OBJECTIVE:Large new children's cohorts now allow embedded trials for prevention and early intervention, offering rare opportunities to reduce the burden of childhood and future chronic disease. We explored parents' preferences for interventions targeting children's current and future health, to guide measurement and intervention choices in such cohorts. DESIGN:Two discrete choice experiments (DCEs) were conducted between February and July 2024. Parents made pairwise choices between hypothetical childhood interventions aimed at preventing or mitigating eight high-burden chronic conditions (DCE1: eight diseases of ageing; DCE2: the four most important from DCE1 plus four common childhood conditions). We analysed mean (range 1-8, 1 being highest) and median rankings, using inverse probability weighting to estimate to all Generation Victoria (GenV) parents (n>70 000). PARTICIPANTS:Convenience samples of parents of children aged 6-24 months in GenV, Australia's largest population-representative interventional cohort. RESULTS:1866 parents completed DCE1 and 1343 completed DCE2, with response rates of 11.4% and 9.8%, respectively. In DCE1, 'cardiovascular health' ranked highest (mean=3.2), followed by 'cognition', 'lung health', 'blood sugar levels', 'bone and muscle strength', 'vision and eye health', 'kidney health' and 'hearing' (mean=5.8). In DCE2, 'anxiety and/or depression' ranked highest (mean=3.6), followed by 'cardiovascular health', 'autism' and 'asthma'. Rankings changed only marginally on weighting. CONCLUSIONS:Parents' priorities are wide-ranging. While low response rates limit translation to policy, rankings weighted to adjust for missing data emerge with compelling themes for future work to triangulate with other consultation methods.

Introduction:The life course phenotypic pathways leading to noncommunicable diseases (NCDs) provide information needed to plan and test preventive interventions. However, most NCD-relevant phenotypes are not routinely measured until diagnosis and their pre-clinical trajectories are therefore not in linked population datasets. Objectives:In the context of planning phenotypic collection waves in an Australian early and pre-midlife mega-cohort, we aimed to undertake (1) a scoping review to identify knowledge availability and gaps and (2) a comparative map of trajectories from available data. Methods:We searched PubMed and MEDLINE (September 2024) for trajectory studies on phenotypes underlying NCDs with the highest late life disease burden (excluding cancer and back pain, with no clear precursor phenotypes): cardiovascular, chronic obstructive pulmonary and kidney diseases, diabetes, falls, hearing and vision loss, and dementia. Eligible studies had ≥3 timepoints spanning ≥5 years in childhood or ≥10 years in adulthood. Using the R ggplot package, we fitted loess curves to create lifetime trajectory visualisations in absolute values and units standardised for comparison. Results:From 3770 abstracts, we included 36 studies. Most (n == 19) examined cardiovascular trajectories, collectively spanning ages 5-105 years for blood pressure. Ten studies reported cognition trajectories, but could not be synthesised due to measurement diversity. Twelve studies mapped lung, glucose, kidney or musculoskeletal phenotypes but with discontinuities at varying life stages. No studies tracked vision or hearing trajectories. Our syntheses confirmed some known trajectory patterns, such as peaking of musculoskeletal phenotypes in early adulthood and the rise in cardiovascular and glucose markers beyond healthy ranges from midlife. Conclusions:Our mapping confirmed expected patterns for some phenotypes, but highlighted significant gaps for others on pathways to high-burden NCDs. If long-running population cohorts collectively tracked all major phenotypes over time, embedded real-world or simulated trials could accelerate progress in prevention and treatment across all major NCDs.

