Compositional Associations of Sleep and Activities within the 24-h Cycle with Cardiometabolic Health Markers in Adults

Compositional associations of 24-h physical activities, sedentary time and sleep with depressive symptoms in urban and rural residents: a cross-sectional study

BACKGROUND

Studies investigating the associations of 24-h movement behaviours (including moderate-to-vigorous-intensity physical activity (MVPA), light-intensity PA (LPA), sedentary time (ST) and sleep) with depressive symptoms are scarce. It is also unclear whether possible associations differ between urban and rural residents. Hence, we aimed to investigate these associations in a population-based sample of middle-aged Finnish adults.

METHODS

The study population consisted of 4295 adults, aged 46 years, from the Northern Finland Birth Cohort 1966. The participants wore a hip-worn accelerometer for 14 days. Time spent in sedentary, LPA and MVPA was obtained from accelerometer data and then combined with self-reported sleep duration to obtain the 24-h composition. The residential environment was classified as urban or rural based on the participants' home addresses. Depressive symptoms were assessed using the Beck Depression Inventory-II (BDI-II). Multivariable adjusted regression analysis using a compositional data analysis approach based on isometric log-ratio transformation was used to determine the associations between movement behaviours and depressive symptoms in urban and rural residential environments.

RESULTS

The 24-h movement behaviour composition was significantly associated with the BDI-II score both in urban and rural residential environment. More time spent in sleep relative to other behaviours was associated with lower BDI-II score in rural residential environments. More time spent in ST among urban residents and in LPA among rural residents was associated with higher BDI-II scores. When modelling pairwise reallocations of time, more MVPA or more sleep at the expense of LPA or ST was associated with lower BDI-II score among rural residents. For urban residents, reallocating time from ST to any other behaviour was associated with lower BDI-II score.

CONCLUSIONS

Our findings showed that more relative time spent in MVPA and sleep was associated with lower levels of depressive symptoms among rural residents, and more relative time spent in any other behaviour at the expense of ST was associated with lower levels of depressive symptoms among urban residents. These differences should be considered in the prevention and treatment of depressive symptoms. Due to the cross-sectional design of this study, causality cannot be inferred, and further research exploring the mechanisms underlying these associations in diverse populations and longitudinal study settings are needed.

Isotemporal Substitution Effect of 24-h Movement Behaviors on Well-Being, Cognition, and BMI Among Older Adults

Background: This study investigated the interdependent relationships among older adults' daily engagement in physical activity (PA), sedentary time (ST), sleep, and their well-being, cognition, and body mass index (BMI). Method: Forty healthy older adults (31 females; Mean [age] = 70.8 ± 5.58) were included in the analysis. Participants wore a Fitbit tracker for an average of 23 h a day, five days a week, over six months. The Fitbit device tracked lightly active time, active time, ST, and sleep durations. Quality of life and cognitive flexibility were assessed using validated instruments. BMI was calculated using participants' self-reported height and weight. A compositional analysis (CODA) investigated the codependent associations among these variables and model time reallocation between behaviors. Results: Regression models utilizing CODA indicated significant associations between the outcomes of BMI (p = 0.05; Adj. R2 = 0.20), while cognitive flexibility and quality of life revealed no association (p > 0.05). Shifting 10 min from ST to active time is associated with a theoretical decrease of -0.76 (95% CI, -1.49 to -0.04) units in BMI. Similarly, reallocating 10 min from active time to ST is associated with a theoretical increase of 1.17 (95% CI, 0.03 to 2.3) units in BMI. Reallocating 10 min between other movement behaviors yielded no statistical significance. Conclusions: Our study highlights the importance of promoting active time to improve BMI in this population. Encouraging 10 min bouts of PA among older adults, in place of ST, is vital for improving national PA guideline adherence.

24-hour movement behaviours and cardiometabolic markers in women with polycystic ovary syndrome (PCOS): a compositional data analysis

STUDY QUESTION

Are 24-h movement composition and time reallocations between the movement behaviours (moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behaviour (SB), and sleep) differentially associated with cardiometabolic markers in women with polycystic ovary syndrome (PCOS) relative to women without PCOS?

SUMMARY ANSWER

There was no difference in 24-h movement composition between the groups, although among women without PCOS, reducing SB time while increasing either MVPA or LPA time was associated with beneficial differences in cardiometabolic markers, whereas in women with PCOS beneficial differences were observed only when SB time was replaced with MVPA.

WHAT IS KNOWN ALREADY

Women with PCOS display lower levels of physical activity, higher sedentary time, and less total sleep than women without the syndrome. Exercise interventions among women with PCOS have shown improvements in body composition and insulin sensitivity, while the findings regarding blood pressure, insulin resistance, and lipid profiles are contradictory.

STUDY DESIGN, SIZE, DURATION

This study was part of a prospective, general population-based Northern Finland Birth Cohort 1966 (NFBC1966) (n = 5889 women). At the 31-year and 46-year follow-up, data collection was performed through postal and clinical examinations, including fasting blood samples and anthropometric measurements. Accelerometer data collection of 14 days (n = 2602 women) and a 2-h oral glucose tolerance test (n = 2780 women) were performed at the 46-year follow-up. Participants were identified as women with or without PCOS at age 31 (n = 1883), and the final study population included those who provided valid accelerometer data at age 46 (n = 857).

PARTICIPANTS/MATERIALS, SETTING, METHODS

Women with PCOS (n = 192) were identified based on the 2023 International Evidence-based Guideline, while those who exhibited no PCOS features were considered women without PCOS (controls; n = 665). Accelerometer-measured MVPA, LPA, and SB were combined with self-reported sleep to obtain 24-h compositions. Multivariable regression analysis based on compositional data analysis and isotemporal reallocations were performed to investigate the associations between 24-h movement composition and cardiometabolic markers. Isotemporal reallocations were expressed as differences (%Δ) from the sample's mean.

MAIN RESULTS AND THE ROLE OF CHANCE

There was no difference in overall 24-h movement composition between women with PCOS and controls in midlife. The 24-h movement composition was associated with waist circumference, triglycerides, fasting serum insulin, and Homeostatic Model Assessment-insulin resistance (HOMA-IR) in both controls and women with PCOS. Reallocating 15 min from SB to MVPA was associated with favourable differences in cardiometabolic markers in both controls (%Δ range from -1.7 to -4.9) and women with PCOS (%Δ range from -1.9 to -8.6). Reallocating 15 min from SB to LPA was also associated with favourable differences in cardiometabolic markers among controls (%Δ range from -0.5 to -1.6) but not among women with PCOS.

LIMITATIONS, REASONS FOR CAUTION

The substitution technique used in this study is theoretical, which can be considered as a limitation. Other limitations of this study are the use of self-reported sleeping time and the difference in the group sample sizes.

