Physical activity and sedentary time in a rural adult population in Malawi compared with an age-matched US urban population

Abstract

ObjectivesThis study was designed to assess patterns of objectively measured physical activity (PA) and sedentary behaviour in a sample of adults in a rural setting from a low-income Sub-Saharan African country (Malawi). The patterns of PA and sedentary behaviour in Malawi were compared with US data collected and analysed using the same methodology.MethodsThe Malawi PA data were collected as part of a survey experiment on the measurement of agricultural labor conducted under the World Bank Living Standards Measurement Study program. ActiGraph accelerometers (model GT3X) were worn on the right hip in a household-based sample of 414 working-age adults (15–85 years).ResultsMean total and 95% CIs for PA by category in min/day for Malawi adults were: sedentary 387.6 (377.4–397.8), low-light 222.1 (214.7–229.5), high-light 136.3 (132.7–139.9), moderate 71.6 (68.8–74.5), vigorous 1.1 (0.5–1.8) and moderate-to-vigorous physical activity (MVPA) 72.8 (69.7–75.9). Mean of PA and sedentary behavior (min/day) summed across age and sex groups are compared between Malawi and US samples: sedentary behaviour, 387.6 vs 525.8 (p<0.001); low-light, 222.1 vs 217.0 (p=ns); high-light, 136.3 vs 45.6 (p<0.001); moderate, 71.6 vs 28.0 (p<0.001); vigorous, 1.1 vs 2.5 (p<0.001); MVPA, 72.8 vs 30.5 (p<0.001). Compared with the USA, Malawi participants averaged consistently less sedentary time/day and more minutes/day in all intensity levels of PA, except for low-light and vigorous PA.ConclusionOverall, levels of MVPA and high-light activity in adults in Malawi were substantially higher and sedentary time was substantially lower than those observed in US samples using near identical data collection, scoring and analysis.

Quantifying population levels of physical activity in Africa using wearable sensors: implications for global physical activity surveillance

“…13 Rural populations of Kenyan Lou, Kamba and Masaai accumulated even higher levels of activity, with time spent above the highest accelerometry cutpoint Figure 1 Overview of objectively-measured moderate-to-vigorous intensity physical activity (MVPA) estimates of adult men (top panel) and women (bottom panel) residing in rural and urban areas of Africa, compared with a few measurement method-matched estimates from the USA and the UK. 3 12-16 18-20 being slightly higher than the equivalent in the Malawian men but slightly lower in the women. 18 Estimates from Ghana and Seychelles using hip accelerometry were however much lower, suggesting substantial variation between African populations.…”

“…Pratt and colleagues describe the levels of objectively measured physical activity in 414 working-age adults, sampled from agricultural (rural) households in Malawi, a low–middle-income country as assessed by the World Bank. 3 Physical activity was measured during the farming season using a hip-mounted accelerometer, and participants were asked to wear the device for 2 weeks, except when sleeping and bathing. The researchers quantified both total activity volume and time spent in different intensities; two of the uniaxial accelerometry cutpoints, denoted high-light and moderate intensity activity, represent the movement intensity equivalent to that of slow walking and normal walking speeds, respectively.…”

“… Overview of objectively-measured moderate-to-vigorous intensity physical activity (MVPA) estimates of adult men (top panel) and women (bottom panel) residing in rural and urban areas of Africa, compared with a few measurement method-matched estimates from the USA and the UK. 3 12–16 18–20 …”

The Distribution of Effort: Physical Activity, Gender Roles, and Bargaining Power in an Agrarian Setting

“…The accelerometers were left with the individual household members for 17 days and study participants were instructed to always wear them during the day, except for sleep time and during washing/bathing. To ensure technical standards, the investigators partnered with the Active Living Research Team of the University of California at San Diego, who participated in the enumerator training and provided quality control (Pratt et al 2020).…”

“…For more information on the physical activity tracking data management, seePratt et al (2020).8 We also computed active calories using the method outlined inFreedson, Melanson and Sirard (1998). However, the Freedson-algorithm was calibrated for participants engaged in moderate to vigorous activities (i.e.…”