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).

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.

Defining and reporting activity patterns: a modified Delphi study.

BACKGROUND: Despite significant interest in assessing activity patterns in different populations, there has been no consensus concerning the definition and operationalisation of this term. This has limited the comparability, interpretability, and synthesis of study findings to date. The aim of this study was to establish a consensus regarding the way in which activity patterns and activity pattern components are defined and reported. METHODS: The activity patterns literature was searched to identify experts to be invited to participate and to develop a proposed definition of activity patterns and activity pattern components. A three-round modified Delphi survey was conducted online (November 2021 to May 2022). In Round 1, participants were asked to rate their agreement with a proposed activity patterns definition, which also included six activity pattern components (e.g., activity intensity, activity bout, transitions), six examples of activity patterns (e.g., frequency of postural transitions in discrete time periods) and eight items for reporting activity patterns in future research (n = 21 items). Open-ended questions enabled participants to provide further comments and suggestions for additional items. Consensus was defined a priori as ≥ 80% participants rating their agreement with an item. In Round 2, participants were asked to rate their agreement with 25 items (13 original items, eight amended, and four new). In Round 3, participants rated their agreement with 10 items (five original items, four amended, and one new). RESULTS: Twenty experts in activity patterns research participated in Round 1, with response rates of 80% and 60% in Rounds 2 and 3, respectively. The proposed activity pattern definition, all activity pattern components definitions, four of the six activity pattern examples, and 10 items in the activity patterns reporting framework achieved consensus. The removal of one activity component item between Rounds 1 and 2 achieved consensus. CONCLUSION: This modified Delphi study achieved consensus for defining and reporting activity patterns for the first time. This consensus definition enables standardisation of activity patterns terminology, which is important given the significant interest in quantifying how individuals accumulate their physical activity and sedentary behaviour across the lifespan to inform the development of future public health guidelines and interventions efforts.

Socioeconomic position as a predictor of youth's movement trajectory profiles between ages 10 and 14 years.

BACKGROUND: Combinations of movement behaviors (i.e., physical activity, sedentary behavior, sleep) are associated with health and developmental outcomes in youth. Youth vary in how they accumulate these behaviors, both in volume and specific domains (e.g., sedentary time spent on recreational screen activities vs homework). The aim of this study was to examine how youth's combined general and domain-specific movement trajectories differ by socioeconomic position. METHODS: We conducted a longitudinal, group-based multi-trajectory analysis to identify general and domain-specific movement trajectory profiles for 2457 youth from age 10 to 14 years from the Longitudinal Study of Australian Children from 2014-2018. We used multinomial logistic regression to test if socioeconomic position predicted profile membership. RESULTS: We identified three general movement trajectory profiles for both sexes, four domain-specific profiles for males, and five for females. For general movement trajectories, females from lower socioeconomic positions were more likely to be a combination of less active and more sedentary than females from higher socioeconomic positions. Males across socioeconomic positions spend similar amounts of time in physical activity, sedentary time, and sleep. For domain-specific movement trajectories, youth from lower socioeconomic positions were likely to spend a combination of less time in education-based sedentary behavior and more time in recreational screen activities than their higher socioeconomic position peers. CONCLUSION: Our results indicate that socioeconomic position predicted in which domains youth accumulate their movements. Future observational research and interventions targeting different socioeconomic groups should therefore consider domain-specific movement trajectories.

Prioritizing a research agenda on built environments and physical activity: a twin panel Delphi consensus process with researchers and knowledge users.

