Reallocating just 10 minutes to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults

Reallocating just 10 minutes to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reallocating just 10 minutes to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults Yemeng Ji, Muhammed Atakan, Xu Yan, Jinlong Wu, Jujiao Kuang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3866812/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: As components of a 24-hour day, sedentary behavior (SB), physical activity (PA), and sleep are all independently linked to cardiovascular health (CVH). However, insufficient understanding of components’ mutual exclusion limits the exploration of the associations between all movement behaviors and health outcomes. The aim of this study was to employ compositional data analysis (CoDA) approach to investigate the associations between 24-hour movement behaviors and overall CVH. Methods: Data from 581 participants, including 230 women, were collected from the 2005-2006 wave of the US National Health and Nutrition Examination Survey (NHANES). This dataset included information on the duration of SB and PA, derived from ActiGraph accelerometers, as well as self-reported sleep duration. The assessment of CVH was conducted in accordance with the criteria outlined in Life's Simple 7, encompassing the evaluation of both health behaviors and health factors. Compositional linear regression was utilized to examine the cross-sectional associations of 24-hour movement behaviors and each component with CVH score. Furthermore, the study predicted the potential differences in CVH score that would occur by reallocating 10 to 60 minutes among different movement behaviors. Results: A significant association was observed between 24-hour movement behaviors and overall CVH ( p <0.001) after adjusting for potential confounders. Substituting moderate-to-vigorous physical activity (MVPA) for other components was strongly associated with favorable differences in CVH score ( p <0.05), whether in one-for-one reallocations or one-for-remaining reallocations. Allocating time away from MVPA consistently resulted in larger negative differences in CVH score ( p <0.05). For instance, replacing 10 minutes of light physical activity (LPA) with MVPA was related to an increase of 0.21 in CVH score (95% confidence interval (95% CI) 0.11 to 0.31). Conversely, when the same duration of MVPA was replaced with LPA, CVH score decreased by 0.67 (95% CI -0.99 to -0.35). No such significance was discovered for all duration reallocations involving only LPA, SB, and sleep ( p >0.05). Conclusions: MVPA seems to be as a pivotal determinant for enhancing cardiovascular health among general adult population, relative to other movement behaviors. Consequently, optimization of MVPA duration is an essential element in promoting overall health and well-being. Cardiovascular health Compositional data analysis Isotemporal substitution Moderate-to-vigorous physical activity Figures Figure 1 Figure 2 Figure 3 Background Daily behaviors are inseparably bound to individuals’ cardiovascular health (CVH). As components of movement behaviors, the independent associations of sedentary behavior (SB), physical activity (PA), and sleep with CVH have been well established [ 1 – 4 ]. However, it is essential to recognize that SB, PA, and sleep collectively constitute a fixed 24-hour period, with each component interrelating and interacting with others, suggesting that changes in one component are likely to have a substantial effect on the associations between other components and health outcomes. Therefore, studies in isolation may lead to underestimation of the true associations between movement behaviors and CVH. In recent years, there has been growing acknowledgement of the feature of perfect multicollinearity among 24-hour movement behaviors and the limitations of the previous paradigm [ 5 , 6 ]. Following the introduction of the isotemporal substitution model (ISM) by Mekary and colleagues into the field of physical activity epidemiology [ 7 ], SB, PA, and sleep have been validated as a finite whole, and this new paradigm has gained widespread application [ 8 – 10 ]. Furthermore, the ISM has undergone continuous refinement [ 11 , 12 ], and has evolved into a more scientifically rigorous method known as compositional data analysis (CoDA) [ 13 , 14 ]. Numerous studies have employed CoDA to investigate the associations between movement behaviors and CVH. For instance, Farrahi et al. [ 15 ] utilized this approach and confirmed that components of 24-hour movement behaviors were significantly associated with cardiometabolic outcomes, including fasting plasma glucose (FPG) and blood lipid, among middle-aged Finnish adults. Similarly, another study verified a strong association between movement behaviors during the waking day and cardiometabolic biomarkers in children and youth aged 6–17 years [ 16 ]. However, despite the abundance of existing studies [ 15 – 20 ], researchers have primarily concentrated on examining the separate factors of cardiovascular risk, particularly the biochemical markers, while neglecting a comprehensive assessment of CVH. It is critical to realize that daily behaviors, such as smoking and unhealthy diets, also have important implications for CVH [ 21 ], and should be taken into consideration. “Life's Simple 7” (LS7), proposed by the American Heart Association (AHA) [ 22 ], provides a comprehensive framework to assess overall CVH by considering both health behaviors and health factors, therefore, has become one of the most widely adopted evaluation criteria [ 23 , 24 ]. Given the comparative lack of findings concerning the associations between 24-hour movement behaviors and overall CVH as determined by CoDA, this study had a two-fold aim: first, to examine the association between 24-hour movement behaviors and CVH score, including the relative associations of individual SB, PA, and sleep with CVH score, evaluated according to LS7’s criteria; second, to explore the estimated differences in CVH score resulting from reallocating fixed time from one component to another, or to the remaining movement behaviors. Methods Study design and participants Data were collected from the National Health and Nutrition Examination Survey (NHANES) 2005–2006, a cross-sectional study that used a stratified, multistage probability design to obtain a large, ethnically diverse representative sample of the USA civilian noninstitutionalized population [ 25 ]. Detailed study methods can be found at: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005 . Data including demographic data, dietary data, examination data, laboratory data, and questionnaire data were collected through a household interview and a visit to a mobile examination center [ 26 ]. Publicly available data were used for this study as a secondary analysis only. In compliance with the Declaration of Helsinki, the protocols of the original study were approved by the Ethics Review Board of the National Center for Health Statistics (#2005-06), and written informed consent was obtained from participants. Measurement of 24-hour Movement Behaviors Data from the 2005–2006 wave were selected due to the inclusion of accelerometer measurements and a sleep questionnaire. ActiGraph AM-7164 (Pensacola, FL, USA) accelerometers were used to objectively access time spent in SB and PA. Except when sleeping or water-based activities, participants wore the device recorded at 100 Hz on the waist over 7 consecutive days. Data with at least 4 days and ≥ 10 h/d of wear time were considered valid [ 27 ]. Using standard count per minutes (cpm) thresholds each 1-min epoch was classified as SB ( 2020 cpm) [ 28 , 29 ]. Non-wear time was defined if cpm was 0 for more than 60 consecutive minutes, with allowance for up to 2 minutes of counts between 1 and 100 [ 29 ]. Sleep duration was counted as a whole integer from 1 to 12 (over 12 hours were treated as 12), depending on the response to the question “How much sleep do you usually get at night on weekdays or workdays?”. Time spent in each component of 24-hour movement behaviors was tallied daily, averaged across all valid days, and expressed as a proportion of 24 hours [ 11 ]. Assessment of CVH Metrics and Calculation of CVH Score Life’s Simple 7, published by the AHA to systematically assess CVH, consists of health behaviors and health factors [ 30 ]. Health behaviors contain smoking status, PA duration, healthy diet and body mass index (BMI). Health factors comprise 3 cardiometabolic risk factors: blood pressure (BP), total cholesterol (TC), and FPG. PA was intentionally omitted from the computation of CVH score as movement behaviors were the primary focus of interest in this analysis [ 31 ]. CVH score was evaluated according to the definitions of LS7 [ 32 ], and each metric was further categorized as ideal, intermediate, and poor, and given a point score of 2, 1, or 0, respectively. Subsequently, all the points were totaled, with “10 to 12 points” signifying an ideal CVH, “6 to 9 points” indicating an intermediate score, and “0 to 5 points” denoting a poor score [ 33 ]. The total CVH score was considered as continuous variable because of its sensitivity and vulnerability to errors [ 34 ]. Smoking status was determined based on responses to questions from the cigarette use questionnaire, which included inquiries such as “Do you now smoke cigarettes?” and “How long since quit smoking cigarettes?”. Participants who never smoked obtained a score of 2, 1 for former smokers who had quit more than 12 months, otherwise, 0 point. The dietary score was derived from responses to a 139-question food frequency questionnaire, on basis of which we integrated the average intake of added-sugar and sodium from two 24-hour diet recall. Due to a lack of complete information on sugar-sweetened beverage consumption, added-sugar intake was utilized instead [ 35 ]. Participants received 2 points if meeting four or five of the following 5 ideal dietary recommendations, score of 1 if meeting two or three, and 0 points for meeting one or zero [ 30 ]: fruits and vegetables ≥ 4.5 cups per day, fiber-rich whole grains ≥ 3 servings per day, fish ≥ 2 servings per week, sodium < 1500 mg/day, added sugar < 37.5 g/day for men, < 25 g/day for women. The weight and height of participants were measured by trained personnel during examination to calculate BMI. BMI < 25 kg/m2 was considered as ideal, 25 ≤ BMI < 30 kg/m2 indicated intermediate status, ≥ 30 kg/m2 categorized as poor [ 36 ]. Resting BP was recorded 3 to 4 times and the final values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were computed from the average of 2 readings, omitting the questionable values. Self-reported use of antihypertensive medications and questionnaire of prescription medications were collected to assist in determining BP categories. Without treatment, BP readings of < 120/80 mmHg were regarded as ideal and corresponded to a score of 2, participants whose SBP was between 120 and 139 mmHg and/or DBP was between 80 and 89 mmHg, or those whose BP was effectively managed with treatment, were classified as intermediate and awarded 1 point. BP readings ≥ 140/90 mmHg were categorized as poor and resulted in a score of 0. Laboratory values of TC and FPG were collected from blood samples in the mobile examination center, and medication use for both were obtained from the same questionnaire of prescription medications. Untreated TC < 200 mg/dl was categorized as ideal, TC in the range of 200 to 239 mg/dl or treatment to target was regarded as intermediate, ≥ 240 mg/dl indicated poor. FPG was categorized as ideal ( 125 mg/dl). Covariates To control for confounding effects, the following variables, including age, gender, race, education, marital status, household income, employment status, and alcohol consumption were regarded as covariables based on the classification of original data in NHANES 2005–2006. The socio-demographic data were obtained through household interview. Employment status was determined by answers to “Type of work done last week” and “Main reason did not work last week”. The frequency of alcohol consumption was evaluated by alcohol use questionnaire. All confounders were treated as categorical variables with exception of age as continuous variable in the model. Statistical Analysis IBM SPSS Statistics Version 26 (IBM Corp., Armonk, NY, USA) served to produce descriptive statistics. Continuous variables were characterized by mean ± standard deviation, while categorical variables were represented as percentage. Compositional data analyses were performed using the “compositions” [ 37 ], “robCompositions” [ 38 ], and “lmtest” packages in Rstudio 2022.12.0 (The R Foundation for Statistical Computing, Vienna, Austria). The central tendency of 24-hour movement behaviors was expressed as compositional mean, with the geometric mean being adjusted linearly to ensure that the total sum of time-use compositions equaled 1440 minutes. The dispersion was described by the compositional variation matrix. The sequential binary partition was utilized to randomly allocate two components of 24-hour movement behaviors to the numerator of the first isometric log-ratio (ilr) coordinate, while the rest two components were assigned to the denominator. The method for constructing the complete set of ilrs coordinates was described in detail elsewhere [ 39 ]. As explanatory variables, the ilrs coordinates and covariables were taken to fit the compositional multiple linear regression model, which intended to display the association between all time-use compositions and CVH score. The model’s linearity, including quadratic terms for the ilrs, and normality were checked [ 40 , 41 ], and the significance was examined using the “car::Anova” function. Subsequently, in accordance with the procedure in study by Dumuid et al. [ 13 ], each component of 24-hour movement behaviors was consecutively transformed into the numerator of the first ilr coordinate, thereby representing its relative association in relation to the health outcome. Consequently, four ilr coordinate systems were established. The relative associations of each component compared to other three with CVH score were ascertained by fitting 4 separate multiple linear regression models. The basic parameters for the first ilr coordinate of 4 models were reported, including the beta coefficients, standard error (SE) of beta, t-statistic, and p-values. On basis of the above analyses, the principle of isotemporal substitution was adopted to predict the differences in CVH score when reallocating fixed time (in 10-mintue increments within 60 minutes) between SB, LPA, MVPA, and sleep while compositional mean of 24-hour movement behaviors was set as the initial value. 