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Introduction: The early years are a critical period for establishing healthy 24-hour movement behaviours (physical activity, sedentary behaviour and sleep), yet studies examining prospective associations between all 24-hour movement behaviours and young children's growth and development are lacking. The My Little Moves study aims to (1) examine the prospective association between 24-hour movement behaviours of young children (ie, 0-4 years) and their growth, motor and social-emotional development; and (2) explore potential determinants of young children's 24-hour movement behaviours from an ecological perspective, to inform public health strategies aimed at promoting healthy behaviours and development., Methods and Analysis: My Little Moves is a longitudinal observational cohort study, with data collection at baseline, and after 9 and 18 months follow-up. Data are collected in three subcohorts. In all subcohorts, 24-hour movement behaviours are assessed by parent-report. Additionally in subcohort 1, data on potential determinants are collected by parental questionnaires, including child, parental and environmental factors. In subcohort 2, social-emotional development is assessed using the Dutch version of the Bayley Scales of Infant and Toddler Development-third edition (Bayley-III-NL) Social Emotional Scale. In subcohort 3, data on height and weight, gross motor development, using the Bayley-III-NL Gross Motor Scale, and 7 consecutive days of 24-hour accelerometer data are collected. Hybrid model analyses are used to assess the prospective associations of 24-hour movement behaviours with young children's growth and development. Potential determinants of young children's 24-hour movement behaviours are explored using regression analysis., Ethics and Dissemination: The Medical Ethics Committee of the VU University Medical Center approved the protocol for the My Little Moves study (2022.0020). The results of this study will be disseminated through the network of all authors, to inform public health strategies for promoting healthy 24-hour movement behaviours and contribute to the evidence-base of recommendations for ideal 24-hour movement behaviours in young children., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Accurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children., Methods: We searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN)., Results: Sixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes., Conclusions: Up to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality., Prospero Registration Number: CRD42020184751., (© 2022. The Author(s).)

Recently, sedentary behavior recommendations have been included in the public health guidelines of multiple countries, pointing to new opportunities for prevention of chronic disease as well as a potential strategy for initiating long-term behavior change., Objective: To propose an evidence-informed approach to physical activity counseling that starts with a focus on reducing sedentary time., Methods: We put forward a case for addressing changes in sedentary behavior in clinical practice using a narrative review. We also propose a new approach for the assessment and counselling of patients with respect to movement behaviors., Results: There is evidence to support a stepwise approach to physical activity counseling that starts with targeting sedentary behavior, particularly in those who are highly sedentary, or those who have chronic disease, or physical impairments., Conclusions: Our approach encourages clinicians to consider sedentary behavior counseling as a critical first step to physical activity counseling. For many patients, this initial step of reducing sedentary behavior could build a pathway to an active lifestyle., Practical Implications: A shift from long periods of sedentary time to daily routines incorporating more light intensity physical activity could result in meaningful health improvements. Importantly, this approach may be more feasible for highly inactive patients., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Accurate proxy-report questionnaires, adapted to the child's developmental stage, are required to monitor 24-h movement behaviors in young children, especially for large samples and low-resource settings., Objectives: This review aimed to summarize available studies evaluating measurement properties of proxy-report questionnaires assessing physical activity, sedentary behavior and/or sleep in children aged 0-5 years., Methods: Systematic literature searches were carried out in the PubMed, Embase and SPORTDiscus databases, up to January 2021. For physical activity and sedentary behavior questionnaires this is a review update, whereas for sleep questionnaires we included all relevant studies published up to now. Studies had to evaluate at least one of the measurement properties of a proxy-report questionnaire assessing at least duration and/or frequency of physical activity, sedentary behavior and/or sleep in 0- to 5-year-old children. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guideline was used to evaluate the quality of evidence., Results: Thirty-three studies were included, examining a total of 37 questionnaires. Ten questionnaires were designed for infants, two for toddlers, 11 for preschoolers, and 14 for a broader age range targeting multiple of these age groups. Twenty questionnaires assessed constructs of sleep, four assessed constructs of physical activity, two assessed screen behavior, five assessed constructs of both physical activity and sedentary behavior, and six assessed constructs of all 24-h movement behaviors. Content validity was evaluated for six questionnaires, structural validity for two, internal consistency for three, test-retest reliability for 16, measurement error for one, criterion validity for one, and construct validity for 26 questionnaires. None of the questionnaires were considered sufficiently valid and/or reliable for assessing one or more movement behaviors in 0- to 5-year-old children, and the quality of evidence was mostly low or very low., Conclusions: Valid and/or reliable questionnaires assessing 24-h movement behaviors in 0- to 5-year-olds are lacking. High-quality studies are therefore required, to develop proxy-report questionnaires and evaluate their measurement properties., Prospero Registration Number: CRD42020169268., (© 2022. The Author(s).)

