Physical Activity Behaviors of a Middle-Age South African Cohort as Determined by Integrated Hip and Thigh Accelerometry

Abstract

PurposeDescriptive studies of objectively measured physical activity behaviors in African populations are rare. We developed a method of combining hip and thigh accelerometery signals to quantify and describe physical behaviors in middle-age South African men and women.MethodsWe integrated signals from two triaxial accelerometers worn simultaneously during free-living, in a subsample of the Middle-age Soweto Cohort (n = 794; mean (SD) age, 53.7 (6.3) yr). Acceleration time series from the accelerometers were combined and movement-related acceleration was derived using Euclidean Norm Minus One (in milligrams), to determine total movement volume (mean Euclidean Norm Minus One) and nonmovement time (<28 mg), light-intensity physical activity (LPA; 28–85 mg), and moderate- to vigorous-intensity physical activity (MVPA; >85 mg); thigh pitch angle and a sleep diary were used to divide nonmovement time (in minutes per day) into sleep, awake sitting/lying, and standing. Sociodemographic factors were self-reported, and weight and height were measured.ResultsMean (SD) wear time was 128 (48) h. Movement volume was 15.0 (6.5) mg for men and 12.2 (3.4) mg for women. Men spent more time in MVPA and sitting/lying, whereas women spent more time standing. Age was inversely associated with movement volume, MVPA, and LPA. When compared with their normal-weight counterparts, men who were overweight or obese spent less time in MVPA, whereas women who were overweight or obese spent less time in LPA and more time sitting/lying. Socioeconomic status was inversely associated with total movement volume, MVPA, and time spent sleeping, and positively associated with time spent sitting/lying, in both men and women.ConclusionsIntegrating signals from hip and thigh accelerometers enables characterization of physical behaviors that can be applied in an African population.

Harmonization of three different accelerometers to classify the 24 h activity cycle

“…Two recent studies have examined alternative approaches for harmonizing data from two different device combinations to classify free-living 24-HAC (Micklesfield et al 2022, Dooley et al 2023. In a study by Dooley et al (2023), a cohort of 1285 community-dwelling older women concurrently wore the ActiGraph wGT3X+ on the waist during waking hours and the Actiwatch 2 continuously on the wrist for up to seven days as part of the Study Of Women's Health Across the Nation (SWAN).…”

“…Once wake intervals were determined, Evenson cut points (Evenson et al 2015) were used to classify SED, LPA, and MVPA from the ActiGraph wGT3X+ vector magnitude counts. In contrast, Micklesfield et al (2022) used a different approach to derive 24-HAC estimates from hip-worn Actigraph GT3X+ and thigh-worn activPAL data in 794 middle-age South-African men and women. They first identified sleep timing using a decision tree based on raw acceleration data signals from the activPAL combined with sleep log information.…”

Physical Activity and Its Association With Body Mass Index: A Cross-Sectional Analysis in Middle-Aged Adults From 4 Sub-Saharan African Countries

“…participants from the Soweto site. 39 Higher work and transport-related PA could arise from work in Africa typically involving manual labor. It is also possible that the transport cost may be too high for a significant proportion of the studied population.…”

Physical Behaviors and Their Association With Adiposity in Men and Women From a Low-Resourced African Setting

“…25 Participants with complete accelerometry and dual-energy X-ray absorptiometry (DXA) data were included and resulted in a final sample of 692 participants (384 men and 308 women). 26 During the first clinic visit, participants completed sociodemographic questionnaires, human immunodeficiency virus (HIV) testing, venous blood collection, and DXA. Participants were fitted with 2 accelerometers and returned the equipment and sleep diaries to the clinic after 7 days of wear.…”

“…Data were summarized at participant level to derive total movement volume, and the magnitude and angle of the signals were used to derive time spent in postures and intensities of activity. 26 The variables reported are total movement volume (Euclidean norm minus one expressed as milligravitational units) and time (in minutes per day) spent in sleeping, sedentary (awake sitting or lying), standing, LPA, and MVPA behaviors.…”