WIDER IMPLICATIONS OF THE FINDINGS

These findings suggest that women with PCOS should be targeted with interventions involving physical activity of at least moderate intensity to improve their cardiometabolic health and underline the importance of developing tailored activity guidelines for women with PCOS.

STUDY FUNDING/COMPETING INTEREST(S)

This study was funded by the Jenny and Antti Wihuri Foundation, Sigrid Juselius Foundation, Novo Nordisk (NNF21OC0070372), Research Council of Finland (315921/2018, 321763/2019, 6GESS 336449), Ministry of Education and Culture of Finland (OKM/54/626/2019, OKM/85/626/2019, OKM/1096/626/2020, OKM/20/626/2022, OKM/76/626/2022, and OKM/68/626/2023), and Roche Diagnostics International Ltd. L.J.M. is supported by a Veski Fellowship. M.Nu. has received funding from Fibrobesity-project, a strategic profiling project at the University of Oulu, which is supported by Research Council of Finland (Profi6 336449). NFBC1966 follow-ups received financial support from University of Oulu (Grant no. 65354, 24000692), Oulu University Hospital (Grant no. 2/97, 8/97, 24301140), Ministry of Health and Social Affairs (Grant no. 23/251/97, 160/97, 190/97), National Institute for Health and Welfare, Helsinki (Grant no. 54121), Regional Institute of Occupational Health, Oulu, Finland (Grant no. 50621, 54231), and ERDF European Regional Development Fund (Grant no. 539/2010 A31592). T.T.P. declares consulting fees from Gedeon Richter, Organon, Astellas, Roche; speaker's fees from Gedeon Richter, Exeltis, Roche, Stragen, Merck, Organon; and travel support from Gedeon Richter. The remaining authors declare no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

A systematic review of research reporting practices in observational studies examining associations between 24-h movement behaviors and indicators of health using compositional data analysis

Background

Compositional data analysis (CoDA) techniques are well suited for examining associations between 24-h movement behaviors (i.e., sleep, sedentary behavior, physical activity) and indicators of health given they recognize these behaviors are co-dependent, representing relative parts that make up a whole day. Accordingly, CoDA techniques have seen increased adoption in the past decade, however, heterogeneity in research reporting practices may hinder efforts to synthesize and quantify these relationships via meta-analysis. This systematic review described reporting practices in studies that used CoDA techniques to investigate associations between 24-h movement behaviors and indicators of health.

Methods

A systematic search of eight databases was conducted, in addition to supplementary searches (e.g., forward/backward citations, expert consultation). Observational studies that used CoDA techniques involving log-ratio transformation of behavioral data to examine associations between time-based estimates of 24-h movement behaviors and indicators of health were included. Reporting practices were extracted and classified into seven areas: (1) methodological justification, (2) behavioral measurement and data handling strategies, (3) composition construction, (4) analytic plan, (5) composition-specific descriptive statistics, (6) model results, and (7) auxiliary information. Study quality and risk of bias were assessed by the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies.

Results

102 studies met our inclusion criteria. Reporting practices varied considerably across areas, with most achieving high standards in methodological justification, but inconsistent reporting across all other domains. Some items were reported in all studies (e.g., how many parts the daily composition was partitioned into), whereas others seldom reported (e.g., definition of a day: midnight-to-midnight versus wake-to-wake). Study quality and risk of bias was fair in most studies (85%).

Conclusions

Current studies generally demonstrate inconsistent reporting practices. Consistent, clear and detailed reporting practices are evidently needed moving forward as the field of time-use epidemiology aims to accurately capture and analyze movement behavior data in relation to health outcomes, facilitate comparisons across studies, and inform public health interventions and policy decisions. Achieving consensus regarding reporting recommendations is a key next step.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44167-024-00062-8.

A comparative analysis of 24-hour movement behaviors features using different accelerometer metrics in adults: Implications for guideline compliance and associations with cardiometabolic health

BACKGROUND

Movement behavior features such as time use estimates, average acceleration and intensity gradient are crucial in understanding associations with cardiometabolic health. The aim of this study was to 1) compare movement behavior features processed by commonly used accelerometer metrics among adults (i.e. Euclidian Norm Minus One (ENMO), Mean Amplitude Deviation (MAD) and counts per minute (CPM)), 2) investigate the impact of accelerometer metrics on compliance with movement behavior guidelines, and 3) explore potential variations in the association between movement behavior features and cardiometabolic variables depending on the chosen metric.

METHODS

This cross-sectional study collected movement behavior features (Actigraph GT3X+) and cardiometabolic variables. Accelerometer data were analyzed by four metrics, i.e. ENMO, MAD, and CPM vertical axis and CPM vector magnitude (GGIR). Intraclass correlations and Bland‒Altman plots identified metric differences for time use in single movement behaviors (physical activity, sedentary behavior), average acceleration and intensity gradient. Regression models across the four metrics were used to explore differences in 24-hour movement behaviors (24h-MBs; compositional variable) as for exploration of associations with cardiometabolic variables.

RESULTS

Movement behavior data from 213 Belgian adults (mean age 45.8±10.8 years, 68.5% female) differed according to the metric used, with ENMO representing the most sedentary movement behavior profile and CPM vector magnitude representing the most active profile. Compliance rates for meeting integrated 24h-MBs guidelines varied from 0-25% depending on the metric used. Furthermore, the strength and direction of associations between movement behavior features and cardiometabolic variables (body mass index, waist circumference, fat% and HbA1c) differed by the choice of metric.

CONCLUSION

The metric used during data processing markedly influenced cut-point dependent time use estimates and cut-point independent average acceleration and intensity gradient, impacting guideline compliance and associations with cardiometabolic variables. Consideration is necessary when comparing findings from accelerometry studies to inform public health guidelines.

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

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 min 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 min 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 CVH 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.

Associations of 24 h time-use compositions of sitting, standing, physical activity and sleeping with optimal cardiometabolic risk and glycaemic control: The Maastricht Study

AIMS/HYPOTHESIS

The associations of sitting, standing, physical activity and sleep with cardiometabolic health and glycaemic control markers are interrelated. We aimed to identify 24 h time-use compositions associated with optimal metabolic and glycaemic control and determine whether these varied by diabetes status.