The present study cross-validated various cut-points to assess physical activity and sedentary behaviour in preschoolers, using hip- and wrist-worn accelerometers and both vertical axis and vector magnitude data. Secondly, we examined the influence of epoch length on time estimates of physical activity and sedentary behaviour. Sixty-four preschoolers (34 girls) wore two accelerometers, on their right hip and dominant wrist, during 1 hour of free play. Preschoolers' activities were observed by two trained researchers. Area under the curve (AUC) was calculated for the receiving operating characteristic (ROC) curves as a measure of precision. AUC ranges were 0.603-0.723 for sedentary behaviour, 0.472-0.545 for light physical activity and 0.503-0.661 for moderate-to-vigorous physical activity (MVPA), indicating poor to fair precision. Percentage of time classified as sedentary behaviour, light or MVPA according to observation and accelerometer data varied largely between cut-points, accelerometer placements and axes. The influence of epoch length on time estimates was minimal across cut-points, except for one hip-based vector magnitude cut-point. Across all accelerometer placements and data axes, no set of cut-points demonstrated adequate precision for sedentary behaviour, light physical activity and MVPA. The highly variable and omnidirectional activity pattern of preschoolers may explain the lack of adequate cut-points.

We examined the influence of using different epoch lengths on the classification accuracy of laboratory-controlled sedentary behaviour (SB), and free-living total time and time spent in bouts of SB and moderate-to-vigorous physical activity (MVPA), in children and adolescents. We used two studies including accelerometer-derived data of: 1) controlled activities, i.e. seven sedentary, one standing and one dancing (n = 90); 2) free-living activities (n = 902). For the controlled-activity data, we calculated percentages of time classified as SB and MVPA. For the free-living data, we calculated medians (25th-75th percentiles) of total time and time spent in bouts of SB and MVPA. Applying 8counts/5seconds, 25counts/15seconds and 100counts/60seconds for SB on controlled-activity data revealed respectively (1) 92-96%, 89-99% and 98-100% of sedentary time accurately classified as SB (activity- and age-dependent); (2) 91-98%, 88-99% and 97-100% of standing time classified as SB (age-dependent); (3) 25-37%, 20-25% and 25-38% of dancing time classified as SB (age-dependent). Using longer epochs, children's total time in SB and MVPA decreased while time accumulated in bouts of SB and MVPA accumulated in bouts increased. We conclude that a 60-second epoch seems preferable when the aim is to classify sedentary behaviour, while a shorter epoch length is needed to capture children's short bursts of MPVA. Furthermore, we should be aware that a longer epoch results in averaging of intensities to the middle category., Competing Interests: The authors have declared that no competing interests exist.

Background: To gain more understanding of the potential health effects of sedentary time, knowledge is required about the accumulation and longitudinal development of young people's sedentary time. This study examined tracking of young peoples' total and prolonged sedentary time as well as their day-to-day variation using the International Children's Accelerometry Database., Methods: Longitudinal accelerometer data of 5991 children (aged 4-17y) was used from eight studies in five countries. Children were included if they provided valid (≥8 h/day) accelerometer data on ≥4 days, including ≥1 weekend day, at both baseline and follow-up (average follow-up: 2.7y; range 0.7-8.2). Tracking of total and prolonged (i.e. ≥10-min bouts) sedentary time was examined using multilevel modelling to adjust for clustering of observations, with baseline levels of sedentary time as predictor and follow-up levels as outcome. Standardized regression coefficients were interpreted as tracking coefficients (low: < 0.3; moderate: 0.3-0.6; high: > 0.6)., Results: Average total sedentary time at study level ranged from 246 to 387 min/day at baseline and increased annually by 21.4 min/day (95% confidence interval [19.6-23.0]) on average. This increase consisted almost entirely of prolonged sedentary time (20.9 min/day [19.2-22.7]). Total (standardized regression coefficient (B) = 0.48 [0.45-0.50]) and prolonged sedentary time (B = 0.43 [0.41-0.45]) tracked moderately. Tracking of day-to-day variation in total (B = 0.04 [0.02-0.07]) and prolonged (B = 0.07 [0.04-0.09]) sedentary time was low., Conclusion: Young people with high levels of sedentary time are likely to remain among the people with highest sedentary time as they grow older. Day-to-day variation in total and prolonged sedentary time, however, was rather variable over time.

