Movement Behaviors Profiles and Their Transition Among Brazilian Adolescents Before and After a School Year

This study examined concurrent changes in all components of 24-h movement behaviours (24-h MB) (sleep, sedentary behaviour [SB] and physical activity [PA]) and compliance with the Australian 24-Hour Movement Guidelines over the primary to the secondary school transition period. The analytical sample included 83 children (60.2% girls) who provided valid accelerometer-measured 24-h MB data during their final year of primary school (T1) and first year of secondary school (T2). Self-reported participation in domain-specific SB and PA, socio-demographic characteristics and weight status were also assessed. Change in 24-h MB composition from T1 to T2 was analysed using a compositional multivariate linear model for repeated measures. The difference in the proportion of meeting the 24-hour integrated movement guidelines was assessed using a McNemar–Bowker test. An unfavourable change was observed in the 24-h MB composition (p < .0001), with increased time spent in SB (+58 min/day) and decreased time in sleep (−13 min/day), Iight-intensity PA (−13 min/day) and moderate- to vigorous-intensity PA (−14 min/day). Domain-specific SB results indicated an increase in recreational screen time (+45 min/day) and out-of-school educational activities (+25 min/day). No significant changes were observed for domain-specific PA. The proportion of children meeting the 24-hour integrated movement guidelines also declined (20.5% vs. 3.6%; p < .0001). Change in 24-h MB was larger on weekdays than weekends (p < .0001); but this was not moderated by socio-demographic characteristics or weight status. These findings suggest that an integrated intervention approach targeting weekdays may be beneficial to promote adherence to healthy 24-h MB during the primary to the secondary school transition period. Highlights This study investigated how the 24-hour movement behaviour composition (i.e., time spent in sleep, sedentary behaviour and physical activity) changes and its impact on children's compliance with the 24-hour integrated movement guidelines during the primary to secondary school transition period. There was an unfavourable change in the accelerometer-measured 24-hour movement behaviour composition, with increased time spent in sedentary behaviour and decreased time in sleep, light-intensity physical activity and moderate- to vigorous-intensity physical activity. The change in weekday composition was significantly more prominent than change on weekends. The observed increase in sedentary behaviour may be attributed in part to an increase in recreational screen time and out-of-school educational activities in secondary schools. These behavioural changes were reflected in decreased compliance rates with the individual and integrated 24-hour movement guidelines, with the largest decline observed in the sleep guideline. Our findings highlight the need for an integrated intervention approach to support children to develop and/or maintain healthy movement behaviour habits throughout the school transition period.

Promoting healthy movement behaviours among children during the COVID-19 pandemic

Adolescent movement behaviour profiles are associated with indicators of mental wellbeing

Public health restrictions caused by the COVID-19 pandemic have dramatically impacted children and youth’s ability to engage in the recommended amount of physical activity (PA), sedentary behaviour (SB), and sleep (‘movement behaviours’) for optimal health. There is heightened concern of the impact of this sudden ‘time out’ of active play for our most vulnerable children and youth including those living with disabilities who have a higher prevalence of multiple chronic conditions and less access to PA. PURPOSE: To assess the acute and longer-term impact of COVID-19 restrictions on the movement behaviours and guideline adherence in Canadian children and youth with disabilities. METHODS: Parents of school-aged children and youth with disabilities completed an online survey to assess their child’s previous 7-day PA, SB, and sleep. Parents’ perceived changes in their child’s movement behaviours between pre-COVID-19 and (a) April-May 2020 (time 1; N = 151) and (b) April-November 2020 (time 2; N = 87) were also assessed. Descriptive statistics were used to analyze child behavioural patterns and guideline adherence. RESULTS: At time 1, 5.3%, 13.2%, and 66.2% of children and youth met the individual guidelines for PA, SB, and sleep, respectively. Only 1.3% of children and youth met all three movement guidelines. Similarly, at time 2, 7.2%, 20.7%, and 66.3% of children and youth met the individual guidelines for PA, SB and sleep, respectively; none met all three movement guidelines. Parents reported their child to spend less time in both outdoor (60% vs. 62.1%) and indoor (46% vs. 60.9%) PA, and more time in recreational screen time (83.5% vs. 77%) across time 1 and 2, respectively. The majority of parents reported their child’s sleep quantity and quality to be the same as pre-pandemic levels for both time 1 (53.1% and 57.3%) and time 2 (77.6% and 64.4%). CONCLUSIONS: These results show very concerning PA and SB profiles of children and youth with disabilities over the early and subsequent periods of the COVID-19 pandemic compared to pre-COVID-19. Immediate action at the programmatic and policy levels is need for more accessible and inclusive strategies to increase PA and reduce SB in this vulnerable population of children and youth. Supported by Canadian Tire Jumpstart Charities

