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.

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.

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

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

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

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

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.

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.

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

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

A collaborative approach to adopting/adapting guidelines. The Australian 24-hour movement guidelines for children (5-12 years) and young people (13-17 years): An integration of physical activity, sedentary behaviour, and sleep.

BACKGROUND: In 2018, the Australian Government updated the Australian Physical Activity and Sedentary Behaviour Guidelines for Children and Young People. A requirement of this update was the incorporation of a 24-hour approach to movement, recognising the importance of adequate sleep. The purpose of this paper was to describe how the updated Australian 24-Hour Movement Guidelines for Children and Young People (5 to 17 years): an integration of physical activity, sedentary behaviour and sleep were developed and the outcomes from this process. METHODS: The GRADE-ADOLOPMENT approach was used to develop the guidelines. A Leadership Group was formed, who identified existing credible guidelines. The Canadian 24-Hour Movement Guidelines for Children and Youth best met the criteria established by the Leadership Group. These guidelines were evaluated based on the evidence in the GRADE tables, summaries of findings tables and recommendations from the Canadian Guidelines. We conducted updates to each of the Canadian systematic reviews. A Guideline Development Group reviewed, separately and in combination, the evidence for each behaviour. A choice was then made to adopt or adapt the Canadian recommendations for each behaviour or create de novo recommendations. We then conducted an online survey (n=237) along with three focus groups (n=11 in total) and 13 key informant interviews. Stakeholders used these to provide feedback on the draft guidelines. RESULTS: Based on the evidence from the Canadian systematic reviews and the updated systematic reviews in Australia, the Guideline Development Group agreed to adopt the Canadian recommendations and, apart from some minor changes to the wording of good practice statements, maintain the wording of the guidelines, preamble, and title of the Canadian Guidelines. The Australian Guidelines provide evidence-informed recommendations for a healthy day (24-hours), integrating physical activity, sedentary behaviour (including limits to screen time), and sleep for children (5-12 years) and young people (13-17 years). CONCLUSIONS: To our knowledge, this is only the second time the GRADE-ADOLOPMENT approach has been used to develop movement behaviour guidelines. The judgments of the Australian Guideline Development Group did not differ sufficiently to change the directions and strength of the recommendations and as such, the Canadian Guidelines were adopted with only very minor alterations. This allowed the Australian Guidelines to be developed in a shorter time frame and at a lower cost. We recommend the GRADE-ADOLOPMENT approach, especially if a credible set of guidelines that was developed using the GRADE approach is available with all supporting materials. Other countries may consider this approach when developing and/or revising national movement guidelines.

The effect of height on estimates of the change in BMI-based prevalence of childhood obesity.

BACKGROUND/OBJECTIVES: Body mass index (BMI, body mass/height2) is biased toward height in children. Here we investigate how change in population height affected change in BMI-based estimates of the prevalence of overweight and obesity in Australian children. SUBJECTS/METHODS: Height, weight, and percent body fat (%BF) were measured at ages 8, 10, and 12 years (1855 sets of measures). Age-specific relationships between BMI and height were derived, adjusting for %BF, to estimate the degree of height bias inherent in BMI. Then, from cross-sectional measurements recorded in 1985 (N = 2388) and 1995 (N = 2148) in 8, 10, and 12 year olds, changes in overweight/obesity prevalences were calculated before and after accounting for the BMI-height bias. RESULTS: Estimates of the effect of height on BMI following adjustment for %BF were similar across age groups and all were significant at p < 0.001. Referring to 12 year olds, at the same %BF for a 1% increase in height there was 0.77% (95% CI 0.55, 0.99) increase in BMI in boys, and 0.74% (0.28, 1.02) increase in girls. Between 1985 and 1995, mean height of 12-year-old boys and girls increased 3.9 and 3.2 cm, respectively. In 1985 unadjusted prevalences of combined overweight/obesity in boys and girls were 13.5% and 13.0%, respectively, and in 1995 were 24% and 24.5%. The latter values were reduced to 21.6% and 22.6% after adjusting for increased height. CONCLUSIONS: Previously reported increases in childhood overweight/obesity in Australia between 1985 and 1995 were likely to be moderately overestimated as a result of increased population height; suggesting that population height be taken into account in any pediatric investigation of changes in overweight/obesity prevalence over time.

