Annual and Seasonal Patterns of Dietary Intake in Australian Adults: A Prospective Cohort Study.

Poor diet is a major risk factor for non-communicable disease. The aims of this study were to describe temporal patterns and seasonal changes in diet across the year in Australian adults. A total of 375 adults from a prospective cohort study conducted between 1 December 2019 and 31 December 2021 in Adelaide, Australia, were asked to complete the Dietary Questionnaire for Epidemiological Studies at eight timepoints over a year. Average intakes over the previous month of total energy, macronutrients, healthy food groups, and discretionary foods and beverages were derived. Temporal patterns in diet were analysed descriptively. Multilevel linear regression modelling was used to assess seasonal differences in diet. Of the 375 participants recruited, 358 provided sufficient data for analysis. Intake of total energy, all macronutrients, and most discretionary foods and beverages peaked in December. Total energy intake was higher in summer than in autumn, winter, and spring. Fruit intake was higher in summer than in winter. Consumption of alcoholic beverages was higher in summer than in autumn, winter, and spring. Consumption of non-alcoholic beverages was higher in summer than in autumn and winter. This study identified temporal differences in dietary intake among Australian adults. Seasonal effects appear to be driven largely by increases in consumption of foods and beverages over the December (summer) holiday period. These findings can inform the design and timing of dietary interventions.

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.

BACKGROUND: Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain. OBJECTIVE: This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events. METHODS: Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. RESULTS: A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%). CONCLUSIONS: Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability. TRIAL REGISTRATION: Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.

It's about time to exercise: development of the Exercise Participation Explained in Relation to Time (EXPERT) model.

A perceived 'lack of time' is consistently the most commonly reported barrier to exercise. However, the term fails to capture the multifaceted nature of time-related factors. Recognising the need for a more comprehensive analysis of 'lack of time' as a barrier to exercise, the aim of this study was to develop the exercise participation explained in relation to time (EXPERT) model. The model was developed through a sequential process including (1) an umbrella literature review of time as a barrier, determinant, and correlate of physical activity; (2) a targeted review of existing temporal models; (3) drafting the model and refining it via discussions between eight authors; (4) a three-round Delphi process with eight panel members; and (5) consultations with seven experts and potential end-users. The final EXPERT model includes 31 factors within four categories: (1) temporal needs and preferences for exercise (ie, when and how long does an individual need/want to exercise), (2) temporal autonomy for exercise (ie, autonomy in scheduling free time for exercise), (3) temporal conditions for exercise (ie, available time for exercise) and (4) temporal dimensions of exercise (ie, use of time for exercise). Definitions, examples and possible survey questions are presented for each factor. The EXPERT model provides a comprehensive framework for understanding the multi-dimensional nature of 'time' as it relates to exercise participation. It moves beyond the simplistic notion of 'lack of time' and delves into the complexity of time allocation in the context of exercise. Empirical and cross-cultural validations of the model are warranted.

Associations of 24 h time-use compositions of sitting, standing, physical activity and sleeping with optimal cardiometabolic risk and glycaemic control: The Maastricht Study.

