{"paper_id":"3cbe1ddd-8e2d-45d2-87af-d25440ca09ec","body_text":"Association of 24-hour movement behaviors and non-communicable diseases among adults of Udupi, Karnataka, India | 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 behaviors and non-communicable diseases among adults of Udupi, Karnataka, India Manaswi Reddy, Baskaran Chandrasekaran, Karppasamy Govindasamy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4340364/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 Background Optimizing 24-hour movement behaviors (physical activity, sleep, and sedentary time) is crucial for prevention of non-communicable diseases (NCDs). However, the association between the aforementioned 24-hour movement behaviors and the risk of NCDs remains largely obscured in low-middle income countries. The present study aimed to explore the association between 24-hour movement behaviors and the occurrence of NCDs. Methods An online survey was administered among 310 community dwelling adults (age 34.3 ± 11.3 years) of coastal Karnataka, enquiring sociodemographic profiles, 24-hour movement behaviors (physical activity levels, sedentary time and sleep time) and presence of NCDs’. Linear and logistic regression models were employed to explore the association. Results While spending 7.5 hours (33%) on sleep, the respondents spend 30% (425 min/day) in LIPA, 19% (270 min/day) of typical day sedentary and 18% (264 min/day) of day involved in MVPA. Higher sleep time was associated with higher odds of NCDs (estimate, β = 0.20, odds ratio = 0.82, p = 0.025). Subgroup analyses showed age and body mass were positively associated with NCDs, while lower socio-economic status was associated with lower sitting time (β = -3.84, p = 0.009) and lower light physical activity time (-318.49, p = 0.018). Conclusion High 24-hour movement behaviors and lower odds of NCDs were found among inhabitants of Udupi. Higher age, body mass index and long sleep hours were associated with higher risk of NCDs. The study warrants the need of addressing body mass and sleep levels along with physical activity levels in the prevention of NCDs. movement behavior physical activity non-communicable disease sustainability sleep sedentary behavior Figures Figure 1 Figure 2 Figure 3 Contributions to the literature First study to explore the 24-hour movement behaviors and chronic disease risk among inhabitants of urban area from India Altered sleep levels were associated with chronic disease while physical activity and sedentary behavior levels remain naïve. High physical activity and low sedentary behavior which are common among individuals from low-middle income countries, may not be determine chronic disease risk. Background Non-communicable chronic diseases (NCDs) attributes to 41 million deaths annually, accounting for 71% of global fatalities and more than 74% of the total fatalities due to NCD occur in low- and middle-income countries [ 1 ]. The goal of World Health Organization's (WHO) action plan is to decrease risk factors related to lifestyle especially optimizing 24 hour movement behaviors such as reducing physical inactivity, sedentary behavior and sleep dysfunction [ 2 ]. To have an improved cardiometabolic health, individuals should have optimized 24-hour movement behavior, incorporating following: (1) reaching 150 minutes of moderate intensity or 75 minutes of vigorous physical activity (PA) in a week; (2) restricting sedentary time (ST) less than eight hours; (3) ensuring an optimal sleep time of 7–8 hours per day [ 3 ]. Though these behaviors are interdependent upon each other, the existing studies have studied these behaviors as isolated entities [ 4 – 6 ]. Recent observational studies have started exploring this interdependency of these 24-hour movement behaviors and its association with the cardiometabolic health however emerging from the high-income countries [ 7 – 9 ]. In South India, particularly in Karnataka, a high prevalence of elevated blood sugar (12%) and elevated blood pressure (19%) has been identified [ 10 ]. Inhabitants of Udupi Taluk, coastal area in Karnataka with a population of 16,60,080 from a two-lakh household are explored less for its 24-hour movement behaviors, though cardiometabolic risk is on rise [ 11 ]. Additionally, social factors such as gender and economic status, in conjunction with physical activity, display variations and are associated with the onset or worsening of underlying NCDs [ 12 ]. However, this interdependency among 24-hour movement behaviors, socio-demographic factors and chronic diseases are least explored in this coastal region. The first phase of behavioral epidemiology framework is to establish links between the behavior and health which might help to develop appropriate measures of behaviors (second phase), identify influences (third phase) and develop culturally appropriate interventions (fourth phase) [ 13 ]. Hence the present study aimed to understand the association of 24-hour movement behaviors and sociodemographic factors with occurrence of NCDs in the adults of the coastal Karnataka. Through the present study, the levels of movement behaviors in inhabitants on coastal Karnataka based on internationally recognized benchmarks such as global physical activity guidelines or international sleep duration recommendations could be understand and would aid in the designing and implementation of efficient lifestyle strategies. Materials and Methods Study design and ethical consideration This cross-sectional study was conducted among the community dwelling adults of Udupi taluk, Karnataka, India during January 2024 – February 2024. The ethical committee of university approved the study (IEC2:582/2023) and the study was prospectively registered in the Clinical Trial Registry of India (CTRI/2023/12/060421). The research was conducted in accordance with the ethical standards laid by Helsinki Declaration of 1975, as revised in 2008. The study is reported as per the reporting guidelines of STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE checklist is attached as supplementary file S1. Phases of the study The study was carried out in phases: (1) phase 1 – administering a literature review and identifying potential movement behaviors influencing chronic diseases. Further addition of other micro-constructs such as sociodemographic profile was added; (2) phase 2 – the developed questionnaire was validated for content (comprehensibility, appropriateness, structure and relevance) by three public health experts and one physician who were at least three years in questionnaire development, administration of online surveys and epidemiological studies; (3) phase 3 involved the development of anonymized online questionnaire using Google forms along with the consent forms and administered online to the potential residents across Udupi area. Supplementary file S2 depicts the three phases of the study described above. Participants For phase 2, the developed questionnaire was pilot tested in three public health experts and one physician with mean age of 45 ± 8 with at least three years in survey questionnaire development and administration. For the phase 3, the participants were identified from Udupi taluk of Karnataka, whose population was estimated to be 16,60,080 from household of two lakhs with 56% was found to be literate. Adults (18–65 years), both men and women who were residents of Udupi taluk were included in the study. To be included in the study, the participants should understand English and access to their own email ids. An internet connection was required since the survey was solely provided in a web-based format. The sampling technique used for recruitment was chain-referral sampling. The survey invitation and the eligibility criteria were announced via email, social media and grocery service providers of Udupi area. The survey link was embedded in this invitation. Participation in the study was voluntary and the respondents were assured irreversible anonymity. The survey was accessible online from October 4, 2021. Data collection remained open until February 15, 2024. Sample size calculation From the earlier literature, we learnt that the inhabitants living in Udupi region to have a moderate level of physical inactivity (46.3%) [ 14 ]. At α of 0.05 and power 95%, the estimated sample size was 306 participants. The sample size was estimated using G*Power statistical software (version 3.1.9.6, University of Kiel, Germany). Variables Socio-demographic data such as age, gender, occupation, education level, lifestyle habits such as smoking and alcohol and socio-economic status (upper, middle and lower) were assessed. Further demographic variables such as height, weight, and hip waist ratio were also included in the questionnaire. The probing questions on the physical activity levels (light, moderate or vigorous) and sedentary time were also included in the questionnaire. The sleep time was estimated from the time out of bed in the morning till the time going to bed at night in a typical day. The presence of chronic diseases were probed for common non-communicable diseases of the coastal area of Karnataka: hypertension, obesity, polycystic ovarian syndrome, diabetes, cardiovascular diseases and other disorders. Online survey administration The survey was administered exploring the sociodemographic profile, physical activity, sedentary levels and the presence of chronic diseases with the following sections: Sociodemographic profile: This section comprised of items related