Cost-effectiveness of reducing children's sedentary time and increasing physical activity at school: the Transform-Us! intervention.

BACKGROUND: Improving physical activity and reducing sedentary behavior represent important areas for intervention in childhood in order to reduce the burden of chronic disease related to obesity and physical inactivity in later life. This paper aims to determine the cost-effectiveness of a multi-arm primary school-based intervention to increase physical activity and/or reduce sedentary time in 8-9 year old children (Transform-Us!). METHODS: Modelled cost-utility analysis, using costs and effects from a cluster randomized controlled trial of a 30-month intervention that used pedagogical and environmental strategies to reduce and break up sedentary behaviour (SB-I), promote physical activity (PA-I), or a combined approach (PA + SB-I), compared to current practice. A validated multiple-cohort lifetable model (ACE-Obesity Policy model) estimated the obesity and physical activity-related health outcomes (measured as change in body mass index and change in metabolic equivalent task minutes respectively) and healthcare cost-savings over the cohort's lifetime from the public-payer perspective, assuming the intervention was delivered to all 8-9 year old children attending Australian Government primary schools. Sensitivity analyses tested the impact on cost-effectiveness of varying key input parameters, including maintenance of intervention effect assumptions. RESULTS: Cost-effectiveness results demonstrated that, when compared to control schools, the PA-I and SB-I intervention arms were "dominant", meaning that they resulted in net health benefits and healthcare cost-savings if the intervention effects were maintained. When the costs and effects of these intervention arms were extrapolated to the Australian population, results suggested significant potential as obesity prevention measures (PA-I: 60,780 HALYs saved (95% UI 15,007-109,413), healthcare cost-savings AUD641M (95% UI AUD165M-$1.1B); SB-I: 61,126 HALYs saved (95% UI 11,770 - 111,249), healthcare cost-savings AUD654M (95% UI AUD126M-1.2B)). The PA-I and SB-I interventions remained cost-effective in sensitivity analysis, assuming the full decay of intervention effect after 10 years. CONCLUSIONS: The PA-I and SB-I Transform-Us! intervention arms represent good value for money and could lead to health benefits and healthcare cost-savings arising from the prevention of chronic disease in later life if intervention effects are sustained. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN83725066). Australia and New Zealand Clinical Trials Registry Number (ACTRN12609000715279).

Movement behaviour typologies and their associations with adiposity indicators in children and adolescents: a latent profile analysis of 24-h compositional data.

OBJECTIVES: Growing evidence supports the important role of 24-hour movement behaviours (MB) in preventing childhood obesity. However, research to understand the heterogeneity and variability of MB among individuals and what kind of typologies of individuals are at risk of developing obesity is lacking. To bridge this gap, this study identified typologies of 24-hour MB in children and adolescents and investigated their associations with adiposity indicators. METHODS: In this cross-sectional study, 374 children and 317 adolescents from the Czech Republic wore wrist-worn accelerometers for seven consecutive days. Time spent in moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behaviour (SB), and sleep was quantified using raw accelerometery data. Adiposity indicators included body mass index (BMI) z-score, fat mass percentage (FM%), fat mass index (FMI), and visceral adipose tissue (VAT). Bias-adjusted latent profile analysis was used on the 24-hour MB data to identify MB typologies and their associations with adiposity indicators. The models were adjusted for potential confounders. The identified typologies were labelled to reflect the behavioural profiles of bees to aid interpretability for the general public. RESULTS: Two typologies were identified in children: highly active Workers characterised by high levels of MVPA and LPA, and inactive Queens characterised by low levels of MVPA and LPA, high levels of SB and longer sleep duration compared to Workers. In adolescents, an additional typology labelled as Drones was characterised by median levels of MVPA, LPA, SB and longest sleep duration. After controlling for covariates, we found that children labelled as Queens were associated with 1.38 times higher FM%, 1.43 times higher FMI, and 1.67 times higher VAT than Workers. In adolescents, Drones had 1.14 times higher FM% and Queens had 1.36 higher VAT in comparison with Workers, respectively. CONCLUSION: Our study highlights the importance of promoting active lifestyles in children and adolescents to potentially reduce adiposity. These findings can provide insights for interventions aimed at promoting healthy MB and preventing childhood obesity.

The association between device-measured sitting time and cardiometabolic health risk factors in children.