10 minutes was chosen as the unit of reallocation according to its health effect [ 11 , 42 ]. The reallocations away from remaining movement behaviors proportionally to one component were conducted firstly (referred to as one-for-remaining reallocations). The duration of one component in the numerator of the first ilr coordinate was changed, and time spent in remaining movement behaviors in the denominator was corresponding altered in proportion to maintain a total of 1440 minutes, and to predict the differences in CVH scores. Furthermore, equivalent procedures were executed for one-for-one reallocations, where one component of 24-hour movement behaviors was substituted for another, while maintaining the constancy of the other two behaviors. The statistical significance was established at p < 0.05. Results Descriptive statistics Participants who were younger than 20 years, pregnant or breastfeeding at the time of survey, or missing any of the required data were excluded. Eventually, a total of 581 participants from the NHANES 2005–2006 dataset met the inclusion criteria for this study and were consequently included in the analysis. Figure 1 shows the participant flow. Table 1 presents the demographic characteristics of the included participants, and Table 2 illustrates the distribution characteristics of CVH score. Table 1 Descriptive characteristics of demography for the final sample from NHANES ( n = 581). Covariates Category Number (%) or Mean ± SD Age 54.9 ± 16.8 Gender , n (%) Male 351 (60.4%) Female 230 (39.6%) Race , n (%) Mexican American 89 (15.3%) Other Hispanic 12 (2.1%) Non-Hispanic White 352 (60.6%) Non-Hispanic Black 103 (17.7%) Other Race, Including Multi-Racial 25 (4.3%) Education , n (%) Less Than 9th Grade 51 (8.8%) 9-11th Grade (Includes 12th grade with no diploma) 98 (16.9%) High School Graduate 165 (28.4%) Some College or AA degree 166 (28.6%) College Graduate or above 101 (17.4%) Marital status , n (%) Married 332 (57.1%) Widowed 52 (9%) Divorced 69 (11.9%) Separated 18 (3.1%) Never Married 57 (9.8%) Living with Partner 53 (9.1%) Household income , n (%) 0-4999$ 17 (2.9%) 5000–9999$ 31 (5.3%) 10000–14999$ 48 (8.3%) 15000–19999$ 43 (7.4%) 20000–24999$ 54 (9.3%) 25000–34999$ 83 (14.3%) 35000–44999$ 72 (12.4%) 45000–54999$ 56 (9.6%) 55000–64999$ 36 (6.2%) 65000–74999$ 33 (5.7%) Over 75000$ 108 (18.6%) Employment status , n (%) Work now 314 (54%) Not Work now 267 (46%) Alcohol consumption , n (%) < 1 time/month 380 (65.4%) ≥ 1 time/month 201 (34.6%) Table 2 Descriptive characteristics of CVH score for the study population ( n = 581). CVH metrics Category Number (%) or Mean ± SD Smoking status , n (%) Poor 226 (38.9%) Intermediate 27 (4.6%) Ideal 328 (56.5%) Healthy diet , n (%) Poor 477 (82.1%) Intermediate 104 (17.9%) Ideal 0 (0.0%) BMI, kg/m 2 BMI , n (%) 28.61 ± 6.37 Poor 204 (35.1%) Intermediate 195 (33.6%) Ideal 182 (31.3%) BP , n (%) Poor 136 (23.4%) Intermediate 338 (58.2%) Ideal 107 (18.4) SBP, mmHg 126.67 ± 18.53 DBP, mmHg 69.31 ± 12.05 TC, mmol/L TC , n (%) 200.07 ± 44.76 Poor 100 (17.2%) Intermediate 267 (46%) Ideal 214 (36.8%) FPG, mmol/L FPG , n (%) 106.63 ± 31.01 Poor 68 (11.7%) Intermediate 220 (37.9%) Ideal 293 (50.4%) CVH score CVH score , n (%) 5.85 ± 1.91 Poor 230 (39.6%) Intermediate 337 (58%) Ideal 14 (2.4%) BMI body mass index, BP blood pressure, CVH cardiovascular health, DBP diastolic blood pressure, FPG fasting plasma glucose, SBP systolic blood pressure, SD standard deviation, TC total cholesterol Removing non-wear invalid time, the average accelerometer wear time was 1312.2 ± 99.7 min/d. Compositional mean of 24-hour movement behaviors was calculated after linearly adjusting to 1440 min/d. Participants spent an average of 604.1 min/d (42.0%) in SB, 465.6 min/d (32.3%) in sleep, 358.8 min/d (24.9%) in LPA, and 11.5 min/d (0.8%) in MVPA, respectively. While around 33.2% of the included participants met the recommended MVPA duration (≥ 21.4 min/d [ 43 ]), more than 71.3% of individuals spent over 8 hours per day in SB. Table 3 shows the variability of compositional data, the values represent the log variance of two components of 24-hour movement behaviors. The smallest variance of log (sleep/LPA) indicated that time spent in sleep was highly co-dependent with LPA. The highest log-ratio variances were observed for MVPA, demonstrating the least likelihood of conversion for MVPA with other components. Table 3 Compositional variation matrix of 24-hour movement behaviors. LPA MVPA SB Sleep LPA 0.000000 1.581860 0.16325 0.083061 MVPA 1.581860 0.000000 2.06873 1.805471 SB 0.163255 2.068731 0.00000 0.109166 Sleep 0.083061 1.805471 0.10917 0.000000 The values represent the log-ratio variance of each two components. LPA light physical activity, MVPA moderate-to-vigorous physical activity, SB sedentary behavior Compositional regression analyses The results from compositional multiple linear regression showed that the first ilr coordinate of 24-hour movement behaviors was strongly associated with CVH score (p < 0.00001), controlling for all covariables. Although the effect size (ES) was relatively small (R²adj = 0.1036), 24-hour movement behaviors emerged as a significant predictor of CVH score. No quadratic relationships between time-use compositions and CVH score existed (p > 0.05). Since the regression coefficient for the first ilrs in the model could not directly reflect the relative associations of individual components with health outcome [ 13 ], 4 models were then conducted to obtain 4 regression coefficients for the first ilr of each component relative to others. The beta coefficients and p-values for 4 models are reported in Table 4 . The results showed that the first ilr coefficient of Model 2 was positive and p < 0.001, indicating a significant positive association between MVPA (relative to remaining movement behaviors) and CVH score. By contrast, the p-values for Model 1, Model 3, and Model 4 emerged as greater than 0.05, suggesting that SB, LPA, and sleep lacked significant relative associations with CVH score. Table 4 Results of four multiple linear regression models for the first ilr. Model Each component relative to remaining Beta coefficients for ilr1 SE t -value p -value Model 1 LPA: remaining -0.38 0.24 -1.60 0.11 Model 2 MVPA: remaining 0.38 0.09 4.40 < 0.001** Model 3 SB: remaining -0.36 0.24 -1.52 0.13 Model 4 Sleep: remaining 0.36 0.29 1.22 0.22 ** p < 0.01. ilr isometric log-ratio, LPA light physical activity, MVPA moderate-to-vigorous physical activity, SB sedentary behavior, SE standard error Compositional isotemporal substitution analyses: One-for-remaining reallocations Figure 2 presents the predicted differences in CVH score when 10 to 60 minutes were reallocated from individual component to remaining movement behaviors in proportion. Given that compositional data must be non-negative [ 13 ] and compositional mean of MVPA was 11.5min/d, MVPA duration was only increased, but not decreased when reallocations of time exceeded 10 minutes. As the dominant component of reallocations, an increase in MVPA (accompanied by the proportionate decrease in other components) exhibited a significant association with a positive change in CVH score, displaying a curve increasing trend. Meanwhile, the variations in CVH score were not equivalent when MVPA replaced other movement behaviors in proportion or was replaced with the same reallocation time, indicating an asymmetrical relationship. For example, reallocating 10 minutes away from remaining movement behaviors to MVPA, there was an associated increase of + 0.22 in CVH score (95% confidence interval (95% CI) 0.12 to 0.31). Conversely, reallocating the same 10 minutes duration from MVPA resulted in a decrease of 0.80 in CVH score (95% CI -1.16 to -0.44). There were no statistically significant differences in CVH score when LPA, SB, and sleep were the dominant component in reallocations (p > 0.05). Compositional isotemporal substitution analyses: One-for-one reallocations Keeping the rest two components at the compositional mean, we systematically reallocated time (in the unit of 10 minutes) from one component of 24-hour movement behavior to another in turn, in order to predict the differences in CVH score. The results demonstrated that all compositional isotemporal substitutions involving MVPA were significantly associated with overall CVH (p < 0.05), similarly to the findings for one-for-remaining reallocations. Generally, there were favorable improvements in CVH score when MVPA replaced other three components, but the rate of increase in CVH score gradually diminished with continuous maximization of MVPA duration. The differences in CVH score were slightly predominant when MVPA replaced LPA (ES = 0.64 for 60 minutes, p < 0.05), compared to the same time reallocations from either SB or sleep (ES = 0.62/0.56 when MVPA replaced SB/sleep, p < 0.05). In addition, the asymmetry of predicted differences in CVH score persisted in one-for-one reallocations when MVPA replaced other components or was replaced, but the magnitudes of negative change were generally smaller than values in one-for-remaining reallocations. For example, when 10 minutes was reallocated from MVPA to LPA, SB, or sleep, a decrease of 0.67/0.67/0.66 points was observed in CVH score (95% CI -0.99 to -0.35/ -0.98 to -0.35/ -0.97 to -0.34), smaller than − 0.80 when MVPA was replaced with remaining components (95% CI -1.16 to -0.44). However, reallocations of the same time from other individual components to MVPA subsequently resulted in increase of 0.21/0.21/0.20 (95% CI 0.11 to 0.31/ 0.11 to 0.30/ 0.10 to 0.29), resembling the effects in one-for-remaining reallocations (95% CI 0.12 to 0.31). Again, no statistical significance was observed in CVH score when reallocations only involved LPA, SB, and sleep (p > 0.05). All results for one-for-one reallocations are depicted in Fig. 3 . Discussion The primary finding of this cross-sectional study indicated a significant association between 24-hour movement behaviors as a whole and overall CVH in general adult population. Moreover, reallocations of fixed time to MVPA from other components individually or proportionally from remaining movement behaviors were consistently associated with beneficial CVH. Our study is novel for setting CVH score, calculated based on the criteria in LS7, as the health outcome. Additionally, we employed CoDA approach to investigate the combined and relative associations of 24-hour movement behaviors and each component with overall CVH. Furthermore, compositional isotemporal substitution was adopted to predict the differences in overall CVH resulting from theoretical reallocations. The results of this study aligned with previous research, which emphasized the importance of thoroughly evaluating cardiometabolic health and utilized a calculated score of individual cardiometabolic risk factors to explore the relationships between movement behaviors and heart health. For instance, Simone et al. [ 44 ] employed CoDA and discovered a notable association between time-use compositions (including 9 components: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; other time) and cardiometabolic risk score (CMR score) among 782 youths aged 7–13 years, where CMR score was assessed based on data of BP, waist circumference, lipoprotein cholesterol, and triglycerides. Several studies focused on metabolic syndrome score, which was computed based on similar risk factors, and demonstrated the correlations between movement behaviors and score in various age groups including children, adults, and the elderly [ 45 , 46 ]. Remarkably, most of these studies neglected the potential implications of daily behaviors such as diet and smoking, even only few studies controlled them as covariates. In light of the limitations of such research, we combined daily behaviors with cardiovascular risk factors, resulting in the derivation of a comprehensive CVH score, and revealed the association between 24-hour movement behaviors and overall CVH in adults. The findings of our study on the differences in health outcomes caused by the reallocations of fixed time between movement behaviors were partially in line with the findings of German et al. [ 31 ], who firstly used CVH score as a measure of health outcome and performed isotemporal substitution analyses using the non-CoDA approach. The results related to MVPA exhibited consistency across both studies. German et al. [ 31 ] found that substituting 30 minutes of SB with MVPA was associated with a 0.485-point increase in CVH score, close to the 0.427 increase in our study (95% CI 0.23 to 0.62). However, the results pertaining to other components were mixed. German and colleagues observed a favorable association of a 0.077 rise in CVH score when replacing 30 minutes of SB