Association of 24-hour Movement Behaviour with Loneliness and Happiness in Japanese Adults: A Compositional Data Analysis with Isotemporal Substitution Model

Association of 24-hour Movement Behaviour with Loneliness and Happiness in Japanese Adults: A Compositional Data Analysis with Isotemporal Substitution Model | 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 Research Article Association of 24-hour Movement Behaviour with Loneliness and Happiness in Japanese Adults: A Compositional Data Analysis with Isotemporal Substitution Model Yu-Tai Liu, Ai Shibata, Kaori Ishii, Sayaka Kurosawa, Koichiro Oka This is a preprint; it has not been peer reviewed by a journal.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-7322612/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 Introduction: Loneliness and happiness levels are common regular emotions and can contribute to adults’ mental health. daily behaviours, such as sleep, sedentary behaviour (SB), moderate to vigorous-intensity physical activity (MVPA), and light-intensity physical activity (LPA), have been linked to various mental health outcomes. However, no prior study has employed compositional data analysis (CoDA) to examine these behaviours within 24 hours and their relationship to adults' loneliness and happiness. This study investigates how adults’ 24-hour movement behaviour patterns relate to these emotions. Methods: This cross-sectional study included 2,718 participants aged 20-59 from a Japanese online health survey conducted in 2023. Participants' 24-hour movement behaviours (sleep, SB, MVPA, LPA) were assessed using a questionnaire about their time use on a typical day. Loneliness was assessed using the UCLA 3-Item Loneliness Scale, and happiness was self-reported based on their current level of happiness. Compositional data analysis with logistic regression was performed to examine the associations between daily behaviour composition and the experiences of loneliness and happiness. Results: Among the 2,718 adults, 1,214 reported feeling lonely, and 739 feeling unhappy. More time spent in SB, relative to other behaviours, was associated with increased loneliness (OR=1.20, 95% CI: 1.06-1.37) and reduced happiness (OR=0.83, 95% CI: 0.72-0.95). Replacing SB time with sleep was linked to improved estimates of both loneliness and happiness. Substituting 5 to 30 minutes of SB with LPA was associated with lower odds of loneliness while substituting SB with MVPA was associated with higher odds of happiness. Conclusions: Greater time spent in SB was associated with increased loneliness and reduced happiness, suggesting that replacing SB with sleep and physical activity might be essential for improving adults' emotional well-being. These findings give adults insights into how their daily routines impact well-being. 24-hour movement behaviours Loneliness Happiness Japanese adults Compositional data analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Good mental health enables people to deal with stress, work well, and advance overall well-being 1 . Among the factors shaping mental health, loneliness and happiness tend to exist in people’s daily emotions. Loneliness, a common unpleasant experience, arises when an individual’s network of social relations is deficient 2 . In Japan, A 2023 governmental survey found that 39.3% of Japanese aged 16 or older felt lonely occasionally or more frequently 3 . On the other hand, happiness, which is defined as subjective enjoyment, reflects positive experiences over negative ones and satisfaction with one’s personal life 4 . Research has shown that both persistent loneliness and low levels of happiness are associated with more severe mental illness 4 , 5 . Identifying the factors contributing to loneliness and happiness is therefore crucial to promoting a better life for adults. Daily behaviours, including light-intensity physical activity (LPA), moderate- to vigorous-intensity physical activity (MVPA), sedentary behaviour (SB), and sleep, have been shown to influence loneliness and happiness, respectively. Engaging in any mode of physical activity, such as exercise or walking, has been reported to be associated with reduced loneliness, and even a small amount of weekly physical activity may help enhance happiness 6 , 7 , 8 , 9 . In contrast, SB, which is widely recognised to harm numerous health indicators, has been found to be associated with negative emotional states 10 , 11 . Similarly, insufficient sleep, such as sleep deprivation or sleep disturbance, has been found to higher loneliness 12 , 13 . A shorter sleep duration (≤ 6 hours/day) has been demonstrated to be associated with lower levels of happiness 14 . These behaviours within a day, termed 24-hour movement behaviours, are interdependent. For example, spending more or less time on physical activity inevitably affects the time available for SB or sleep. With growing interest in understanding the integrated effects of 24-hour movement behaviours, the compositional data analysis (CoDA) approach has been increasingly applied to account for their co-dependent nature and explore how the composition or reallocation of behaviours within a day contributes to health 15 , 16 . Using this robust framework, many studies have examined the associations between 24-hour movement behaviours and various mental health outcomes. A recent review study identified more MVPA or LPA, reduced SB, and adequate sleep as the healthiest combinations of daily movement behaviours for mental health 17 . Loneliness and happiness are particularly relevant to the mental health of working-age adults, reflecting interpersonal connections and subjective well-being, respectively 18 , 19 , and are often shaped by occupational and social stress and may influence mental well-being. However, only one CoDA-based study has investigated daily behaviours in relation to loneliness and happiness, focusing on older adults. This study found that spending more time in MVPA, including by reallocating 30 minutes from other daily behaviours (LPA, SB, or sleep), was associated with lower loneliness and greater happiness 20 . To date, no studies have applied CoDA to examine these associations among working-age adults. Consequently, the influence of reallocating time between daily behaviours on working-age adults’ loneliness and happiness remains unclear. To address this gap, this study employed CoDA to investigate the associations between relative time spent in SB, sleep, MVPA, and LPA and levels of loneliness and happiness among working-age adults. This study also sought to estimate the influence of reallocating time between 24-hour movement behaviours on loneliness and happiness, potentially informing targeted strategies to arrange working-age adults’ daily time use. Methods Study population This cross-sectional study analysed data from a 2023 Japanese online survey conducted by MyVoice Com Inc., a Japanese internet survey company managing a panel of approximately one million voluntarily registered individuals across Japan with detailed sociodemographic profiles. From this database, 19,081 adults aged 20–59 were randomly selected and stratified equally by sex and age groups (20–29, 30–39, 40–49, and 50–59 years) to ensure balanced representation and minimize selection bias. Participants received an email invitation through the company’s system, containing a link to an online questionnaire and information outlining the study’s purpose, data handling procedures, and ethical considerations. Of those invited, 3,000 participants completed the questionnaire (response rate of 15.7%) and received reward points valued at 123 JPY for partner facilities as an incentive. The analysis included 2,718 participants, excluding those with invalid behavioural information (Zero time spent in SB, sleep, or LAP; MVPA time > 16 hours). All responses were anonymized, and personal information was handled in accordance with MyVoice Com Inc.’s privacy policy. This study was approved by the Institutional Ethics Committee of Waseda University (2022 − 407). Details of the questionnaire are available in Supplementary Material 1. Exposures: 24-hour movement behaviours measurement The 24-hour movement behaviour composition was defined as the daily time proportion of sleep, SB, MVPA, and LPA. This information was assessed by a questionnaire created to assess 24-hour physical behaviours with sound validity and reliability 21 . Participants were asked to recall their time per day in a typical week and report each behaviour’s time in hours and minutes. Sleep time was estimated for the total time spent sleeping. SB time was defined as total sitting and lying time, excluding sleep. MVPA was defined as activities causing at least a slight increase in heart rate or breathing. The LPA time was defined as the time spent in low-intensity physical activity other than the behaviours mentioned above. When the sum of four behaviours times did not sum to 24 hours, the LPA was calculated as the remaining time by subtracting sleep, SB, and MVPA durations from 1440 minutes. These four behaviour durations were expressed as proportions of the 24-hour day. Outcome: Loneliness and happiness Outcome: Loneliness and happiness Loneliness was measured using the three-item University of California, Los Angeles (UCLA) Loneliness Scale, asking: “How often do you feel you lack companionship?”, “How often do you feel left out?”, and “How often do you feel isolated from others?” 