The relationship between the physical activity intensity, and the risk of type 2 diabetes in active and sedentary adults: Zahedan Adult Cohort Study (ZACS), Iran

The relationship between the physical activity intensity, and the risk of type 2 diabetes in active and sedentary adults: Zahedan Adult Cohort Study (ZACS), Iran | 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 The relationship between the physical activity intensity, and the risk of type 2 diabetes in active and sedentary adults: Zahedan Adult Cohort Study (ZACS), Iran Tahereh Dehdari, Fariba Shahraki-Sanavi, Amir Nasiri, Roghayeh Nouri, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4117125/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: Given the effect of lifestyle on Type 2 Diabetes Mellitus (T2DM), the present study was conducted to determine the association between PA intensity and the risk of T2DM in sedentary and active adults in Zahedan based on a cohort study. Method: This cross-sectional study was conducted as a component of the population-based cohort known as prospective epidemiological research studies in Iran. The baseline data from the Zahedan Adult Cohort Study (ZACS) was utilized for this study. A total of 10,004 adults aged between 35 and 70 years were selected as participants from 2015 to 2019. Data on general information, socio-economic status, sleep status, medical history, and PA were collected through self-report questionnaires. Anthropometric measurements and biochemical parameters were obtained from participants after a 12-hour fasting period. Finally, data were analyzed using descriptive statistics, as well as independent-samples t-test, chi-square, and logistic regression tests in SPSS 22 . Results: Out of 10,004 adults studied, 81.2% were sedentary, with worse health indicators such as higher weight, waist size, and poor blood metrics than active participnts. Sedentary lifestyle correlated strongly with higher rates of hypertension, heart diseases, and stroke. Diabetes prevalence was higher in sedentary (77%) compared to active (16%) participants. The findings showed that the chance of developing T2DM in active individuals were less than sedentary individuals (OR=0.62, p<0.001). Moreover, this relationship was significant after adjusting variables demographic factors (OR=0.65, p<0.001), triglyceride and cholesterol (OR=0.68, p=0.001), sleep status (OR=0.72, p=0.001), and family history of diabetes (OR=0.66, p=0.001). Conclusion: The findings showed a clear association between sedentary lifestyles and an increased risk of T2DM. Physical inactivity correlates with adverse health markers and an elevated incidence of T2DM, which is mitigated by an active lifestyle. These results underscore the imperative for public health initiatives to promote PA as a critical strategy for diabetes prevention. physical activity type 2 diabetes mellitus anthropometric Background Diabetes mellitus is a significant global public health issue characterized by changes in insulin secretion, insulin resistance, or both, resulting in the impaired metabolism of carbohydrates, proteins, and fats [ 1 ]. The classic classification of diabetes includes Type 1 Diabetes Mellitus (T1DM), which is an early autoimmune form, and Type 2 Diabetes Mellitus (T2DM), a late non-autoimmune form. However, other clinical subgroups also exist, such as maturity onset diabetes of the young, neonatal diabetes (monogenic diabetes), latent autoimmune diabetes in adults (gestational diabetes), and possibly a late-onset autoimmune form, such as maturity-onset diabetes of young or neonatal diabetes (monogenic diabetes) or latent autoimmune diabetes in adults (gestational diabetes), and possibly a late-onset autoimmune form [ 2 ]. The condition known as T2DM is characterized by high blood sugar levels, which occur due to issues with the uptake of glucose by the liver and peripheral tissues, as well as with insulin secretion, or a combination of these factors [ 3 ]. As individuals age, they are more susceptible to issues such as reduced bone mineral density [ 4 ], muscle loss [ 5 ], physical dysfunction, and metabolic disorders [ 6 ]. Consequently, older individuals are at a higher risk of developing diabetes and other metabolic conditions compared to middle-aged and young individuals [ 7 ]. In 2019, the global prevalence of T2DM in 20–79 year olds was estimated to be 463 million (11%), with this figure expected to rise further to 700 million by 2040 [ 8 ]. Over the last three decades, the number of people with diabetes has more than doubled and it is the fifth leading cause of mortality globally [ 3 ]. While genetic predisposition plays a significant role in the heightened susceptibility to T2DM, an unhealthy diet and a sedentary lifestyle are two key factors driving the current global epidemic [ 9 – 11 ]. Increasing Physical Activity (PA) along with other lifestyle modification is one of the most effective ways to prevent T2DM as well as glycemic control for those who already have diabetes. Studies consistently showed that leisure-time PA, vigorous PA and resistance exercise were associated with reduced risk of type 2 diabetes [ 12 – 14 ]. The rising prevalence of T2DM is closely linked to the worldwide surge in obesity [ 15 ], which is influenced by sedentary behavior and modern lifestyles, including the growing prevalence of sedentary office-based jobs [ 16 ]. Sedentary behavior is a major factor contributing to the rise in the prevalence of T2DM [ 17 ]. Among adults with T2DM, breaking up sedentary time with short, regular bouts of light PA such as walking or simple resistance exercises helps reduce post-meal glucose, insulin, peptide, and TG levels [ 18 ] as well as decreases waist circumference, Body Mass Index (BMI) [ 19 ] and blood pressure (20). Given that genetic conditions cannot be altered, lifestyle control and management can help prevent the onset of T2DM. Simple anthropometric measurements, like waist circumference, neck circumference, waist-hip ratio, and waist-height ratio, serve as surrogate markers for obesity and are more practically valuable in both clinical practice and large-scale epidemiological studies. Physical measures such as BMI provide a simple way to assess the prevalence of overweight and obesity in the population [ 21 , 22 ]. Waist circumference is the most effective measure for both intra-abdominal fat mass and total fat. However, BMI can be misleading, particularly for individuals with a high proportion of lean muscle mass. Waist circumference, offering a more precise measure of body fat distribution, has demonstrated stronger associations with morbidity and mortality. Recently, the waist-to-stature ratio has been suggested as a superior screening tool compared to waist circumference and BMI for adult metabolic risk factors [ 21 , 23 – 25 ]. Given the significance of a healthy lifestyle in T2DM prevention. Therefore, the present study was conducted with the purpose of examining the correlation between the intensity of PA and risk of T2DM in adults. Methods Study design This cross-sectional study utilized the baseline data from the Zabol Cohort Study (ZACS) in Southeast Iran. The ZACS was a component of the Prospective Epidemiological Research Studies of Iran (PERSIAN) and involved 10,016 individuals aged 35–70. Recruitment and data collection took place between October 2015 and January 2019 in Zahedan. The rationale, objectives, and design of this study have been previously published [ 26 ]. Ethical considerations and data confidentiality were carefully managed throughout the study. Prior to participation, all individuals provided written informed consent and were informed of their right to withdraw from the study at any time. The Ethics Committee of Zahedan University of Medical Sciences approved the study (IR.ZAUMS.REC.1402.110). The study's inclusion criteria comprised Iranian citizenship, age between 35 and 70 at the time of the baseline survey, residency in Zahedan for at least 9 months for local residents, and at least 1 year of residency for immigrants from other areas. Data collection took