Financial Incentives for Physical Activity and Sports Participation in Young People.

Physical inactivity is a global health problem. Childhood is an opportune time to establish healthy physical activity behaviors, including the participation in organized physical activity, such as sports. We hypothesize that financial incentives can improve young people's participation in physical activity and sports. The design of the incentive and the context in which it operates are crucial to its success.

The Annual Rhythms in Sleep, Sedentary Behavior, and Physical Activity of Australian Adults: A Prospective Cohort Study.

BACKGROUND: Sleep, sedentary behavior, and physical activity have fundamental impacts on health and well-being. Little is known about how these behaviors vary across the year. PURPOSE: To investigate how movement-related behaviors change across days of the week and seasons, and describe movement patterns across a full year and around specific temporal events. METHODS: This cohort study included 368 adults (mean age = 40.2 years [SD = 5.9]) who wore Fitbit activity trackers for 12 months to collect minute-by-minute data on sleep, sedentary behavior, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data were analyzed descriptively, as well as through multilevel mixed-effects linear regression to explore associations with specific temporal cycles (day-of-the-week, season) and events. RESULTS: Movement patterns varied significantly by day-of-the-week and season, as well as during annual events like Christmas-New Year and daylight saving time (DST) transitions. For example, sleep was longer on weekends (+32 min/day), during autumn and winter relative to summer (+4 and +11 min/day), and over Christmas-New Year (+24 min/day). Sedentary behavior was longer on weekdays, during winter, after Christmas-New Year, and after DST ended (+45, +7, +12, and +8 min/day, respectively). LPA was shorter in autumn, winter, and during and after Christmas-New Year (-6, -15, -17, and -31 min/day, respectively). Finally, there was less MVPA on weekdays and during winter (-5 min/day and -2 min/day, respectively). CONCLUSIONS: Across the year, there were notable variations in movement behaviors. Identifying high-risk periods for unfavorable behavior changes may inform time-targeted interventions and health messaging.

Sleep, sedentary behavior, and physical activity have fundamental impacts on health and well-being, yet little is known about how these behaviors vary across the year. This study investigated how these behaviors change across days of the week, seasons, and a year, and around specific temporal events. The study included 368 middle-aged adults who wore Fitbit activity trackers for 12 months to collect minute-by-minute movement data. Statistical analyses showed movement patterns varied significantly by day-of-the-week and season, as well as during annual events like Christmas-New Year and daylight saving time transitions. For example, sleep was longer on weekends, during autumn and winter relative to summer, and over Christmas-New Year. Sedentary behavior was longer on weekdays, during winter, after Christmas-New Year, and after daylight savings time ended. Light physical activity was shorter in autumn, winter, and during and after Christmas-New Year. Finally, there was less moderate-to-vigorous physical activity on weekdays and during winter. Across the year, there were notable variations in movement patterns. Identifying high-risk periods for unfavorable behavior changes may inform time-targeted interventions and health messaging.

The Wearable Activity Tracker Checklist for Healthcare (WATCH): a 12-point guide for the implementation of wearable activity trackers in healthcare.

Increasing physical activity in patients offers dual benefits, fostering improved patient health and recovery, while also bolstering healthcare system efficiency by minimizing costs related to extended hospital stays, complications, and readmissions. Wearable activity trackers offer valuable opportunities to enhance physical activity across various healthcare settings and among different patient groups. However, their integration into healthcare faces multiple implementation challenges related to the devices themselves, patients, clinicians, and systemic factors. This article presents the Wearable Activity Tracker Checklist for Healthcare (WATCH), which was recently developed through an international Delphi study. The WATCH provides a comprehensive framework for implementation and evaluation of wearable activity trackers in healthcare. It covers the purpose and setting for usage; patient, provider, and support personnel roles; selection of relevant metrics; device specifications; procedural steps for issuance and maintenance; data management; timelines; necessary adaptations for specific scenarios; and essential resources (such as education and training) for effective implementation. The WATCH is designed to support the implementation of wearable activity trackers across a wide range of healthcare populations and settings, and in those with varied levels of experience. The overarching goal is to support broader, sustained, and systematic use of wearable activity trackers in healthcare, therefore fostering enhanced physical activity promotion and improved patient outcomes.

How do 24-h movement behaviours change during and after vacation? A cohort study.