with sleep, whereas our study found a modest and statistically insignificant increase in CVH score (ES = 0.028, 95% CI -0.02 to 0.08) when 30 minutes SB were replaced with sleep. In terms of reallocations between LPA and SB, radically distinct results were observed in two studies. Specifically, the investigators observed a significant increase in CVH score when 30 minutes LPA substituted for SB (ES = 0.039) [ 31 ], but our study suggested that the association was opposite and insignificant (ES=-0.007, 95% CI -0.05 to 0.03). The discrepancy between the results of two studies may be attributed to various reasons, including the followings: First, the types of ISM. The accuracy of obtained results may be compromised [ 5 ] because of the failure to treat movement behaviors as compositional data during the analysis using traditional ISM [ 7 ]. Second, the diversity of participants should not be ignored. In comparison to the broader adult population (aged 20–85 years) included in our study, participants in their study were middle-age and older adults (aged 45–84 years), with a higher average age. It is worth noting that for the elderly, engaging in LPA yields comparatively greater advantages when reallocating equivalent time from SB [ 19 , 47 ], which may result in disparities in findings. Third, the initial baseline levels of components have undeniably impact on the correlations. For instance, the average sleep duration of participants before reallocations in our study was longer compared to the study by German et al.. Since participants already obtained ample sleep, the favorable influence of increasing sleep duration became less significant when sleep substituted SB. As a result, the predicted positive association was attenuated or even disappeared. Besides, compositional mean of MVPA was lower in our study, thereby leading to stronger associations between isotemporal substitution involving MVPA and CVH score. These findings may reflect the nature of compositional data: A small value in one component leads to a greater variability in response to relative changes [ 13 ]. This study provided further evidence supporting the positive association of MVPA with cardiovascular health, consistent with the previous literatures [ 15 , 48 – 51 ]. In addition, employing CoDA, we found a notable asymmetry of the predicted differences in CVH score when substitution involved MVPA, regardless of in one-for-one reallocations or one-for-remaining reallocations [ 12 , 39 , 52 , 53 ]. This characteristic led to the formation of a dose-response curve for MVPA duration [ 59 ], illustrating that a sustained increase in MVPA duration cannot generate the benefit with same growth trend [ 54 ]. Hence, it is advisable for general population to prioritize maximizing MVPA while also maintaining a high level of MVPA duration. Nonetheless, because the curve did not exhibit a slowing or decreasing trend, it is possible that we were unable to determine the exact duration of MVPA that would provide the greatest cardiovascular health benefits [ 41 ], so further research is required. The results for the relative associations between substitution with LPA and CVH were controversial in prior research [ 44 , 46 , 55 , 59 ], which was in line with our finding that did not appreciably demonstrate the significant associations with CVH score when LPA replaced other components or was replaced. This finding implies that the intensity of PA may have a more significant impact on overall CVH compared to its duration. The association of sleep as a component of 24-hour movement behaviors with CVH has garnered growing interest in recent years. A few studies identified the U-shaped association between sleep and various health outcomes [ 15 , 17 , 40 , 56 ]. Nevertheless, considering that most participants in our study reported sleep duration within the range of 7–9 hours, with only 1.9% exceeding 9 hours per day, it was reasonable to conclude that such a relationship did not exist in this study. More importantly, self-reported sleep duration was prone to recall biases, which may affect the accuracy of relevant results. Further efforts should be made to measure sleep in more precise ways and to explore the association of sleep as a component with CVH in populations with sleep problems, such as sleep-deprived workers or people with circadian reversal. Strengths and Limitations A key strength of this study lies in its utilization of CoDA to further investigate the association between 24-hour movement behaviors and overall cardiovascular health in general adult population. The limitations of this study need to be considered. Firstly, causal inferences between time-use compositions and CVH score cannot be established due to the cross-sectional nature of NHANES 2005–2006. Secondly, while main confounders such as socio-demographic variables were adjusted for during analyzing, possible residual confounding by other unmeasured factors could not be ruled out. Thirdly, participants lacking any information on CVH metrics were excluded, resulting in a reduced sample size of only 581 participants meeting the inclusion criteria. Consequently, there may be some degree of bias in the results. Fourthly, participants in our study had a wide age-span (from 20 to 85 years) and thus the results, such as compositional mean of 24-hour movement behaviors, merely reflected a relative indication of the general adult population, cannot be directly interpreted as an absolute representation of the specific population. Fifthly, self-reported data, such as sleep duration and dietary intake, may deviate from actual measurements, thereby introducing potential inaccuracies into the analysis. Lastly, waist-worn accelerometers are limited in accurately identifying postural standing or sitting [ 57 ], thereby the actual time spent in SB and LPA may be misestimated. Meanwhile, the cut points used to define movement behaviors were set according to the characteristics of the general adult population, which may lead to an underestimation of MVPA duration in the elderly [ 58 ]. Conclusions Our study provided evidence of the cross-sectional association between 24-hour movement behaviors and overall cardiovascular health in adults. The findings suggested that reallocations of time from any other time-use compositions to MVPA were associated with more favorable CVH, highlighting the importance of MVPA and supporting the inclusion of MVPA in the assessment of cardiovascular health in LS7. However, the curve positive association between MVPA and CVH score, relative to other components, also suggested that we should maintain the current MVPA duration while optimizing it. Future studies should pay more attention to conduct longitudinal studies across various age cohorts and establish optimal time-use zones that effectively balance outcomes and cater to the needs of the public. Abbreviations AHA American Heart Association BMI Body mass index BP Blood pressure CI Confidence interval CoDA Compositional data analysis cpm count per minutes CVH Cardiovascular health DBP Diastolic blood pressure FPG Fasting plasma glucose ilr isometric log-ratio LPA Light physical activity ISM Isotemporal substitution model LS7 Life's Simple 7 MVPA Moderate-to-vigorous physical activity NHANES National Health and Nutrition Examination Survey PA Physical activity SB Sedentary behavior SBP Systolic blood pressure SE Standard error TC Total cholesterol Declarations Ethics approval and consent to participate The original study was approved by the Ethics Review Board of the National Center for Health Statistics (#2005-06). Participants provided written informed consent. This study involved secondary analysis of above available data only. Consent for publication Not applicable. Availability of data and materials The data of original study are available in the NHANES 2005-2006 repository, [https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005], and the datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors’ Contributions YJ participated in the design of the study, contributed to data collection and data reduction/analysis, and drafted the manuscript. MMA improved the manuscript framework. XY, JW and JK provided writing support. LP supervised all the study process. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors. Acknowledgments All data used are from the US National Health and Nutrition Examination Survey, we thank all NHANES researchers, staff, and participants for their contributions. The findings reported in this article are solely those of the author. Without the help of all authors, this study could not have been completed. Therefore, we would like to thank Dr. MMA and WL for their assistance in writing, Dr. LP for her guidance, and especially to Dr. XY and Dr. JK for their support in this research. References Hadgraft NT, Winkler E, Climie RE, et al. Effects of sedentary behaviour interventions on biomarkers of cardiometabolic risk in adults: systematic review with meta-analyses. Br J Sports Med. 2021;55(3):144–54. Jackson CL, Redline S, Emmons KM. Sleep as a potential fundamental contributor to disparities in cardiovascular health. Annu Rev Public Health. 2015;36:417–40. Tobaldini E, Fiorelli EM, Solbiati M, et al. Short sleep duration and cardiometabolic risk: from pathophysiology to clinical evidence. Nat Rev Cardiol. 2019;16(4):213–24. Vásquez PM, Durazo-Arvizu RA, Marquez DX, et al. Association of accelerometer-measured physical activity and cardiovascular health in the hispanic community health study/study of latinos. Hisp Health Care Int. 2022;20(1):15–24. Zeljko, Pedišić. Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology. 2017;2(49):252–69. Zeljko Pedišić. Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research: the focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology. 2014;1(46):135–46. Mekary RA, Willett WC, Hu FB, et al. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519–27. Buman MP, Winkler EAH, Kurka JM, et al. Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005–2006. Am J Epidemiol. 2014;179(3):323–34. Wellburn S, Ryan CG, Azevedo LB, et al. Displacing sedentary time: association with cardiovascular disease prevalence. Med Sci Sports Exerc. 2016;48(4):641–7. Yasunaga A, Shibata A, Ishii K et al. Associations of sedentary behavior and physical activity with older adults’ physical function: an isotemporal substitution approach. BMC Geriatr 2017;17(1). Chastin SFM, Palarea-Albaladejo J, Dontje ML, et al. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS ONE. 2015;10(10):e0139984. Dumuid D, Stanford TE, Pedišić Z et al. Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: a compositional data analysis approach. BMC Public Health 2018;18(1). Dumuid D, Stanford TE, Martin-Fernández J, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–38. Dumuid D, Wake M, Clifford S, et al. The association of the body composition of children with 24-hour activity composition. J Pediatr. 2019;208:43–49e9. Farrahi V, Kangas M, Walmsley R, et al. Compositional associations of sleep and activities within the 24-h cycle with cardiometabolic health markers in adults. Med Sci Sports Exerc. 2021;53(2):324–32. Carson V, Tremblay MS, Chaput J, et al. Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometabolic biomarkers among children and youth from the united states. PLoS ONE. 2019;14(7):e0220009. Full KM, Gallo LC, Malhotra A et al. Modeling the cardiometabolic benefits of sleep in older women: exploring the 24-hour day. Sleep 2020;43(1). Johansson MS, Holtermann A, Marott JL, et al. The physical activity health paradox and risk factors for cardiovascular disease: a cross-sectional compositional data analysis in the copenhagen city heart study. PLoS ONE. 2022;17(4):e0267427. Powell C, Browne LD, Carson BP, et al. Use of compositional data analysis to show estimated changes in cardiometabolic health by reallocating time to light-intensity physical activity in older adults. Sports Med. 2020;50(1):205–17. Swindell N, Rees P, Fogelholm M et al. Compositional analysis of the associations between 24-h movement behaviours and cardio-metabolic risk factors in overweight and obese adults with pre-diabetes from the preview study: cross-sectional baseline analysis. Int J Behav Nutr Phys Act 2020;17(1). Ng R, Sutradhar R, Yao Z, et al. Smoking, drinking, diet and physical activity—modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020;49(1):113–30. Lloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction. Circulation. 2010;121(4):586–613. van Sloten TT, Tafflet M, Périer M, et al. Association of change in cardiovascular risk factors with incident cardiovascular events. JAMA. 2018;320(17):1793. King DE, Xiang J. Retirement and healthy lifestyle: a national health and nutrition examination survey (nhanes) data report. J Am Board Family Med. 2017;30(2):213–9. Zipf G, Chiappa M, Porter KS et al. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1 2013;(56):1–37. Curtin LR, Mohadjer LK, Dohrmann SM et al. The national health and nutrition examination survey: sample design, 1999–2006. Vital Health Stat 2 2012;(155):1–39. Colley R, Connor GS, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Health Rep. 2010;21(1):63–9. Freedson PS, Melanson E, Sirard J. Calibration of the computer science and applications, inc. Accelerometer. Med Sci Sports Exerc. 1998;30(5):777–81. Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the united states measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8. Lloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction. Circulation. 2010;121(4):586–613. German C, Makarem N, Fanning J, et al. Sleep, sedentary behavior, physical activity, and cardiovascular health: mesa. Med Sci Sports Exerc. 