Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach-Based Psychological Determinants.

BACKGROUND: Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored. OBJECTIVE: The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition. METHODS: The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used. RESULTS: The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%). CONCLUSIONS: This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance. TRIAL REGISTRATION: ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-019-3456-7.

Sex-specific typologies of older adults' sedentary behaviors and their associations with health-related and socio-demographic factors: a latent profile analysis.

BACKGROUND: Some types of sedentary behaviors tend to cluster in individuals or groups of older adults. Insight into how these different types of sedentary behavior cluster is needed, as recent research suggests that not all types of sedentary behavior may have the same negative effects on physical and mental health. Therefore, the aim of this study was to identify sex-specific typologies of older adults' sedentary behavior, and to examine their associations with health-related and socio-demographic factors. METHODS: Cross-sectional data were collected as part of the BEPAS Seniors, and the Busschaert study among 696 Flemish older adults (60+). Typologies of self-reported sedentary behavior were identified using latent profile analysis, and associations with health-related and sociodemographic factors were examined using analyses of variances. RESULTS: Five distinct typologies were identified from seven sedentary behaviors (television time, computer time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in men, and three typologies were identified from six sedentary behaviors (television time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in women. Typologies that are characterized by high television time seem to be related to more negative health outcomes, like a higher BMI, less grip strength, and a lower physical and mental health-related quality-of-life. Typologies that are represented by high computer time and motorized transport seem to be related to more positive health outcomes, such as a lower body mass index, more grip strength and a higher physical and mental health-related quality-of-life. CONCLUSIONS: Although causal direction between identified typologies and health outcomes remains uncertain, our results suggests that future interventions should better focus on specific types of sedentary behavior (e.g. television time), or patterns of sedentary behavior, rather than on total sedentary behavior.

Engagement, Acceptability, Usability, and Preliminary Efficacy of a Self-Monitoring Mobile Health Intervention to Reduce Sedentary Behavior in Belgian Older Adults: Mixed Methods Study.

BACKGROUND: Although healthy aging can be stimulated by the reduction of sedentary behavior, few interventions are available for older adults. Previous studies suggest that self-monitoring might be a promising behavior change technique to reduce older adults' sedentary behavior. However, little is known about older adults' experiences with a self-monitoring-based intervention aimed at the reduction of sedentary behavior. OBJECTIVE: The aim of this study is to evaluate engagement, acceptability, usability, and preliminary efficacy of a self-monitoring-based mHealth intervention developed to reduce older adults' sedentary behavior. METHODS: A mixed methods study was performed among 28 community-dwelling older adults living in Flanders, Belgium. The 3-week intervention consisted of general sedentary behavior information as well as visual and tactile feedback on participants' sedentary behavior. Semistructured interviews were conducted to explore engagement with, and acceptability and usability of, the intervention. Sitting time was measured using the thigh-worn activPAL (PAL Technologies) accelerometer before and after the intervention. System usage data of the app were recorded. Quantitative data were analyzed using descriptive statistics and paired-samples t tests; qualitative data were thematically analyzed and presented using pen profiles. RESULTS: Participants mainly reported positive feelings regarding the intervention, referring to it as motivating, surprising, and interesting. They commonly reported that the intervention changed their thinking (ie, they became more aware of their sedentary behavior) but not their actual behavior. There were mixed opinions on the kind of feedback (ie, tactile vs visual) that they preferred. The intervention was considered easy to use, and the design was described as clear. Some problems were noticed regarding attaching and wearing the self-monitoring device. System usage data showed that the median frequency of consulting the app widely differed among participants, ranging from 0 to 20 times a day. No significant reductions were found in objectively measured sitting time. CONCLUSIONS: Although the intervention was well perceived by the majority of older adults, no reductions in sitting time were found. Possible explanations for the lack of reductions might be the short intervention duration or the fact that only bringing the habitual sedentary behavior into conscious awareness might not be sufficient to achieve behavior change. TRIAL REGISTRATION: ClinicalTrials.gov NCT04003324; https://tinyurl.com/y2p4g8hx.