BACKGROUND: For adults, vacations represent a break from daily responsibilities of work - offering the opportunity to re-distribute time between sleep, sedentary behaviour, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) across the 24-h day. To date, there has been minimal research into how activity behaviour patterns change on vacation, and whether any changes linger after the vacation. This study examined how daily movement behaviours change from before, to during and after vacations, and whether these varied based on the type of vacation and vacation duration. METHODS: Data collected during the Annual Rhythms In Adults' lifestyle and health (ARIA) study were used. 308 adults (mean age 40.4 years, SD 5.6) wore Fitbit Charge 3 fitness trackers 24 h a day for 13 months. Minute-by-minute movement behaviour data were aggregated into daily totals. Multi-level mixed-effects linear regressions were used to compare movement behaviours during and post-vacation (4 weeks) to pre-vacation levels (14 days), and to examine the associations with vacation type and duration. RESULTS: Participants took an average of 2.6 (SD = 1.7) vacations of 12 (SD = 14) days' (N = 9778 days) duration. The most common vacation type was outdoor recreation (35%) followed by family/social events (31%), rest (17%) and non-leisure (17%). Daily sleep, LPA and MVPA all increased (+ 21 min [95% CI = 19,24] p < 0.001, + 3 min [95% CI = 0.4,5] p < 0.02, and + 5 min [95% CI = 3,6] p < 0.001 respectively) and sedentary behaviour decreased (-29 min [95% CI = -32,-25] p < 0.001) during vacation. Post-vacation, sleep remained elevated for two weeks; MVPA returned to pre-vacation levels; and LPA and sedentary behaviour over-corrected, with LPA significantly lower for 4 weeks, and sedentary behaviour significantly higher for one week. The largest changes were seen for "rest" and "outdoor" vacations. The magnitude of changes was smallest for short vacations (< 3 days). CONCLUSIONS: Vacations are associated with favourable changes in daily movement behaviours. These data provide preliminary evidence of the health benefits of vacations. TRIAL REGISTRATION: The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

Weather associations with physical activity, sedentary behaviour and sleep patterns of Australian adults: a longitudinal study with implications for climate change.

BACKGROUND: Weather is a potentially important influence on how time is allocated to sleep, sedentary behaviour and physical activity across the 24-h day. Extremes of weather (very hot, cold, windy or wet) can create undesirable, unsafe outdoor environments for exercise or active transport, impact the comfort of sleeping environments, and increase time indoors. This 13-month prospective cohort study explored associations between weather and 24-h movement behaviour patterns. METHODS: Three hundred sixty-eight adults (mean age 40.2 years, SD 5.9, 56.8% female) from Adelaide, Australia, wore Fitbit Charge 3 activity trackers 24 h a day for 13 months with minute-by-minute data on sleep, sedentary behaviour, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) collected remotely. Daily weather data included temperature, rainfall, wind, cloud and sunshine. Multi-level mixed-effects linear regression analyses (one model per outcome) were used. RESULTS: Ninety thousand eight hundred one days of data were analysed. Sleep was negatively associated with minimum temperature (-12 min/day change across minimum temperature range of 31.2 °C, p = 0.001). Sedentary behaviour was positively associated with minimum temperature (+ 12 min/day, range = 31.2 oC, p = 0.006) and wind speed (+ 10 min/day, range = 36.7 km/h, p< 0.001), and negatively associated with sunshine (-17 min/day, range = 13.9 h, p < 0.001). LPA was positively associated with minimum temperature (+ 11 min/day, range = 31.2 °C, p = 0.002), cloud cover (+ 4 min/day, range = 8 eighths, p = 0.008) and sunshine (+ 17 min/day, range = 13.9 h, p < 0.001), and negatively associated with wind speed (-8 min/day, range = 36.7 km/h, p < 0.001). MVPA was positively associated with sunshine (+ 3 min/day, range = 13.9 h, p < 0.001) and negatively associated with minimum temperature (-13 min/day, range = 31.2 oC, p < 0.001), rainfall (-3 min/day, range = 33.2 mm, p = 0.006) and wind speed (-4 min/day, range = 36.7 km/h, p < 0.001). For maximum temperature, a significant (p < 0.05) curvilinear association was observed with sleep (half-U) and physical activity (inverted-U), where the decrease in sleep duration appeared to slow around 23 °C, LPA peaked at 31 oC and MVPA at 27 °C. CONCLUSIONS: Generally, adults tended to be less active and more sedentary during extremes of weather and sleep less as temperatures rise. These findings have the potential to inform the timing and content of positive movement behaviour messaging and interventions. TRIAL REGISTRATION: The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

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.

The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface.