to respondents’ gender, age, weight, height, annual income, living status and highest education level. The socio economic status was graded as five grades using Modified Kuppuswamy scale (updated 2023) as follows: upper, upper middle, lower middle, upper lower and lower income categories [ 15 ]. The composite scores from individual scores for education, occupation and total monthly income were calculated. The grades of socio-economic status were based on the composite scores: (1) upper (scores 26–29); (2) upper middle (16–25); (3) lower middle (11–15); (4) upper lower (5–10) and (5) lower (< 5). PA and sedentary time levels: This section is modified from International Physical Activity Questionnaire (IPAQ) [ 16 ]. This section comprised of signaling questions on PA levels (light – walking, moderate and vigorous intensities, frequencies and duration of each activities) for the previous week. The sitting time was estimated in hours per day while light intensity PA (walking) was estimated in minutes per day and moderate-vigorous PA was estimated in minutes per week. The MET minutes per week for each participant was calculated as per developer standards. Presence of chronic disease: This section comprised of questions probing for the presence of chronic disease was adapted from our observational studies in coastal areas of Karnataka [ 17 ]. Statistical methods All the anonymized data were descriptively analyzed. The results of the descriptive statistics were presented as frequencies and percentages for categorical data. For numerical data, means and standard deviations were indicated for numerical data as Shapiro Wilk test. Internal consistency among the health experts was assessed using Cronbach’s alpha. The prevalence of 24-hour movement behaviors (light intensity PA, moderate-vigorous intensity PA, sitting time and sleep time) was expressed in percentages. The primary objective (association between 24-hour movement behaviors and chronic diseases) and secondary objectives (association of socio demographic profile with the NCD occurrence and 24-hour movement behaviors) were explored used multivariate logistic and linear regression models. All the individual variables of 24-hour movement behavior were included in the model. The estimates, 95% confidence intervals and odds ratio of the models were calculated controlling for education and lifestyle (smoking and alcohol) factors. Low multicollinearity (variable inflation factor > 2) was observed for all the models. Significance was set at p < 0.05 in priori. Conditional estimation plots with 95% confidence intervals also were drawn. All the analyses were performed using Jeffreys's Amazing Statistics Program (JASP, version 0.17.1., University of Amsterdam, Netherlands). Results The present cross-sectional study was carried on with sending email to the first wave of potential participants on 3rd February, 2024 and 2nd wave on 18th February, 2024. Of 525 potential participants emailed for survey responses, 332 (63%) responses were obtained and data of 310 participants were analyzed. The flow of the email responses and the data included and analyzed is depicted as Fig. 1 . ------------------------------------------- Insert Fig. 1 here --------------------------------------------- Baseline characteristics Baseline characteristics of the respondents are depicted in Table 1 . Majority of the respondents were women and had a mean age of 34.3 years. Almost half of the participants belong to overweight and obese category (≈ 40%). Half of the respondents (n = 152, 49%) belong to lower income category. Almost 80% of the respondents had optimum sleep (7–9 hours per day) and optimum sitting time (less than 6 hours/day). Majority of the participants (n = 177, 57%) reached the weekly MVPA goal of 150 minutes. Of 310 respondents, 11% of the respondents have expressed about the presence of chronic diseases with half of them were hypertensive (Table 1 ). Table 1 Baseline characteristics of the participants Characteristics Total (n = 310) Expressed in mean ± standard deviation Age (years) 34.3 ± 11.3 BMI (kg/m 2 ) 23.9 ± 5.3 Physical activity levels LIPA (min/day) 425.18 ± 237.54 MVPA (min/day) 264.23 ± 167.3 Sitting time (hours/ day) 4.5 ± 2.8 Sleep time (hours/ day) 7.5 ± 1.4 Expressed in number, percentages Female 208 (67.1%) BMI category Underweight 40 (12.9%) Normal 144 (46.5%) Overweight 89 (28.71%) Obese 37 (11.9%) Socio-economic status Upper income 6 (1.9%) Upper – middle income 50 (16.1%) Lower – middle income 102 (32.9%) Lower income 152 (49.3%) Physical activity levels Low 97 (31.3%) Moderate 90 (29.0%) High 122 (39.4%) Reached weekly MVPA (yes) 177 (57.1%) Optimum sleep time (7–9 hrs/day) 242 (78.1%) Optimum sitting time (< 6 hrs/day) 245 (79.0%) Presence of chronic diseases n = 34 (10.9%) Hypertension 15 (4.8%) Diabetes 6 (1.9%) PCOS 5 (1.6%) Obesity 4 (1.3%) Other diseases 4 (1.3%) Abbreviations: BMI – body mass index, LIPA – light intensity physical activity, MVPA – moderate to vigorous physical activity, PCOS – polycystic ovarian syndrome ------------------------------------------ Insert Table 1 here --------------------------------------------- Prevalence of 24-hour movement behaviors While spending 7.5 hours (33%) on sleep, the respondents spend 30% (425 min/day) in LIPA, 19% (270 min/day) of typical day sedentary and 18% (264 min/day) of day involved in MVPA. The overall prevalence of 24-hour movement behaviors is depicted in Supplementary file S3. Association of 24-hour movement behaviors with chronic diseases Of all movement behaviors, only sleep time showed a significant association with the chronic disease occurrence (Table 2 ). Figure 4 shows the significant association of sleep time and levels (low, optimum and high) with the chronic disease risk. While sitting time showed a positive (detrimental) association and LIPA along with MVPA showed a negative (favorable) association with the occurrence of chronic disease, none of the association reached statistical significance. Table 2 Association of 24-hour movement behaviors with the chronic diseases 24-hour movement behaviors β (95% CI) Odds Ratio p Sleep time (hours/day) 0.20 (0.036, 0.36) 0.82 0.025* Sitting time (hours/day) 0.12 (0.03, 0.26) 0.89 0.122 LIPA (min/day) -0.01 (-0.03, 0.01) 1.00 0.184 MVPA (min/day) -0.01 (-0.01, 0.01) 0.99 0.357 LIPA – light intensity physical activity, MVPA – moderate-vigorous physical activity; *p < 0.05 -------------------------------------- Insert Table 2 and Fig. 2 here ------------------------------------ Sub-group analysis Association of socio-demographic variables with chronic diseases occurrence Age and BMI showed positive association with chronic disease, however the strength of association was low (Table 3 ). Higher odds ratio (> 1) demonstrates the exposure (age and BMI) was associated with higher odds of outcome (chronic disease). Female was found to have higher occurrence of chronic disease compared to male however, the odds of occurrence was low in both of the gender. No significant association of any socio-economic class with the chronic disease. Figure 3 (a), (b), (c) depicts the significant association of age, gender and body mass index respectively with the probability of occurrence of chronic disease. Table 3 Association of demographic variables with 24-hour movement behaviors and chronic diseases Demographic variables 24-hour movement behavior Chronic diseases Sleep Sitting time (hours/day) LIPA (min/day) MVPA (min/week) β (95% CI) p β (95% CI) p β (95% CI) p β (95% CI) p β (95% CI) Odds Ratio p Age (years) -0.01 (-0.04, 0.02) 0.678 -0.01 (-0.04, 0.02) 0.503 2.89 (0.41, 5.38) 0.023 0.12 (-1.65, 1.90) 0.892 0.1 (0.01, 0.1) 1.045 0.014 Gender Male 8.11 (5.48, 10.74) < 0.001 6.19 (3.67, 8.71) < 0.001 520.70 (291.63, 749.77) < 0.001 306.64 (143.06, 470.23) < 0.001 5.3 (3.2, 7.4) 0.005 < 0.001 Female 7.75 (5.13, 10.37) < 0.001 6.40 (3.89, 8.91) < 0.001 536.88 (308.80, 764.97) < 0.001 288.85 (125.97, 451.73) < 0.001 5.9 (3.7, 7.9) 0.003 < 0.001 BMI -0.03 (-0.09, 0.03) 0.292 0.02 (-0.04, 0.08) 0.491 -6.05 (-11.15, -0.95) 0.020 -0.12 (-3.76, 3.52) 0.948 0.1 (0.01, 0.1) 1.087 0.019 Socio-economic class Upper middle 0.56 (-1.72, 2.85) 0.629 -1.28 (-3.47, 0.91) 0.250 -45.74 (-244.87, 153.39) 0.652 -71.32 (-213.53, 70.88) 0.324 -1.1 (-3.6, 1.3) 0.313 0.358 Lower- middle 0.84 (-1.39, 3.06) 0.460 -2.12 (-4.25, 0.01) 0.051 -78.53 (-272.28, 115.23) 0.426 -124.59 (-262.96, 13.77) 0.077 -2.4 (-4.9, 0.1) 0.093 0.063 Lower 0.92 (-2.10, 3.95) 0.548 -3.84 (-6.73, -0.95) 0.009 -318.49 (-581.45, -55.53) 0.018 -183.50 (-371.29, 4.28) 0.055 0.3 (-2.7, 3.2) 1.282 0.868 LIPA – light intensity physical activity, MVPA – moderate-vigorous physical activity; *p < 0.05 -------------------------------- Insert Table 3 and Fig. 3 here ------------------------------------------ Association of socio-demographic variables with 24-hour movement behaviors While age was positively (favorably) associated with LIPA levels, BMI was negatively (detrimentally) associated with LIPA levels (Table 3 and Fig. 6). Women found to have higher sitting time and LIPA levels, while lower sleep time and MVPA levels than their male counterparts. Lower income participants were found to have lower sleep and LIPA levels comparing their higher income counterparts (supplementary file S4). Discussion The present study explored the prevalence and the association of the 24-hour movement behaviors and socio-demographic profile with the chronic diseases. The study found the respondents exhibited