Time-use differences in 24-hour movement behaviours by sociodemographic and health- related factors among Japanese adults

Time-use differences in 24-hour movement behaviours by sociodemographic and health- related factors among Japanese adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Time-use differences in 24-hour movement behaviours by sociodemographic and health- related factors among Japanese adults Yu-Tai Liu, Ai Shibata, Kaori Ishii, Sayaka Kurosawa, Koichiro Oka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453049/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Adults’ daily time spent in sedentary behaviour (SB), sleep, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA) form the 24-hour movement behaviour composition, where more time in one behaviour means less time in others. Differences in daily time-use patterns across sociodemographic and health-related groups may drive health inequalities. This study examined these differences using compositional data analysis (CoDA) to inform tailored public health strategies. Using 2023 survey data from 2,718 Japanese adults aged 20–59, we applied compositional MANOVA to test variations across sociodemographic (sex, age, marital status, education, income, residential area, occupational type) and health-related factors (smoking, alcohol, BMI). Back-transformed log-ratio differences with 95% bootstrap confidence intervals were used for interpretation. Participants spent 29.5%, 33.6%, 0.6%, and 36.3% of their day in SB, sleep, MVPA, and LPA. Significant differences were found by sex, marital status, living arrangement, and occupation: unmarried, those living alone, and desk-based workers engaged in more SB and less LPA, while men and physically demanding workers had higher MVPA. Sleep showed minimal variation. This first CoDA study in Japanese working-age adults highlights the need for contextual strategies, such as reducing SB among desk-based workers and promoting MVPA among women and socially isolated groups. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors 24-hour movement behaviours Compositional data analysis Sociodemographic factors Health-related factors Japanese adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Adults’ daily time-use consists of light-intensity physical activity (LPA), moderate-vigorous intensity physical activity (MVPA), sedentary behaviours (SB), and sleep, which are collectively referred as “24-hour movement behaviours” 1 . Abundant evidence shows that sufficient physical activity and adequate sleep duration beneficially contribute to various health outcomes 2 , 3 , 4 , whereas excessive sedentary time introduces detrimental health effects 5 , 6 . To develop practical interventions and targeted policies to promote healthier lifestyles, researchers increasingly identify differences in behaviours across individuals’ sociodemographic and health-relevant factors (e.g. smoking, alcohol consumption, and body mass index) 7 , 8 , 9 , 10 . Several significant correlates of behavioural patterns have been reported. For example, men may be likely to engage in MVPA than women 11 , 12 , and individuals with a higher socioeconomic status may tend to spend more time on SB and have an adequate sleep duration (7–8 hours per day) 13 , 14 . Because the 24-hour day is finite, increases in one behaviour inevitably reduce time available for other behaviours 15 . The differences in daily time allocation, together with the 24-hour time-budget constraint, highlight the need to consider individual characteristics and the interdependent nature of time use when studying movement behaviours. However, many previous studies have examined behaviours in isolation, overlooking their co-dependency within a single day. To address this limitation and advance the integration of daily behaviours, a novel compositional data analysis approach (CoDA) has drawn attention in the behavioural epidemiology field 16 , 17 . Compared to the traditional approach using absolute time measures, CoDA provides more robust interpretations by analysing behaviours’ relative composition within the limited 24-hour day (e.g. the SB’s role relative to LPA, MVAP, and sleep), allowing researchers to explore individuals’ 24-hour movement behaviours in a time-constrained nature. Despite the increasing applications of CoDA to examine the associations between 24-hour movement behaviours and health outcomes (such as all-cause mortality and mental well-being) 18 , only a few studies have used CoDA to investigate the sociodemographic factors and their relationship with time-use patterns 19 , 20 , 21 , 22 , 23 . Two Japanese studies found sex differences in physical activity participation, with women engaging in more LPA and less SB than men. Research with UK working-age adults showed that individuals of lower socioeconomic status spend less time sitting, whereas those with higher socioeconomic status allocate more time to exercise. A study of New Zealand’s children revealed that ethnicity, annual household income, or deprivation status significantly influence children’s 24-hour movement behaviours. For Japanese working-age adults, time spent in daily components may often be influenced by family structure and occupational cultures 24 . Because working-age adults are in a life stage critical for shaping long-term health, understanding their time-use patterns is essential for informing targeted public health efforts 25 . Despite growing evidence on correlates of 24-hour movement behaviours, no previous study has applied the CoDA approach to investigate how Japanese adults’ behavioural patterns vary by diverse sociodemographic and health-related factors. Therefore, our study aimed to apply the CoDA to examine how sociodemographic and health-related factors influence Japanese adults’ time use of 24-hour movement behaviours, including LPA, MVPA, SB, and sleep. Methods Study population Data for this cross-sectional study were obtained from a nationwide online survey conducted in 2023 by MyVoiceCom Inc., a Japanese internet research company that maintains a panel of approximately one million registered individuals with various sociodemographic profiles. Using proportionate stratified random sampling based on the 2020 Japanese census, potential participants aged 20 to 59 years were randomly selected within eight groups defined by sex (men, women) and 10-year age group (20–29, 30–39, 40–49, 50–59 years) to ensure demographic balance and reduce sampling bias. A total of 19,081 registered participants were invited via email to participate by accessing an online platform, which included an explanation of the study’s objectives, data use, and ethical statements. Of those invited, 3,000 individuals completed the questionnaire (response rate: 15.7%) and received incentive points redeemable at affiliated partner facilities. After excluding respondents with invalid behavioural data (e.g., zero time reported for SB, sleep, or LPA; MVPA exceeding 16 hours), 2,718 participants were retained for the final analysis. All responses were anonymised, and personal information was handled according to the company’s privacy policy. The study protocol was reviewed and approved by the Institutional Ethics Committee of Waseda University (approval number: 2022 − 407). All participants provided electronic informed consent prior to completing the online survey. On the first survey page, they were required to read the study information and explicitly agree to participate before proceeding. Participation was entirely voluntary, and respondents could withdraw at any time without consequence. The study adhered to the ethical principles of the Declaration of Helsinki. 