BACKGROUND: How time is allocated influences health. However, any increase in time allocated to one behaviour must be offset by a decrease in others. Recently, studies have used compositional data analysis (CoDA) to estimate the associations with health when reallocating time between different behaviours. The aim of this scoping review was to provide an overview of studies that have used CoDA to model how reallocating time between different time-use components is associated with health. METHODS: A systematic search of four electronic databases (MEDLINE, Embase, Scopus, SPORTDiscus) was conducted in October 2022. Studies were eligible if they used CoDA to examine the associations of time reallocations and health. Reallocations were considered between movement behaviours (sedentary behaviour (SB), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA)) or various activities of daily living (screen time, work, household chores etc.). The review considered all populations, including clinical populations, as well as all health-related outcomes. RESULTS: One hundred and three studies were included. Adiposity was the most commonly studied health outcome (n = 41). Most studies (n = 75) reported reallocations amongst daily sleep, SB, LPA and MVPA. While other studies reported reallocations amongst sub-compositions of these (work MVPA vs. leisure MVPA), activity types determined by recall (screen time, household chores, passive transport etc.) or bouted behaviours (short vs. long bouts of SB). In general, when considering cross-sectional results, reallocating time to MVPA from any behaviour(s) was favourably associated with health and reallocating time away from MVPA to any behaviour(s) was unfavourably associated with health. Some beneficial associations were seen when reallocating time from SB to both LPA and sleep; however, the strength of the association was much lower than for any reallocations involving MVPA. However, there were many null findings. Notably, most of the longitudinal studies found no associations between reallocations of time and health. Some evidence also suggested the context of behaviours was important, with reallocations of leisure time toward MVPA having a stronger favourable association for health than reallocating work time towards MVPA. CONCLUSIONS: Evidence suggests that reallocating time towards MVPA from any behaviour(s) has the strongest favourable association with health, and reallocating time away from MVPA toward any behaviour(s) has the strongest unfavourable association with health. Future studies should use longitudinal and experimental study designs, and for a wider range of outcomes.

Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions.

OBJECTIVE: Physical inactivity is a leading modifiable cause of death and disease worldwide. Population-based interventions to increase physical activity are needed. Existing automated expert systems (e.g., computer-tailored interventions) have significant limitations that result in low long-term effectiveness. Therefore, innovative approaches are needed. This special communication aims to describe and discuss a novel mHealth intervention approach that proactively offers participants with hyper-personalised intervention content adjusted in real-time. METHODS: Using machine learning approaches, we propose a novel physical activity intervention approach that can learn and adapt in real-time to achieve high levels of personalisation and user engagement, underpinned by a likeable digital assistant. It will consist of three major components: (1) conversations: to increase user's knowledge on a wide range of activity-related topics underpinned by Natural Language Processing; (2) nudge engine: to provide users with hyper-personalised cues to action underpinned by reinforcement learning (i.e., contextual bandit) and integrating real-time data from activity tracking, GPS, GIS, weather, and user provided data; (3) Q&A: to facilitate users asking any physical activity related questions underpinned by generative AI (e.g., ChatGPT, Bard) for content generation. RESULTS: The detailed concept of the proposed physical activity intervention platform demonstrates the practical application of a just-in-time adaptive intervention applying various machine learning techniques to deliver a hyper-personalised physical activity intervention in an engaging way. Compared to traditional interventions, the novel platform is expected to show potential for increased user engagement and long-term effectiveness due to: (1) using new variables to personalise content (e.g., GPS, weather), (2) providing behavioural support at the right time in real-time, (3) implementing an engaging digital assistant and (4) improving the relevance of content through applying machine learning algorithms. CONCLUSION: The use of machine learning is on the rise in every aspect of today's society, however few attempts have been undertaken to harness its potential to achieve health behaviour change. By sharing our intervention concept, we contribute to the ongoing dialogue on creating effective methods for promoting health and well-being in the informatics research community. Future research should focus on refining these techniques and evaluating their effectiveness in controlled and real-world circumstances.

A Digital Lifestyle Program for Psychological Distress, Wellbeing and Return-to-Work: A Proof-of-Concept Study.

OBJECTIVE: To demonstrate proof-of-concept for a chatbot-led digital lifestyle medicine program in aiding rehabilitation for return-to-work. DESIGN: Retrospective cohort study with pre-post measures. SETTING: Community setting, Australia. PARTICIPANTS: 78 adult participants (mean age 46 years, 32% female) with an active workers' compensation claim (N=78). INTERVENTIONS: A 6-week digital lifestyle medicine program led by an artificially intelligent virtual health coach and weekly telehealth calls with a health coach. MAIN OUTCOME MEASURES: Adherence (% program completions) and engagement (% of daily and weekly sessions completed), changes in depression, anxiety and distress (K10), psychological wellbeing (WHO-5), return-to-work confidence and anxiety and change in work status. RESULTS: Sixty participants completed the program (72%), with improvements in psychological distress (P≤.001, r=.47), depression (P<.001, r=.55), anxiety (P<.001, r=.46) and wellbeing (P<.001, r=.62) were noted, as well as increased confidence about returning to work (P≤.001, r=.51) and improved work status (P≤.001). Anxiety about returning to work remained unchanged. Participants completed an average of 73% of daily virtual coach sessions and 95% of telehealth coaching sessions. CONCLUSIONS: Artificial intelligence technology may be able to provide a practical, supportive, and low-cost intervention to improve psychosocial outcomes among individuals on an active workers' compensation claim. Further, controlled research is needed to confirm these findings.