2021;53(4):724–31. Lopez-Neyman SM, Davis K, Zohoori N et al. Racial disparities and prevalence of cardiovascular disease risk factors, cardiometabolic risk factors, and cardiovascular health metrics among us adults: NHANES 2011–2018. Sci Rep 2022;12(1). van Sloten TT, Tafflet M, Périer M, et al. Association of change in cardiovascular risk factors with incident cardiovascular events. JAMA. 2018;320(17):1793. Ragland DR. Dichotomizing continuous outcome variables: dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology. 1992;3(5):434–40. King DE, Xiang J. Retirement and healthy lifestyle: a national health and nutrition examination survey (NHANES) data report. J Am Board Family Med. 2017;30(2):213–9. Kalra R, Patel N, Arora P, et al. Cardiovascular health and disease among asian-americans (from the national health and nutrition examination survey). Am J Cardiol. 2019;124(2):270–7. van den Boogaart KG, Tolosana-Delgado R. compositions: a unified R package to analyze compositional data. Comput Geosci. 2008;34(4):320–38. Templ M, Hron K, Filzmoser P. Robcompositions: an R-package for robust statistical analysis of compositional data.; 2011. p.341–355. Dumuid D, Pedišić Z, Stanford TE, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28(3):846–57. Dumuid D, Simm P, Wake M, et al. The goldilocks day for children's skeletal health: compositional data analysis of 24-hour activity behaviors. J Bone Miner Res. 2020;35(12):2393–403. Dumuid D, Wake M, Burgner D, et al. Balancing time use for children’s fitness and adiposity: evidence to inform 24-hour guidelines for sleep, sedentary time and physical activity. PLoS ONE. 2021;16(1):e0245501. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the american college of sports medicine and the american heart association. Med Sci Sports Exerc. 2007;39(8):1423–34. Bull FC, Al-Ansari SS, Biddle S, et al. World health organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451–62. Verswijveren SJJM, Lamb KE, Martín-Fernández JA, et al. Using compositional data analysis to explore accumulation of sedentary behavior, physical activity and youth health. J Sport Health Sci. 2022;11(2):234–43. Lee J, Walker ME, Matthews KA, et al. Associations of physical activity and sleep with cardiometabolic risk in older women. Prev Med Rep. 2020;18:101071. Matricciani L, Dumuid D, Paquet C, et al. Sleep and cardiometabolic health in children and adults: examining sleep as a component of the 24-h day. Sleep Med. 2021;78:63–74. Lacroix AZ, Bellettiere J, Rillamas-Sun E, et al. Association of light physical activity measured by accelerometry and incidence of coronary heart disease and cardiovascular disease in older women. JAMA Netw Open. 2019;2(3):e190419. Kinoshita K, Ozato N, Yamaguchi T et al. Association of sedentary behaviour and physical activity with cardiometabolic health in japanese adults. Sci Rep 2022;12(1). Rossen J, Von Rosen P, Johansson UB, et al. Associations of physical activity and sedentary behavior with cardiometabolic biomarkers in prediabetes and type 2 diabetes: a compositional data analysis. Phys Sportsmed. 2020;48(2):222–8. Whitaker KM, Pettee Gabriel K, Buman MP et al. Associations of accelerometer-measured sedentary time and physical activity with prospectively assessed cardiometabolic risk factors: the cardia study. J Am Heart Assoc 2019;8(1). Yates T, Edwardson CL, Henson J, et al. Prospectively reallocating sedentary time: associations with cardiometabolic health. Med Sci Sports Exerc. 2020;52(4):844–50. Biddle G, Edwardson C, Henson J, et al. Associations of physical behaviours and behavioural reallocations with markers of metabolic health: a compositional data analysis. Int J Environ Res Public Health. 2018;15(10):2280. Dumuid D, Lewis LK, Olds TS, et al. Relationships between older adults’ use of time and cardio-respiratory fitness, obesity and cardio-metabolic risk: a compositional isotemporal substitution analysis. Maturitas. 2018;110:104–10. Knaeps S, De Baere S, Bourgois J, et al. Substituting sedentary time with light and moderate to vigorous physical activity is associated with better cardiometabolic health. J Phys Activity Health. 2018;15(3):197–203. Madden KM, Feldman B, Chase J. Sedentary time and metabolic risk in extremely active older adults. Diabetes Care. 2021;44(1):194–200. Mellow ML, Crozier AJ, Dumuid D, et al. How are combinations of physical activity, sedentary behaviour and sleep related to cognitive function in older adults? A systematic review. Exp Gerontol. 2022;159:111698. Kozey-Keadle S, Libertine A, Lyden K, et al. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43(8):1561–7. Copeland JL, Esliger DW. Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act. 2009;17(1):17–30. Miatke1 A, Olds T, Maher C et al. The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface. Int J Behav Nutr Phys Activity 2023; 20–127. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Mar, 2024 Reviews received at journal 11 Mar, 2024 Reviewers agreed at journal 11 Mar, 2024 Reviewers invited by journal 28 Jan, 2024 Editor assigned by journal 28 Jan, 2024 Editor invited by journal 24 Jan, 2024 Submission checks completed at journal 24 Jan, 2024 First submitted to journal 15 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3866812","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269740154,"identity":"60c35b1b-85ed-4623-b7d6-db8c18b190f5","order_by":0,"name":"Yemeng Ji","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Yemeng","middleName":"","lastName":"Ji","suffix":""},{"id":269740155,"identity":"f396a371-fc46-43fc-839c-6648204d1aaf","order_by":1,"name":"Muhammed Atakan","email":"","orcid":"","institution":"Hacettepe University","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"Atakan","suffix":""},{"id":269740156,"identity":"bc6ea706-9c99-4da3-8597-9271818d2e21","order_by":2,"name":"Xu Yan","email":"","orcid":"","institution":"Victoria University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Yan","suffix":""},{"id":269740157,"identity":"f44a2853-0ab3-40df-9f31-b1b2d2b48bd2","order_by":3,"name":"Jinlong Wu","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Jinlong","middleName":"","lastName":"Wu","suffix":""},{"id":269740158,"identity":"b508c16d-5fa3-4249-9cb9-7d2fc5baaaa1","order_by":4,"name":"Jujiao Kuang","email":"","orcid":"","institution":"Victoria University","correspondingAuthor":false,"prefix":"","firstName":"Jujiao","middleName":"","lastName":"Kuang","suffix":""},{"id":269740159,"identity":"4e91d21e-bf75-4e43-9725-ccd08f5825e7","order_by":5,"name":"Li Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACfmaGBOOffyTk+NkbiNQi2d7woJixwcJYsucAkVoMzhx88JmxoSLR4EYCsbbMSE7cXLhDIsHg5uONNxhqbKIJauGXSEs2nnlGIk/ydlqxBcOxtNwGwrbkpBnwsEkU893OMZNgbDhMWIvBjfzvP4BaEhtuniFWy5kDCca8bRKJE27wEKkFGMgJhjPOSAADGeiXBGL8AopKgw8VdcCoPLzxxocaG8JaUBwpkUCKcogWUnWMglEwCkbByAAAAi5EHaj3znwAAAAASUVORK5CYII=","orcid":"","institution":"Southwest University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-01-15 14:34:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3866812/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3866812/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50330211,"identity":"a1e7851e-2e21-47f3-a748-357984b9eeff","added_by":"auto","created_at":"2024-01-29 21:37:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208732,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow. \u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eBP\u003c/em\u003e \u0026nbsp;blood pressure, \u003cem\u003eCVH\u003c/em\u003e cardiovascular health, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eMVPA\u003c/em\u003emoderate-to-vigorous physical activity, \u003cem\u003eNHANES\u003c/em\u003e National Health and Nutrition Examination Survey, \u003cem\u003eTC\u003c/em\u003e total cholesterol\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3866812/v1/421f2b157f3cf73be0f08844.png"},{"id":50330208,"identity":"597d5de9-d330-4457-844a-b42c42b84eda","added_by":"auto","created_at":"2024-01-29 21:37:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113732,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated differences in CVH score associated with time reallocations from remaining components of 24-hour movement behaviors in proportion to one component. \u003cem\u003eCVH\u003c/em\u003e cardiovascular health, \u003cem\u003eLPA\u003c/em\u003e light physical activity, \u003cem\u003eMVPA\u003c/em\u003e moderate-to-vigorous physical activity, \u003cem\u003eSB\u003c/em\u003e sedentary behavior\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3866812/v1/0846432e721225d2e5ee7c18.png"},{"id":50330209,"identity":"a3be8eb8-cdd4-48fa-8bf0-d94a07fc47a7","added_by":"auto","created_at":"2024-01-29 21:37:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122723,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated differences in CVH score associated with time reallocations from one component to another, keeping the rest two constant. \u003cem\u003eCVH\u003c/em\u003e cardiovascular health, \u003cem\u003eLPA\u003c/em\u003e light physical activity, \u003cem\u003eMVPA\u003c/em\u003e moderate-to-vigorous physical activity, \u003cem\u003eSB\u003c/em\u003e sedentary behavior\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3866812/v1/eef0dd3e013e6312cb35c244.png"},{"id":50331085,"identity":"d65926c7-4d9c-4d76-9bf6-571877f897b3","added_by":"auto","created_at":"2024-01-29 21:45:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1007332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3866812/v1/11ae8701-7af0-4315-8a3b-96e0e90a8f31.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reallocating just 10 minutes to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults","fulltext":[{"header":"Background","content":"\u003cp\u003eDaily behaviors are inseparably bound to individuals\u0026rsquo; cardiovascular health (CVH). As components of movement behaviors, the independent associations of sedentary behavior (SB), physical activity (PA), and sleep with CVH have been well established [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, it is essential to recognize that SB, PA, and sleep collectively constitute a fixed 24-hour period, with each component interrelating and interacting with others, suggesting that changes in one component are likely to have a substantial effect on the associations between other components and health outcomes. Therefore, studies in isolation may lead to underestimation of the true associations between movement behaviors and CVH.\u003c/p\u003e \u003cp\u003eIn recent years, there has been growing acknowledgement of the feature of perfect multicollinearity among 24-hour movement behaviors and the limitations of the previous paradigm [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Following the introduction of the isotemporal substitution model (ISM) by Mekary and colleagues into the field of physical activity epidemiology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], SB, PA, and sleep have been validated as a finite whole, and this new paradigm has gained widespread application [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, the ISM has undergone continuous refinement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and has evolved into a more scientifically rigorous method known as compositional data analysis (CoDA) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous studies have employed CoDA to investigate the associations between movement behaviors and CVH. For instance, Farrahi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] utilized this approach and confirmed that components of 24-hour movement behaviors were significantly associated with cardiometabolic outcomes, including fasting plasma glucose (FPG) and blood lipid, among middle-aged Finnish adults. Similarly, another study verified a strong association between movement behaviors during the waking day and cardiometabolic biomarkers in children and youth aged 6\u0026ndash;17 years [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, despite the abundance of existing studies [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], researchers have primarily concentrated on examining the separate factors of cardiovascular risk, particularly the biochemical markers, while neglecting a comprehensive assessment of CVH. It is critical to realize that daily behaviors, such as smoking and unhealthy diets, also have important implications for CVH [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and should be taken into consideration. \u0026ldquo;Life's Simple 7\u0026rdquo; (LS7), proposed by the American Heart Association (AHA) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], provides a comprehensive framework to assess overall CVH by considering both health behaviors and health factors, therefore, has become one of the most widely adopted evaluation criteria [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the comparative lack of findings concerning the associations between 24-hour movement behaviors and overall CVH as determined by CoDA, this study had a two-fold aim: first, to examine the association between 24-hour movement behaviors and CVH score, including the relative associations of individual SB, PA, and sleep with CVH score, evaluated according to LS7\u0026rsquo;s criteria; second, to explore the estimated differences in CVH score resulting from reallocating fixed time from one component to another, or to the remaining movement behaviors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eData were collected from the National Health and Nutrition Examination Survey (NHANES) 2005\u0026ndash;2006, a cross-sectional study that used a stratified, multistage probability design to obtain a large, ethnically diverse representative sample of the USA civilian noninstitutionalized population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Detailed study methods can be found at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Data including demographic data, dietary data, examination data, laboratory data, and questionnaire data were collected through a household interview and a visit to a mobile examination center [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Publicly available data were used for this study as a secondary analysis only. In compliance with the Declaration of Helsinki, the protocols of the original study were approved by the Ethics Review Board of the National Center for Health Statistics (#2005-06), and written informed consent was obtained from participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of 24-hour Movement Behaviors\u003c/h2\u003e \u003cp\u003eData from the 2005\u0026ndash;2006 wave were selected due to the inclusion of accelerometer measurements and a sleep questionnaire. ActiGraph AM-7164 (Pensacola, FL, USA) accelerometers were used to objectively access time spent in SB and PA. Except when sleeping or water-based activities, participants wore the device recorded at 100 Hz on the waist over 7 consecutive days. Data with at least 4 days and \u0026ge;\u0026thinsp;10 h/d of wear time were considered valid [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Using standard count per minutes (cpm) thresholds each 1-min epoch was classified as SB (\u0026lt;\u0026thinsp;100 cpm), light physical activity (LPA) (100 to 2020 cpm) or moderate-to-vigorous physical activity (MVPA) (\u0026gt;\u0026thinsp;2020 cpm) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Non-wear time was defined if cpm was 0 for more than 60 consecutive minutes, with allowance for up to 2 minutes of counts between 1 and 100 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sleep duration was counted as a whole integer from 1 to 12 (over 12 hours were treated as 12), depending on the response to the question \u0026ldquo;How much sleep do you usually get at night on weekdays or workdays?