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.

Background: Physical activity has beneficial effects on the health of cancer survivors. We aimed to investigate accelerometer-assessed physical activity and sedentary time in cancer survivors, and describe activity profiles. Additionally, we identify demographic and clinical correlates of physical activity, sedentary time and activity profiles., Methods: Accelerometer, questionnaire and clinical data from eight studies conducted in four countries (n = 1447) were pooled. We calculated sedentary time and time spent in physical activity at various intensities using Freedson cut-points. We used latent profile analysis to identify activity profiles, and multilevel linear regression analyses to identify demographic and clinical variables associated with accelerometer-assessed moderate to vigorous physical activity (MVPA), sedentary time, the highly active and highly sedentary profile, adjusting for confounders identified using a directed acyclic graph., Results: Participants spent on average 26 min (3%) in MVPA and 568 min (66%) sedentary per day. We identified six activity profiles. Older participants, smokers and participants with obesity had significantly lower MVPA and higher sedentary time. Furthermore, men had significantly higher MVPA and sedentary time than women and participants who reported less fatigue had higher MVPA time. The highly active profile included survivors with high education level and normal body mass index. Haematological cancer survivors were less likely to have a highly active profile compared to breast cancer survivors. The highly sedentary profile included older participants, males, participants who were not married, obese, smokers, and those < 12 months after diagnosis., Conclusions: Cancer survivors engage in few minutes of MVPA and spend a large proportion of their day sedentary. Correlates of MVPA, sedentary time and activity profiles can be used to identify cancer survivors at risk for a sedentary and inactive lifestyle.

Purpose: To date, epidemiological studies have focused on the potential health effects of total volume of physical activity (PA) or sedentary behavior (SB). However, two persons may have the same volume of PA or SB but accumulated in a completely different sequence. The pattern of accumulating PA and SB might be more important for health effects than the total volume. Therefore, the aim was to develop a sophisticated algorithm translating accelerometer data into detailed sequence maps considering how PA and SB are accumulated throughout the day., Methods: We developed a novel algorithm to convert accelerometer counts into a sequence map based on behavior states defined by a combination of intensity (SB, light, moderate, and vigorous intensity) and duration (sporadic accumulation or in bouts of different duration). In addition, hierarchical cluster analysis was applied to identify clusters of children with similar behavioral sequence maps., Results: Clustering resulted in seven clusters of children with similar PA and SB sequence maps: an average cluster (33% of children); a cluster with relatively more SB, light, and moderate PA in bouts (SB and PA bouters, 31%); a cluster characterized by more sporadic SB and light PA (light activity breakers, 26%); and four smaller clusters with 7% of the children or less., Conclusion: This novel algorithm is a next step in more sophisticated analyses of accelerometer data considering how PA and SB are accumulated throughout the day. The next step is identifying whether specific patterns of accumulating PA and SB are associated with improved health outcomes.

Background and Objective: This review is an update of a previous review published in 2010, and aims to summarize the available studies on the measurement properties of physical activity questionnaires for young people under the age of 18 years., Methods: Systematic literature searches were carried out using the online PubMed, EMBASE, and SPORTDiscus databases up to 2018. Articles had to evaluate at least one of the measurement properties of a questionnaire measuring at least the duration or frequency of children's physical activity, and be published in the English language. The standardized COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist was used for the quality assessment of the studies., Results: This review yielded 87 articles on 89 different questionnaires. Within the 87 articles, 162 studies were conducted: 103 studies assessed construct validity, 50 assessed test-retest reliability, and nine assessed measurement error. Of these studies, 38% were of poor methodological quality and 49% of fair methodological quality. A questionnaire with acceptable validity was found only for adolescents, i.e., the Greek version of the 3-Day Physical Activity Record. Questionnaires with acceptable test-retest reliability were found in all age categories, i.e., preschoolers, children, and adolescents., Conclusion: Unfortunately, no questionnaires were identified with conclusive evidence for both acceptable validity and reliability, partly due to the low methodological quality of the studies. This evidence is urgently needed, as current research and practice are using physical activity questionnaires of unknown validity and reliability. Therefore, recommendations for high-quality studies on measurement properties of physical activity questionnaires were formulated in the discussion., Prospero Registration Number: CRD42016038695.