BACKGROUND:The transition from primary-to-secondary school significantly impacts students' well-being. However, existing research provides limited insight into the long-term impact of school transition on well-being, and no studies have disentangled age-related changes from transition-specific effects. This study leverages a natural experiment, where an educational reform resulted in two different age cohorts transitioning simultaneously, to disentangle age effects from transition effects on student well-being. METHODS:This study analyzed longitudinal data from the Well-being and Engagement Collection census (2019-2025) in South Australia. Participants were two cohorts of students who simultaneously started secondary school in 2022: one transitioning at Year 7 and the other at Year 8. Well-being was measured across eight domains. Linear mixed-effects regression models examined transition effects and tested interactions with sociodemographic factors. RESULTS:A total of 20,910 participants (Male: 52.1%, Age in 2019: 9.7 ± 0.6) contributing 104,800 observations (5.0 responses/participant) across the 7 years were included. In the first 2 years post-transition, well-being experienced adverse changes across all domains (marginal effects for positively-worded measures: -0.44 to -0.18; negatively-worded measures: 0.08 to 0.13). The largest declines were observed in cognitive engagement (-0.44) and perseverance (-0.31). Younger and older cohorts experienced similar adverse changes; however, the younger cohort showed a larger well-being decline in the second-year post-transition. Females experienced more pronounced declines than males. The well-being decline among students residing in remote and very remote areas persisted until the third year after the transition. CONCLUSIONS:School transitions negatively affect students' well-being, with impacts that persist for more than 2 years. This decline was largely attributable to the school transition rather than age-related progression. Females and students residing in remote areas experienced greater declines in well-being than their counterparts. These findings highlight the need for transition-specific support strategies for vulnerable groups that extend beyond the first year of secondary schooling.

The daily allocation of the finite 24-h time budget is strongly associated with physical, mental, and cognitive health. While predictive models can estimate the relationship between time-use compositions and health outcomes such as body mass index, life satisfaction, and cognition, most optimization approaches focus only on maximizing expected benefit and do not consider the uncertainty inherent in data-driven prediction. Ignoring uncertainty in health-related decisions can lead to unrealistic time-use recommendations. To address this gap, we introduce an uncertainty quantification Quality Diversity (QD) framework for a more reliable time-use recommendation. Objective functions are derived using compositional data analysis using a large child cohort dataset n > 1000 , to capture the relationship between daily activity compositions and multiple health indicators. We develop a new approach that incorporates predictive uncertainty into QD processes and produces more reliable recommendations that balance the expected health benefits with the confidence of the model. We explore the solution space through variable-based and objective-based behavioral representations, revealing diverse high-quality time-use composition and explicit relationships between health outcomes under uncertainty. By embedding uncertainty directly into optimization, our framework shifts the time-use recommendations toward regions of lower uncertainty while preserving high-quality structures for more reliable decision-making in behavioral health.

Abstract Background Current methods for assessing the healthfulness of 24-hour movement behaviours (sleep, sedentary time, light physical activity, moderate-to-vigorous physical activity) use binary classifications that fail to capture their continuous and compositional nature. This study introduces a percentile-based scoring and visualization approach to evaluate the healthfulness of movement behaviour time-use compositions, using social-emotional development in early childhood as an example. Methods This cross-sectional study includes 560 children aged 1.2–2.9 years and 1,500 children aged 3.0-4.9 years from Sleep and Activity Database for the Early Years (SADEY), an international accelerometer repository of young children’s movement behaviours. Sedentary time, light physical activity, and moderate-to-vigorous physical activity were measured using accelerometers. Sleep duration was parent-reported. Social-emotional development was assessed using age- and sex-normalized scores from the Strengths and Difficulties Questionnaire. Linear regression models with compositional covariates were used to model associations between movement behaviours and Strengths and Difficulties Questionnaire scores. Representative grids containing all possible time-use compositions (in 5 min/d increments) of sleep, sedentary time, light physical activity, and moderate-to-vigorous physical activity were developed. The regression models were applied to each time-use composition in the grid, and the predicted scores were ranked to create percentile scores for different movement behaviour time-use compositions. Results The 24-hour movement behaviour composition was associated with all five Strengths and Difficulties Questionnaire scores in both age groups (p ≤ 0.01). The grids contained 17,577 and 16,535 possible time-use compositions for 1–2 and 3–4-year-olds, respectively. Time-use compositions ranked at the 0th percentile had the least sleep and highest sedentary time, while those ranked at the 100th percentile had the most sleep and least sedentary time. Across the central range of the percentile score distribution (e.g., rankings between the 25th to 75th percentiles), some very different time-use compositions had the same percentile score. Interactive visualization tools were presented to enable real-time exploration of percentile scores for various movement behaviour time-use compositions. Conclusions This study introduces a novel approach to evaluate the health benefits of movement behaviours. This approach moves beyond traditional binary cutoffs to recognize the gradual improvements in health that occur with small changes in behaviours, and that there are multiple pathways to achieving the same health benefits.

