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Predicting Physical Activity Energy Expenditure Using Accelerometry in Adults From Sub‐Sahara Africa
Author(s) -
Assah Felix K.,
Ekelund Ulf,
Brage Soren,
Corder Kirsten,
Wright Antony,
Mbanya Jean C.,
Wareham Nicholas J.
Publication year - 2009
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1038/oby.2009.39
Subject(s) - accelerometer , energy expenditure , medicine , physical activity , doubly labeled water , population , demography , zoology , physical therapy , environmental health , endocrinology , physics , quantum mechanics , sociology , biology
Lack of physical activity may be an important etiological factor in the current epidemiological transition characterized by increasing prevalence of obesity and chronic diseases in sub‐Sahara Africa. However, there is a dearth of data on objectively measured physical activity energy expenditure (PAEE) in this region. We sought to develop regression equations using body composition and accelerometer counts to predict PAEE. We conducted a cross‐sectional study of 33 adult volunteers from an urban ( n = 16) and a rural ( n = 17) residential site in Cameroon. Energy expenditure was measured by doubly labeled water (DLW) over a period of seven consecutive days. Simultaneously, a hip‐mounted Actigraph accelerometer recorded body movement. PAEE prediction equations were derived using accelerometer counts, age, sex, and body composition variables, and cross‐validated by the jack‐knife method. The Bland and Altman limits of agreement (LOAs) approach was used to assess agreement. Our results show that PAEE (kJ/kg/day) was significantly and positively correlated with activity counts from the accelerometer ( r = 0.37, P = 0.03). The derived equations explained 14–40% of the variance in PAEE. Age, sex, and accelerometer counts together explained 34% of the variance in PAEE, with accelerometer counts alone explaining 14%. The LOAs between DLW and the derived equations were wide, with predicted PAEE being up to 60 kJ/kg/day below or above the measured value. In summary, the derived equations performed better than existing published equations in predicting PAEE from accelerometer counts in this population. Accelerometry could be used to predict PAEE in this population and, therefore, has important applications for monitoring population levels of total physical activity patterns.