The primary aim was to examine child- and parent-related correlates of accelerometer-assessed overall total and prolonged (i.e., accumulated in bouts of ≥10 consecutive minutes) sedentary time (SED) in 5- to 6-year-old children. Second, child- and parent-related correlates of total and prolonged SED during weekend days and the after school period were examined, as associations with parent-related correlates may be stronger during these periods. SED and moderate-to-vigorous-intensity physical activity (MVPA) were assessed by ActiGraph accelerometers in children ( n = 836) and one of their parents/carers. Parents completed a questionnaire examining potential parent-related correlates. Multilevel models examined associations between potential correlates and children's total and prolonged SED. Children's MVPA was the only correlate that was consistently negatively associated with both total and prolonged SED across the different time periods (overall, after school, and weekend days). Higher total SED in parents was associated with higher overall total SED and weekend total SED in children. Higher body mass index z -scores of children were associated with lower overall total and prolonged SED. Girls had lower prolonged SED after school than boys. Older children had lower total SED during the weekend. In conclusion, few potential correlates were associated with young children's total or prolonged SED and most associations differed by time period.

Evidence of adverse health effects of TV viewing is stronger than for overall sedentary behaviour in youth. One explanation may be that TV viewing involves less body movement than other sedentary activities. Variations in body movement across sedentary activities are currently unknown, as are age differences in such variations. This study examined body movement differences across various sedentary activities in children and adolescents, assessed by hip-, thigh- and wrist-worn accelerometers, muscle activity and heart rate. Body movement differences between sedentary activities and standing were also examined. Fifty-three children (aged 10⁻12 years) and 37 adolescents (aged 16⁻18 years) performed seven different sedentary activities, a standing activity, and a dancing activity (as a control activity) in a controlled setting. Each activity lasted 10 minutes. Participants wore an Actigraph on their hip and both wrists, an activPAL on their thigh and a heart rate monitor. The muscle activity of weight-bearing leg muscles was measured in a subgroup ( n = 38) by surface electromyography. Variations in body movement across activities were examined using general estimation equations analysis. Children showed significantly more body movement during sedentary activities and standing than adolescents. In both age groups, screen-based sedentary activities involved less body movement than non-screen-based sedentary activities. This may explain the stronger evidence for detrimental health effects of TV viewing while evidence for child sedentary behaviour in general is inconsistent. Differences in body movement during standing and sedentary activities were relatively small. Future research should examine the potential health effects of differences in body movement between screen-based versus non-screen based and standing versus sedentary activities.

Todays children spend a large amount of their time sedentary. There is limited evidence on the determinants of sedentary behavior in children, and qualitative studies are especially lacking. Therefore, this study aimed to explore determinants of children's sedentary behavior from the child- and parent perspective. Qualitative data were collected during concept mapping sessions with four groups of 11-13 years old children ( n = 38) and two online sessions with parents ( n = 21). Children and parents generated sedentary behavior motives, sorted related motives, and rated their importance in influencing children's sedentary time. Next, multidimensional scaling and hierarchical cluster analysis was performed to create clusters of motives resulting in a concept map. Finally, the researchers named the clusters in the concept map. Concept maps of children yielded eight to ten perceived determinants, and concept maps of parents six to seven. Children and parents identified six similar potential determinants, and both rated as important: Sitting because… "it is the norm (I have to)", and "I can work/play better that way". In addition, children rated "there is nobody to play with" as an important potential determinant for engaging in sedentary behavior. The most important child- and parent perceived determinants were related to the social/cultural and physical environment, indicating that these are promising targets for future interventions., Competing Interests: The authors declare no conflict of interest.

Background: This study examined the frequency of and differences in sedentary bouts of different durations and the total time spent in sedentary bouts on a weekday, a weekend day, during school hours, during after-school hours and in the evening period in a sample of 10- to 12-year-old Belgian children., Methods: Accelerometer data were collected as part of the ENERGY-project in Belgium (n = 577, 10.9 ± 0.7 years, 53% girls) in 2011. Differences in total sedentary time, sedentary bouts of 2-5, 5-10, 10-20, 20-30 and ≥30 min and total time accumulated in those bouts were examined on a weekday, a weekend day, during school hours, during after-school hours and in the evening period, using multilevel analyses in MLwiN 2.22., Results: More than 60% of the participants' waking time was spent sedentary. Children typically engaged in short sedentary bouts of 2-5 and 5-10 min, which contributed almost 50% towards their total daily sedentary time. Although the differences were very small, children engaged in significantly fewer sedentary bouts of nearly all durations during after-school hours compared to during school hours and in the evening period. Children also engaged in significantly fewer sedentary bouts of 5-10, 10-20, and 20-30 min per hour on a weekend day than on a weekday., Conclusions: Although primary school children spend more than 60% of their waking time sedentary, they generally engaged in short sedentary bouts. Children's sedentary bouts were slightly longer on weekdays, particularly during school hours and in the evening period, although the differences were very small. These results suggest that in this age group, interventions focusing on reducing total sedentary time rather than interrupting prolonged sedentary time are needed.