\u0026rdquo;. Time spent in each component of 24-hour movement behaviors was tallied daily, averaged across all valid days, and expressed as a proportion of 24 hours [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of CVH Metrics and Calculation of CVH Score\u003c/h2\u003e \u003cp\u003eLife\u0026rsquo;s Simple 7, published by the AHA to systematically assess CVH, consists of health behaviors and health factors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Health behaviors contain smoking status, PA duration, healthy diet and body mass index (BMI). Health factors comprise 3 cardiometabolic risk factors: blood pressure (BP), total cholesterol (TC), and FPG. PA was intentionally omitted from the computation of CVH score as movement behaviors were the primary focus of interest in this analysis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CVH score was evaluated according to the definitions of LS7 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and each metric was further categorized as ideal, intermediate, and poor, and given a point score of 2, 1, or 0, respectively. Subsequently, all the points were totaled, with \u0026ldquo;10 to 12 points\u0026rdquo; signifying an ideal CVH, \u0026ldquo;6 to 9 points\u0026rdquo; indicating an intermediate score, and \u0026ldquo;0 to 5 points\u0026rdquo; denoting a poor score [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The total CVH score was considered as continuous variable because of its sensitivity and vulnerability to errors [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmoking status was determined based on responses to questions from the cigarette use questionnaire, which included inquiries such as \u0026ldquo;Do you now smoke cigarettes?\u0026rdquo; and \u0026ldquo;How long since quit smoking cigarettes?\u0026rdquo;. Participants who never smoked obtained a score of 2, 1 for former smokers who had quit more than 12 months, otherwise, 0 point. The dietary score was derived from responses to a 139-question food frequency questionnaire, on basis of which we integrated the average intake of added-sugar and sodium from two 24-hour diet recall. Due to a lack of complete information on sugar-sweetened beverage consumption, added-sugar intake was utilized instead [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Participants received 2 points if meeting four or five of the following 5 ideal dietary recommendations, score of 1 if meeting two or three, and 0 points for meeting one or zero [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]: fruits and vegetables\u0026thinsp;\u0026ge;\u0026thinsp;4.5 cups per day, fiber-rich whole grains\u0026thinsp;\u0026ge;\u0026thinsp;3 servings per day, fish\u0026thinsp;\u0026ge;\u0026thinsp;2 servings per week, sodium\u0026thinsp;\u0026lt;\u0026thinsp;1500 mg/day, added sugar\u0026thinsp;\u0026lt;\u0026thinsp;37.5 g/day for men, \u0026lt;\u0026thinsp;25 g/day for women. The weight and height of participants were measured by trained personnel during examination to calculate BMI. BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m2 was considered as ideal, 25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m2 indicated intermediate status, \u0026ge;\u0026thinsp;30 kg/m2 categorized as poor [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResting BP was recorded 3 to 4 times and the final values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were computed from the average of 2 readings, omitting the questionable values. Self-reported use of antihypertensive medications and questionnaire of prescription medications were collected to assist in determining BP categories. Without treatment, BP readings of \u0026lt;\u0026thinsp;120/80 mmHg were regarded as ideal and corresponded to a score of 2, participants whose SBP was between 120 and 139 mmHg and/or DBP was between 80 and 89 mmHg, or those whose BP was effectively managed with treatment, were classified as intermediate and awarded 1 point. BP readings\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg were categorized as poor and resulted in a score of 0. Laboratory values of TC and FPG were collected from blood samples in the mobile examination center, and medication use for both were obtained from the same questionnaire of prescription medications. Untreated TC\u0026thinsp;\u0026lt;\u0026thinsp;200 mg/dl was categorized as ideal, TC in the range of 200 to 239 mg/dl or treatment to target was regarded as intermediate, \u0026ge;\u0026thinsp;240 mg/dl indicated poor. FPG was categorized as ideal (\u0026lt;\u0026thinsp;100 mg/dl without treatment), intermediate (100\u0026thinsp;\u0026minus;\u0026thinsp;125 mg/dl or treated to goal), or poor (\u0026gt;\u0026thinsp;125 mg/dl).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eTo control for confounding effects, the following variables, including age, gender, race, education, marital status, household income, employment status, and alcohol consumption were regarded as covariables based on the classification of original data in NHANES 2005\u0026ndash;2006. The socio-demographic data were obtained through household interview. Employment status was determined by answers to \u0026ldquo;Type of work done last week\u0026rdquo; and \u0026ldquo;Main reason did not work last week\u0026rdquo;. The frequency of alcohol consumption was evaluated by alcohol use questionnaire. All confounders were treated as categorical variables with exception of age as continuous variable in the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIBM SPSS Statistics Version 26 (IBM Corp., Armonk, NY, USA) served to produce descriptive statistics. Continuous variables were characterized by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables were represented as percentage. Compositional data analyses were performed using the \u0026ldquo;compositions\u0026rdquo; [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], \u0026ldquo;robCompositions\u0026rdquo; [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and \u0026ldquo;lmtest\u0026rdquo; packages in Rstudio 2022.12.0 (The R Foundation for Statistical Computing, Vienna, Austria). The central tendency of 24-hour movement behaviors was expressed as compositional mean, with the geometric mean being adjusted linearly to ensure that the total sum of time-use compositions equaled 1440 minutes. The dispersion was described by the compositional variation matrix.\u003c/p\u003e \u003cp\u003eThe sequential binary partition was utilized to randomly allocate two components of 24-hour movement behaviors to the numerator of the first isometric log-ratio (ilr) coordinate, while the rest two components were assigned to the denominator. The method for constructing the complete set of ilrs coordinates was described in detail elsewhere [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As explanatory variables, the ilrs coordinates and covariables were taken to fit the compositional multiple linear regression model, which intended to display the association between all time-use compositions and CVH score. The model\u0026rsquo;s linearity, including quadratic terms for the ilrs, and normality were checked [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the significance was examined using the \u0026ldquo;car::Anova\u0026rdquo; function. Subsequently, in accordance with the procedure in study by Dumuid et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], each component of 24-hour movement behaviors was consecutively transformed into the numerator of the first ilr coordinate, thereby representing its relative association in relation to the health outcome. Consequently, four ilr coordinate systems were established. The relative associations of each component compared to other three with CVH score were ascertained by fitting 4 separate multiple linear regression models. The basic parameters for the first ilr coordinate of 4 models were reported, including the beta coefficients, standard error (SE) of beta, t-statistic, and p-values.\u003c/p\u003e \u003cp\u003eOn basis of the above analyses, the principle of isotemporal substitution was adopted to predict the differences in CVH score when reallocating fixed time (in 10-mintue increments within 60 minutes) between SB, LPA, MVPA, and sleep while compositional mean of 24-hour movement behaviors was set as the initial value. 10 minutes was chosen as the unit of reallocation according to its health effect [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The reallocations away from remaining movement behaviors proportionally to one component were conducted firstly (referred to as one-for-remaining reallocations). The duration of one component in the numerator of the first ilr coordinate was changed, and time spent in remaining movement behaviors in the denominator was corresponding altered in proportion to maintain a total of 1440 minutes, and to predict the differences in CVH scores. Furthermore, equivalent procedures were executed for one-for-one reallocations, where one component of 24-hour movement behaviors was substituted for another, while maintaining the constancy of the other two behaviors. The statistical significance was established at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eParticipants who were younger than 20 years, pregnant or breastfeeding at the time of survey, or missing any of the required data were excluded. Eventually, a total of 581 participants from the NHANES 2005\u0026ndash;2006 dataset met the inclusion criteria for this study and were consequently included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the participant flow. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic characteristics of the included participants, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the distribution characteristics of CVH score.\u003c/p\u003e \u003cp\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 demography for the final sample from NHANES (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;581).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber (%) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e351 (60.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (39.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMexican American\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOther Hispanic\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNon-Hispanic White\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNon-Hispanic Black\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOther Race, Including Multi-Racial\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLess Than 9th Grade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e9-11th Grade (Includes 12th grade with no diploma)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh School Graduate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSome College or AA degree\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCollege Graduate or above\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMarried\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWidowed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDivorced\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSeparated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNever Married\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLiving with Partner\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u003cb\u003eHousehold income\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0-4999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e5000\u0026ndash;9999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10000\u0026ndash;14999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e15000\u0026ndash;19999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e20000\u0026ndash;24999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e25000\u0026ndash;34999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e35000\u0026ndash;44999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e45000\u0026ndash;54999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e55000\u0026ndash;64999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e65000\u0026ndash;74999$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOver 75000$\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWork now\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (54%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNot Work now\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;1 time/month\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (65.