24-hour movement behaviours measurement The 24-hour movement behaviour was defined as the daily time spent in sleep, SB, MVPA, and LPA, expressed as proportions of a 24-hour day. These data were collected using a questionnaire specifically developed to assess 24-hour physical behaviours, with established validity and reliability 26 . Participants reported the average time (in hours and minutes) they typically spent per day in each behaviour over a usual week. Sleep time referred to the total duration of sleep. SB was defined as the combined time spent sitting and lying down, excluding sleep. MVPA referred to activities that can cause a slight to substantial increase in heart rate or breathing. LPA was defined as the time engaged in any other physical activity not captured by the other three categories. When the reported durations did not sum to 24 hours, LPA was estimated as the residual time by subtracting the reported sleep, SB, and MVPA from 1440 minutes. Sociodemographic and health-related factors Sociodemographic and health-related factors were assessed via an online questionnaire. The sociodemographic variables included sex (men, women), age group (20–39 years, 40–59 years), marital status (unmarried, married), living arrangement (living alone, living with others), education level (high school or below, above high school), annual household income (less than JPY 5 million, JPY 5 million or more), residential area (urban, suburban, rural), and occupational activity type (unemployed, desk-based, standing, walking, or manual labour). Health-related factors included smoking status (non-smoker, current smoker), alcohol consumption (non-current drinker, current drinker), and body mass index (BMI). BMI was calculated from self-reported height and weight and classified into three categories: underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), and overweight/obese (≥ 25 kg/m²). Statistical Analysis The distribution of participants across sociodemographic and health-related characteristics was summarised. In the compositional data analysis (CoDA) approach 17 , zero values in any movement behaviour component were imputed using the expectation–maximisation (EM) algorithm to enable log-ratio transformation. Absolute time values were then converted to compositional means and standardised to a 1440-minute day. Compositional mean bar plots were generated to visualise the relative distribution of the four movement behaviours (sleep, SB, LPA, and MVPA) across sociodemographic and health-related groups, in comparison to the overall compositional mean. To examine group-level differences in 24-hour movement behaviour compositions, the four-part composition was expressed as isometric log-ratio (ilr) coordinates. These coordinates were used as dependent variables in compositional multivariate analysis of variance (MANOVA), with sociodemographic and health-related variables (e.g., sex, age group, marital status, living arrangement, education, income, residential area, occupational activity type, BMI, smoking, alcohol use, and chronic disease diagnosis) as independent factors. For sociodemographic and health-related factor groups showing significant compositional differences, 95% bootstrap percentile confidence intervals (CIs) were calculated for the log-ratio of group-specific compositional means relative to the overall mean, in order to identify which behaviours contributed to the observed differences 27 , 28 . Statistical significance was set at p < 0.05. All data processing and analyses were conducted using SAS version 9.4 and R version 4.3.3. Results Among the 2718 participants (mean age: 41.8 years), the sex distribution was approximately equal, with 50.5% men and 49.5% women. A total of 58.2% of participants were aged 40-59 years, and a substantial proportion had higher education levels, annual household incomes of 5 million JPY or more, and resided in suburban areas. Around one-fifth lived alone (21.4%) or were unemployed (22.3%), nearly half reported being married (49.8%). Regarding health-related factors, most participants had a normal BMI (65.9%), over half were current drinkers (54.2%), and 17.8% were current smokers (Table 1). Based on the compositional means, participants spent an average of 424.8 minutes in SB, 483.5 minutes sleeping, 9.0 minutes in MVPA, and 522.6 minutes in LPA, corresponding to 29.5%, 33.6%, 0.6%, and 36.3% of the 24-hour day, respectively (Table 1). Table 1 (listed at the end of the manuscript) Compositional MANOVA indicated significant differences in overall 24-hour movement behaviour compositions across several factors, including sex, age group, marital status, living arrangement, annual household income, occupation activity type, smoking status, and alcohol consumption (Table 2). Table 2 (listed at the end of the manuscript) Unmarried individuals, those living alone, and individuals with desk-based occupations tend to allocate more time to SB and less time to LPA than the overall compositional mean (Figures 1, 2, 3a, 3b). For example, bootstrap estimations showed that unmarried participants spent 8.9% (4.7% to 13.8%) more time in SB and 8.4% (-12.5% to -4.8%) less time in LPA. Occupational differences were even more evident: desk-based workers spent 20% (15.5% to 25.2%) more time in SB, while those in standing, walking, or labour-intensive jobs spent over 30% less time in SB. These groups all showed inverse patterns for LPA time. Figure 1, 2, 3a, and 3b (listed at the end of the manuscript) MVPA differences were most pronounced across sex, age groups, annual household income levels, occupation activity types, smoking, and drinking status. Figure 4 with bootstrap estimates indicated that men engaged in 36.9% (19.3% to 56.1%) more MVPA, while women engaged in 27.4% (-37.5% to -16.7%) less than the average. Occupation activity type was a strong correlate of MVPA: those in physically demanding roles (e.g., standing, walking, or labour-based) performed significantly more MVPA, with walking and labour workers reporting over 100% more MVPA than the overall average (Figure 3a, 3b). In contrast, unemployed and desk-based individuals performed considerably less MVPA. Similar higher MVPA patterns were observed among current smokers and drinkers. Figure 4 (listed at the end of the manuscript) Lastly, no significant differences in sleep duration were observed across most factors, with the exception of occupation type. Bootstrap estimations suggested slightly lower sleep durations among participants in standing- or walking-based jobs, while other occupation activity groups showed no substantial deviation from the average sleep time. The differences across age groups, annual household income levels, smoking status, and alcohol consumption are presented in the Supplementary Figures 1 to 4. Discussion Our study revealed distinct time-use patterns in 24-hour movement behaviours across sociodemographic and health-related factors. Unmarried individuals, those living alone, and desk-based workers accumulated more sedentary time and less light physical activity, while men, individuals in physically demanding jobs, and those with smoking or drinking habits reported higher MVPA. These findings highlight how individuals’ social and occupational contexts shape behavioural allocation within a finite 24-hour day. Differences in SB were particularly evident across marital status, living arrangement, and occupation activity type, aligning with previous findings using non-compositional approaches. Unmarried individuals and those living alone have been reported to accumulate more sedentary time. Two Japanese studies found that unmarried adults were more likely to exceed 8 hours of sedentary time per day, and that older adults living alone reported longer durations of television viewing 29 , 30 . These patterns may reflect fewer family-related commitments and greater independent time, contributing to prolonged sedentary behaviour. Desk-based workers accumulated significantly more SB than those engaging in physically demanding occupations, consistent with findings from both Japanese and international studies 31 , 32 , 33 , 34 , 35 , 36 . In Japan, workers in sitting-based jobs exhibited elevated sedentary time during both work and non-work periods. These patterns may be attributed to job demands and limited infrastructure (e.g., the lack of standing desks) and cultural factors that emphasize responsibility and performance, which may discourage active breaks. An inverse relationship between SB and LPA was also evident across marital status, living arrangement, and occupational activity types. This finding aligns with previous studies showing that reductions in SB are more likely to translate into increases in LPA rather than MVPA 37 . In contrast to MVPA, which often involves structured activities, LPA encompasses more incidental movements, such as standing or slow walking 38 . Our findings suggest that promoting LPA may represent a more feasible strategy for reducing sedentary time, particularly among unmarried individuals, those living alone, and desk-based workers. MVPA differed significantly by sex, age groups, annual household income, occupational activity type, smoking, and drinking status. Men reported significantly higher MVPA levels compared with women, corroborating findings from international studies 11 , 12 , 39 , 40 , 41 . However, previous CoDA-based studies among Japanese adults found no significant