lower sedentary time (19%), optimum MVPA (18%) and optimum sleep time (33%). The overall prevalence of NCD among our respondents was found to be 11% with hypertension as common NCD encountered. Among 24-hour movement variables, only higher sleep time (more than nine hours per day) was associated with occurrence of chronic diseases. Higher age and higher BMI were associated with higher odds of chronic disease. BMI was negatively associated with light intensity PA while lower socioeconomic status was associated positively with light intensity PA and negatively with the sitting time. Prevalence of 24-hour movement behaviors and chronic disease The respondents from Udupi were found to have optimum sedentary time and MVPA recommended by global policy makers and associations. The present findings concur with the previous epidemiological study [ 14 ] that have observed high MVPA and low sedentary time among the inhabitants of Udupi, coastal Karnataka region. The high MVPA and low sedentary time among the study respondents might due to one of the following reasons: (1) the coastal Karnataka, Udupi is a blended mixture of blue and green spaces with surrounding mangrove, natural forests and beaches which are less accessible to public transport [ 18 ]. The inhabitants of Udupi access to nearby places maximum by walking and indulge in unorganized sports and leisure activities which might have contributed to the high MVPA and less sedentary behavior; (2) the second possibility might be the use of self-reported questionnaire that might have caused recall bias and may not determine type, intensity, and amount of PA recommended by public health agencies [ 19 ]. Association of 24-hour movement behaviors with chronic diseases High sleep time (more than nine hours/day) was found to be associated with the higher chances of chronic disease in the present study. The above finding confirms the earlier population-based studies that concluded long sleep hours were associated with cardiovascular diseases. [ [ 20 ] ] Though, these previous studies [ 20 , 21 ] claimed positive association between the short duration sleep hours with the multi-morbidity, the present study findings did not observe any such association. Though existing epidemiological studies [ 22 – 24 ] have found higher and lower risks of NCDs sedentary time and PA of any intensities respectively, the present study findings failed to find significant associations. This may be due to the high MVPA and low ST and low prevalence of chronic diseases (11%) among the present study respondents. We hypothesize that all respondents who accessed the internet were educated and remained aware of the negative effects of sedentary behaviors and physical inactivity on chronic diseases. This awareness might have led to high baseline movement behaviors and low NCDs and a non-association between above both. Sub-group analyses In the present study, higher age and higher BMI were associated with higher chances of chronic disease. The above finding concur with the recent systematic reviews that concluded the association of BMI with plurality of chronic diseases (type 2 diabetes, hypertension, heart failure, stroke, cancers, stroke, Alzheimer’s and arthritis) [ 25 , 26 ]. Female was found to have higher occurrence of chronic disease compared to male however, the odds of occurrence was low in both of the gender. No significant association of any socio-economic class with the chronic disease. While age was positively (favorably) associated with LIPA levels, BMI was negatively (detrimentally) associated with LIPA levels (Table 3 and Fig. 3 ). Women found to have higher sitting time and LIPA levels, while lower sleep time and MVPA levels than their male counterparts. Lower income participants were found to have lower sleep and LIPA levels comparing their higher income counterparts Limitations The present study presents few limitations: (1) 24-hour movement behaviors were self-reported which represents inherent bias of recall and social-desirability [ 27 ]. Further the presence of chronic disease was also self-reported. We recommend future population-based studies should advocate accelerometers (24-hour movement behaviors) and biochemical risk markers (chronic disease risk) to establish the relation among the later among the inhabitants of Udupi, west coastal Karnataka [ 28 ]; (2) the present study administered online survey which inherently means our respondents were educated [ 29 ]. The present study might have failed to see significant association between 24-hour movement behaviors and chronic diseases probably due to awareness of negative effects of inactivity which is shown in optimum 24-hour movement behaviors. Future we recommend that population-based studies should reach through door to door survey (interviewer-based survey) rather than through online; (3) Though we aimed to include until 65 years, the respondents presented with median age of 34 years and hence it’s not surprising to find high movement behaviors and low occurrence of chronic diseases [ 30 ]. We reckon future studies to stratify the sampling and include aged population to confirm our findings. Conclusion High movement behaviors of a typical day and lower odds of NCDs among inhabitants of Udupi was found. Higher age, body mass index and long sleep hours were associated with higher risk of chronic diseases. Though the findings favor the optimum movement behaviors and lower odds of chronic diseases, readers are cautioned about the respondents with high baseline education and use of self-reported questionnaires which remain potential barriers in interpreting the present study results. We reckon further studies employing objective measures to quantify 24-hour movement behaviors (accelerometers) and chronic diseases (medical records, biochemical markers of cardiometabolic risk) at the population level to confirm our findings. Declarations The authors declare that the article is not under consideration for publication elsewhere. Further all the authors approved the final version of the manuscript. The authors also declare that if the article is accepted, it will not be published elsewhere by the authors, including electronically in the same form, in English or in any other language, without the written consent of the copyright-holder. Funding: The authors declare that the manuscript was not supported by any funding sources Ethical approval: The ethical committee of university approved the study (IEC2:582/2023) and the study was prospectively registered in the Clinical Trial Registry of India (CTRI/2023/12/060421). Declaration of competing interest: None of the authors has any financial or personal relationships with other people or organizations that could inappropriately influence their work. Consent to participate and publish: All the participants were given written informed consent for participation and publishing the study. Research data policy and data availability statements: Data extraction sheet will be made available based on the reasonable request to the corresponding author. Acknowledgement: Authors wish to thank Manipal Academy of Higher Education for providing conducive research environment and supporting us the publishing support. Authors contribution: Baskaran Chandrasekaran, Dr Chythra R Rao conceived the idea. Eswaran TPM and Manaswi Reddy administered the data collection. Manaswi Reddy and Baskaran Chandrasekaran wrote the original draft. Dr Chythra R Rao and Karuppasamy Govindaswamy provided critical inputs and revised the manuscript. 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Associations of Accelerometer-Measured Sedentary Time and Physical Activity With Prospectively Assessed Cardiometabolic Risk Factors: The CARDIA Study. J Am Heart Assoc. 2019;8(1):e010212. Lefever S, Dal M, Matthíasdóttir A. Online data collection in academic research:: advantages and limitations. British Journal of Educational Technology. 2007 JUL 2007;38(4):574-82. Rector J, Marceau K, Friedman E. Moderation of the Association Between Chronic Medical Conditions and Functional Limitations Over Time by Physical Activity: Effects of Age. Journals of Gerontology Series a-Biological Sciences and Medical Sciences. 2020;75(1):168-74. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4340364\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":298403918,\"identity\":\"35f8f05f-8aa2-4ebd-a79a-4d9a822f58e0\",\"order_by\":0,\"name\":\"Manaswi Reddy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Manipal Academy of Higher Education\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Manaswi\",\"middleName\":\"\",\"lastName\":\"Reddy\",\"suffix\":\"\"},{\"id\":298403920,\"identity\":\"30aac56d-e18b-4371-b60e-8de9777825af\",\"order_by\":1,\"name\":\"Baskaran Chandrasekaran\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACAwYGZiiT+QCQkGBgYAdSPMRpYUuAaGEmXguPAdQ2AlrM2Y8/Nvi5xy6Pgf3M1w0/d1jk8TczMD5424Zbi2VPjnFiz7PkYgae3G03e89IFEscZmA2nItHi8GBHOYDPAeYExskeLfd4G2TSGw4zMAmzYtPy/nnjw/+OVAP1MLz7OZfoJb5hxnYf+PVciPBOJnnwGGQFrbbIFs2AG1hxqfFcsYbY2OZA8cT23jSzG7LtkkUGx5mbJaccw63FnP+9MeSbw5UJ/azH352821bXZ7c8eaDH96U4dYCB2xQOoGBgbGBCPVIIIE05aNgFIyCUTASAABrl1ELWATrbgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Manipal Academy of Higher Education\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Baskaran\",\"middleName\":\"\",\"lastName\":\"Chandrasekaran\",\"suffix\":\"\"},{\"id\":298403922,\"identity\":\"3b2a074f-8741-47db-b05d-b1ebbe2d1098\",\"order_by\":2,\"name\":\"Karppasamy Govindasamy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"SRM Institute of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Karppasamy\",\"middleName\":\"\",\"lastName\":\"Govindasamy\",\"suffix\":\"\"},{\"id\":298403924,\"identity\":\"c84f80e3-29db-4997-9f8d-ffba158ab46d\",\"order_by\":3,\"name\":\"Eswaran TPM\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Manipal Academy of Higher Education\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Eswaran\",\"middleName\":\"\",\"lastName\":\"TPM\",\"suffix\":\"\"},{\"id\":298403925,\"identity\":\"6349cb19-06ea-48a9-a7cf-c77a9993ef3f\",\"order_by\":4,\"name\":\"Chythra R Rao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Manipal Academy of Higher Education\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chythra\",\"middleName\":\"R\",\"lastName\":\"Rao\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-29 05:29:39\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4340364/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4340364/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":56067028,\"identity\":\"9afa5fef-522f-429a-a6ee-9d420b936318\",\"added_by\":\"auto\",\"created_at\":\"2024-05-08 06:33:05\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":268125,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSTROBE Flowchart depicting the email responses included and analyzed\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4340364/v1/aba496f205ed332581fed7b1.png\"},{\"id\":56067025,\"identity\":\"b0bf6cc8-411c-4967-be4d-ec0223073514\",\"added_by\":\"auto\",\"created_at\":\"2024-05-08 06:33:05\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":102465,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFigure depicting the association between the daily sleep time and the probability of chronic disease occurrence. (a) depicts association of daily sleep time (as a continuous variable) with the occurrence of chronic diseases, while (b) shows the association between the tertiles of sleep time and the occurrence of chronic diseases.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4340364/v1/80d7eb18de095031b20e914b.png\"},{\"id\":56067026,\"identity\":\"e4887ef9-3ffd-441b-95c6-28c015fafe87\",\"added_by\":\"auto\",\"created_at\":\"2024-05-08 06:33:05\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":133916,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFigure depicting the association between the socio-demographic variables and the probability of chronic disease occurrence. While (a) \\u0026amp; (c) depicts association of age and body mass (as continuous variable) with the occurrence of chronic diseases, the figure (b) shows the association between the gender and the occurrence of chronic diseases.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4340364/v1/739a91d06c1a31a672fb402a.png\"},{\"id\":56308813,\"identity\":\"7e61de69-7b73-43fc-9541-30b20735d099\",\"added_by\":\"auto\",\"created_at\":\"2024-05-11 13:54:18\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1138487,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4340364/v1/9caab99f-c91c-4fca-beca-d3a14b53202d.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association of 24-hour movement behaviors and non-communicable diseases among adults of Udupi, Karnataka, India\",\"fulltext\":[{\"header\":\"Contributions to the literature\",\"content\":\"\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"652\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eFirst study to explore the 24-hour movement behaviors and chronic disease risk among inhabitants of urban area from India\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eAltered sleep levels were associated with chronic disease while physical activity and sedentary behavior levels remain na\\u0026iuml;ve.\\u0026nbsp;\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd width=\\\"100%\\\" valign=\\\"top\\\"\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003eHigh physical activity and low sedentary behavior which are common among individuals from low-middle income countries, may not be determine chronic disease risk.\\u0026nbsp;\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\"},{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eNon-communicable chronic diseases (NCDs) attributes to 41\\u0026nbsp;million deaths annually, accounting for 71% of global fatalities and more than 74% of the total fatalities due to NCD occur in low- and middle-income countries [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The goal of World Health Organization's (WHO) action plan is to decrease risk factors related to lifestyle especially optimizing 24 hour movement behaviors such as reducing physical inactivity, sedentary behavior and sleep dysfunction [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTo have an improved cardiometabolic health, individuals should have optimized 24-hour movement behavior, incorporating following: (1) reaching 150 minutes of moderate intensity or 75 minutes of vigorous physical activity (PA) in a week; (2) restricting sedentary time (ST) less than eight hours; (3) ensuring an optimal sleep time of 7\\u0026ndash;8 hours per day [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Though these behaviors are interdependent upon each other, the existing studies have studied these behaviors as isolated entities [\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Recent observational studies have started exploring this interdependency of these 24-hour movement behaviors and its association with the cardiometabolic health however emerging from the high-income countries [\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn South India, particularly in Karnataka, a high prevalence of elevated blood sugar (12%) and elevated blood pressure (19%) has been identified [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Inhabitants of Udupi Taluk, coastal area in Karnataka with a population of 16,60,080 from a two-lakh household are explored less for its 24-hour movement behaviors, though cardiometabolic risk is on rise [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Additionally, social factors such as gender and economic status, in conjunction with physical activity, display variations and are associated with the onset or worsening of underlying NCDs [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. However, this interdependency among 24-hour movement behaviors, socio-demographic factors and chronic diseases are least explored in this coastal region.\\u003c/p\\u003e \\u003cp\\u003eThe first phase of behavioral epidemiology framework is to establish links between the behavior and health which might help to develop appropriate measures of behaviors (second phase), identify influences (third phase) and develop culturally appropriate interventions (fourth phase) [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Hence the present study aimed to understand the association of 24-hour movement behaviors and sociodemographic factors with occurrence of NCDs in the adults of the coastal Karnataka. Through the present study, the levels of movement behaviors in inhabitants on coastal Karnataka based on internationally recognized benchmarks such as global physical activity guidelines or international sleep duration recommendations could be understand and would aid in the designing and implementation of efficient lifestyle strategies.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy design and ethical consideration\\u003c/h2\\u003e \\u003cp\\u003eThis cross-sectional study was conducted among the community dwelling adults of Udupi taluk, Karnataka, India during January 2024 \\u0026ndash; February 2024. The ethical committee of university approved the study (IEC2:582/2023) and the study was prospectively registered in the Clinical Trial Registry of India (CTRI/2023/12/060421). The research was conducted in accordance with the ethical standards laid by Helsinki Declaration of 1975, as revised in 2008. The study is reported as per the reporting guidelines of STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE checklist is attached as supplementary file S1.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePhases of the study\\u003c/h2\\u003e \\u003cp\\u003eThe study was carried out in phases: (1) phase 1 \\u0026ndash; administering a literature review and identifying potential movement behaviors influencing chronic diseases. Further addition of other micro-constructs such as sociodemographic profile was added; (2) phase 2 \\u0026ndash; the developed questionnaire was validated for content (comprehensibility, appropriateness, structure and relevance) by three public health experts and one physician who were at least three years in questionnaire development, administration of online surveys and epidemiological studies; (3) phase 3 involved the development of anonymized online questionnaire using Google forms along with the consent forms and administered online to the potential residents across Udupi area. Supplementary file S2 depicts the three phases of the study described above.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipants\\u003c/h2\\u003e \\u003cp\\u003eFor phase 2, the developed questionnaire was pilot tested in three public health experts and one physician with mean age of 45\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8 with at least three years in survey questionnaire development and administration.