European Journal of Clinical Nutrition (2017) 71, 331–339; doi:10.1038/ejcn.2017.2; published online 1 February 2017

Usefulness of motion sensors to estimate energy expenditure in children and adults: a narrative review of studies using DLW

L B Sardinha1 and P B Júdice1

1Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Lisboa, Portugal

Correspondence: Professor LB Sardinha, Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Lisboa 1499-002, Portugal. E-mail:

Received 1 November 2016; Accepted 1 November 2016
Advance online publication 1 February 2017



It is well documented that meeting moderate-to-vigorous physical activity guidelines of 150min per week is protective against chronic disease, and this is likely explained by higher energy expenditure (EE). In opposition, sedentary behavior (low EE) seems to impair health outcomes. There are gold standard methods to measure EE such as the doubly labeled water (DLW) or calorimetry. These methods are highly expensive and rely on complex techniques. Motion sensors present a good alternative to estimate EE and have been validated against these reference methods. This review summarizes findings from previous reviews and the most recently published studies on the validity of different motion sensors to estimate physical activity energy expenditure (PAEE) and total energy expenditure (TEE) against DLW, and whether adding other indicators may improve these estimations in children and adults. Regardless of the recognized validity of motion sensors to estimate PAEE and TEE at the group level, individual bias is very high even when combining biometric or physiological indicators. In children, accelerometers explained 13% of DLW’s PAEE variance and 31% of TEE variance. In adults, DLW’s explained variance was higher, 29 and 44% for PAEE and TEE, respectively. There is no ideal device, but identifying postures seems to be relevant for both children and adults’ PAEE estimates. The variance associated with the number of methodological choices that these devices require invite investigators to work with the raw data in order to standardize all these procedures and potentiate the accelerometer signal-derived information. Models that consider biometric covariates seem only to improve TEE estimations, but adding heart rate enhances PAEE estimations in both children and adults.

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