22 . Responses were scored on a three-point scale: ‘never or hardly ever,’ ‘sometimes,’ and ‘often. The overall loneliness score ranges from 3–9, and higher scores reflect greater levels of loneliness. UCLA-3 data were also categorized, with scores of 3–5 as “not lonely” and 6–9 as “lonely” 23 . Happiness was assessed using a single-item self-rated question, “How happy do you feel about yourself now?” The participants responded on a reverse Likert-type scale ranging from 1 (happy), 2 (somewhat happy), 3 (somewhat unhappy), and 4 (unhappy). Responses of 1 or 2 were classified as “happy”, and 3 or 4 as “unhappy” 24 . Covariates Sociodemographic variables, lifestyle factors, and body mass index (BMI) were collected via the online questionnaire. Sociodemographic variables included age (years), sex (men, women), marital status (unmarried, married), living arrangements (living alone, living with others), education level (high school or below, above high school), household annual income (JPY < 5 million, ≥ 5 million), were obtained from pre-existing data in the MyVoice Com Inc. panel database. Occupation type (unemployed, desk-based, standing, walking, manual labour), lifestyle factors, including smoking status (non-smoker, current smoker) and alcohol consumption (non-drinker, current drinker), and BMI were measured specifically for this study through the questionnaire. BMI was calculated from self-reported height and weight, categorized as underweight (< 18.5), normal (18.5–24.9), or overweight/obese (≥ 25 kg/m²). Statistical Analysis Data processing and statistical analyses were performed using SAS version 9.4 and R version 4.3.3 software. Chi-square tests or t-tests were used to compare means, standard deviations (SD), and percentages (%) of participants’ characteristics across outcome categories (happy vs. unhappy; lonely vs. not lonely). 24-hour movement behaviour durations were expressed as geometric means, normalized to a 1440-minute day, and their log-ratio differences were compared by loneliness and happiness status. Zero values in any behaviour duration were imputed using the log ratio Expectation–maximisation algorithm in the R function to avoid mathematical indeterminacy. In the compositional data analysis approach, isometric log-ratio ( ilr ) transformation mapped the interdependent 24-hour movement data into real space for standard statistical methods 15 . The four-part composition (sleep, SB, MVPA, and LPA) was transformed into a set of ilr coordinates (z1, z2, z3) by a sequential binary partition. These coordinates capture relative balances: z1 contrasts a selected behaviour (e.g. sleep) with the remaining behaviours, z2 balances MVPA relative SB and LPA, and z3 compares LPA to SB. The ilr transformation allows any behaviour (sleep, SB, MVPA, or LPA) to be placed in z1 sequentially, with this first ilr coordinate (z1) interpreted as containing the primary information. These transformed ilr coordinates set then served as exposure variables in a multivariate logistic regression model assessing the associations with loneliness and happiness. Model 1 was adjusted for age and sex; Model 2 added education level, marital status, living arrangements, household annual income, and occupation type; Model 3 further included BMI, smoking status, and alcohol consumption. Odds ratios (ORs) indicated the changes in loneliness or happiness odds per unit of ilr increase. Compositional isotemporal substitution (ISM) models estimated the influence of pairwise time reallocations (in 5-minute increments up to 30 minutes) on loneliness and happiness odds 16 . Based on the mean 24-hour composition, a simulated behavioural composition was generated by reallocating time between two behaviours (e.g., reducing SB and increasing sleep while keeping other behaviours constant). Then the new set of ilr coordinates was used to predict ORs and 95% confidence intervals (95% CI). Results were further plotted to illustrate changes in loneliness and happiness. Statistical significance was set at p < 0.05. Results Table 1 shows that among the 2,718 participants (mean age: 41.8 years), 1,214 (44.7%) reported feeling lonely, and 739 (27.2%) reported feeling unhappy. Females were more likely to feel lonely, while males were more likely to feel unhappy. Individuals reporting loneliness or unhappiness were more likely to be unmarried, live alone, have lower education and household annual income, be unemployed, and be non-drinkers. Table 1 Characteristics of the study sample with loneliness and happiness status Loneliness Happiness Total sample (n = 2718) Not lonely 1504 (55.3) Lonely 1214 (44.7) P-value Unhappy 739 (27.2) Happy 1979 (72.8) P-value Age, mean years (SD) 41.8 (10.7) 42.26 (10.75) 40.95 (10.56) 0.002 41.04 (10.56) 42.09 (10.74) 0.024 Sex 0.001 < 0.001 Male 1372 (50.48) 931 (67.86) 441 (32.14) 428 (31.2) 944 (68.8) Female 1346 (49.52) 834 (61.96) 512 (38.04) 311 (23.11) 1035 (76.89) Marital status < 0.001 < 0.001 Unmarried 1364 (50.18) 794 (58.21) 570 (41.79) 520 (38.12) 844 (61.88) Married 1354 (49.82) 971 (71.71) 383 (28.29) 219 (16.17) 1135 (83.83) Living arrangements 0.004 < 0.001 Living alone 581 (21.38) 348 (59.9) 233 (40.1) 225 (38.73) 356 (61.27) Living with others 2137 (78.62) 1417 (66.31) 720 (33.69) 514 (24.05) 1623 (75.95) Education level 0.003 < 0.001 High school and below 994 (36.57) 610 (61.37) 384 (38.63) 322 (32.39) 672 (67.61) Above high school 1724 (63.43) 1155 (67) 569 (33) 417 (24.19) 1307 (75.81) Household annual income < 0.001 < 0.001 < 5 million 1165 (42.86) 653 (56.05) 512 (43.95) 456 (39.14) 709 (60.86) ≥ 5 million 1553 (57.14) 1112 (71.6) 441 (28.4) 283 (18.22) 1270 (81.78) Occupation type < 0.001 0.035 Unemployed 605 (22.26) 337 (55.7) 268 (44.3) 188 (31.07) 417 (68.93) Desk-based 1296 (47.68) 894 (68.98) 402 (31.02) 322 (24.85) 974 (75.15) Standing 394 (14.5) 257 (65.23) 137 (34.77) 119 (30.2) 275 (69.8) Walking 349 (12.84) 224 (64.18) 125 (35.82) 90 (25.79) 259 (74.21) Manual labour 74 (2.72) 53 (71.62) 21 (28.38) 20 (27.03) 54 (72.97) Body mass index, BMI 0.184 0.001 Underweight 395 (14.53) 242 (61.27) 153 (38.73) 121 (30.63) 274 (69.37) Normal 1791 (65.89) 1182 (66) 609 (34) 445 (24.85) 1346 (75.15) Overweight/obese 532 (19.57) 341 (64.1) 191 (35.9) 173 (32.52) 359 (67.48) Smoking status < 0.001 0.153 Current smoker 483 (17.77) 348 (72.05) 135 (27.95) 144 (29.81) 339 (70.19) Non-current smoker 2235 (82.23) 1417 (63.4) 818 (36.6) 595 (26.62) 1640 (73.38) Alcohol consumption < 0.001 < 0.001 Current drinker 1474 (54.23) 1019 (69.13) 455 (30.87) 347 (23.54) 1127 (76.46) Non-current drinker 1244 (45.77) 746 (59.97) 498 (40.03) 392 (31.51) 852 (68.49) Data are represented as n (%), unless otherwise specified *P-value based on t-test for continuous variables, and based on Chi-square test for categorical variables. The compositional mean of time spent in SB, sleep, MVPA, and LPA was 424.8, 483.5, 9.0, and 522.6 minutes, respectively (Table 2 ). Compared with the overall sample, participants reporting loneliness spent 9.8% more time in SB, 0.3% more in sleep, 7.2% less in MVPA, and 8.9% less in LPA. Those reporting being unhappy spent 6.2% more time in SB, 1.6% less in sleep, 22.8% less in MVPA, and 3.5% less in LPA. Figure 1 and Fig. 2 show the log ratio differences in compositional means. Table 2 Compositional Differences in 24-hour Movement Behaviours by Loneliness and Happiness Status Loneliness Happiness Total sample (n = 2718) Not lonely 1504 (55.3) Lonely 1214 (44.7) Unhappy 739 (27.2) Happy 1979 (72.8) Compositional mean a , mins/day SB 424.8 403.3 452.1 452.2 414.8 Sleep 483.5 482.5 483.8 476.0 486.1 MVPA 9.0 9.3 8.6 7.2 9.8 LPA 522.6 544.8 495.4 504.6 529.2 Log-ratio of group compositional mean to total sample compositional mean b (%) SB 0 -5.5 9.8 6.2 -2.4 Sleep 0 -0.4 0.3 -1.6 0.5 MVPA 0 3.6 -7.2 -22.8 8.5 LPA 0 4.6 -8.9 -3.5 1.3 a The geometric mean is calculated from the proportion of daily time spent in each behavior and scaled back to 24 hours, ensuring symmetry for compositional data analysis. b The relative time spent in each behaviour is expressed as the log ratio of the group's compositional mean to the full sample's compositional mean. For example, compared to the full sample, unhappy individuals spent 6.2% more time in sleep, 1.6% less time in SB, 22.8% less time in MVPA, and 3.5% less time in LPA. In the fully adjusted compositional logistic regression model, greater SB time, relative to other behaviours, was positively associated with loneliness and negatively associated with happiness. More time spent in sleep and MVPA relative to other behaviours, was significantly associated with higher odds of happiness. No significant associations were found between LPA time and either loneliness or happiness (Table 3 ). Table 3 Associations between 24-hour movement behaviours and loneliness and happiness. (n = 2718) Being lonely Being happy OR (95% CI) P-value OR (95% CI) P-value Model 1 Behaviours relative to others SB 1.22 (1.08–1.38) 0.003 0.82 (0.72–0.93) 0.004 Sleep 0.91 (0.74–1.12) 0.372 1.22 (0.98–1.52) 0.088 MVPA 0.99 (0.96–1.02) 0.686 1.05 (1.02–1.09) 0.004 LPA 0.91 (0.82–1.01) 0.099 0.95 (0.85–1.06) 0.389 Model 2 Behaviours relative to others SB 1.21 (1.06–1.37) 0.005 0.83 (0.72–0.95) 0.009 Sleep 0.88 (0.71–1.09) 0.245 1.29 (1.02–1.62) 0.034 MVPA 1.00 (0.97–1.04) 0.826 1.05 (1.01–1.09) 0.027 LPA 0.94 (0.84–1.05) 0.272 0.89 (0.80–1.01) 0.082 Model 3 Behaviours relative to others SB 1.20 (1.06–1.37) 0.006 0.83 (0.72–0.95) 0.010 Sleep 0.88 (0.71–1.09) 0.247 1.29 (1.02–1.63) 0.034 MVPA 1.01 (0.98–1.04) 0.651 1.04 (1.01–1.08) 0.047 LPA 0.94 (0.84–1.04) 0.258 0.90 (0.79–1.01) 0.086 Time-use compositions were expressed as isometric log-ratio (ILR) coordinates, with each result derived from the initial ILR coordinates. The odds ratio corresponded to a one-unit increase in ILR coordinates. Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for education level, marital status, living arrangements, household annual income, and occupation type. Model 3 additionally adjusted for BMI, smoking status, and alcohol consumption. Abbreviations: OR: odds ratio; CI: confidence interval; SB: sedentary behaviours; MVPA: moderate-to-vigorous physical activity; LPA: light-intensity physical activity. The isotemporal substitution model evaluated the influence of reallocating 24-hour movement behaviours. Replacing SB with sleep was associated with lower odds of loneliness and higher odds of happiness. Substituting 5 to 30 minutes of SB with LPA while holding other behaviours constant was associated with lower odds of loneliness but not with happiness odds (Figs. 3 and 4 ). For happiness, substituting SB with MVPA was associated with higher odds of being happy. (Fig. 4 ). Although the influence of reallocating time across 24-hour movement behaviours were small, sleep prominently influenced loneliness, and MVPA notably affected happiness. For example, reallocating 30 minutes/day from SB to sleep was associated with a 1.8% reduction in loneliness odds, while substituting 30 minutes of SB with MVPA was linked to a 6.6% increase in happiness odds (Supplementary Material 2. Tables 1 and 2 ). Discussion Using a compositional data analysis (CoDA) approach and a representative sample of Japanese middle-aged adults, we found that greater time spent in SB, relative to sleep, LPA, and MVPA, was associated with increased loneliness and reduced happiness. Isotemporal substitution analyses further showed that replacing SB with LPA or sleep was associated with reduced loneliness, and substituting SB with sleep or MVPA was associated with increased happiness, while LPA showed no significant association with happiness. These findings suggest that reallocating time from SB to sleep or specific physical activities may be associated with improved emotional well-being in middle-aged adults, with sleep more strongly linked to reduced loneliness and MVPA more strongly linked to increased happiness. This study is the first to use CoDA to investigate adults’ 24-hour movement behaviours and their associations with loneliness and happiness. Supporting the rising consensus about the integration of whole day’s behaviours and less sedentary lifestyle 25 , 26 , our results underscore that adequate sleep duration is critical for adults’ emotional well-being, offering a practical target for interventions. Moreover, physical activity engagement alleviates loneliness and enhances happiness, with MVPA showing a stronger influence on happiness than LPA. Our findings strengthen the growing evidence of SB’s adverse effects, demonstrating how SB is harmfully associated with higher loneliness and lower happiness. Previous non-compositional studies have linked greater SB time to higher levels of loneliness in specific groups, such as obese adults and older Chinese populations 27 , 28 . Similarly, another Japanese study of adult populations found that more passive SB (e.g., TV viewing or screen use) was associated with reduced happiness 24 . In contrast, the only prior CoDA-based study reported no significant association between SB with loneliness and happiness among older adults 17 . The inconsistency may be due to its focus on older adults, whose outcomes tend to be more influenced by physical activity than SB 29 . Despite varying study designs, our findings align with the public health message, suggesting that reducing time spent in SB can improve mental well-being. Our findings indicated that longer sleep time, relative to other behaviours, was associated with greater happiness but not with loneliness. In line with previous evidence, no clear associations have been established between sleep duration and loneliness. The traditional design studies reported inconsistent associations between longer sleep duration 13 , 30 . Studies focusing on happiness reported that longer sleep duration is associated with increased happiness, while shorter sleep duration is linked to lower happiness and higher negative emotions 14 , 31 , 32 . Notably, both insufficient and excessive sleep durations were well-proven to harm mental health outcomes 33 . For CoDA-based studies, most focused on different mental health outcomes (e.g. depression and anxiety), while no results about adults’ loneliness and happiness can be compared 18 . Similar beneficial associations of sleep were found in a study with Japanese middle-aged workers samples, indicating that replacing SB or LPA with sleep could result in lowering psychological distress 34 . When viewing the whole-day behaviour integration, our findings align with the Canadian 24-hour Movement guidelines, which recommend adults sleep for 7 to 9 hours per day to support better health 26 . With a recommended mean sleep duration (approximately 8 hours/day), our ISM results suggest that reallocating time from other behaviours to sleep effectively reduces loneliness and enhances happiness. These findings emphasize sleep’s vital role in emotional well-being and prioritize sleep in the Japanese lifestyle. The present findings indicated that the associations of physical activity with loneliness and happiness vary by intensity, with LPA showing no significant associations, while MVPA, relative to other behaviours, was related to happiness level. Though traditional studies suggest that LPA promotes happiness in middle-aged adults 9 , these findings were established without considering the whole day’s behaviours. The no associations between LPA time and loneliness were also observed in several studies with the CoDA approach 17 , 18 , 35 . Our middle-aged Japanese sample, whose LPA time likely consisted of work-related tasks or chores 36 , may offset LPA’s positive influence on emotion and further explain the no association. However, our ISM results showed that reallocating time from SB to LPA was associated with reduced loneliness. This result may be attributed to the diverse nature of LPA, as individuals who replace SB with LPA may engage in more social or leisure activities that mitigate feelings of isolation. Greater time spent in MVPA relative to other behaviours showed a significant association with higher happiness levels, with similar beneficial patterns observed when reallocating time from SB or LPA to MVPA. These results align with studies showing regular MVPA may boost subjective well-being and reduce negative mental health outcomes 37 , 38 , possibly through MVPA’s stronger neurobiological activation compared to lighter physical activity 39 . Unlike the results from a CoDA study, we did not find that MVPA is significantly associated with reduced loneliness. This discrepancy may be because our study focused on middle-aged adults, whereas the prior CoDA study targeted older adults, who may benefit more from MVPA’s effect against loneliness 17 . Our cross-sectional design raises the possibilities of reverse causality, where happier or less lonely individuals may be more likely to engage in MVPA or LPA 40 . Nevertheless, this study demonstrates differential associations of physical activity intensity with mental well-being. In CoDA, MVPA was associated with higher happiness but not with loneliness, while LPA showed limited associations with either outcome. In contrast, ISM results indicate reallocating time from SB to MVPA is primarily associated with higher happiness, whereas reallocating time to LPA is more strongly associated with reduced loneliness. Strengths and Limitations Our study novelty applies compositional data analysis (CoDA) and isotemporal substitution models (ISM) to examine the substitution of SB time with sleep and physical activity. Based on a nationally representative sample, our findings suggest that Japanese middle-aged adults can enhance mental well-being by reducing their sitting time at work, limiting screen time at home, increasing physical activity, or ensuring adequate sleep. However, several limitations should be noted. First, our cross-sectional design limits the interpretation of causality. Bidirectional associations might exist; for example, loneliness could impair sleep quality and further affect their daily behaviour patterns. Nonetheless, by considering the interdependence of all 24-hour behaviours, our study offers robust insights into their integration. Future longitudinal studies could further clarify the association of behavioural compositions with happiness and loneliness. Second, our questionnaire-based measurement of 24-hour behaviours is less reliable than accelerometer measurements, potentially introducing recall and social desirability biases. The derived over- or underestimated time for sleep, physical activity, and SB may influence their time composition under the CoDA approach. However, most previous CoDA-based studies combined accelerometers and sleep surveys and had diverse algorithms or intensity cut-points, which could alter their measurement validity 41 . Our integrated questionnaire has also demonstrated acceptable reliability and validity. Third, we acknowledge that unmeasured confounders, such as social participation or diagnosed mental diseases, might interfere with happiness and loneliness despite adjusting for sociodemographic factors correlated with behavioural engagement and moods 5 , 42 . Fourth, our online survey may introduce selection biases, as participants could be limited to individuals with higher digital literacy or internet access 43 . This may potentially underrepresent those within remote areas with limited connectivity. Lastly, the questionnaire invitation's low response rate may further restrict our findings' generalizability, as the samples could tend to be Internet-active or health-oriented individuals 44 , which could possibly overestimate the prevalence of healthy behaviours. Future studies could consider hybrid recruitment strategies, combining online and offline methods, to reach a broader population. Conclusion Greater SB time, relative to sleep, LPA, and MVPA, is associated with higher loneliness and decreased happiness. Substituting SB with sleep time can mitigate SB’s associations with both higher loneliness and lower happiness. Lower- and higher-intensity physical activity may contribute to different emotions: replacing SB with LPA may link to alleviating loneliness, while MVPA may relate to boosting happiness. Our findings provide valuable insights into how daily behaviour durations are associated with individuals’ emotional well-being. For middle-aged adults, reducing SB and reallocating time to sleep or physical activity offers a practical approach to enhance emotional well-being, highlighting the potential of behaviour-based interventions Abbreviations SB Sedentary behaviour MVPA Moderate to vigorous-intensity physical activity LPA Light-intensity physical activity CoDA Compositional data analysis ISM Isotemporal substitution model BMI Body mass index Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of Waseda University (Approval No. 2022 − 407). All participants provided informed consent electronically before answering the online survey. On the first survey page, participants were required to read the study explanation and explicitly agree to participate; otherwise, they could not proceed to the questionnaire. Participation was voluntary, and participants could discontinue at any time without disadvantage. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests The authors declare that they have no competing interests 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. conceptualised and designed the study. Y.T.L. conducted the data analysis and prepared the initial draft of the manuscript. A.S., K.I., S.K., and K.O. contributed to the manuscript writing and supported the interpretation of the findings. All authors reviewed and approved the final version of the manuscript. Acknowledgements Not applicable Availability of data and materials Anonymised data are available from the corresponding author on reasonable requests. References Slade M. 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Sleep Med. 2024;119:565–73. Kitano N, Kai Y, Jindo T, Tsunoda K, Arao T. Compositional data analysis of 24-hour movement behaviors and mental health in workers. Prev Med Rep. 2020;20:101213. Miatke A, Olds T, Maher C, Fraysse F, Mellow ML, Smith AE, et al. The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface. Int J Behav Nutr Phys Act. 2023;20(1):127. Chancellor J, Layous K, Lyubomirsky S. Recalling positive events at work makes employees feel happier, move more, but interact less: a 6-week randomized controlled intervention at a Japanese workplace. J Happiness Stud. 2015;16(4):871–87. Booth JN, Ness AR, Joinson C, Tomporowski PD, Boyle JME, Leary SD, et al. Associations between physical activity and mental health and behaviour in early adolescence. Ment Health Phys Act. 2023;24:100497. Ku PW, Fox KR, Liao Y, Sun WJ, Chen LJ. Prospective associations of objectively assessed physical activity at different intensities with subjective well-being in older adults. Qual Life Res. 2016;25(11):2909–19. Nakagawa T, Koan I, Chen C, Matsubara T, Hagiwara K, Lei H et al. Regular moderate- to vigorous-intensity physical activity rather than walking is associated with enhanced cognitive functions and mental health in young adults. Int J Environ Res Public Health . 2020;17(2):Article 2. Liao Y, Chou CP, Huh J, Leventhal A, Dunton G. Examining acute bi-directional relationships between affect, physical feeling states, and physical activity in free-living situations using electronic ecological momentary assessment. J Behav Med. 2017;40(3):445–57. Quante M, Kaplan ER, Rueschman M, Cailler M, Buxton OM, Redline S. Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep. Sleep Health. 2015;1(4):275–84. Dawson-Townsend K. Social participation patterns and their associations with health and well-being for older adults. SSM Popul Health. 2019;8:100424. Bethlehem J. Selection bias in web surveys. Int Stat Rev. 2010;78(2):161–88. Dodge HH, Katsumata Y, Zhu J, Mattek N, Bowman M, Gregor M et al. Characteristics associated with willingness to participate in a randomized controlled behavioral clinical trial using home-based personal computers and a webcam. Trials . 2014;15(1):Article 1. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial120250819.docx Supplementalmaterial220250819.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:49:28","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137889,"visible":true,"origin":"","legend":"","description":"","filename":"c9e9ba1ca4d74beb8732d7fc81e2eae21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/131b8fcdff764ff07929fef6.xml"},{"id":91837713,"identity":"c6d8e712-1ca1-4989-86a3-1e3208d51119","added_by":"auto","created_at":"2025-09-22 09:41:28","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145481,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/903c2dccd7d922674a354cc1.html"},{"id":91837703,"identity":"9b981a4c-2307-4c65-8362-fee68f98cb8a","added_by":"auto","created_at":"2025-09-22 09:41:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147574,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative difference in time spent in each 24-hour movement behaviour by loneliness status. (Group's compositional mean compared to the overall sample's compositional mean)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/67285bfe062f46257f5045f1.jpeg"},{"id":91838724,"identity":"40fd3c29-54f5-4232-9128-24c765941952","added_by":"auto","created_at":"2025-09-22 09:49:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139056,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative difference in time spent in each 24-hour movement behaviour by happiness status. (Group's compositional mean compared to the overall sample's compositional mean)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/4a3481d7c79372cedf9283a8.jpeg"},{"id":91837710,"identity":"482bb222-b557-4442-8ffe-f5c63328fb7b","added_by":"auto","created_at":"2025-09-22 09:41:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":482001,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated influences on loneliness of reallocating SB with other behaviours (MVPA, LPA, and sleep).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/c749c0de1ad984a09a84fcae.jpeg"},{"id":91838727,"identity":"7d65dfb9-0d5e-4b7a-a18b-0da37ca1ddee","added_by":"auto","created_at":"2025-09-22 09:49:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":498176,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated influences on happiness of reallocating SB with other behaviours (MVPA, LPA, and sleep).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/a72d17bd137a8937a05ff980.jpeg"},{"id":93033064,"identity":"aef475f7-06af-4cf0-8249-719775c8a0b7","added_by":"auto","created_at":"2025-10-08 10:32:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2172295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/4dbecb5f-1cce-4927-9097-8e5ec3c2acd2.pdf"},{"id":91838723,"identity":"1fe3f926-6ed6-4c38-a37d-c361c9af2384","added_by":"auto","created_at":"2025-09-22 09:49:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24640,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial120250819.docx","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/ddb2971d74ab2f4073b1f292.docx"},{"id":91837700,"identity":"b0c81937-45d3-4303-99f1-abe9ca2b5fc2","added_by":"auto","created_at":"2025-09-22 09:41:28","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25857,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial220250819.docx","url":"https://assets-eu.researchsquare.com/files/rs-7322612/v1/e4202a0e1e70089f48a3e4ac.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of 24-hour Movement Behaviour with Loneliness and Happiness in Japanese Adults: A Compositional Data Analysis with Isotemporal Substitution Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGood mental health enables people to deal with stress, work well, and advance overall well-being\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among the factors shaping mental health, loneliness and happiness tend to exist in people\u0026rsquo;s daily emotions. Loneliness, a common unpleasant experience, arises when an individual\u0026rsquo;s network of social relations is deficient\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In Japan, A 2023 governmental survey found that 39.3% of Japanese aged 16 or older felt lonely occasionally or more frequently\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. On the other hand, happiness, which is defined as subjective enjoyment, reflects positive experiences over negative ones and satisfaction with one\u0026rsquo;s personal life\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Research has shown that both persistent loneliness and low levels of happiness are associated with more severe mental illness\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Identifying the factors contributing to loneliness and happiness is therefore crucial to promoting a better life for adults.\u003c/p\u003e\u003cp\u003eDaily behaviours, including light-intensity physical activity (LPA), moderate- to vigorous-intensity physical activity (MVPA), sedentary behaviour (SB), and sleep, have been shown to influence loneliness and happiness, respectively. Engaging in any mode of physical activity, such as exercise or walking, has been reported to be associated with reduced loneliness, and even a small amount of weekly physical activity may help enhance happiness\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\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\u003c/sup\u003e. In contrast, SB, which is widely recognised to harm numerous health indicators, has been found to be associated with negative emotional states\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Similarly, insufficient sleep, such as sleep deprivation or sleep disturbance, has been found to higher loneliness\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A shorter sleep duration (\u0026le;\u0026thinsp;6 hours/day) has been demonstrated to be associated with lower levels of happiness\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese behaviours within a day, termed 24-hour movement behaviours, are interdependent. For example, spending more or less time on physical activity inevitably affects the time available for SB or sleep. With growing interest in understanding the integrated effects of 24-hour movement behaviours, the compositional data analysis (CoDA) approach has been increasingly applied to account for their co-dependent nature and explore how the composition or reallocation of behaviours within a day contributes to health\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Using this robust framework, many studies have examined the associations between 24-hour movement behaviours and various mental health outcomes. A recent review study identified more MVPA or LPA, reduced SB, and adequate sleep as the healthiest combinations of daily movement behaviours for mental health\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLoneliness and happiness are particularly relevant to the mental health of working-age adults, reflecting interpersonal connections and subjective well-being, respectively\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and are often shaped by occupational and social stress and may influence mental well-being. However, only one CoDA-based study has investigated daily behaviours in relation to loneliness and happiness, focusing on older adults. This study found that spending more time in MVPA, including by reallocating 30 minutes from other daily behaviours (LPA, SB, or sleep), was associated with lower loneliness and greater happiness\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To date, no studies have applied CoDA to examine these associations among working-age adults. Consequently, the influence of reallocating time between daily behaviours on working-age adults\u0026rsquo; loneliness and happiness remains unclear.