\\u003c/p\\u003e \\u003cp\\u003eFor the phase 3, the participants were identified from Udupi taluk of Karnataka, whose population was estimated to be 16,60,080 from household of two lakhs with 56% was found to be literate. Adults (18\\u0026ndash;65 years), both men and women who were residents of Udupi taluk were included in the study. To be included in the study, the participants should understand English and access to their own email ids. An internet connection was required since the survey was solely provided in a web-based format. The sampling technique used for recruitment was chain-referral sampling. The survey invitation and the eligibility criteria were announced via email, social media and grocery service providers of Udupi area. The survey link was embedded in this invitation. Participation in the study was voluntary and the respondents were assured irreversible anonymity. The survey was accessible online from October 4, 2021. Data collection remained open until February 15, 2024.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample size calculation\\u003c/h2\\u003e \\u003cp\\u003eFrom the earlier literature, we learnt that the inhabitants living in Udupi region to have a moderate level of physical inactivity (46.3%) [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. At α of 0.05 and power 95%, the estimated sample size was 306 participants. The sample size was estimated using G*Power statistical software (version 3.1.9.6, University of Kiel, Germany).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eVariables\\u003c/h2\\u003e \\u003cp\\u003eSocio-demographic data such as age, gender, occupation, education level, lifestyle habits such as smoking and alcohol and socio-economic status (upper, middle and lower) were assessed. Further demographic variables such as height, weight, and hip waist ratio were also included in the questionnaire. The probing questions on the physical activity levels (light, moderate or vigorous) and sedentary time were also included in the questionnaire. The sleep time was estimated from the time out of bed in the morning till the time going to bed at night in a typical day. The presence of chronic diseases were probed for common non-communicable diseases of the coastal area of Karnataka: hypertension, obesity, polycystic ovarian syndrome, diabetes, cardiovascular diseases and other disorders.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOnline survey administration\\u003c/h2\\u003e \\u003cp\\u003eThe survey was administered exploring the sociodemographic profile, physical activity, sedentary levels and the presence of chronic diseases with the following sections:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eSociodemographic profile: This section comprised of items related to respondents\\u0026rsquo; gender, age, weight, height, annual income, living status and highest education level. The socio economic status was graded as five grades using Modified Kuppuswamy scale (updated 2023) as follows: upper, upper middle, lower middle, upper lower and lower income categories [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. The composite scores from individual scores for education, occupation and total monthly income were calculated. The grades of socio-economic status were based on the composite scores: (1) upper (scores 26\\u0026ndash;29); (2) upper middle (16\\u0026ndash;25); (3) lower middle (11\\u0026ndash;15); (4) upper lower (5\\u0026ndash;10) and (5) lower (\\u0026lt;\\u0026thinsp;5).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003ePA and sedentary time levels: This section is modified from International Physical Activity Questionnaire (IPAQ) [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. This section comprised of signaling questions on PA levels (light \\u0026ndash; walking, moderate and vigorous intensities, frequencies and duration of each activities) for the previous week. The sitting time was estimated in hours per day while light intensity PA (walking) was estimated in minutes per day and moderate-vigorous PA was estimated in minutes per week. The MET minutes per week for each participant was calculated as per developer standards.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003ePresence of chronic disease: This section comprised of questions probing for the presence of chronic disease was adapted from our observational studies in coastal areas of Karnataka [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical methods\\u003c/h2\\u003e \\u003cp\\u003eAll the anonymized data were descriptively analyzed. The results of the descriptive statistics were presented as frequencies and percentages for categorical data. For numerical data, means and standard deviations were indicated for numerical data as Shapiro Wilk test. Internal consistency among the health experts was assessed using Cronbach\\u0026rsquo;s alpha. The prevalence of 24-hour movement behaviors (light intensity PA, moderate-vigorous intensity PA, sitting time and sleep time) was expressed in percentages. The primary objective (association between 24-hour movement behaviors and chronic diseases) and secondary objectives (association of socio demographic profile with the NCD occurrence and 24-hour movement behaviors) were explored used multivariate logistic and linear regression models. All the individual variables of 24-hour movement behavior were included in the model. The estimates, 95% confidence intervals and odds ratio of the models were calculated controlling for education and lifestyle (smoking and alcohol) factors. Low multicollinearity (variable inflation factor\\u0026thinsp;\\u0026gt;\\u0026thinsp;2) was observed for all the models. Significance was set at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 in priori. Conditional estimation plots with 95% confidence intervals also were drawn. All the analyses were performed using Jeffreys's Amazing Statistics Program (JASP, version 0.17.1., University of Amsterdam, Netherlands).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe present cross-sectional study was carried on with sending email to the first wave of potential participants on 3rd February, 2024 and 2nd wave on 18th February, 2024. Of 525 potential participants emailed for survey responses, 332 (63%) responses were obtained and data of 310 participants were analyzed. The flow of the email responses and the data included and analyzed is depicted as Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e-------------------------------------------\\u003cem\\u003eInsert\\u003c/em\\u003e Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cem\\u003ehere ---------------------------------------------\\u003c/em\\u003e\\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline characteristics\\u003c/h2\\u003e \\u003cp\\u003eBaseline characteristics of the respondents are depicted in Table \\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Majority of the respondents were women and had a mean age of 34.3 years. Almost half of the participants belong to overweight and obese category (\\u0026asymp;\\u0026thinsp;40%). Half of the respondents (n\\u0026thinsp;=\\u0026thinsp;152, 49%) belong to lower income category. Almost 80% of the respondents had optimum sleep (7\\u0026ndash;9 hours per day) and optimum sitting time (less than 6 hours/day). Majority of the participants (n\\u0026thinsp;=\\u0026thinsp;177, 57%) reached the weekly MVPA goal of 150 minutes. Of 310 respondents, 11% of the respondents have expressed about the presence of chronic diseases with half of them were hypertensive (Table \\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\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\\u003eBaseline characteristics of the participants\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTotal (n\\u0026thinsp;=\\u0026thinsp;310)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eExpressed in mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePhysical activity levels\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLIPA (min/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e425.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;237.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMVPA (min/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e264.23\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;167.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSitting time (hours/ day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSleep time (hours/ day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eExpressed in number, percentages\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e208 (67.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eBMI category\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnderweight\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40 (12.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNormal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e144 (46.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverweight\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e89 (28.71%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eObese\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e37 (11.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eSocio-economic status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUpper income\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUpper \\u0026ndash; middle income\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50 (16.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLower \\u0026ndash; middle income\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e102 (32.