METHODS

Thigh-worn activPAL data from 2388 participants aged 40-75 years (48.7% female; mean age 60.1 [SD = 8.1] years; n=684 with type 2 diabetes) in The Maastricht Study were examined. Compositional isometric log ratios were generated from mean 24 h time use (sitting, standing, light-intensity physical activity [LPA], moderate-to-vigorous physical activity [MVPA] and sleeping) and regressed with outcomes of waist circumference, fasting plasma glucose (FPG), 2 h plasma glucose, HbA1c, the Matsuda index expressed as z scores, and with a clustered cardiometabolic risk score. Overall analyses were adjusted for demographics, smoking, dietary intake and diabetes status, and interaction by diabetes status was examined separately. The estimated difference when substituting 30 min of one behaviour with another was determined with isotemporal substitution. To identify optimal time use, all combinations of 24 h compositions possible within the study footprint (1st-99th percentile of each behaviour) were investigated to determine those cross-sectionally associated with the most-optimal outcome (top 5%) for each outcome measure.

RESULTS

Compositions lower in sitting time and with greater standing time, physical activity and sleeping had the most beneficial associations with outcomes. Associations were stronger in participants with type 2 diabetes (p<0.05 for interactions), with larger estimated benefits for waist circumference, FPG and HbA1c when sitting was replaced by LPA or MVPA in those with type 2 diabetes vs the overall sample. The mean (range) optimal compositions of 24 h time use, considering all outcomes, were 6 h (range 5 h 40 min-7 h 10 min) for sitting, 5 h 10 min (4 h 10 min-6 h 10 min) for standing, 2 h 10 min (2 h-2 h 20 min) for LPA, 2 h 10 min (1 h 40 min-2 h 20 min) for MVPA and 8 h 20 min (7 h 30 min-9 h) for sleeping.

CONCLUSIONS/INTERPRETATION

Shorter sitting time and more time spent standing, undergoing physical activity and sleeping are associated with preferable cardiometabolic health. The substitutions of behavioural time use were significantly stronger in their associations with glycaemic control in those with type 2 diabetes compared with those with normoglycaemic metabolism, especially when sitting time was balanced with greater physical activity.

Associations of accelerometer-estimated free-living daily activity impact intensities with 10-year probability of osteoporotic fractures in adults

PURPOSE

Accelerometers are used to objectively measure physical activity; however, the relationship between accelerometer-based activity parameters and bone health is not well understood. This study examines the association between accelerometer-estimated daily activity impact intensities and future risk estimates of major osteoporotic fractures in a large population-based cohort.

METHODS

Participants were 3165 adults 46 years of age from the Northern Finland Birth Cohort 1966 who agreed to wear a hip-worn accelerometer during all waking hours for 14 consecutive days. Raw accelerometer data were converted to resultant acceleration. Impact magnitude peaks were extracted and divided into 32 intensity bands, and the osteogenic index (OI) was calculated to assess the osteogenic effectiveness of various activities. Additionally, the impact peaks were categorized into three separate impact intensity categories (low, medium, and high). The 10-year probabilities of hip and all major osteoporotic fractures were estimated with FRAX-tool using clinical and questionnaire data in combination with body mass index collected at the age of 46 years. The associations of daily activity impact intensities with 10-year fracture probabilities were examined using three statistical approaches: multiple linear regression, partial correlation, and partial least squares (PLS) regression.

RESULTS

On average, participants' various levels of impact were 8331 (SD = 3478) low; 2032 (1248) medium; and 1295 (1468) high impacts per day. All three statistical approaches found a significant positive association between the daily number of low-intensity impacts and 10-year probability of hip and all major osteoporotic fractures. In contrast, increased number of moderate to very high daily activity impacts was associated with a lower probability of future osteoporotic fractures. A higher OI was also associated with a lower probability of future major osteoporotic fractures.

CONCLUSION

Low-intensity impacts might not be sufficient for reducing fracture risk in middle-aged adults, while high-intensity impacts could be beneficial for preventing major osteoporotic fractures.

Longitudinal Associations between 24-h Movement Behaviors and Cardiometabolic Biomarkers: A Natural Experiment over Retirement

INTRODUCTION

Physical activity, sedentary behavior, and sleep, that is, 24-h movement behaviors, often change in the transition from work to retirement, which may affect cardiometabolic health. This study investigates the longitudinal associations between changes in 24-h movement behaviors and cardiometabolic biomarkers during the retirement transition.

METHODS

Retiring public sector workers ( n = 212; mean (SD) age, 63.5 (1.1) yr) from the Finnish Retirement and Aging study used a thigh-worn Axivity accelerometer and filled out a diary to obtain data on daily time spent in sedentary behavior (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), and sleep before and after retirement (1 yr in-between). Cardiometabolic biomarkers, including LDL-cholesterol, HDL-cholesterol, total/HDL-cholesterol ratio, triglycerides, C-reactive protein, fasting glucose, and insulin, were measured. Associations between changes in 24-h movement behaviors and cardiometabolic biomarkers were analyzed using compositional robust regression and isotemporal substitution analysis.

RESULTS

Increasing LPA in relation to remaining behaviors was associated with an increase in HDL-cholesterol and decrease in total/HDL-cholesterol ratio ( P < 0.05 for both). For instance, reallocation of 30 min from sleep/SED to LPA was associated with an increase in HDL-cholesterol by 0.02 mmol·L -1 . Moreover, increasing MVPA in relation to remaining behaviors was associated with a decrease in triglycerides ( P = 0.02). Reallocation of 30 min from SED/sleep to MVPA was associated with 0.07-0.08 mmol·L -1 decrease in triglycerides. Findings related to LDL-cholesterol, C-reactive protein, fasting glucose, and insulin were less conclusive.

CONCLUSIONS

During the transition from work to retirement, increasing physical activity at the expense of passive behaviors was associated with a better lipid profile. Our findings suggest that life transitions like retirement could be utilized more as an optimal time window for promoting physical activity and health.

Association between movement behavior patterns and cardiovascular risk among Chinese adults aged 40-75: a sex-specific latent class analysis

BACKGROUND

Cardiovascular disease (CVD) is a major global health threat, particularly in China, contributing to over 40% of deaths. While sleep behaviors, sedentary behaviors, and physical activities are recognized as independent lifestyle risk factors for CVD, there remains limited understanding of specific movement behavior patterns and their CVD risks, especially considering sex-specific differences. This study examines movement behavior patterns among Chinese adults (40-75) and their associations with cardiovascular risk, with a focus on sleep, physical activity (PA), and sedentary behavior (SB).

METHODS

Data pertaining to 13,465 male participants and 15,613 female participants, collected from the Chronic Disease and Risk Factor Surveillance Survey in Nanjing from February 2020 to December 2022. The latent class analysis method was employed to identify underlying movement patterns across sexes. Multinomial logistic regression models assessed CVD risk, and the China-PAR model calculated 10-year risk.