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.

Low-Cost Consumer-Based Trackers to Measure Physical Activity and Sleep Duration Among Adults in Free-Living Conditions: Validation Study.

BACKGROUND: Wearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. OBJECTIVE: This study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. METHODS: A free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. RESULTS: Large variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. CONCLUSIONS: This study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

Built Environment, Physical Activity, and Obesity: Findings from the International Physical Activity and Environment Network (IPEN) Adult Study.

Creating more physical activity-supportive built environments is recommended by the World Health Organization for controlling noncommunicable diseases. The IPEN (International Physical Activity and Environment Network) Adult Study was undertaken to provide international evidence on associations of built environments with physical activity and weight status in 12 countries on 5 continents (n > 14,000). This article presents reanalyzed data from eight primary papers to identify patterns of findings across studies. Neighborhood environment attributes, whether measured objectively or by self-report, were strongly related to all physical activity outcomes (accelerometer-assessed total physical activity, reported walking for transport and leisure) and meaningfully related to overweight/obesity. Multivariable indexes of built environment variables were more strongly related to most outcomes than were single-environment variables. Designing activity-supportive built environments should be a higher international health priority. Results provide evidence in support of global initiatives to increase physical activity and control noncommunicable diseases while achieving sustainable development goals.

Influences of Parental Snacking-Related Attitudes, Behaviours and Nutritional Knowledge on Young Children's Healthy and Unhealthy Snacking: The ToyBox Study.

This study investigated parental influences on preschool children's healthy and unhealthy snacking in relation to child obesity in a large cross-sectional multinational sample. Parents and 3-5 year-old child dyads (n = 5185) in a kindergarten-based study provided extensive sociodemographic, dietary practice and food intake data. Parental feeding practices that were derived from questionnaires were examined for associations with child healthy and unhealthy snacking in adjusted multilevel models, including child estimated energy expenditure, parental education, and nutritional knowledge. Parental healthy and unhealthy snacking was respectively associated with their children's snacking (both p < 0.0001). Making healthy snacks available to their children was specifically associated with greater child healthy snack intake (p < 0.0001). Conversely, practices that were related to unhealthy snacking, i.e., being permissive about unhealthy snacking and acceding to child demands for unhealthy snacks, were associated with greater consumption of unhealthy snacks by children, but also less intake of healthy snacks (all p < 0.0001). Parents having more education and greater nutritional knowledge of snack food recommendations had children who ate more healthy snacks (all p < 0.0001) and fewer unhealthy snacks (p = 0.002, p < 0.0001, respectively). In the adjusted models, child obesity was not related to healthy or unhealthy snack intake in these young children. The findings support interventions that address parental practices and distinguish between healthy and unhealthy snacking to influence young children's dietary patterns.

Older Adults' Perceptions of Sedentary Behavior: A Systematic Review and Thematic Synthesis of Qualitative Studies.

BACKGROUND AND OBJECTIVES: Reducing sedentary behavior contributes to healthy aging. In order to develop sedentary behavior interventions, insight is needed into older adults' perceptions of (reducing) sedentary behavior. Therefore, this systematic review aims to synthesize qualitative evidence of older adults' perceptions towards (a) the concept of sedentary behavior, (b) barriers and facilitators of sedentary behavior, and (c) solutions and strategies to reduce sedentary behavior. RESEARCH DESIGN AND METHODS: A systematic search was conducted in four electronic databases. Inclusion criteria comprised qualitative and mixed-methods studies investigating the perceptions of older adults (mean age: ≥60 years) towards (reducing) sedentary behavior. Quality of the included studies was rated using the Critical Appraisal Skills Programme (CASP) Qualitative Checklist. Relevant data on older adults' perceptions were extracted and imported into NVivo. Two independent reviewers analyzed the data by means of thematic synthesis (line-by-line coding, constructing descriptive (sub)themes, developing analytical themes). RESULTS: Fifteen studies were included. Four analytical themes were developed to be considered when aiming to reduce sedentary behavior in older adults: the lack of knowledge on/awareness of sedentary behavior, the habitual nature of sedentary behavior, the importance of enjoyment and convenience, and the key role of aging. DISCUSSION AND IMPLICATIONS: The reduction of older adults' sedentary behavior will likely be challenging as sedentary behavior seems to be firmly incorporated into older adults' daily routines, and strongly linked with positive reinforcement. Both aspects deserve thoughtful attention by intervention developers and health care professionals who aim to promote healthy aging by reducing sedentary behavior.