The aim of this study was to investigate the association between neighbourhood environment characteristics and meeting the Australian 24-hour movement guidelines. In total, 1216 Australian schoolchildren (10-12 years; 50.2% female) completed questionnaires and wore accelerometers to assess screen time, moderate-to-vigorous physical activity (MVPA), and sleep. Parents reported neighbourhood environment characteristics including general safety, access to destinations and services, social capital and cohesion. Logistic regression analyses examined these associations, stratified by place of residence (69.2% in major cities vs 30.8% in regional and remote areas). Adherence to all guidelines was achieved by 10.1% of participants. Among schoolchildren in regional and remote areas, better access to destinations and services was associated with higher adherence to sleep guidelines (OR = 1.48; 95% CI: 1.14, 1.92), and higher parent-reported social capital and cohesion was associated with higher adherence to MVPA (OR = 1.67; 95% CI: 1.15, 2.43) and screen time (OR = 1.68; 95% CI: 1.07, 2.63) guidelines. There were no statistically significant associations observed among schoolchildren living in major cities. Neighbourhood environment may influence adherence to the 24-hour movement guidelines among regional/remote schoolchildren. Future research is needed to better understand the association between neighbourhood environment and compliance with movement guidelines in urban schoolchildren.

BACKGROUND: Students’ participation in after-school activities changed during the COVID-19 pandemic. However, it remains unclear whether these changes were primarily attributable to the pandemic itself or to age-related developmental trends. This study aims to examine how activity patterns shifted during the pandemic and distinguish temporal trends related to the pandemic from normal developmental changes. METHODS: This repeated-measure cross-sectional study included 112,358 participants contributed 229,700 observations in Australia. Students who were in grades 4 to 9 between 2019 and 2022 were included. Weekly frequency of eleven activities were measured, and categorized as none, moderate, or high. Ordinal logistic regression models were used to examine the temporal trend of after-school activity participation. RESULTS: Clear temporal trends were observed across the four years: participation in social media and e-games increased over the four years, with the greatest rise in social media. Conversely, participation in sports, reading, study, friends, and clubs declined, while TV, arts, chores remained stable. These temporal trends were largely consistent across grade levels, indicating that the changes reflect pandemic-related shifts at the population level rather than age-related developmental effects. An exception was Grade 7, the first year of secondary school, which exhibited the greatest increase in social media use, and largest declines in music, arts, and reading compared to other grades. DISCUSSION: The pandemic was associated with substantial shifts in students’ activity participation, with adverse temporal trends persisting after lockdown ended. Targeted interventions are needed to support students to re-engage in beneficial activities, with special attention to groups experiencing transitioning to secondary school.

Background Alzheimer's disease and related dementias have long prodromal phases during which addressing modifiable lifestyle factors may help delay onset. Digital dementia risk screening tools are emerging as an accessible method to convey risk information and promote preventive strategies. However, little is known about the characteristics, intended users, and risk factor coverage of these tools. Objective To identify and summarize publicly available digital dementia risk screening tools, including their components (risk factors), administration methods, target populations, and implementation settings. Methods A two-phase systematic search was conducted. Phase 1 involved searching Embase, MEDLINE (Ovid), APA PsycINFO, and Google Scholar for digital dementia screening tools. To be included in this review, tools needed to be publicly available, focused on dementia, containing at least one modifiable risk factor, targeted at adults, and published in English. Phase 2 examined the psychometric properties of the identified tools (when administered to older adults). Results Eleven tools met inclusion criteria: CogDrisk, ANU-ADRI, BDSI, DemPort, UKBDRS, CAIDE, LIBRA, Alzhe Alert, DRA, UKB-DRP, and the Knight Alzheimer's Disease Risk Calculator. Tools were web- or app-based and designed for use by individuals, clinicians, or researchers. While the most recent Lancet Commission outlines 14 modifiable risk factors, only 11 were covered across these tools. Only one tool had reported psychometric properties. Conclusions Several digital tools for dementia risk screening are publicly available, but most remain in early development and require further validation in real-world adult populations. Future research should evaluate their reliability and effectiveness to support early identification and behavior change.