sex differences in MVPA 19 , 20 . This inconsistency may be attributed to sample differences, as earlier studies were limited to specific prefectures and covered older adults. In contrast, our study included a nationwide working-age population, who may maintain MVPA levels due to occupational and stable physical functions. Occupational activity type emerged as a primary determinant of MVPA. Individuals with physically demanding jobs reported substantially higher MVPA, consistent with prior studies 33 , 42 , 43 . In Japan, Kurita et al. (2019) found that workers in physically demanding jobs, especially manual labour, accumulated the most MVPA. Their occupational classification matched our study, supporting the relevance of our findings. While previous studies have suggested that work-derived MVPA may not necessarily translate into increased non-work activity, our results underscore the significant contribution of occupational demands to overall MVPA levels among the working-age population. Additionally, the clustering of high MVPA levels with smoking and drinking behaviours may reflect sociocultural patterns specific to certain occupational sectors in Japan 44 , 45 , 46 . Beyond occupational factors, our findings indicated that older middle-aged adults (40–59 years) and individuals with higher annual household income also reported modestly higher MVPA participation. These may reflect greater health awareness, increased flexibility in daily schedules, or improved access to physical activity opportunities among economically advantaged groups 47 , 48 . In accordance with previous CoDA-based studies, sleep duration did not significantly differ across most sociodemographic and health-related factors 21 , 22 , 23 . Participants in this study reported an average sleep duration of approximately eight hours, aligning with widely recommended guidelines. Given this adequacy, it is plausible that participants maintained relatively stable sleep time while adjusting their waking-time behaviours by reallocating time between physical activity and sedentary behaviours. This may explain the near absence of significant sleep differences. Minor differences were observed only by occupational activity type, with participants in physically demanding jobs (e.g., standing and walking-based roles) reporting slightly less sleep, potentially due to early shifts or longer work hours in specific industries such as healthcare, logistics, or manufacturing 49 , 50 . These findings have practical implications for designing targeted movement behaviour interventions. Rather than applying uniform strategies, behaviour promotion should be adapted to individuals’ social and occupational contexts. For example, reducing SB may be a more feasible target than promoting MVPA among individuals who live alone or have sedentary jobs. In contrast, those with physically demanding work may require strategies to balance occupational demands with adequate recovery and to incorporate structured leisure-time physical activity to reduce health risks from heavy workloads. This study is among the few to apply compositional data analysis (CoDA) to examine differences in 24-hour movement behaviour compositions by sociodemographic and health-related factors, and it is the first to focus on Japanese working-age adults. Though our approach offered more robust findings based on the compositional nature, several limitations should be noted. First, the cross-sectional design limits our ability to ascertain causal relationships between sociodemographic or health-related factors and time-use behaviours. Second, movement behaviour data were collected through self-reported questionnaires, which possibly introduced recall and social desirability bias 51 . Under- or overestimating time spent in sleep, physical activity, or sedentary behaviour may have affected the precision of the compositional estimates. Third, our questionnaire lacked contextual details about behaviours (e.g., active vs. passive SB, occupational vs. leisure-time PA), which may influence health outcomes differently. Understanding these behavioural contexts could provide a more comprehensive perspective on adults’ daily routines and inform tailored public health interventions. Fourth, we did not differentiate between weekday and weekend behaviours, despite potential variation in time-use patterns across the week among working-age adults 33 . Fifth, the online survey format may have introduced selection bias, particularly under-representing individuals with limited digital literacy or internet access 52 , 53 . Sixth, our study was limited to Japanese adult participants, which may restrict the generalizability of the findings to other populations. Additionally, the relatively low response rate could introduce selection bias and further limit the representativeness of the results. Conclusion This study identified key sociodemographic and health-related factors associated with 24-hour movement behaviour composition among Japanese working-age adults. By applying the CoDA approach to a nationwide representative sample, we offer an integrated understanding of how time is allocated across daily behaviours in diverse population subgroups. These findings underscore the importance of recognising social and occupational contexts when designing strategies to promote healthier and more balanced time-use patterns in working-age adults. Declarations Funding: This study was conducted as part of a project supported by the MHLW Program (Grant Number JPMH20FA0601 and 22FA1004) and the JSPS Grants-in-Aid for Scientific Research (19H04008, 20H04113, 21K11693, and 21K21233). Author Contribution Y.T.L. and K.O. conceived and designed the study. A.S., K.I., S.K., and K.O. contributed to data collection and establishment. 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[Trends in smoking prevalence by occupations defined in the Japan Standard Occupational Classification: A repeated cross-sectional analysis of the Comprehensive Survey of Living Conditions, 2001-2016]. Nihon Koshu Eisei Zasshi Jpn. J. Public Health 68 , 433–443 (2021). https://doi.org/10.11236/jph.20-118 Cerin, E. & Leslie, E. How socio-economic status contributes to participation in leisure-time physical activity. Soc. Sci. Med . 66 , 2596–2609 (2008). https://doi.org/10.1016/j.socscimed.2008.02.012 Spiteri, K. et al. Barriers and Motivators of Physical Activity Participation in Middle-Aged and Older Adults—A Systematic Review. J Aging Phys Act . 27 , 929-944 (2019) https://doi.org/10.1123/japa.2018-0343 Trinkoff, A. M., Storr, C. L. & Lipscomb, J. A. Physically Demanding Work and Inadequate Sleep, Pain Medication Use, and Absenteeism in Registered Nurses. J. Occup. Environ. Med . 43 , 355 (2001). https://doi.org/10.1097/00043764-200104000-00012 Luckhaupt, S. E., Tak, S., & Calvert, G. M. The Prevalence of Short Sleep Duration by Industry and Occupation in the National Health Interview Survey. Sleep , 33 , 149–159. (2010). https://doi.org/10.1093/sleep/33.2.149 Adams, S. A. et al. The Effect of Social Desirability and Social Approval on Self-Reports of Physical Activity. Am. J. Epidemiol . 161 , 389–398 (2005). https://doi.org/10.1093/aje/kwi054 Bethlehem, J. Selection Bias in Web Surveys. Int. Stat. Rev . 78 , 161–188 (2010). https://doi.org/10.1111/j.1751-5823.2010.00112.x Dodge, H. H. et al. Characteristics associated with willingness to participate in a randomized controlled behavioral clinical trial using home-based personal computers and a webcam. Trials 15 , 508 (2014). https://doi.org/10.1186/1745-6215-15-508 Tables Table 1. Characteristics of participants (n=2718) Sociodemographic and health-related factors, n(%) Sex Men 1372 (50.5) Women 1346 (49.5) Age group 20-39 years 1135 (41.8) 40-59 years 1583 (58.2) Marital status Unmarried 1364 (50.2) Married 1354 (49.8) Living arrangement Living alone 581 (21.4) Living with others 2137 (78.6) Education level High school or below 994 (36.6) Above high school 1724 (63.4) Annual household income < 5 millions JPY 1165 (42.9) ≥ 5 millions JPY 1553 (57.1) Residential area Urban 974 (35.9) Suburban 1564 (57.5) Rural 180 (6.6) Occupational activity type Unemployed 605 (22.3) Desk-based 1296 (47.7) Standing 394 (14.5) Walking 349 (12.8) Manual labour 74 (2.7) BMI categories Underweight 395 (14.5) Normal 1791 (65.9) Overweight or obese 532 (19.6) Smoking status Non-smoker 2235 (82.2) Current smoker 483 (17.8) Alcohol consumption Non-current drinker 1244 (45.8) Current drinker 1474 (54.2) Behavioural composition, mean (% of a day) 24-hour movement behaviours Sedentary behaviours (minutes/day) 424.8 (29.5) Sleep (minutes/day) 483.5 (33.6) Moderate-vigorous physcial activity (minutes/day) 9.0 (0.6) Light physical activity (minutes/day) 522.6 (36.3) Table 2. Results of compositional MANOVA of differences in the compositions of 24-hour movement behaviours between sociodemographic and health-related factors Variable Pillai's trace F η²p p-value Sex 0.014 13.262 0.014 <0.001 Age group 0.003 3.159 0.003 0.024 Marital status 0.013 11.677 0.013 <0.001 Education level 0.002 1.435 0.002 0.230 Living arrangement 0.003 3.027 0.003 0.028 A nnual household income 0.003 3.059 0.003 0.027 Residential area 0.002 1.416 0.002 0.236 Occupational activity type 0.084 83.436 0.084 <0.001 BMI categories 0.002 1.736 0.002 0.158 Smoking status 0.005 4.210 0.005 0.006 Alcohol consumption 0.011 10.389 0.011 <0.001 Bold values represent significant differences in the overall composition of 24-hour movement behaviours within factor levels. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7453049","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511583773,"identity":"1626d517-bc90-4b8c-875b-70df44597fd5","order_by":0,"name":"Yu-Tai Liu","email":"data:image/png;base64,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","orcid":"","institution":"Waseda University","correspondingAuthor":true,"prefix":"","firstName":"Yu-Tai","middleName":"","lastName":"Liu","suffix":""},{"id":511583774,"identity":"2bb9ce85-4857-445d-a277-55c4241a2220","order_by":1,"name":"Ai Shibata","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Ai","middleName":"","lastName":"Shibata","suffix":""},{"id":511583775,"identity":"5099001d-aef7-471f-a8f5-24e6ece86b12","order_by":2,"name":"Kaori Ishii","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Kaori","middleName":"","lastName":"Ishii","suffix":""},{"id":511583776,"identity":"7cb23f85-2241-4eca-a831-2aefbc60cf8a","order_by":3,"name":"Sayaka Kurosawa","email":"","orcid":"","institution":"Rikkyo University","correspondingAuthor":false,"prefix":"","firstName":"Sayaka","middleName":"","lastName":"Kurosawa","suffix":""},{"id":511583777,"identity":"d586f103-2bd7-4b42-8613-81e077b5bf6f","order_by":4,"name":"Koichiro Oka","email":"","orcid":"","institution":"Waseda University","correspondingAuthor":false,"prefix":"","firstName":"Koichiro","middleName":"","lastName":"Oka","suffix":""}],"badges":[],"createdAt":"2025-08-25 11:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7453049/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7453049/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91072336,"identity":"43103784-3b5d-492f-a303-a74204b3fae2","added_by":"auto","created_at":"2025-09-11 10:54:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47726,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage differences with bootstrap 95 confidence intervals in 24-hour movement behaviour composition between marital status and the sample mean.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7453049/v1/4004c0502e5e45284fb212f7.png"},{"id":91070749,"identity":"f564299c-12e1-45d2-a7a2-479619faf4a5","added_by":"auto","created_at":"2025-09-11 10:46:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49897,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage differences with bootstrap 95 confidence intervals in 24-hour movement behaviour composition between living arrangement and the sample mean.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7453049/v1/f9bb94c2d09936382593720a.png"},{"id":91070751,"identity":"c60d7c41-ab47-4716-8451-07eabac8e590","added_by":"auto","created_at":"2025-09-11 10:46:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95130,"visible":true,"origin":"","legend":"\u003cp\u003ea. Compositional mean bar plot for the log-ratio differences between occupational activity type and the sample mean.\u003c/p\u003e\n\u003cp\u003eb. The percentage differences with bootstrap 95 confidence intervals in 24-hour movement behaviour composition between occupational activity type and the sample mean.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7453049/v1/9d09816c5e01e1b94165c3a1.png"},{"id":91072337,"identity":"188183dd-fdaa-4b17-9baf-5dd09483139d","added_by":"auto","created_at":"2025-09-11 10:54:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49758,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage differences with bootstrap 95 confidence intervals in 24-hour movement behaviour composition between sex and the sample mean.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7453049/v1/c2a317b31f9c03c9bbfa56f8.png"},{"id":93394866,"identity":"a0d4c338-c1fc-495b-9341-052ec4dc7fb5","added_by":"auto","created_at":"2025-10-13 11:23:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":959045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7453049/v1/16c861ef-b863-457d-93c5-f4bfcd0755f3.pdf"},{"id":91076450,"identity":"f442856d-7e64-4744-a8ea-7936a4b00805","added_by":"auto","created_at":"2025-09-11 11:10:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":221874,"visible":true,"origin":"","legend":"","description":"","filename":"v1SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7453049/v1/cb7df0c95bf3325e8333f88f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-use differences in 24-hour movement behaviours by sociodemographic and health- related factors among Japanese adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdults\u0026rsquo; daily time-use consists of light-intensity physical activity (LPA), moderate-vigorous intensity physical activity (MVPA), sedentary behaviours (SB), and sleep, which are collectively referred as \u0026ldquo;24-hour movement behaviours\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Abundant evidence shows that sufficient physical activity and adequate sleep duration beneficially contribute to various health outcomes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, whereas excessive sedentary time introduces detrimental health effects\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. To develop practical interventions and targeted policies to promote healthier lifestyles, researchers increasingly identify differences in behaviours across individuals\u0026rsquo; sociodemographic and health-relevant factors (e.g. smoking, alcohol consumption, and body mass index)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Several significant correlates of behavioural patterns have been reported. For example, men may be likely to engage in MVPA than women\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and individuals with a higher socioeconomic status may tend to spend more time on SB and have an adequate sleep duration (7\u0026ndash;8 hours per day)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBecause the 24-hour day is finite, increases in one behaviour inevitably reduce time available for other behaviours\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The differences in daily time allocation, together with the 24-hour time-budget constraint, highlight the need to consider individual characteristics and the interdependent nature of time use when studying movement behaviours. However, many previous studies have examined behaviours in isolation, overlooking their co-dependency within a single day. To address this limitation and advance the integration of daily behaviours, a novel compositional data analysis approach (CoDA) has drawn attention in the behavioural epidemiology field\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Compared to the traditional approach using absolute time measures, CoDA provides more robust interpretations by analysing behaviours\u0026rsquo; relative composition within the limited 24-hour day (e.g. the SB\u0026rsquo;s role relative to LPA, MVAP, and sleep), allowing researchers to explore individuals\u0026rsquo; 24-hour movement behaviours in a time-constrained nature.\u003c/p\u003e\u003cp\u003eDespite the increasing applications of CoDA to examine the associations between 24-hour movement behaviours and health outcomes (such as all-cause mortality and mental well-being)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, only a few studies have used CoDA to investigate the sociodemographic factors and their relationship with time-use patterns\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Two Japanese studies found sex differences in physical activity participation, with women engaging in more LPA and less SB than men. Research with UK working-age adults showed that individuals of lower socioeconomic status spend less time sitting, whereas those with higher socioeconomic status allocate more time to exercise. A study of New Zealand\u0026rsquo;s children revealed that ethnicity, annual household income, or deprivation status significantly influence children\u0026rsquo;s 24-hour movement behaviours.