed people to deviate from physical distancing measures in a deliberate manner. Potential areas for future interventions aimed at promoting adherence to physical distancing measures and enhancing psychosocial well-being are discussed.

Adherence to the Physical Distancing Measures during the COVID-19 Pandemic: A HAPA-Based Perspective.

BACKGROUND: The COVID-19 pandemic requires massive and rapid behavior change. The Health Action Process Approach (HAPA) describes personal determinants that play a key role in behavior change. This study investigated whether these determinants are associated with adherence to physical distancing measures to prevent the spread of COVID-19 (i.e. keeping 1.5 m physical distance and staying at home). Decreased psychosocial well-being and lack of social support were explored as barriers to adherence. METHODS: Two cross-sectional surveys were conducted among adults in Belgium. The first survey (N = 2,379; March 2020) focused on adherence to physical distancing measures. The second survey (N = 805; April 2020) focused on difficulty with, and perseverance in, adhering to these measures. Linear regression models were fitted to examine associations with HAPA determinants, psychosocial well-being, and social support. RESULTS: Self-efficacy, outcome expectancies, intention, action planning, and coping planning were related to adhering to, difficulty with, and perseverance in, adhering to physical distancing measures. Decreased psychosocial well-being and lack of social support were related to more difficulties with adhering to physical distancing and lower perseverance. CONCLUSIONS: Health action process approach determinants are associated with adherence to physical distancing measures. Future work could design HAPA-based interventions to support people in adhering to these measures.

A Self-Regulation-Based eHealth and mHealth Intervention for an Active Lifestyle in Adults With Type 2 Diabetes: Protocol for a Randomized Controlled Trial.

BACKGROUND: Adoption of an active lifestyle plays an important role in the management of type 2 diabetes. Online interventions targeting lifestyle changes in adults with type 2 diabetes have provided mixed results. Previous research highlights the importance of creating theory-based interventions adapted to the population's specific needs. The online intervention "MyPlan 2.0" targets physical activity and sedentary behavior in adults with type 2 diabetes. This intervention is grounded in the self-regulation framework and, by incorporating the feedback of users with type 2 diabetes, iteratively adapted to its target population. OBJECTIVE: The aim of this paper is to thoroughly describe "MyPlan 2.0" and the study protocol that will be used to test the effectiveness of this intervention to alter patients' levels of physical activity and sedentary behavior. METHODS: A two-arm superiority randomized controlled trial will be performed. Physical activity and sedentary behavior will be measured using accelerometers and questionnaires. Furthermore, using questionnaires and diaries, patients' stressors and personal determinants for change will be explored in depth. To evaluate the primary outcomes of the intervention, multilevel analyses will be conducted. RESULTS: The randomized controlled trial started in January 2018. As participants can start at different moments, we aim to finish all testing by July 2019. CONCLUSIONS: This study will increase our understanding about whether and how a theory-based online intervention can help adults with type 2 diabetes increase their level of physical activity and decrease their sedentary time. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12413.