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLower income\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e152 (49.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003ePhysical activity levels\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e97 (31.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90 (29.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e122 (39.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eReached weekly MVPA (yes)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e177 (57.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eOptimum sleep time (7\\u0026ndash;9 hrs/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e242 (78.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eOptimum sitting time (\\u0026lt;\\u0026thinsp;6 hrs/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e245 (79.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ePresence of chronic diseases\\u003c/p\\u003e \\u003cp\\u003en\\u0026thinsp;=\\u0026thinsp;34 (10.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (4.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDiabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6 (1.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePCOS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 (1.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eObesity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (1.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOther diseases\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (1.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eAbbreviations: BMI \\u0026ndash; body mass index, LIPA \\u0026ndash; light intensity physical activity, MVPA \\u0026ndash; moderate to vigorous physical activity, PCOS \\u0026ndash; polycystic ovarian syndrome\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e------------------------------------------\\u003cem\\u003eInsert\\u003c/em\\u003e Table \\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cem\\u003ehere ---------------------------------------------\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePrevalence of 24-hour movement behaviors\\u003c/h2\\u003e \\u003cp\\u003eWhile spending 7.5 hours (33%) on sleep, the respondents spend 30% (425 min/day) in LIPA, 19% (270 min/day) of typical day sedentary and 18% (264 min/day) of day involved in MVPA. The overall prevalence of 24-hour movement behaviors is depicted in Supplementary file S3.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation of 24-hour movement behaviors with chronic diseases\\u003c/h2\\u003e \\u003cp\\u003eOf all movement behaviors, only sleep time showed a significant association with the chronic disease occurrence (Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Figure\\u0026nbsp;4 shows the significant association of sleep time and levels (low, optimum and high) with the chronic disease risk. While sitting time showed a positive (detrimental) association and LIPA along with MVPA showed a negative (favorable) association with the occurrence of chronic disease, none of the association reached statistical significance.\\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\\u003eAssociation of 24-hour movement behaviors with the chronic diseases\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e24-hour movement behaviors\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eβ (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSleep time (hours/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.20 (0.036, 0.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.025*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSitting time (hours/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.12 (0.03, 0.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.122\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLIPA (min/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.01 (-0.03, 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.184\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMVPA (min/day)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.01 (-0.01, 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.357\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003eLIPA \\u0026ndash; light intensity physical activity, MVPA \\u0026ndash; moderate-vigorous physical activity; *p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e--------------------------------------\\u003cem\\u003eInsert\\u003c/em\\u003e Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e \\u003cem\\u003eand\\u003c/em\\u003e Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e \\u003cem\\u003ehere ------------------------------------\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSub-group analysis\\u003c/h2\\u003e \\u003cp\\u003eAssociation of socio-demographic variables with chronic diseases occurrence\\u003c/p\\u003e \\u003cp\\u003eAge and BMI showed positive association with chronic disease, however the strength of association was low (Table \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Higher odds ratio (\\u0026gt;\\u0026thinsp;1) demonstrates the exposure (age and BMI) was associated with higher odds of outcome (chronic disease). Female was found to have higher occurrence of chronic disease compared to male however, the odds of occurrence was low in both of the gender. No significant association of any socio-economic class with the chronic disease. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e (a), (b), (c) depicts the significant association of age, gender and body mass index respectively with the probability of occurrence of chronic disease.\\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\\u003eAssociation of demographic variables with 24-hour movement behaviors and chronic diseases\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"13\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eDemographic variables\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c10\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003e24-hour movement behavior\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" morerows=\\\"1\\\" nameend=\\\"c13\\\" namest=\\\"c11\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eChronic diseases\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSleep\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSitting time (hours/day)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eLIPA (min/day)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMVPA (min/week)\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eβ (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eβ (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eβ (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eβ (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eβ (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eOdds Ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003ep\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.01 (-0.04, 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.678\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.01 (-0.04, 0.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.503\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.89 (0.41, 5.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.023\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.12 (-1.65, 1.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.892\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.1 (0.01, 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.014\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.11 (5.48, 10.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.19 (3.67, 8.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e520.70 (291.63, 749.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e306.64 (143.06, 470.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.3 (3.2, 7.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.75 (5.13, 10.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.40 (3.89, 8.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e536.88 (308.80, 764.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e288.85 (125.97, 451.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e5.9 (3.7, 7.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.03 (-0.09, 0.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.292\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.02 (-0.04, 0.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.491\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-6.05 (-11.15, -0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.020\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.12 (-3.76, 3.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.948\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.1 (0.01, 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1.087\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.019\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eSocio-economic class\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUpper middle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.56 (-1.72, 2.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.629\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-1.28 (-3.47, 0.91)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.250\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-45.74 (-244.87, 153.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.652\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-71.32 (-213.53, 70.