place after obtaining written informed consent from the participants. Individuals who did not meet the study requirements or had severe physical or mental illnesses preventing them from completing questionnaires or visiting the cohort center were excluded. The reference population was chosen using a multi-stage random sampling method. Initially, Zahedan city regions were categorized based on socioeconomic status. Subsequently, three socioeconomic status categories (low, middle, and high) were randomly selected, and all eligible residents in these areas were invited to participate in the study. Ultimately, the study included 10,004 individuals. Measurements The questionnaires used in this study were previously validated in the PERSIAN cohort study [ 27 ]. These questionnaires included sections on general information, socio-economic status, sleep and rest, disease history, and physical activity, consisting of 42 items. Participants' physical activity levels were classified into two categories (low, < 41 METs; high, ≥ 41) based on the 24-hour activity level and Metabolic Equivalent Task (MET) index [ 28 ]. Anthropometric measurements were conducted in the morning and in a fasting state. To measure weight, a standing hand scale calibrated daily with a 10 kg weight was used. Participants were instructed to remove bulky and heavy clothing, as well as shoes, bags, and phones, and stand on the scale with hands on both sides, motionless. Weight was recorded in kilograms. To measure height, participants were asked to remove their shoes and sandals. They were instructed to remove any headwear, clips, or hair accessories. Participants stood in front of the researcher with legs together, heels against the wall, knees straight, and gaze straight ahead without tilting their heads. The measuring rod was lowered to the top of the person's head, and they were asked to breathe slowly. Height was recorded in centimeters. For waist circumference measurement, participants stood with legs together, hands on both sides, palms facing inward, and exhaled slowly. The waist size was measured at the top of the hip bone and recorded with an accuracy of 0.1 cm. Similarly, for hip circumference measurement, participants removed items such as wallets and belts. They stood with equal weight distribution on both legs, hands on both sides, palms facing inward, and exhaled slowly. The measurement was taken at the position of the maximum hip circumference and recorded with an accuracy of 0.1 cm. Blood biochemistry tests (Fasting Blood Sugar (FBS), Triglyceride (TG), and Cholesterol levels) were performed after a 12-hour fasting period. Participants were advised to avoid consuming fruit juice, tea, coffee, smoking, alcohol, caffeine, stimulant drinks, chewing gum, mint tablets, flavorings, similar foods, high-fat foods, intramuscular injections, physical exercise, and heavy exercise during this fasting period. Blood samples were taken at rest and 5 minutes after activity, with 5 cc of blood collected.Participants were advised to avoid consuming fruit juice, tea, coffee, smoking, alcohol, caffeine, stimulant drinks, chewing gum, mint tablets, flavorings, similar foods, high-fat foods, intramuscular injections, physical exercise, and heavy exercise during this fasting period. Blood samples were taken at rest and 5 minutes after activity, with 5 cc of blood collected. Statistical analysis Data were analyzed in SPSS (version 22) by using descriptive statistics and independent-samples t-test, chi-square and logistic regression test. Statistical significance was set at two-sided p < 0.05. Results The results revealed that out of the 10,004 adults in this study, 81.2% (n = 8123) were classified as sedentary, while 18.8% (n = 1881) were categorized as active. The mean age, years of education, duration of nighttime sleep, mid-day napping, time spent lying down without falling asleep, weight, waist circumference, hip circumference, BMI, waist–hip ratio, FBS, TG, and cholesterol were significantly higher in sedentary participants compared to active ones. Furthermore, the frequency of hypertension, cardiac ischemia, myocardial infarction, and stroke was significantly higher in sedentary individuals compared to active individuals (Table 1 ). Table 1 Descriptive characteristics of participants and comparative analysis between physically active and sedentary partcipants Variable Metabolic equivalent task p . value* Sedentary(< 41) Active(≥ 41) Mean ± SD Mean ± SD Age 50.78 ± 9.19 49.03 ± 9.01 0.001 Education years 6.99 ± 5.35 5.82 ± 4.75 0.001 Socioeconomic status 0.06 ± 0.97 0.25 ± 1.05 0.001 Night sleep duration (hour) 7.27 ± 1.67 6.37 ± 1.50 0.001 Mid-day napping (min) 0.69 ± 0.82 0.44 ± 0.66 0.001 Laying down without falling asleep (hour) 1.66 ± 1.47 1.16 ± 0.92 0.001 Height (Cm) 161.80 ± 10.25 161.75 ± 8.91 0.833 Weight (Kg) 72.03 ± 14.70 69.14 ± 13.62 0.001 Waist circumference (Cm) 96.31 ± 12.64 93.58 ± 12.21 0.001 Hip circumference (Cm) 101.69 ± 10.26 99.99 ± 9.53 0.001 Body mass index 27.48 ± 5.35 26.45 ± 4.91 0.001 Waist–hip ratio 0.94 ± 0.07 0.93 ± 0.07 0.001 Fast blood sugar 106.13 ± 43.85 99.58 ± 35.84 0.001 Triglyceride 147.95 ± 92.63 129.35 ± 91.57 0.001 Cholesterol 182.95 ± 40.09 181.20 ± 37.91 0.085 Variable n (%) n (%) p . value** Gender Male 3181(81.2) 736(18.8) 0.486 Female 4950(81.2) 1149(18.8) Marital status Single 147(84.5) 27(15.5) 0.182 Married 7150(80.9) 1691(19.1) Widowed 706(83.1) 144(16.9) Divorced 126(84.6) 23(15.4) Hypertension Yes 2219(86.5) 346(13.5) 0.001 No 5912(79.3) 1539(20.7) Cardiac ischemic Yes 813(88.9) 101(11.1) 0.001 No 7318(80.4) 1784(19.6) Miocardial infarction Yes 293(91.3) 28(8.7) 0.001 No 7838(80.8) 1857(19.2) Stroke Yes 141(90.4) 15(9.6) 0.002 No 7990(81.0) 1870(19.0) p < 0.05 significant. *The results of Independent-samples t-test, ** The results of Chi-square exam Findings showed that 2175 (21.7%) of participants had T2DM. The mean age, sleep duration at mid-day, 24-hour rest duration, weight, waist circumference, hip circumference, BMI, waist–hip ratio, FBS, and TG were significantly higher in individuals with diabetes. Additionally, the frequency of diabetes was significantly higher in female participants, as well as in those who were widowed or had a deceased spouse, and in individuals with a family history of diabetes among first and second-degree relatives (Table 2 ). Table 2 Descriptive characteristics of participants with and without type 2 diabetes mellitus and a comparative analysis between these two groups Variable Type 2 Diabetes Mellitus p . value* No Yes Mean ± SD Mean ± SD Age 49.18 ± 9.09 55.01 ± 8.00 0.001 Education years 7.06 ± 5.22 5.72 ± 5.30 0.001 Socioeconomic status 0.00 ± 1.00 0.16 ± 0.97 0.504 Night sleep duration (hour) 7.13 ± 1.66 7.00 ± 1.73 0.002 Mid-day napping (min) 0.62 ± 0.79 0.73 ± 0.85 0.001 Laying down without falling asleep (hour) 1.48 ± 1.32 1.89 ± 1.63 0.001 Height (Cm) 162.06 ± 9.44 160.82 ± 11.76 0.001 Weight (Kg) 70.69 ± 14.52 74.33 ± 14.26 0.001 Waist circumference (Cm) 94.53 ± 12.60 100.36 ± 11.52 0.001 Hip circumference (Cm) 101.10 ± 10.09 102.37 ± 10.28 0.001 Body mass index 26.92 ± 5.28 28.61 ± 5.10 0.001 Waist–hip ratio 0.93 ± 0.07 0.98 ± 0.06 0.001 Fast blood sugar 90.72 ± 11.02 160.03 ± 67.46 0.001 Triglyceride 135.41 ± 84.15 177.03 ± 112.65 0.001 Cholesterol 183.11 ± 37.88 180.87 ± 45.61 0.020 Variable n (%) n (%) p . value** Gender Male 3115(79.5) 802(20.5) 0.008 Female 4726(77.5) 1373(22.5) Marital status Single 158(90.8) 16(9.2) 0.001 Married 6961(78.7) 1880(21.3) Widowed 598(70.4) 252(29.6) Divorced 123(82.6) 26(17.4) Family history of diabetes in a first-degree relative No 5258(84.5) 968(15.5) 0.001 Yes 2582(68.1) 1207(31.9) Family history of diabetes in a second-degree relative No 6381(79.0) 1695(21.0) 0.001 Yes 1459(75.2) 480(24.8) p < 0.05 significant. *The results of Independent-samples t-test, ** The results of Chi-square exam The prevalence of diabetes was 77% in sedentary individuals and 16% in active individuals. The unadjusted and adjusted odds ratios for the relationship between activity and diabetes are presented in Table 3 . Active individuals were found to have a lower likelihood of being diabetic (Model 1: OR = 0.62, p < 0.001). The odds of diabetes were significantly lower in active individuals after adjusting for demographic factors (Model 2: OR = 0.65, p < 0.001), TG and cholesterol levels (Model 3: OR = 0.68, p = 0.001), sleep status (Model 4: OR = 0.72, p = 0.001), and family history of diabetes (OR = 0.66, p = 0.001). Table 3 Odds ratio of diabetes in physically active versus sedentary participants OR (active vs sedentary) 95% CI p . value* Model 1 0.62 (0.53–0.71) < 0.001 Model 2 0.65 (0.56, 0.76) < 0.001 Model 3 0.68 (0.60, 0.78) 0.001 Model 4 0.72 (0.62, 0.82) 0.001 Model 5 0.66 (0.58, 0.76) 0.001 *p < 0.05 significant. Model 1: Unadjusted Model 2: Adjusted for Age & Education Model 3: Adjuster for TG & CHOL Model 4: Adjuster for Sleep Duration 24h, Sleep Duration Mid-Day & Rest1 Model 5: Adjuster for FH1_Diabetes & FH2_Diabetes Discussion PA includes all movements that increase energy expenditure, improve blood glucose control in type 2 diabetes, reduce cardiovascular risk factors, contribute to weight loss, and enhance well-being [ 29 ]. Our results from a cohort study indicate an inverse association between PA and incidence of type 2 diabetes, which was consistently observed across the different studies [ 29 – 32 ]. Our study has presented evidence linking sedentary behavior to conditions such as hypertension, ischemic heart disease, myocardial infarction, and stroke. In alignment with our findings, Soares-Miranda et al. conducted a study examining the potential connection between PA and sedentary behavior with the risk of coronary heart disease and stroke in older adults. They discovered that among US men and women with an average age of 73 years at baseline, increased levels of walking, leisure-time activity, and exercise intensity were inversely linked to coronary heart disease, stroke (especially ischemic stroke), and overall cardiovascular disease [ 33 ]. Similarly, Kim et al. also found significant association between PA and lowest risk of stroke, hypertension and T2DM [ 34 ]. Conversely, Doherty et al. conducted a recent one-sample Mendelian randomization analysis to examine potential associations between device-measured physical activity and sedentary behavior with coronary artery disease, stroke, heart failure, blood pressure, hypertension, and anthropometric traits such as BMI and body fat percentage [ 35 ]. The differences between our and previous findings may be explained by the definitions and assessments of PA and sedentary behavior. Our findings revealed a significant increase in weight, waist circumference, hip circumference, BMI, waist–hip ratio, FBS, TG, and cholesterol among sedentary participants compared to active participants. This aligns with the results of the study by Salahshoornezhad et al., which aimed to investigate the effects of a multi-disciplinary program on anthropometric and biochemical parameters in obese and overweight elementary school girls. The intervention group in their study demonstrated more favorable outcomes in terms of weight loss, waist circumference, hip circumference, total cholesterol, low-density lipoprotein cholesterol, TG, and FBS when compared to the control group [ 36 ]. Furthermore, in another study, the findings revealed that An intervention group that participated in training sessions for 8 weeks. These sessions included exercise for 3 days a week for one hour (60–70% of the maximum heart rate), a noteworthy increase in vaspin level and a significant decrease in body weight, BMI, waist circumference, fat percentage, fat mass, fat-free mass, total cholesterol, FBS, and insulin level. However, there were no significant changes in hip circumference, waist-to-hip ratio, TG, HOMA-IR, and Quantitative Insulin Sensitivity Check Index (QUICKI) in intervention group compared to control groups [ 37 ]. Additionally, the duration of night sleep, mid-day napping, and laying down without falling asleep were significantly higher in sedentary participants compared to active participants. Several previous studies have identified a positive association between sedentary behavior and these factors [ 38 – 40 ]. According to the documents of the World Health Organization, inactivity or lack of regular PA is the fourth risk factor for global mortality, which accounts for approximately 7% of deaths worldwide. The level of inactivity is increasing in many countries and has unfortunate consequences such as high blood pressure, increased blood sugar, overweight and obesity. Therefore, PA is a low-cost solution to prevent many diseases [ 41 ]. The study revealed that the average age, mid-day sleep duration, 24-hour rest duration, weight, waist circumference, hip circumference, BMI, waist–hip ratio, FBS, and TG were higher among participants with T2DM. According to Al-Abri et al., there were no significant differences in daytime sleepiness and daytime naps between the T2DM and control groups. However, patients with diabetes had significantly higher BMI, fasting glucose, and TG, as well as lower total cholesterol and low-density lipoprotein levels compared to controls. High-density lipoprotein levels were similar in both groups [ 42 ]. Several studies have reported a high prevalence of these factors in patients with diabetes, finding higher BMI, waist circumference, waist-hip ratio, and FBS in patients with T2DM [ 43 , 44 ]. A screening measure should be both efficient and practical. While BMI is calculated using weight and height, it does not reflect the distribution of an individual's fat, such as general versus abdominal obesity. It has been noted that individuals with the same BMI may have different waist circumferences. Since people with abdominal obesity are more susceptible to cardiovascular diseases and hypertension, it is crucial to utilize a measure that takes into account waist circumference and either the waist–hip ratio or waist-to-height ratio [ 44 ]. Conclusion In conclusion, the comprehensive data presented in this study highlights a significant association between a sedentary lifestyle and an increased risk of T2DM among an adult population of 10,004 people. A sedentary lifestyle was markedly more prevalent, with 81.2% of the participants being classified in this category. These individuals showed higher mean values for age, years of education, duration of nighttime sleep and mid-day napping, as well as increased measurements in weight, waist and hip circumference, BMI, waist-to-hip ratio, FBS, TG, and cholesterol levels when compared to their active counterparts. Additionally, the study revealed a staggering 77% prevalence of diabetes among the sedentary individuals, a figure drastically higher than the 16% noted in active participants, underlining the strong influence of PA on mitigating diabetes risk. This risk reduction is further corroborated through multivariate logistic regression models, indicating that active individuals have consistently lower odds of developing diabetes across various adjusted models considering demographic factors, lipid profiles, sleep status, and family history of diabetes. The findings assert that promoting an active lifestyle may serve as a formidable approach to combating the rising tide of diabetes and other associated health conditions. Furthermore, the significant occurrence of diabetes among females, the bereaved (those with a deceased spouse), and amongst individuals with a familial history of the disease, underscores the need for targeted intervention strategies that address both lifestyle and genetic vulnerabilities. Public health policies must prioritize PA as a critical component of T2DM prevention and management to reduce the burden of this chronic disease and its concomitant health complications. These findings emphasize the urgency for public health interventions to focus on augmenting PA levels in the general population. Such measures could potentially reduce the prevalence of non-communicable diseases such as T2DM and improve overall population health. With the unadjusted and adjusted odds ratios demonstrating the benefits of being active, this study provides empirical support for healthcare policies that promote active lifestyles to prevent T2DM and related health issues. Strength and limitation The current study's robustness was demonstrated by its extensive sample size, encompassing the Sistani and Baloch populations in southeastern Iran. Furthermore, the study utilized a community-based multi-stage cluster sampling method. However, several limitations were identified, such as the absence of Hemoglobin A1c measurements, reliance on self-reported questionnaires, and a specific focus on an urban demographic. Abbreviations T2DM Patients with Type 2 Diabete PA Physical Activity BMI Body Mass Index FBS Fasting Blood Sugar TG Triglyceride ZACS Zahedan Adult Cohort Study Declarations Acknowledgments We appreciate the adults involved in the study for their cooperation. Funding This study was supported by Zahedan University of Medical Sciences. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Please contact the corresponding author for the data requests. Authors’ contributions FSh.S, AN and TD contributed to the design of the study. AAM, MM performed data analysis. FSh.S and RN interpreted the findings and drafted the manuscript. TD, AN and F.Sh.S. revised the manuscript for important intellectual content. All the authors have read and approved the final version of the manuscript. Ethics approval and consent to participate While each cohort center received the ethical approval from local universities, for the purpose of this study and pooling all PERSIAN data, the ethics committee of Zahedan University of Medical Sciences approved the study (IR.ZAUMS.REC.1402.110). All participants in Zahedan Adult Cohort Study were informed about the study objectives and voluntary nature of their participation. Also, a consent form was obtained from them. They were assured that their information were kept secured and coded according to each participant’s identification number Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. 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Predictability of body mass index for diabetes: affected by the presence of metabolic syndrome? 2011;11:1–9. https://pubmed.ncbi.nlm.nih.gov/21609497/ . Wei W, Xin X, Shao B, Zeng F-F, Love EJ, Wang B-YJPHN. The relationship between anthropometric indices and type 2 diabetes mellitus among adults in north-east China. 2015;18(9):1675–83. https://pubmed.ncbi.nlm.nih.gov/25358425/ . Shahraki-Sanavi F, Woodward M, Ansari-Moghaddam A, Okati-Aliabad H, Mohammadi M, Khorram A et al. Cohort Profile: The Zahedan Adult Cohort Study (ZACS)—a prospective study of non-communicable diseases in Sistani and Baluch populations. Int J Epidemiol. 2022. pmid35138365. https://pubmed.ncbi.nlm.nih.gov/35138365/ . Poustchi H, Eghtesad S, Kamangar F, et al. Prospective epidemiological research studies in Iran (the Persian Cohort Study): rationale, objectives, and design. Am J Epidemiol. 2018;187:647–55. https://pubmed.ncbi.nlm.nih.gov/29145581/ . Kazemi Karyani A, Karmi Matin B, Soltani S, et al. Socioeconomic gradient in physical activity: findings from the PERSIAN cohort study. BMC Public Health. 2019;19:1312. https://doi.org/10.1186/s12889-019-7715-z . Colberg SR et al. Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes care, 2016. 39(11): p. 2065. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6908414/ . Smith AD, et al. Physical activity and incident type 2 diabetes mellitus: a systematic review and dose–response meta-analysis of prospective cohort studies. Diabetologia. 2016;59(12):2527–45. https://pubmed.ncbi.nlm.nih.gov/27747395/ . Wahid A, et al. Quantifying the association between physical activity and cardiovascular disease and diabetes: a systematic review and meta-analysis. J Am Heart Association. 2016;5(9):e002495. https://pubmed.ncbi.nlm.nih.gov/27628572/ . González K, Fuentes J, Márquez JL. Physical inactivity, sedentary behavior and chronic diseases. Korean J family Med. 2017;38(3):111. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451443/ . Soares-Miranda L, et al. Physical activity and risk of coronary heart disease and stroke in older adults: the cardiovascular health study. Circulation. 2016;133(2):147–55. https://pubmed.ncbi.nlm.nih.gov/26538582/ . Kim Y, et al. Exercise and incidence of myocardial infarction, stroke, hypertension, type 2 diabetes and site-specific cancers: prospective cohort study of 257 854 adults in South Korea. BMJ open. 2019;9(3):e025590. https://bmjopen.bmj.com/content/9/3/e025590 . Doherty A, Smith-Byrne K, Ferreira T, Holmes MV, Holmes C, Pulit SL, et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat Commun. 2018;9(1):5257. https://www.nature.com/articles/s41467-018-07743-4 . Salahshoornezhad S, et al. Effect of a multi-disciplinary program on anthropometric and biochemical parameters in obese and overweight elementary school girls: A randomized clinical trial. Nutr Metabolism Cardiovasc Dis. 2022;32(8):1982–9. https://pubmed.ncbi.nlm.nih.gov/35610083/ . Shahraki Z, Eftekhari E. Impact of Aerobic Exercise on Serum Vaspin Level in Female Patients With Type 2 Diabetes Mellitus. Crescent J Med Biol Sci, 2018. 5(3). https://www.cjmb.org/text.php?id=166 . Pengpid S, Peltzer K. Sedentary behaviour and 12 sleep problem indicators among middle-aged and elderly adults in South Africa. Int J Environ Res Public Health. 2019;16(8):1422. https://pubmed.ncbi.nlm.nih.gov/31010026/ . Creasy SA, et al. Higher amounts of sedentary time are associated with short sleep duration and poor sleep quality in postmenopausal women. Sleep. 2019;42(7):zsz093. https://pubmed.ncbi.nlm.nih.gov/30994175/ . Thejaswini P. A Study on Factors Associated with Insomnia among Aged Population in Urban Field Practice Area of Kempegowda Institute of Medical Sciences. Rajiv Gandhi University of Health Sciences (India); 2018. Organization World Health. Global status report on non-communicable diseases. Geneva Switz 2014. https://www.who.int/publications-detail-redirect/9789241564854 . Al-Abri MA, et al. Habitual sleep deprivation is associated with type 2 diabetes: a case-control study. Oman Med J. 2016;31(6):399. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5099401/ . Khan MM, et al. Effect of age and body mass index on various clinical and anthropometric parameters of type 2 diabetic patients: a case-control study. Int J health Sci Res. 2016;6(11):132–42. https://www.ijhsr.org/IJHSR_Vol.6_Issue.11_Nov2016/20.pdf . Moosaie F, et al. Waist-to-height ratio is a more accurate tool for predicting hypertension than waist-to-hip circumference and BMI in patients with type 2 diabetes: A prospective study. Front Public Health. 2021;9:726288. https://pubmed.ncbi.nlm.nih.gov/34692623/ . Additional Declarations No competing interests reported. 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The classic classification of diabetes includes Type 1 Diabetes Mellitus (T1DM), which is an early autoimmune form, and Type 2 Diabetes Mellitus (T2DM), a late non-autoimmune form. However, other clinical subgroups also exist, such as maturity onset diabetes of the young, neonatal diabetes (monogenic diabetes), latent autoimmune diabetes in adults (gestational diabetes), and possibly a late-onset autoimmune form, such as maturity-onset diabetes of young or neonatal diabetes (monogenic diabetes) or latent autoimmune diabetes in adults (gestational diabetes), and possibly a late-onset autoimmune form [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The condition known as T2DM is characterized by high blood sugar levels, which occur due to issues with the uptake of glucose by the liver and peripheral tissues, as well as with insulin secretion, or a combination of these factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As individuals age, they are more susceptible to issues such as reduced bone mineral density [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], muscle loss [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], physical dysfunction, and metabolic disorders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, older