\u003c/p\u003e\u003cp\u003eFor Japanese working-age adults, time spent in daily components may often be influenced by family structure and occupational cultures\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Because working-age adults are in a life stage critical for shaping long-term health, understanding their time-use patterns is essential for informing targeted public health efforts\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Despite growing evidence on correlates of 24-hour movement behaviours, no previous study has applied the CoDA approach to investigate how Japanese adults\u0026rsquo; behavioural patterns vary by diverse sociodemographic and health-related factors. Therefore, our study aimed to apply the CoDA to examine how sociodemographic and health-related factors influence Japanese adults\u0026rsquo; time use of 24-hour movement behaviours, including LPA, MVPA, SB, and sleep.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eData for this cross-sectional study were obtained from a nationwide online survey conducted in 2023 by MyVoiceCom Inc., a Japanese internet research company that maintains a panel of approximately one million registered individuals with various sociodemographic profiles. Using proportionate stratified random sampling based on the 2020 Japanese census, potential participants aged 20 to 59 years were randomly selected within eight groups defined by sex (men, women) and 10-year age group (20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, 50\u0026ndash;59 years) to ensure demographic balance and reduce sampling bias. A total of 19,081 registered participants were invited via email to participate by accessing an online platform, which included an explanation of the study\u0026rsquo;s objectives, data use, and ethical statements. Of those invited, 3,000 individuals completed the questionnaire (response rate: 15.7%) and received incentive points redeemable at affiliated partner facilities. After excluding respondents with invalid behavioural data (e.g., zero time reported for SB, sleep, or LPA; MVPA exceeding 16 hours), 2,718 participants were retained for the final analysis. All responses were anonymised, and personal information was handled according to the company\u0026rsquo;s privacy policy. The study protocol was reviewed and approved by the Institutional Ethics Committee of Waseda University (approval number: 2022\u0026thinsp;\u0026minus;\u0026thinsp;407). All participants provided electronic informed consent prior to completing the online survey. On the first survey page, they were required to read the study information and explicitly agree to participate before proceeding. Participation was entirely voluntary, and respondents could withdraw at any time without consequence. The study adhered to the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003e\u003cb\u003e24-hour movement behaviours measurement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 24-hour movement behaviour was defined as the daily time spent in sleep, SB, MVPA, and LPA, expressed as proportions of a 24-hour day. These data were collected using a questionnaire specifically developed to assess 24-hour physical behaviours, with established validity and reliability\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Participants reported the average time (in hours and minutes) they typically spent per day in each behaviour over a usual week. Sleep time referred to the total duration of sleep. SB was defined as the combined time spent sitting and lying down, excluding sleep. MVPA referred to activities that can cause a slight to substantial increase in heart rate or breathing. LPA was defined as the time engaged in any other physical activity not captured by the other three categories. When the reported durations did not sum to 24 hours, LPA was estimated as the residual time by subtracting the reported sleep, SB, and MVPA from 1440 minutes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSociodemographic and health-related factors\u003c/h3\u003e\n\u003cp\u003eSociodemographic and health-related factors were assessed via an online questionnaire. The sociodemographic variables included sex (men, women), age group (20\u0026ndash;39 years, 40\u0026ndash;59 years), marital status (unmarried, married), living arrangement (living alone, living with others), education level (high school or below, above high school), annual household income (less than JPY 5\u0026nbsp;million, JPY 5\u0026nbsp;million or more), residential area (urban, suburban, rural), and occupational activity type (unemployed, desk-based, standing, walking, or manual labour). Health-related factors included smoking status (non-smoker, current smoker), alcohol consumption (non-current drinker, current drinker), and body mass index (BMI). BMI was calculated from self-reported height and weight and classified into three categories: underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5\u0026ndash;24.9 kg/m\u0026sup2;), and overweight/obese (\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;).\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe distribution of participants across sociodemographic and health-related characteristics was summarised. In the compositional data analysis (CoDA) approach\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, zero values in any movement behaviour component were imputed using the expectation\u0026ndash;maximisation (EM) algorithm to enable log-ratio transformation. Absolute time values were then converted to compositional means and standardised to a 1440-minute day. Compositional mean bar plots were generated to visualise the relative distribution of the four movement behaviours (sleep, SB, LPA, and MVPA) across sociodemographic and health-related groups, in comparison to the overall compositional mean. To examine group-level differences in 24-hour movement behaviour compositions, the four-part composition was expressed as isometric log-ratio (ilr) coordinates. These coordinates were used as dependent variables in compositional multivariate analysis of variance (MANOVA), with sociodemographic and health-related variables (e.g., sex, age group, marital status, living arrangement, education, income, residential area, occupational activity type, BMI, smoking, alcohol use, and chronic disease diagnosis) as independent factors. For sociodemographic and health-related factor groups showing significant compositional differences, 95% bootstrap percentile confidence intervals (CIs) were calculated for the log-ratio of group-specific compositional means relative to the overall mean, in order to identify which behaviours contributed to the observed differences\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All data processing and analyses were conducted using SAS version 9.4 and R version 4.3.3.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 2718 participants (mean age: 41.8 years), the sex distribution was approximately equal, with 50.5% men and 49.5% women. A total of 58.2% of participants were aged 40-59 years, and a substantial proportion had higher education levels, annual household incomes of 5 million JPY or more, and resided in suburban areas. Around one-fifth lived alone (21.4%) or were unemployed (22.3%), nearly half reported being married (49.8%). Regarding health-related factors, most participants had a normal BMI (65.9%), over half were current drinkers (54.2%), and 17.8% were current smokers (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the compositional means, participants spent an average of 424.8 minutes in SB, 483.5 minutes sleeping, 9.0 minutes in MVPA, and 522.6 minutes in LPA, corresponding to 29.5%, 33.6%, 0.6%, and 36.3% of the 24-hour day, respectively (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 (listed at the end of the manuscript)\u003c/p\u003e\n\u003cp\u003eCompositional MANOVA indicated significant differences in overall 24-hour movement behaviour compositions across several factors, including sex, age group, marital status, living arrangement, annual household income, occupation activity type, smoking status, and alcohol consumption (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2 (listed at the end of the manuscript)\u003c/p\u003e\n\u003cp\u003eUnmarried individuals, those living alone, and individuals with desk-based occupations tend to allocate more time to SB and less time to LPA than the overall compositional mean (Figures 1, 2, 3a, 3b). For example, bootstrap estimations showed that unmarried participants spent 8.9% (4.7% to 13.8%) more time in SB and 8.4% (-12.5% to -4.8%) less time in LPA. Occupational differences were even more evident: desk-based workers spent 20% (15.5% to 25.2%) more time in SB, while those in standing, walking, or labour-intensive jobs spent over 30% less time in SB. These groups all showed inverse patterns for LPA time.