ObjectivesTo investigate levels of recreational physical activity, screen time, sleep quality and mood in undergraduate medicine and nursing students.DesignObservational, cross-sectional study using an online survey administered during the academic term in 2024.SettingInternational Health Professions University in Bahrain.Participants279 undergraduate students from the school of medicine and school of nursing.Primary and secondary outcome measuresPhysical activity levels (International Physical Activity Questionnaire-Short Form), recreational screen time (Sedentary Behaviour Questionnaire), sleep quality (Pittsburgh Sleep Quality Index) and mood (Brief Mood Introspection Scale) were measured and compared across groups, and associations between measures were assessed.ResultsParticipants reported high rates of not meeting physical activity recommendations (46.6%), high levels of recreational screen time (median=32 hours per week) and poor-quality sleep (63.1%). Males reported higher levels of physical activity, screen time and sleep quality. Higher sleep quality was observed for the school of medicine, the preclinical stage of study and participants living alone. Overweight and obese participants had significantly higher recreational screen time and more unpleasant and tired moods. Higher levels of screen time and lower sleep quality were associated with tired, unpleasant and negative moods, while not meeting physical activity recommendations was associated with poor sleep in addition to unpleasant, tired and negative moods.ConclusionsPhysical activity levels are positively associated with mood and sleep quality in young adult healthcare students. Recreational screen time is negatively associated with mood but has no relationship with sleep quality. Intervention programmes to increase physical activity are warranted for young adults in healthcare training.Data availability statementStudy data is available on reasonable request from the corresponding author.

This chapter summarizes the associations between children’s digital screen media use (DSMU) and their health, within the 24-h movement behavior framework (physical activities, sedentary behaviors, sleep), provides recommendations for healthy DSMU, and highlights future research directions. Key concepts include behavior displacement, combined associations, and DSMU context and content. Displacement examples include more DSMU decreasing time for healthier behaviors (e.g., physical activity or sleep) or replacing reading books and magazines with DSMU alternatives (e.g., texting, social media). How DSMU affects the relationships between various combinations of movement behaviors and children’s health is largely unknown. Total DSMU is most frequently studied, but a deeper understanding of all movement behaviors requires examining the content and context of DSMUs. Insufficiently examining context and content inhibits a fulsome understanding of the health impact of child DSMU within a 24-h movement paradigm. Measurement limitations include overreliance on self- or proxy-report measures. Preliminary evidence may suggest that high DSMU contributes to an unhealthy movement behavior profile, but DSMU could also contribute to a healthy movement behavior profile (e.g., active video gaming, goal setting apps). Whether and how much DSMU can be part of a healthy combination of physical activities, sedentary behaviors, and sleep requires further study.

