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

ABSTRACT: 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 is crucial to its success.

Children's Health, Wellbeing and Academic Outcomes over the Summer Holidays: A Scoping Review.

BACKGROUND: The school day provides a supportive and stimulating environment that may protect children and adolescents (5-18 years) from behaviours that are adverse for health and wellbeing. OBJECTIVE: To review the literature regarding changes in children's academic achievement or overall wellbeing during the extended school summer break and evaluate if the outcomes are different for children experiencing disadvantage. METHODS: The peer-reviewed literature was searched across six electronic databases for studies tracking changes in any academic, health or wellbeing outcome in children over the summer holidays. Studies were screened in duplicate for inclusion. Data were extracted using a standardized data extraction form. Outcomes were coded as decline (suggestive or significant), increase (suggestive or significant) or mixed/neutral and then compared to the school year or according to disadvantaged. RESULTS: Seventy-six studies (n = 14,230,846 participants) were included. Strong evidence was found of a decline in academic outcomes and increases in adiposity, sedentary behaviour and screen time. There was moderate evidence of declines in cardiovascular fitness and physical activity. These patterns were magnified for disadvantaged children. Limited data were available on muscular fitness, sleep, diet quality and social, emotional or mental wellbeing. A total of 80% of studies were from the United States. Most data were from children 12 years of age and younger. CONCLUSIONS: Over the summer break, children's academic and health outcomes decline. Children experiencing disadvantage display magnified losses that warrant further investigation. The summer holidays present an opportunity to improve children's health and wellbeing.

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.

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.

Time use and dimensions of healthy sleep: A cross-sectional study of Australian children and adults.

BACKGROUND: Sleep is increasingly recognized as a multidimensional construct that occurs within the 24-hour day. Despite advances in our understanding, studies continue to consider the relationship between sleep, sedentary time and physical activity separately, and not as part of the 24-hour day. AIMS: To determine the association between the 24-hour activity composition and dimensions of healthy sleep. METHODS: This study examined data on 1168 children (mean age 12years; 49% female) and 1360 adults (mean age 44years; 87% female) collected as part of the Child Health CheckPoint study. Participants were asked to wear a GENEActiv monitor (Activinsights, Cambs, UK) on their nondominant wrist for eight consecutive days to measure 24-hour time-use. Compositional data analysis was used to examine the association between time use (actigraphy-derived sleep duration, sedentary time, light physical activity and moderate-vigorous physical activity) and dimensions of healthy sleep. Healthy sleep was conceptualized in terms of continuity/efficiency, timing, alertness/sleepiness, satisfaction/quality, and regularity. Time allocations were also examined. RESULTS: The 24-hour activity composition was significantly associated with all objectively measured and self-report dimensions of healthy sleep in both children and adults. Allocating more time to sleep was associated with earlier sleep onsets, later sleep offsets, less efficient and more consistent sleep patterns for both children and adults. CONCLUSION: This study highlights the integral relationship between daily activities and dimensions of sleep. Considering sleep within the 24-hour day activity composition framework may help inform lifestyle decisions to improve sleep health.

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.

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.

The Association Between Socioeconomic Status and Use of Time in Australian Children and Adolescents.

PURPOSE: There are well-known socioeconomic status (SES) gradients in children and adolescents' health which may be associated with time use. Our aim was to evaluate the association between Australian children's 24-hour time use and SES using four separate surveys from 2005 to 2021. METHODS: Time use was assessed in 4526 8-19-year-olds from the 2005 Health of Young Victorians, 2007 National Children's Nutrition and Physical Activity, 2015 Child Health CheckPoint, and 2019-21 Life on Holidays study. Each survey used the same reliable, valid, 24-hour recall instrument. SES was quantified using tertiles of household income, education, and postcode-level measures. Compositional data analysis was used to compare 24-hour time use between SES categories, adjusting for age, sex, and puberty. RESULTS: Time-use compositions differed significantly by SES in each survey. Relative to the lowest SES, children from the highest SES accumulated on average 31 min/day more School-related time, 6 min/day more Passive Transport and 6 min/day more Self-care. Conversely, they accumulated 30 min/day less Screen Time (which included computer time), 11 min/day less sleep, and spent 7 min/day less in Domestic/Social activities. There were only small differences in Quiet Time and Physical Activity. DISCUSSION: SES-related differences in time use were robust across ages 8-19, a 16-year timespan, diverse Australian geographical regions, and using different SES metrics. The exchange of about 30 min/day between School-related activities and Screen Time amounts to >180 hours extra exposure to School-related activities annually in the highest SES category relative to the lowest, equivalent to >6 weeks of school time per year.