Are young children with asthma more likely to be less physically active?

BACKGROUND: Previous research suggests that children who experience asthma may be less physically active; however, results have been inconclusive. This study aimed to investigate whether the presence of asthma or wheeze is associated with lower physical activity levels in children, and whether sex, body mass index or earlier asthma or wheeze status modifies the association. METHODS: This study was conducted in 391 HealthNuts participants in Melbourne, Australia. Asthma and wheeze data were collected via questionnaire at age 4 and 6, and physical activity was measured through accelerometry. Using adjusted linear regression models, the cross-sectional and longitudinal associations were investigated. RESULTS: There was no evidence of a difference in time spent in moderate-to-vigorous physical activity (MVPA) at age 6 years between children with and without asthma at age 4; children with asthma spent 8.3 minutes more time physically active per day (95% CI: -5.6, 22.1, P = .24) than children without asthma. Similar results were seen for children with current wheeze (5.8 minutes per day more, 95% CI: -5.9, 17.5, P = .33) or ever wheeze or asthma (7.7 minutes per day more, 95% CI: -4.8, 20.2, P = .23) at age 4 years. Comparable null results were observed in the cross-sectional analyses. Interaction with BMI could not be assessed; however, previous asthma or wheeze status and sex were not found to modify these associations. CONCLUSION: This analysis found no evidence of asthma hindering physical activity in these young children. These results are encouraging, as they indicate that the Australian asthma and physical activity public health campaigns are being effectively communicated and adopted by the public.

Children's moderate-to-vigorous physical activity on weekdays versus weekend days: a multi-country analysis.

PURPOSE: The Structured Days Hypothesis (SDH) posits that children's behaviors associated with obesity - such as physical activity - are more favorable on days that contain more 'structure' (i.e., a pre-planned, segmented, and adult-supervised environment) such as school weekdays, compared to days with less structure, such as weekend days. The purpose of this study was to compare children's moderate-to-vigorous physical activity (MVPA) levels on weekdays versus weekend days using a large, multi-country, accelerometer-measured physical activity dataset. METHODS: Data were received from the International Children's Accelerometer Database (ICAD) July 2019. The ICAD inclusion criteria for a valid day of wear, only non-intervention data (e.g., baseline intervention data), children with at least 1 weekday and 1 weekend day, and ICAD studies with data collected exclusively during school months, were included for analyses. Mixed effects models accounting for the nested nature of the data (i.e., days within children) assessed MVPA minutes per day (min/day MVPA) differences between weekdays and weekend days by region/country, adjusted for age, sex, and total wear time. Separate meta-analytical models explored differences by age and country/region for sex and child weight-status. RESULTS/FINDINGS: Valid data from 15 studies representing 5794 children (61% female, 10.7 ± 2.1 yrs., 24% with overweight/obesity) and 35,263 days of valid accelerometer data from 5 distinct countries/regions were used. Boys and girls accumulated 12.6 min/day (95% CI: 9.0, 16.2) and 9.4 min/day (95% CI: 7.2, 11.6) more MVPA on weekdays versus weekend days, respectively. Children from mainland Europe had the largest differences (17.1 min/day more MVPA on weekdays versus weekend days, 95% CI: 15.3, 19.0) compared to the other countries/regions. Children who were classified as overweight/obese or normal weight/underweight accumulated 9.5 min/day (95% CI: 6.9, 12.2) and 10.9 min/day (95% CI: 8.3, 13.5) of additional MVPA on weekdays versus weekend days, respectively. CONCLUSIONS: Children from multiple countries/regions accumulated significantly more MVPA on weekdays versus weekend days during school months. This finding aligns with the SDH and warrants future intervention studies to prioritize less-structured days, such as weekend days, and to consider providing opportunities for all children to access additional opportunities to be active.