RESULTS

Three male and four female movement patterns emerged. Active Movers (17.10% males, 5.93% females) adhered to PA recommendations but had poorer sleep quality. Moderate Achievers (61.42% males, 45.32% females) demonstrated moderate behavior. Sedentary Sleepers (21.48% males, 10.20% females) exhibited minimal PA but good sleep. Female Moderate Physical Activity (MPA) Dominant Movers demonstrated a prevalent adherence to recommended MPA levels. Active movers had the lowest CVD risk. After adjusting for potential confounders, moderate achievers (OR = 1.462, 95% CI 1.212, 1.764) and sedentary sleepers (OR = 1.504, 95% CI 1.211, 1.868) were both identified as being associated with a high-risk of cardiovascular diseases (CVDs) compared to active movers in males, demonstrating a similar trend for intermediate risk. Such associations were not statistically significant among females.

CONCLUSIONS

Our study revealed sex-specific movement patterns associated with CVD risks among middle-aged Chinese adults. We suggest that adopting an active movement behavior pattern, characterized by meeting or exceeding recommended levels of vigorous physical activity (VPA) and reducing sedentary behavior, is beneficial for all middle-aged adults, particularly males. An active lifestyle could help counteract the adverse effects of relatively poor sleep quality on the risk of developing CVD in this population. Integrating sleep, PA, and SB information provides a holistic framework for understanding and mitigating CVD risks.

Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method

Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = -0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture.

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

BACKGROUND

Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders.

METHODS

Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20-65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003-2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data.

RESULTS

Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as "Early-morning movers" and the profile characterized as "Highest activity" both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the "Lowest activity" profile. No significant differences were observed for the "Least sedentary movers" profile compared to the "Lowest activity" profile.

CONCLUSIONS

Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.

Cross-sectional associations between 24-hour movement behaviors and cardiometabolic health among adults with type 2 diabetes mellitus: A comparison according to weight status

OBJECTIVES

Type 2 diabetes mellitus (T2DM) is a chronic disease associated with overweight and obesity. Evidence suggests that 24-hour movement behaviors (24 h-MBs) play a crucial role in cardiometabolic health. However, it is not yet known if 24 h-MBs differ between weight status groups among people with T2DM (PwT2DM) and how 24 h-MBs are associated with their cardiometabolic health.

DESIGN

Cross-sectional study.

METHODS

Cardiometabolic variables (i.e. Body Mass Index (BMI), waist circumference (WC), HbA1c, fasting glucose, triglycerides, total-cholesterol, HDL-cholesterol, LDL-cholesterol, blood pressure) and 24 h-MBs (accelerometry and sleep-diary) of 1001 PwT2DM were collected. Regression models using compositional data analysis explored differences in 24 h-MBs between weight status groups and analyzed associations with cardiometabolic variables.

RESULTS

The 24 h-MBs of PwT2DM being obese consisted of less sleep, light physical activity (LPA) and moderate to vigorous physical activity (MVPA) and more sedentary time (ST) per day as compared to PwT2DM being overweight or normal weight (p < 0.001). Regardless of weight status, the largest associations were found when reallocating 20 min a day from ST into MVPA for BMI (-0.32 kg/m2; [-0.55; -0.09], -1.09 %), WC (-1.44 cm, [-2.26; -0.62], -1.35 %) and HDL-cholesterol (0.02 mmol/l, [0.01, 0.02], +1.59 %), as well as from ST into LPA for triglycerides (-0.04 mmol/l, [-0.05; -0.03], -2.3 %). Moreover, these associations were different when stratifying people by short-to-average (7.7 h/night) versus long sleep (9.3 h/night) period.

CONCLUSIONS

This study highlights the importance of 24 h-MBs in the cardiometabolic health of PwT2DM. Shifting time from ST and/or sleep toward LPA or MVPA might theoretically benefit cardiometabolic health among relatively inactive PwT2DM, irrespective of weight status.

Regularity of bedtime, wake-up time, and time in bed in mid-life: associations with cardiometabolic health markers with adjustment for physical activity and sedentary time

BACKGROUND

Insufficient sleep has been linked to the accumulation of cardiometabolic risks while physical activity acts as a protective factor. Also, sleep regularity may play a critical role in maintaining optimal cardiometabolic health. This cross-sectional study examined the association between device-based sleep regularity, waking activity behaviors, and cardiometabolic health markers, including blood pressure level; abdominal adiposity level; and blood glucose, insulin, and cholesterol.

METHODS

We included 3698 members of the Northern Finland Birth cohort 1966 who participated in the follow-up study at the age of 46 years between 2012 and 2014 (women 61%). We used seven-day standard deviations of device-based bedtime, wake-up time, and time in bed to reflect sleep regularities. As covariates in linear regression models, we used commonly known potential risk factors in (gender, education, marital status, work schedule, smoking status, alcohol risk use, seven-day time in bed mean, chronotype). In addition to the previous, we used either sedentary time or total physical activity as a covariate (B coefficients with 95% confidence intervals CI).

RESULTS

When we considered sedentary time with other covariates, irregularities in bedtime, wake-up time, and time in bed were associated with unfavorable cardiometabolic health markers, such as higher body mass index (bedtime regularity: 0.194, 95% CI [0.072, 0.316], p = 0.002); higher diastolic blood pressure levels (time in bed regularity: 0.175, 95% CI [0.044, 0.306], p = 0.009); and higher 2-h glucose levels (wake-up time regularity: 0.107, 95% CI [0.030, 0.184], p = 0.006). When we considered total physical activity with other covariates, only irregular bedtime was associated with higher waist circumference (B 0.199, 95% CI [0.042, 0.356], p = 0.013). Irregularities in bedtime and wake-up time were not associated with higher diastolic blood pressure, higher visceral fat area or higher fasting insulin level after considering sedentary time or total physical activity with other covariates (in all, p > 0.05).

CONCLUSIONS

In middle-aged, physical activity appears to weaken the adverse relationship between irregular sleep and cardiometabolic health markers, although the interpretation of the impact of sedentary time remains less conclusive. The clinical significance and extent of the observed associations warrant further investigation.

The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface

BACKGROUND

How time is allocated influences health. However, any increase in time allocated to one behaviour must be offset by a decrease in others. Recently, studies have used compositional data analysis (CoDA) to estimate the associations with health when reallocating time between different behaviours. The aim of this scoping review was to provide an overview of studies that have used CoDA to model how reallocating time between different time-use components is associated with health.

METHODS

A systematic search of four electronic databases (MEDLINE, Embase, Scopus, SPORTDiscus) was conducted in October 2022. Studies were eligible if they used CoDA to examine the associations of time reallocations and health. Reallocations were considered between movement behaviours (sedentary behaviour (SB), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA)) or various activities of daily living (screen time, work, household chores etc.). The review considered all populations, including clinical populations, as well as all health-related outcomes.

