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.

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.

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.

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.

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.

What factors explain socioeconomic inequalities in adults' television-related sitting time?

BACKGROUND: There are considerable socioeconomic inequalities in television-related sitting time, but there is little evidence for the explanatory mechanisms. We used a cohort of Belgian adults (25-60 years) and older adults (≥65 years) to examine the social cognitive, home environmental and health-related factors contributing to socioeconomic differences in television-related sitting. METHODS: We included 301 adults and 258 older adults (total n = 559). Linear regression analyses were used to examine the associations of education and occupational status with television-related sitting time, adjusted for age and gender. We assessed the explanatory power of social cognitive, home environmental and health-related factors using the traditional 'change-in-estimation method'. RESULTS: Those with low and medium education, respectively, engaged in 54 and 28 minutes per day more television-related sitting time than those with high education. We found no association between occupational status and television-related sitting time. Social cognitive factors explained 54% of the difference in television-related sitting time between those with low and high education, while home environmental factors only explained 6%, and health-related variables explained 10% of these differences. CONCLUSION: We found no occupational inequalities in television-related sitting time. Social cognitive variables such as attitude and modelling of the partner explained a large part of the educational inequalities in television-related sitting time. If confirmed by future studies, a focus on social cognition may help reduce sedentary behaviours in low-educated adults and diminish inequalities in sedentary behaviours.

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.

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.

Effect and process evaluation of implementing standing desks in primary and secondary schools in Belgium: a cluster-randomised controlled trial.

BACKGROUND: Children and adolescents spend a lot of time sitting at school. Implementing standing desks in the classroom is one potential strategy to reduce and break up sitting time. The first aim was to evaluate the effect of implementing standing desks in classrooms in primary and secondary schools on pupils' sitting-related behaviour and determinants. The second aim was to quantitatively and qualitatively evaluate the process of implementing the desks in the classroom. METHODS: We conducted a cluster-randomised controlled trial with a pre-, mid-, and post-test design including 10 intervention schools (5 primary, 5 secondary schools) and 9 control schools (5 primary, 4 secondary schools) across Flanders, Belgium. Three standing desks were placed in one class in each intervention school for 6 months. At pre-, mid- and post-test, all pupils (n = 311; 54.5% girls) completed a questionnaire whilst a subsample of three pupils per class wore an activPAL inclinometer for one school week. Focus groups with pupils and interviews with teachers were conducted at mid-test. Process evaluation questions were added to the mid- and post-test questionnaire for the intervention group. Qualitative data were analysed using NVivo 11. Multilevel regression analyses were conducted in MLwiN 2.31. RESULTS: Few significant intervention effects were observed, although activPAL data showed favourable intervention effects on primary school pupils' sitting and standing time and bouts. Focus groups and interviews showed a generally positive attitude towards using standing desks in both teachers and pupils, although some barriers and suggestions for future implementation were noted, for example regarding the amount of desks per classroom. Quantitative process evaluation data showed a low individual use of standing desks (between 57 and 83 min per week), which significantly decreased across the school year for primary school pupils only. CONCLUSIONS: Although pupils and teachers were generally positive about the desks, relatively few intervention effects were found. Future studies should consider how to optimise the use of standing desks in classrooms to impact on sitting time, by for example, determining the most feasible intervention design and by encouraging the continued use of standing desks throughout the school year. Moreover, additional intervention strategies (e.g. educational strategies) might be needed. TRIAL REGISTRATION: NCT03163004 . ClinicalTrials.gov. Registered 22 May 2017 (retrospectively registered).

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.

Exploring the cross-sectional association between outdoor recreational facilities and leisure-time physical activity: the role of usage and residential self-selection.

