BACKGROUND: The increasing burden of non-communicable diseases in sub-Saharan Africa (SSA) warrants rigorous studies of contributing lifestyle factors. Combined heart rate (HR) and movement monitoring make it possible to objectively measure physical activity in free-living individuals. We examined the validity of a combined HR and motion sensor in estimating physical activity energy expenditure (PAEE) in free-living adults in rural and urban Cameroon compared with doubly-labelled water (DLW) as criterion. METHODS: PAEE was measured in 33 free-living rural and urban dwellers by DLW over 7 consecutive days. Simultaneously, the combined sensor recorded HR and uni-axial acceleration. Individual HR vs PAEE calibration was done by a step test. Branched equation modelling was used to estimate PAEE from HR and acceleration. Validity and accuracy of prediction were expressed as mean bias and root mean square error (RMSE). Agreement was analysed using Bland and Altman limits of agreement (LOA). RESULTS: There was no significant mean bias between PAEE estimated from the combined sensor or measured by DLW [mean bias (standard error): -5.4 (5.1) kJ/kg/day; P = 0.3; RMSE = 29.3 kJ/kg/day]. The bias doubled for group compared with individual calibration of HR [-9.1 (5.0) kJ/kg/day, P = 0.08]. PAEE prediction was more accurate in urban compared with rural volunteers. The 95% LOAs between predicted and measured PAEE were ∼50-60 kJ/kg/day above or below perfect agreement. CONCLUSIONS: Combined HR and movement sensing is a valid method for estimating free-living PAEE on group level in adults in SSA.
BACKGROUND: The increasing burden of non-communicable diseases in sub-Saharan Africa (SSA) warrants rigorous studies of contributing lifestyle factors. Combined heart rate (HR) and movement monitoring make it possible to objectively measure physical activity in free-living individuals. We examined the validity of a combined HR and motion sensor in estimating physical activity energy expenditure (PAEE) in free-living adults in rural and urban Cameroon compared with doubly-labelled water (DLW) as criterion. METHODS: PAEE was measured in 33 free-living rural and urban dwellers by DLW over 7 consecutive days. Simultaneously, the combined sensor recorded HR and uni-axial acceleration. Individual HR vs PAEE calibration was done by a step test. Branched equation modelling was used to estimate PAEE from HR and acceleration. Validity and accuracy of prediction were expressed as mean bias and root mean square error (RMSE). Agreement was analysed using Bland and Altman limits of agreement (LOA). RESULTS: There was no significant mean bias between PAEE estimated from the combined sensor or measured by DLW [mean bias (standard error): -5.4 (5.1) kJ/kg/day; P = 0.3; RMSE = 29.3 kJ/kg/day]. The bias doubled for group compared with individual calibration of HR [-9.1 (5.0) kJ/kg/day, P = 0.08]. PAEE prediction was more accurate in urban compared with rural volunteers. The 95% LOAs between predicted and measured PAEE were ∼50-60 kJ/kg/day above or below perfect agreement. CONCLUSIONS: Combined HR and movement sensing is a valid method for estimating free-living PAEE on group level in adults in SSA.
Authors: David M Almeida; David Marcusson-Clavertz; David E Conroy; Jinhyuk Kim; Matthew J Zawadzki; Martin J Sliwinski; Joshua M Smyth Journal: J Behav Med Date: 2019-05-28
Authors: O F Sotunde; H S Kruger; H H Wright; L Havemann-Nel; I M Kruger; E Wentzel-Viljoen; A Kruger; M Tieland Journal: J Nutr Health Aging Date: 2015-06 Impact factor: 4.075
Authors: A M Silva; D A Santos; C N Matias; P B Júdice; J P Magalhães; U Ekelund; L B Sardinha Journal: Eur J Clin Nutr Date: 2014-04-02 Impact factor: 4.016