BACKGROUND: There is limited evidence of the associations between postural-derived sitting time, waist-worn derived sedentary time and children's health and the moderation effect of physical activity (PA). This study examined associations of children's device-measured sitting time with cardiometabolic health risk factors, including moderation by physical activity. METHODS: Cross-sectional baseline data from children (mean-age 8.2 ± 0.5 years) in Melbourne, Australia (2010) participating in the TransformUs program were used. Children simultaneously wore an activPAL to assess sitting time and an ActiGraph GT3X to assess sedentary time and physical activity intensity. Cardiometabolic health risk factors included: adiposity (body mass index [BMI], waist circumference [WC]), systolic and diastolic blood pressure (SBP, DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, triglycerides, fasting plasma glucose (FPG), serum insulin, and 25-hydroxyvitaminD (25[OH]D). Linear regression models (n = 71-113) assessed associations between sitting time with each health risk factor, adjusted for different PA intensities (i.e. light [LIPA], moderate-vigorous intensities [MVPA], separately on each model), age, sex, adiposity, and clustering by school. Interaction terms examined moderation. The analyses were repeated using device-measured sedentary time (i.e. ActiGraph GT3X) for comparison. RESULTS: Sitting time was positively associated with SBP (b = 0.015; 95%CI: 0.004, 0.026), DBP (b = 0.012; 95%CI:0.004, 0.020), and FPG (b = 0.001; 95%CI: 0.000, 0.000), after adjusting for higher PA intensities. The association between sitting time and insulin (b = 0.003; 95%CI: 0.000, 0.006) was attenuated after adjusting for higher PA intensities. When the models were adjusted for LIPA and MVPA, there was a negative association with LDL (b=-0.001; 95%CI: -0.002, -0.000 and b=-0.001; 95%CI: -0.003, -0.000, respectively). There was a negative association of sedentary time with WCz (b=-0.003; 95%CI: -0.005, 0.000) and BMIz (b=-0.003; 95%CI: -0.006, -0.000) when the models were adjusted by MVPA. Sedentary time was positively associated with triglycerides (b = 0.001; 95%CI: 0.000, 0.001) but attenuated after adjusting for MVPA. No evidence of moderation effects was found. CONCLUSIONS: Higher volumes of sitting and sedentary time were associated with some adverse associations on some cardiometabolic health risk factors in children. These associations were more evident when sitting time was the predictor. This suggests that reducing time spent sitting may benefit some cardiometabolic health outcomes, but future experimental research is needed to confirm causal relationships and identify the biological mechanisms that might be involved. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12609000715279.

Transform-Us! cluster RCT: 18-month and 30-month effects on children's physical activity, sedentary time and cardiometabolic risk markers.

OBJECTIVE: To test the efficacy of the Transform-Us! school- and home-based intervention on children's physical activity (PA), sedentary behaviour (SB) and cardiometabolic risk factor profiles. METHODS: A 30-month 2×2 factorial design cluster randomised controlled trial delivered in 20 primary schools (148 Year 3 classes) in Melbourne, Australia (2010-2012), that used pedagogical and environmental strategies to reduce and break up SB, promote PA or a combined approach, compared with usual practice. Primary outcomes (accelerometry data; n=348) were assessed at baseline, 18 and 30 months. Secondary outcomes included body mass index (BMI) and waist circumference (WC) (n=564), blood pressure (BP) (n=537) and biomarkers (minimum n=206). Generalised linear mixed models estimated the interactive effects of the PA and SB interventions on the outcomes. If there was no interaction, the main effects were assessed. RESULTS: At 18 months, there were intervention effects on children's weekday SB (-27 min, 95% CI: -47.3 to -5.3) for the PA intervention, and on children's average day PA (5.5 min, 95% CI: 0.1 to 10.8) for the SB intervention. At 30 months, there was an intervention effect for children's average day SB (-33.3 min, 95% CI: -50.6 and -16.0) for the SB intervention. Children's BMI (PA and SB groups) and systolic BP (combined group) were lower, and diastolic BP (PA group) was higher. There were positive effects on WC at both time points (SB intervention) and mixed effects on blood parameters. CONCLUSIONS: The Transform-Us! PA and SB interventions show promise as a pragmatic approach for reducing children's SB and adiposity indicators; but achieving substantial increases in PA remains challenging. TRIAL REGISTRATION: ISRCTN83725066; ACTRN12609000715279.

Exploring activity compensation amongst youth and adults: a systematic review.

BACKGROUND: Globally, significant efforts have focused on increasing physical activity and reducing sedentary behaviour in youth and adults across a range of settings (e.g., schools, workplaces, community, and home). Despite this, interventions have had varied efficacy and typically have failed to sustain changes in behaviours over time. One explanation that has been put forth to explain the mixed success of interventions is activity compensation. However, little is known about activity compensation, including whether compensation occurs, and perceptions and potential mechanisms of activity compensation. Understanding activity compensation would assist in tailoring and targeting of potential intervention strategies. The primary aim of this review was to synthesise research that has investigated activity compensation in youth and adults. The secondary aim was to identify potential reasons for and/or awareness of compensatory changes that may have occurred. METHODS: An electronic search of the EBSCOhost (via Academic Search Complete, CINAHL Complete, Education Source, Health Source: Nursing/Academic Edition, PsycINFO, SPORTdiscus with Full Text), MEDLINE Complete, Global Health, EMBASE, Scopus and Web of Science databases up to May 2021 was conducted. Quality assessment of included quantitative studies used a modified compensation-specific McMaster Quality Assessment Tool. RESULTS: A total of 44 studies met the inclusion criteria (22 = adult populations; 22 = youth populations) and were classified as (1) quantitative (n = 31); (2) combination of quantitative and behavioural (n = 11); (3) behavioural only (n = 1); and (4) qualitative (n = 1). Of the 42 studies that included a quantitative component, 11 (26%) reported compensation occurred. Within the 13 studies examining specific behaviours, 35 behaviours were assessed, and evidence of compensation was inconsistent. Compensation mechanisms included fatigue, time constraints, lack of motivation, drive to be inactive, fear of overexertion, and autonomous motivation. CONCLUSION: Little evidence of compensation was reported in the included quantitative studies; however, inconsistencies between studies makes comparisons difficult. There was considerable variability in the types of behaviours assessed in quantitative studies, and few studies examined potential compensatory mechanisms. Future research, using compensation specific study designs, methods, and analytic techniques, within different population sub-groups, should address these evidence gaps.

