A pilot study of physical activity and sedentary behavior distribution patterns in older women

Abstract

The study aims were to investigate free-living physical activity and sedentary behavior distribution patterns in a group of older women, and assess the cross-sectional associations with body mass index (BMI). Eleven older women (mean (SD) age: 77 (9) yrs) wore custom-built activity monitors, each containing a triaxial accelerometer ( ± 16 g, 100 Hz), on the waist and ankle for lab-based walking trials and 4 days in free-living. Daily active time, step counts, cadence, and sedentary break number were estimated from acceleration data. The sedentary bout length distribution and sedentary time accumulation pattern, using the Gini index, were investigated. Associations of the parameters’ total daily values and coefficients of variation (CVs) of their hourly values with BMI were assessed using linear regression. The algorithm demonstrated median sensitivity, positive predictive value, and agreement values > 98% and < 1% mean error in cadence calculations with video identification during lab trials. Participants’ sedentary bouts were found to be power law distributed with 56% of their sedentary time occurring in 20 min bouts or longer. Meaningful associations were detectable in the relationships of total active time, step count, sedentary break number and their CVs with BMI. Active time and step counts had moderate negative associations with BMI while sedentary break number had a strong negative association. Active time, step count and sedentary break number CVs also had strong positive associations with BMI. The results highlight the importance of measuring sedentary behavior and suggest a more even distribution of physical activity throughout the day is associated with lower BMI.

Differences in Physical Activity and Sedentary Behavior Patterns of Postmenopausal Women With Normal vs. Low Total Hip Bone Mineral Density

“…The daily active and sedentary time estimates observed in the current study were similar to previous reports in the literature (Arnardottir et al, 2013 ; Wanner et al, 2013 ). The mean daily sedentary break numbers reported in this study were also comparable to previous studies (Bankoski et al, 2011 ; Fortune et al, 2017 ). Furthermore, participants in both groups spent a similar percentage (70 and 72%) of their day being sedentary which was also reported in previous studies on older adults (68–78%) (Arnardottir et al, 2013 ; Godfrey et al, 2013 ; Jefferis et al, 2015 ; Diaz et al, 2016 ; Fortune et al, 2017 ).…”

“…We additionally previously found that there were no significant differences between the daily step counts yielded by our step detection algorithm and the step counts yielded by the ActiLife low frequency extension step detection algorithm using the free-living data from the cohort of postmenopausal women also used in the current manuscript (Madansingh et al, 2020 ). Similar to Fortune et al ( 2017 ), in this study, acceleration data were classified as either active (upright dynamic activity) or sedentary (static and/or lying down posture) on a second by second basis.…”

“…s −1 and during a simulated free-living protocol in the lab using Mayo Clinic accelerometer-based AMs (Fortune et al, 2014a ; Lugade et al, 2014 ), and for older adult participants with gait velocities ranging from 0.5 (0.02) to 1.7 (0.06) m . s −1 using the ActiGraph GT3X+ AMs (Fortune et al, 2015 , 2017 ). We additionally previously found that there were no significant differences between the daily step counts yielded by our step detection algorithm and the step counts yielded by the ActiLife low frequency extension step detection algorithm using the free-living data from the cohort of postmenopausal women also used in the current manuscript (Madansingh et al, 2020 ).…”

Objective evaluation of physical activity pattern using smart devices

“…Only a few metrics have been proposed to characterize the physical activity pattern, of which the Gini coefficient has been studied with respect to the patterns of a sedentary behaviour 4,13 and physical activity inequality 15 . In these studies, the Gini coefficient was calculated taking into account only time periods that contain some amount of physical activity.…”

Associations Between Objectively Measured Physical Activity, Body Composition and Sarcopenia: Findings from the Hertfordshire Sarcopenia Study (HSS)

“…Our results regarding associations between PA and adiposity are consistent with the literature. Time spent in non-sedentary activity as well as MVPA has been shown to be associated with lower adiposity and lower BMI in several population-based studies [ 4 , 33 , 34 ] and a systematic review by Fuzeki et al concluded that engagement in light PA was associated with less risk of developing obesity [ 35 ].…”