BACKGROUND: The availability of outdoor recreational facilities is associated with increased leisure-time physical activity (PA). We investigated how much of this association is attributable to selection effects, and explored whether usage of recreational facilities was an explanatory mechanism. METHODS: We analysed data from 5199 participants in the SPOTLIGHT survey residing in five European urban regions. Adults completed a survey and a Google Street View-based virtual audit was conducted to objectively measure the availability of outdoor recreational facilities in the residential neighbourhood. We used negative binomial GEE models to examine the association between objective and subjective availability of outdoor recreational facilities and leisure-time PA, and explored whether this association was attenuated after adjustment for socioeconomic status and preference for neighbourhoods with recreational facilities (as indicators of self-selection). We examined whether reported use of recreational facilities was associated with leisure-time PA (as explanatory mechanism), and summarized the most important motivations for (not) using recreational facilities. RESULTS: Subjective - but not objective - availability of outdoor recreational facilities was associated with higher levels of total leisure-time PA. After adjustment for self-selection (which attenuated the association by 25%), we found a 25% difference in weekly minutes of total leisure-time PA between individuals with and without self-reported availability of outdoor recreational facilities. For our study population, this translates to about 28 min per week. Participants who reported outdoor recreational facilities to be present but indicated not to use them (RR = 1.19, 95% CI = 1.03;1.22), and those reporting outdoor recreational facilities to be present and to use them (RR = 1.33, 95% CI = 1.22, 1.45) had higher levels of total leisure-time PA than those who reported outdoor recreational facilities not to be present. Proximity to outdoor recreational facilities was the most important motivation for use. CONCLUSION: The modest attenuation in the association between availability of outdoor recreational facilities and self-reported leisure-time PA suggests that individuals' higher activity levels may be due more to the perceived availability of outdoor recreational facilities than to self-selection. The use of these facilities seemed to be an important underlying mechanism, and proximity was the main motivator for using recreational facilities.

Subgroups of adolescents differing in physical and social environmental preferences towards cycling for transport: A latent class analysis.

In order to be able to tailor environmental interventions to adolescents at risk for low levels of physical activity, the aim of the present study is to identify subgroups of adolescents with different physical and social environmental preferences towards cycling for transport and to determine differences in individual characteristics between these subgroups. In this experimental study, 882 adolescents (12-16 years) completed 15 choice tasks with manipulated photographs. Participants chose between two possible routes to cycle to a friend's house which differed in seven physical micro-environmental factors, cycling distance and co-participation in cycling (i.e. cycling alone or with a friend). Latent class analysis was performed. Data were collected from March till October 2016 across Flanders (Belgium). Three subgroups could be identified. Subgroup 1 attached most importance to separation of the cycle path and safety-related aspects. Subgroup 2 attached most importance to being able to cycle together with a friend and had the highest percentage of regular cyclists. In subgroup 3, the importance of cycling distance clearly stood out. This subgroup included the lowest percentage of regular cyclists. Results showed that in order to stimulate the least regular cyclists, and thus also the subgroup most at risk for low levels of active transport, cycling distances should be as short as possible. In general, results showed that providing well-separated cycle paths which enable adolescents to cycle side by side and introducing shortcuts for cyclists may encourage different subgroups of adolescents to cycle for transport without discouraging other subgroups.

Lack of interest in physical activity - individual and environmental attributes in adults across Europe: The SPOTLIGHT project.

A considerable proportion of European adults report little or no interest in physical activity. Identifying individual-level and environmental-level characteristics of these individuals can help designing effective interventions and policies to promote physical activity. This cross-sectional study additionally explored associations between level of interest and physical activity, after controlling for other individual and environmental variables. Measures of objective and perceived features of the physical environment of residence, self-reported physical activity and other lifestyle behaviors, barriers towards physical activity, general health, and demographics were obtained from 5205 European adults participating in the 2014 online SPOTLIGHT survey. t-Tests, chi-square tests, and generalized estimating equations with negative binomial log-link function were conducted. Adults not interested in physical activity reported a higher BMI and a lower self-rated health, were less educated, and to a smaller extent female and less frequently employed. They were more prone to have less healthy eating habits, and to perceive more barriers towards physical activity. Only minor differences were observed in environmental attributes: the non-interested were slightly more likely to live in neighborhoods objectively characterized as less aesthetic and containing more destinations, and perceived as less functional, safe, and aesthetic. Even after controlling for other individual and environmental factors, interest in physical activity remained a significant correlate of physical activity, supporting the importance of this association. This study is among the first to describe characteristics of individuals with reduced interest in physical activity, suggesting that (lack of) interest is a robust correlate of physical activity in several personal and environmental conditions.

Associations of neighborhood environmental attributes with adults' objectively-assessed sedentary time: IPEN adult multi-country study.