Background/objectivesAn element of obesity prevention is increasing total physical activity energy expenditure. However, this approach does not incorporate the balance of various movement behaviors—physical activity, sedentary behaviors and sleep—across domains of the day. We aimed to identify time-use profiles over work and leisure, termed ‘movement behavior profiles’ and to investigate their association with obesity.Subjects/methodsEight-hundred-and-seven workers completed (a) thigh accelerometry and diaries to determine their 24-h composition of behaviors (sedentary and standing, light physical activity and moderate-to-vigorous physical activity at work and leisure, and time in bed) and (b) obesity measurements. Movement behavior profiles were determined using latent profile analyses of isometric log-ratios of the 24-h composition, and labeled according to animal movement behavior traits. Linear models were applied to determine the association between profiles and obesity.ResultsFour profiles were identified, labeled as “Chimpanzees” (n = 226), “Lions” (n = 179), “Ants” (n = 244), and “Koalas” (n = 158). “Chimpanzees” work time was evenly distributed between behaviors while their leisure time was predominantly active. Compared to Chimpanzees, “Lions” were more active at work and sedentary during leisure and spent more time in bed; “Ants” were more active at work and during leisure; “Koalas” were more sedentary at work and leisure and spent similar time in bed. With “Chimpanzees” as reference, “Lions” had least favorable obesity indicators: +2.0 (95% confidence interval [CI] 0.6, 3.4) %body fat, +4.3 cm (1.4, 7.3) waist circumference and +1.0 (2.0, 0.0) Body Mass Index (BMI), followed by “Koalas” +2.0 (0.4, 3.7) %body fat, +3.1 cm (0.1, 6.0) waist circumference, and +0.8 (−0.30, 1.94) BMI. No significant differences were found between “Chimpanzees” and “Ants”.ConclusionsMovement behavior profiles across work and leisure time-use compositions are associated with obesity. Achieving adequate balance between work and leisure movement behaviors should be further investigated as a potential obesity prevention strategy.

Obesity-related behaviours such as physical activity, sedentary behaviour, sleep, screen time and diet often cluster in children. There is limited evidence on the clustering of movement behaviours among young children from low- and middle-income countries. This paper reports how 24-h movement behaviours cluster in Mongolian preschool children and their associations with eating behaviours and adiposity. Cross-sectional study involved 201 children aged 3-4 years attending kindergartens in urban and rural areas of Ulaanbaatar city, Tuv and Uvurkhangai provinces, Mongolia. Children wore accelerometers to measure physical activity, sedentary behaviour and sleep. Parents completed a questionnaire to report screen time and eating behaviours. To derive clusters, hierarchical and k-means cluster analyses were performed sequentially. Associations between clusters, eating behaviours and BMI z-score were analysed using ANOVA. Logistic regression was applied to estimate the odds of being overweight depending on cluster membership. Three clusters were identified: All-rounders, Non-active Sleepers and Screeners. We found that around half the children exhibited at least one unhealthy behaviour and were classified into clusters with a mix of healthy and unhealthy behaviours. The clusters did not differ by sociodemographic characteristics. No significant association was found between cluster membership and BMI z-score of children. The cluster at highest risk for being overweight was Screeners (odds ratio = 1.7, 95% CI: 0.67-4.33), who exhibited two obesogenic behaviours simultaneously, screen time for > 4 h per day and regular consumption of unhealthy snacks (53%, p = 0.033) and sugary drinks (53%, p = 0.014). Obesity prevention measures should begin in early childhood and target high-risk clusters, considering the co-occurrence of healthy and unhealthy behaviours. More research is needed in Mongolia to provide evidence for obesity prevention policies and inform targeted interventions to promote healthy behaviours from a young age.