Importance:Social media's association with adolescent well-being remains debated. Heavy use has been associated with distress, while abstinence may cause missed connections. Objective:To investigate 3-year longitudinal associations between after-school social media use and adolescent well-being using a large longitudinal cohort dataset modeled within a repeated cross-sectional framework. Design, Setting, and Participants:This cohort study included Australian students in grades 4 through 12 (2019-2022). After-school social media use was self-reported and grouped as none, moderate, or highest. Well-being was assessed using 8 validated indicators (eg, happiness, life satisfaction, emotional regulation), dichotomized as high vs low. Well-being was assessed concurrently with social media use during the annual school-based survey in each year of data collection. Data analysis was conducted from June to July 2025. Exposures:Self-reported after-school social media use between 3 pm and 6 pm (weekdays), classified into 3 categories: none (0 h/wk), moderate (>0 to <12.5 h/wk), and highest (≥12.5 h/wk). Main Outcomes and Measures:The primary outcome was overall well-being, measured as the mean score across 8 validated domains (happiness, optimism, life satisfaction, worry, sadness, perseverance, emotional regulation, and cognitive engagement), dichotomized as high vs low (<3 on a scale of 1-5). Secondary outcomes were each individual well-being indicator, similarly dichotomized. Mixed-effects logistic models were used for analyses, stratified by sex and adjusted for demographic covariates. Results:The analytic sample included 100 991 adolescents, contributing 173 533 observations (86 582 [49.9%] observations from female participants; mean [SD] age, 13.5 [2.2] years). A U-shaped association was observed between after-school social media use and well-being. Compared with moderate users, adolescents with the highest use had greater odds of low well-being (grades 7-9, girls: odds ratio [OR], 3.13 [95% CI, 2.88-3.39]; boys: OR, 2.25 [95% CI, 1.86-2.72]), while nonusers also had higher odds of low well-being in later adolescence (grades 10-12, girls: OR, 1.79 [95% CI, 1.41-2.27]; boys: OR, 3.00 [95% CI, 2.01-4.46]). These patterns were consistent across survey years and robust to sensitivity analyses. Conclusions and Relevance:In this cohort study of students in grades 4 through 12, social media's association with adolescent well-being was complex and nonlinear, varying by age and sex. While heavy use was associated with poorer well-being and abstinence sometimes coincided with less favorable outcomes, these findings are observational and should be interpreted cautiously.

This study examined the associations between 24-h movement behaviors and cognitive function in cognitively unimpaired older adults, and whether Alzheimer's disease brain signatures, including gray matter mean diffusivity (GMMD) and thickness/volume signatures, moderated these associations. A total of 91 participants were enrolled in the AGUEDA trial (NCT05186090), with 89 included in this cross-sectional analysis (mean age = 71.61 ± 3.85 years; 56.2% females). Moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behavior (SB), and sleep were objectively assessed using 9-day wrist-worn accelerometry. Cognitive function was assessed across attention/inhibitory control, episodic memory, processing speed, visuospatial processing, and working memory. Thickness/volume and GMMD signatures were derived from cortical and hippocampal regions. Compositional linear regression models (CoDA) examined associations, adjusting for age, sex, and education, and tested interactions between movement behaviors and Alzheimer's disease brain signatures. No direct associations were observed between 24-h movement behaviors and cognitive domains (all p > 0.05). However, GMMD signature, but not thickness/volume signature, moderated several movement behavior-cognition associations. Moderation was most evident for episodic memory, with significant interactions for MVPA, LPA, and sleep, while LPA exhibited the broadest pattern of moderation across cognitive domains, interacting with attention/inhibitory control, episodic memory, processing speed, and working memory (all p for interaction ≤ 0.04). In conclusion, the associations between 24-h movement behaviors and cognitive function may depend on microstructural brain integrity (i.e., GMMD). These findings highlight the importance of considering Alzheimer's disease-related brain signatures when investigating lifestyle-cognition associations and support a more personalized approach to promoting cognitive health in older adults. Future prospective longitudinal studies and CoDA interventional trials are needed to establish temporal precedence and evaluate whether targeted time reallocations can actively attenuate cognitive trajectories across distinct microstructural vulnerability profiles. Trial Registration: NCT05186090.