AIMS/HYPOTHESIS: The associations of sitting, standing, physical activity and sleep with cardiometabolic health and glycaemic control markers are interrelated. We aimed to identify 24 h time-use compositions associated with optimal metabolic and glycaemic control and determine whether these varied by diabetes status. METHODS: Thigh-worn activPAL data from 2388 participants aged 40-75 years (48.7% female; mean age 60.1 [SD = 8.1] years; n=684 with type 2 diabetes) in The Maastricht Study were examined. Compositional isometric log ratios were generated from mean 24 h time use (sitting, standing, light-intensity physical activity [LPA], moderate-to-vigorous physical activity [MVPA] and sleeping) and regressed with outcomes of waist circumference, fasting plasma glucose (FPG), 2 h plasma glucose, HbA1c, the Matsuda index expressed as z scores, and with a clustered cardiometabolic risk score. Overall analyses were adjusted for demographics, smoking, dietary intake and diabetes status, and interaction by diabetes status was examined separately. The estimated difference when substituting 30 min of one behaviour with another was determined with isotemporal substitution. To identify optimal time use, all combinations of 24 h compositions possible within the study footprint (1st-99th percentile of each behaviour) were investigated to determine those cross-sectionally associated with the most-optimal outcome (top 5%) for each outcome measure. RESULTS: Compositions lower in sitting time and with greater standing time, physical activity and sleeping had the most beneficial associations with outcomes. Associations were stronger in participants with type 2 diabetes (p<0.05 for interactions), with larger estimated benefits for waist circumference, FPG and HbA1c when sitting was replaced by LPA or MVPA in those with type 2 diabetes vs the overall sample. The mean (range) optimal compositions of 24 h time use, considering all outcomes, were 6 h (range 5 h 40 min-7 h 10 min) for sitting, 5 h 10 min (4 h 10 min-6 h 10 min) for standing, 2 h 10 min (2 h-2 h 20 min) for LPA, 2 h 10 min (1 h 40 min-2 h 20 min) for MVPA and 8 h 20 min (7 h 30 min-9 h) for sleeping. CONCLUSIONS/INTERPRETATION: Shorter sitting time and more time spent standing, undergoing physical activity and sleeping are associated with preferable cardiometabolic health. The substitutions of behavioural time use were significantly stronger in their associations with glycaemic control in those with type 2 diabetes compared with those with normoglycaemic metabolism, especially when sitting time was balanced with greater physical activity.

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.

A Digitally Enabled, Pharmacist service to detecT medicine harms in residential aged care (nursing home) (ADEPT): protocol for a feasibility study.

INTRODUCTION: This feasibility study aims to develop and test a new model of practice in Australia using digital technologies to enable pharmacists to monitor early signs and symptoms of medicine-induced harms in residential aged care. METHODS AND ANALYSIS: Thirty residents will be recruited from an aged care facility in South Australia. The study will be conducted in two phases. In phase I, the study team will work with aged care software providers and developers of digital technologies (a wearable activity tracker and a sleep tracking sensor) to gather physical activity and sleep data, as well as medication and clinical data from the electronic medication management system and aged care clinical software. Data will be centralised into a cloud-based monitoring platform (TeleClinical Care (TCC)). The TCC will be used to create dashboards that will include longitudinal visualisations of changes in residents' health, function and medicine use over time. In phase II, the on-site pharmacist will use the centralised TCC platform to monitor each resident's medicine, clinical, physical activity and sleep data to identify signs of medicine-induced harms over a 12-week period.A mixed methods process evaluation applying the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) evaluation framework will be used to assess the feasibility of the service. Outcome measures include service reach, changes in resident symptom scores (measured using the Edmonton Symptom Assessment System), number of medication adverse events detected, changes in physical activity and sleep, number of pharmacist recommendations provided, cost analysis and proportion of all pharmacists' recommendations implemented at 4-week, 8-week and 12-week postbaseline period. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the University of South Australia's Human Research Ethics Committee (205098). Findings will be disseminated through published manuscripts, conference presentations and reporting to the study funder. TRIAL REGISTRATION NUMBER: ACTRN12623000506695.

Cross-sectional associations between 24-hour time-use composition, grey matter volume and cognitive function in healthy older adults.