Uptake of a Universal Sports Subsidy Program for School-Aged Children: A Population Data Analysis.

BACKGROUND: Around 40% of Australian children do not participate in sport. Cost is a major barrier to participation, particularly for children from low socioeconomic backgrounds. This study aimed to evaluate the uptake of a population-level children's sports subsidy scheme, including sociodemographic differences in uptake. METHODS: A state-wide cross-sectional analysis comparing sports voucher claimants (primary school-aged children with a valid Medicare or Australian visa number) from the 2019 financial year with population census data from South Australia. Chi-square was used to examine whether the percentage of eligible children who claimed a voucher differed based on age, sex, socioeconomic status (SES), and geographical remoteness. Subgroup analyses were conducted for the lowest 2 socioeconomic disadvantage deciles, split by gender. Scatterplots were used to compare sports between high and low SES children. RESULTS: A total of 74,668 children claimed sports vouchers (45.5% of eligible children). Children who were relatively younger, female, from low socioeconomic backgrounds, and from major cities were least likely to claim the voucher. The 5 most common sports were Australian rules football (30.2%), netball (13.6%), soccer (13.1%), gymnastics (10.4%), and basketball (5.7%), with the popular sports similar for high and low SES children. CONCLUSIONS: Future work is needed to understand how Sports Voucher, and sport participation rates have changed over time, and to improve voucher uptake among girls, city dwellers, and low SES children.

Effects of an mHealth physical activity intervention to prevent osteoporosis in premenopausal women. A randomized controlled trial.

OBJECTIVES: It is critical to develop prevention strategies for osteoporosis that reduce the burden fractures place on individuals and health systems. This study evaluated the effects of an mHealth intervention that delivered and monitored a non-supervised exercise program on bone mineral density (BMD). DESIGN: Randomized controlled trial. METHODS: 60 premenopausal women aged 35-50 years were divided into an intervention group (IG) and a control group (CG). The IG followed a 6-month intervention aimed at increasing osteogenic physical activity, guided by two daily goals: walking at least 10,000 steps and completing 60 impacts over 4 g of acceleration. These goals were monitored using a wearable accelerometer linked to an mHealth app. The CG maintained their regular lifestyle and wore the accelerometer without feedback. BMD was estimated using dual-energy X-ray absorptiometry (DXA) scans at baseline and after 6 months, with group-by-time analyses conducted using ANCOVA. The intervention's impact on physical fitness and activity habits was also evaluated. RESULTS: 46 participants completed the study (IG = 24; CG = 22). The IG showed significant improvements compared to the CG in femoral neck BMD (IG: +0.003 ±â€¯0.029 g/cm2 vs CG: -0.027 ±â€¯0.031 g/cm2), trochanter BMD (IG: +0.004 ±â€¯0.023 g/cm2 vs CG: -0.026 ±â€¯0.030 g/cm2), and total hip BMD (IG: +0.006 ±â€¯0.043 g/cm2 vs CG: -0.040 ±â€¯0.048 g/cm2). The IG also demonstrated significant improvements in physical fitness measures, including peak torque and power at various speeds and positions. No adverse events related to the intervention were reported. CONCLUSIONS: This non-supervised physical activity intervention delivered by wearable-technology and an mHealth app was effective in improving BMD, suggesting its potential for osteoporosis prevention.