Background: Cancer survivors face elevated risks of mortality from cardiovascular disease (CVD). The potential importance of physical activity (PA) and other behaviours across the 24-hour day (e.g. sedentary behaviour (SB) and sleep) for CVD-mortality risk is not well understood in this at-risk population. Objectives: To assess the importance of 24-hour movement behaviour, using a compositional approach, for mitigating CVD-mortality amongst cancer survivors. Methods: Participants with a prior cancer diagnosis were drawn from the UK Biobank accelerometry sub-study (n=6,158). Accelerometer-derived movement (moderate-to-vigorous PA (MVPA), vigorous PA (VPA), moderate PA (MPA), light PA (LPA), SB, sleep) was examined in relation to CVD-mortality, identified from health record linkage data (using Fine-Gray Cox proportional-hazards models adjusted for demographic, health, lifestyle covariates). Results: Median follow-up was 8.0 years (Q1-Q3: 7.4-8.5), with n=500 (8.2%) deaths (CVD-deaths: n=118). Greater MVPA, in place of any other behaviour, was inversely associated with CVD-mortality with e.g. 10% lower hazard if MVPA theoretically replaced 7 minutes (mins)/day SB (Hazard ratio (HR): 0.91, (95% Confidence Interval: 0.86-0.95)), 9 mins/day LPA (HR: 0.90, 0.83-0.97), or 11 mins/day sleep (HR: 0.90, 0.83-0.97). The VPA component of MVPA proved critical, requiring only ~1-2 additional mins/day for equivalent hazard reduction. Sleep duration, was also inversely associated with CVD-mortality. A 10% lower hazard required replacing 29 mins/day of SB with sleep (HR: 0.90, 0.84-0.96); no other behavioural replacement amongst SB, sleep or LPA could provide an equivalent risk reduction. Conclusions: Among cancer survivors, the most potent reduction in CVD-mortality followed theoretically reallocating time to higher intensity movement.

Time compositions of physical behaviours are associated with premature mortality, but the moderating role of sleep remains unclear. Using data from the UK Biobank accelerometry subsample, we examined associations of time reallocations between five device-measured physical behaviours (sleep, sedentary behaviour (SB), standing, light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA)) with all-cause, cardiovascular disease (CVD) and physical activity-related cancer mortality, and the potential effect modification by sleep duration and regularity. Compositional Cox regression was used to examine associations of behavioural reallocations with mortality. In 58,149 adults, 2,209 deaths occurred over a mean follow-up of 8.0 years. Among participants who meet sleep duration guidelines, reallocating 30 minutes from sleep to standing, LPA or MVPA was favourably associated with all-cause mortality with HRs of 0.86 (95%CI 0.79, 0.93), 0.87 (0.80, 0.95), and 0.80 (0.73, 0.87), respectively. Reallocating 30 minutes from sleep to SB, standing, or LPA was adversely associated with CVD risk (HRs 1.08 (1.02, 1.15), 1.10 (1.01, 1.20), and 1.11 (1.03, 1.20)) among those not meeting guidelines. Beneficial associations of reallocating SB to sleep were evident only amongst short (<7h/day) or regular (SRI>87.8) sleepers across mortality outcomes. Our findings support incorporating sleep characteristics into future personalised behavioural interventions design and behavioural targets. ### Competing Interest Statement ES is a paid consultant and holds equity in Complement One, a US-based commercial entity whose products and services relate to heathy lifestyle behaviours. All other authors disclose no conflict of interest for this work. ### Funding Statement This study is funded by an Australian National Health and Medical Research Council (NHMRC) Investigator Grant (APP1194510). The funder had no specific role in any of the following study aspects: the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. DD was supported by an ARC DECRA fellowship (DE230101174). JMB is supported through a British Heart Foundation grant (SP/F/ 20/150002). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The ethical approval was received from the UK National Health service (NHS) and National Research Ethics Service for the UK (No. 11/NW/0382) and participants provided written informed consent. All information and materials in the manuscript are original and have not been submitted for publication elsewhere. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are available from the UK Biobank, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with the permission of the UK Biobank.