\u003c/p\u003e\u003cp\u003eTo address this gap, this study employed CoDA to investigate the associations between relative time spent in SB, sleep, MVPA, and LPA and levels of loneliness and happiness among working-age adults. This study also sought to estimate the influence of reallocating time between 24-hour movement behaviours on loneliness and happiness, potentially informing targeted strategies to arrange working-age adults\u0026rsquo; daily time use.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis cross-sectional study analysed data from a 2023 Japanese online survey conducted by MyVoice Com Inc., a Japanese internet survey company managing a panel of approximately one million voluntarily registered individuals across Japan with detailed sociodemographic profiles. From this database, 19,081 adults aged 20\u0026ndash;59 were randomly selected and stratified equally by sex and age groups (20\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, and 50\u0026ndash;59 years) to ensure balanced representation and minimize selection bias. Participants received an email invitation through the company\u0026rsquo;s system, containing a link to an online questionnaire and information outlining the study\u0026rsquo;s purpose, data handling procedures, and ethical considerations. Of those invited, 3,000 participants completed the questionnaire (response rate of 15.7%) and received reward points valued at 123 JPY for partner facilities as an incentive. The analysis included 2,718 participants, excluding those with invalid behavioural information (Zero time spent in SB, sleep, or LAP; MVPA time\u0026thinsp;\u0026gt;\u0026thinsp;16 hours). All responses were anonymized, and personal information was handled in accordance with MyVoice Com Inc.\u0026rsquo;s privacy policy. This study was approved by the Institutional Ethics Committee of Waseda University (2022\u0026thinsp;\u0026minus;\u0026thinsp;407). Details of the questionnaire are available in Supplementary Material 1.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExposures: 24-hour movement behaviours measurement\u003c/h3\u003e\n\u003cp\u003eThe 24-hour movement behaviour composition was defined as the daily time proportion of sleep, SB, MVPA, and LPA. This information was assessed by a questionnaire created to assess 24-hour physical behaviours with sound validity and reliability\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Participants were asked to recall their time per day in a typical week and report each behaviour\u0026rsquo;s time in hours and minutes. Sleep time was estimated for the total time spent sleeping. SB time was defined as total sitting and lying time, excluding sleep. MVPA was defined as activities causing at least a slight increase in heart rate or breathing. The LPA time was defined as the time spent in low-intensity physical activity other than the behaviours mentioned above. When the sum of four behaviours times did not sum to 24 hours, the LPA was calculated as the remaining time by subtracting sleep, SB, and MVPA durations from 1440 minutes. These four behaviour durations were expressed as proportions of the 24-hour day.\u003c/p\u003e\n\u003ch3\u003eOutcome: Loneliness and happiness\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome: Loneliness and happiness\u003c/div\u003e\u003cp\u003eLoneliness was measured using the three-item University of California, Los Angeles (UCLA) Loneliness Scale, asking: \u0026ldquo;How often do you feel you lack companionship?\u0026rdquo;, \u0026ldquo;How often do you feel left out?\u0026rdquo;, and \u0026ldquo;How often do you feel isolated from others?\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Responses were scored on a three-point scale: \u0026lsquo;never or hardly ever,\u0026rsquo; \u0026lsquo;sometimes,\u0026rsquo; and \u0026lsquo;often. The overall loneliness score ranges from 3\u0026ndash;9, and higher scores reflect greater levels of loneliness. UCLA-3 data were also categorized, with scores of 3\u0026ndash;5 as \u0026ldquo;not lonely\u0026rdquo; and 6\u0026ndash;9 as \u0026ldquo;lonely\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Happiness was assessed using a single-item self-rated question, \u0026ldquo;How happy do you feel about yourself now?\u0026rdquo; The participants responded on a reverse Likert-type scale ranging from 1 (happy), 2 (somewhat happy), 3 (somewhat unhappy), and 4 (unhappy). Responses of 1 or 2 were classified as \u0026ldquo;happy\u0026rdquo;, and 3 or 4 as \u0026ldquo;unhappy\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eSociodemographic variables, lifestyle factors, and body mass index (BMI) were collected via the online questionnaire. Sociodemographic variables included age (years), sex (men, women), marital status (unmarried, married), living arrangements (living alone, living with others), education level (high school or below, above high school), household annual income (JPY\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026nbsp;million, \u0026ge;\u0026thinsp;5\u0026nbsp;million), were obtained from pre-existing data in the MyVoice Com Inc. panel database. Occupation type (unemployed, desk-based, standing, walking, manual labour), lifestyle factors, including smoking status (non-smoker, current smoker) and alcohol consumption (non-drinker, current drinker), and BMI were measured specifically for this study through the questionnaire. BMI was calculated from self-reported height and weight, categorized as underweight (\u0026lt;\u0026thinsp;18.5), normal (18.5\u0026ndash;24.9), or overweight/obese (\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData processing and statistical analyses were performed using SAS version 9.4 and R version 4.3.3 software. Chi-square tests or t-tests were used to compare means, standard deviations (SD), and percentages (%) of participants\u0026rsquo; characteristics across outcome categories (happy vs. unhappy; lonely vs. not lonely). 24-hour movement behaviour durations were expressed as geometric means, normalized to a 1440-minute day, and their log-ratio differences were compared by loneliness and happiness status. Zero values in any behaviour duration were imputed using the log ratio Expectation\u0026ndash;maximisation algorithm in the R function to avoid mathematical indeterminacy. In the compositional data analysis approach, isometric log-ratio (\u003cem\u003eilr\u003c/em\u003e) transformation mapped the interdependent 24-hour movement data into real space for standard statistical methods\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The four-part composition (sleep, SB, MVPA, and LPA) was transformed into a set of ilr coordinates (z1, z2, z3) by a sequential binary partition. These coordinates capture relative balances: z1 contrasts a selected behaviour (e.g. sleep) with the remaining behaviours, z2 balances MVPA relative SB and LPA, and z3 compares LPA to SB. The ilr transformation allows any behaviour (sleep, SB, MVPA, or LPA) to be placed in z1 sequentially, with this first ilr coordinate (z1) interpreted as containing the primary information. These transformed ilr coordinates set then served as exposure variables in a multivariate logistic regression model assessing the associations with loneliness and happiness. Model 1 was adjusted for age and sex; Model 2 added education level, marital status, living arrangements, household annual income, and occupation type; Model 3 further included BMI, smoking status, and alcohol consumption. Odds ratios (ORs) indicated the changes in loneliness or happiness odds per unit of ilr increase. Compositional isotemporal substitution (ISM) models estimated the influence of pairwise time reallocations (in 5-minute increments up to 30 minutes) on loneliness and happiness odds\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Based on the mean 24-hour composition, a simulated behavioural composition was generated by reallocating time between two behaviours (e.g., reducing SB and increasing sleep while keeping other behaviours constant). Then the new set of ilr coordinates was used to predict ORs and 95% confidence intervals (95% CI). Results were further plotted to illustrate changes in loneliness and happiness. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that among the 2,718 participants (mean age: 41.8 years), 1,214 (44.7%) reported feeling lonely, and 739 (27.2%) reported feeling unhappy. Females were more likely to feel lonely, while males were more likely to feel unhappy. Individuals reporting loneliness or unhappiness were more likely to be unmarried, live alone, have lower education and household annual income, be unemployed, and be non-drinkers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study sample with loneliness and happiness status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLoneliness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHappiness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal sample (n\u0026thinsp;=\u0026thinsp;2718)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot lonely\u003c/p\u003e\u003cp\u003e1504 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLonely\u003c/p\u003e\u003cp\u003e1214 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUnhappy\u003c/p\u003e\u003cp\u003e739 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHappy\u003c/p\u003e\u003cp\u003e1979 (72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, mean years (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.8 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.26 (10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.95 (10.