Neighborhood environmental attributes have been found to be associated with residents' time spent walking and in physical activity, in studies from single countries and in multiple-country investigations. There are, however, mixed findings on such environmental relationships with sedentary (sitting) time, which primarily have used evidence derived from single-country investigations with self-reported behavioral outcome measures. We examined potential relationships of neighborhood environmental attributes with objectively-assessed sedentary time using data from 5712 adults recruited from higher and lower socio-economic status neighborhoods in 12 sites in 10 countries, between 2002 and 2011. Ten perceived neighborhood attributes, derived from an internationally-validated scale, were assessed by questionnaire. Sedentary time was derived from hip-worn accelerometer data. Associations of individual environmental attributes and a composite environmental index with sedentary time were estimated using generalized additive mixed models. In fully adjusted models, higher street connectivity was significantly related to lower sedentary time. Residential density, pedestrian infrastructure and safety, and lack of barriers to walking were related to higher sedentary time. Aesthetics and safety from crime were related to less sedentary time in women only. The predicted difference in sedentary time between those with the minimum versus maximum composite environmental index values was 71 min/day. Overall, certain built environment attributes, including street connectivity, land use mix and aesthetics were found to be related to sedentary behavior in both expected and unexpected directions. Further research using context-specific measures of sedentary time is required to improve understanding of the potential role of built environment characteristics as influences on adults' sedentary behavior.

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.

Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study.

BACKGROUND: Physical inactivity is a global pandemic responsible for over 5 million deaths annually through its effects on multiple non-communicable diseases. We aimed to document how objectively measured attributes of the urban environment are related to objectively measured physical activity, in an international sample of adults. METHODS: We based our analyses on the International Physical activity and Environment Network (IPEN) adult study, which was a coordinated, international, cross-sectional study. Participants were sampled from neighbourhoods with varied levels of walkability and socioeconomic status. The present analyses of data from the IPEN adult study included 6822 adults aged 18-66 years from 14 cities in ten countries on five continents. Indicators of walkability, public transport access, and park access were assessed in 1·0 km and 0·5 km street network buffers around each participant's residential address with geographic information systems. Mean daily minutes of moderate-to-vigorous-intensity physical activity were measured with 4-7 days of accelerometer monitoring. Associations between environmental attributes and physical activity were estimated using generalised additive mixed models with gamma variance and logarithmic link functions. RESULTS: Four of six environmental attributes were significantly, positively, and linearly related to physical activity in the single variable models: net residential density (exp[b] 1·006 [95% CI 1·003-1·009]; p=0·001), intersection density (1·069 [1·011-1·130]; p=0·019), public transport density (1·037 [1·018-1·056]; p=0·0007), and number of parks (1·146 [1·033-1·272]; p=0·010). Mixed land use and distance to nearest public transport point were not related to physical activity. The difference in physical activity between participants living in the most and least activity-friendly neighbourhoods ranged from 68 min/week to 89 min/week, which represents 45-59% of the 150 min/week recommended by guidelines. INTERPRETATION: Design of urban environments has the potential to contribute substantially to physical activity. Similarity of findings across cities suggests the promise of engaging urban planning, transportation, and parks sectors in efforts to reduce the health burden of the global physical inactivity pandemic. FUNDING: Funding for coordination of the IPEN adult study, including the present analysis, was provided by the National Cancer Institute of National Institutes of Health (CA127296) with studies in each country funded by different sources.

The effectiveness of asking behaviors among 9-11 year-old children in increasing home availability and children's intake of fruit and vegetables: results from the Squire's Quest II self-regulation game intervention.

BACKGROUND: Home environment has an important influence on children's fruit and vegetable (FV) consumption, but children may in turn also impact their home FV environment, e.g. by asking for FV. The Squire's Quest II serious game intervention aimed to increase asking behaviors to improve home FV availability and children's FV intake. This study's aims were to assess: 1) did asking behaviors at baseline predict home FV availability at baseline (T0) (RQ1); 2) were asking behaviors and home FV availability influenced by the intervention (RQ2); 3) did increases in asking behaviors predict increased home FV availability (RQ3); and 4) did increases in asking behaviors and increases in home FV availability mediate increases in FV intake among children (RQ4)? METHODS: This is a secondary analysis of a study using a randomized controlled trial, with 4 groups (each n = 100 child-parent dyads). All groups were analyzed together for this paper since groups did not vary on components relevant to our analysis. All children and parents (n = 400 dyads) received a self-regulation serious game intervention and parent material. The intervention ran for three months. Measurements were taken at baseline, immediately after intervention and at 3-month follow-up. Asking behavior and home FV availability were measured using questionnaires; child FV intake was measured using 24-h dietary recalls. ANCOVA methods (research question 1), linear mixed-effect models (research question 2), and Structural Equation Modeling (research questions 3 and 4) were used. RESULTS: Baseline child asking behaviors predicted baseline home FV availability. The intervention increased child asking behaviors and home FV availability. Increases in child asking behaviors, however, did not predict increased home FV availability. Increased child asking behaviors and home FV availability also did not mediate the increases in child FV intake. CONCLUSIONS: Children influence their home FV environment through their asking behaviors, which can be enhanced via a serious game intervention. The obtained increases in asking behavior were, however, insufficient to affect home FV availability or intake. Other factors, such as child preferences, sample characteristics, intervention duration and parental direct involvement may play a role and warrant examination in future research. TRIALS REGISTRATION: ClinicalTrials.gov NCT01004094 . Date registered 10/28/2009.