Intervention effects on children's movement behaviour accumulation as a result of the Transform-Us! school- and home-based cluster randomised controlled trial.

BACKGROUND: It is unknown if and how children's movement behaviour accumulation patterns change as a result of physical activity and/or sedentary behaviour interventions. It is important to establish the effectiveness of interventions targeting changes in such accumulation patterns. This study aimed to investigate the effect of the Transform-Us! school- and home-based intervention program on children's movement behaviour accumulation patterns, focusing on sporadic accumulation versus time in bouts. METHODS: Baseline and post-intervention (18 months) accelerometer data from the Transform-Us! 2 × 2 factorial design cluster randomised controlled trial was used (Melbourne, 2010-2012; analytical sample n = 267; aged 8-9 years). Linear mixed models were fitted to examine effects of three different interventions (targeting increases in physical activity [PA-I], reductions in sedentary time [SB-I], or both [PA + SB-I]) compared to a usual practice (control) group on post-intervention movement behaviour accumulation compositions with eight components, including sporadic time and bouts of sedentary time, and light-, moderate- and vigorous-intensity physical activity. RESULTS: Intervention effects on distribution of time in the post-intervention waking movement behaviour accumulation composition (adjusted for baseline composition) were small and not significant. However, visual inspection of the change in compositions over time revealed that only groups with a sedentary behaviour intervention component (SB-I and PA + SB-I) reduced time in sedentary bouts, compared to the overall sample compositional mean. In addition, the SB-I group was the only group with an increase in vigorous-intensity physical activity. The combined intervention group (PA + SB-I) was characterized by the largest proportional increase in MPA bouts. The usual practice group was characterized by the largest proportional increases in both sporadic and bouts of sedentary time. CONCLUSIONS: This study showed some early evidence to suggest that the "break up your sitting" message may result in greater impact than the "move more" message. Future research, including larger sample sizes, should investigate if this type of messaging is indeed more effective in changing movement behaviours and ultimately child health. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number ISRCTN83725066 ; Australian New Zealand Clinical Trials Registry Number ACTRN12609000715279 .

Is level of implementation linked with intervention outcomes? Process evaluation of the TransformUs intervention to increase children's physical activity and reduce sedentary behaviour.

BACKGROUND: TransformUs was a four-arm school-based intervention to increase physical activity and reduce sedentary behaviour among primary school children. Pedagogical and environmental strategies targeted the classroom, school grounds and family setting. The aims of this study were to evaluate program fidelity, dose, appropriateness, satisfaction and sustainability, and associations between implementation level and outcomes among the three intervention arms. METHODS: At baseline, 18-months (mid-intervention) and 30-months (post-intervention), teachers, parents and children completed surveys, and children wore GT3X ActiGraph accelerometers for 8 days at each time point to determine physical activity and sedentary time. Implementation data were pooled across the three intervention groups and teachers were categorised by level of implementation: (i) 'Low' (< 33% delivered); (ii) 'Moderate' (33-67% delivered); and (iii) 'High' (> 67% delivered). Linear and logistic mixed models examined between group differences in implementation, and the association with children's physical activity and sedentary time outcomes. Qualitative survey data were analysed thematically. RESULTS: Among intervention recipients, 52% (n = 85) of teachers, 29% (n = 331) of parents and 92% (n = 407) of children completed baseline evaluation surveys. At 18-months, teachers delivered on average 70% of the key messages, 65% set active/standing homework, 30% reported delivering > 1 standing lesson/day, and 56% delivered active breaks per day. The majority of teachers (96%) made activity/sports equipment available during recess and lunch, and also used this equipment in class (81%). Fidelity and dose of key messages and active homework reduced over time, whilst fidelity of standing lessons, active breaks and equipment use increased. TransformUs was deemed appropriate for the school setting and positively received. Implementation level and child behavioural outcomes were not associated. Integration of TransformUs into existing practices, children's enjoyment, and teachers' awareness of program benefits all facilitated delivery and sustainability. CONCLUSIONS: This study demonstrated that intervention dose and fidelity increased over time, and that children's enjoyment, senior school leadership and effective integration of interventions into school practices facilitated improved intervention delivery and sustainability. Teacher implementation level and child behavioural outcomes were unrelated, suggesting intervention efficacy was achieved irrespective of implementation variability. The potential translatability of TransformUs into practice contexts may therefore be increased. Findings have informed scale-up of TransformUs across Victoria, Australia. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number ISRCTN83725066; Australian New Zealand Clinical Trials Registry Number ACTRN12609000715279. Registered 19 August 2009. Available at: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=308387&isReview=true.