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41.04 (10.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e42.09 (10.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1372 (50.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e931 (67.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e441 (32.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e428 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e944 (68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1346 (49.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e834 (61.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e512 (38.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e311 (23.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1035 (76.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1364 (50.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e794 (58.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e570 (41.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e520 (38.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e844 (61.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1354 (49.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e971 (71.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e383 (28.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e219 (16.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1135 (83.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLiving arrangements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e581 (21.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e348 (59.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e233 (40.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e225 (38.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e356 (61.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2137 (78.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1417 (66.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e720 (33.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e514 (24.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1623 (75.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e994 (36.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e610 (61.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e384 (38.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e322 (32.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e672 (67.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1724 (63.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1155 (67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e569 (33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e417 (24.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1307 (75.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHousehold annual income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1165 (42.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e653 (56.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e512 (43.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e456 (39.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e709 (60.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5 million\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1553 (57.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1112 (71.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e441 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e283 (18.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1270 (81.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOccupation type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e605 (22.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e337 (55.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e268 (44.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e188 (31.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e417 (68.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDesk-based\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1296 (47.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e894 (68.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e402 (31.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e322 (24.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e974 (75.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStanding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e257 (65.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137 (34.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e119 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e275 (69.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWalking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e349 (12.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e224 (64.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e125 (35.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e90 (25.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e259 (74.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManual labour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (2.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (71.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (28.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20 (27.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54 (72.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBody mass index, BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e395 (14.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e242 (61.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e153 (38.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e121 (30.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e274 (69.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1791 (65.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1182 (66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e609 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e445 (24.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1346 (75.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverweight/obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e532 (19.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e341 (64.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191 (35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e173 (32.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e359 (67.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e483 (17.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e348 (72.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e135 (27.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e144 (29.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e339 (70.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-current smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2235 (82.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1417 (63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e818 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e595 (26.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1640 (73.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAlcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1474 (54.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1019 (69.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e455 (30.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e347 (23.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1127 (76.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-current drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1244 (45.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e746 (59.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e498 (40.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e392 (31.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e852 (68.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eData are represented as n (%), unless otherwise specified\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003e*P-value based on t-test for continuous variables, and based on Chi-square test for categorical variables.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe compositional mean of time spent in SB, sleep, MVPA, and LPA was 424.8, 483.5, 9.0, and 522.6 minutes, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the overall sample, participants reporting loneliness spent 9.8% more time in SB, 0.3% more in sleep, 7.2% less in MVPA, and 8.9% less in LPA. Those reporting being unhappy spent 6.2% more time in SB, 1.6% less in sleep, 22.8% less in MVPA, and 3.5% less in LPA. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the log ratio differences in compositional means.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCompositional Differences in 24-hour Movement Behaviours by Loneliness and Happiness Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLoneliness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHappiness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal sample (n\u0026thinsp;=\u0026thinsp;2718)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNot lonely\u003c/p\u003e\u003cp\u003e1504 (55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLonely\u003c/p\u003e\u003cp\u003e1214 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUnhappy\u003c/p\u003e\u003cp\u003e739 (27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHappy\u003c/p\u003e\u003cp\u003e1979 (72.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eCompositional mean\u003csup\u003ea\u003c/sup\u003e, mins/day\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e424.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e403.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e452.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e452.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e414.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e483.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e482.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e483.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e476.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e486.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e522.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e544.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e495.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e504.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e529.