Background: The prominence of sedentary behavior research in health science has grown rapidly. With this growth there is increasing urgency for clear, common and accepted terminology and definitions. Such standardization is difficult to achieve, especially across multi-disciplinary researchers, practitioners, and industries. The Sedentary Behavior Research Network (SBRN) undertook a Terminology Consensus Project to address this need., Method: First, a literature review was completed to identify key terms in sedentary behavior research. These key terms were then reviewed and modified by a Steering Committee formed by SBRN. Next, SBRN members were invited to contribute to this project and interested participants reviewed and provided feedback on the proposed list of terms and draft definitions through an online survey. Finally, a conceptual model and consensus definitions (including caveats and examples for all age groups and functional abilities) were finalized based on the feedback received from the 87 SBRN member participants who responded to the original invitation and survey., Results: Consensus definitions for the terms physical inactivity, stationary behavior, sedentary behavior, standing, screen time, non-screen-based sedentary time, sitting, reclining, lying, sedentary behavior pattern, as well as how the terms bouts, breaks, and interruptions should be used in this context are provided., Conclusion: It is hoped that the definitions resulting from this comprehensive, transparent, and broad-based participatory process will result in standardized terminology that is widely supported and adopted, thereby advancing future research, interventions, policies, and practices related to sedentary behaviors.

Background: Accurate measurement of child sedentary behavior is necessary for monitoring trends, examining health effects, and evaluating the effectiveness of interventions., Objectives: We therefore aimed to summarize studies examining the measurement properties of self-report or proxy-report sedentary behavior questionnaires for children and adolescents under the age of 18 years. Additionally, we provided an overview of the characteristics of the evaluated questionnaires., Methods: We performed systematic literature searches in the EMBASE, PubMed, and SPORTDiscus electronic databases. Studies had to report on at least one measurement property of a questionnaire assessing sedentary behavior. Questionnaire data were extracted using a standardized checklist, i.e. the Quality Assessment of Physical Activity Questionnaire (QAPAQ) checklist, and the methodological quality of the included studies was rated using a standardized tool, i.e. the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist., Results: Forty-six studies on 46 questionnaires met our inclusion criteria, of which 33 examined test-retest reliability, nine examined measurement error, two examined internal consistency, 22 examined construct validity, eight examined content validity, and two examined structural validity. The majority of the included studies were of fair or poor methodological quality. Of the studies with at least a fair methodological quality, six scored positive on test-retest reliability, and two scored positive on construct validity., Conclusion: None of the questionnaires included in this review were considered as both valid and reliable. High-quality studies on the most promising questionnaires are required, with more attention to the content validity of the questionnaires. PROSPERO registration number: CRD42016035963.

Evidence for adverse health effects of excessive sedentary behaviour in children is predominantly based on cross-sectional studies, measuring TV viewing as proxy for sedentary behaviour. This systematic review and meta-analysis summarizes the evidence on the prospective relationship between childhood sedentary behaviour and biomedical health indicators, overall and stratified by type of sedentary behaviour (TV viewing, computer use/games, screen time and objective sedentary time). PubMed, EMBASE, PsycINFO and Cochrane were systematically searched till January 2015. Methodological quality of all included studies was scored, and a best evidence synthesis was applied. We included 109 studies of which 19 were of high quality. We found moderate-to-strong evidence for a relationship of overall sedentary time with some anthropometrics (overweight/obesity, weight-for-height), one cardiometabolic biomarker (HDL-cholesterol) and some fitness indicators (fitness, being unfit). For other health indicators, we found no convincing evidence because of inconsistent or non-significant findings. The evidence varied by type of sedentary behaviour. The meta-analysis indicated that each additional baseline hour of TV viewing (β = 0.01, 95%CI = [-0.002; 0.02]) or computer use (β = 0.00, 95%CI = [-0.004; 0.01]) per day was not significantly related with BMI at follow-up. We conclude that the evidence for a prospective relationship between childhood sedentary behaviour and biomedical health is in general unconvincing., (© 2016 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of International Association for the Study of Obesity (IASO).)