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026ge;\u0026thinsp;1 time/month\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (34.6%)\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 \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 CVH score for the study population (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;581).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVH metrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber (%) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHealthy diet\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e477 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.67\u0026thinsp;\u0026plusmn;\u0026thinsp;18.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP, mmHg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTC, mmol/L\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.07\u0026thinsp;\u0026plusmn;\u0026thinsp;44.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267 (46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFPG, mmol/L\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFPG\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.63\u0026thinsp;\u0026plusmn;\u0026thinsp;31.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (37.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCVH score\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCVH score\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePoor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (39.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIntermediate\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337 (58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIdeal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (2.4%)\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 \u003cem\u003eBMI\u003c/em\u003e body mass index, \u003cem\u003eBP\u003c/em\u003e blood pressure, \u003cem\u003eCVH\u003c/em\u003e cardiovascular health, \u003cem\u003eDBP\u003c/em\u003e diastolic blood pressure, \u003cem\u003eFPG\u003c/em\u003e fasting plasma glucose, \u003cem\u003eSBP\u003c/em\u003e systolic blood pressure, \u003cem\u003eSD\u003c/em\u003e standard deviation, \u003cem\u003eTC\u003c/em\u003e total cholesterol\u003c/p\u003e \u003cp\u003eRemoving non-wear invalid time, the average accelerometer wear time was 1312.2\u0026thinsp;\u0026plusmn;\u0026thinsp;99.7 min/d. Compositional mean of 24-hour movement behaviors was calculated after linearly adjusting to 1440 min/d. Participants spent an average of 604.1 min/d (42.0%) in SB, 465.6 min/d (32.3%) in sleep, 358.8 min/d (24.9%) in LPA, and 11.5 min/d (0.8%) in MVPA, respectively. While around 33.2% of the included participants met the recommended MVPA duration (\u0026ge;\u0026thinsp;21.4 min/d [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]), more than 71.3% of individuals spent over 8 hours per day in SB. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the variability of compositional data, the values represent the log variance of two components of 24-hour movement behaviors. The smallest variance of log (sleep/LPA) indicated that time spent in sleep was highly co-dependent with LPA. The highest log-ratio variances were observed for MVPA, demonstrating the least likelihood of conversion for MVPA with other components.\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\u003eCompositional variation matrix of 24-hour movement behaviors.\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eLPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMVPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.581860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMVPA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.581860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.06873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.805471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.163255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.068731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.083061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.805471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe values represent the log-ratio variance of each two components. \u003cem\u003eLPA\u003c/em\u003e light physical activity, \u003cem\u003eMVPA\u003c/em\u003e moderate-to-vigorous physical activity, \u003cem\u003eSB\u003c/em\u003e sedentary behavior\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCompositional regression analyses\u003c/h2\u003e \u003cp\u003eThe results from compositional multiple linear regression showed that the first ilr coordinate of 24-hour movement behaviors was strongly associated with CVH score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001), controlling for all covariables. Although the effect size (ES) was relatively small (R\u0026sup2;adj\u0026thinsp;=\u0026thinsp;0.1036), 24-hour movement behaviors emerged as a significant predictor of CVH score. No quadratic relationships between time-use compositions and CVH score existed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Since the regression coefficient for the first ilrs in the model could not directly reflect the relative associations of individual components with health outcome [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], 4 models were then conducted to obtain 4 regression coefficients for the first ilr of each component relative to others. The beta coefficients and p-values for 4 models are reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results showed that the first ilr coefficient of Model 2 was positive and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a significant positive association between MVPA (relative to remaining movement behaviors) and CVH score. By contrast, the p-values for Model 1, Model 3, and Model 4 emerged as greater than 0.05, suggesting that SB, LPA, and sleep lacked significant relative associations with CVH score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of four multiple linear regression models for the first ilr.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEach component relative to remaining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta coefficients\u003c/p\u003e \u003cp\u003efor ilr1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPA: remaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMVPA: remaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSB: remaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep: remaining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01. \u003cem\u003eilr\u003c/em\u003e isometric log-ratio, \u003cem\u003eLPA\u003c/em\u003e light physical activity, \u003cem\u003eMVPA\u003c/em\u003e moderate-to-vigorous physical activity, \u003cem\u003eSB\u003c/em\u003e sedentary behavior, \u003cem\u003eSE\u003c/em\u003e standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCompositional isotemporal substitution analyses: One-for-remaining reallocations\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the predicted differences in CVH score when 10 to 60 minutes were reallocated from individual component to remaining movement behaviors in proportion. Given that compositional data must be non-negative [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and compositional mean of MVPA was 11.5min/d, MVPA duration was only increased, but not decreased when reallocations of time exceeded 10 minutes. As the dominant component of reallocations, an increase in MVPA (accompanied by the proportionate decrease in other components) exhibited a significant association with a positive change in CVH score, displaying a curve increasing trend. Meanwhile, the variations in CVH score were not equivalent when MVPA replaced other movement behaviors in proportion or was replaced with the same reallocation time, indicating an asymmetrical relationship. For example, reallocating 10 minutes away from remaining movement behaviors to MVPA, there was an associated increase of +\u0026thinsp;0.22 in CVH score (95% confidence interval (95% CI) 0.12 to 0.31). Conversely, reallocating the same 10 minutes duration from MVPA resulted in a decrease of 0.80 in CVH score (95% CI -1.16 to -0.44). There were no statistically significant differences in CVH score when LPA, SB, and sleep were the dominant component in reallocations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCompositional isotemporal substitution analyses: One-for-one reallocations\u003c/h2\u003e \u003cp\u003eKeeping the rest two components at the compositional mean, we systematically reallocated time (in the unit of 10 minutes) from one component of 24-hour movement behavior to another in turn, in order to predict the differences in CVH score. The results demonstrated that all compositional isotemporal substitutions involving MVPA were significantly associated with overall CVH (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), similarly to the findings for one-for-remaining reallocations. Generally, there were favorable improvements in CVH score when MVPA replaced other three components, but the rate of increase in CVH score gradually diminished with continuous maximization of MVPA duration. The differences in CVH score were slightly predominant when MVPA replaced LPA (ES\u0026thinsp;=\u0026thinsp;0.64 for 60 minutes, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), compared to the same time reallocations from either SB or sleep (ES\u0026thinsp;=\u0026thinsp;0.62/0.56 when MVPA replaced SB/sleep, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, the asymmetry of predicted differences in CVH score persisted in one-for-one reallocations when MVPA replaced other components or was replaced, but the magnitudes of negative change were generally smaller than values in one-for-remaining reallocations. For example, when 10 minutes was reallocated from MVPA to LPA, SB, or sleep, a decrease of 0.67/0.67/0.66 points was observed in CVH score (95% CI -0.99 to -0.35/ -0.98 to -0.35/ -0.97 to -0.34), smaller than \u0026minus;\u0026thinsp;0.80 when MVPA was replaced with remaining components (95% CI -1.16 to -0.44). However, reallocations of the same time from other individual components to MVPA subsequently resulted in increase of 0.21/0.21/0.20 (95% CI 0.11 to 0.31/ 0.11 to 0.30/ 0.10 to 0.29), resembling the effects in one-for-remaining reallocations (95% CI 0.12 to 0.31). Again, no statistical significance was observed in CVH score when reallocations only involved LPA, SB, and sleep (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). All results for one-for-one reallocations are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary finding of this cross-sectional study indicated a significant association between 24-hour movement behaviors as a whole and overall CVH in general adult population. Moreover, reallocations of fixed time to MVPA from other components individually or proportionally from remaining movement behaviors were consistently associated with beneficial CVH. Our study is novel for setting CVH score, calculated based on the criteria in LS7, as the health outcome. Additionally, we employed CoDA approach to investigate the combined and relative associations of 24-hour movement behaviors and each component with overall CVH. Furthermore, compositional isotemporal substitution was adopted to predict the differences in overall CVH resulting from theoretical reallocations.\u003c/p\u003e \u003cp\u003eThe results of this study aligned with previous research, which emphasized the importance of thoroughly evaluating cardiometabolic health and utilized a calculated score of individual cardiometabolic risk factors to explore the relationships between movement behaviors and heart health. For instance, Simone et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] employed CoDA and discovered a notable association between time-use compositions (including 9 components: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; other time) and cardiometabolic risk score (CMR score) among 782 youths aged 7\u0026ndash;13 years, where CMR score was assessed based on data of BP, waist circumference, lipoprotein cholesterol, and triglycerides. Several studies focused on metabolic syndrome score, which was computed based on similar risk factors, and demonstrated the correlations between movement behaviors and score in various age groups including children, adults, and the elderly [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Remarkably, most of these studies neglected the potential implications of daily behaviors such as diet and smoking, even only few studies controlled them as covariates. In light of the limitations of such research, we combined daily behaviors with cardiovascular risk factors, resulting in the derivation of a comprehensive CVH score, and revealed the association between 24-hour movement behaviors and overall CVH in adults.