Which physical and social environmental factors are most important for adolescents' cycling for transport? An experimental study using manipulated photographs.

BACKGROUND: Ecological models emphasize that cycling for transport is determined by an interplay between individual, physical and social environmental factors. The current study investigated (a) which physical and social environmental factors determine adolescents' preferences towards cycling for transport and (b) which individual, physical and social environmental factors are associated with their intention to actually cycle for transport. METHODS: An online questionnaire consisting of questions on individual and social environmental variables, and 15 choice-based conjoint tasks with manipulated photographs was completed by 882 adolescents (55.3% male; 13.9 ± 1.6 years). Within the choice tasks, participants were asked to indicate which of two situations they would prefer to cycle to a friend's house. The manipulated photographs were all modified versions of one semi-urban street which differed in the following physical micro-environmental attributes (separation of cycle path, evenness of cycle path, speed limit, speed bump, traffic density, amount of vegetation and maintenance). In addition, each photograph was accompanied by two sentences which described varying cycling distances and co-participation in cycling (i.e. cycling alone or with a friend). After each choice task participants were also asked if they would actually cycle in that situation in real life (i.e. intention). Hierarchical Bayes analyses were performed to calculate relative importances and part-worth utilities of environmental attributes. Logistic regression analyses were performed to investigate which individual, physical and social environmental factors were associated with adolescents' intention to actually cycle for transport. RESULTS: Adolescents' preference to cycle for transport was predominantly determined by separation of cycle path, followed by shorter cycling distance and co-participation in cycling. Higher preferences were observed for a separation between the cycle path and motorized traffic by means of a hedge versus a curb, versus a marked line. Similar findings were observed for intention to cycle. Furthermore, evenness of the cycle path and general maintenance of the street were also of considerable importance among adolescents, but to a lesser extent. CONCLUSIONS: Results of this experimental study justify investment by local governments in well-separated cycling infrastructure, which seemed to be more important than cycling distance and the social environment.

Objectively measured physical environmental neighbourhood factors are not associated with accelerometer-determined total sedentary time in adults.

BACKGROUND: The physical neighbourhood environment may influence adults' sedentary behaviour. Yet, most studies examining the association between the physical neighbourhood environment and sedentary behaviour rely on self-reported data of either the physical neighbourhood environment and/or sedentary behaviour. The aim of this study was to investigate the associations between objectively measured physical environmental neighbourhood factors and accelerometer-determined total sedentary time in adults. METHODS: In total, 219 Dutch and 128 Belgian adults (mean age ± SD: 55.8 ± 15.4 years) were recruited between March and August 2014 as part of the European SPOTLIGHT project. Physical environmental neighbourhood factors, grouped into eight domains, i.e. walking, cycling, public transport, aesthetics, land use mix, grocery stores, food outlets and recreational facilities, were assessed using the SPOTLIGHT Virtual Audit Tool. Sedentary time was collected using ActiGraph GT3X+ accelerometers. General linear mixed models were conducted to examine associations between physical environmental neighbourhood factors and total sedentary time. RESULTS: Participants were sedentary, on average, for 542.9 min/day (SD: 84.3), or 9.1 h/day. None of the examined physical environmental neighbourhood factors were significantly related to total sedentary time. CONCLUSIONS: Our findings do not support associations of objectively measured physical environmental neighbourhood factors with adults' objectively sedentary time in Dutch and Belgian adults. More research on sedentary behaviours in settings such as the home and work setting is needed to examine the influence of more specific physical environmental factors on these context-specific sedentary behaviours.