Associations between the neighbourhood social environment and preschool children's physical activity and screen time.

BACKGROUND: The neighbourhood social environment (NSE) has been associated with physical activity and screen time behaviours in adults and youth however less is known about this relationship in preschool-aged children (2-5 years). This study seeks to explore associations between the NSE and the physical activity and screen time behaviours of preschool-aged children. METHOD: Cross-sectional data was collected in 2019. Parents (n = 214) of preschool-aged children (m = 3.8 ± 0.8 years), from 187 different Australian postcodes representing all states and territories were invited to complete an online survey where they answered questions about their NSE (perceived social cohesion, social interaction, sense of community, social norms and neighbourhood crime) and proxy-reported their child's usual physical activity and screen time (minutes/day). Two hierarchical linear regressions were run separately to assess relationships between NSE predictor variables and physical activity and screen time. Three logistic regressions were run to determine associations between NSE constructs and the likelihood of meeting: 1) physical activity (≥ 180 min/day including ≥ 60 min of moderate-to-vigorous-intensity), 2) screen time (≤ 60 min/day) and 3) both physical activity and screen time guidelines. Child age, gender, childcare attendance, and neighbourhood level socioeconomic status (SES) were controlled for in all analyses. RESULTS: Social interaction was associated with increased daily physical activity (b = 17.76, 95%CI = 0.81, 34.71), decreased daily screen time (b = -12.77, 95%CI = -23.23, -2.23) and improved the likelihood of meeting physical activity (OR = 1.81, 95%CI = 1.20, 2.75) and combined physical activity and screen time guidelines (OR = 1.51, 95%CI = 1.03, 2.21). Higher neighbourhood crime was associated with a lower likelihood of meeting screen time guidelines (OR = 0.47, 95%CI = 0.47, 0.99). Social cohesion, sense of community and social norms were not statistically significant predictors of daily physical activity, screen time or meeting guidelines. CONCLUSION: Social interaction showed the most consistent associations with physical activity and screen time. Future research should consider potential mediators of this relationship, including parental facilitation of children's outdoor time. Improving understanding of the relationship between the NSE and physical activity and screen time in young children can help to guide community-based initiatives striving to optimise behavioural, health and social outcomes.

"Everything kind of revolves around technology": a qualitative exploration of families' screen use experiences, and intervention suggestions.

BACKGROUND: Managing children's screen time is challenging for most families. Interventions have had limited success in reducing screen time, potentially due to a lack of understanding of the experiences, needs and recommendations of families. This study aimed to 1) understand the screen time experiences of families, particularly during COVID-19 lockdowns; and 2) explore parent and child suggestions for the design, components, and content of a screen time management program. METHODS: Parents and children from 30 families living in Victoria, Australia completed a semi-structured interview (63 interviews) via Zoom in October-November 2021. Parents were maged 40.8 (± 8.9) years and predominantly female (90%). Children were maged 11.4 (± 2.4) years and 47% female. The interviews were audio recorded, transcribed verbatim and analysed using inductive thematic analysis combined with a summative content analysis approach. RESULTS: Three themes under Aim 1 emerged. Theme 1) 'Screen time management experiences and practices', including rules and strategies, challenges, and the impact of COVID-19 lockdowns. Theme 2) 'Impact of screens on family interaction and communication' including conflicts within the family, reduced face-to-face interactions, and negative impact on child's behaviour and wellbeing. Theme 3) 'Benefits of increased screen time due to COVID-19 lockdowns' including continuation of social interactions, extracurricular activities, improved technology skills and using screens as a 'babysitter'. Findings from Aim 2 suggest that families want a screen time management program delivered online to parents and children, which includes static and interactive content that incorporates health information, alternative activities, cyber-safety information, tips for goal setting and rewards, screen monitoring tools, links to reputable information, and parent social connections. Reminders via text message or through the online platform would help maintain engagement in the program. CONCLUSIONS: Families are experiencing challenges in managing the complex balance between the increased need for screens and the impact it has on the family. These findings provide valuable parent and child insights to assist in developing screen time management programs that are created with an understanding of the needs and challenges of families.

Test-retest reliability of a measure of perceived activity compensation in primary school children and their parents: a mixed methods study.