BACKGROUND: The growth of urban dwelling populations globally has led to rapid increases of research and policy initiatives addressing associations between the built environment and physical activity (PA). Given this rapid proliferation, it is important to identify priority areas and research questions for moving the field forward. The objective of this study was to identify and compare research priorities on the built environment and PA among researchers and knowledge users (e.g., policy makers, practitioners). METHODS: Between September 2022 and April 2023, a three-round, modified Delphi survey was conducted among two independent panels of international researchers (n = 38) and knowledge users (n = 23) to identify similarities and differences in perceived research priorities on the built environment and PA and generate twin 'top 10' lists of the most important research needs. RESULTS: From a broad range of self-identified issues, both panels ranked in common the most pressing research priorities including stronger study designs such as natural experiments, research that examines inequalities and inequities, establishing the cost effectiveness of interventions, safety and injuries related to engagement in active transportation (AT), and considerations for climate change and climate adaptation. Additional priorities identified by researchers included: implementation science, research that incorporates Indigenous perspectives, land-use policies, built environments that support active aging, and participatory research. Additional priorities identified by knowledge users included: built environments and PA among people living with disabilities and a need for national data on trip chaining, multi-modal travel, and non-work or school-related AT. CONCLUSIONS: Five common research priorities between the two groups emerged, including (1) to better understand causality, (2) interactions with the natural environment, (3) economic evaluations, (4) social disparities, and (5) preventable AT-related injuries. The findings may help set directions for future research, interdisciplinary and intersectoral collaborations, and funding opportunities.

Results from the Australian 2022 Report Card on physical activity for children and young people.

Background: Past Physical Activity Report Cards have indicated a minority of Australian children and young people are sufficiently active. The purpose of this paper is to summarise grades across 10 indicators of the 2022 Australian Physical Activity Report Card, to assess physical activity behaviours and supports. Methods: A development team consisting of research experts synthesised and evaluated national and state level data to inform grades for each indicator. Data were drawn from nationally and state/territory representative datasets spanning 2016-2021. Results: Overall Physical Activity Levels and Screen Time were both assigned grades of D-, remaining the worst performing indicators. Australia's best performing indicator was Community and the Built Environment (A-), followed by Organised Sport and Physical Activity (B-). Remaining indicators were Family and Peers (C+), School (C+), Strategies and Investments (C-), Active Transport (D-) and Physical Fitness (D-). Active Play was unable to be graded, due to lack of consensus on a primary metric for this indicator and a lack of representative data. Conclusion: Evidence suggests that physical activity levels of Australian children remain consistently low, despite access to and availability of facilities and open spaces. There is a strong need for a National Physical Activity Plan to address this. The theme for the 2022 Australian Physical Activity Report Card, REBOOT! Reimagining physically active lives encourages us all to think more imaginatively about how we might engage all children and young people through diverse physical activity opportunities to be more active.

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 .

CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children.

BACKGROUND: Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. METHODS: Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). RESULTS: Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. CONCLUSIONS: There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

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.

A systematic review of temporal body weight and dietary intake patterns in adults: implications on future public health nutrition interventions to promote healthy weight.

PURPOSE: The global prevalence of overweight remains high; effective strategies that consider patterns of body weight changes to identify periods when adults are susceptible to weight gain are warranted. This systematic review aimed to investigate body weight patterns, and how they were associated with dietary intake and/or dietary behaviours (Prospero CRD42020161977). METHODS: Systematic literature search was conducted in the Medline, Embase, and CINAHL databases until November 2020. Observational studies in adults (18 years and over) that reported at least two measurements of weight and dietary intake in a year were included. Risk of bias was conducted using the Evidence Analysis Library by the Academy of Nutrition and Dietetics tool. This review included 16 unique studies after title, abstract, and full-text screening, and findings were narratively synthesised. RESULTS: Of the six studies conducted in the farming populations, five were conducted in countries with two seasons (dry vs. rainy seasons) and all studies observed higher body weight during the dry season (up to 3.1 kg difference between seasons). The remaining study was conducted in a sub-tropical country and did not observe temporal weight patterns. Higher dietary intake was also reported during the dry season in the tropical countries. In non-farming populations (n = 10), temporal patterns were also seen, where higher body weight and adiposity was observed during colder seasons (autumn and winter). However, the opposite was found in a study conducted in Iran, where higher weight was seen in summer. Concurrent with higher body weight, higher energy, fat, carbohydrate and soda consumption, and lower fiber and vegetable intake were observed. CONCLUSION: Temporal weight and dietary patterns exist, and they were country- and context-specific; these patterns were also related to factors such as activity levels, seasons and occupation. Future interventions should consider temporal patterns in the design and delivery of timely and tailored dietary interventions to promote optimal body weight. PROSPERO REGISTRATION: PROSPERO Registration: CRD42020161977.

