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

Abstract

PurposeThis 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.MethodsThe 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.ResultsFour 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).ConclusionsAfter 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.

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

“…Consistent with our results, previous studies have shown that after adjustment for CRF and sedentary time, higher levels of MVPA are associated with better cardiometabolic health. 6,12,29 However, although evidence is accumulating that adults may gain cardiometabolic health benefits by performing more LPA, 3,5,[30][31][32][33] it remains unclear whether this intensity of movement would lead to better cardiometabolic health irrespective of CRF. Our findings, while supporting the robust evidence for the health benefits of MVPA, 1,34 further indicate that physical activity from light intensity upward is beneficially associated with markers of cardiometabolic health, even after accounting for 24-h movement and non-movement behaviors and CRF.…”

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

“…Due to their feasibility and applicability, many studies have utilized wearable activity monitors as an objective means of monitoring daily activities [ 4 – 7 ]. The robust evidence derived from these studies suggests human movement behaviors have considerable consequences on various health indicators in adults, including the risk of cardiometabolic diseases and mortality [ 7 – 9 ].…”

“…Adults engage in three types of movement behaviors in their daily living during waking hours—moderate-to-vigorous physical activity (MVPA), light intensity activities, and sedentary behaviors [ 9 ]. While the health benefits of MVPA are well-documented [ 10 ], the availability of device-based techniques has provided an unprecedented opportunity to explore whether and how the full spectrum of movement behaviors are related to health, especially when coupled with advanced machine learning and pattern recognition techniques [ 1 , 4 , 11 ].…”

“…While the health benefits of MVPA are well-documented [ 10 ], the availability of device-based techniques has provided an unprecedented opportunity to explore whether and how the full spectrum of movement behaviors are related to health, especially when coupled with advanced machine learning and pattern recognition techniques [ 1 , 4 , 11 ]. Existing studies, utilizing wearable devices to monitor human movement behaviors, provide compelling evidence that excessive sedentary time, especially when combined with a low physical activity level, have detrimental effects on health [ 9 ]. Conversely, a higher level of physical activity from light intensity upwards is shown to be associated with improved cardiometabolic health [ 12 ].…”

“…By considering movement behaviors as compositional data, these approaches allow for a comprehensive understanding of their relative contributions to health markers and indicators, and how reallocations within these compositions may impact health outcomes [ 20 , 23 , 24 ]. More recently, machine learning and data-driven techniques have also been employed for movement behavior profiling and studying the joint associations of sedentary behaviors and physical activity with different health indicators [ 9 , 25 , 26 ]. For instance, machine-learned profiles of sedentary and activity behaviors, characterized by performing more physical activity at light-intensity upwards throughout an entire week, have been linked to better cardiometabolic health in adults [ 9 ].…”