An increasing number of interventions targeting sedentary behaviour in children have emerged in recent years. Recently published reviews included sedentary behaviour and physical activity interventions. This review critically summarizes evidence on the effectiveness of intervention strategies that exclusively targeted reducing sedentary time in children and adolescents. We performed a systematic literature search in Pubmed, Embase and the Cochrane Library through November 2015. Two independent reviewers selected eligible studies, extracted relevant data and rated the methodological quality using the assessment tool for quantitative studies. We included 21 intervention studies, of which 8 studies scored moderate on methodological quality and 13 studies scored weak. Four out of eight moderate quality studies reported significant beneficial intervention effects.Although descriptions of intervention strategies were not always clearly reported, we identified encouragement of a TV turnoff week and implementing standing desks in classrooms as promising strategies. Due to a lack of high quality studies and inconsistent findings, we found no convincing evidence for the effectiveness of existing interventions targeting solely sedentary behaviour. We recommend that future studies apply mediation analyses to explore which strategies are most effective. Furthermore, to increase the effectiveness of interventions, knowledge of children's motives to engage in sedentary behavior is required, as well as their opinion on potentially effective intervention strategies.

Existing research evidence indicates that children and adolescents of parents with a low socioeconomic position spend more time on sedentary behaviour than their counterparts. However, the mechanisms driving these differences remain poorly understood. The main aim of this systematic review was to summarize the evidence regarding the association between socioeconomic position and correlates of sedentary behaviour among youth (0-18 years) from developed countries. The literature search was conducted using MEDLINE, Embase, CINAHL and PsycINFO. A total of 37 studies were included. All but three studies examined screen-based sedentary behaviours only. Methodological quality ranged from low to moderate. Education was the most commonly used indicator of socioeconomic position, followed by income. Socioeconomic position was inversely related to the presence of a TV in the child's bedroom, parental modelling for TV viewing, parental co-viewing and eating meals in front of the TV. We found no/indeterminate evidence for an association between socioeconomic position and rules and regulations about screen time. The findings suggest possible factors that could be targeted in future intervention studies to decrease screen-based sedentary behaviour in lower socioeconomic groups in particular., (© 2015 World Obesity.)

Increasing attention is paid to the potential health effects of children's sedentary bouts and breaks, rather than total sedentary time. This paper discusses currently used operational definitions of sedentary bouts and breaks in studies among children. Remarkably, these studies applied different operational definitions, complicating comparison between studies. To advance the field of sedentary behaviour epidemiology, we need a clear and 'evidence-based' operationalization for such sedentary bouts and breaks., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: This study examined the occurrence and duration of sedentary bouts and explored the cross-sectional association with health indicators in children applying various operational definitions of sedentary bouts., Methods: Accelerometer data of 647 children (10-13 years old) were collected in five European countries. We analyzed sedentary time (<100 cpm) accumulated in bouts of at least 5, 10, 20 or 30 min based on four operational definitions, allowing 0, 30 or 60s ≥100 cpm within bouts. Health indicators included anthropometrics (i.e. waist circumference and body mass index (BMI)) and in a subsample from two European countries (n=112) fasting capillary blood levels of glucose, C-peptide, high-density- and low-density cholesterol, and triglycerides. Data collection took place from March to July 2010. Associations were adjusted for age, gender, moderate-to-vigorous physical activity, total wear time and country., Results: Occurrence of sedentary bouts varied largely between the various definitions. Children spent most of their sedentary time in bouts of ≥5 min while bouts of ≥20 min were rare. Linear regression analysis revealed few significant associations of sedentary time accumulated in bouts of ≥5-30 min with health indicators. Moreover, we found that more associations became significant when allowing no tolerance time within sedentary bouts., Conclusion: Despite a few significant associations, we found no convincing evidence for an association between sedentary time accumulated in bouts and health indicators in 10-13 year old children., (Copyright © 2015. Published by Elsevier Inc.)

Aim: This study aims to establish evidence-based accelerometer data reduction criteria to accurately assess total sedentary time and sedentary patterns in children., Methods: Participants (n = 1057 European children; 9-13 yrs) were invited to wear an accelerometer for at least 6 consecutive days. We explored 1) non-wear time criteria; 2) minimum daily valid wear time; 3) differences between weekday and weekend day; and 4) minimum number of days of accelerometer wear by comparing the effects of commonly used data reduction decisions on total sedentary time, and duration and number of prolonged sedentary bouts., Results: More than 60 consecutive minutes of zero counts was the optimal criterion for non-wear time. Increasing the definition of a valid day from 8 to 10 hours wear time hardly influenced the sedentary outcomes, while the sample size of children with more than 4 valid days increased from 69 to 81%. On weekdays, children had on average 1 hour more wear time, 50 minutes more total sedentary time, 26 minutes more sedentary time accumulated in bouts, and 1 more sedentary bout. At least 6 days of accelerometer data were needed to accurately represent weekly sedentary time and patterns., Conclusions: Based on our results we recommend 1) a minimum of 60 minutes of consecutive zeros as the most realistic criterion for non-wear time; and 2) including at least six days with minimum eight valid hours to characterize children's usual total sedentary time and patterns, preferably including one weekend day.