individuals are at a higher risk of developing diabetes and other metabolic conditions compared to middle-aged and young individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2019, the global prevalence of T2DM in 20\u0026ndash;79 year olds was estimated to be 463\u0026nbsp;million (11%), with this figure expected to rise further to 700\u0026nbsp;million by 2040 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Over the last three decades, the number of people with diabetes has more than doubled and it is the fifth leading cause of mortality globally [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile genetic predisposition plays a significant role in the heightened susceptibility to T2DM, an unhealthy diet and a sedentary lifestyle are two key factors driving the current global epidemic [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Increasing Physical Activity (PA) along with other lifestyle modification is one of the most effective ways to prevent T2DM as well as glycemic control for those who already have diabetes. Studies consistently showed that leisure-time PA, vigorous PA and resistance exercise were associated with reduced risk of type 2 diabetes [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The rising prevalence of T2DM is closely linked to the worldwide surge in obesity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which is influenced by sedentary behavior and modern lifestyles, including the growing prevalence of sedentary office-based jobs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Sedentary behavior is a major factor contributing to the rise in the prevalence of T2DM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among adults with T2DM, breaking up sedentary time with short, regular bouts of light PA such as walking or simple resistance exercises helps reduce post-meal glucose, insulin, peptide, and TG levels [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] as well as decreases waist circumference, Body Mass Index (BMI) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and blood pressure (20).\u003c/p\u003e \u003cp\u003eGiven that genetic conditions cannot be altered, lifestyle control and management can help prevent the onset of T2DM. Simple anthropometric measurements, like waist circumference, neck circumference, waist-hip ratio, and waist-height ratio, serve as surrogate markers for obesity and are more practically valuable in both clinical practice and large-scale epidemiological studies. Physical measures such as BMI provide a simple way to assess the prevalence of overweight and obesity in the population [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Waist circumference is the most effective measure for both intra-abdominal fat mass and total fat. However, BMI can be misleading, particularly for individuals with a high proportion of lean muscle mass. Waist circumference, offering a more precise measure of body fat distribution, has demonstrated stronger associations with morbidity and mortality. Recently, the waist-to-stature ratio has been suggested as a superior screening tool compared to waist circumference and BMI for adult metabolic risk factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the significance of a healthy lifestyle in T2DM prevention. Therefore, the present study was conducted with the purpose of examining the correlation between the intensity of PA and risk of T2DM in adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis cross-sectional study utilized the baseline data from the Zabol Cohort Study (ZACS) in Southeast Iran. The ZACS was a component of the Prospective Epidemiological Research Studies of Iran (PERSIAN) and involved 10,016 individuals aged 35\u0026ndash;70. Recruitment and data collection took place between October 2015 and January 2019 in Zahedan. The rationale, objectives, and design of this study have been previously published [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEthical considerations and data confidentiality were carefully managed throughout the study. Prior to participation, all individuals provided written informed consent and were informed of their right to withdraw from the study at any time. The Ethics Committee of Zahedan University of Medical Sciences approved the study (IR.ZAUMS.REC.1402.110).\u003c/p\u003e \u003cp\u003eThe study's inclusion criteria comprised Iranian citizenship, age between 35 and 70 at the time of the baseline survey, residency in Zahedan for at least 9 months for local residents, and at least 1 year of residency for immigrants from other areas. Data collection took place after obtaining written informed consent from the participants. Individuals who did not meet the study requirements or had severe physical or mental illnesses preventing them from completing questionnaires or visiting the cohort center were excluded. The reference population was chosen using a multi-stage random sampling method. Initially, Zahedan city regions were categorized based on socioeconomic status. Subsequently, three socioeconomic status categories (low, middle, and high) were randomly selected, and all eligible residents in these areas were invited to participate in the study. Ultimately, the study included 10,004 individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003eThe questionnaires used in this study were previously validated in the PERSIAN cohort study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These questionnaires included sections on general information, socio-economic status, sleep and rest, disease history, and physical activity, consisting of 42 items. Participants' physical activity levels were classified into two categories (low, \u0026lt;\u0026thinsp;41 METs; high, \u0026ge;\u0026thinsp;41) based on the 24-hour activity level and Metabolic Equivalent Task (MET) index [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnthropometric measurements were conducted in the morning and in a fasting state. To measure weight, a standing hand scale calibrated daily with a 10 kg weight was used. Participants were instructed to remove bulky and heavy clothing, as well as shoes, bags, and phones, and stand on the scale with hands on both sides, motionless. Weight was recorded in kilograms. To measure height, participants were asked to remove their shoes and sandals. They were instructed to remove any headwear, clips, or hair accessories. Participants stood in front of the researcher with legs together, heels against the wall, knees straight, and gaze straight ahead without tilting their heads. The measuring rod was lowered to the top of the person's head, and they were asked to breathe slowly. Height was recorded in centimeters. For waist circumference measurement, participants stood with legs together, hands on both sides, palms facing inward, and exhaled slowly. The waist size was measured at the top of the hip bone and recorded with an accuracy of 0.1 cm. Similarly, for hip circumference measurement, participants removed items such as wallets and belts. They stood with equal weight distribution on both legs, hands on both sides, palms facing inward, and exhaled slowly. The measurement was taken at the position of the maximum hip circumference and recorded with an accuracy of 0.1 cm.