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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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

• Work-related disability poses a significant burden on society due to the loss of workforce productivity and increased health care costs. • This proof-of-concept study suggests that a chatbot-led digital lifestyle medicine program may improve psychological and return-to-work outcomes. • Controlled studies to examine the long-term effects on psychosocial outcomes, return-to-work rates, and health care costs are warranted. To demonstrate proof-of-concept for a chatbot-led digital lifestyle medicine program in aiding rehabilitation for return-to-work. Retrospective cohort study with pre-post measures. Community setting, Australia. 78 adult participants (mean age 46 years, 32% female) with an active workers' compensation claim (N=78). A 6-week digital lifestyle medicine program led by an artificially intelligent virtual health coach and weekly telehealth calls with a health coach. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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). Randomized controlled trial. 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. 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 ± 0.029 g/cm2 vs CG: − 0.027 ± 0.031 g/cm2), trochanter BMD (IG: + 0.004 ± 0.023 g/cm2 vs CG: − 0.026 ± 0.030 g/cm2), and total hip BMD (IG: + 0.006 ± 0.043 g/cm2 vs CG: − 0.040 ± 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. 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. [ABSTRACT FROM AUTHOR]

Objective: To describe secular trends in the prevalence of overweight and obesity in Australian children in each state and territory.Design: Systematic search and numerical meta-analysis.Methods: A systematic search was conducted to identify all sources that objectively measured the height and weight of Australian children (aged 2-18 years) and had a sample size of at least 300. Raw and summary data were requested from authors and divided into age×sex×state×yearly slices to derive estimates of the prevalence of overweight and obesity. Following a double arcsine transformation to facilitate meta-analysis, all estimates were standardised for age, stratified by sex and analysed using sample-weighted non-linear regressions.Results: The systematic search identified 73 eligible sources (47 raw and 26 summary datasets), with 72.8% of data sourced from Victoria and South Australia. Prevalence trends varied from state to state, with three states or territories showing a marked plateau, two showing a decline in the more recent years and three showing continued linear increases. Tasmania and Northern Territory generally had the highest prevalence (30.2% and 24.3% overweight and obesity respectively), and the Australian Capital Territory had the lowest (12.4% overweight and obesity).Conclusions: Prevalence, as well as prevalence trends, varied amongst Australian states and territories. At a national level, the prevalence trend has nearly plateaued for the past 15 years. However, upward prevalence trends appear to be persisting in Western Australia, South Australia and Tasmania. Findings highlight the need for ongoing efforts to address the issue of childhood obesity. [ABSTRACT FROM AUTHOR]

Objectives: To examine the relationships between children's moderate-to-vigorous physical activity (MVPA), sedentary behaviours, and academic performance.Design: This study investigated cross-sectional relationships between children's accelerometer-measured physical activity and sedentary behaviour patterns, and academic performance using a standardised, nationally-administered academic assessment.Methods: A total of 285 Australian children aged 9-11 years from randomly selected schools undertook 7-day 24h accelerometry to objectively determine their MVPA and sedentary behaviour. In the same year, they completed nationally-administered standardised academic testing (National Assessment Program-Literacy and Numeracy; NAPLAN). BMI was measured, and socio-demographic variables were collected in a parent-reported survey. Relationships between MVPA, sedentary behaviour and academic performance across five domains were examined using Generalised Linear Mixed Models, adjusted for a wide variety of socio-demographic variables.Results: Higher academic performance was strongly and consistently related to higher sedentary time, with significant relationships seen across all five academic domains (range F=4.13, p=0.04 through to F=18.65, p=<0.01). In contrast, higher academic performance was only related to higher MVPA in two academic domains (writing F=5.28, p=0.02, and numeracy F=6.28, p=0.01) and was not related to language, reading and spelling performance.Conclusions: Findings highlight that sedentary behaviour can have positive relationships with non-physical outcomes. Positive relationships between MVPA and literacy and numeracy, as well as the well documented benefits for MVPA on physical and social health, suggest that it holds an important place in children's lives, both in and outside of school. [ABSTRACT FROM AUTHOR]