Do associations of sex, age and education with transport and leisure-time physical activity differ across 17 cities in 12 countries?

BACKGROUND: Leisure-time and transport activity domains are studied most often because they are considered more amenable to intervention, but to date evidence on these domains is limited. The aim of the present study was to examine patterns of socio-demographic correlates of adults' leisure-time and transport physical activity and how these associations varied across 17 cities in 12 countries. METHODS: Participants (N = 13,745) aged 18-66 years in the IPEN Adult study and with complete data on socio-demographic and self-reported physical activity characteristics were included. Participants reported frequency and duration of leisure-time and transport activities in the last 7 days using the self-administered International Physical Activity Questionnaire-Long Form. Six physical activity outcomes were examined in relation with age, education, and sex, and analyses explored variations by city and curvilinear associations. RESULTS: Sex had the most consistent results, with five of six physical activity outcomes showing females were less active than males. Age had the most complex associations with self-report transport and leisure-time physical activity. Compared to older people, younger adults were less likely to engage in transport physical activity, but among those who did, younger people were likely to engage in more active minutes. Curvilinear associations were found between age and all three leisure-time physical activity outcomes, with the youngest and the oldest being more active. Positive associations with education were found for leisure-time physical activity only. There were significant interactions of city with sex and education for multiple physical activity outcomes. CONCLUSIONS: Although socio-demographic correlates of physical activity are widely studied, the present results provide new information. City-specific findings suggest there will be value in conducting more detailed case studies. The curvilinear associations of age with leisure-time physical activity as well as significant interactions of leisure-time activity with sex and education should be further investigated. The findings of lower leisure-time physical activity among females as well as people with low education suggest that greater and continued efforts in physical activity policies and programs tailored to these high-risk groups are needed internationally.

Effect of integrating a video intervention on parenting practices and related parental self-efficacy regarding health behaviours within the Feel4Diabetes-study in Belgian primary schoolchildren from vulnerable families: A cluster randomized trial.

BACKGROUND: This study aimed to investigate the effect of integrating a video intervention "Movie Models" within the Feel4Diabetes-study on specific parenting practices and related parental self-efficacy regarding children's physical activity, screen-time and eating behaviour in vulnerable families (i.e. families living in low socioeconomic municipalities and at risk for developing type 2 diabetes). Additionally, there was examination of how the intervention was perceived by the parents. METHODS: Within randomly selected low socioeconomic municipalities in Belgium, families were recruited through primary schools. Families at risk for developing type 2 diabetes were identified using the FINDRISC questionnaire (n = 457). Afterwards, the municipalities were randomly assigned to the intervention or control condition. At risk families assigned to the intervention group were invited to participate in six Feel4Diabetes counselling sessions in which families were encouraged to adopt a healthier lifestyle. The "Movie Models" videos were integrated within two sessions by using a face-to-face group discussion approach. Parenting-related factors were assessed before and after the integration of the videos, using a questionnaire. After integrating the videos, some extra evaluation questions were assessed. In total, 126 families were included in a per protocol evaluation and Repeated Measures ANOVAs were conducted to evaluate the potential intervention effects. RESULTS: Some favourable intervention effects were found on parenting practices and related parental self-efficacy regarding children's eating behaviours, however almost no effects were found on parenting-related factors regarding children's physical activity and screen-time. In total, 60.0% of the participants indicated that they applied tips regarding parenting practices and 52.0% indicated that discussions with other participants regarding the videos were useful for them. CONCLUSION: The integration of "Movie Models" within the Feel4Diabetes-study was effective in improving some parenting-related factors regarding children's health behaviours, however most parenting-related factors could not be improved. The implementation of "Movie Models" as a face-to-face group discussion approach was relatively well received and may be a promising way to improve parenting-related factors in vulnerable families. TRIAL REGISTRATION: ClinicalTrials.gov NCT02278809.