\u003c/p\u003e \u003cp\u003eBlood biochemistry tests (Fasting Blood Sugar (FBS), Triglyceride (TG), and Cholesterol levels) were performed after a 12-hour fasting period. Participants were advised to avoid consuming fruit juice, tea, coffee, smoking, alcohol, caffeine, stimulant drinks, chewing gum, mint tablets, flavorings, similar foods, high-fat foods, intramuscular injections, physical exercise, and heavy exercise during this fasting period. Blood samples were taken at rest and 5 minutes after activity, with 5 cc of blood collected.Participants were advised to avoid consuming fruit juice, tea, coffee, smoking, alcohol, caffeine, stimulant drinks, chewing gum, mint tablets, flavorings, similar foods, high-fat foods, intramuscular injections, physical exercise, and heavy exercise during this fasting period. Blood samples were taken at rest and 5 minutes after activity, with 5 cc of blood collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed in SPSS (version 22) by using descriptive statistics and independent-samples t-test, chi-square and logistic regression test. Statistical significance was set at two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe results revealed that out of the 10,004 adults in this study, 81.2% (n\u0026thinsp;=\u0026thinsp;8123) were classified as sedentary, while 18.8% (n\u0026thinsp;=\u0026thinsp;1881) were categorized as active. The mean age, years of education, duration of nighttime sleep, mid-day napping, time spent lying down without falling asleep, weight, waist circumference, hip circumference, BMI, waist\u0026ndash;hip ratio, FBS, TG, and cholesterol were significantly higher in sedentary participants compared to active ones. Furthermore, the frequency of hypertension, cardiac ischemia, myocardial infarction, and stroke was significantly higher in sedentary individuals compared to active individuals (Table\u0026nbsp;\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\u003eDescriptive characteristics of participants and comparative analysis between physically active and sedentary partcipants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMetabolic equivalent task\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e. value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSedentary(\u0026lt;\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActive(\u0026ge;\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNight sleep duration (hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMid-day napping (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLaying down without falling asleep (hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHeight (Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.03\u0026thinsp;\u0026plusmn;\u0026thinsp;14.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.14\u0026thinsp;\u0026plusmn;\u0026thinsp;13.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWaist circumference (Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.58\u0026thinsp;\u0026plusmn;\u0026thinsp;12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHip circumference (Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.99\u0026thinsp;\u0026plusmn;\u0026thinsp;9.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.48\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWaist\u0026ndash;hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFast blood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.13\u0026thinsp;\u0026plusmn;\u0026thinsp;43.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.58\u0026thinsp;\u0026plusmn;\u0026thinsp;35.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.95\u0026thinsp;\u0026plusmn;\u0026thinsp;92.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.35\u0026thinsp;\u0026plusmn;\u0026thinsp;91.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182.95\u0026thinsp;\u0026plusmn;\u0026thinsp;40.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181.20\u0026thinsp;\u0026plusmn;\u0026thinsp;37.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e. \u003cb\u003evalue**\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e3181(81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e736(18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.486\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e4950(81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1149(18.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7150(80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1691(19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e706(83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144(16.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126(84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2219(86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e346(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5912(79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1539(20.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCardiac ischemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e813(88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7318(80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1784(19.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMiocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293(91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7838(80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1857(19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141(90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7990(81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1870(19.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant.\u003c/p\u003e \u003cp\u003e*The results of Independent-samples t-test, ** The results of Chi-square exam\u003c/p\u003e \u003cp\u003eFindings showed that 2175 (21.7%) of participants had T2DM. The mean age, sleep duration at mid-day, 24-hour rest duration, weight, waist circumference, hip circumference, BMI, waist\u0026ndash;hip ratio, FBS, and TG were significantly higher in individuals with diabetes. Additionally, the frequency of diabetes was significantly higher in female participants, as well as in those who were widowed or had a deceased spouse, and in individuals with a family history of diabetes among first and second-degree relatives (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eDescriptive characteristics of participants with and without type 2 diabetes mellitus and a comparative analysis between these two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e. value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.01\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNight sleep duration (hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMid-day napping (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLaying down without falling asleep (hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHeight (Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.82\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.69\u0026thinsp;\u0026plusmn;\u0026thinsp;14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWaist circumference (Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHip circumference (Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.37\u0026thinsp;\u0026plusmn;\u0026thinsp;10.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWaist\u0026ndash;hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFast blood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160.03\u0026thinsp;\u0026plusmn;\u0026thinsp;67.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.41\u0026thinsp;\u0026plusmn;\u0026thinsp;84.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177.03\u0026thinsp;\u0026plusmn;\u0026thinsp;112.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183.11\u0026thinsp;\u0026plusmn;\u0026thinsp;37.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180.87\u0026thinsp;\u0026plusmn;\u0026thinsp;45.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e. \u003cb\u003evalue**\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e3115(79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e802(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.008\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e4726(77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1373(22.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6961(78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1880(21.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e598(70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e252(29.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123(82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily history of diabetes in a first-degree relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5258(84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e968(15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2582(68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1207(31.