Objectives The objective of this study was to investigate changes in use of time when undertaking a structured exercise program. Design This study used a randomized, multi-arm, controlled trial design. Methods A total of 129 insufficiently active adults aged 18–60 years were recruited and randomly allocated to one of three groups, a Moderate or Extensive six-week exercise group (150 and 300 additional minutes of exercise per week, respectively) or a Control group. Prescribed exercise was accumulated through both group and individual sessions. Use of time was measured at baseline and end-program using the Multimedia Activity Recall for Children and Adults, a computerized 24-h recall instrument. Daily minutes of activity in activity domains and energy expenditure zones were determined. Results Relative to changes in the control group, daily time spent in the physical activity [ F (2, 108) = 20.21, p < 0.001] and Active Transport [ F (2, 108) = 3.71, p = 0.03] time use domains significantly increased in the intervention groups by 21–45 min/day. Comparatively, the intervention groups spent significantly less time watching television [ F (2, 108) = 5.02, p = 0.008; −50–52 min/day], relative to Controls. Additionally, time spent in the moderate to vigorous energy expenditure zone had significantly increased in the intervention groups by end-program [ F (2, 108) = 6.35, p = 0.002; 48–50 min/day], relative to Controls. Conclusions This study is the first to comprehensively map changes in time use across an exercise program. The results suggest that exercise interventions should be mindful not only of compliance but also of “isotemporal displacement” of behaviors. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To describe time use clusters and correlate-cluster profiles of Australian youth. Design: Secondary analysis of cross-sectional national survey. Methods: Data were from the National Children's Nutrition and Physical Activity Survey, a random sample (n =1853) of 9–16 years old Australians (February–August 2007). Time use data were collected using the Multimedia Activity Recall for Children and Adults, and collapsed into 17 age-adjusted variables for sex-specific cluster analysis. Cluster associations with socio-demographic, anthropometric, health and dietary variables were analysed. Results: For boys (n =930), the Social tasker cluster was characterised by social interaction and chores & work, the Techno-active cluster by team sport and TV and the Techno-studious cluster by video games and study. Average daily pedometer steps, age and remoteness were significant cluster correlates. For the girls (n =923), the Social screenie cluster was characterised by TV and social interaction, the Quiet actives cluster by quiet time and non-team sport and the Techno-studious cluster by video games and study. Pedometer steps, age, parental income and education, parent–child age difference, “extra foods”, fat and fruit intakes were significant correlates. Conclusions: Distinct sex-specific time use clusters and profiles exist among Australian youth. These findings may assist the development of targeted time use interventions to improve health and well-being. [Copyright &y& Elsevier]

Abstract: Objective: To review the literature for the criterion validity of pedometers for use in child and adult populations with physical disabilities. Data Sources: Academic Search Premier, ERIC, SPORTDiscus, MEDLINE, AMED, Scopus, CINAHL, Web of Science, and EMBASE databases, searched from inception to September 7, 2011. Study Selection: Studies were included if they were peer-reviewed articles, included populations with physical disabilities, and reported primary data for pedometer validity in comparison with direct observation. A consensus approach was used to apply the inclusion and exclusion criteria. Of the 163 articles identified in the database searches (excluding duplicates), 7 studies met the inclusion criteria. Data Extraction: The quality of the studies was assessed independently by 2 reviewers, using a purpose-designed appraisal tool, with a consensus approach used to settle disagreement. A single reviewer extracted data relating to sample size, participant characteristics, pedometer model, main variables tested, duration of tests, and method of direct observation. Data Synthesis: The methodologic quality of the studies was generally high; however, there was a wide variation of population and methodology between studies. The correlation between pedometer step counts and directly observed step counts was moderate to excellent (intraclass correlation coefficient, .52–.87), and percent errors ranged from 0.5% to 24.7%. Secondary variables reported included the effect of speed of movement, pedometer placement, comparison of pedometer makes/models, and test-retest reliability. Conclusions: Available evidence suggests that pedometers are valid for use in clinical and research settings in people with physical disabilities. Further research examining the validity of pedometers in less heterogeneous populations of people with disabilities is warranted to determine validity for specific disability populations and to determine optimal pedometer ... [ABSTRACT FROM AUTHOR]