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eLog-ratio of group compositional mean to total sample compositional mean\u003csup\u003eb\u003c/sup\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-22.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThe geometric mean is calculated from the proportion of daily time spent in each behavior and scaled back to 24 hours, ensuring symmetry for compositional data analysis.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eThe relative time spent in each behaviour is expressed as the log ratio of the group's compositional mean to the full sample's compositional mean. For example, compared to the full sample, unhappy individuals spent 6.2% more time in sleep, 1.6% less time in SB, 22.8% less time in MVPA, and 3.5% less time in LPA.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the fully adjusted compositional logistic regression model, greater SB time, relative to other behaviours, was positively associated with loneliness and negatively associated with happiness. More time spent in sleep and MVPA relative to other behaviours, was significantly associated with higher odds of happiness. No significant associations were found between LPA time and either loneliness or happiness (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between 24-hour movement behaviours and loneliness and happiness. (n\u0026thinsp;=\u0026thinsp;2718)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eBeing lonely\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBeing happy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eBehaviours relative to others\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22 (1.08\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82 (0.72\u0026ndash;0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91 (0.74\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.22 (0.98\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05 (1.02\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91 (0.82\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95 (0.85\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eBehaviours relative to others\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.21 (1.06\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83 (0.72\u0026ndash;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88 (0.71\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29 (1.02\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.97\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05 (1.01\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94 (0.84\u0026ndash;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89 (0.80\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eBehaviours relative to others\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20 (1.06\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83 (0.72\u0026ndash;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSleep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88 (0.71\u0026ndash;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29 (1.02\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMVPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.651\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04 (1.01\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94 (0.84\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.90 (0.79\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eTime-use compositions were expressed as isometric log-ratio (ILR) coordinates, with each result derived from the initial ILR coordinates. The odds ratio corresponded to a one-unit increase in ILR coordinates.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eModel 1 was adjusted for age and sex. Model 2 was additionally adjusted for education level, marital status, living arrangements, household annual income, and occupation type. Model 3 additionally adjusted for BMI, smoking status, and alcohol consumption.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: OR: odds ratio; CI: confidence interval; SB: sedentary behaviours; MVPA: moderate-to-vigorous physical activity; LPA: light-intensity physical activity.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe isotemporal substitution model evaluated the influence of reallocating 24-hour movement behaviours. Replacing SB with sleep was associated with lower odds of loneliness and higher odds of happiness. Substituting 5 to 30 minutes of SB with LPA while holding other behaviours constant was associated with lower odds of loneliness but not with happiness odds (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For happiness, substituting SB with MVPA was associated with higher odds of being happy. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAlthough the influence of reallocating time across 24-hour movement behaviours were small, sleep prominently influenced loneliness, and MVPA notably affected happiness. For example, reallocating 30 minutes/day from SB to sleep was associated with a 1.8% reduction in loneliness odds, while substituting 30 minutes of SB with MVPA was linked to a 6.6% increase in happiness odds (Supplementary Material 2. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing a compositional data analysis (CoDA) approach and a representative sample of Japanese middle-aged adults, we found that greater time spent in SB, relative to sleep, LPA, and MVPA, was associated with increased loneliness and reduced happiness. Isotemporal substitution analyses further showed that replacing SB with LPA or sleep was associated with reduced loneliness, and substituting SB with sleep or MVPA was associated with increased happiness, while LPA showed no significant association with happiness. These findings suggest that reallocating time from SB to sleep or specific physical activities may be associated with improved emotional well-being in middle-aged adults, with sleep more strongly linked to reduced loneliness and MVPA more strongly linked to increased happiness. This study is the first to use CoDA to investigate adults\u0026rsquo; 24-hour movement behaviours and their associations with loneliness and happiness. Supporting the rising consensus about the integration of whole day\u0026rsquo;s behaviours and less sedentary lifestyle\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, our results underscore that adequate sleep duration is critical for adults\u0026rsquo; emotional well-being, offering a practical target for interventions. Moreover, physical activity engagement alleviates loneliness and enhances happiness, with MVPA showing a stronger influence on happiness than LPA.\u003c/p\u003e\u003cp\u003eOur findings strengthen the growing evidence of SB\u0026rsquo;s adverse effects, demonstrating how SB is harmfully associated with higher loneliness and lower happiness. Previous non-compositional studies have linked greater SB time to higher levels of loneliness in specific groups, such as obese adults and older Chinese populations\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Similarly, another Japanese study of adult populations found that more passive SB (e.g., TV viewing or screen use) was associated with reduced happiness\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In contrast, the only prior CoDA-based study reported no significant association between SB with loneliness and happiness among older adults\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The inconsistency may be due to its focus on older adults, whose outcomes tend to be more influenced by physical activity than SB\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Despite varying study designs, our findings align with the public health message, suggesting that reducing time spent in SB can improve mental well-being.\u003c/p\u003e\u003cp\u003eOur findings indicated that longer sleep time, relative to other behaviours, was associated with greater happiness but not with loneliness. In line with previous evidence, no clear associations have been established between sleep duration and loneliness. The traditional design studies reported inconsistent associations between longer sleep duration\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Studies focusing on happiness reported that longer sleep duration is associated with increased happiness, while shorter sleep duration is linked to lower happiness and higher negative emotions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Notably, both insufficient and excessive sleep durations were well-proven to harm mental health outcomes\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For CoDA-based studies, most focused on different mental health outcomes (e.g. depression and anxiety), while no results about adults\u0026rsquo; loneliness and happiness can be compared\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Similar beneficial associations of sleep were found in a study with Japanese middle-aged workers samples, indicating that replacing SB or LPA with sleep could result in lowering psychological distress\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. When viewing the whole-day behaviour integration, our findings align with the Canadian 24-hour Movement guidelines, which recommend adults sleep for 7 to 9 hours per day to support better health\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. With a recommended mean sleep duration (approximately 8 hours/day), our ISM results suggest that reallocating time from other behaviours to sleep effectively reduces loneliness and enhances happiness. These findings emphasize sleep\u0026rsquo;s vital role in emotional well-being and prioritize sleep in the Japanese lifestyle.