"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.

Validity evidence for the Pictorial Scale of Perceived Water Competence short form (PSPWC-4).

Promoting swimming and water safety is an important public health issue. The Pictorial Scale of Perceived Water Competence (PSPWC) requires children to indicate their perceived competence using three difficulty levels for 17 swimming scenarios. The aim was to purposefully select four critical scenarios of the PSPWC to create the PSPWC-short form and test for associations with actual swim competence. Four of 17 scenarios were selected and extended to a four-point response scale by adding an extra difficulty level. Parents whose children had participated in at least 1-week swim-intensive programme in 2021 were invited to participate in the study, and perceptions of 139 children (mean age 6.9 years, SD = 1.9) were matched with certificate data of actual swim competence pre- (n = 139) and post-programme (n = 29). Moderate positive correlations were found between swim level at programme start and perception of: retrieving an object in deep water (rho = 0.57), swimming on front (rho = 0.60), swimming on back (rho = 0.69), treading water (rho = 0.63) and the summed score of all four (rho = 0.71). After adjusting for sex and age, higher perceived skill was still significantly associated with increasing achieved skill levels. The PSPWC-short form appears to provide a valid measure to be used as a brief screening assessment.

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.

The Influence of Sitting, Standing, and Stepping Bouts on Cardiometabolic Health Markers in Older Adults.

Aside from total time spent in physical activity behaviors, how time is accumulated is important for health. This study examined associations between sitting, standing, and stepping bouts, with cardiometabolic health markers in older adults. Participants from the Mitchelstown Cohort Rescreen Study (N = 221) provided cross-sectional data on activity behaviors (assessed via an activPAL3 Micro) and cardiometabolic health. Bouts of ≥10-, ≥30-, and ≥60-min sitting, standing, and stepping were calculated. Linear regression models were fitted to examine the associations between bouts and cardiometabolic health markers. Sitting (≥10, ≥30, and ≥60 min) and standing (≥10 and ≥30 min) bouts were detrimentally associated with body composition measures, lipid markers, and fasting glucose. The effect for time spent in ≥60-min sitting and ≥30-min standing bouts was larger than shorter bouts. Fragmenting sitting with bouts of stepping may be targeted to benefit cardiometabolic health. Further insights for the role of standing need to be elicited.

Effect of commercial wearables and digital behaviour change resources on the physical activity of adolescents attending schools in socio-economically disadvantaged areas: the RAW-PA cluster-randomised controlled trial.

BACKGROUND: There has been increasing interest in using wearable activity trackers to promote physical activity in youth. This study examined the short- and longer-term effects of a wearable activity tracker combined with digital behaviour change resources on the physical activity of adolescents attending schools in socio-economically disadvantaged areas. METHODS: The Raising Awareness of Physical Activity (RAW-PA) Study was a 12-week, multicomponent intervention that combined a Fitbit Flex (and accompanying app), and online digital behaviour change resources and weekly challenges delivered via Facebook. RAW-PA was evaluated using a cluster-randomised controlled trial with 275 adolescents (50.2% female; 13.7 ± 0.4 years) from 18 Melbourne secondary schools (intervention n = 9; wait-list control group n = 9). The primary outcome was moderate- to vigorous-intensity physical activity (MVPA), measured using hip-worn ActiGraph accelerometers. The secondary outcome was self-reported physical activity. Data were collected at baseline, 12-weeks (immediately post-intervention), and 6-months post-intervention (follow-up). Multilevel models were used to determine the effects of the intervention on daily MVPA over time, adjusting for covariates. RESULTS: No significant differences were observed between intervention and wait-list control adolescents' device-assessed MVPA immediately post-intervention. At 6-months post-intervention, adolescents in the intervention group engaged in 5 min (95% CI: - 9.1 to - 1.0) less MVPA per day than those in the wait-list control group. Males in the intervention group engaged in 11 min (95% CI: - 17.6 to - 4.5) less MVPA than males in the wait-list control group at 6-months post-intervention. No significant differences were observed for females at either time point. For self-reported physical activity, no significant effects were found at 12-weeks and 6-months post-intervention. CONCLUSIONS: Combining a wearable activity tracker with digital behaviour change resources and weekly challenges did not increase inactive adolescents' accelerometer-derived and self-reported physical activity levels immediately post-intervention. This contrasts previous research that has suggested wearable activity tracker may increase youth physical activity levels in the short-term. Lower engagement in MVPA 6-months post-intervention was observed for males but not for females, though it is unclear why this finding was observed. The results suggest wearable activity trackers, in combination with supporting materials, may not be effective for increasing physical activity levels in adolescents. TRIAL REGISTRATION: ACTRN12616000899448 . Australian and New Zealand Clinical Trials Registry. Registered 7 July 2016.