There is a lack of specific tools for assessing children's activity compensatory responses. This study 1) determined test-retest reliability and internal consistency of survey items assessing children's self-reported and parents' proxy-reported perceived compensatory responses; and 2) described children's and parents' views of potential compensatory mechanisms. Children (n = 55; mean age 10.2 ± 0.9) and their parents (n = 60) completed a survey twice, seven days apart. A sub-sample (17 parents; 13 children) participated in a short, semi-structured interview. Intraclass Correlation Coefficients (ICCs) and Cronbach's alpha assessed absolute agreement and internal consistency. Interviews were analysed via thematic analysis. Seven self- and proxy-reported survey sub-scales had excellent test-retest reliability (ICC ≥ 0.75), three had good (ICC ≥ 0.56) and one fair (ICC = 0.44). All survey items and sub-scales had acceptable internal consistency (alpha >0.67). Thematic analysis identified two overarching themes: awareness of compensation and mechanisms of compensation. After an active day at school, most participants perceived that compensation occurred later that day. Mechanisms of compensation included psychological, physiological, environmental, and interpersonal mechanisms. This reliable survey provides a new tool for assessing children's and their parents' perceptions of activity compensation and may inform future intervention designs. Future research is needed to establish concordance between perceived and device-assessed compensation.

Socioecological correlates associated with muscle-strengthening exercise at home during COVID-19 among adolescents: The our life at home study.

This study examined adolescent muscle-strengthening exercise (MSE) participation at home and associated socioecological correlates during Australia's initial COVID-19 lockdown restrictions. Adolescents (N = 731, Mage = 16.3, SD = 1.2 years, 73% female) self-reported their MSE participation in February 2020 (pre-lockdown; at a gym or at home) and April/May (during lockdown; at home only as gyms were closed). They also reported a range of potential individual, family, and home environment correlates. Remoteness and area-level socioeconomic disadvantage were also considered. Logistic regression models examined potential correlates of participation in any MSE and MSE engagement ≥3 times/week during April/May. Fewer adolescents participated in MSE during April/May (48%) than February (54%), however, the proportions that engaged in MSE ≥3 times/week were the same (30%). Prioritising being active every day (OR = 2.43, 95% CI = 1.52, 3.90), being active with sibling/s ≥ 5 days/week (OR = 2.24, 95% CI = 1.00, 5.00) and access to weights at home (OR = 2.98, 95% CI = 1.94, 4.57) were associated with higher odds of any MSE participation at home during April/May. These variables were also positively associated with MSE participation at home ≥3 times/week. Understanding how to support adolescents to prioritise being active, engage in MSE with siblings, and provide equipment may assist adolescents to engage in home-based MSE.

Is replacing sedentary time with bouts of physical activity associated with inflammatory biomarkers in children?

This study aimed to investigate the theoretical impact of reallocating a specific amount of sedentary time with an equal amount of (a) total and (b) ≥1-minute bout-accumulated time spent in different activity intensities, on inflammatory biomarkers in 8- to 9-year-old children. Accelerometry and inflammatory biomarker baseline data from the Transform-Us! Study (complete cases n = 149) were utilized. Isotemporal linear models with the Gaussian distribution and identity link functions were used to assess associations between the activity replacements and seven individual inflammatory biomarkers, including C-reactive protein (CRP), and Interleukin (IL)-2, IL-6, IL-8, and IL-10, as well as combined inflammatory and pro-inflammatory composite scores. Eighty-five percent of children met physical activity recommendations. Replacing 10 minutes of sedentary time per day with VPA, regardless of how this was accumulated, was beneficially associated with CRP and both combined composite scores. In contrast, replacing 10 min/day of sedentary time with ≥ 1-minute MPA bouts was detrimentally associated with CRP and the inflammatory composite score. Substitutions with other activity intensities were not significantly associated with any individual inflammatory biomarkers, or combined inflammatory and pro-inflammatory composite scores. In healthy and active school-aged children, evidence of the theoretical impact of replacing sedentary time with physical activity, regardless of intensity or accumulation, on markers of systemic inflammation was limited. Longitudinal research is needed to investigate the long-term impacts of reallocating sedentary time with physical activity, and particularly VPA, for inflammatory biomarkers in children, including those with increased risk of inflammation.

Effectiveness and moderators of a multicomponent school-based intervention on screen time devices: the Movimente cluster-randomized controlled trial.