Adults' Preferences for Behavior Change Techniques and Engagement Features in a Mobile App to Promote 24-Hour Movement Behaviors: Cross-Sectional Survey Study.

BACKGROUND: There is a limited understanding of components that should be included in digital interventions for 24-hour movement behaviors (physical activity [PA], sleep, and sedentary behavior [SB]). For intervention effectiveness, user engagement is important. This can be enhanced by a user-centered design to, for example, explore and integrate user preferences for intervention techniques and features. OBJECTIVE: This study aimed to examine adult users' preferences for techniques and features in mobile apps for 24-hour movement behaviors. METHODS: A total of 86 participants (mean age 37.4 years [SD 9.2]; 49/86, 57% female) completed a Web-based survey. Behavior change techniques (BCTs) were based on a validated taxonomy v2 by Abraham and Michie, and engagement features were based on a list extracted from the literature. Behavioral data were collected using Fitbit trackers. Correlations, (repeated measures) analysis of variance, and independent sample t tests were used to examine associations and differences between and within users by the type of health domain and users' behavioral intention and adoption. RESULTS: Preferences were generally the highest for information on the health consequences of movement behavior self-monitoring, behavioral feedback, insight into healthy lifestyles, and tips and instructions. Although the same ranking was found for techniques across behaviors, preferences were stronger for all but one BCT for PA in comparison to the other two health behaviors. Although techniques fit user preferences for addressing PA well, supplemental techniques may be able to address preferences for sleep and SB in a better manner. In addition to what is commonly included in apps, sleep apps should consider providing tips for sleep. SB apps may wish to include more self-regulation and goal-setting techniques. Few differences were found by users' intentions or adoption to change a particular behavior. Apps should provide more self-monitoring (P=.03), information on behavior health outcome (P=.048), and feedback (P=.04) and incorporate social support (P=.048) to help those who are further removed from healthy sleep. A virtual coach (P<.001) and video modeling (P=.004) may provide appreciated support to those who are physically less active. PA self-monitoring appealed more to those with an intention to change PA (P=.03). Social comparison and support features are not high on users' agenda and may not be needed from an engagement point of view. Engagement features may not be very relevant for user engagement but should be examined in future research with a less reflective method. CONCLUSIONS: The findings of this study provide guidance for the design of digital 24-hour movement behavior interventions. As 24-hour movement guidelines are increasingly being adopted in several countries, our study findings are timely to support the design of interventions to meet these guidelines.

Associations Between Bystander Reactions to Cyberbullying and Victims' Emotional Experiences and Mental Health.

Bystanders in cyberbullying may play a crucial role in reducing cyberbullying's harm for victims. This study assessed bystander responses, and the associations with adolescent victims' emotional reactions to cyberbullying and victims' mental health outcomes (symptoms of depression, anxiety, and stress; suicidal ideation). A total of 1037 adolescents (49.8 percent female, mean age = 15.17 years) participated in the cross-sectional study and filled out an anonymous questionnaire. Victimization was measured with a single-item scale (cybervictims) and a multiple-item scale with cyberbullying examples (youth experiencing offensive practices). Associations of positive (e.g., defending) and negative (e.g., ignoring) bystander responses with victims' emotions (8 types of emotions rated on 5-point Likert scale) were assessed through correlations; and with victims' mental health outcomes (Depression, Anxiety, Stress Scale [DASS-21], single item for suicidal ideation) using regression analyses, adjusting for the influence of gender and coping styles (KIDCOPE). Cybervictims (single-item scale) showed more victimization experiences, and more negative emotional and mental health outcomes than youth only experiencing offensive practices. Negative bystander responses predicted some mental health outcomes among cybervictims, but not among youth only experiencing offensive practices. Positive bystander behavior did not predict any mental health outcome. There is a clear need for cyberbullying prevention programs to include components that target bystander responses, to alleviate victims' emotional and mental health harm after cyberbullying. Attention is needed to create effective programs to reduce negative bystander behavior, while most current programs are focused on positive bystander 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.