Count- versus MAD-based accelerometry-assessed movement behaviors and associations with child adiposity and fitness.

Estimations of time spent sedentary and in various physical activity intensities may vary according to data reduction methods applied. This study compared associations between children's accelerometer data and adiposity and fitness markers using open source (mean amplitude deviation, MAD) and proprietary (counts) data reduction methods. Complete-case accelerometer, adiposity (Body Mass Index z-score, waist circumference), and fitness (cardiorespiratory, musculoskeletal) data from 118 children (10.4 ± 0.6 years, 49% girls) were analyzed. Estimates of sedentary behavior, light-, moderate-, vigorous- (VPA), and moderate- to vigorous-intensity (MVPA) physical activity were calculated using count- and MAD-based data reduction methods. Linear regression models between time in movement behaviours and fitness and adiposity markers were conducted. Significant differences in estimates of time spent in all intensities were observed between MAD-based and count-based methods. Both methods produced evidence to suggest that sedentary behavior was detrimentally, and physical activity (any intensity) was beneficially, associated with waist circumference. MVPA and VPA were beneficially associated with fitness markers using both data reduction measures. Overall, findings suggest that estimates of sedentary time and physical activity were not comparable. However, the strength and direction of the associations obtained between the different data reduction methods and adiposity and fitness outcomes were fairly comparable, with both methods finding stronger associations for VPA compared to MVPA. This suggests that future studies may be able to pool data using different data reduction approaches when examining associations between activity and health risk factors, albeit with caution.

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.

Physical activity accumulation along the intensity spectrum differs between children and adults.

PURPOSE: Detailed exploration of physical activity accumulation with fine grading along the intensity spectrum has indicated the potential pragmatic utility of such an approach. However, it is currently unclear what sorts of accumulation patterns along particular intensity bands are found in the children and adult populations. Therefore, we conducted a comparison of activity accumulation in specific intensity bands between four distinct populations: children, adults with sedentary lifestyles, habitual joggers, habitual marathon runners. METHODS: Free-living waist-worn accelerometry records from 28 children aged 7 to 11, and 61 adults aged 25 to 35 were analysed. Activity intensity was evaluated in 5 s non-overlapping epochs as mean amplitude deviation (MAD) and normalised to acceleration intensities corresponding to walking at 3 metabolic equivalents of a task (METs). Adult data were normalised to 0.091 g MAD based on literature, and data from children to 0.170 g MAD based on laboratory experimentation. The normalised epoch values were divided into 100 intensity gradations. RESULTS: Children accumulated more activity in 0.74 to 1.58 normalised acceleration intensities (all p < 0.005) compared to adults. Adult joggers/runners accumulated more activity in normalised acceleration intensities from 7.1 to 11.1 compared to the other groups (p < 0.008). CONCLUSION: The primary bulk of children's free-living activities are of relatively low intensity not likely to provoke cardiometabolic improvement. These sorts of explorations could be used in informing intervention development aiming at optimising healthy development. Evidence is mounting to justify randomised controlled trials based on intervention targets identified based on exploring the intensity spectrum.