RESULTS

One hundred and three studies were included. Adiposity was the most commonly studied health outcome (n = 41). Most studies (n = 75) reported reallocations amongst daily sleep, SB, LPA and MVPA. While other studies reported reallocations amongst sub-compositions of these (work MVPA vs. leisure MVPA), activity types determined by recall (screen time, household chores, passive transport etc.) or bouted behaviours (short vs. long bouts of SB). In general, when considering cross-sectional results, reallocating time to MVPA from any behaviour(s) was favourably associated with health and reallocating time away from MVPA to any behaviour(s) was unfavourably associated with health. Some beneficial associations were seen when reallocating time from SB to both LPA and sleep; however, the strength of the association was much lower than for any reallocations involving MVPA. However, there were many null findings. Notably, most of the longitudinal studies found no associations between reallocations of time and health. Some evidence also suggested the context of behaviours was important, with reallocations of leisure time toward MVPA having a stronger favourable association for health than reallocating work time towards MVPA.

CONCLUSIONS

Evidence suggests that reallocating time towards MVPA from any behaviour(s) has the strongest favourable association with health, and reallocating time away from MVPA toward any behaviour(s) has the strongest unfavourable association with health. Future studies should use longitudinal and experimental study designs, and for a wider range of outcomes.

Modifications of 24-h movement behaviors to prevent obesity in retirement: a natural experiment using compositional data analysis

BACKGROUND

Retirement often leads to a more passive lifestyle and may therefore lead to weight gain. This study aims to investigate longitudinal associations between changes in 24-h movement behaviors and BMI and waist circumference in relation to the transition from work to retirement.

METHODS

The study population included 213 retiring public sector workers (mean age 63.5 years, standard deviation 1.1) from the Finnish Retirement and Aging study. Before and after retirement participants wore an Axivity accelerometer on their thigh and filled in a daily log for at least four days to measure daily time spent sleeping, in sedentary behavior (SED), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Also, their body mass index (BMI) and waist circumference were measured repeatedly. Compositional linear regression analysis and isotemporal substitution analysis were used to study associations between one-year changes in 24-h movement behaviors and concurrent changes in BMI and waist circumference.

RESULTS

An increase in MVPA in relation to sleep, SED and LPA was associated with a decreasing BMI (β = -0.60, p = 0.04) and waist circumference (β = -2.14, p = 0.05) over one year from before retirement to after retirement. In contrast, increasing sleep in relation to SED, LPA and MVPA was associated with an increasing BMI (β = 1.34, p = 0.02). Reallocating 60 min from MVPA to SED or sleep was estimated to increase BMI by on average 0.8-0.9 kg/m2 and waist circumference by 3.0 cm during one year.

CONCLUSIONS

During the transition from work to retirement, increasing MVPA was associated with a slight decrease in BMI and waist circumference, whereas increasing sleep was associated with an increasing BMI. Common life transitions, like retirement, should be considered when giving recommendations and guidance for physical activity and sleep.

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

BACKGROUND

Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue.

OBJECTIVE

This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design.

METHODS

Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources.

RESULTS

A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets.

CONCLUSIONS

This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

The Way to Spend a Workday Matters in School Principals' Somatic and Psychological Discomfort

BACKGROUND

School principals usually have to sacrifice health and family obligations to obtain sufficient work time. This study investigates school principals' somatic and psychological discomfort related to their time allocation to diverse work contexts and life domains, so as to test the optimal allocation of time to each context and domain.

METHODS

This study is based on survey data of 347 school principals, from the preexisting 2021 Survey of School Teachers' Living Conditions in Shanghai. Generalized linear regression modeling was adopted to analyze the data according to the research purpose.

RESULTS

This study finds that school principals' daily time spent on work at home, sleep, breakfast, exercise, and family obligations significantly predict their somatic or psychological discomfort. However, their time spent on work at school, daytime napping, lunch, and dinner are not of significance.

CONCLUSIONS

This study reveals several unhealthy ways of working and lifestyle habits among school principals from a perspective of time allocation, such as extended periods working at home, sleep deficits, hurried breakfast, lack exercise, and failure to meet familial obligations.

Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range

Accelerometer data can be used to estimate incident oxygen consumption (VO2) during physical activity. Relationships between the accelerometer metrics and VO2 are typically determined using specific walking or running protocols on a track or treadmill. In this study, we compared the predictive performance of three different metrics based on the mean amplitude deviation (MAD) of the raw three-dimensional acceleration signal during maximal tests performed on a track or treadmill. A total of 53 healthy adult volunteers participated in the study, 29 performed the track test and 24 the treadmill test. During the tests, the data were collected using hip-worn triaxial accelerometers and metabolic gas analyzers. Data from both tests were pooled for primary statistical analysis. For typical walking speeds at VO2 less than 25 mL/kg/min, accelerometer metrics accounted for 71-86% of the variation in VO2. For typical running speeds starting from VO2 of 25 mL/kg/min up to over 60 mL/kg/min, 32-69% of the variation in VO2 could be explained, while the test type had an independent effect on the results, except for the conventional MAD metrics. The MAD metric is the best predictor of VO2 during walking, but the poorest during running. Depending on the intensity of locomotion, the choice of proper accelerometer metrics and test type may affect the validity of the prediction of incident VO2.

Substituting device-measured sedentary time with alternative 24-hour movement behaviours: compositional associations with adiposity and cardiometabolic risk in the ORISCAV-LUX 2 study

BACKGROUND

There is a considerable burden of sedentary time in European adults. We aimed to quantify the differences in adiposity and cardiometabolic health associated with theoretically exchanging sedentary time for alternative 24 h movement behaviours.

METHODS

This observational cross-sectional study included Luxembourg residents aged 18-79 years who each provided  ≥ 4 valid days of triaxial accelerometry (n = 1046). Covariable adjusted compositional isotemporal substitution models were used to examine if statistically replacing device-measured sedentary time with more time in the sleep period, light physical activity (PA), or moderate-to-vigorous PA (MVPA) was associated with adiposity and cardiometabolic health markers. We further investigated the cardiometabolic properties of replacing sedentary time which was accumulated in prolonged (≥ 30 min) with non-prolonged (< 30 min) bouts.

RESULTS

Replacing sedentary time with MVPA was favourably associated with adiposity, high-density lipoprotein cholesterol, fasting glucose, insulin, and clustered cardiometabolic risk. Substituting sedentary time with light PA was associated with lower total body fat, fasting insulin, and was the only time-exchange to predict lower triglycerides and a lower apolipoprotein B/A1 ratio. Exchanging sedentary time with more time in the sleep period was associated with lower fasting insulin, and with lower adiposity in short sleepers. There was no significant evidence that replacing prolonged with non-prolonged sedentary time was related to outcomes.