Hashtag fitspiration: credibility screening and content analysis of Instagram fitness accounts.

BACKGROUND: Fitspiration is a social media phenomenon purported to inspire viewers to lead healthier lifestyles but can result in negative psychological outcomes such as body dissatisfaction. This study aimed to develop a tool to audit Instagram fitspiration accounts and screen for content that could have potentially negative psychological effects. METHODS: This study developed and implemented an audit tool to (1) identify credible fitspiration accounts (i.e., accounts that do not portray potentially harmful or unhealthy content) and (2) describe the content of identified accounts. The most recent 15 posts of 100 leading Instagram fitspiration accounts were audited. Accounts were deemed non-credible and were excluded if they contained fewer than four fitness-related posts or portrayed nudity or inappropriate clothing, sexualisation or objectification, extreme body types, "thinspiration", or negative messages. RESULTS: Many accounts contained fewer than four fitness-related posts (n = 41), sexualisation or objectification (n = 26), nudity or inappropriate clothing (n = 22), and/or extreme body types (n = 15). Three accounts failed on all four criteria, while 13, 10 and 33 failed on three, two, or one criterion, respectively. Therefore, only 41% of accounts were considered credible. Inter-rater reliability (percentage agreement and Brennan and Prediger's coefficient κq) was high (Stage 1: 92% agreement [95% CI 87, 97], κq 0.84 [95% CI 0.73, 0.95]; Stage 2: 93% agreement [95% CI 83, 100], κq 0.85 [95% CI 0.67, 1.00]). Account holders of credible fitspiration accounts were predominantly female (59%), aged 25-34 (54%), Caucasian (62%), and from the United States (79%). Half held a qualification related to physical activity or physical health (e.g., personal trainer, physiotherapy; 54%). Most included accounts included an exercise video (93%) and example workout (76%). CONCLUSION: While many popular Instagram fitspiration accounts offered credible content such as example workouts, many accounts contained sexualisation, objectification or promotion of unhealthy or unrealistic body shapes. The audit tool could be used by Instagram users to ensure the accounts they follow do not portray potentially harmful or unhealthy content. Future research could use the audit tool to identify credible fitspiration accounts and examine whether exposure to these accounts positively influences physical activity.

A needs assessment for self-management services for adults awaiting community-based mental health services.

BACKGROUND: High demand for services has resulted in lengthy waiting times being experienced across mental health services, both across Australia and internationally. Timely access to services is necessary to optimise the effectiveness of treatment, and prevent further mental health decline, risk of suicidality and hospitalisation for clients waiting for services to commence. The present study aims to better understand the experiences of individuals who are waiting for ongoing mental health services to commence and their preferences for additional support whilst on the waitlist, as a means to recommend alternative supports. METHODS: A link to the cross-sectional, anonymous survey was sent via text message to 2,147 clients of a mental health service, with a reminder text message sent approximately one week subsequent to those who did not opt out of the communication. Eligibility criteria included having been a client of the service in the previous 12 months, having spent time on the waiting list, being aged 16 or over and having sufficient English proficiency. RESULTS: A total of 334 participants responded to the needs assessment survey, 277 (82.9%) of which resided in the metropolitan region and 57 (17.1%) residing in the country region. Of the respondents, the majority presented with generalised anxiety/panic attacks (n = 205, 61.4%), followed by life stressors (e.g., financial concerns, relationships, n = 196, 58.7%) and lack of motivation/loss of interest (n = 196, 58.7%). Most respondents (52.7%) waited 4-12 months for ongoing services to commence and almost half (47%) reported that their mental health deteriorated during this time. Of the additional support options, most participants expressed interest in additional mental health supports (78.4%, n = 262), such as telephone support and access to online materials. There was significant interest in other supports such as exercise support (57.4%, n = 192), sleep education (56.6%, n = 190) and healthy eating support (41%, n = 137). CONCLUSION: Mental health services are experiencing significant waiting times, increasing the risk of mental health deterioration for persons waiting for services to commence. However, the findings demonstrate that there is interest for alternative support options, such as lifestyle interventions, in the interim. Desire for lifestyle support services, particularly in-person exercise programs and self-directed sleep, was especially high amongst the population of respondents within this study. Future work to rigorously develop and evaluate such lifestyle support services for mental health clients is warranted.