Background: Evidence on the association between different screen behaviours and cardiometabolic biomarkers in children is limited. We examined the independent relationship of TV time and PC time with cardiometabolic biomarkers in Dutch 5-6 year old children., Methods: Cross-sectional analyses were conducted December 2012-March 2013 using data from a multi-ethnic cohort (the ABCD study, n = 1,961). TV and PC time and physical activity were assessed by parent-report. Body weight, height, waist circumference and blood pressure were measured using a standard protocol. Fasting capillary blood samples were collected. A cardiometabolic function score was computed as the mean of the inverted standardised values of waist circumference, mean of systolic and diastolic blood pressure, glucose, HDLC (not inverted), and triglycerides., Results: Mean TV time was 1.2 (± 0.8) hr/day and mean PC time was 0.2 (± 0.4) hr/day. After adjustment for birth weight, height, maternal education, PC time, and physical activity, excessive TV time (>2 hrs/day) was adversely associated with waist circumference (b = 0.39, 95% CI: 0.004;0.78) while PC time was beneficially associated with HDLC levels (b = 0.04, 95% CI: 0.001;0.08). We found no additional significant associations of TV time, or PC time with any of the cardiometabolic biomarkers., Conclusions: We found no convincing evidence for an association between TV or PC time and cardiometabolic function in apparently healthy 5-6 yr olds.

Background: Sedentary time has been identified as an important and independent risk factor for the development of type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD) in adults. However, to date most studies have focused on TV time, few also included other sedentary behaviours such as computer use and reading, and most studies had a cross-sectional design. We aimed to examine the prospective relationship between time spent on sedentary behaviours in different domains with individual and clustered cardiometabolic risk in adults., Methods: Longitudinal data of 622 adults aged 30-50 years (42% males) at increased cardiometabolic risk were used. Leisure time TV viewing, computer use, reading and other sedentary activities (e.g. passive transport) were assessed using a subscale of the Activity Questionnaire for Adolescents and Adults (AQuAA), and summed into overall sedentary behaviour (min/day). Weight and blood pressure were measured, waist-to-hip ratio and BMI calculated, and fasting plasma levels of glucose, HbA1c, total cholesterol, HDL-cholesterol, LDL-cholesterol and triglycerides determined. T2DM risk score was estimated according to the ARIC formula and CVD mortality risk according to the SCORE formula., Results: Generalized Estimating Equation analysis demonstrated that over a two-year period higher levels of overall sedentary time and TV time were weakly but negatively associated with one out of 13 studied cardiometabolic risk factors (i.e. HDL cholesterol)., Conclusion: Overall sedentary time, as well as sedentary time in different domains, was virtually not related with cardiometabolic risk factors.

Background: The aim of the study was to examine the association of daily variations in rainfall and temperature with sedentary time (ST) and physical activity (PA) in European children., Methods: Children were included from 5 countries (Belgium, Greece, Hungary, the Netherlands, Switzerland) as part of the ENERGY-project. We used cross-sectional data from 722 children aged 10-12 years (47% boys). ST and PA were measured by accelerometers for 6 consecutive days, including weekend days. Weather data were collected from online national weather reports. Multilevel regression models were used for data analyses., Results: Maximum temperature was positively associated with light PA (β = 3.1 min/day; 95% CI = 2.4-3.8), moderate-to-vigorous PA (β = 0.6 min/day; 95% CI = 0.4-0.8), and average PA [β = 4.1 counts per minute (cpm); 95% CI = 1.6-6.5, quadratic relationship]. Rainfall was inversely and quadratically associated with light PA (β = -1.3 min/day; 95% CI = -1.9 to -0.6), moderate-to-vigorous PA (β = -0.6 min/day; 95% CI = -0.8 to -0.3), and average PA (β = -1.6 cpm; 95% CI = -2.2 to -0.9). Maximum temperature was not significantly associated with ST (β = -0.2 min/day; 95% CI = -1.0 to 0.6), while rainfall was positively associated with ST (β = 0.9 min/day; 95% CI = 0.6-1.3)., Conclusion: The current study shows that temperature and rainfall are significantly associated with PA and ST in 10- to 12-year-old European children.

