Can energy expenditure be accurately assessed using accelerometry-based wearable motion detectors for physical activity monitoring in post-stroke patients in the subacute phase?

计步器 能源消耗 医学 可穿戴计算机 冲程(发动机) 运动传感器 物理医学与康复 物理疗法 回廊的 体力活动 计算机科学 外科 人工智能 内科学 机械工程 工程类 嵌入式系统
作者
Stéphane Mandigout,Justine Lacroix,Béatrice Ferry,Nicolas Vuillerme,Maxence Compagnat,Jean‐Christophe Daviet
出处
期刊:European Journal of Preventive Cardiology [Oxford University Press]
卷期号:24 (18): 2009-2016 被引量:33
标识
DOI:10.1177/2047487317738593
摘要

In the subacute stroke phase, the monitoring of ambulatory activity and activities of daily life with wearable sensors may have relevant clinical applications. Do current commercially available wearable activity trackers allow us to objectively assess the energy expenditure of these activities? The objective of the present study was to compare the energy expenditure evaluated by indirect calorimetry during the course of a scenario consisting of everyday activities while estimating the energy expenditure using several commercialised wearable sensors in post-stroke patients (less than six months since stroke). Twenty-four patients (age 68.2 ± 13.9; post-stroke delay 34 ± 25 days) voluntarily participated in this study. Each patient underwent a scenario of various everyday tasks (transfer, walking, etc.). During the implementation, patients wore 14 wearable sensors (Armband, Actigraph GT3X, Actical, pedometer) to obtain an estimate of the energy expenditure. The actual energy expenditure was concurrently determined by indirect calorimetry. Except for the Armband worn on the non-plegic side, the results of our study show a significant difference between the energy expenditure values estimated by the various sensors and the actual energy expenditure when the scenario is considered as a whole. The present results suggest that, for a series of everyday tasks, the wearable sensors underestimate the actual energy expenditure values in post-stroke patients in the subacute phase and are therefore not accurate. Several factors are likely to confound the results: types of activity, prediction equations, the position of the sensor and the hemiplegia side.
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