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.

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.

A factorial randomised controlled trial to identify efficacious self-regulation techniques in an e- and m-health intervention to target an active lifestyle: study protocol.

BACKGROUND: Sufficient physical activity and a limited amount of sedentary behaviour can prevent a range of chronic diseases. However, most adults do not meet the recommendations for physical activity and sedentary behaviour. Effective and engaging interventions are needed to change people's behaviour. E- and m-health interventions are promising, but unfortunately they result in small effects and suffer from high attrition rates. Improvements to intervention content and design are required. Qualitative research has revealed the need for clear and concise interventions. Furthermore, many interventions use a range of behaviour-change techniques, and it is yet unknown whether these techniques are equally important to obtain behaviour change. It may well be that a limited set of these techniques is sufficient. In this study, the aim is to experimentally investigate the efficacy of three behaviour-change techniques (i.e. action planning, coping planning and self-monitoring) on physical activity, sedentary behaviour and related determinants among adults. METHODS: In a 2 x 2 x 2 factorial trial participants will be randomly allocated to eight groups (including one control group). Each group will receive a different version of the self-regulation-based e- and m-health intervention 'MyPlan 2.0', in which three behaviour-change techniques (i.e. action planning, coping planning, self-monitoring) will be combined in order to achieve self-formulated goals about physical activity or sedentary behaviour. Goal attainment, and levels of physical activity and sedentary behaviour will be measured via self-report questionnaires. DISCUSSION: This study should provide insight into the role of various behaviour-change techniques in changing health behaviour and its determinants. Its experimental and longitudinal design, with repeated measures of several determinants of behaviour change, allows an in-depth analysis of the processes underlying behaviour change, enabling the authors to provide guidance for the development of future e- and m-health interventions. TRIAL REGISTRATION: This study is registered as MyPlan 2.0 as a clinical trial (ID number: NCT03274271 ). Release date: 20 October 2017.

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.

Framework, principles and recommendations for utilising participatory methodologies in the co-creation and evaluation of public health interventions.

PLAIN ENGLISH SUMMARY: Background: Society has to cope with a large burden of health issues. There is need to find solutions to prevent diseases and help individuals live healthier lifestyles. Individual needs and circumstances vary greatly and one size fit all solutions do not tend to work well. More tailored solutions centred on individuals' needs and circumstances can be developed in collaboration with these individuals. This process, known as co-creation, has shown promise but it requires guiding principles to improve its effectiveness. The aim of this study was to identify a key set of principles and recommendations for co-creating public health interventions.Methods: These principles were collaboratively developed through analysing a set of case studies targeting different health behaviours (such as reducing sitting and improving strength and balance) in different groups of people (such as adolescent schoolgirls and older adults living in the community).Results: The key principles of co-creation are presented in four stages: Planning (what is the purpose of the co-creation; and who should be involved?); Conducting (what activities can be used during co-creation; and how to ensure buy-in and commitment?); Evaluating (how do we know the process and the outcome are valid and effective?) and Reporting (how to report the findings?). Three models are proposed to show how co-created solutions can be scaled up to a population level.Conclusions: These recommendations aim to help the co-creation of public health interventions by providing a framework and governance to guide the process. ABSTRACT: Background: Due to the chronic disease burden on society, there is a need for preventive public health interventions to stimulate society towards a healthier lifestyle. To deal with the complex variability between individual lifestyles and settings, collaborating with end-users to develop interventions tailored to their unique circumstances has been suggested as a potential way to improve effectiveness and adherence. Co-creation of public health interventions using participatory methodologies has shown promise but lacks a framework to make this process systematic. The aim of this paper was to identify and set key principles and recommendations for systematically applying participatory methodologies to co-create and evaluate public health interventions. Methods: These principles and recommendations were derived using an iterative reflection process, combining key learning from published literature in addition to critical reflection on three case studies conducted by research groups in three European institutions, all of whom have expertise in co-creating public health interventions using different participatory methodologies. Results: Key principles and recommendations for using participatory methodologies in public health intervention co-creation are presented for the stages of: Planning (framing the aim of the study and identifying the appropriate sampling strategy); Conducting (defining the procedure, in addition to manifesting ownership); Evaluating (the process and the effectiveness) and Reporting (providing guidelines to report the findings). Three scaling models are proposed to demonstrate how to scale locally developed interventions to a population level. Conclusions: These recommendations aim to facilitate public health intervention co-creation and evaluation utilising participatory methodologies by ensuring the process is systematic and reproducible.