Association between the use of weight management strategies and weight change among Australian adults over 12 months: an observational study.

BACKGROUND: Obesity is a growing, global public health issue. This study aimed to describe the weight management strategies used by a sample of Australian adults; examine the socio-demographic characteristics of using each strategy; and examine whether use of each strategy was associated with 12-month weight change. METHODS: This observational study involved a community-based sample of 375 healthy adults (mean age: 40.1 ± 5.8 years, 56.8% female). Participants wore a Fitbit activity monitor, weighed themselves daily, and completed eight online surveys on socio-demographic characteristics. Participants also recalled their use of weight management strategies over the past month, at 8 timepoints during the 12-month study period. RESULTS: Most participants (81%) reported using at least one weight management strategy, with exercise/physical activity being the most common strategy at each timepoint (40-54%). Those who accepted their current bodyweight were less likely to use at least one weight management strategy (Odds ratio = 0.38, 95% CI = 0.22-0.64, p < 0.01) and those who reported being physically active for weight maintenance had a greater reduction in bodyweight, than those who did not (between group difference: -1.2 kg, p < 0.01). The use of supplements and fasting were associated with poorer mental health and quality of life outcomes (p < 0.01). CONCLUSIONS: The use of weight management strategies appears to be common. Being physically active was associated with greater weight loss. Individuals who accepted their current body weight were less likely to use weight management strategies. Fasting and the use of supplements were associated with poorer mental health. Promoting physical activity as a weight management strategy appears important, particularly considering its multiple health benefits.

Changes in fitness and fatness in Australian schoolchildren during the summer holidays: fitness lost, fatness regained? A cohort study.

BACKGROUND: Emerging evidence suggests that children's fatness increases and fitness declines at a greater rate during the summer holiday period, compared with the school year. The aim of this study was to compare rates of change in fitness and fatness over the in-term and summer holiday periods among Australian schoolchildren. A secondary aim was to explore whether rates of change differed according to the child's sex, socio-economic status (SES), pubertal status and weight status. METHODS: Children (n = 381) initially in Grade 4 (age 9) were recruited for this 2-year longitudinal study. Fatness (% body fat, BMI z-score, waist-to-height ratio) and fitness (20-m shuttle run and standing broad jump) were measured at the start and end of two consecutive years. Rates of change were calculated for the two in-school periods (Grades 4 and 5) and for the summer holiday period. Rates of change in fatness and fitness between in-school and holiday periods were compared, and differences in rates of change according to sex, socio-economic status, and weight status were explored. RESULTS: During the holidays, percentage body fat increased at a greater rate (annualised rate of change [RoC]: +3.9 vs. Grade 4 and + 4.7 vs. Grade 5), and aerobic fitness declined at a greater rate (RoC - 4.7 vs. Grade 4 and - 4.4 vs. Grade 5), than during the in-school periods. There were no differences in rates of change for BMI z-score, waist-to-height ratio or standing broad jump. Body fatness increased faster in the holidays (relative to the in-school period) in children who are overweight and from low-SES families. Aerobic fitness declined more rapidly in the holidays in children who are overweight. CONCLUSION: This study highlights that during the summer holiday period, children experience greater increases in fatness and declines in fitness, with children who live with low-SES families and are overweight being more affected. The findings suggest the need for targeted interventions during this period to address these negative health trends. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry, identifier ACTRN12618002008202. Retrospectively registered on 14 December 2018.

Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews.

OBJECTIVE: To synthesise the evidence on the effects of physical activity on symptoms of depression, anxiety and psychological distress in adult populations. DESIGN: Umbrella review. DATA SOURCES: Twelve electronic databases were searched for eligible studies published from inception to 1 January 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Systematic reviews with meta-analyses of randomised controlled trials designed to increase physical activity in an adult population and that assessed depression, anxiety or psychological distress were eligible. Study selection was undertaken in duplicate by two independent reviewers. RESULTS: Ninety-seven reviews (1039 trials and 128 119 participants) were included. Populations included healthy adults, people with mental health disorders and people with various chronic diseases. Most reviews (n=77) had a critically low A MeaSurement Tool to Assess systematic Reviews score. Physical activity had medium effects on depression (median effect size=-0.43, IQR=-0.66 to -0.27), anxiety (median effect size=-0.42, IQR=-0.66 to -0.26) and psychological distress (effect size=-0.60, 95% CI -0.78 to -0.42), compared with usual care across all populations. The largest benefits were seen in people with depression, HIV and kidney disease, in pregnant and postpartum women, and in healthy individuals. Higher intensity physical activity was associated with greater improvements in symptoms. Effectiveness of physical activity interventions diminished with longer duration interventions. CONCLUSION AND RELEVANCE: Physical activity is highly beneficial for improving symptoms of depression, anxiety and distress across a wide range of adult populations, including the general population, people with diagnosed mental health disorders and people with chronic disease. Physical activity should be a mainstay approach in the management of depression, anxiety and psychological distress. PROSPERO REGISTRATION NUMBER: CRD42021292710.