BACKGROUND: Interventions targeting reduce screen time in adolescents are urgently needed, mainly in low and middle-income countries because of the lack of evidence. Thus, the aims of the study were to examine the effect of a cluster-randomized controlled trial on screen time (ST) devices among Brazilian adolescents and to identify possible moderators. METHODS: Movimente was a multicomponent school-based intervention that was performed in 2017 and consisted of teacher training, education curriculum, and environmental improvements. Baseline and post-intervention assessments (over one academic year) were conducted with students aged 10-16 years at baseline (baseline n = 921, [n = 538 intervention group; n = 383 control group]). A self-report questionnaire was used to measure daily minutes of device specific screen time (TV, computer, video games and smartphone) and demographic variables. Linear mixed models were used to examine intervention effects and an exploratory moderation analysis (sex, grade and socioeconomic status) was performed. RESULTS: The intervention had no significant effects on TV time (ß = - 6.4, 95% CI: - 6.1;13.4), game time (ß = - 8.2, 95% CI: - 7.2;10.8), computer time (ß = 1.1, 95% CI: - 6.3;18.5), smartphone time (ß = - 10.2, 95% CI: - 32.5;12.1), screen time (ß = - 12.8, 95% CI: - 50.5;24.8), meeting screen time guidelines (OR: 1.29, 95% CI: 0.65,2.57) and meeting screen time guidelines with smartphone (OR: 1.66, 95% CI: 0.37,7.40). There was a significant intervention effect on reducing TV time (ß = - 37.1, 95% CI: - 73.0, - 1.3) among 8th grade students only. CONCLUSIONS: The Movimente intervention was effective only for TV time among 8th grade students. Understanding how school-based interventions can improve adolescents' device specific screen time across age groups is needed. Future strategies should cover all screen-based devices. Further, there is a need for more studies in low- and-middle income countries to assist in the development of effective strategies. TRIAL REGISTRATION: Clinicaltrials.gov identifier NCT02944318 (25/10/2016).

The Use of Digital Platforms for Adults' and Adolescents' Physical Activity During the COVID-19 Pandemic (Our Life at Home): Survey Study.

BACKGROUND: Government responses to managing the COVID-19 pandemic may have impacted the way individuals were able to engage in physical activity. Digital platforms are a promising way to support physical activity levels and may have provided an alternative for people to maintain their activity while at home. OBJECTIVE: This study aimed to examine associations between the use of digital platforms and adherence to the physical activity guidelines among Australian adults and adolescents during the COVID-19 stay-at-home restrictions in April and May 2020. METHODS: A national online survey was distributed in May 2020. Participants included 1188 adults (mean age 37.4 years, SD 15.1; 980/1188, 82.5% female) and 963 adolescents (mean age 16.2 years, SD 1.2; 685/963, 71.1% female). Participants reported demographic characteristics, use of digital platforms for physical activity over the previous month, and adherence to moderate- to vigorous-intensity physical activity (MVPA) and muscle-strengthening exercise (MSE) guidelines. Multilevel logistic regression models examined differences in guideline adherence between those who used digital platforms (ie, users) to support their physical activity compared to those who did not (ie, nonusers). RESULTS: Digital platforms include streaming services for exercise (eg, YouTube, Instagram, and Facebook); subscriber fitness programs, via an app or online (eg, Centr and MyFitnessPal); facilitated online live or recorded classes, via platforms such as Zoom (eg, dance, sport training, and fitness class); sport- or activity-specific apps designed by sporting organizations for participants to keep up their skills (eg, TeamBuildr); active electronic games (eg, Xbox Kinect); and/or online or digital training or racing platforms (eg, Zwift, FullGaz, and Rouvy). Overall, 39.5% (469/1188) of adults and 26.5% (255/963) of adolescents reported using digital platforms for physical activity. Among adults, MVPA (odds ratio [OR] 2.0, 95% CI 1.5-2.7), MSE (OR 3.3, 95% CI 2.5-4.5), and combined (OR 2.7, 95% CI 2.0-3.8) guideline adherence were higher among digital platform users relative to nonusers. Adolescents' MVPA (OR 2.4, 95% CI 1.3-4.3), MSE (OR 3.1, 95% CI 2.1-4.4), and combined (OR 4.3, 95% CI 2.1-9.0) guideline adherence were also higher among users of digital platforms relative to nonusers. CONCLUSIONS: Digital platform users were more likely than nonusers to meet MVPA and MSE guidelines during the COVID-19 stay-at-home restrictions in April and May 2020. Digital platforms may play a critical role in helping to support physical activity engagement when access to facilities or opportunities for physical activity outside the home are restricted.

Reallocating sedentary time with total physical activity and physical activity bouts in children: Associations with cardiometabolic biomarkers.

This cross-sectional study examined theoretical effects of reallocating sedentary time (SED) with total physical activity, and physical activity bouts of varying intensities, on children's cardiometabolic biomarkers. Baseline data from the Transform-Us! trial (Melbourne, Australia) was used. Participant data were included if accelerometer and blood biomarker data were complete (n = 169; 8.7 [0.4] years; 56% girls). Isotemporal substitution models assessed the impact of replacing 10 minutes of SED with 10 minutes of total physical activity or physical activity in bouts of varying intensities on cardiometabolic biomarkers. In adjusted models, replacing 10 minutes of SED with vigorous-intensity physical activity (VPA) was associated with lower triglycerides in the whole sample. Replacing SED with VPA was associated with better high-density lipoprotein cholesterol (HDL-C) and triglycerides in children with healthy weight. Replacing SED with MPA was associated with better homoeostatic model assessment of insulin resistance (HOMA-IR) and HDL-C, in children with healthy weight and overweight, respectively. Substituting SED with VPA specifically accumulated in ≥1-min bouts was detrimentally associated with HOMA-IR in children with healthy weight but beneficially with the cardiometabolic summary score in the overweight sample. This suggests that replacing SED with MPA or VPA may have some benefits on cardiometabolic health.