Background: Screen time has been associated with pediatric overweight. However, it is unclear whether overweight predicts or is predicted by excessive amounts of screen time. The aim of this study was to examine the direction of the association between screen time and body fatness in Dutch adolescents., Methods: Longitudinal data of 465 Dutch adolescents (mean age at baseline 13 years, 53% boys) was used. Body fatness (objectively measured BMI, four skin folds and waist- and hip circumference), self-reported time spent watching TV and computer use, and aerobic fitness (shuttle run test) were assessed in all participants at three time points during 12 months. Multi-level linear autoregressive analyses was used to examine whether screen time predicted body fatness in the following time period and whether body fatness predicted screen time. Analyses were performed for boys and girls separately and adjusted for ethnicity and aerobic fitness., Results: Time spent TV viewing did predict changes in BMI and hip circumference in boys, but not in girls, in the subsequent period. Computer time significantly predicted increases in skinfolds in boys and girls and increases in BMI in girls. Body fatness did not predict any changes in screen time., Conclusion: The present study only partly supports the widely posited hypothesis that higher levels of screen time cause increases in body fatness. In addition, this study demonstrates that high levels of body fatness do not predict increases in screen time.

Background: The association between objectively assessed sedentary time and metabolic risk factors in childhood have rarely been studied. Therefore, we examined the independent relationship between objectively assessed and self-rated sedentary time and indicators of metabolic health in Dutch and Hungarian 10-12 year olds., Methodology/principal Findings: We performed a cross-sectional survey in primary schools. Participants were Dutch and Hungarian girls (n = 73, aged 12.2 ± 0.6 years, 18% overweight/obese) and boys (n = 69, aged 12.2 ± 0.7 years, 38% overweight/obese). Sedentary time and physical activity were assessed by the Actigraph accelerometer. TV and PC time were assessed by self-report. Adiposity indicators included body weight, height, and waist circumference (WC). Fasting plasma glucose, C-peptide, total cholesterol, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides were determined in capillary blood and summed into a metabolic risk score. Linear regression analyses were adjusted for physical activity, number of sedentary bouts and WC. Children spent on average 7.6 hours of their daily waking time in sedentary behavior and self-reported 116 ± 64 min/day watching TV and 85 ± 57 min/day using the computer. Comparing the 1(st) and 4(th) quartile of objectively assessed sedentary time, C-Peptide levels, WC and BMI were significantly higher in the most sedentary quartile, while the difference in metabolic risk score was borderline significant (p = 0.09). Comparing the 1(st) and 4(th) quartile of TV time, BMI was significantly higher in the most sedentary quartile, while the difference in WC score was borderline significant (p = 0.06). In the adjusted linear regression analysis we found no significant association of sedentary time with metabolic risk., Conclusions/significance: Although BMI and WC were higher in the most sedentary versus the least sedentary children; we found no further evidence that more sedentary children were at increased metabolic risk.

Background: It is not clear whether the association between sedentary time and cardiometabolic risk exists among obese adolescents. We examined the association between screen time (TV and computer time) and cardiometabolic risk in obese Dutch adolescents., Methods and Findings: For the current cross-sectional study, baseline data of 125 Dutch overweight and obese adolescents (12-18 years) participating in the Go4it study were included. Self-reported screen time (Activity Questionnaire for Adolescents and Adults) and clustered and individual cardiometabolic risk (i.e. body composition, systolic and diastolic blood pressure, low-density (LDL-C), high-density (HDL-C) and total cholesterol (TC), triglycerides, glucose and insulin) were assessed in all participants. Multiple linear regression analyses were used to assess the association between screen time and cardiometabolic risk, adjusting for age, gender, pubertal stage, ethnicity and moderate-to-vigorous physical activity. We found no significant relationship between self-reported total screen time and clustered cardiometabolic risk or individual risk factors in overweight and obese adolescents. Unexpectedly, self-reported computer time, but not TV time, was slightly but significantly inversely associated with TC (B = -0.002; CI = [-0.003;-0.000]) and LDL-C (B = -0.002; CI = [-0.001;0.000])., Conclusions: In obese adolescents we could not confirm the hypothesised positive association between screen time and cardiometabolic risk. Future studies should consider computer use as a separate class of screen behaviour, thereby also discriminating between active video gaming and other computer activities.