The feasibility of collecting the physiotherapy outcomes airway clearance, physical activity and fitness for the Australian Cystic Fibrosis Data Registry.

BACKGROUND: Physiotherapy-related data, such as airway clearance techniques (ACTS), physical activity and aerobic fitness are not consistently included in international cystic fibrosis (CF) data registries. This study aimed to pilot the collection of ACTS, physical activity and fitness in a hospital CF clinic, as a step towards informing future national implementation. METHODS: This study was undertaken in a CF clinic within a major tertiary hospital. Patients and families were invited to participate. Participants completed self-report questionnaires on ACT use and those aged ≥ 10 years completed a physical activity questionnaire (Core Indicators and Measures of Youth Health Survey) and aerobic fitness test (the A-STEP test). Participants also completed a survey to explore the tolerance and acceptability of the fitness test, and the perceived accuracy of the self-reported data collection. RESULTS: Forty patients agreed to participate in the study (mean age = 9.8, SD = 4.1 years old; 52.5% female). All patients and/or families that were approached agreed to participate and completion rate for the ACTs and physical activity surveys was 98% and 100% (respectively). Completion rate for the fitness test was 55%, due to time constraints. Most participants agreed (≥ 90%) they could accurately provide ACT and physical activity data, and the assessments were tolerable and acceptable. CONCLUSIONS: Patients with CF and their families are able to and can acceptably provide physiotherapy-related data, and collecting self-report ACTs and physical activity data is highly feasibly during routine CF clinic visits. However, aerobic fitness testing using the A-STEP test may be less feasible in clinic environments, due to time constraints.

Physical activity and screen time in outside school hours care services across Australia: current versus best practice.

BACKGROUND: Outside school hours care (OSHC) is accessed by millions of children internationally. Recently, physical activity and screen time guidelines in OSHC were developed. This study described the current physical activity and screen time scheduling in Australian OSHC, obtained sector feedback on the guidelines and compared current- with best-practice. METHODS: A cross-sectional online survey was administered to n = 3551 Australian OSHC directors. Participants reported scheduling for physical activity and screen time opportunities in before- and after-school care. Feedback was sought on the new guidelines, including barriers and enablers for implementation. Scheduling data were used to evaluate whether services were currently meeting the new guidelines; that is if time allocated matched with time recommended. RESULTS: Five hundred and sixty-six directors participated (response rate 16%). Physical activity and screen time practices varied widely (e.g., after-school physical activity opportunity ranged from 15 to 150 min, mean 74, SD 28; after-school screen time opportunity ranged from 15 to 195 min, mean 89, SD 43), with state (p = 0.002) and socioeconomic (based on postcode; p < 0.001) differences. Most participants (54-81%) agreed that the guideline's recommended physical activity and screen time durations were appropriate, however, only 40% of participants' OSHC services' programs actually met the guidelines. CONCLUSIONS: Physical activity and screen time scheduling in OSHC is highly variable. Despite support for the guidelines, current scheduling practice in the majority of OSHC services surveyed do not meet best practice guidelines.

The relationships between school children's wellbeing, socio-economic disadvantage and after-school activities: a cross-sectional study.

BACKGROUND: Lower socioeconomic status is associated with poorer wellbeing among children. Identifying how children participate in after-school activities and how after-school activities are associated with wellbeing may inform interventions to improve wellbeing among children from low socioeconomic backgrounds. This study explored whether children's after-school activities varied by socioeconomic status and examined the associations between after-school activities and wellbeing in low socioeconomic status children. METHODS: This study analysed cross-sectional data from 61,759 school students in years 4 to 9 who completed the 2018 South Australian Wellbeing and Engagement Collection. Students reported the number of days per week they participated in 12 activities (after-school care, homework, music lessons or practice, youth organisations, sports, television, videogames, social media, reading, chores, arts and crafts, and socialising with friends) during the after-school period (3-6 pm) and their wellbeing (happiness, sadness, worry, engagement, perseverance, optimism, emotion regulation, and life satisfaction). Socioeconomic status was measured by parents' highest education level obtained from school enrolment data. Linear multilevel models were used to examine whether frequency of after-school activities varied by socioeconomic status. Multilevel ordered logit models were used to analyse the association between after-school activities and wellbeing amongst participants in the low socioeconomic status category. RESULTS: After-school activities differed according to socioeconomic status; high socioeconomic status children did more frequent sport, homework, and reading and low socioeconomic status children did more frequent screen-based activities (TV, videogames and social media). Among children from low socioeconomic status backgrounds, higher wellbeing was associated most consistently with more frequent sports participation, homework, reading and spending time with friends and less frequent videogames, social media and after-school care. CONCLUSIONS: Children's wellbeing is positively associated with socioeconomic status. Amongst children from disadvantaged backgrounds, participating in sport, spending time with friends and getting less screen time may be protective for wellbeing. The results suggest that programming targeted at increasing sports participation and reducing screen time amongst children from low socioeconomic status backgrounds may support their wellbeing.