The Moderating Role of Social Neighbourhood Factors in the Association between Features of the Physical Neighbourhood Environment and Weight Status.

BACKGROUND: This paper investigated the independent and joint associations between aspects of the physical neighbourhood environment and social neighbourhood factors with BMI and overweight status in European adults. METHODS: Data from 5,199 participants in the SPOTLIGHT survey were analysed. Participants reported on their height, weight and perceptions of the neighbourhood. Objectively measured aspects of the physical neighbourhood environment included: presence of recreational facilities, features of the active transportation environment, neighbourhood aesthetics and presence of different types of food outlets. Social factors included the self-reported variables social network, social cohesion, social trust and perceived crime and the census variable neighbourhood socioeconomic status. Outcome measures were BMI and overweight status. Main associations between physical and social factors and BMI/overweight status were analysed using multilevel regression analyses adjusted for confounders. Moderation analysis was conducted by adding the interaction terms between physical and social neighbourhood factors one by one to the multivariable models. Significant interaction terms were then stratified. RESULTS: Significant associations with BMI/overweight status were found for features of the active transportation environment and all social factors, except perceived crime. Several significant interaction terms were detected, but no significant associations between the physical neighbourhood environment and BMI/overweight status were found after stratification. CONCLUSION: We did not find consistent interactions between physical and social neighbourhood factors to explain BMI and overweight status.

Citizen Science to Communicate about Public Health Messages: The Reach of a Playful Online Survey on Sitting Time and Physical Activity.

There is a lack of research on how to communicate public health guidelines. Citizen science (CS) has been an effective way to involve the public in research. This study analyses the reach of a well-established CS experiment, launched during an annual national science event, to understand if it could be used as communication strategy for public health issues. A short playful online survey contained tailored health-related messages associated to an "animal totem" profile, based on the combination of sitting and physical activity levels (koala: high sitting, low activity; gorilla: high sitting, high activity; zebra: low sitting, low activity; bee: low sitting, high activity). Tweets, radio interviews, radio and online advertisements, press articles, and a press conference were used to promote the CS experiment. Google Analytics and Facebook Graph API (application programming interface) (use and spread of experiment) and descriptive statistics (attributes of adults completing the experiment) were used. A total of 6,246 adults completed the experiment, with a peak of views (n = 5,103) and completions (n = 1,209) a couple of days before the event. Completers were mostly female (65.8%), on average 37.5 years old, and had a healthy body mass index (23.8 kg/m2). Nearly half (46.4%) had the most beneficial profile ("bee"), 26.5% had the least healthy profile ("koala"). CS as part of a national science event is a good platform for health communication as 1 in 1,000 Flemish adults were reached. However, those completing the experiment were not representative of the general Flemish adult population and reported to be more physically active. Abbreviations: API: application programming interface; BMI: body mass index; CVD: cardiovascular disease; METs: metabolic equivalents.