Background:Sleep, physical activity, and nutrition (SPAN) are key determinants of both life expectancy (lifespan) and disease-free life expectancy (healthspan), yet are often studied in isolation. This study aimed to determine the minimum combined SPAN improvements needed for a longer lifespan and healthspan. Methods:This prospective cohort comprised 59,078 participants from the UK Biobank, recruited between 2006 and 2010 (median age: 64.0 years; 45.4% male). Between 2013 and 2015, a subsample of participants was invited to wear a wrist worn accelerometer for 7 days. Moderate to vigorous physical activity (MVPA; mins/day) and sleep (hours/day) were calculated using a validated wearables-based algorithm. Diet was assessed using a 10-item diet quality score (DQS), including intake of vegetables, fruits, grains, meats, fish, dairy, oils, and sugar-sweetened beverages (ranging 0-100; higher indicates better quality). Lifespan and healthspan (free of cardiovascular disease (CVD), cancer, type II diabetes, chronic obstructive pulmonary disease (COPD), and dementia) were estimated across 27 joint tertile SPAN combinations and a composite SPAN score using life tables. Findings:Over an 8.1-year median follow-up, 2458 deaths, 9996 CVD, 7681 cancers, 2971 type II diabetes, 1540 COPD, and 508 dementia events occurred. Compared to the least favourable tertiles, the optimal tertiles (7.2-8.0 h/day of sleep; >42 min/day of MVPA; DQS of 57.5-72.5) had 9.35 additional years of lifespan (95% CI: 6.67, 11.63) and 9.45 years of healthspan (95% CI: 5.45, 13.61). Compared to the 5th percentile, a minimum combined improvement of 5 min/day of sleep, 1.9 min/day MVPA, and a 5-point increase in DQS (e.g., additional ½ serving of vegetables/day or additional 1.5 servings of whole grains per day) was associated with 1 additional year of lifespan (95% CI: 0.69, 1.15). For healthspan, a combined improvement of 24 min/day of sleep, 3.7 min/day of MVPA, and a 23-point DQS increase was associated with 4.0 additional years (95% CI: 0.50, 8.61). Interpretation:Modest concurrent improvements in sleep, physical activity, and diet were associated with meaningful gains in lifespan and healthspan. Funding:Australian National Health and Medical Research Council.

Abstract Background Insufficient physical activity (PA) and excessive sedentary behaviour is associated with several cancers. Personalised approaches to increasing healthy movement behaviours over unhealthy behaviours may be more effective than a one-size-fits-all approach. Exploring both postures, and intensities across the 24-hour day may reveal actionable behavioural alternatives from current guidance. Methods Using a novel dual-compositional approach, we assessed how differences in participant’s composition of 24-hour daily movement (postures and intensities) and sleep are differentially associated with PA-related cancer incidence (a composite of 13 sites linked with physical inactivity). This prospective analysis involved adults drawn from the UK Biobank accelerometry subsample each followed-up by health linkage. Participant’s daily movement was classified into two 24-hour compositions. Composition 1 (posture-focused): sleep duration, sedentary behaviour (SB), standing, moving at any intensity. Composition 2 (intensity-focused): sleep duration, sedentary time (ST), light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Secondary analysis combined VPA and MPA as moderate-to-vigorous PA (MVPA). PA-related cancer diagnoses were captured from health registry data for up to 9.5 years (y). Cox-proportional hazards models were adjusted for age, sex, education, smoking, alcohol, diet, parental cancer history, cardiovascular disease and medication use. Results Analyses included 59,218 (55% female) participants (mean [SD] age: 61.7 [7.8]y), with a median follow-up of 8.0y [IQR: 7.4-8.5y; 464,640 person years] with 2,385 (4%) incident cancer events. Among the average, active participant, greater moving in place of other behaviours was associated with lower cancer risk, e.g. theoretically replacing 15 min of sleep or SB with 15 min of moving was associated with hazard ratios (HR) of 0.98 (95% Confidence Interval (95%CI): 0.97–0.99) and 0.98 (95%CI: 0.97–0.99), respectively. Similar risk reduction was observed with 30 min additional standing in place of sleep or SB. Regarding intensity, greater MVPA, in place of any behaviour proved most robustly associated with lower risk, although notably, the VPA component within MVPA proved the critical intensity. Conclusions Beyond MVPA, moving at any intensity, in place of other postures, was associated with reduced risk of cancer. However, greater standing may also provide a plausible behavioural adjunct or alternative, warranting further investigation.

How people spend their finite time budget of 24 hours on daily activities is linked to their wellbeing. Yet, how to best allocate time to optimise multi-dimensional wellbeing (physical, mental and cognitive) remains unknown. Here, we utilise a number of (objective) functions derived using compositional data analysis and a large child cohort ( \(n>1{,}000\) ), to predict how time allocation is associated with wellbeing outcomes such as body mass index, life satisfaction and cognition. We develop and advocate joint cumulative distribution function constraints to ensure the feasible solutions do not extrapolate the sampled data for which the objective function is derived from. Moreover, we incorporate quality diversity (QD) approaches to study these objective functions. We define two types of behavioural spaces (BSs), one based on the activities, called the variable-based behavioural space (VBS), and the other based on the objectives, called the objective-based behavioural space (OBS). The VBS allows us to generate a set of high-quality solutions with different activity durations, while the OBS allows us to tradeoff different wellbeing dimensions against each other. We also demonstrate a web application, Time allocation optimiser, for creating personalised, optimised time-use plans.