BACKGROUND: Increasing physical activity (PA) is an effective strategy to slow reductions in cortical volume and maintain cognitive function in older adulthood. However, PA does not exist in isolation, but coexists with sleep and sedentary behaviour to make up the 24-hour day. We investigated how the balance of all three behaviours (24-hour time-use composition) is associated with grey matter volume in healthy older adults, and whether grey matter volume influences the relationship between 24-hour time-use composition and cognitive function. METHODS: This cross-sectional study included 378 older adults (65.6 ± 3.0 years old, 123 male) from the ACTIVate study across two Australian sites (Adelaide and Newcastle). Time-use composition was captured using 7-day accelerometry, and T1-weighted magnetic resonance imaging was used to measure grey matter volume both globally and across regions of interest (ROI: frontal lobe, temporal lobe, hippocampi, and lateral ventricles). Pairwise correlations were used to explore univariate associations between time-use variables, grey matter volumes and cognitive outcomes. Compositional data analysis linear regression models were used to quantify associations between ROI volumes and time-use composition, and explore potential associations between the interaction between ROI volumes and time-use composition with cognitive outcomes. RESULTS: After adjusting for covariates (age, sex, education), there were no significant associations between time-use composition and any volumetric outcomes. There were significant interactions between time-use composition and frontal lobe volume for long-term memory (p = 0.018) and executive function (p = 0.018), and between time-use composition and total grey matter volume for executive function (p = 0.028). Spending more time in moderate-vigorous PA was associated with better long-term memory scores, but only for those with smaller frontal lobe volume (below the sample mean). Conversely, spending more time in sleep and less time in sedentary behaviour was associated with better executive function in those with smaller total grey matter volume. CONCLUSIONS: Although 24-hour time use was not associated with total or regional grey matter independently, total grey matter and frontal lobe grey matter volume moderated the relationship between time-use composition and several cognitive outcomes. Future studies should investigate these relationships longitudinally to assess whether changes in time-use composition correspond to changes in grey matter volume and cognition.

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.

Cross-sectional associations between 24-hour movement behaviors and cardiometabolic health among adults with type 2 diabetes mellitus: A comparison according to weight status.

OBJECTIVES: Type 2 diabetes mellitus (T2DM) is a chronic disease associated with overweight and obesity. Evidence suggests that 24-hour movement behaviors (24 h-MBs) play a crucial role in cardiometabolic health. However, it is not yet known if 24 h-MBs differ between weight status groups among people with T2DM (PwT2DM) and how 24 h-MBs are associated with their cardiometabolic health. DESIGN: Cross-sectional study. METHODS: Cardiometabolic variables (i.e. Body Mass Index (BMI), waist circumference (WC), HbA1c, fasting glucose, triglycerides, total-cholesterol, HDL-cholesterol, LDL-cholesterol, blood pressure) and 24 h-MBs (accelerometry and sleep-diary) of 1001 PwT2DM were collected. Regression models using compositional data analysis explored differences in 24 h-MBs between weight status groups and analyzed associations with cardiometabolic variables. RESULTS: The 24 h-MBs of PwT2DM being obese consisted of less sleep, light physical activity (LPA) and moderate to vigorous physical activity (MVPA) and more sedentary time (ST) per day as compared to PwT2DM being overweight or normal weight (p < 0.001). Regardless of weight status, the largest associations were found when reallocating 20 min a day from ST into MVPA for BMI (-0.32 kg/m2; [-0.55; -0.09], -1.09 %), WC (-1.44 cm, [-2.26; -0.62], -1.35 %) and HDL-cholesterol (0.02 mmol/l, [0.01, 0.02], +1.59 %), as well as from ST into LPA for triglycerides (-0.04 mmol/l, [-0.05; -0.03], -2.3 %). Moreover, these associations were different when stratifying people by short-to-average (7.7 h/night) versus long sleep (9.3 h/night) period. CONCLUSIONS: This study highlights the importance of 24 h-MBs in the cardiometabolic health of PwT2DM. Shifting time from ST and/or sleep toward LPA or MVPA might theoretically benefit cardiometabolic health among relatively inactive PwT2DM, irrespective of weight status.

Are reallocations of time between physical activity, sedentary behaviour and sleep associated with low back pain? A compositional data analysis.