CONCLUSIONS

Artificial time-use substitutions indicate that replacing sedentary time with MVPA is beneficially associated with the widest range of cardiometabolic risk factors. Light PA confers some additional and unique metabolic benefit. Extending sleep, by substituting sedentary time with more time in the sleep period, may lower obesity risk in short sleepers.

AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments

OBJECTIVE

Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers.

METHODS

Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18-91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories.

RESULTS

Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%-30% on unseen data.

CONCLUSION

AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction.

The Individual-Level Productivity Costs of Physical Inactivity

PURPOSE

This study estimated the long-term individual-level productivity costs of physical inactivity.

METHODS

The data were drawn from the Northern Finland Birth Cohort 1966, to which the productivity cost variables (sick leaves and disability pensions) from Finnish registries were linked. Individuals ( N = 6261) were categorized into physical activity groups based on their level of physical activity, which was measured in three ways: 1) self-reported leisure-time moderate- to vigorous-intensity physical activity (MVPA) at 46 yr old, 2) longitudinal self-reported leisure-time MVPA at 31-46 yr old, and 3) accelerometer-measured overall MVPA at 46 yr old. The human capital approach was applied to calculate the observed costs (years 2012-2020) and the expected costs (years 2012-2031).

RESULTS

The results showed that the average individual-level productivity costs were higher among physically inactive compared with the costs among physically active. The results were consistent regardless of the measurement type of physical activity or the period used. On average, the observed long-term productivity costs among physically inactive individuals were €1900 higher based on self-reported MVPA, €1800 higher based on longitudinal MVPA, and €4300 higher based on accelerometer-measured MVPA compared with the corresponding productivity costs among physically active individuals. The corresponding difference in the expected costs was €2800, €1200, and €8700, respectively.

CONCLUSIONS

The results provide evidence that productivity costs differ according to an individual's level of physical activity. Therefore, investments in physical activity may decrease not only the direct healthcare costs but also the indirect productivity costs paid by the employee, the employer, and the government.

Replacing sedentary time with physical activity and sleep: Associations with cardiometabolic health markers in adults

This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 h of movement and non-movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4585 members of the Northern Finland Birth Cohort 1966, who wore a hip-worn accelerometer at the age of 46 years for 14 consecutive days. Time spent in sedentary behaviors, light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) were determined from the accelerometer and combined with self-reported sleep duration to obtain the 24-h time use. CRF was estimated from the peak heart rate in a submaximal step test. An isotemporal substitution paradigm was used to examine how sedentary time and substituting sedentary time with an equal amount of LPA, MVPA, or sleep were associated with adiposity markers, blood lipid levels, and fasting glucose and insulin. Sedentary time was independently and adversely associated with the markers of cardiometabolic health, even after adjustment for CRF, but not in partition models including LPA, MVPA, sleep, and CRF. Substituting 60, 45, 30, and 15 min/day of sedentary time with LPA or MVPA was associated with 0.2%-13.7% favorable differences in the cardiometabolic health markers after accounting for LPA, MVPA, sleep, CRF, and other confounders. After adjustment for movement and non-movement behaviors within the 24-h cycle, reallocating additional time to both LPA and MVPA was beneficially associated with markers of cardiometabolic health in middle-aged adults regardless of their CRF level.

Joint Profiles of Sedentary Time and Physical Activity in Adults and Their Associations with Cardiometabolic Health

PURPOSE

This study aimed to identify and characterize joint profiles of sedentary time and physical activity among adults and to investigate how these profiles are associated with markers of cardiometabolic health.

METHODS

The participants included 3702 of the Northern Finland Birth Cohort 1966 at age 46 yr, who wore a hip-worn accelerometer during waking hours and provided seven consecutive days of valid data. Sedentary time, light-intensity physical activity, and moderate- to vigorous-intensity physical activity on each valid day were obtained, and a data-driven clustering approach ("KmL3D") was used to characterize distinct joint profiles of sedentary time and physical activity intensities. Participants self-reported their sleep duration and performed a submaximal step test with continuous heart rate measurement to estimate their cardiorespiratory fitness (peak heart rate). Linear regression was used to determine the association between joint profiles of sedentary time and physical activities with cardiometabolic health markers, including adiposity markers and blood lipid, glucose, and insulin levels.

RESULTS

Four distinct groups were identified: "active couch potatoes" ( n = 1173), "sedentary light movers" ( n = 1199), "sedentary exercisers" ( n = 694), and "movers" ( n = 636). Although sufficiently active, active couch potatoes had the highest daily sedentary time (>10 h) and lowest light-intensity physical activity. Compared with active couch potatoes, sedentary light movers, sedentary exercisers, and movers spent less time in sedentary by performing more physical activity at light-intensity upward and had favorable differences in their cardiometabolic health markers after accounting for potential confounders (1.1%-25.0% lower values depending on the health marker and profile).

CONCLUSIONS

After accounting for sleep duration and cardiorespiratory fitness, waking activity profiles characterized by performing more physical activity at light-intensity upward, resulting in less time spent in sedentary, were associated with better cardiometabolic health.

Physical Activity, Body Composition, and Cardiometabolic Health during Pregnancy: A Compositional Data Approach

PURPOSE

The aim of this study was to examine the cross-sectional and longitudinal associations of 24-h movement behaviors (sleep, sedentary behavior (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA)) with body composition and cardiometabolic health in i) early and ii) late pregnancy (gestational weeks 14 and 37).

METHODS

This observational study utilized cross-sectional ( n = 273) and longitudinal data ( n = 242) from the HealthyMoms trial. Time spent in movement behaviors over seven consecutive 24-h periods (ActiGraph wGT3x-BT accelerometer), body composition (Bod Pod), and cardiometabolic health indicators (glucose levels, homeostatic model for insulin resistance (HOMA-IR), systolic and diastolic blood pressure, metabolic syndrome (MetS) score) were measured in early and late pregnancy.

RESULTS

In early pregnancy, reallocating time to MVPA from LPA, SB, and sleep was associated with lower MetS score (adjusted γ = -0.343, P = 0.002). Correspondingly, reallocating time to LPA from SB and sleep in early pregnancy was associated with lower body weight (adjusted γ = -5.959, P = 0.047) and HOMA-IR (adjusted γ = -0.557, P = 0.031) at the same time point. Furthermore, reallocating time to LPA from SB and sleep in early pregnancy was associated with lower fat mass index (adjusted γ = -0.668, P = 0.028), glucose levels (adjusted γ = -0.315, P = 0.006), HOMA-IR (adjusted γ = -0.779, P = 0.004), and MetS score (adjusted γ = -0.470, P = 0.027) in late pregnancy. The changes in behaviors throughout pregnancy were not associated with body weight, body composition, and MetS score in late pregnancy.