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.324\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-1.1 (-3.6, 1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.313\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0.358\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLower- middle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.84 (-1.39, 3.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.460\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.12 (-4.25, 0.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.051\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-78.53 (-272.28, 115.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.426\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-124.59 (-262.96, 13.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.077\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-2.4 (-4.9, 0.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.093\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0.063\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLower\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.92 (-2.10, 3.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.548\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-3.84 (-6.73, -0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.009\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-318.49 (-581.45, -55.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.018\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-183.50 (-371.29, 4.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.055\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.3 (-2.7, 3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1.282\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0.868\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"13\\\"\\u003e\\u003cem\\u003eLIPA \\u0026ndash; light intensity physical activity, MVPA \\u0026ndash; moderate-vigorous physical activity; *p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e--------------------------------\\u003cem\\u003eInsert\\u003c/em\\u003e Table \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e \\u003cem\\u003eand\\u003c/em\\u003e Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e \\u003cem\\u003ehere ------------------------------------------\\u003c/em\\u003e\\u003c/p\\u003e \\u003cp\\u003eAssociation of socio-demographic variables with 24-hour movement behaviors\\u003c/p\\u003e \\u003cp\\u003eWhile age was positively (favorably) associated with LIPA levels, BMI was negatively (detrimentally) associated with LIPA levels (Table \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Fig.\\u0026nbsp;6). Women found to have higher sitting time and LIPA levels, while lower sleep time and MVPA levels than their male counterparts. Lower income participants were found to have lower sleep and LIPA levels comparing their higher income counterparts (supplementary file S4).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe present study explored the prevalence and the association of the 24-hour movement behaviors and socio-demographic profile with the chronic diseases. The study found the respondents exhibited lower sedentary time (19%), optimum MVPA (18%) and optimum sleep time (33%). The overall prevalence of NCD among our respondents was found to be 11% with hypertension as common NCD encountered. Among 24-hour movement variables, only higher sleep time (more than nine hours per day) was associated with occurrence of chronic diseases. Higher age and higher BMI were associated with higher odds of chronic disease. BMI was negatively associated with light intensity PA while lower socioeconomic status was associated positively with light intensity PA and negatively with the sitting time.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePrevalence of 24-hour movement behaviors and chronic disease\\u003c/h2\\u003e \\u003cp\\u003eThe respondents from Udupi were found to have optimum sedentary time and MVPA recommended by global policy makers and associations. The present findings concur with the previous epidemiological study [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] that have observed high MVPA and low sedentary time among the inhabitants of Udupi, coastal Karnataka region. The high MVPA and low sedentary time among the study respondents might due to one of the following reasons: (1) the coastal Karnataka, Udupi is a blended mixture of blue and green spaces with surrounding mangrove, natural forests and beaches which are less accessible to public transport [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. The inhabitants of Udupi access to nearby places maximum by walking and indulge in unorganized sports and leisure activities which might have contributed to the high MVPA and less sedentary behavior; (2) the second possibility might be the use of self-reported questionnaire that might have caused recall bias and may not determine type, intensity, and amount of PA recommended by public health agencies [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation of 24-hour movement behaviors with chronic diseases\\u003c/h2\\u003e \\u003cp\\u003eHigh sleep time (more than nine hours/day) was found to be associated with the higher chances of chronic disease in the present study. The above finding confirms the earlier population-based studies that concluded long sleep hours were associated with cardiovascular diseases.\\u003csup\\u003e[\\u003c/sup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003csup\\u003e]\\u003c/sup\\u003e Though, these previous studies [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e] claimed positive association between the short duration sleep hours with the multi-morbidity, the present study findings did not observe any such association.\\u003c/p\\u003e \\u003cp\\u003eThough existing epidemiological studies [\\u003cspan additionalcitationids=\\\"CR23\\\" citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] have found higher and lower risks of NCDs sedentary time and PA of any intensities respectively, the present study findings failed to find significant associations. This may be due to the high MVPA and low ST and low prevalence of chronic diseases (11%) among the present study respondents. We hypothesize that all respondents who accessed the internet were educated and remained aware of the negative effects of sedentary behaviors and physical inactivity on chronic diseases. This awareness might have led to high baseline movement behaviors and low NCDs and a non-association between above both.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSub-group analyses\\u003c/h2\\u003e \\u003cp\\u003eIn the present study, higher age and higher BMI were associated with higher chances of chronic disease. The above finding concur with the recent systematic reviews that concluded the association of BMI with plurality of chronic diseases (type 2 diabetes, hypertension, heart failure, stroke, cancers, stroke, Alzheimer\\u0026rsquo;s and arthritis) [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Female was found to have higher occurrence of chronic disease compared to male however, the odds of occurrence was low in both of the gender. No significant association of any socio-economic class with the chronic disease.\\u003c/p\\u003e \\u003cp\\u003eWhile age was positively (favorably) associated with LIPA levels, BMI was negatively (detrimentally) associated with LIPA levels (Table \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Women found to have higher sitting time and LIPA levels, while lower sleep time and MVPA levels than their male counterparts. Lower income participants were found to have lower sleep and LIPA levels comparing their higher income counterparts\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLimitations\\u003c/h2\\u003e \\u003cp\\u003eThe present study presents few limitations: (1) 24-hour movement behaviors were self-reported which represents inherent bias of recall and social-desirability [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Further the presence of chronic disease was also self-reported. We recommend future population-based studies should advocate accelerometers (24-hour movement behaviors) and biochemical risk markers (chronic disease risk) to establish the relation among the later among the inhabitants of Udupi, west coastal Karnataka [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]; (2) the present study administered online survey which inherently means our respondents were educated [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. The present study might have failed to see significant association between 24-hour movement behaviors and chronic diseases probably due to awareness of negative effects of inactivity which is shown in optimum 24-hour movement behaviors. Future we recommend that population-based studies should reach through door to door survey (interviewer-based survey) rather than through online; (3) Though we aimed to include until 65 years, the respondents presented with median age of 34 years and hence it\\u0026rsquo;s not surprising to find high movement behaviors and low occurrence of chronic diseases [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. We reckon future studies to stratify the sampling and include aged population to confirm our findings.