Objectives: The aim of this cross-sectional study was to explore the associations of reallocating time between moderate- to vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behaviour (SB) and sleep with occurrence, frequency and intensity of low back pain (LBP) among adults using compositional isotemporal substitution analysis. Methods: A total of 2333 participants from the general adult population completed the Daily Activity Behaviours Questionnaire asking about their time-use composition consisting of sleep, SB, LPA and MVPA, and they self-reported their frequency and intensity of LBP in the past year. Results: Regression analyses adjusted for age, sex, body mass index, smoking, stress, education and socioeconomic status found that the time-use composition is associated with the frequency (p=0.009) and intensity of LBP (p<0.001). Reallocating time from SB or LPA to sleep was associated with lower frequency and intensity of LBP (p<0.05). Reallocating time from MVPA to sleep, SB or LPA and from SB to LPA was associated with a lower intensity of LBP (p<0.05). For example, reallocating 30 min/day from SB to sleep was associated with 5% lower odds (95% CI: 2% to 8%, p=0.001) of experiencing LBP more frequently, and 2% lower LBP intensity (95% CI: 1% to 3%, p<0.001). Conclusion: LBP sufferers may benefit from getting additional sleep and spending more time in LPA, while engaging less in SB and MVPA. These reallocations of time may be meaningful from clinical and public health perspectives.

Towards responsive policy and actions to address non-communicable disease risks amongst adolescents in Indonesia: insights from key stakeholders.

Background: Non-communicable diseases (NCDs) such as cancer, diabetes, heart disease, mental disorder and chronic lung conditions are the leading cause of death and disability in Indonesia. Adolescence is when risks for NCDs emerge and it is also an important life stage for intervention, yet young people are often at the margins of NCD policy and actions. This study aimed to understand how policies and actions should address NCD risks (tobacco smoking, inadequate physical activity, and diet) for adolescents in Indonesia, and how young people can be meaningfully involved. Methods: Qualitative in-depth interviews over videoconference (n = 21) were conducted in English or Bahasa with stakeholders in Indonesia. Participants included policymakers, implementation partners, and advocates who were focused on adolescent health or NCDs. Interviews were recorded, transcribed, translated, and thematically analysed using NVivo12. Findings were disseminated to participants for validation and feedback. Youth participants (n = 7) attended an additional workshop and considered recommendations and actions arising from this research. Findings: Participants identified that government and non-government organisations are acting on NCDs in Indonesia, but few of the existing initiatives target adolescents, and adolescent services rarely addressed NCD risks. Participants also felt that policies to protect adolescents from NCD risks (i.e., smoke-free areas in public) were not always enforced. For programs or initiatives focused on adolescent health, those that had engaged adolescents as co-creators and leaders were perceived to be more successful. As such, participants recommended more meaningful engagement of young people, including young people's leadership of initiatives. Additional recommendations included the need for intersectoral engagement and a 'whole-of-government' approach to prevention given the complex determinants of NCD risks, and the need for evidence-based actions that are underpinned by quality data to enable monitoring of progress. Interpretation: There is a recognised need to strengthen policies and actions to address NCD risks amongst adolescents in Indonesia. Meaningful youth engagement that allows young people to take the lead, intersectoral actions, and evidence-based data driven responses were key strategies identified. Funding: UNICEF East Asia and Pacific Regional Office.

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 Goldilocks Day for healthy adiposity measures among children and adolescents.

Background: The optimal balance of time spent on daily movement behaviors ("The Goldilocks Day") associated with childhood obesity remains unknown. Objective: To estimate the optimal durations of sleep, sedentary behavior (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MPVA) associated with excess adiposity in a paediatric population. Methods: Accelerometer-measured 24-h movement behaviors were obtained from 659 Czech children and adolescents (8-18-year-olds). Adiposity indicators were body mass index z-score, fat mass percentage, fat-free mass index, and visceral adipose tissue. Excess adiposity was defined as exceeding the 85th percentile for an adiposity indicator. Compositional regression analyses were used investigate the associations between movement behaviors and adiposity indicators and estimating "The Goldilocks Day." Results: The movement behavior composition was associated with visceral adipose tissue (Fdf1 = 3,df2 = 317 = 3.672, p = 0.013) and fat mass percentage (Fdf1 = 3,df2 = 289 = 2.733, p = 0.044) among children and adolescents. The Goldilocks Day consisted of 8.5 h of sleep, 10.8 h of SB, 3.9 h of LPA, and 0.8 h of MVPA among children and 7.5 h of sleep, 12.4 h of SB, 3.6 h of LPA, and 0.5 h of MVPA among adolescents. Conclusion: Optimizing the time spent sleeping, and in sedentary and physical activities appears to be important in the prevention of excess adiposity.