\u003c/p\u003e\u003cp\u003eThe present findings indicated that the associations of physical activity with loneliness and happiness vary by intensity, with LPA showing no significant associations, while MVPA, relative to other behaviours, was related to happiness level. Though traditional studies suggest that LPA promotes happiness in middle-aged adults\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, these findings were established without considering the whole day\u0026rsquo;s behaviours. The no associations between LPA time and loneliness were also observed in several studies with the CoDA approach\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Our middle-aged Japanese sample, whose LPA time likely consisted of work-related tasks or chores\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, may offset LPA\u0026rsquo;s positive influence on emotion and further explain the no association. However, our ISM results showed that reallocating time from SB to LPA was associated with reduced loneliness. This result may be attributed to the diverse nature of LPA, as individuals who replace SB with LPA may engage in more social or leisure activities that mitigate feelings of isolation.\u003c/p\u003e\u003cp\u003eGreater time spent in MVPA relative to other behaviours showed a significant association with higher happiness levels, with similar beneficial patterns observed when reallocating time from SB or LPA to MVPA. These results align with studies showing regular MVPA may boost subjective well-being and reduce negative mental health outcomes\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, possibly through MVPA\u0026rsquo;s stronger neurobiological activation compared to lighter physical activity\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Unlike the results from a CoDA study, we did not find that MVPA is significantly associated with reduced loneliness. This discrepancy may be because our study focused on middle-aged adults, whereas the prior CoDA study targeted older adults, who may benefit more from MVPA\u0026rsquo;s effect against loneliness\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur cross-sectional design raises the possibilities of reverse causality, where happier or less lonely individuals may be more likely to engage in MVPA or LPA\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Nevertheless, this study demonstrates differential associations of physical activity intensity with mental well-being. In CoDA, MVPA was associated with higher happiness but not with loneliness, while LPA showed limited associations with either outcome. In contrast, ISM results indicate reallocating time from SB to MVPA is primarily associated with higher happiness, whereas reallocating time to LPA is more strongly associated with reduced loneliness.\u003c/p\u003e\n\u003ch3\u003eStrengths and Limitations\u003c/h3\u003e\n\u003cp\u003eOur study novelty applies compositional data analysis (CoDA) and isotemporal substitution models (ISM) to examine the substitution of SB time with sleep and physical activity. Based on a nationally representative sample, our findings suggest that Japanese middle-aged adults can enhance mental well-being by reducing their sitting time at work, limiting screen time at home, increasing physical activity, or ensuring adequate sleep. However, several limitations should be noted. First, our cross-sectional design limits the interpretation of causality. Bidirectional associations might exist; for example, loneliness could impair sleep quality and further affect their daily behaviour patterns. Nonetheless, by considering the interdependence of all 24-hour behaviours, our study offers robust insights into their integration. Future longitudinal studies could further clarify the association of behavioural compositions with happiness and loneliness. Second, our questionnaire-based measurement of 24-hour behaviours is less reliable than accelerometer measurements, potentially introducing recall and social desirability biases. The derived over- or underestimated time for sleep, physical activity, and SB may influence their time composition under the CoDA approach. However, most previous CoDA-based studies combined accelerometers and sleep surveys and had diverse algorithms or intensity cut-points, which could alter their measurement validity\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our integrated questionnaire has also demonstrated acceptable reliability and validity. Third, we acknowledge that unmeasured confounders, such as social participation or diagnosed mental diseases, might interfere with happiness and loneliness despite adjusting for sociodemographic factors correlated with behavioural engagement and moods\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Fourth, our online survey may introduce selection biases, as participants could be limited to individuals with higher digital literacy or internet access\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This may potentially underrepresent those within remote areas with limited connectivity. Lastly, the questionnaire invitation's low response rate may further restrict our findings' generalizability, as the samples could tend to be Internet-active or health-oriented individuals\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, which could possibly overestimate the prevalence of healthy behaviours. Future studies could consider hybrid recruitment strategies, combining online and offline methods, to reach a broader population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGreater SB time, relative to sleep, LPA, and MVPA, is associated with higher loneliness and decreased happiness. Substituting SB with sleep time can mitigate SB\u0026rsquo;s associations with both higher loneliness and lower happiness. Lower- and higher-intensity physical activity may contribute to different emotions: replacing SB with LPA may link to alleviating loneliness, while MVPA may relate to boosting happiness. Our findings provide valuable insights into how daily behaviour durations are associated with individuals\u0026rsquo; emotional well-being. For middle-aged adults, reducing SB and reallocating time to sleep or physical activity offers a practical approach to enhance emotional well-being, highlighting the potential of behaviour-based interventions\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSedentary behaviour\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMVPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eModerate to vigorous-intensity physical activity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLight-intensity physical activity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCoDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCompositional data analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eISM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIsotemporal substitution model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Waseda University (Approval No. 2022\u0026thinsp;\u0026minus;\u0026thinsp;407). All participants provided informed consent electronically before answering the online survey. On the first survey page, participants were required to read the study explanation and explicitly agree to participate; otherwise, they could not proceed to the questionnaire. Participation was voluntary, and participants could discontinue at any time without disadvantage. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\u003c/p\u003e\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. conceptualised and designed the study. Y.T.L. conducted the data analysis and prepared the initial draft of the manuscript. A.S., K.I., S.K., and K.O. contributed to the manuscript writing and supported the interpretation of the findings. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eAnonymised data are available from the corresponding author on reasonable requests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSlade M. Mental illness and well-being: the central importance of positive psychology and recovery approaches. BMC Health Serv Res. 2010;10(1):26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeutel ME, Klein EM, Br\u0026auml;hler E, Reiner I, J\u0026uuml;nger C, Michal M, et al. 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However, no prior study has employed compositional data analysis (CoDA) to examine these behaviours within 24 hours and their relationship to adults' loneliness and happiness. This study investigates how adults’ 24-hour movement behaviour patterns relate to these emotions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis cross-sectional study included 2,718 participants aged 20-59 from a Japanese online health survey conducted in 2023. Participants' 24-hour movement behaviours (sleep, SB, MVPA, LPA) were assessed using a questionnaire about their time use on a typical day. Loneliness was assessed using the UCLA 3-Item Loneliness Scale, and happiness was self-reported based on their current level of happiness. Compositional data analysis with logistic regression was performed to examine the associations between daily behaviour composition and the experiences of loneliness and happiness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eAmong the 2,718 adults, 1,214 reported feeling lonely, and 739 feeling unhappy. More time spent in SB, relative to other behaviours, was associated with increased loneliness (OR=1.20, 95% CI: 1.06-1.37) and reduced happiness (OR=0.83, 95% CI: 0.72-0.95). Replacing SB time with sleep was linked to improved estimates of both loneliness and happiness. Substituting 5 to 30 minutes of SB with LPA was associated with lower odds of loneliness while substituting SB with MVPA was associated with higher odds of happiness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Greater time spent in SB was associated with increased loneliness and reduced happiness, suggesting that replacing SB with sleep and physical activity might be essential for improving adults' emotional well-being. These findings give adults insights into how their daily routines impact well-being.\u003c/p\u003e","manuscriptTitle":"Association of 24-hour Movement Behaviour with Loneliness and Happiness in Japanese Adults: A Compositional Data Analysis with Isotemporal Substitution Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:41:23","doi":"10.21203/rs.3.rs-7322612/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 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T10:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:41:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7322612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7322612","identity":"rs-7322612","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}