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily history of diabetes in a second-degree relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6381(79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1695(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1459(75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e480(24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant.\u003c/p\u003e \u003cp\u003e*The results of Independent-samples t-test, ** The results of Chi-square exam\u003c/p\u003e \u003cp\u003eThe prevalence of diabetes was 77% in sedentary individuals and 16% in active individuals. The unadjusted and adjusted odds ratios for the relationship between activity and diabetes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Active individuals were found to have a lower likelihood of being diabetic (Model 1: OR\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The odds of diabetes were significantly lower in active individuals after adjusting for demographic factors (Model 2: OR\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TG and cholesterol levels (Model 3: OR\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;0.001), sleep status (Model 4: OR\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;=\u0026thinsp;0.001), and family history of diabetes (OR\u0026thinsp;=\u0026thinsp;0.66, p\u0026thinsp;=\u0026thinsp;0.001).\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\u003eOdds ratio of diabetes in physically active versus sedentary participants\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (active vs sedentary)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e. value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.53\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.56, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.60, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.62, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.58, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant.\u003c/p\u003e \u003cp\u003eModel 1: Unadjusted\u003c/p\u003e \u003cp\u003eModel 2: Adjusted for Age \u0026amp; Education\u003c/p\u003e \u003cp\u003eModel 3: Adjuster for TG \u0026amp; CHOL\u003c/p\u003e \u003cp\u003eModel 4: Adjuster for Sleep Duration 24h, Sleep Duration Mid-Day \u0026amp; Rest1\u003c/p\u003e \u003cp\u003eModel 5: Adjuster for FH1_Diabetes \u0026amp; FH2_Diabetes\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePA includes all movements that increase energy expenditure, improve blood glucose control in type 2 diabetes, reduce cardiovascular risk factors, contribute to weight loss, and enhance well-being [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our results from a cohort study indicate an inverse association between PA and incidence of type 2 diabetes, which was consistently observed across the different studies [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has presented evidence linking sedentary behavior to conditions such as hypertension, ischemic heart disease, myocardial infarction, and stroke. In alignment with our findings, Soares-Miranda et al. conducted a study examining the potential connection between PA and sedentary behavior with the risk of coronary heart disease and stroke in older adults. They discovered that among US men and women with an average age of 73 years at baseline, increased levels of walking, leisure-time activity, and exercise intensity were inversely linked to coronary heart disease, stroke (especially ischemic stroke), and overall cardiovascular disease [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, Kim et al. also found significant association between PA and lowest risk of stroke, hypertension and T2DM [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Conversely, Doherty et al. conducted a recent one-sample Mendelian randomization analysis to examine potential associations between device-measured physical activity and sedentary behavior with coronary artery disease, stroke, heart failure, blood pressure, hypertension, and anthropometric traits such as BMI and body fat percentage [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The differences between our and previous findings may be explained by the definitions and assessments of PA and sedentary behavior.\u003c/p\u003e \u003cp\u003eOur findings revealed a significant increase in weight, waist circumference, hip circumference, BMI, waist\u0026ndash;hip ratio, FBS, TG, and cholesterol among sedentary participants compared to active participants. This aligns with the results of the study by Salahshoornezhad et al., which aimed to investigate the effects of a multi-disciplinary program on anthropometric and biochemical parameters in obese and overweight elementary school girls. The intervention group in their study demonstrated more favorable outcomes in terms of weight loss, waist circumference, hip circumference, total cholesterol, low-density lipoprotein cholesterol, TG, and FBS when compared to the control group [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, in another study, the findings revealed that An intervention group that participated in training sessions for 8 weeks. These sessions included exercise for 3 days a week for one hour (60\u0026ndash;70% of the maximum heart rate), a noteworthy increase in vaspin level and a significant decrease in body weight, BMI, waist circumference, fat percentage, fat mass, fat-free mass, total cholesterol, FBS, and insulin level. However, there were no significant changes in hip circumference, waist-to-hip ratio, TG, HOMA-IR, and Quantitative Insulin Sensitivity Check Index (QUICKI) in intervention group compared to control groups [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, the duration of night sleep, mid-day napping, and laying down without falling asleep were significantly higher in sedentary participants compared to active participants. Several previous studies have identified a positive association between sedentary behavior and these factors [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. According to the documents of the World Health Organization, inactivity or lack of regular PA is the fourth risk factor for global mortality, which accounts for approximately 7% of deaths worldwide. The level of inactivity is increasing in many countries and has unfortunate consequences such as high blood pressure, increased blood sugar, overweight and obesity. Therefore, PA is a low-cost solution to prevent many diseases [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study revealed that the average age, mid-day sleep duration, 24-hour rest duration, weight, waist circumference, hip circumference, BMI, waist\u0026ndash;hip ratio, FBS, and TG were higher among participants with T2DM. According to Al-Abri et al., there were no significant differences in daytime sleepiness and daytime naps between the T2DM and control groups. However, patients with diabetes had significantly higher BMI, fasting glucose, and TG, as well as lower total cholesterol and low-density lipoprotein levels compared to controls. High-density lipoprotein levels were similar in both groups [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Several studies have reported a high prevalence of these factors in patients with diabetes, finding higher BMI, waist circumference, waist-hip ratio, and FBS in patients with T2DM [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A screening measure should be both efficient and practical. While BMI is calculated using weight and height, it does not reflect the distribution of an individual's fat, such as general versus abdominal obesity. It has been noted that individuals with the same BMI may have different waist circumferences. Since people with abdominal obesity are more susceptible to cardiovascular diseases and hypertension, it is crucial to utilize a measure that takes into account waist circumference and either the waist\u0026ndash;hip ratio or waist-to-height ratio [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the comprehensive data presented in this study highlights a significant association between a sedentary lifestyle and an increased risk of T2DM among an adult population of 10,004 people. A sedentary lifestyle was markedly more prevalent, with 81.2% of the participants being classified in this category. These individuals showed higher mean values for age, years of education, duration of nighttime sleep and mid-day napping, as well as increased measurements in weight, waist and hip circumference, BMI, waist-to-hip ratio, FBS, TG, and cholesterol levels when compared to their active counterparts. Additionally, the study revealed a staggering 77% prevalence of diabetes among the sedentary individuals, a figure drastically higher than the 16% noted in active participants, underlining the strong influence of PA on mitigating diabetes risk. This risk reduction is further corroborated through multivariate logistic regression models, indicating that active individuals have consistently lower odds of developing diabetes across various adjusted models considering demographic factors, lipid profiles, sleep status, and family history of diabetes.