Trends in Well-Being Among Youth in Australia, 2017-2022.

Importance: Little is known about temporal trends in children's well-being and how the COVID-19 pandemic may have influenced the well-being of young Australians. Certain demographic groups may be more vulnerable to experiencing declines in well-being. Objective: To examine well-being trends over 6 consecutive years among South Australian students and explore the influence of sociodemographic characteristics. Design, Setting, and Participants: Longitudinal analyses of annual (2017 to 2022) cross-sectional data of students in grades 4 through 9 (n = 40 392 to 56 897 per year) attending South Australian government schools from the Well-being and Engagement Collection (WEC) census. Exposures: Calendar year (2017-2022) and sociodemographic characteristics (sex, school grade, parental education, language spoken at home, residential region) from school enrollment records. Main Outcomes and Measures: Students self-reported life satisfaction, optimism, happiness, cognitive engagement, emotional regulation, perseverance, worry, and sadness. Results: Over 6 years (2017 to 2022), a total of 119 033 students (mean [SD] age, 12.1 y; 51.4% male) participated in this study. Most well-being measures declined over time, with consistent worsening of well-being from 2020 onward. For example, compared with 2017, sadness was 0.26 (95% CI, 0.25-0.27) points higher in 2020 (standardized mean difference [SMD], 0.27) and remained elevated by more than 0.26 points (SMD, 0.27) in 2021 and 2022. At almost every time point, greatest well-being was reported by students of male sex (except cognitive engagement and perseverance), in earlier school grades, with highest parental education, speaking a language other than English at home, and residing in outer regional and remote settings (for satisfaction, optimism, and emotional regulation). Sociodemographic differences in well-being were generally consistent over time; however, sex differences widened from 2020 for all indicators except cognitive engagement and perseverance. For example, between 2017 and 2022, sadness increased by 0.27 (95% CI, 0.25-0.29) more points among females than males (SMD, 0.28). Conclusions and Relevance: In this longitudinal analysis of annual census data, there were downward trends in students' well-being, especially since 2020. The largest sociodemographic disparities were observed for students of female sex, those in later school grades, and those with lowest parental education. Urgent and equitable support for the well-being of all young people, particularly those facing disparity, is imperative.

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.

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.

Weekly, Seasonal, and Festive Period Weight Gain Among Australian Adults.

Importance: Obesity is a major global health concern. A better understanding of temporal patterns of weight gain will enable the design and implementation of interventions with potential to alter obesity trajectories. Objective: To describe changes in daily weight across 12 months among Australian adults. Design, Setting, and Participants: This cohort study conducted between December 1, 2019, and December 31, 2021 in Adelaide, South Australia, involved 375 community-dwelling adults aged 18 to 65 years. Participants wore a fitness tracker and were encouraged to weigh themselves, preferably daily but at least weekly, using a body weight scale. Data were remotely gathered using custom-developed software. Exposure: Time assessed weekly, seasonally, and at Christmas/New Year and Easter. Main Outcomes and Measures: Data were visually inspected to assess the overall yearly pattern in weight change. Data were detrended (to remove systematic bias from intraindividual gradual increases or decreases in weight) by calculating a line of best fit for each individual's annual weight change relative to baseline and subtracting this from each participant's weight data. Multilevel mixed-effects linear regression analysis was used to compare weight across days of the week and seasons and at Christmas/New Year and Easter. Results: Of 375 participants recruited, 368 (mean [SD] age, 40.2 [5.9] years; 209 [56.8%] female; mean [SD] baseline weight, 84.0 [20.5] kg) provided at least 7 days of weight data for inclusion in analyses. Across the 12-month period, participants gained a median of 0.26% body weight (218 g) (range, -29.4% to 24.0%). Weight fluctuated by approximately 0.3% (252 g) each week, with Mondays and Tuesdays being the heaviest days of the week. Relative to Monday, participants' weight gradually decreased from Tuesday, although not significantly so (mean [SE] weight change, 0.01% [0.03%]; P = .83), to Friday (mean [SE] weight change, -0.18% [0.03%]; P < .001) and increased across the weekend to Monday (mean [SE] weight change for Saturday, -0.16% [0.03%]; P < .001; mean [SE] weight change for Sunday, -0.10% [0.03%]; P < .001). Participants' weight increased sharply at Christmas/New Year (mean [SE] increase, 0.65% [0.03%]; z score, 25.30; P < .001) and Easter (mean [SE] weight change, 0.29% [0.02%], z score, 11.51; P < .001). Overall, participants were heaviest in summer (significantly heavier than in all other seasons), were lightest in autumn (mean [SE] weight change relative to summer, -0.47% [0.07%]; P < .001), regained some weight in winter (mean [SE] weight change relative to summer, -0.23% [0.07%]; P = .001), and became lighter in spring (mean [SE] weight change relative to summer, -0.27% [0.07%]; P < .001). Conclusions and Relevance: In this cohort study of Australian adults with weekly and yearly patterns in weight gain observed across 12 months, high-risk times for weight gain were Christmas/New Year, weekends, and winter, suggesting that temporally targeted weight gain prevention interventions may be warranted.