Social ecological factors associated with physical activity and screen time amongst mothers from disadvantaged neighbourhoods over three years.

BACKGROUND: Mothers from socioeconomically disadvantaged neighbourhoods are at elevated risk of physical inactivity and high levels of screen time. Yet, little is known regarding the social ecological factors that are longitudinally associated with physical activity and screen time in this target group, and whether the age of their children impacts these relationships. This study aimed to longitudinally examine the social ecological factors associated with physical activity and screen time amongst mothers living in socioeconomically disadvantaged neighbourhoods, and whether these differed according to their child's age. METHODS: Data were from 895 mothers living in socioeconomically disadvantaged neighbourhoods (mean age 36.7 years) at baseline and three-year follow-up. Mothers self-reported weekly discretionary physical activity (leisure-time, LTPA; transport-related, TRPA) and screen time durations. Linear regression models assessed associations between five intrapersonal, three social and five physical environmental factors and LTPA, TRPA and screen time, adjusting for confounding factors, clustering by neighbourhood and baseline variables. Interaction analysis was conducted for age of children (younger and older children (n = 442) and mothers with older children (aged 5-12 years) only (n = 453). RESULTS: In adjusted models, all intrapersonal factors (self-efficacy, enjoyment, outcome expectations, behavioural intentions and behavioural skill), social support from friends, neighbourhood cohesion and number of televisions were longitudinally associated with LTPA amongst all mothers. Interaction models showed that findings were generally consistent across groups (i.e., those with both younger and older children compared to those with older children only), with three exceptions. Physical activity enjoyment and social support from family were associated with LTPA only among mothers with older children. Neighbourhood cohesion was associated with screen time only amongst mothers with both younger and older children. No associations were detected for TRPA. CONCLUSION: Intrapersonal, social and physical environmental factors were longitudinally associated with mother's LTPA, whilst neighbourhood cohesion was longitudinally associated with screen time behaviours amongst mothers. Interventions aimed at increasing LTPA amongst mothers (particularly those from socioeconomically disadvantaged neighbourhoods) may need to target all domains of the social ecological model and may require some tailoring according to the age of children. Further work is needed to identify longitudinal associations with screen time and TRPA in this population group.

Home-based screen time behaviors amongst youth and their parents: familial typologies and their modifiable correlates.

BACKGROUND: Excessive screen time behaviors performed by children and parents at home is a major public health concern. Identifying whether child and parent screen time behaviors cluster and understanding correlates of these familial clusters can help inform interventions for the whole family. This study characterized familial typologies of screen time behaviors and identified key modifiable correlates of these typologies. METHODS: Parents participating in the cross-sectional Sitting in the Home (SIT) study reported the duration (mins/day) they and their child (aged 11.2 ± 2.62 years) spent in six screen time behaviors at home (computer/laptop for home/work, computer/laptop for leisure, TV/videos/DVDs, tablet/smart phone for home/work, tablet/smart phone for leisure, and electronic games) and completed items related to 21 potential correlates framed by an adapted Social Cognitive Theory, Family Perspective. Latent Class Analysis was used to identify typologies based on parent and child data for the six behaviors. Multinomial logistic regression analysis assessed the relative risk of typology membership for each potential correlate, adjusting for child and parent age and sex. RESULTS: The sample comprised 542 parent-child dyads (parents: 40.7 ± 6.3 yrs., 94% female; children: 11.2 ± 2.6 yrs., 46% female). Three typologies were identified: 1) high computer/moderate TV (n = 197); 2) high TV/tablet/smartphone, low computer (n = 135); and 3) low-screen users (n = 210). 'Low-screen users' spent the least amount of time in all screen time behaviors (assigned as reference category). Greater child preference for screen time behaviors, parental support for screen time behaviors and frequency of homework requiring a tablet/laptop were associated with higher odds of being in the 'high computer/moderate TV' typology. The odds of being in the 'high TV/tablet/smartphone, low computer' typology were greater amongst children with a higher preference for screen time behaviors, and lower among more active parents. CONCLUSIONS: Three familial typologies of screen time behaviors were identified. The findings highlight that screen time in the home can be influenced by the home environment, parental behaviours and role modelling, child preferences as well as school policies. Findings can inform the development of family screen time interventions, however more research exploring the influence of factors outside of the home is warranted.

Exploring when and how adolescents sit: cross-sectional analysis of activPAL-measured patterns of daily sitting time, bouts and breaks.