\u003c/p\u003e\n\u003cp\u003eFigure 1, 2, 3a, and 3b (listed at the end of the manuscript)\u003c/p\u003e\n\u003cp\u003eMVPA differences were most pronounced across sex, age groups, annual household income levels, occupation activity types, smoking, and drinking status. Figure 4 with bootstrap estimates indicated that men engaged in 36.9% (19.3% to 56.1%) more MVPA, while women engaged in 27.4% (-37.5% to -16.7%) less than the average. Occupation activity type was a strong correlate of MVPA: those in physically demanding roles (e.g., standing, walking, or labour-based) performed significantly more MVPA, with walking and labour workers reporting over 100% more MVPA than the overall average (Figure 3a, 3b). In contrast, unemployed and desk-based individuals performed considerably less MVPA. Similar higher MVPA patterns were observed among current smokers and drinkers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4 (listed at the end of the manuscript)\u003c/p\u003e\n\u003cp\u003eLastly, no significant differences in sleep duration were observed across most factors, with the exception of occupation type. Bootstrap estimations suggested slightly lower sleep durations among participants in standing- or walking-based jobs, while other occupation activity groups showed no substantial deviation from the average sleep time. The differences across age groups, annual household income levels, smoking status, and alcohol consumption are presented in the Supplementary Figures 1 to 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study revealed distinct time-use patterns in 24-hour movement behaviours across sociodemographic and health-related factors. Unmarried individuals, those living alone, and desk-based workers accumulated more sedentary time and less light physical activity, while men, individuals in physically demanding jobs, and those with smoking or drinking habits reported higher MVPA. These findings highlight how individuals\u0026rsquo; social and occupational contexts shape behavioural allocation within a finite 24-hour day.\u003c/p\u003e\u003cp\u003eDifferences in SB were particularly evident across marital status, living arrangement, and occupation activity type, aligning with previous findings using non-compositional approaches. Unmarried individuals and those living alone have been reported to accumulate more sedentary time. Two Japanese studies found that unmarried adults were more likely to exceed 8 hours of sedentary time per day, and that older adults living alone reported longer durations of television viewing\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These patterns may reflect fewer family-related commitments and greater independent time, contributing to prolonged sedentary behaviour.\u003c/p\u003e\u003cp\u003eDesk-based workers accumulated significantly more SB than those engaging in physically demanding occupations, consistent with findings from both Japanese and international studies\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In Japan, workers in sitting-based jobs exhibited elevated sedentary time during both work and non-work periods. These patterns may be attributed to job demands and limited infrastructure (e.g., the lack of standing desks) and cultural factors that emphasize responsibility and performance, which may discourage active breaks.\u003c/p\u003e\u003cp\u003eAn inverse relationship between SB and LPA was also evident across marital status, living arrangement, and occupational activity types. This finding aligns with previous studies showing that reductions in SB are more likely to translate into increases in LPA rather than MVPA\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In contrast to MVPA, which often involves structured activities, LPA encompasses more incidental movements, such as standing or slow walking\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Our findings suggest that promoting LPA may represent a more feasible strategy for reducing sedentary time, particularly among unmarried individuals, those living alone, and desk-based workers.\u003c/p\u003e\u003cp\u003eMVPA differed significantly by sex, age groups, annual household income, occupational activity type, smoking, and drinking status. Men reported significantly higher MVPA levels compared with women, corroborating findings from international studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, previous CoDA-based studies among Japanese adults found no significant sex differences in MVPA\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This inconsistency may be attributed to sample differences, as earlier studies were limited to specific prefectures and covered older adults. In contrast, our study included a nationwide working-age population, who may maintain MVPA levels due to occupational and stable physical functions.\u003c/p\u003e\u003cp\u003eOccupational activity type emerged as a primary determinant of MVPA. Individuals with physically demanding jobs reported substantially higher MVPA, consistent with prior studies\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In Japan, Kurita et al. (2019) found that workers in physically demanding jobs, especially manual labour, accumulated the most MVPA. Their occupational classification matched our study, supporting the relevance of our findings. While previous studies have suggested that work-derived MVPA may not necessarily translate into increased non-work activity, our results underscore the significant contribution of occupational demands to overall MVPA levels among the working-age population. Additionally, the clustering of high MVPA levels with smoking and drinking behaviours may reflect sociocultural patterns specific to certain occupational sectors in Japan\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Beyond occupational factors, our findings indicated that older middle-aged adults (40\u0026ndash;59 years) and individuals with higher annual household income also reported modestly higher MVPA participation. These may reflect greater health awareness, increased flexibility in daily schedules, or improved access to physical activity opportunities among economically advantaged groups\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn accordance with previous CoDA-based studies, sleep duration did not significantly differ across most sociodemographic and health-related factors\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Participants in this study reported an average sleep duration of approximately eight hours, aligning with widely recommended guidelines. Given this adequacy, it is plausible that participants maintained relatively stable sleep time while adjusting their waking-time behaviours by reallocating time between physical activity and sedentary behaviours. This may explain the near absence of significant sleep differences. Minor differences were observed only by occupational activity type, with participants in physically demanding jobs (e.g., standing and walking-based roles) reporting slightly less sleep, potentially due to early shifts or longer work hours in specific industries such as healthcare, logistics, or manufacturing\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings have practical implications for designing targeted movement behaviour interventions. Rather than applying uniform strategies, behaviour promotion should be adapted to individuals\u0026rsquo; social and occupational contexts. For example, reducing SB may be a more feasible target than promoting MVPA among individuals who live alone or have sedentary jobs. In contrast, those with physically demanding work may require strategies to balance occupational demands with adequate recovery and to incorporate structured leisure-time physical activity to reduce health risks from heavy workloads.\u003c/p\u003e\u003cp\u003eThis study is among the few to apply compositional data analysis (CoDA) to examine differences in 24-hour movement behaviour compositions by sociodemographic and health-related factors, and it is the first to focus on Japanese working-age adults.