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BACKGROUND: All 24-h movement behaviors, i.e. physical activity, sedentary behavior and sleep, are important for optimal health in children. Currently, no tools exist that include all 24-h behaviors and have been proven to be both reliable and valid. Potential reasons for the inadequate validity and reliability of existing questionnaires are the lack of focus on the content validity and lack of involvement of children in the development. Therefore, the aim of this study was to co-create a 24-h movement behavior tool together with 9-12-year-old children.METHODS: Concept mapping and photovoice meetings were held to identify children's physical activity behaviors. During concept mapping meetings with four groups of children (n = 40), children generated an extensive list of physical activities they engaged in, sorted the activities in categories and rated the frequency and perceived intensity of these activities. Using photovoice, three groups of children (n = 24) photographed their physical activities during one weekday and one weekend day, named the photographs, and placed them on a timeline. Furthermore, researchers obtained information on relevant items regarding sleep and sedentary behavior by screening existing questionnaires. Thereafter, we developed the first version of MyDailyMoves. Subsequently, we examined the content validity of the tool together with three groups of children (n = 22) and one group of researchers (n = 7) using focus group meetings.RESULTS: MyDailyMoves has a timeline format, onto which children add the activities they performed the previous day. Based on the concept mapping and photovoice studies, eight physical activity categories were included: playing inside, playing outside, sports, hobbies, chores, personal care, transport, and others. Sleep questions and two more sedentary categories (schoolwork and screen time) were added to MyDailyMoves to define and complete the timeline. The content validity study showed that all items in the tool were relevant. However, children mentioned that the activity category 'eating' was missing and the understandability of how to use the tool should be improved by adding an explanatory video. Both suggestions were adopted in the second version.CONCLUSION: Including the children's perceptions throughout the tool development process resulted in a comprehensive and practical tool which is easy for children to use.

The aim of the present study was to explore potential factors of inadequate sleep health (i.e. sleep duration, quality, and timing) of school-aged children. Data were collected among 382 primary school children (aged 4-13 years) and their parents. Personal characteristics (i.e. age, sex), individual lifestyle behaviours (i.e. screen use, sleep hygiene behaviour), social and community factors (i.e. parental sleep-related practices, parental barriers, perceived ethnicity), and living conditions (i.e. parental educational level, sleep environment) were assessed with a parental questionnaire. Sleep duration, quality, and timing were assessed with a sleep diary. Associations were analysed using linear mixed models and logistic regression analyses. In total, 332 children, with a mean (range) age of 7.5 (4-13) years, were included in the analyses. The mean sleep duration was 632 min/night, the mean sleep quality score was 40, on a scale from 10 to 50, and 25% had a bedtime that varied >40 min between weekdays. Factors negatively associated with children's sleep health included older age, perceived non-Dutch cultural background, lower parental pre-sleep emotional support, the parental barrier to get their child to bed on time when siblings have a later bedtime, high parental educational level, sleeping in a darkened bedroom, and being brought to bed after falling asleep. On average, children in the present study had adequate sleep health. The factors found to be associated with children's sleep health are useful for future healthy sleep research and intervention development.

An increasing number of children experience inadequate sleep, which negatively effects their health. To promote healthy sleep among children, it is essential to understand the underlying determinants. This online concept mapping study therefore explores potential determinants of children&rsquo, s inadequate sleep as perceived by professionals with expertise in the sleep health of children aged 4&ndash, 12 years. Participants (n = 27) were divided in three groups: (1) doctors (n = 9), (2) nurses (n = 11), (3) sleep experts (n = 7). Participants generated potential determinants (i.e., ideas) of children&rsquo, s inadequate sleep. Subsequently, they sorted all ideas by relatedness and rated their importance. These data were analysed using multidimensional scaling and hierarchical cluster analysis. The results of all three groups were combined and validated by an additional group of professionals (n = 16). A large variety of perceived determinants were identified. The most important determinants perceived by all groups belonged to the categories psychosocial determinants (i.e., worrying, a change in daily life), daytime and evening activities (i.e., screen use before bedtime, stimulating game play before bedtime, inadequate amount of daytime physical activity), and pedagogical determinants (i.e., inconsistent sleep schedule, lack of a bedtime routine). These perspectives are valuable for future longitudinal studies on the determinants of children&rsquo, s sleep and the development of future healthy sleep interventions.

