Hallal, PC; Reichert, FF; Clark, VL; Cordeira, KL; Menezes, AM; Eaton, S; Ekelund, U; Hallal, PC; Reichert, FF; Clark, VL; Cordeira, KL; Menezes, AM; Eaton, S; Ekelund, U; Wells, JC; - view fewer (2013) Energy expenditure compared to physical activity measured by accelerometry and self-report in adolescents: a validation study. PLoS One , 8 (11) , Article e77036. 10.1371/journal.pone.0077036.

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Abstract

Background Physical inactivity is responsible for 5.3 million deaths annually worldwide. To measure physical activity energy expenditure, the doubly labeled water (DLW) method is the gold standard. However, questionnaires and accelerometry are more widely used. We compared physical activity measured by accelerometer and questionnaire against total (TEE) and physical activity energy expenditure (PAEE) estimated by DLW. Methods TEE, PAEE (TEE minus resting energy expenditure) and body composition were measured using the DLW technique in 25 adolescents (16 girls) aged 13 years living in Pelotas, Brazil. Physical activity was assessed using the Actigraph accelerometer and by self-report. Physical activity data from accelerometry and self-report were tested against energy expenditure data derived from the DLW method. Further, tests were done to assess the ability of moderate-to-vigorous intensity physical activity (MVPA) to predict variability in TEE and to what extent adjustment for fat and fat-free mass predicted the variability in TEE. Results TEE varied from 1,265 to 4,143 kcal/day. It was positively correlated with physical activity (counts) estimated by accelerometry (rho = 0.57; p = 0.003) and with minutes per week of physical activity by questionnaire (rho = 0.41; p = 0.04). An increase of 10 minutes per day in moderate-to-vigorous intensity physical activity (MVPA) relates to an increase in TEE of 141 kcal/day. PAEE was positively correlated with accelerometry (rho = 0.64; p = 0.007), but not with minutes per week of physical activity estimated by questionnaire (rho = 0.30; p = 0.15). Physical activity by accelerometry explained 31% of the vssariability in TEE. By incorporating fat and fat-free mass in the model, we were able to explain 58% of the variability in TEE. Conclusion Objectively measured physical activity significantly contributes to the explained variance in both TEE and PAEE in Brazilian youth. Independently, body composition also explains variance in TEE, and should ideally be taken into account when using accelerometry to predict energy expenditure values.

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