BackgroundIndividuals of lower socioeconomic position (SEP) are at increased risk of developing cardiovascular disease (CVD). Variability in movement behaviours, including physical activity, sedentary behaviour, and sleep, contributes to CVD risk and socioeconomic gradients in cardiovascular health. Movement behaviour habits often begin early in life, with adolescence representing a sensitive period for establishing behaviour patterns that persist into adulthood. This study aims to evaluate the extent to which socioeconomic inequalities in adult CVD risk can be mitigated by using causal decomposition to model interventional scenarios on unhealthy movement behaviours during adolescence.MethodsWe will conduct a population-based cohort study of adolescents from the Add Health study, recruited in 1994-1995 from the United States and followed into adulthood. Unhealthy movement behaviours - low moderate-to-vigorous physical activity, high recreational screen time, and short sleep duration - will be operationalized based on the 24-hour Movement Guidelines and measured twice during adolescence (12-24 years). Parental education and family financial hardship will be used to measure SEP. The outcome will be the 10-year risk of CVD, as measured in adulthood (33-41 years) using a validated risk score. G-formula-based causal decomposition with time-varying covariates will be used to quantify the change of socioeconomic inequalities in adult CVD risk under two interventional scenarios: (1) elimination (unhealthy movement behaviours are eliminated in the population during adolescence) and, (2) equalization (the distributions of unhealthy movement behaviours in the low SEP group during adolescence are equalized to those in the high SEP group).ConclusionsThis study will use causal decomposition methods to estimate policy-relevant effects, providing evidence to guide interventions targeting adolescent movement behaviours and reduce socioeconomic inequalities in CVD risk.

Background: the relationship between behavior clusters and weight status, mainly in low- and middle-income countries, remains unclear. This study aimed to examine the association between profiles of physical activity (PA), diet and sedentary behavior (SB) with weight status in adolescents from a southern Brazilian city, according to sex. Methods: data from the Movimente Intervention study were analyzed (n = 812 / mean age 13.0 years (sd 1.04). Data on SB hours per day, PA minutes per week and weekly consumption frequencies of fruits, vegetables, salty snacks, candies and soda were self-reported on the validated Movimente questionnaire. Classes of healthy and unhealthy behaviors were derived by latent class analysis. Logistic regression analysis was used to estimate the associations between adolescents’ weight status and classes. Results: two classes were identified for the whole sample and for boys and girls. All classes had high probabilities of engaging high time in SB. Male adolescents in the unhealthy class had low probabilities of being active and high probability of consuming a low-quality diet. In contrast, girls’ healthiest profile presented lower probabilities of being active compared to boys’ healthiest profiles. No association was found between weight status and classes. Conclusion: All classes had at least one unhealthy behavior, for both the whole sample, and for girls and boys. Girls’ profiles were unhealthier compared to boys’ profiles. Hence, it is recommended that intervention strategies to change behaviors need to be distinct according to sex, targeting more than one obesogenic behavior at the same time.

The prevalence of cannabis use among youth is rising globally, making it crucial to understand its role in health and well-being. While cannabis use is linked to various health outcomes, evidence on its relationship with 24-h movement behaviours (moderate-to-vigorous physical activity, sedentary behaviour, and sleep) in youth is limited. This study uses a digital citizen science approach to examine these associations among Canadian youth aged 13 to 21 years. As a part of the Smart Platform, a digital citizen science initiative for ethical population health surveillance and policy interventions, this study engaged with 208 youth citizen scientists from Saskatchewan, Canada (August to December 2018). Participants used their smartphones to report moderate-to-vigorous physical activity, sedentary behaviour, sleep, substance use, mental health, and sociodemographic data over eight consecutive days. Linear regression models assessed associations between cannabis use and 24-h movement behaviours. Sedentary behaviour was further stratified into recreational screen time and other sedentary behaviour to explore distinct relationships with cannabis use. After adjusting for age, gender, parental education, and school, cannabis use was associated with higher hours/day of sedentary behaviour in the overall (β = 8.92, 95% CI = 1.11, 16.72; p-value = 0.02) and weekend models (β = 5.32, 95% CI = 0.89, 9.75; p-value = 0.02). Cannabis use was also associated with higher recreational screen time in both overall (β = 4.65, 95% CI = 0.19, 9.13; p-value = 0.04) and weekend models (β = 2.70, 95% CI = 0.08, 5.32; p-value = 0.04). These findings need to be confirmed with longitudinal studies to develop holistic population health interventions focusing on policy solutions to address complex negative behaviours among youth.

