Charlotte L Edwardson1,2, Joseph Henson1,2, Stuart J H Biddle3, Melanie J Davies1,2,4, Kamlesh Khunti1,4,5, Benjamin Maylor1, Thomas Yates1,2. 1. Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UNITED KINGDOM. 2. NIHR Leicester Biomedical Research Centre, Leicester General Hospital, Leicester, UNITED KINGDOM. 3. Institute for Resilient Regions, University of Southern Queensland, Education City, Springfield Central, QLD, AUSTRALIA. 4. Leicester Diabetes Centre, University Hospitals of Leicester, Leicester General Hospital, Leicester, UNITED KINGDOM. 5. NIHR Collaboration for Leadership in Applied Health Research and Care-East Midlands (CLAHRC-EM), Leicester Diabetes Centre, Leicester, UNITED KINGDOM.
Abstract
PURPOSE: To establish whether associations between sedentary behavior and cardiometabolic health differ when assessed by thigh-worn and waist-worn accelerometry. METHODS: Participants were recruited from several areas in the United Kingdom. Sedentary behavior was assessed using the activPAL worn on the thigh and ActiGraph worn on the waist. Average total (TST), prolonged (bouts ≥30 min; PST) and breaks (BST) in sedentary time were calculated. Cardiometabolic health markers included: adiposity (body fat) and surrogate markers of adiposity ((waist circumference, body mass index [BMI]), lipids (total, low density lipoprotein, and high-density lipoprotein [HDL] cholesterol, triglycerides), blood pressure, and glucose (fasting, 2 h and glycated hemoglobin A1c). A clustered cardiometabolic risk score was calculated. Linear regression analysis examined the associations with cardiometabolic health. RESULTS: There were 1457 participants (mean age [± standard deviation], 59.38 ± 11.85 yr; 51.7% male; mean BMI, 30.19 ± 5.59 kg·m) included in the analyses. ActivPAL and ActiGraph sedentary variables were moderately correlated (0.416-0.511, P < 0.01); however, all variables were significantly different from each other (P < 0.05). Consistency was observed across devices in the direction and magnitude of associations of TST and PST with adiposity, surrogate markers of adiposity, HDL, triglycerides, and cardiometabolic risk score and for BST with adiposity, surrogate markers of adiposity, and cardiometabolic risk. Differences across devices were observed in associations of TST and PST with diastolic blood pressure, for TST with 2-h glucose and for BST with HDL. No other associations were observed for any other health marker for either device. CONCLUSIONS: Results suggest that associations with cardiometabolic health are largely comparable across the two common assessments of sedentary behavior but some small differences may exist for certain health markers.
PURPOSE: To establish whether associations between sedentary behavior and cardiometabolic health differ when assessed by thigh-worn and waist-worn accelerometry. METHODS:Participants were recruited from several areas in the United Kingdom. Sedentary behavior was assessed using the activPAL worn on the thigh and ActiGraph worn on the waist. Average total (TST), prolonged (bouts ≥30 min; PST) and breaks (BST) in sedentary time were calculated. Cardiometabolic health markers included: adiposity (body fat) and surrogate markers of adiposity ((waist circumference, body mass index [BMI]), lipids (total, low density lipoprotein, and high-density lipoprotein [HDL] cholesterol, triglycerides), blood pressure, and glucose (fasting, 2 h and glycated hemoglobin A1c). A clustered cardiometabolic risk score was calculated. Linear regression analysis examined the associations with cardiometabolic health. RESULTS: There were 1457 participants (mean age [± standard deviation], 59.38 ± 11.85 yr; 51.7% male; mean BMI, 30.19 ± 5.59 kg·m) included in the analyses. ActivPAL and ActiGraph sedentary variables were moderately correlated (0.416-0.511, P < 0.01); however, all variables were significantly different from each other (P < 0.05). Consistency was observed across devices in the direction and magnitude of associations of TST and PST with adiposity, surrogate markers of adiposity, HDL, triglycerides, and cardiometabolic risk score and for BST with adiposity, surrogate markers of adiposity, and cardiometabolic risk. Differences across devices were observed in associations of TST and PST with diastolic blood pressure, for TST with 2-h glucose and for BST with HDL. No other associations were observed for any other health marker for either device. CONCLUSIONS: Results suggest that associations with cardiometabolic health are largely comparable across the two common assessments of sedentary behavior but some small differences may exist for certain health markers.
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