\u003c/p\u003e \u003cp\u003eThe findings of our study on the differences in health outcomes caused by the reallocations of fixed time between movement behaviors were partially in line with the findings of German et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], who firstly used CVH score as a measure of health outcome and performed isotemporal substitution analyses using the non-CoDA approach. The results related to MVPA exhibited consistency across both studies. German et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] found that substituting 30 minutes of SB with MVPA was associated with a 0.485-point increase in CVH score, close to the 0.427 increase in our study (95% CI 0.23 to 0.62). However, the results pertaining to other components were mixed. German and colleagues observed a favorable association of a 0.077 rise in CVH score when replacing 30 minutes of SB with sleep, whereas our study found a modest and statistically insignificant increase in CVH score (ES\u0026thinsp;=\u0026thinsp;0.028, 95% CI -0.02 to 0.08) when 30 minutes SB were replaced with sleep. In terms of reallocations between LPA and SB, radically distinct results were observed in two studies. Specifically, the investigators observed a significant increase in CVH score when 30 minutes LPA substituted for SB (ES\u0026thinsp;=\u0026thinsp;0.039) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but our study suggested that the association was opposite and insignificant (ES=-0.007, 95% CI -0.05 to 0.03). The discrepancy between the results of two studies may be attributed to various reasons, including the followings: First, the types of ISM. The accuracy of obtained results may be compromised [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] because of the failure to treat movement behaviors as compositional data during the analysis using traditional ISM [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Second, the diversity of participants should not be ignored. In comparison to the broader adult population (aged 20\u0026ndash;85 years) included in our study, participants in their study were middle-age and older adults (aged 45\u0026ndash;84 years), with a higher average age. It is worth noting that for the elderly, engaging in LPA yields comparatively greater advantages when reallocating equivalent time from SB [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which may result in disparities in findings. Third, the initial baseline levels of components have undeniably impact on the correlations. For instance, the average sleep duration of participants before reallocations in our study was longer compared to the study by German et al.. Since participants already obtained ample sleep, the favorable influence of increasing sleep duration became less significant when sleep substituted SB. As a result, the predicted positive association was attenuated or even disappeared. Besides, compositional mean of MVPA was lower in our study, thereby leading to stronger associations between isotemporal substitution involving MVPA and CVH score. These findings may reflect the nature of compositional data: A small value in one component leads to a greater variability in response to relative changes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study provided further evidence supporting the positive association of MVPA with cardiovascular health, consistent with the previous literatures [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In addition, employing CoDA, we found a notable asymmetry of the predicted differences in CVH score when substitution involved MVPA, regardless of in one-for-one reallocations or one-for-remaining reallocations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This characteristic led to the formation of a dose-response curve for MVPA duration [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], illustrating that a sustained increase in MVPA duration cannot generate the benefit with same growth trend [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Hence, it is advisable for general population to prioritize maximizing MVPA while also maintaining a high level of MVPA duration. Nonetheless, because the curve did not exhibit a slowing or decreasing trend, it is possible that we were unable to determine the exact duration of MVPA that would provide the greatest cardiovascular health benefits [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], so further research is required. The results for the relative associations between substitution with LPA and CVH were controversial in prior research [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which was in line with our finding that did not appreciably demonstrate the significant associations with CVH score when LPA replaced other components or was replaced. This finding implies that the intensity of PA may have a more significant impact on overall CVH compared to its duration.\u003c/p\u003e \u003cp\u003eThe association of sleep as a component of 24-hour movement behaviors with CVH has garnered growing interest in recent years. A few studies identified the U-shaped association between sleep and various health outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Nevertheless, considering that most participants in our study reported sleep duration within the range of 7\u0026ndash;9 hours, with only 1.9% exceeding 9 hours per day, it was reasonable to conclude that such a relationship did not exist in this study. More importantly, self-reported sleep duration was prone to recall biases, which may affect the accuracy of relevant results. Further efforts should be made to measure sleep in more precise ways and to explore the association of sleep as a component with CVH in populations with sleep problems, such as sleep-deprived workers or people with circadian reversal.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eA key strength of this study lies in its utilization of CoDA to further investigate the association between 24-hour movement behaviors and overall cardiovascular health in general adult population.\u003c/p\u003e \u003cp\u003eThe limitations of this study need to be considered. Firstly, causal inferences between time-use compositions and CVH score cannot be established due to the cross-sectional nature of NHANES 2005\u0026ndash;2006. Secondly, while main confounders such as socio-demographic variables were adjusted for during analyzing, possible residual confounding by other unmeasured factors could not be ruled out. Thirdly, participants lacking any information on CVH metrics were excluded, resulting in a reduced sample size of only 581 participants meeting the inclusion criteria. Consequently, there may be some degree of bias in the results. Fourthly, participants in our study had a wide age-span (from 20 to 85 years) and thus the results, such as compositional mean of 24-hour movement behaviors, merely reflected a relative indication of the general adult population, cannot be directly interpreted as an absolute representation of the specific population. Fifthly, self-reported data, such as sleep duration and dietary intake, may deviate from actual measurements, thereby introducing potential inaccuracies into the analysis. Lastly, waist-worn accelerometers are limited in accurately identifying postural standing or sitting [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], thereby the actual time spent in SB and LPA may be misestimated. Meanwhile, the cut points used to define movement behaviors were set according to the characteristics of the general adult population, which may lead to an underestimation of MVPA duration in the elderly [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study provided evidence of the cross-sectional association between 24-hour movement behaviors and overall cardiovascular health in adults. The findings suggested that reallocations of time from any other time-use compositions to MVPA were associated with more favorable CVH, highlighting the importance of MVPA and supporting the inclusion of MVPA in the assessment of cardiovascular health in LS7. However, the curve positive association between MVPA and CVH score, relative to other components, also suggested that we should maintain the current MVPA duration while optimizing it. Future studies should pay more attention to conduct longitudinal studies across various age cohorts and establish optimal time-use zones that effectively balance outcomes and cater to the needs of the public.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Heart Association\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\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCoDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCompositional data analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecpm\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecount per minutes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting plasma glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eilr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eisometric log-ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLight physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIsotemporal substitution model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLS7\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLife's Simple 7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMVPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModerate-to-vigorous physical activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\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\"\u003eSB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSedentary behavior\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original study was approved by the Ethics Review Board of the National Center for Health Statistics (#2005-06). Participants provided written informed consent. This study involved secondary analysis of above available data only.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of original study are available in the NHANES 2005-2006 repository, [https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2005], and the datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\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\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYJ participated in the design of the study, contributed to data collection and data reduction/analysis, and drafted the manuscript. MMA improved the manuscript framework. XY, JW and JK provided writing support. LP supervised all the study process. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used are from the US National Health and Nutrition Examination Survey, we thank all NHANES researchers, staff, and participants for their contributions. The findings reported in this article are solely those of the author. Without the help of all authors, this study could not have been completed. Therefore, we would like to thank Dr. MMA and WL for their assistance in writing, Dr. LP for her guidance, and especially to Dr. XY and Dr. JK for their support in this research.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHadgraft NT, Winkler E, Climie RE, et al. Effects of sedentary behaviour interventions on biomarkers of cardiometabolic risk in adults: systematic review with meta-analyses. Br J Sports Med. 2021;55(3):144\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson CL, Redline S, Emmons KM. Sleep as a potential fundamental contributor to disparities in cardiovascular health. Annu Rev Public Health. 2015;36:417\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTobaldini E, Fiorelli EM, Solbiati M, et al. Short sleep duration and cardiometabolic risk: from pathophysiology to clinical evidence. Nat Rev Cardiol. 2019;16(4):213\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026aacute;squez PM, Durazo-Arvizu RA, Marquez DX, et al. Association of accelerometer-measured physical activity and cardiovascular health in the hispanic community health study/study of latinos. Hisp Health Care Int. 2022;20(1):15\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeljko, Pedišić. Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology. 2017;2(49):252\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeljko Pedišić. Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research: the focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology. 2014;1(46):135\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekary RA, Willett WC, Hu FB, et al. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuman MP, Winkler EAH, Kurka JM, et al. Reallocating time to sleep, sedentary behaviors, or active behaviors: associations with cardiovascular disease risk biomarkers, NHANES 2005\u0026ndash;2006. Am J Epidemiol. 2014;179(3):323\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellburn S, Ryan CG, Azevedo LB, et al. Displacing sedentary time: association with cardiovascular disease prevalence. Med Sci Sports Exerc. 2016;48(4):641\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasunaga A, Shibata A, Ishii K et al. Associations of sedentary behavior and physical activity with older adults\u0026rsquo; physical function: an isotemporal substitution approach. BMC Geriatr 2017;17(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChastin SFM, Palarea-Albaladejo J, Dontje ML, et al. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS ONE. 2015;10(10):e0139984.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Stanford TE, Pedišić Z et al. Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: a compositional data analysis approach. BMC Public Health 2018;18(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Stanford TE, Martin-Fern\u0026aacute;ndez J, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Wake M, Clifford S, et al. The association of the body composition of children with 24-hour activity composition. J Pediatr. 2019;208:43\u0026ndash;49e9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrahi V, Kangas M, Walmsley R, et al. Compositional associations of sleep and activities within the 24-h cycle with cardiometabolic health markers in adults. Med Sci Sports Exerc. 