BACKGROUND: This study describes patterns of adolescents' objectively-measured sitting volume, sitting bouts, and breaks in sitting during different days and periods of the day, and explored differences by sex and weekdays versus weekend days. METHODS: ActivPAL™ data were collected in August 2014-December 2015 from adolescents attending secondary government schools in Melbourne Australia. Eight periods (early morning, mid-morning, morning break, late morning, lunch, early afternoon, late-afternoon and evening) were extracted for each day. School time, class time and out-of-school time were also extracted for weekdays. The percentage of time sitting, percentage of each hour in prolonged sitting (sitting bout ≥10 min), and number of sitting breaks/hour were calculated for each period. Differences by sex, and week and weekend days were determined using t-tests. RESULTS: Participants (n = 297, 15.4 ± 1.6 years) spent 68% of their day sitting; ~ 30% of each hour in prolonged sitting and 3.1 sitting breaks/hour. Sitting time was greater during class time (75%) and school (70%) compared to out-of-school time (65%). Sitting patterns differed between week and weekend days for all periods except the evening period. Girls had higher proportion of sitting during class than boys (76% vs 72% respectively) and school hours (72% vs 67%), more prolonged sitting during school hours (27% vs 23%), and more sitting breaks per hour during out-of-school time (2.6 vs 2.4), but fewer during class (2.5 vs 3.3) and school hours (2.7 vs 3.3). Sitting patterns did not differ by sex on weekend days. CONCLUSIONS: Adolescents spent two-thirds of their waking hours sitting, with distinct patterns on weekdays and weekend days. Even though boys and girls were exposed to the same school day routine, girls spent more time sitting and had fewer sitting breaks. Class times, school breaks and the evening period were identified as key intervention periods. Further research is needed to understand the behavioural differences, and guide future intervention design.

A systematic review of the prevalence of sedentary behavior during the after-school period among children aged 5-18 years.

BACKGROUND: Independent of physical activity levels, youth sedentary behaviors (SB) have negative health outcomes. SB prevalence estimates during discretionary periods of the day (e.g., after-school), inform the need for targeted period-specific interventions. This systematic review aimed to determine children's and adolescents' SB prevalence during the after-school period. METHODS: A computerized search was conducted in October 2015 (analysed November 2015). Inclusion criteria were: published in a peer-reviewed English journal; participants aged 5-18 years; measured overall after-school sedentary time (ST) objectively, and/or specific after-school SBs (e.g., TV viewing) objectively or subjectively; and provided the percentage of the after-school period spent in ST/SB or duration of behavior and period to calculate this. Where possible, findings were analyzed by location (e.g., after-school care/'other' locations). The PRISMA guidelines were followed. RESULTS: Twenty-nine studies were included: 24 included children (≤12 years), four assessed adolescents (>12 years) and one included both; 20 assessed ST and nine assessed SB. On average, children spent 41% and 51% of the after-school period in ST when at after-school care and other locations respectively. Adolescents spent 57% of the after-school period in ST. SBs that children and adolescents perform include: TV viewing (20% of the period), non-screen based SB (including homework; 20%), screen-based SB (including TV viewing; 18%), homework/academics (13%), motorised transport (12%), social SB (9%), and screen-based SB (excluding TV viewing; 6%). CONCLUSION: Children spent up to half of the after-school period in ST and this is higher among adolescents. A variety of screen- and non-screen based SBs are performed after school, providing key targets for interventions. TRIAL REGISTRATION: PROSPERO registration number CRD42015010437.

The correlates of after-school sedentary behavior among children aged 5-18 years: a systematic review.

BACKGROUND: Children and adolescents spend a large proportion of the after-school period in sedentary behaviors (SB). Identifying context-specific correlates is important for informing strategies to reduce these behaviors. This paper systematically reviews the correlates of children's and adolescents' after-school SB. METHODS: A computerized literature search was performed in October 2015 for peer-reviewed original research journal articles published in English before October 2015. Eligibility criteria included: 1) sample aged 5-18 years; 2) quantified the amount of SB or component of this that the children/adolescents were performing after school; 3) a measure of SB as the dependent outcome; and 4) the association between potential correlates and after-school SB. RESULTS: Data were synthesized in October 2015. Thirty-one studies met the eligibility criteria: 22 studies among children (≤ 12 years), six among adolescents (>12 years), two had a combined sample of children and adolescents and one cohort followed children from childhood to adolescence. Findings were separated by after-school location i.e. after-school programs (n = 4 studies) and unidentified locations (n = 27). There was insufficient evidence to draw conclusions on all but two of the 58 potential correlates: sex and age. Among children at unidentified locations there was a null association between sex (male) and overall after-school SB, a null association between sex (male) and after-school TV viewing, a positive association between age and overall after-school SB and an inconsistent association between age and after-school TV viewing. No correlates of after-school sedentary behaviour while at after-school programs were identified. CONCLUSIONS: Only two correlates have been investigated frequently enough to determine an overall association; neither correlate is modifiable. Due to the lack of consistent investigation of potential correlates, further evidence is required to accurately identify potential intervention targets. PROSPERO REGISTRATION NUMBER: CRD42014009180.