BACKGROUND:Behaviours across a 24-hour day, including physical activity, sedentary time and sleep, are disrupted following cancer and contribute to cancer-related outcomes. This study describes the day-to-day 24-hour behaviour profiles of individuals with and without cancer, considering time since diagnosis and cancer types. METHODS:Seven days of accelerometer data from the UK Biobank (M±SDage=62.3±7.9 years; 56.4% female) were derived from machine learning models to assess the 24-hour behaviours in individuals with cancer (n=10 152; M±SDyears since diagnosis=7.4±6.1 years) compared with healthy (free of diseases) individuals (n=13 722). Diagnoses were identified using the International Classification of Disease codes within cancer registries. Bayesian compositional data analysis compared profiles between individuals with and without cancer, across time since diagnosis (<1 year, 1-5 years, >5 years) and 14 cancer types. RESULTS:The least physically active profiles were observed for individuals within 1 year following cancer diagnosis and in cancers with poor prognoses. Compared with healthy individuals, those within 1 year following cancer diagnosis had 40 min/day less physical activity (light plus moderate-to-vigorous intensities), compensated by 40 min/day more inactive time (sedentary plus sleep periods). Differences also varied across cancer types, ranging from 22-75 min/day less physical activity and 22-75 min/day more inactive time, between individuals with cancers and healthy individuals. Cancers with poorer prognoses (eg, lung, gastrointestinal tract) had the least optimal profiles, whereas cancers with better prognoses (eg, prostate, skin) showed profiles closer to healthy individuals. CONCLUSION:The 24-hour behaviour profiles differed by cancer history, prognosis and type. Supporting a healthy balance of behaviours, that can feasibly be achieved within a 24-hour day, should be considered for cancer survivors, particularly in the year after diagnosis and in poor prognosis cancers.

OBJECTIVES:To identify profiles of compositional movement behaviour patterns among children and examine cross-sectional and 12-month associations with adiposity markers and health-related quality of life (HRQoL). DESIGN:Secondary analysis of data from the TransformUs cluster randomised controlled trial with cross-sectional and 12-month follow-up analyses. SETTING:Primary schools in metropolitan and regional areas of Victoria, Australia. PARTICIPANTS:Children aged 7-11 years with valid accelerometer at baseline, regardless of demographic, adiposity and HRQoL data available (n=792), were included in the analytical sample for the latent profile analysis. MEASURES:Sedentary time, light-intensity physical activity (LPA) and moderate- to vigorous-intensity physical activity (MVPA) along with their respective mean bout lengths were derived from raw acceleration data. Latent profile analysis used these measures (total times, as isometric log ratios and mean bout lengths) as input variables to classify distinct profiles for us as a categorical exposure variable in regression models. Primary outcomes were age- and sex-standardised body mass index, waist circumference and parent-reported HRQoL at baseline. Secondary outcomes were the same measures assessed at 12-month follow-up. RESULTS:Four distinct profiles were identified. The high MVPA-short sedentary bout profile (n=184) was characterised by the highest levels of MVPA, moderate sedentary time and the shortest mean sedentary bout duration. The low sedentary-high LPA profile (n=54) had the lowest sedentary time, the highest LPA and the longest mean LPA bout duration. Two profiles were characterised by high sedentary time: the high sedentary-long sedentary bout profile (n=149), which had the longest mean sedentary bout durations, and the high sedentary-shorter bouts profile (n=405), which also had high sedentary time but shorter bout durations for all intensities. While the omnibus Wald test for differences across profiles indicated uncertainty in the overall profile effect, the high MVPA-short sedentary bout profile had favourable adiposity levels cross-sectionally compared with the high sedentary-long sedentary bout reference profile in pairwise comparisons. No longitudinal associations were detected. CONCLUSIONS:Four distinct movement profiles were identified. Few pairwise differences between health outcomes were observed. While MVPA remains a key factor for promoting healthy body weight, our findings suggest that a variety of movement patterns - including those characterised by lower sedentary time and higher LPA - may also support health in children. TRIAL REGISTRATION:This study is a secondary analysis of the TransformUs effectiveness-implementation trial, registered with the Australian Clinical Trials Registry (ACTRN12617000204347; 1 April 2017).