2021;53(2):324\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarson V, Tremblay MS, Chaput J, et al. Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometabolic biomarkers among children and youth from the united states. PLoS ONE. 2019;14(7):e0220009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFull KM, Gallo LC, Malhotra A et al. Modeling the cardiometabolic benefits of sleep in older women: exploring the 24-hour day. Sleep 2020;43(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohansson MS, Holtermann A, Marott JL, et al. The physical activity health paradox and risk factors for cardiovascular disease: a cross-sectional compositional data analysis in the copenhagen city heart study. PLoS ONE. 2022;17(4):e0267427.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowell C, Browne LD, Carson BP, et al. Use of compositional data analysis to show estimated changes in cardiometabolic health by reallocating time to light-intensity physical activity in older adults. Sports Med. 2020;50(1):205\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwindell N, Rees P, Fogelholm M et al. Compositional analysis of the associations between 24-h movement behaviours and cardio-metabolic risk factors in overweight and obese adults with pre-diabetes from the preview study: cross-sectional baseline analysis. Int J Behav Nutr Phys Act 2020;17(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg R, Sutradhar R, Yao Z, et al. Smoking, drinking, diet and physical activity\u0026mdash;modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020;49(1):113\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction. Circulation. 2010;121(4):586\u0026ndash;613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Sloten TT, Tafflet M, P\u0026eacute;rier M, et al. Association of change in cardiovascular risk factors with incident cardiovascular events. JAMA. 2018;320(17):1793.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing DE, Xiang J. Retirement and healthy lifestyle: a national health and nutrition examination survey (nhanes) data report. J Am Board Family Med. 2017;30(2):213\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZipf G, Chiappa M, Porter KS et al. National health and nutrition examination survey: plan and operations, 1999\u0026ndash;2010. Vital Health Stat 1 2013;(56):1\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurtin LR, Mohadjer LK, Dohrmann SM et al. The national health and nutrition examination survey: sample design, 1999\u0026ndash;2006. Vital Health Stat 2 2012;(155):1\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColley R, Connor GS, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Health Rep. 2010;21(1):63\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreedson PS, Melanson E, Sirard J. Calibration of the computer science and applications, inc. Accelerometer. Med Sci Sports Exerc. 1998;30(5):777\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroiano RP, Berrigan D, Dodd KW, et al. Physical activity in the united states measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd-Jones DM, Hong Y, Labarthe D, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction. Circulation. 2010;121(4):586\u0026ndash;613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerman C, Makarem N, Fanning J, et al. Sleep, sedentary behavior, physical activity, and cardiovascular health: mesa. Med Sci Sports Exerc. 2021;53(4):724\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Neyman SM, Davis K, Zohoori N et al. Racial disparities and prevalence of cardiovascular disease risk factors, cardiometabolic risk factors, and cardiovascular health metrics among us adults: NHANES 2011\u0026ndash;2018. Sci Rep 2022;12(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Sloten TT, Tafflet M, P\u0026eacute;rier M, et al. Association of change in cardiovascular risk factors with incident cardiovascular events. JAMA. 2018;320(17):1793.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRagland DR. Dichotomizing continuous outcome variables: dependence of the magnitude of association and statistical power on the cutpoint. Epidemiology. 1992;3(5):434\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing DE, Xiang J. Retirement and healthy lifestyle: a national health and nutrition examination survey (NHANES) data report. J Am Board Family Med. 2017;30(2):213\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalra R, Patel N, Arora P, et al. Cardiovascular health and disease among asian-americans (from the national health and nutrition examination survey). Am J Cardiol. 2019;124(2):270\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Boogaart KG, Tolosana-Delgado R. compositions: a unified R package to analyze compositional data. Comput Geosci. 2008;34(4):320\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTempl M, Hron K, Filzmoser P. Robcompositions: an R-package for robust statistical analysis of compositional data.; 2011. p.341\u0026ndash;355.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Pedišić Z, Stanford TE, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. 2019;28(3):846\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Simm P, Wake M, et al. The goldilocks day for children's skeletal health: compositional data analysis of 24-hour activity behaviors. J Bone Miner Res. 2020;35(12):2393\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Wake M, Burgner D, et al. Balancing time use for children\u0026rsquo;s fitness and adiposity: evidence to inform 24-hour guidelines for sleep, sedentary time and physical activity. PLoS ONE. 2021;16(1):e0245501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the american college of sports medicine and the american heart association. Med Sci Sports Exerc. 2007;39(8):1423\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBull FC, Al-Ansari SS, Biddle S, et al. World health organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerswijveren SJJM, Lamb KE, Mart\u0026iacute;n-Fern\u0026aacute;ndez JA, et al. Using compositional data analysis to explore accumulation of sedentary behavior, physical activity and youth health. J Sport Health Sci. 2022;11(2):234\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Walker ME, Matthews KA, et al. Associations of physical activity and sleep with cardiometabolic risk in older women. Prev Med Rep. 2020;18:101071.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatricciani L, Dumuid D, Paquet C, et al. Sleep and cardiometabolic health in children and adults: examining sleep as a component of the 24-h day. Sleep Med. 2021;78:63\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacroix AZ, Bellettiere J, Rillamas-Sun E, et al. Association of light physical activity measured by accelerometry and incidence of coronary heart disease and cardiovascular disease in older women. JAMA Netw Open. 2019;2(3):e190419.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinoshita K, Ozato N, Yamaguchi T et al. Association of sedentary behaviour and physical activity with cardiometabolic health in japanese adults. Sci Rep 2022;12(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossen J, Von Rosen P, Johansson UB, et al. Associations of physical activity and sedentary behavior with cardiometabolic biomarkers in prediabetes and type 2 diabetes: a compositional data analysis. Phys Sportsmed. 2020;48(2):222\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitaker KM, Pettee Gabriel K, Buman MP et al. Associations of accelerometer-measured sedentary time and physical activity with prospectively assessed cardiometabolic risk factors: the cardia study. J Am Heart Assoc 2019;8(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYates T, Edwardson CL, Henson J, et al. Prospectively reallocating sedentary time: associations with cardiometabolic health. Med Sci Sports Exerc. 2020;52(4):844\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiddle G, Edwardson C, Henson J, et al. Associations of physical behaviours and behavioural reallocations with markers of metabolic health: a compositional data analysis. Int J Environ Res Public Health. 2018;15(10):2280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumuid D, Lewis LK, Olds TS, et al. Relationships between older adults\u0026rsquo; use of time and cardio-respiratory fitness, obesity and cardio-metabolic risk: a compositional isotemporal substitution analysis. Maturitas. 2018;110:104\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnaeps S, De Baere S, Bourgois J, et al. Substituting sedentary time with light and moderate to vigorous physical activity is associated with better cardiometabolic health. J Phys Activity Health. 2018;15(3):197\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadden KM, Feldman B, Chase J. Sedentary time and metabolic risk in extremely active older adults. Diabetes Care. 2021;44(1):194\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMellow ML, Crozier AJ, Dumuid D, et al. How are combinations of physical activity, sedentary behaviour and sleep related to cognitive function in older adults? A systematic review. Exp Gerontol. 2022;159:111698.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKozey-Keadle S, Libertine A, Lyden K, et al. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43(8):1561\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopeland JL, Esliger DW. Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act. 2009;17(1):17\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiatke1 A, Olds T, Maher C et al. The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface. Int J Behav Nutr Phys Activity 2023; 20\u0026ndash;127.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular health, Compositional data analysis, Isotemporal substitution, Moderate-to-vigorous physical activity","lastPublishedDoi":"10.21203/rs.3.rs-3866812/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3866812/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e As components of a 24-hour day, sedentary behavior (SB), physical activity (PA), and sleep are all independently linked to cardiovascular health (CVH). However, insufficient understanding of components’ mutual exclusion limits the exploration of the associations between all movement behaviors and health outcomes. The aim of this study was to employ compositional data analysis (CoDA) approach to investigate the associations between 24-hour movement behaviors and overall CVH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from 581 participants, including 230 women, were collected from the 2005-2006 wave of the US National Health and Nutrition Examination Survey (NHANES). This dataset included information on the duration of SB and PA, derived from ActiGraph accelerometers, as well as self-reported sleep duration. The assessment of CVH was conducted in accordance with the criteria outlined in Life's Simple 7, encompassing the evaluation of both health behaviors and health factors. Compositional linear regression was utilized to examine the cross-sectional associations of 24-hour movement behaviors and each component with CVH score. Furthermore, the study predicted the potential differences in CVH score that would occur by reallocating 10 to 60 minutes among different movement behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA significant association was observed between 24-hour movement behaviors and overall CVH (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) after adjusting for potential confounders. Substituting moderate-to-vigorous physical activity (MVPA) for other components was strongly associated with favorable differences in CVH score (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), whether in one-for-one reallocations or one-for-remaining reallocations. Allocating time away from MVPA consistently resulted in larger negative differences in CVH score (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). For instance, replacing 10 minutes of light physical activity (LPA) with MVPA was related to an increase of 0.21 in CVH score (95% confidence interval (95% CI) 0.11 to 0.31). Conversely, when the same duration of MVPA was replaced with LPA, CVH score decreased by 0.67 (95% CI -0.99 to -0.35). No such significance was discovered for all duration reallocations involving only LPA, SB, and sleep (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMVPA seems to be as a pivotal determinant for enhancing cardiovascular health among general adult population, relative to other movement behaviors. Consequently, optimization of MVPA duration is an essential element in promoting overall health and well-being.\u003c/p\u003e","manuscriptTitle":"Reallocating just 10 minutes to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 21:37:36","doi":"10.21203/rs.3.rs-3866812/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-12T16:10:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-11T21:23:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0e40cf7b-63c5-413e-93a0-d68f77a93285","date":"2024-03-11T21:00:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-28T13:52:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-28T13:48:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-24T14:51:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-24T12:43:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-01-15T14:33:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23e87963-b2e8-4a50-8bd7-ec63140def67","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-24T10:47:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 21:37:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3866812","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3866812","identity":"rs-3866812","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}