\u003c/p\u003e \u003cp\u003eThe findings assert that promoting an active lifestyle may serve as a formidable approach to combating the rising tide of diabetes and other associated health conditions. Furthermore, the significant occurrence of diabetes among females, the bereaved (those with a deceased spouse), and amongst individuals with a familial history of the disease, underscores the need for targeted intervention strategies that address both lifestyle and genetic vulnerabilities. Public health policies must prioritize PA as a critical component of T2DM prevention and management to reduce the burden of this chronic disease and its concomitant health complications.\u003c/p\u003e \u003cp\u003eThese findings emphasize the urgency for public health interventions to focus on augmenting PA levels in the general population. Such measures could potentially reduce the prevalence of non-communicable diseases such as T2DM and improve overall population health. With the unadjusted and adjusted odds ratios demonstrating the benefits of being active, this study provides empirical support for healthcare policies that promote active lifestyles to prevent T2DM and related health issues.\u003c/p\u003e"},{"header":"Strength and limitation","content":"\u003cp\u003eThe current study's robustness was demonstrated by its extensive sample size, encompassing the Sistani and Baloch populations in southeastern Iran. Furthermore, the study utilized a community-based multi-stage cluster sampling method. However, several limitations were identified, such as the absence of Hemoglobin A1c measurements, reliance on self-reported questionnaires, and a specific focus on an urban demographic.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatients with Type 2 Diabete\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting Blood Sugar\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZahedan Adult Cohort Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the adults involved in the study for their cooperation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Zahedan University of Medical Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding authors upon reasonable request. Please contact the corresponding author for the data requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFSh.S, AN and TD contributed to the design of the study. AAM, MM performed data analysis. FSh.S and RN interpreted the findings and drafted the manuscript. TD, AN and F.Sh.S. revised the manuscript for important intellectual content. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile each cohort center received the ethical approval from local universities, for the purpose of this study and pooling all PERSIAN data, the ethics committee of Zahedan University of Medical Sciences approved the study\u0026nbsp;(IR.ZAUMS.REC.1402.110).\u003c/p\u003e\n\u003cp\u003eAll participants in\u0026nbsp;Zahedan Adult Cohort Study\u0026nbsp;were informed about the study objectives and voluntary nature of their participation. Also, a consent form was obtained from them. They were assured that their information were kept secured and coded according to each participant\u0026rsquo;s identification number\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Health Promotion Research Center, Iran University of Medical Sciences. \u003csup\u003e2\u003c/sup\u003e Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran. \u003csup\u003e3\u0026nbsp;\u003c/sup\u003eDepartment of Health Education and Health Promotion, School of Public Health, Iran University of Medical Sciences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, Hu FB, Kahn CR, Raz I, Shulman GI, Simonson DC, Testa MA, Weiss R. 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Front Public Health. 2021;9:726288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/34692623/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/34692623/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"physical activity, type 2 diabetes mellitus, anthropometric","lastPublishedDoi":"10.21203/rs.3.rs-4117125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGiven the effect of lifestyle on Type 2 Diabetes Mellitus (T2DM), the present study was conducted to determine the association between PA intensity and the risk of T2DM in sedentary and active adults in Zahedan based on a cohort study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u0026nbsp;\u003c/strong\u003eThis cross-sectional study was conducted as a component of the population-based cohort known as prospective epidemiological research studies in Iran. The baseline data from the Zahedan Adult Cohort Study (ZACS) was utilized for this study. A total of 10,004 adults aged between 35 and 70 years were selected as participants from 2015 to 2019. Data on general information, socio-economic status, sleep status, medical history, and PA were collected through\u0026nbsp; self-report questionnaires. Anthropometric measurements and biochemical parameters were obtained from participants after a 12-hour fasting period. Finally, data were analyzed using descriptive statistics, as well as independent-samples t-test, chi-square, and logistic regression tests in SPSS\u003csub\u003e22\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOut of 10,004 adults studied, 81.2% were sedentary, with worse health indicators such as higher weight, waist size, and poor blood metrics than active participnts. Sedentary lifestyle correlated strongly with higher rates of hypertension, heart diseases, and stroke. Diabetes prevalence was higher in sedentary (77%) compared to active (16%) participants. The findings showed that the chance of developing T2DM in active individuals were less than sedentary individuals (OR=0.62, p\u0026lt;0.001). Moreover, this relationship was significant after adjusting variables demographic factors (OR=0.65, p\u0026lt;0.001), triglyceride and cholesterol (OR=0.68, p=0.001), sleep status (OR=0.72, p=0.001), and family history of diabetes (OR=0.66, p=0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe findings showed a clear association between sedentary lifestyles and an increased risk of T2DM. Physical inactivity correlates with adverse health markers and an elevated incidence of T2DM, which is mitigated by an active lifestyle. These results underscore the imperative for public health initiatives to promote PA as a critical strategy for diabetes prevention.\u003c/p\u003e","manuscriptTitle":"The relationship between the physical activity intensity, and the risk of type 2 diabetes in active and sedentary adults: Zahedan Adult Cohort Study (ZACS), Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 09:09:35","doi":"10.21203/rs.3.rs-4117125/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b99b701-8d73-4345-8d5e-ef5266bde111","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-06T15:59:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 09:09:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4117125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117125","identity":"rs-4117125","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}