A study on prospective associations between adiposity and 7-year changes in movement behaviors among older women based on compositional data analysis.

INTRODUCTION: It is unclear whether adiposity leads to changes in movement behaviors, and there is a lack of compositional analyses of longitudinal data which focus on these associations. Using a compositional approach, this study aimed to examine the associations between baseline adiposity and 7-year changes in physical activity (PA) and sedentary behavior (SB) among elderly women. We also explored the longitudinal associations between change in adiposity and change in movement-behavior composition. METHODS: This longitudinal study included 176 older women (mean baseline age 62.8 (4.1) years) from Central Europe. Movement behavior was assessed by accelerometers and adiposity was measured by bioelectrical impedance analysis at baseline and follow-up. A set of multivariate least-squares regression analyses was used to examine the associations of baseline adiposity and longitudinal changes in adiposity as explanatory variables with longitudinal changes in a 3-part movement-behavior composition consisting of SB, light PA (LPA) and moderate-to-vigorous PA (MVPA) as outcome variables. RESULTS: No significant associations were found between baseline adiposity and longitudinal changes in the movement-behavior composition (p > 0.05). We found significant associations of changes in body mass index (BMI) and fat mass percentage (FM%) with changes in the movement-behavior composition. An increase in BMI was associated with an increase of SB at the expense of LPA and MVPA (ß = 0.042, p = 0.009) and with a decrease of MVPA in favor of SB and LPA (ß = - 0.059, p = 0.037). An increase in FM% was significantly associated only with an increase of SB at the expense of LPA and MVPA (ß = 0.019, p = 0.031). CONCLUSIONS: This study did not support the assumption that baseline adiposity is associated with longitudinal changes in movement behaviors among elderly women, but we found evidence for change-to-change associations, suggesting that a 7-year increase in adiposity is associated with a concurrent increase of SB at the expense of LPA and MVPA and with a concurrent decrease of MVPA in favor of LPA and SB. Public health interventions are needed to simultaneously prevent weight gain and promote physically active lifestyle among elderly women.

Annual rhythms in adults' lifestyle and health (ARIA): protocol for a 12-month longitudinal study examining temporal patterns in weight, activity, diet, and wellbeing in Australian adults.

BACKGROUND: Almost one in three Australian adults are now obese, and the rate continues to rise. The causes of obesity are multifaceted and include environmental, cultural and lifestyle factors. Emerging evidence suggests there may be temporal patterns in weight gain related, for example, to season and major festivals such as Christmas, potentially due to changes in diet, daily activity patterns or both. The aim of this study is to track the annual rhythm in body weight, 24 h activity patterns, dietary patterns, and wellbeing in a cohort of Australian adults. In addition, through data linkage with a concurrent children's cohort study, we aim to examine whether changes in children's body mass index, activity and diet are related to those of their parents. METHODS: A community-based sample of 375 parents aged 18 to 65 years old, residing in or near Adelaide, Australia, and who have access to a Bluetooth-enabled mobile device or a computer and home internet, will be recruited. Across a full year, daily activities (minutes of moderate to vigorous physical activity, light physical activity, sedentary behaviour and sleep) will be measured using wrist-worn accelerometry (Fitbit Charge 3). Body weight will be measured daily using Fitbit wifi scales. Self-reported dietary intake (Dietary Questionnaire for Epidemiological Studies V3.2), and psychological wellbeing (WHOQOL-BREF and DASS-21) will be assessed eight times throughout the 12-month period. Annual patterns in weight will be examined using Lowess curves. Associations between changes in weight and changes in activity and diet compositions will be examined using repeated measures multi-level models. The associations between parent's and children's weight, activity and diet will be investigated using multi-level models. DISCUSSION: Temporal factors, such as day type (weekday or weekend day), cultural celebrations and season, may play a key role in weight gain. The aim is to identify critical opportunities for intervention to assist the prevention of weight gain. Family-based interventions may be an important intervention strategy. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry, identifier ACTRN12619001430123 . Prospectively registered on 16 October 2019.