\u003c/p\u003e\u003cp\u003eThough our approach offered more robust findings based on the compositional nature, several limitations should be noted. First, the cross-sectional design limits our ability to ascertain causal relationships between sociodemographic or health-related factors and time-use behaviours. Second, movement behaviour data were collected through self-reported questionnaires, which possibly introduced recall and social desirability bias\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Under- or overestimating time spent in sleep, physical activity, or sedentary behaviour may have affected the precision of the compositional estimates. Third, our questionnaire lacked contextual details about behaviours (e.g., active vs. passive SB, occupational vs. leisure-time PA), which may influence health outcomes differently. Understanding these behavioural contexts could provide a more comprehensive perspective on adults\u0026rsquo; daily routines and inform tailored public health interventions. Fourth, we did not differentiate between weekday and weekend behaviours, despite potential variation in time-use patterns across the week among working-age adults\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Fifth, the online survey format may have introduced selection bias, particularly under-representing individuals with limited digital literacy or internet access\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Sixth, our study was limited to Japanese adult participants, which may restrict the generalizability of the findings to other populations. Additionally, the relatively low response rate could introduce selection bias and further limit the representativeness of the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified key sociodemographic and health-related factors associated with 24-hour movement behaviour composition among Japanese working-age adults. By applying the CoDA approach to a nationwide representative sample, we offer an integrated understanding of how time is allocated across daily behaviours in diverse population subgroups. These findings underscore the importance of recognising social and occupational contexts when designing strategies to promote healthier and more balanced time-use patterns in working-age adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis study was conducted as part of a project supported by the MHLW Program (Grant Number JPMH20FA0601 and 22FA1004) and the JSPS Grants-in-Aid for Scientific Research (19H04008, 20H04113, 21K11693, and 21K21233).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.T.L. and K.O. conceived and designed the study. A.S., K.I., S.K., and K.O. contributed to data collection and establishment. Y.T.L. performed the data analysis and drafted the initial manuscript. A.S., K.I., S.K., and K.O. also contributed to manuscript writing and provided critical input on the interpretation of results. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to participant privacy and data use agreement with the survey company, but anonymised data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRollo, S., Antsygina, O. \u0026amp; Tremblay, M. S. The whole day matters: Understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. \u003cem\u003eJ. Sport Health Sci\u003c/em\u003e. \u003cstrong\u003e9\u003c/strong\u003e, 493\u0026ndash;510 (2020). https://doi.org/10.1016/j.jshs.2020.07.004\u003c/li\u003e\n\u003cli\u003eWarburton, D. E. R., Nicol, C. W. \u0026amp; Bredin, S. S. D. 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Act\u003c/em\u003e. \u003cstrong\u003e17\u003c/strong\u003e, 148 (2020). https://doi.org/10.1186/s12966-020-01055-x\u003c/li\u003e\n\u003cli\u003eLandais, L. L. et al. Office workers\u0026rsquo; perspectives on physical activity and sedentary behaviour: a qualitative study. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 621 (2022). https://doi.org/10.1186/s12889-022-13024-z \u003c/li\u003e\n\u003cli\u003eMansoubi, M., Pearson, N., Biddle, S. J. H. \u0026amp; Clemes, S. The relationship between sedentary behaviour and physical activity in adults: A systematic review. \u003cem\u003ePrev. Med\u003c/em\u003e. \u003cstrong\u003e69\u003c/strong\u003e, 28\u0026ndash;35 (2014). https://doi.org/10.1016/j.ypmed.2014.08.028 \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO guidelines on physical activity and sedentary behaviour. https://www.who.int/publications/i/item/9789240015128 (2020).\u003c/li\u003e\n\u003cli\u003eB\u0026eacute;langer, M., Townsend, N. \u0026amp; Foster, C. Age-related differences in physical activity profiles of English adults. \u003cem\u003ePrev. Med\u003c/em\u003e. \u003cstrong\u003e52\u003c/strong\u003e, 247\u0026ndash;249 (2011). https://doi.org/10.1016/j.ypmed.2011.02.008 \u003c/li\u003e\n\u003cli\u003eNagata, J. M. et al. Moderate-to-vigorous intensity physical activity from young adulthood to middle age and metabolic disease: a 30-year population-based cohort study. \u003cem\u003eBr J Sports Med\u003c/em\u003e. \u003cstrong\u003e56\u003c/strong\u003e, 847-853 (2022). https://doi.org/10.1136/bjsports-2021-104231 \u003c/li\u003e\n\u003cli\u003eSkurvydas, A. et al. Men and women choose moderate-to-vigorous physical activity and sedentary behaviors with a \u0026ldquo;hot\u0026rdquo; mind rather than a \u0026ldquo;cold\u0026rdquo; one. \u003cem\u003eBMC Public Health\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 3364 (2024). https://doi.org/10.1186/s12889-024-20866-2 \u003c/li\u003e\n\u003cli\u003eGay, J. L., Buchner, D. M., Smith, J. \u0026amp; He, C. An examination of compensation effects in accelerometer-measured occupational and non-occupational physical activity. \u003cem\u003ePrev. Med. Rep\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 55\u0026ndash;59 (2017). https://doi.org/10.1016/j.pmedr.2017.07.013 \u003c/li\u003e\n\u003cli\u003eSaint-Maurice, P. F. et al. Amount, Type, and Timing of Domain-Specific Moderate to Vigorous Physical Activity Among US Adults. \u003cem\u003eJ Phys Act Health\u003c/em\u003e. \u003cstrong\u003e18\u003c/strong\u003e, S114-S122. (2021). https://doi.org/10.1123/jpah.2021-0174 \u003c/li\u003e\n\u003cli\u003ePoortinga, W. Associations of physical activity with smoking and alcohol consumption: A sport or occupation effect? \u003cem\u003ePrev. Med\u003c/em\u003e. \u003cstrong\u003e45\u003c/strong\u003e, 66\u0026ndash;70 (2007). https://doi.org/10.1016/j.ypmed.2007.04.013 \u003c/li\u003e\n\u003cli\u003eMorikawa, Y. et al. The Effect of Age on the Relationships between Work-related Factors and Heavy Drinking. \u003cem\u003eJ. Occup. Health\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 141\u0026ndash;149 (2014). https://doi.org/10.1539/joh.13-0136-OA \u003c/li\u003e\n\u003cli\u003eTanaka, H. \u0026amp; Kobayashi, Y. [Trends in smoking prevalence by occupations defined in the Japan Standard Occupational Classification: A repeated cross-sectional analysis of the Comprehensive Survey of Living Conditions, 2001-2016]. Nihon Koshu Eisei Zasshi Jpn. \u003cem\u003eJ. Public Health\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 433\u0026ndash;443 (2021). https://doi.org/10.11236/jph.20-118 \u003c/li\u003e\n\u003cli\u003eCerin, E. \u0026amp; Leslie, E. How socio-economic status contributes to participation in leisure-time physical activity. \u003cem\u003eSoc. Sci. Med\u003c/em\u003e. \u003cstrong\u003e66\u003c/strong\u003e, 2596\u0026ndash;2609 (2008). https://doi.org/10.1016/j.socscimed.2008.02.012 \u003c/li\u003e\n\u003cli\u003eSpiteri, K. et al. Barriers and Motivators of Physical Activity Participation in Middle-Aged and Older Adults\u0026mdash;A Systematic Review. J\u003cem\u003e Aging Phys Act\u003c/em\u003e. \u003cstrong\u003e27\u003c/strong\u003e, 929-944 (2019) https://doi.org/10.1123/japa.2018-0343 \u003c/li\u003e\n\u003cli\u003eTrinkoff, A. M., Storr, C. L. \u0026amp; Lipscomb, J. A. Physically Demanding Work and Inadequate Sleep, Pain Medication Use, and Absenteeism in Registered Nurses. \u003cem\u003eJ. Occup. Environ. Med\u003c/em\u003e. \u003cstrong\u003e43\u003c/strong\u003e, 355 (2001). https://doi.org/10.1097/00043764-200104000-00012\u003c/li\u003e\n\u003cli\u003eLuckhaupt, S. E., Tak, S., \u0026amp; Calvert, G. M. The Prevalence of Short Sleep Duration by Industry and Occupation in the National Health Interview Survey. \u003cem\u003eSleep\u003c/em\u003e, \u003cstrong\u003e33\u003c/strong\u003e, 149\u0026ndash;159. (2010). https://doi.org/10.1093/sleep/33.2.149 \u003c/li\u003e\n\u003cli\u003eAdams, S. A. et al. The Effect of Social Desirability and Social Approval on Self-Reports of Physical Activity. \u003cem\u003eAm. J. Epidemiol\u003c/em\u003e. \u003cstrong\u003e161\u003c/strong\u003e, 389\u0026ndash;398 (2005). https://doi.org/10.1093/aje/kwi054 \u003c/li\u003e\n\u003cli\u003eBethlehem, J. Selection Bias in Web Surveys. \u003cem\u003eInt. Stat. Rev\u003c/em\u003e. \u003cstrong\u003e78\u003c/strong\u003e, 161\u0026ndash;188 (2010). https://doi.org/10.1111/j.1751-5823.2010.00112.x \u003c/li\u003e\n\u003cli\u003eDodge, H. H. et al. Characteristics associated with willingness to participate in a randomized controlled behavioral clinical trial using home-based personal computers and a webcam. \u003cem\u003eTrials\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 508 (2014). https://doi.org/10.1186/1745-6215-15-508 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 99.8195%;\"\u003e\n \u003cp\u003eTable 1. Characteristics of participants (n=2718)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eSociodemographic and health-related factors, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1372 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1346 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003e20-39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1135 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003e40-59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1583 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1364 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1354 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eLiving arrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eLiving alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e581 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eLiving with others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e2137 (78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eHigh school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e994 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1724 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eAnnual\u0026nbsp;household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026lt; 5 millions JPY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1165 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003e\u0026ge; 5 millions JPY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1553 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eResidential area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e974 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eSuburban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1564 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e180 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eOccupational activity type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e605 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eDesk-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1296 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eStanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e394 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eWalking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e349 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eManual labour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e74 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eBMI categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e395 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1791 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eOverweight or obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e532 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e2235 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e483 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eNon-current\u0026nbsp;drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1244 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eCurrent drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1474 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eBehavioural composition, mean (% of a day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003e24-hour movement behaviours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eSedentary behaviours (minutes/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e424.8 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eSleep (minutes/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e483.5 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eModerate-vigorous physcial activity (minutes/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e9.0 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 366px;\"\u003e\n \u003cp\u003eLight physical activity (minutes/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e522.6 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 578px;\"\u003e\n \u003cp\u003eTable 2. Results of compositional MANOVA of differences in the compositions of 24-hour movement behaviours between sociodemographic and health-related factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePillai\u0026apos;s trace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving arrangement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ennual\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehousehold\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eincome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003eResidential area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational activity type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e83.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003eBMI categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 265px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 578px;\"\u003e\n \u003cp\u003eBold values represent significant differences in the overall composition of 24-hour movement behaviours within factor levels.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"24-hour movement behaviours, Compositional data analysis, Sociodemographic factors, Health-related factors, Japanese adults","lastPublishedDoi":"10.21203/rs.3.rs-7453049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7453049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdults\u0026rsquo; daily time spent in sedentary behaviour (SB), sleep, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA) form the 24-hour movement behaviour composition, where more time in one behaviour means less time in others. Differences in daily time-use patterns across sociodemographic and health-related groups may drive health inequalities. This study examined these differences using compositional data analysis (CoDA) to inform tailored public health strategies. Using 2023 survey data from 2,718 Japanese adults aged 20\u0026ndash;59, we applied compositional MANOVA to test variations across sociodemographic (sex, age, marital status, education, income, residential area, occupational type) and health-related factors (smoking, alcohol, BMI). Back-transformed log-ratio differences with 95% bootstrap confidence intervals were used for interpretation. Participants spent 29.5%, 33.6%, 0.6%, and 36.3% of their day in SB, sleep, MVPA, and LPA. Significant differences were found by sex, marital status, living arrangement, and occupation: unmarried, those living alone, and desk-based workers engaged in more SB and less LPA, while men and physically demanding workers had higher MVPA. Sleep showed minimal variation. This first CoDA study in Japanese working-age adults highlights the need for contextual strategies, such as reducing SB among desk-based workers and promoting MVPA among women and socially isolated groups.\u003c/p\u003e","manuscriptTitle":"Time-use differences in 24-hour movement behaviours by sociodemographic and health- related factors among Japanese adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 10:46:07","doi":"10.21203/rs.3.rs-7453049/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79367a19-ef7b-4c47-a589-37dbcebdc08f","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54329602,"name":"Health sciences/Health care"},{"id":54329603,"name":"Biological sciences/Psychology"},{"id":54329604,"name":"Social science/Psychology"},{"id":54329605,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-13T11:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 10:46:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7453049","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7453049","identity":"rs-7453049","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}