Changes in physical activity, sedentary behaviour and sleep across the transition from primary to secondary school: A systematic review

Adolescents' movement behaviours (MB) vary between schooldays and weekends, potentially impacting health-related quality of life (HRQoL) and well-being. This study aimed to identify transitions between 24-h MB profiles on schooldays and weekends and examine their associations with HRQoL and well-being. This is a cross-sectional study of 1070 Czech adolescents (average age: 13.8 years and standard deviation: 2.2 years; 56% girls). Participants wore accelerometers for 7 consecutive days to assess physical activity (PA) of different intensities, sedentary behaviour (SB) and sleep. A subsample of 451 participants provided data on HRQoL, which was measured using the Paediatric Quality of Life Inventory, and 484 provided valid well-being data measured with the 5-item World Health Organisation Well-Being Index. Latent transition analysis was used on the MB variables to identify transitions across MB profiles, and linear regression was used to examine associations between transitions and HRQoL or well-being. Four MB profiles were identified: Excellent (high PA, low SB and high sleep duration), Good (average MB values), Fair (below-average PA and sleep, above-average SB) and Poor (low PA and sleep, high SB). Most adolescents transitioned to less favourable profiles on weekends. Those remaining in the Excellent profile had higher HRQoL than those transitioning to less favourable profiles. Transitions to the Poor profile were associated with the lowest HRQoL and well-being scores. This study underscores the dynamic nature of adolescents' MB and the importance of consistent, healthy routines. Interventions optimizing 24-h MB throughout the week and especially on weekends may enhance adolescent HRQoL and well-being, but further evidence from longitudinal and intervention studies is needed. We observed a contrast in 24-h MB between schooldays and weekends: 29.7% of adolescents were in the Excellent on schooldays, but only 5.8% did so on weekends, while the prevalence of the Poor profile rose from 1.6% on schooldays to 27.7% on weekends. Adolescents who maintained the Excellent profile across the whole week recorded the highest scores for HRQoL and well-being. Moving into the Poor profile on weekend was associated with about 9 points poorer HRQoL and 14 points lower well-being, compared with peers who remained in the Excellent profile. Behaviour change strategies should target the entire week to preserve PA, reduce SB and protect sleep.

Reducing Socioeconomic Inequalities in Adult Cardiovascular Disease Risk by Targeting Unhealthy Movement Behaviours During Adolescence: A Protocol

SummaryBackgroundDuring the coronavirus disease 2019 (COVID‐19) pandemic, the Australian state of Victoria (in particular, its capital, Melbourne) experienced some of the longest lockdowns in the world.ObjectiveThis repeated cross‐sectional study examined changes between March to June 2019 (pre‐pandemic) and April to August 2022 (6 to 11 months following pandemic‐related lockdowns) in overweight and obesity prevalence, physical activity, sedentary behaviour, sleep, diet quality, and health‐related quality of life (HRQoL) among primary school children in north‐east Victoria, Australia.MethodsHeight and weight were measured for Grade 2, 4, and 6 students in 2019 (3889 children) and 2022 (1816 children). Grade 4 and 6 students self‐reported on their movement behaviours, diet quality, and HRQoL.ResultsParticipation declined among schools (2019:56%, 2022:34%) and students (2019:87%, 2022:75%). Compared to children in 2019, children in 2022 had a higher prevalence of overweight and obesity; were less likely to have met guidelines for moderate‐to‐vigorous physical activity, recreational screen time, and vegetable consumption; had higher intakes of takeaway food, energy‐dense nutrient‐poor snacks, and sugar‐sweetened beverages; and had lower HRQoL.ConclusionChildren's health‐related behaviours and outcomes seemed not to have returned to pre‐pandemic levels 6 to 11 months after the final lockdowns lifted for their communities. Continued monitoring and interventions targeting the drivers of childhood obesity are urgently needed.