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.

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.

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.

Compositional functional regression and isotemporal substitution analysis: Methods and application in time-use epidemiology.

The distribution of time that people spend in physical activity of various intensities has important health implications. Physical activity (commonly categorised by the intensity into light, moderate and vigorous physical activity), sedentary behaviour and sleep, should not be analysed separately, because they are parts of a time-use composition with a natural constraint of 24 h/day. To find out how are relative reallocations of time between physical activity of various intensities associated with health, herewith we describe compositional scalar-on-function regression and a newly developed compositional functional isotemporal substitution analysis. Physical activity intensity data can be considered as probability density functions, which better reflects the continuous character of their measurement using accelerometers. These probability density functions are characterised by specific properties, such as scale invariance and relative scale, and they are geometrically represented using Bayes spaces with the Hilbert space structure. This makes possible to process them using standard methods of functional data analysis in the L2 space, via centred logratio (clr) transformation. The scalar-on-function regression with clr transformation of the explanatory probability density functions and compositional functional isotemporal substitution analysis were applied to a dataset from a cross-sectional study on adiposity conducted among school-aged children in the Czech Republic. Theoretical reallocations of time to physical activity of higher intensities were found to be associated with larger and more progressive expected decreases in adiposity. We obtained a detailed insight into the dose-response relationship between physical activity intensity and adiposity, which was enabled by using the compositional functional approach.

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.

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.

Longitudinal reallocations of time between 24-h movement behaviours and their associations with inflammation in children and adolescents: the UP&DOWN study.

BACKGROUND: While there is evidence that physical activity, sedentary behaviour (SB) and sleep may all be associated with modified levels of inflammatory markers in adolescents and children, associations with one movement behaviour have not always been adjusted for other movement behaviours, and few studies have considered all movement behaviours in the 24-hour day as an exposure. PURPOSE: The aim of the study was to explore how longitudinal reallocations of time between moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), SB and sleep are associated with changes in inflammatory markers in children and adolescents. METHODS: A total of 296 children/adolescents participated in a prospective cohort study with a 3-year follow-up. MVPA, LPA and SB were assessed by accelerometers. Sleep duration was assessed using the Health Behavior in School-aged Children questionnaire. Longitudinal compositional regression models were used to explore how reallocations of time between movement behaviours are associated with changes in inflammatory markers. RESULTS: Reallocations of time from SB to sleep were associated with increases in C3 levels (difference for 60 min/d reallocation [d60] = 5.29 mg/dl; 95% confidence interval [CI] = 0.28, 10.29) and TNF-α (d60 = 1.81 mg/dl; 95% CI = 0.79, 15.41) levels. Reallocations from LPA to sleep were also associated with increases in C3 levels (d60 = 8.10 mg/dl; 95% CI = 0.79, 15.41). Reallocations from LPA to any of the remaining time-use components were associated with increases in C4 levels (d60 ranging from 2.54 to 3.63 mg/dl; p < 0.05), while any reallocation of time away from MVPA was associated with unfavourable changes in leptin (d60 ranging from 3088.44 to 3448.07 pg/ml; p < 0.05). CONCLUSIONS: Reallocations of time between 24-h movement behaviours are prospectively associated with some inflammatory markers. Reallocating time away from LPA appears to be most consistently unfavourably associated with inflammatory markers. Given that higher levels of inflammation during childhood and adolescence are associated with an increased risk of chronic diseases in adulthood, children and adolescents should be encouraged to maintain or increase the level of LPA to preserve a healthy immune system.