Objectives: While there is consistent evidence that rural adults in Australia are less active than their urban counterparts, studies relating geographical remoteness to activity patterns in Australian adolescents have yielded inconsistent results. The aim of this study was to describe objectively and subjectively measured patterns of physical activity and sedentary behaviours across remoteness categories in a representative sample of 9-16 year old Australians.Design: Cross-sectional observational study.Methods: 2071 Australian adolescents provided self-report use of time data on four days and wore a pedometer for at least 6 days within the 2007 Australian National Children's Nutrition and Physical Activity Survey. Comparisons of activity patterns were made across four objectively-determined remoteness categories (Major City, Inner Regional, Outer Regional and Remote), adjusting for household income, parental education and age.Results: Adolescents living in major cities self-reported 11-29 min less moderate to vigorous physical activity each day than their counterparts living in geographically more remote areas, and took 150-850 fewer steps each day. While there were no differences in time spent in sport or active transport, differences in free play participation were significant. Males in major cities also reported higher levels of screen time. Differences were somewhat more marked among males than among females.Conclusions: Activity levels among Australian adolescents show contrasting patterns of geographical differences to those found in Australian adults. Higher levels of free play among rural Australian adolescents may be due to more available space and less fear of traffic and stranger risks. [ABSTRACT FROM AUTHOR]

Abstract: Background: Obese adolescents achieve less moderate to vigorous physical activity (MVPA) than normal-weight adolescents, but the nature and extent of the deficit is unclear. This study aimed to describe differences in MVPA across weight status categories by domain of activity (sport, play, and active transport) and specific activity-sets (e.g. team sports, playground games, and chores) using minutes of activity, estimated intensity, and estimated energy expenditure as metrics of MVPA. Method: Anthropometric, use-of-time, and pedometer data were collected on a random sample of 2,200 Australian children aged 9–16 years. Minutes of activity, intensity metabolic equivalents of tasks (METs), and energy expenditure (MET.min) within each domain were estimated using an energy cost compendium. Results: Obese adolescents reported 174 MET.min/d lower MVPA energy expenditure than normal-weight peers (p < .0001), more than accounting for the entire difference in total daily energy expenditure (154 MET.min/d). Of this difference, 121 MET.min/d was associated with lower sports participation (p < .0001) and 45 MET.min/d with less free play (p = .03). There were no differences in minutes of active transport or in reported activity intensity across weight status categories in sport, free play, or active transport. The differences in MVPA participation between obese and normal-weight adolescents were largely because of different levels of participation in team sports, cycling, partner sports (boys), and dancing (girls). Conclusion: More than two-thirds of the difference in energy expenditure between obese and normal-weight Australian adolescents was because of lower participation in sport. Strategies for engaging obese adolescents in sport may help to redress deficits in energy expenditure. [Copyright &y& Elsevier]

Introduction: Appealing approaches to increasing physical activity levels are needed. This study evaluated whether a social and gamified smartphone app (Active Team) could be one such approach.Study Design: A 3-group cluster RCT compared the efficacy of Active Team with a basic self-monitoring app and waitlist control group.Setting/participants: Australian adults (N=444, mean age of 41 years, 74% female) were recruited in teams (n=121) and randomly assigned (1:1:1) to the Active Team (n=141, 39 teams), self-monitoring app (n=160, 42 teams), or waitlist group (n=143, 40 teams). Data were collected in 2016-2017, and analysis was conducted in 2018-2019.Intervention: Active Team is a 100-day app-based, gamified, online social networking physical activity intervention.Main Outcome Measures: The primary outcome was change in objective physical activity from baseline to 3-month follow-up. Secondary outcomes included objective physical activity at 9 months and self-reported physical activity, quality of life, depression, anxiety and stress, well-being, and engagement.Results: Mixed models indicated no significant differences in objective physical activity between groups at 3 (F=0.17, p=0.84; Cohen's d=0.03, 95% CI= -0.21, 0.26) or 9 months (F=0.23, p=0.92; d=0.06, 95% CI= -0.17, 0.29) and no significant differences for secondary outcomes of quality of life, depression, anxiety and stress, or well-being. Self-reported moderate-to-vigorous physical activity was significantly higher in the Active Team group at the 9-month follow-up (F=3.05, p=0.02; d=0.50, 95% CI=0.26, 0.73). Engagement was high; the Active Team group logged steps on an average of 72 (SD=35) days and used the social and gamified features an average of 89 (SD=118) times.Conclusions: A gamified, online social networking physical activity intervention did not change objective moderate-to-vigorous physical activity, though it did increase self-reported moderate-to-vigorous physical activity and achieve high levels of engagement. Future work is needed to understand if gamification, online social networks, and app-based approaches can be leveraged to achieve positive behavior change.Trial Registration: This study is registered at Australian and New Zealand Clinical Trial Registry (protocol: ANZCTR12617000113358). [ABSTRACT FROM AUTHOR]