Are longitudinal reallocations of time between movement behaviours associated with adiposity among elderly women? A compositional isotemporal substitution analysis.

BACKGROUND: This study aimed to use compositional data analysis to: (1) investigate the prospective associations between changes in daily movement behaviours and adiposity among elderly women; and (2) to examine how the reallocation of time between movement behaviours was associated with longitudinal changes in adiposity. SUBJECTS/METHODS: This is a 7-year longitudinal study in Central European older women (n = 158, baseline age 63.9 ± 4.4 years). At baseline and follow-up, light-intensity physical activity (LIPA), moderate-to-vigorous physical activity (MVPA) and sedentary behaviour were measured by accelerometer and body adiposity (body mass index [BMI], body fat percentage [%BF]) was assessed from measured height and weight and bioelectrical impedance analyser. Compositional regression with robust estimators and compositional longitudinal isotemporal substitution analysis explored if, and how, changes in movement behaviours were associated with adiposity. RESULTS: Over 7 years, the prevalence of obesity in the sample increased by 10.1% and 14.6% according to BMI and %BF, respectively, and time spent in sedentary behaviour increased by 14%, while time spent in LIPA and MVPA decreased by 14% and 21%, respectively. The increase in sedentary behaviour at the expense of LIPA and MVPA during the 7-year period was associated with higher BMI and %BF at follow-up (both p < 0.01). The increase in LIPA or MVPA at the expense of sedentary behaviour was associated with reduced BMI and %BF at follow-up. In our sample, the largest change in BMI (0.75 kg/m2; 95% confidence interval [CI]: 0.37-1.13) and %BF (1.28 U; 95% CI: 0.48-2.09) was associated with longitudinal reallocation of 30 min from MVPA to sedentary behaviour. CONCLUSIONS: We found an association between longitudinal changes in daily movement behaviours and adiposity among elderly women in Central Europe. Our findings support public health programmes to increase or maintain time spent in higher-intensity physical activity among elderly women.

Validity and bias on the online active Australia survey: activity level and participant factors associated with self-report bias.

BACKGROUND: This study examined the criterion validity of the online Active Australia Survey, using accelerometry as the criterion, and whether self-report bias was related to level of activity, age, sex, education, body mass index and health-related quality of life. METHODS: The online Active Australia Survey was validated against the GENEActiv accelerometer as a direct measure of activity. Participants (n = 344) wore an accelerometer for 7 days, completed the Active Australia Survey, and reported their health and demographic characteristics. A Spearman's rank coefficient examined the association between minutes of moderate-to-vigorous physical activity recorded on the Active Australia Survey and GENEActiv accelerometer. A Bland-Altman plot illustrated self-report bias (the difference between methods). Linear mixed effects modelling was used to examine whether participant factors predicted self-report bias. RESULTS: The association between moderate-to-vigorous physical activity reported on the online Active Australia Survey and accelerometer was significant (rs = .27, p < .001). Participants reported 4 fewer minutes per day on the Active Australia Survey than was recorded by accelerometry (95% limits of agreement -104 - 96 min) but the difference was not significant (t(343) = -1.40, p = .16). Self-report bias was negatively associated with minutes of accelerometer-recorded moderate-to-vigorous physical activity and positively associated with mental health-related quality of life. CONCLUSIONS: The online Active Australia Survey showed limited criterion validity against accelerometry. Self-report bias was related to activity level and mental health-related quality of life. Caution is recommended when interpreting studies using the online Active Australia Survey.

Breastfeeding and childhood obesity: A 12-country study.