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eHigh movement behaviors of a typical day and lower odds of NCDs among inhabitants of Udupi was found. Higher age, body mass index and long sleep hours were associated with higher risk of chronic diseases. Though the findings favor the optimum movement behaviors and lower odds of chronic diseases, readers are cautioned about the respondents with high baseline education and use of self-reported questionnaires which remain potential barriers in interpreting the present study results. We reckon further studies employing objective measures to quantify 24-hour movement behaviors (accelerometers) and chronic diseases (medical records, biochemical markers of cardiometabolic risk) at the population level to confirm our findings.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eThe authors declare that the\\u0026nbsp;article is not under consideration for publication elsewhere. Further all the authors approved the final version of the manuscript. The authors also declare that if the article is accepted, it will not be published elsewhere by the authors, including electronically in the same form, in English or in any other language, without the written consent of the copyright-holder.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e The authors declare that the manuscript was not supported by any funding sources\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval:\\u0026nbsp;\\u003c/strong\\u003eThe ethical committee of university approved the study (IEC2:582/2023) and the study was prospectively registered in the Clinical Trial Registry of India (CTRI/2023/12/060421).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of competing interest:\\u0026nbsp;\\u003c/strong\\u003eNone of the authors has any financial or personal relationships with other people or organizations that could inappropriately influence their work.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate and publish:\\u0026nbsp;\\u003c/strong\\u003eAll the participants were given written informed consent for participation and publishing the study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResearch data policy and data availability statements:\\u0026nbsp;\\u003c/strong\\u003eData extraction sheet will be made available based on the reasonable request to the corresponding author.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement:\\u003c/strong\\u003e Authors wish to thank Manipal Academy of Higher Education for providing conducive research environment and supporting us the publishing support.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors contribution:\\u003c/strong\\u003e Baskaran Chandrasekaran, Dr Chythra R Rao conceived the idea. Eswaran TPM and Manaswi Reddy administered the data collection. Manaswi Reddy and Baskaran Chandrasekaran wrote the original draft. Dr Chythra R Rao and Karuppasamy Govindaswamy provided critical inputs and revised the manuscript. All the authors approved the present form of manuscript. \\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eRamesh S, Kosalram K. The burden of non-communicable diseases: A scoping review focus on the context of India. Journal of Education and Health Promotion. 2023;12(1):14.\\u003c/li\\u003e\\n\\u003cli\\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-62.\\u003c/li\\u003e\\n\\u003cli\\u003eO\\u0026apos;Neill CD, Vidal-Almela S, Terada T, Way KL, Kamiya K, Sperlich B, et al. Moving Together While Staying Apart: Practical Recommendations for 24-Hour Home-Based Movement Behaviours for Those With Cardiovascular Disease. CJC Open. 2021;3(12):1495-504.\\u003c/li\\u003e\\n\\u003cli\\u003eKoyama T, Ozaki E, Kuriyama N, Tomida S, Yoshida T, Uehara R, et al. Effect of Underlying Cardiometabolic Diseases on the Association Between Sedentary Time and All-Cause Mortality in a Large Japanese Population: A Cohort Analysis Based on the J-MICC Study. J Am Heart Assoc. 2021;10(13):e018293.\\u003c/li\\u003e\\n\\u003cli\\u003eReddigan JI, Ardern CI, Riddell MC, Kuk JL. Relation of physical activity to cardiovascular disease mortality and the influence of cardiometabolic risk factors. Am J Cardiol. 2011;108(10):1426-31.\\u003c/li\\u003e\\n\\u003cli\\u003eKim GS, Im E, Rhee JH. 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Sleep duration, lifestyles and chronic diseases: a cross-sectional population-based study. Sleep Sci. 2018;11(4):217-30.\\u003c/li\\u003e\\n\\u003cli\\u003eChomistek AK, Cook NR, Flint AJ, Rimm EB. Vigorous-intensity leisure-time physical activity and risk of major chronic disease in men. Med Sci Sports Exerc. 2012;44(10):1898-905.\\u003c/li\\u003e\\n\\u003cli\\u003eLi Y, White K, O\\u0026apos;Shields KR, McLain AC, Merchant AT. Light-Intensity Physical Activity and Cardiometabolic Risk Among Older Adults With Multiple Chronic Conditions. Am J Health Promot. 2019;33(4):507-15.\\u003c/li\\u003e\\n\\u003cli\\u003eDohrn IM, Welmer AK, Hagstr\\u0026ouml;mer M. Accelerometry-assessed physical activity and sedentary time and associations with chronic disease and hospital visits - a prospective cohort study with 15\\u0026thinsp;years follow-up. Int J Behav Nutr Phys Act. 2019;16(1):125.\\u003c/li\\u003e\\n\\u003cli\\u003eLarsson SC, Burgess S. Causal role of high body mass index in multiple chronic diseases: a systematic review and meta-analysis of Mendelian randomization studies. BMC Med. 2021;19(1):320.\\u003c/li\\u003e\\n\\u003cli\\u003eJeong YJ, Park S, Yon DK, Lee SW, Tizaoui K, Koyanagi A, et al. Global burden of gout in 1990-2019: A systematic analysis of the Global Burden of Disease study 2019. Eur J Clin Invest. 2023;53(4):e13937.\\u003c/li\\u003e\\n\\u003cli\\u003eWelk GJ, Kim Y, Stanfill B, Osthus DA, Calabro MA, Nusser SM, et al. Validity of 24-h physical activity recall: physical activity measurement survey. Med Sci Sports Exerc. 2014;46(10):2014-24.\\u003c/li\\u003e\\n\\u003cli\\u003eWhitaker KM, Pettee Gabriel K, Buman MP, Pereira MA, Jacobs DR, Reis JP, et al. Associations of Accelerometer-Measured Sedentary Time and Physical Activity With Prospectively Assessed Cardiometabolic Risk Factors: The CARDIA Study. J Am Heart Assoc. 2019;8(1):e010212.\\u003c/li\\u003e\\n\\u003cli\\u003eLefever S, Dal M, Matth\\u0026iacute;asd\\u0026oacute;ttir A. Online data collection in academic research:: advantages and limitations. British Journal of Educational Technology. 2007 JUL 2007;38(4):574-82.\\u003c/li\\u003e\\n\\u003cli\\u003eRector J, Marceau K, Friedman E. Moderation of the Association Between Chronic Medical Conditions and Functional Limitations Over Time by Physical Activity: Effects of Age. Journals of Gerontology Series a-Biological Sciences and Medical Sciences. 2020;75(1):168-74.\\u003c/li\\u003e\\n\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"movement behavior, physical activity, non-communicable disease, sustainability, sleep, sedentary behavior\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4340364/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4340364/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eOptimizing 24-hour movement behaviors (physical activity, sleep, and sedentary time) is crucial for prevention of non-communicable diseases (NCDs). However, the association between the aforementioned 24-hour movement behaviors and the risk of NCDs remains largely obscured in low-middle income countries. The present study aimed to explore the association between 24-hour movement behaviors and the occurrence of NCDs.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eAn online survey was administered among 310 community dwelling adults (age 34.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.3 years) of coastal Karnataka, enquiring sociodemographic profiles, 24-hour movement behaviors (physical activity levels, sedentary time and sleep time) and presence of NCDs\\u0026rsquo;. Linear and logistic regression models were employed to explore the association.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWhile spending 7.5 hours (33%) on sleep, the respondents spend 30% (425 min/day) in LIPA, 19% (270 min/day) of typical day sedentary and 18% (264 min/day) of day involved in MVPA. Higher sleep time was associated with higher odds of NCDs (estimate, β\\u0026thinsp;=\\u0026thinsp;0.20, odds ratio\\u0026thinsp;=\\u0026thinsp;0.82, p\\u0026thinsp;=\\u0026thinsp;0.025). Subgroup analyses showed age and body mass were positively associated with NCDs, while lower socio-economic status was associated with lower sitting time (β = -3.84, p\\u0026thinsp;=\\u0026thinsp;0.009) and lower light physical activity time (-318.49, p\\u0026thinsp;=\\u0026thinsp;0.018).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eHigh 24-hour movement behaviors and lower odds of NCDs were found among inhabitants of Udupi. Higher age, body mass index and long sleep hours were associated with higher risk of NCDs. The study warrants the need of addressing body mass and sleep levels along with physical activity levels in the prevention of NCDs.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association of 24-hour movement behaviors and non-communicable diseases among adults of Udupi, Karnataka, India\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-05-08 06:33:00\",\"doi\":\"10.21203/rs.3.rs-4340364/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"ddbf1788-de80-4110-8fe1-11de19eef200\",\"owner\":[],\"postedDate\":\"May 8th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-05-11T13:53:59+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-05-08 06:33:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4340364\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4340364\",\"identity\":\"rs-4340364\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}