We investigated whether associations of neighborhood social environment attributes and physical activity differed among 12 countries and levels of economic development using World Bank classification (low/lower-middle-, upper-middle- and high- income countries) among 9-11 year old children (N=6161) from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Collective efficacy and perceived crime were obtained via parental/guardian report. Moderate-to-vigorous physical activity (MVPA) was assessed with waist-worn Actigraph accelerometers. Neighborhood environment by country interactions were tested using multi-level statistical models, adjusted for covariates. Effect estimates were reported by country and pooled estimates calculated across World Bank classifications for economic development using meta-analyses and forest plots. Associations between social environment attributes and MVPA varied among countries and levels of economic development. Associations were more consistent and in the hypothesized directions among countries with higher levels economic development, but less so among countries with lower levels of economic development. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate how daily use of time changes across the retirement transition and how these changes may differ according to socio-demographic characteristics.Study Design: This longitudinal cohort study was based on interviews with 124 people at pre-retirement and at three, six and 12 months after retirement.Main Outcome Measures: The Multimedia Activity Recall for Children and Adults (MARCA), a computer-assisted telephone interview, measured use of time. Random effects mixed modelling (REMM) was used to examine time use changes across retirement, and ANCOVA to investigate the extent of the change by sex, education and health.Results: Apart from the decrease in time spent in work across retirement (-122min/day, p<0.001), less time was also spent in both active and passive transport (-26min/day, p<0.001). There were significant increases in time spent on Chores (+55min/day, p<0.001), Screen time (+32min/day, p<0.001), Sleep (+32min/day, p<0.001), Quiet time (+17min/day, p=0.02), Self-care (+8min/day, p=0.01) and Physical activity (+7min/day, p=0.01). There was little variation in change between sex, education and health sub-groups. Most changes occurred at retirement, with time use patterns fairly stable between three and 12 months post-retirement.Conclusions: The small amount of time spent in physical activity, combined with less time spent in active transport and increased time spent watching television, indicate the need for a more active lifestyle. [ABSTRACT FROM AUTHOR]

Background: How people use their time has health implications, but use of time may be influenced by factors such as age, sex, education and health.Objective: This study aimed to provide detailed information on the daily activity patterns of older working people.Study Design: 139 older Australian adults (aged 50-79 years) undertook comprehensive interviews on their use of time. This paper presents a cross-sectional analysis of the baseline findings from a longitudinal study.Main Outcome Measures: Use of time was measured using the Multimedia Activity Recall for Children and Adults (MARCA), administered via computer-assisted phone interview. Activity patterns were described, and compared on the basis of sex, education and health status.Results: The main activities undertaken were sleep (mean 466min/day), work (mean 233min/day) and chores (mean 160min/day), with little time spent on physical activity (mean 13min/day). Women spent more time doing chores (p<0.001) while men spent more time on vigorous activities (p<0.001). Participants with "fair" health spent less time on inside chores (p=0.05) and grooming (p=0.02) than healthier participants.Conclusions: Healthy lifestyle interventions for older workers should aim to increase physical activity levels by targeting specific activities, depending on sex and health status. [ABSTRACT FROM AUTHOR]

Because physical inactivity and unhealthy diets are highly prevalent, there is a need for cost-effective interventions that can reach large populations. Electronic health (eHealth) and mobile health (mHealth) solutions have shown promising outcomes and have expanded rapidly in the past decade. The purpose of this report is to provide an overview of the state of the evidence for the use of eHealth and mHealth in improving physical activity and nutrition behaviors in general and special populations. The role of theory in eHealth and mHealth interventions is addressed, as are methodological issues. Key recommendations for future research in the field of eHealth and mHealth are provided. [ABSTRACT FROM AUTHOR]