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.

Evaluation of a supportive care app for coordinating caring networks: an analysis of the first 19,000 users.

Background: Major illnesses such as cancer, and other traumatic life events, can lead to sudden increases in supportive care needs. This study aimed to describe engagement, acceptability and satisfaction with a supportive care networking app under real-world conditions. Methods: A total of 10,952 individuals used the app during the study period (2018-2022). The app is designed to enable "captains" to assemble a network of friends and family members to provide timely, and individually tailored, supportive care (including assistance with tasks such as taking children to school, cooking meals, grocery shopping, and transport to appointments). Engagement was determined from server data, whilst acceptability and satisfaction were captured using purposed-designed surveys. Results: Users were mostly female (76%) and aged between 30 and 49 years (61%). The most common reason for using the app was sudden illness (web: 81%; mobile: 64%). An average of 42 tasks were requested per network, with a 32% acceptance rate. Significantly more tasks were requested (web: 52.2 tasks per network; mobile: 31.7 tasks per network; p < 0.001) and accepted (web: 43.2%; mobile: 20.2%; p < 0.001) in the web app vs. the mobile app. Task requests in the web app most commonly related to food (43% of requested tasks), social (15% of requested tasks) and children (13% of requested tasks). The task acceptance rate differed by task categories (p < 0.001), with tasks relating to transport, medical appointments and children accepted at the highest rates (56%, 52% and 49%, respectively). Acceptability and satisfaction data suggested that the app was well received and overall, participants were satisfied with the app. Conclusion: Findings suggested that this support care networking app achieved widespread uptake for a wide variety of supportive care tasks. Future research focused on optimizing engagement with the mobile app and examining the effectiveness of the app for improving patient and hospital outcomes is warranted.

Interventions Using Wearable Activity Trackers to Improve Patient Physical Activity and Other Outcomes in Adults Who Are Hospitalized: A Systematic Review and Meta-analysis.