CONCLUSIONS

Our results demonstrated that increasing LPA or MVPA while reducing SB and sleep was associated with lower weight and more favorable cardiometabolic health in early pregnancy. In contrast, LPA in early pregnancy seems to be a stimulus of enough intensity to improve body composition and cardiometabolic health indicators in late pregnancy.

24 h movement behavior and metabolic syndrome study protocol: A prospective cohort study on lifestyle and risk of developing metabolic syndrome in undergraduate students from low-income regions during a pandemic

Introduction

Obesity and its comorbidities are increasingly prevalent in Latin America, with a more rapid growth in individuals with lower income. The composition of movement behaviors within a 24 h period may have important implications for obesity, metabolic and mental health in cross-sectional data. However, a longitudinal study is needed to confirm the findings from the primarily cross-sectional evidence. The COVID-19 pandemic has been associated with cardiometabolic outcomes and has impeded healthy behavior.

Objectives

The first objective is to evaluate the time elapsed since the diagnosis of not meeting 24 h movement guidelines and the potential subsequent onset of metabolic syndrome in undergraduate students from low-income regions within 4 years of follow up. The second objective is to test the association between 24 h movement, mental wellbeing, eating behaviors, and abdominal obesity in the period of this pandemic.

Methods

The 24 h movement behavior and metabolic syndrome (24 h-MESYN) study is a multicentre cohort study that will include participants from two Brazilian cities within the 2022-2025 period to asses the first objective, and also a nested case-control study at the baseline will be carried out to evaluate the second objective. Previously, we conducted a feasibility study in the academic year of 2021 to assessing the psychometric properties of subjective tools, refine our study protocol, and adjust the epidemiological conditions of the cohort's subsequent phases (like as prevalence of exposure of interest, sampling process, and study adherence). Statistical tests as Cohen's kappa agreement; factorial analysis; logistic, Poisson and linear regression; and Kaplan-Meier analysis will be performed, in accordance with the objectives.

Cross-Sectional Associations of Sedentary Behavior and Sitting with Serum Lipid Biomarkers in Midlife

INTRODUCTION

Physical inactivity, excessive total time spent in sedentary behavior (SB) and prolonged sedentary bouts have been proposed to be risk factors for chronic disease morbidity and mortality worldwide. However, which patterns and postures of SB have the most negative impacts on health outcomes is still unclear. This population-based study aimed to investigate the independent associations of the patterns of accelerometer-based overall SB and sitting with serum lipid biomarkers at different moderate- to vigorous-intensity physical activity (MVPA) levels.

METHODS

Physical activity and SB were measured in a birth cohort sample ( N = 3272) at 46 yr using a triaxial hip-worn accelerometer in free-living conditions for 14 d. Raw acceleration data were classified into SB and PA using a machine learning-based model, and the bouts of overall SB and sitting were identified from the classified data. The participants also answered health-related questionnaires and participated in clinical examinations. Associations of overall SB (lying and sitting) and sitting patterns with serum lipid biomarkers were investigated using linear regression.

RESULTS

The overall SB patterns were more consistently associated with serum lipid biomarkers than the sitting patterns after adjustments. Among the participants with the least and the most MVPA, high total time spent in SB and SB bouts of 15-29.99 and ≥30 min were associated with impaired lipid metabolism. Among those with moderate amount of MVPA, higher time spent in SB and SB bouts of 15-29.99 min was unfavorably associated with serum lipid biomarkers.

CONCLUSIONS

The associations between SB patterns and serum lipid biomarkers were dependent on MVPA level, which should be considered when planning evidence-based interventions to decrease SB in midlife.

Effects of physical activity intervention on 24-h movement behaviors: a compositional data analysis

We utilized compositional data analysis (CoDA) to study changes in the composition of the 24-h movement behaviors during an activity tracker based physical activity intervention. A total of 231 recently retired Finnish retirees were randomized into intervention and control groups. The intervention participants were requested to use a commercial activity tracker bracelet with daily activity goal and inactivity alerts for 12 months. The controls received no intervention. The 24-h movement behaviors, i.e., sleep, sedentary time (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were estimated from wrist-worn ActiGraph data using the GGIR R-package. Three balance coordinates describing the composition of movement behaviors were applied: ratio of active vs. passive behaviors, LPA vs. MVPA, and sleep vs. SED. A linear mixed model was used to study changes between the baseline and 6-month time point. Overall, the changes in the 24-h movement behaviors were small and did not differ between the groups. Only the ratio of LPA to MVPA tended to change differently between the groups (group*time interaction p = 0.08) as the intervention group increased LPA similarly to controls but decreased their MVPA. In conclusion, the use of a commercial activity tracker may not be enough to induce changes in the 24-h movement behaviors among retirees.

The association between sedentary behaviour, physical activity and type 2 diabetes markers: A systematic review of mixed analytic approaches

The negative effect of sedentary behaviour on type 2 diabetes markers is established, but the interaction with measures of physical activity is still largely unknown. Previous studies have analysed associations with single-activity models, which ignore the interaction with other behaviours. By including results from various analytical approaches, this review critically summarises the effects of sedentary behaviour on diabetes markers and the benefits of substitutions and compositions of physical activity. Ovid Medline, Embase and Cochrane Library databases were systematically searched. Studies were selected if sedentary behaviour and physical activity were measured by accelerometer in the general population, and if associations were reported with glucose, insulin, HOMA-IR, insulin sensitivity, HbA1c, diabetes incidence, CRP and IL-6. Forty-five studies were included in the review. Conclusive detrimental associations with sedentary behaviour were determined for 2-h insulin (6/12 studies found associations), fasting insulin (15/19 studies), insulin sensitivity (4/6 studies), diabetes (3/4 studies) and IL-6 (2/3 studies). Reallocating sedentary behaviour to light or moderate-to-vigorous activity has a beneficial effect for 2-h glucose (1/1 studies), fasting insulin (3/3 studies), HOMA-IR (1/1 studies) and insulin sensitivity (1/1 studies). Compositional measures of sedentary behaviour were found to affect 2-h glucose (1/1 studies), fasting insulin (2/3 studies), 2-h insulin (1/1 studies), HOMA-IR (2/2 studies) and CRP (1/1 studies). Different analytical methods produced conflicting results for fasting glucose, 2-h glucose, 2-h insulin, insulin sensitivity, HOMA-IR, diabetes, hbA1c, CRP and IL-6. Studies analysing data by quartiles report independent associations between sedentary behaviour and fasting insulin, HOMA-IR and diabetes only for high duration of sedentary time (7-9 hours/day). However, this review could not provide sufficient evidence for a time-specific cut-off of sedentary behaviour for diabetes biomarkers. While substituting sedentary behaviour with moderate-to-vigorous activity brings greater improvements for health, light activity also benefits metabolic health. Future research should elucidate the effects of substituting and combining different activity durations and modalities.