Many children do not meet the recommendations for healthy sleep, which is concerning given the potential negative effects on children&rsquo, s health. To promote healthy sleep, it is crucial to understand its determinants. This concept mapping study therefore explores perspectives of children and parents on potential determinants of children&rsquo, s inadequate sleep. The focus lies on 9&ndash, 12 year old children (n = 45), and their parents (n = 33), from low socioeconomic neighbourhoods, as these children run a higher risk of living in a sleep-disturbing environment (e.g., worries, noise). All participants generated potential reasons (i.e., ideas) for children&rsquo, s inadequate sleep. Next, participants sorted all ideas by relatedness and rated their importance. Subsequently, multidimensional scaling and hierarchical cluster analyses were performed to create clusters of ideas for children and parents separately. Children and parents both identified psychological (i.e., fear, affective state, stressful situation), social environmental (i.e., sleep schedule, family sleep habits), behavioural (i.e., screen behaviour, physical activity, diet), physical environmental (i.e., sleep environment such as temperature, noise, light), and physiological (i.e., physical well-being) determinants. These insights may be valuable for the development of future healthy sleep interventions.

To develop evidence-based healthy sleep interventions for children, this review provides insight into the behavioral determinants of sleep behavior. Hence the objective of this review is to systematically review the longitudinal evidence on determinants of children's sleep behavior. Studies were identified from searches in PubMed, PsycINFO, and Web of Science, until January 2017. Longitudinal studies investigating the association between potential determinants and sleep behavior (duration, quality and timing) in healthy children aged 4–12 years were included. The methodological quality was scored and the results were summarized using a best-evidence synthesis. We followed the PRISMA statement guidelines in order to summarize the evidence accurately and reliably. Twelve of the 45 included studies were rated as ‘high quality’. We found strong evidence for child age and moderate evidence for screen time, past sleep behavior, and a difficult temperament as determinant of sleep duration. For determinants of sleep quality, evidence was either insufficient or inconsistent. We found moderate evidence for week schedule as a determinant of sleep timing, with later bed- and wake times in weekends. More high quality studies, which are extensive, collaborative, and multidisciplinary, are needed into the determinants of all dimensions of sleep behavior.

Background: Interventions delivered through new device technology, including mobile phone apps, appear to be an effective method to reach young adults. Previous research indicates that self-efficacy and social support for physical activity and self-regulation behavior change techniques (BCT), such as goal setting, feedback, and self-monitoring, are important for promoting physical activity; however, little is known about evaluations by the target population of BCTs applied to physical activity apps and whether these preferences are associated with individual personality characteristics. Objective: This study aimed to explore young adults’ opinions regarding BCTs (including self-regulation techniques) applied in mobile phone physical activity apps, and to examine associations between personality characteristics and ratings of BCTs applied in physical activity apps. Methods: We conducted a cross-sectional online survey among healthy 18 to 30-year-old adults (N=179). Data on participants’ gender, age, height, weight, current education level, living situation, mobile phone use, personality traits, exercise self-efficacy, exercise self-identity, total physical activity level, and whether participants met Dutch physical activity guidelines were collected. Items for rating BCTs applied in physical activity apps were selected from a hierarchical taxonomy for BCTs, and were clustered into three BCT categories according to factor analysis: “goal setting and goal reviewing,” “feedback and self-monitoring,” and “social support and social comparison.” Results: Most participants were female (n=146), highly educated (n=169), physically active, and had high levels of self-efficacy. In general, we observed high ratings of BCTs aimed to increase “goal setting and goal reviewing” and “feedback and self-monitoring,” but not for BCTs addressing “social support and social comparison.” Only 3 (out of 16 tested) significant associations between personality characteristics and BCTs were observed: “agreeableness” was related to more positive ratings of BCTs addressing “goal setting and goal reviewing” (OR 1.61, 95% CI 1.06-2.41), “neuroticism” was related to BCTs addressing “feedback and self-monitoring” (OR 0.76, 95% CI 0.58-1.00), and “exercise self-efficacy” was related to a high rating of BCTs addressing “feedback and self-monitoring” (OR 1.06, 95% CI 1.02-1.11). No associations were observed between personality characteristics (ie, personality, exercise self-efficacy, exercise self-identity) and participants’ ratings of BCTs addressing “social support and social comparison.” Conclusions: Young Dutch physically active adults rate self-regulation techniques as most positive and techniques addressing social support as less positive among mobile phone apps that aim to promote physical activity. Such ratings of BCTs differ according to personality traits and exercise self-efficacy. Future research should focus on which behavior change techniques in app-based interventions are most effective to increase physical activity. [JMIR Mhealth Uhealth 2015;3(4):e103]