This study aimed to examine the association between breastfeeding and childhood obesity. A multinational cross-sectional study of 4,740 children aged 9-11 years was conducted from 12 countries. Infant breastfeeding was recalled by parents or legal guardians. Height, weight, waist circumference, and body fat were obtained using standardized methods. The overall prevalence of obesity, central obesity, and high body fat were 12.3%, 9.9%, and 8.1%, respectively. After adjustment for maternal age at delivery, body mass index (BMI), highest maternal education, history of gestational diabetes, gestational age, and child's age, sex, birth weight, unhealthy diet pattern scores, moderate-to-vigorous physical activity, sleeping, and sedentary time, exclusive breastfeeding was associated with lower odds of obesity (odds ratio [OR] 0.76, 95% confidence interval, CI [0.57, 1.00]) and high body fat (OR 0.60, 95% CI [0.43, 0.84]) compared with exclusive formula feeding. The multivariable-adjusted ORs based on different breastfeeding durations (none, 1-6, 6-12, and > 12 months) were 1.00, 0.74, 0.70, and 0.60 for obesity (Ptrend = .020) and 1.00, 0.64, 047, and 0.64 for high body fat (Ptrend = .012), respectively. These associations were no longer significant after adjustment for maternal BMI. Breastfeeding may be a protective factor for obesity and high body fat in 9- to 11-year-old children from 12 countries.

Joint associations between weekday and weekend physical activity or sedentary time and childhood obesity.

OBJECTIVE: To evaluate the single and joint associations of objectively measured moderate-to-vigorous physical activity (MVPA) and sedentary time on week and weekend days with obesity in children from 12 countries across all inhabited continents. METHODS: A multinational, 12-country cross-sectional study of 5779 children aged 9-11 years was conducted. Time spent in MVPA and sedentary behaviors was assessed by waist-worn accelerometry. Logistic regression was used to examine the independent and joint associations of MVPA and sedentary time on weekdays and weekend with the odds of obesity. RESULTS: After adjustment for all confounding factors, the odds ratios (ORs) of childhood obesity were the highest among children with a low level of MVPA on both weekdays and weekend (OR 4.67), high among children with a high level of MVPA on weekdays and a low level of MVPA on weekend (OR 1.99) and high among children with a low level of MVPA on weekdays and a high levels of MVPA on weekend (OR 2.20), compared to those with a high level of MVPA on both weekdays and weekend. Similarly, the ORs of childhood obesity were significantly higher among children with a high level of sedentary time on both weekdays and weekend (OR 1.87) compared with those with low levels of sedentary time on both weekdays and weekend. CONCLUSIONS: Lower levels of MVPA or higher levels of sedentary time on either weekdays or weekend were associated with increased odds of obesity in 9-11 year old children in 12 countries.

The Association of the Body Composition of Children with 24-Hour Activity Composition.

OBJECTIVES: To evaluate how the reallocation of time between sleep, sedentary time, light, and moderate-vigorous activities is associated with children's body composition. STUDY DESIGN: Population-based cross-sectional Child Health CheckPoint within the Longitudinal Study of Australian Children (n = 938 11-12 year-olds, 50% boys). Twenty-four hour activity composition via accelerometry (minutes/day of sleep, sedentary time, light, and moderate-to-vigorous physical activity [MVPA]) and 3-part body composition (percentage truncal fat, percentage nontruncal fat, and percentage fat-free mass) via bioelectrical impedance analysis were measured. We estimated differences in 3-part body composition associated with the incremental reallocation of time between activities, using dual-compositional regression models adjusted for sex, age, puberty, and socioeconomic position. RESULTS: Reallocation of time between MVPA and any other activity was strongly associated with differences in body composition. Adverse body composition differences were larger for a given MVPA decrease than were the beneficial differences for an equivalent MVPA increase. For example, 15 minutes less MVPA (relative to remaining activities) was associated with absolute percentage differences of +1.7% (95% CI 1.2; 2.4) for truncal fat, +0.8% (0.6; 1.2) for nontruncal fat, and -2.6% (-3.5; -1.9) for fat-free mass, and a 15-minute increase was associated with -0.7% (-0.9; -0.5) truncal fat, -0.4% (-0.5; -0.3) nontruncal fat, and +1.1% (0.9; 1.5) fat-free mass. Reallocations between sleep, sedentary time, and light physical activity were not associated with differences in body composition. CONCLUSIONS: Preventing declines in MVPA during inactive periods (eg, holidays) may be an important intervention goal. More MVPA, instead of other activities, may benefit body composition.