Importance: Low levels of physical activity during hospitalization are thought to contribute to a range of poor outcomes for patients. Using wearable activity trackers during hospitalization may help improve patient activity, sedentary behavior, and other outcomes. Objective: To evaluate the association of interventions that use wearable activity trackers during hospitalization with patient physical activity, sedentary behavior, clinical outcomes, and hospital efficiency outcomes. Data Sources: OVID MEDLINE, CINAHL, Embase, EmCare, PEDro, SportDiscuss, and Scopus databases were searched from inception to March 2022. The Cochrane Central Register for Controlled trials, ClinicalTrials.gov, and World Health Organization Clinical Trials Registry were also searched for registered protocols. No language restrictions were imposed. Study Selection: Randomized clinical trials and nonrandomized clinical trials of interventions that used wearable activity trackers to increase physical activity or reduce sedentary behavior in adults (aged 18 years or older) who were hospitalized were included. Data Extraction and Synthesis: Study selection, data extraction, and critical appraisal were conducted in duplicate. Data were pooled for meta-analysis using random-effects models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Main Outcomes and Measures: The primary outcomes were objectively measured physical activity or sedentary behavior. Secondary outcomes included clinical outcomes (eg, physical function, pain, mental health), and hospital efficiency outcomes (eg, length of stay, readmission). Results: Fifteen studies with a total of 1911 participants were included, representing various surgical cohorts (4 studies), stroke rehabilitation (3 studies), orthopedic rehabilitation (3 studies), mixed rehabilitation (3 studies), and mixed medical (2 studies). All studies were included in meta-analyses. There was a significant association between wearable activity tracker interventions with higher overall physical activity (standardized mean difference, 0.35; 95% CI, 0.15 to 0.54; I2 = 72%; P < .002) and less sedentary behavior (mean difference, -35.46 min/d; 95% CI, -57.43 to -13.48 min/d; I2 = 0; P = .002), and a significant association between wearable activity tracker interventions with improvements in physical function (standardized mean difference, 0.27; 95% CI, 0.08 to 0.46; I2 = 0; P = .006) compared with usual care. There was no significant association between wearable activity tracker interventions with pain, mental health, length of stay, or readmission risk. Conclusions and Relevance: In this systematic review and meta-analysis, interventions that used wearable activity trackers with patients who are hospitalized were associated with higher physical activity levels, less sedentary behavior, and better physical functioning compared with usual care.

Systematic review and meta-analysis of the effectiveness of chatbots on lifestyle behaviours.

Chatbots (also known as conversational agents and virtual assistants) offer the potential to deliver healthcare in an efficient, appealing and personalised manner. The purpose of this systematic review and meta-analysis was to evaluate the efficacy of chatbot interventions designed to improve physical activity, diet and sleep. Electronic databases were searched for randomised and non-randomised controlled trials, and pre-post trials that evaluated chatbot interventions targeting physical activity, diet and/or sleep, published before 1 September 2022. Outcomes were total physical activity, steps, moderate-to-vigorous physical activity (MVPA), fruit and vegetable consumption, sleep quality and sleep duration. Standardised mean differences (SMD) were calculated to compare intervention effects. Subgroup analyses were conducted to assess chatbot type, intervention type, duration, output and use of artificial intelligence. Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment tool. Nineteen trials were included. Sample sizes ranged between 25-958, and mean participant age ranged between 9-71 years. Most interventions (n = 15, 79%) targeted physical activity, and most trials had a low-quality rating (n = 14, 74%). Meta-analysis results showed significant effects (all p < 0.05) of chatbots for increasing total physical activity (SMD = 0.28 [95% CI = 0.16, 0.40]), daily steps (SMD = 0.28 [95% CI = 0.17, 0.39]), MVPA (SMD = 0.53 [95% CI = 0.24, 0.83]), fruit and vegetable consumption (SMD = 0.59 [95% CI = 0.25, 0.93]), sleep duration (SMD = 0.44 [95% CI = 0.32, 0.55]) and sleep quality (SMD = 0.50 [95% CI = 0.09, 0.90]). Subgroup analyses showed that text-based, and artificial intelligence chatbots were more efficacious than speech/voice chatbots for fruit and vegetable consumption, and multicomponent interventions were more efficacious than chatbot-only interventions for sleep duration and sleep quality (all p < 0.05). Findings from this systematic review and meta-analysis indicate that chatbot interventions are efficacious for increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality. Chatbot interventions were efficacious across a range of populations and age groups, with both short- and longer-term interventions, and chatbot only and multicomponent interventions being efficacious.

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.