Development of Australian physical activity and screen time guidelines for outside school hours care: an international Delphi study.

BACKGROUND: Children's activity patterns in the periods before and after school make a key contribution to achieving 24-h movement guidelines. There are currently no national-level guidelines informing physical activity and screen time practices in Outside School Hours Care (OSHC) programs anywhere in the world. This study aimed to work with industry, government and academic stakeholders to develop draft physical activity and screen time guidelines for use in Australian OSHC. METHODS: A 4-round online Delphi survey was conducted from May 2019 to January 2020. The Delphi participants included national and international experts and stakeholders from academia, education, government, health and the OSHC sectors. Round 1 consisted of open-ended questions exploring physical activity, screen time and sedentary behaviour in various periods of OSHC (before school, after school and vacation care). In rounds 2 and 3, participants rated the importance of items generated from the first round for inclusion in national guidelines using a Likert scale (1-9). Consensus was defined a priori as ≥80% of respondents rating an item as "critically important" (score 7-9). Between rounds 3 and 4, the guideline development panel used the consensus items, systematic review evidence, and followed the GRADE process, to draft the guidelines. In round 4, participants were invited to provide feedback on the draft guidelines and comment on barriers and enablers to implementation. RESULTS: Sixty-seven stakeholders agreed to participate, with response rates 61, 81, 54 and 72% for the four rounds respectively. Of the 123 items generated across the three rounds, 48 statements achieved consensus agreement as critically important for inclusion in the guidelines. These included offering a variety of physical activities (free play, playground and equipment) and restriction of screen time. The final round provided feedback on the draft guidelines. The wording of the guidelines was found to be appropriate and preliminary enablers and barriers to implementation were identified. CONCLUSIONS: This world-first expert and stakeholder consultation has underpinned the development of the draft Australian guidelines for physical activity and screen time in OSHC. Ongoing work is needed to further refine the guidelines, determine current rates of compliance with the guidelines and implement the guidelines into practice.

Interventions in outside-school hours childcare settings for promoting physical activity amongst schoolchildren aged 4 to 12 years.

