Accuracy of a Portable Multisensor Body Monitor for Predicting Resting Energy Expenditure in Older People: A Comparison with Indirect Calorimetry

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Background: Accurate and comfortable methods are needed to determine resting energy expenditure (REE) in older people who are characterized by a lowered metabolic rate. The portable SenseWear® armband (SWA) body monitor, worn on the right upper arm, can easily be used by this age group in an ambulatory manner. Objective: The purpose of this study was to evaluate the reliability and accuracy of the SWA armband in determining REE in healthy, normal-weight older people. Methods: Participants were 49 older, community-dwelling volunteers aged 60–87 years. Reliability was tested in all subjects. Accuracy was analyzed in 32 subjects by comparing REE estimated by SWA against REE measured by indirect calorimetry as a criterion method. Data were simultaneously and continuously recorded for 20 min in the morning. In the same subjects, REE determined by SWA from night recording was evaluated for accuracy against indirect calorimetry. The agreement between methods was assessed by the Bland-Altman procedure. Results: (1) REE measured by SWA was reliable when comparing 2 consecutive measurements (typical error: 1.9%). (2) REE calculated from morning recording (1,543 ± 181 kcal/24 h) was higher than REE from night recording (1,564 ± 192 kcal/24 h; p = 0.018). (3) Compared with REE by indirect calorimetry (1,377 ± 228 kcal/24 h) for accuracy, the SWA overestimated mean REE in the older people by 12 and 14%, respectively, for morning and night recording (each p < 0.001). Conclusions: The SWA provides a reliable estimate of REE in healthy older subjects and has the advantage of easy handling. The 20-min recording time, which was recommended by the manufacturer, can be applied. However, the SWA overestimates REE in this group, possibly due to age-related changes in skin conductance and thermoregulation, both being measured by sensors of the SWA armband. This requires improving the SWA by developing better fitting algorithms for predicting REE in older people.