Analysing how physical activity competes: a cross-disciplinary application of the Duplication of Behaviour Law.

BACKGROUND: Despite the ongoing promotion of physical activity, the rates of physical inactivity remain high. Drawing on established methods of analysing consumer behaviour, this study seeks to understand how physical activity competes for finite time in a day - how Exercise and Sport compete with other everyday behaviours, and how engagement in physical activity is shared across Exercise and Sport activities. As targeted efforts are common in physical activity intervention and promotion, the existence of segmentation is also explored. METHODS: Time-use recall data (n = 2307 adults) is analysed using the Duplication of Behaviour Law, and tested against expected values, to document what proportion of the population that engage in one activity, also engage in another competing activity. Additionally, a Mean Absolute Deviation approach is used to test for segmentation. RESULTS: The Duplication of Behaviour Law is evident for everyday activities, and Exercise and Sport activities - all activities 'compete' with each other, and the prevalence of the competing activity determines the extent of competition. However, some activities compete more or less than expected, suggesting the combinations of activities that should be used or avoided in promotion efforts. Competition between everyday activities is predictable, and there are no specific activities that are sacrificed to engage in Exercise and Sport. How people share their physical activity across different Exercise and Sport activities is less predictable - Males and younger people (under 20 years) are more likely to engage in Exercise and Sport, and those who engage in Exercise and Sport are slightly more likely to Work and Study. High competition between Team Sports and Non-Team Sports suggests strong preferences for sports of different varieties. Finally, gender and age-based segmentation does not exist for Exercise and Sport relative to other everyday activities; however, segmentation does exist for Team Sports, Games, Active Play and Dance. CONCLUSIONS: The Duplication of Behaviour Law demonstrates that population-level patterns of behaviour can yield insight into the competition between different activities, and how engagement in physical activity is shared across different Exercise and Sport activities. Such insights can be used to describe and predict physical activity behaviour and may be used to inform and evaluate promotion and intervention.

Standardised criteria for classifying the International Classification of Activities for Time-use Statistics (ICATUS) activity groups into sleep, sedentary behaviour, and physical activity.

BACKGROUND: Globally, the International Classification of Activities for Time-Use Statistics (ICATUS) is one of the most widely used time-use classifications to identify time spent in various activities. Comprehensive 24-h activities that can be extracted from ICATUS provide possible implications for the use of time-use data in relation to activity-health associations; however, these activities are not classified in a way that makes such analysis feasible. This study, therefore, aimed to develop criteria for classifying ICATUS activities into sleep, sedentary behaviour (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), based on expert assessment. METHOD: We classified activities from the Trial ICATUS 2005 and final ICATUS 2016. One author assigned METs and codes for wakefulness status and posture, to all subclass activities in the Trial ICATUS 2005. Once coded, one author matched the most detailed level of activities from the ICATUS 2016 with the corresponding activities in the Trial ICATUS 2005, where applicable. The assessment and harmonisation of each ICATUS activity were reviewed independently and anonymously by four experts, as part of a Delphi process. Given a large number of ICATUS activities, four separate Delphi panels were formed for this purpose. A series of Delphi survey rounds were repeated until a consensus among all experts was reached. RESULTS: Consensus about harmonisation and classification of ICATUS activities was reached by the third round of the Delphi survey in all four panels. A total of 542 activities were classified into sleep, SB, LPA, and MVPA categories. Of these, 390 activities were from the Trial ICATUS 2005 and 152 activities were from the final ICATUS 2016. The majority of ICATUS 2016 activities were harmonised into the ICATUS activity groups (n = 143). CONCLUSIONS: Based on expert consensus, we developed a classification system that enables ICATUS-based time-use data to be classified into sleep, SB, LPA, and MVPA categories. Adoption and consistent use of this classification system will facilitate standardisation of time-use data processing for the purpose of sleep, SB and physical activity research, and improve between-study comparability. Future studies should test the applicability of the classification system by applying it to empirical data.