Association of domain-specific physical activity and sedentary behavior with cardiometabolic health among office workers

OBJECTIVE

Previous studies have reported opposite effects of occupational or non-occupational physical activity (PA) and sedentary behavior (SB) on health outcomes. However, no study has investigated the relationship between domain-specific movement behaviors and cardiometabolic health (CMH) among office workers, considering the compositional nature of time-use data. We investigated the associations of accelerometer-measured PA and SB for each domain (working time, non-working time on workday, and non-workday) with CMH indicators among office workers, using compositional data analysis.

METHODS

This cross-sectional study included 1258 Japanese office workers. The time spent on SB, light-intensity PA (LPA), and moderate- to vigorous-intensity PA (MVPA) were assessed using an accelerometer. CMH indicators were retrieved from the annual health check-up data.

RESULTS

Compositional multiple linear regression indicated that PA and SB during non-working time on workdays, but not working time or non-workdays, were significantly associated with CMH. In particular, during non-working time, time reallocations from SB to LPA and from SB to MVPA were associated with favorable changes in cardiometabolic risk score and lipid metabolism, respectively. Paradoxically, a greater proportion of LPA during non-working time was associated with favorable diastolic blood pressure (β = 1.61; 95% confidence interval [CI] = 0.02, 3.19), whereas occupational LPA was detrimental (β = -2.48; 95% CI = -4.87, -0.09).

CONCLUSION

Our results suggested that reducing SB and increasing PA during non-working time on workdays may be effective for managing CMH among office workers. Future longitudinal studies using compositional data analysis are required to confirm our results.

The Compositional Impacts of 2 Distinct 24-Hour Movement Behavior Change Patterns on Physical Fitness in Chinese Adolescents

BACKGROUND

The study aimed to examine predicted differences of 2 different behavior change patterns on physical fitness (PF).

METHODS

Participants were 241 students (51% girls) aged 11-14 years from China. Light physical activity, moderate to vigorous physical activity (MVPA), and sedentary behavior (SB) were objectively measured. Sleep was obtained by subtracting from awake time. According to Chinese National student PF standards, 5 components of PF, including body mass index, cardiorespiratory fitness, speed, muscular explosive power and strength endurance, and flexibility, were assessed. The effects of different time reallocations between 24-hour movement behaviors on PF were estimated based on adjusted compositional multiple linear regression models with isometric log ratios.

RESULTS

Compared with MVPA substituting for the remaining behaviors, MVPA replacing SB or light physical activity produced more favorable changes on the comprehensive PF score, cardiorespiratory fitness, explosive power, and speed. MVPA replacing 30 minutes of SB was associated with favorable changes in PF (+1.9 [0.53, 3.18] points), 50-m run (-0.17 [-0.31, -0.04] s), long-distance running (-5.54 s [for girls]/7.25 s [for boys]), and long jump (+0.05 [0.01, 0.09] m). When sleep replaced SB, PF improved.

CONCLUSIONS

MVPA substituting SB or light physical activity is a strategy with a greater improvement in PF.

Accumulation patterns of sedentary time and breaks and their association with cardiometabolic health markers in adults

Breaking up sedentary time with physical activity (PA) could modify the detrimental cardiometabolic health effects of sedentary time. Our aim was to identify profiles according to distinct accumulation patterns of sedentary time and breaks in adults, and to investigate how these profiles are associated with cardiometabolic outcomes. Participants (n = 4439) of the Northern Finland Birth Cohort 1966 at age 46 years wore a hip-worn accelerometer for 7 consecutive days during waking hours. Uninterrupted ≥1-min sedentary bouts were identified, and non-sedentary bouts in between two consecutive sedentary bouts were considered as sedentary breaks. K-means clustering was performed with 65 variables characterizing how sedentary time was accumulated and interrupted. Linear regression was used to determine the association of accumulation patterns with cardiometabolic health markers. Four distinct groups were formed as follows: "Couch potatoes" (n = 1222), "Prolonged sitters" (n = 1179), "Shortened sitters" (n = 1529), and "Breakers" (n = 509). Couch potatoes had the highest level of sedentariness and the shortest sedentary breaks. Prolonged sitters, accumulating sedentary time in bouts of ≥15-30 min, had no differences in cardiometabolic outcomes compared with Couch potatoes. Shortened sitters accumulated sedentary time in bouts lasting <15 min and performed more light-intensity PA in their sedentary breaks, and Breakers performed more light-intensity and moderate-to-vigorous PA. These latter two profiles had lower levels of adiposity, blood lipids, and insulin sensitivity, compared with Couch potatoes (1.1-25.0% lower values depending on the cardiometabolic health outcome, group, and adjustments for potential confounders). Avoiding uninterrupted sedentary time with any active behavior from light-intensity upwards could be beneficial for cardiometabolic health in adults.

A compositional analysis of time spent in sleep, sedentary behaviour and physical activity with all-cause mortality risk

BACKGROUND

Daily time spent in sleep, sedentary behaviour (SED), light intensity physical activity (LIPA), and moderate-to-vigorous intensity physical activity (MVPA) are compositional, co-dependent variables. The objectives of this study were to use compositional data analysis to: (1) examine the relationship between the movement behaviour composition (daily time spent in sleep, SED, LIPA and MVPA) and all-cause mortality risk, and (2) estimate the extent to which changing time spent in any given movement behaviour (sleep, SED, LIPA, or MVPA) within the movement behaviour composition was associated with changes in risk of all-cause mortality.

METHODS

2838 adult participants from the 2005-2006 cycle of the U.S. National Health and Nutrition Examination Survey were studied using a prospective cohort design. Daily time spent in SED, LIPA and MVPA were determined by accelerometer. Nightly time spent sleeping was self-reported. Survey data were linked with mortality data through to the end of December 2015. Compositional data analysis was used to investigate relationships between the movement behaviour composition and mortality.

RESULTS

The movement behaviour composition was significantly associated with mortality risk. Time spent in MVPA relative to other movement behaviours was negatively associated with mortality risk (HR = .74; 95% CI [.67, .83]) while relative time spent in SED was positively associated with mortality risk (HR = 1.75; 95% CI [1.10, 2.79]). Time displacement estimates revealed that the greatest estimated changes in mortality risk occurred when time spent in MVPA was decreased and replaced with sleep, SED, LIPA or a combination of these behaviours (HRs of 1.76 to 1.80 for 15 min/day displacements).

CONCLUSIONS

The daily movement behaviour composition was related to mortality. Replacing time in MVPA or SED with equivalent time from any other movement behaviour was associated with an increase and decrease in mortality risk, respectively.