BACKGROUND: Insufficient physical activity is one of four primary risk factors for non-communicable diseases such as stroke, heart disease, type 2 diabetes, cancer and chronic lung disease. As few as one in five children aged 5 to 17 years have the physical activity recommended for health benefits. The outside-school hours period contributes around 30% of children's daily physical activity and presents a key opportunity for children to increase their physical activity. Testing the effects of interventions in outside-school hours childcare settings is required to assess the potential to increase physical activity and reduce disease burden. OBJECTIVES: To assess the effectiveness, cost-effectiveness and associated adverse events of interventions designed to increase physical activity in children aged 4 to 12 years in outside-school hours childcare settings. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, ERIC and SportsDISCUS to identify eligible trials on 18 August 2020. We searched two databases, three trial registries, reference lists of included trials and handsearched two physical activity journals in August 2020. We contacted first and senior authors on articles identified for inclusion for ongoing or unpublished potentially relevant trials in August 2020. SELECTION CRITERIA: We included randomised controlled trials, including cluster-randomised controlled trials, of any intervention primarily aimed at increasing physical activity in children aged 4 to 12 years in outside-school hours childcare settings compared to usual care. To be eligible, the interventions must have been delivered in the context of an existing outside-school hours childcare setting (i.e. childcare that was available consistently throughout the school week/year), and not set up in the after-school period for the purpose of research. Two review authors independently screened titles and abstracts of identified papers with discrepancies resolved via a consensus discussion. A third review author was not required to resolve disagreements. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed the risk of bias of included trials with discrepancies resolved via a consensus discussion; a third review author was not required to resolve disagreements. For continuous measures of physical activity, we reported the mean difference (MD) with 95% confidence intervals (CIs) in random-effects models using the generic inverse variance method for each outcome. For continuous measures, when studies used different scales to measure the same outcome, we used standardised mean differences (SMDs). We conducted assessments of risk of bias of all outcomes and evaluated the certainty of evidence (GRADE approach) using standard Cochrane procedures. MAIN RESULTS: We included nine trials with 4458 participants. Five trials examined the effectiveness of staff-based interventions to change practice in the outside-school hours childcare setting (e.g. change in programming, activities offered by staff, staff facilitation/training). Two trials examined the effectiveness of staff- and parent-based interventions (e.g. parent newsletters/telephone calls/messages or parent tool-kits in addition to staff-based interventions), one trial assessed staff- and child-based intervention (e.g. children had home activities to emphasise physical activity education learnt during outside-school hours childcare sessions in addition to staff-based interventions) and one trial assessed child-only based intervention (i.e. only children were targeted).  We judged two trials as free from high risk of bias across all domains. Of those studies at high risk of bias, it was across domains of randomisation process, missing outcome data and measurement of the outcome. There was low-certainty evidence that physical activity interventions may have little to no effect on total daily moderate-to-vigorous physical activity compared to no intervention (MD 1.7 minutes, 95% CI -0.42 to 3.82; P = 0.12; 6 trials; 3042 children). We were unable to pool data on proportion of the OSHC session spent in moderate-to-vigorous physical activity in a meta-analysis. Both trials showed an increase in proportion of session spent in moderate-to-vigorous physical activity (moderate-certainty evidence) from 4% to 7.3% of session time; however, only one trial was statistically significant. There was low-certainty evidence that physical activity interventions may lead to little to no reduction in body mass index (BMI) as a measure of cardiovascular health, compared to no intervention (SMD -0.17, 95% CI -0.44 to 0.10; P = 0.22; 4 trials, 1684 children). Physical activity interventions that were delivered online were more cost-effective than in person. Combined results suggest that staff-and-parent and staff-and-child-based interventions may lead to a small increase in overall daily physical activity and a small reduction or no difference in BMI. Process evaluation was assessed differently by four of the included studies, with two studies reporting improvements in physical activity practices, one reporting high programme satisfaction and one high programme fidelity. The certainty of the evidence for these outcomes was low to moderate. Finally, there was very low-certainty evidence that physical activity interventions in outside-school hours childcare settings may increase cardiovascular fitness. No trials reported on quality of life or adverse outcomes. Trials reported funding from local government health grants or charitable funds; no trials reported industry funding. AUTHORS' CONCLUSIONS: Although the review included nine trials, the evidence for how to increase children's physical activity in outside-school hours care settings remains limited, both in terms of certainty of evidence and magnitude of the effect. Of the types of interventions identified, when assessed using GRADE there was low-certainty evidence that multi-component interventions, with a specific physical activity goal may have a small increase in daily moderate-to-vigorous physical activity and a slight reduction in BMI. There was very low-certainty evidence that interventions increase cardiovascular fitness. By contrast there was moderate-certainty evidence that interventions were effective for increasing proportion of time spent in moderate-to-vigorous physical activity, and online training is cost-effective.

Should the physiotherapy outcomes airway clearance, physical activity and fitness be recorded on the Australian Cystic Fibrosis Data Registry? A consensus approach.

BACKGROUND: Physiotherapy is a cornerstone of cystic fibrosis (CF) management, yet the Australian CF Data Registry (ACFDR) currently does not record physiotherapy-related data. This study aimed to gather opinions from lead Australian CF physiotherapists regarding the importance and feasibility of collecting physiotherapy-related data on the ACFDR. METHODS: A three-round online Delphi survey was conducted to gather expert stakeholder opinion and consensus agreement. Lead physiotherapists from all 23 Australian CF centres were invited to participate. Round one explored the potential benefits, barriers and importance of recording three physiotherapy-related domains on the ACFDR: airway clearance, physical activity and fitness. Subsequent rounds were developed based on the findings from the previous round and sought consensus (80% agreement) for the inclusion of physiotherapy-related data on the ACFDR and for the most appropriate methods of collecting such data. RESULTS: The response rate was > 80% for all rounds. Participants agreed that collection of airway clearance, physical activity and fitness data on the ACFDR was important and feasible. Findings suggested that airway clearance and physical activity should be collected using self-reported questionnaires, while fitness should be measured using a field-based test. CONCLUSIONS: Australian lead CF physiotherapists believe that collection of airway clearance, physical activity and fitness on the ACFDR is important and feasible. Future work is needed to pilot the data collection procedure to examine its feasibility in real-world clinical settings. This study demonstrates how Delphi methodology can provide a contemporary summary of expert clinicians' opinion that may underpin nation-wide health service improvement.