使用集成在助听器中的加速度计估算中老年人在日常生活中的能量消耗。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2024-06-17 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1400535
Jan Stutz, Philipp A Eichenberger, Nina Stumpf, Samuel E J Knobel, Nicholas C Herbert, Isabel Hirzel, Sacha Huber, Chiara Oetiker, Emily Urry, Olivier Lambercy, Christina M Spengler
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引用次数: 0

摘要

背景:传统上,人们将加速度计佩戴在臀部,以估算体力活动期间的能量消耗(EE),但现在越来越多的加速度计被佩戴在手腕上的产品所取代,以提高佩戴的顺应性,尽管这可能会影响 EE 估算的准确性。在听力损失发生率较高的老年人群中,可能会出现一种新的综合选择。因此,本研究旨在调查使用集成在助听器中的加速度计估算 EE 的准确性和精确度,并将其性能与同时佩戴在手腕和臀部的传感器进行比较:60 名中老年人(平均年龄为 64.0 ± 8.0 岁,48% 为女性)参加了此次研究。他们进行了 20 分钟的静息能量消耗测量(隔夜禁食后),然后是标准早餐和 13 种不同的日常生活活动,其中 12 种是从 35 种活动中单独挑选出来的,这些活动既有久坐不动、强度较低的活动,也有动态性较强、对体力要求较高的活动。我们使用间接热量计作为任务代谢当量(MET)的参考,将助听器集成设备(Audéo)与佩戴在臀部的研究设备(ZurichMove)和佩戴在手腕上的消费类设备(Garmin 和 Fitbit)进行的 EE 估算进行了比较。通过布兰-阿尔特曼分析,使用类估计模型和类已知模型来评估 EE 估计值的准确性和精确性:研究结果显示,Audéo(类估计模型)的平均偏差和 95% 的一致性限制为 -0.23 ± 3.33 METs,这表明其与腕戴式消费设备(Garmin:-0.64 ± 3.53 METs 和 Fitbit:-0.67 ± 3.40 METs)相比略有优势。分类了解模型显示,Audéo(-0.21 ± 2.51 METs)和 ZurichMove(-0.13 ± 2.49 METs)的性能相当。子分析表明,不同活动的准确性存在很大差异,而在典型的一天使用 10 小时的活动中,平均准确性较高(+61 ± 302 千卡):本研究显示了助听器集成加速度计在准确估计目标人群各种活动的 EE 方面的潜力,同时也强调了考虑到在消费者和研究设备中观察到的精度限制而进行持续优化的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy expenditure estimation during activities of daily living in middle-aged and older adults using an accelerometer integrated into a hearing aid.

Background: Accelerometers were traditionally worn on the hip to estimate energy expenditure (EE) during physical activity but are increasingly replaced by products worn on the wrist to enhance wear compliance, despite potential compromises in EE estimation accuracy. In the older population, where the prevalence of hearing loss is higher, a new, integrated option may arise. Thus, this study aimed to investigate the accuracy and precision of EE estimates using an accelerometer integrated into a hearing aid and compare its performance with sensors simultaneously worn on the wrist and hip.

Methods: Sixty middle-aged to older adults (average age 64.0 ± 8.0 years, 48% female) participated. They performed a 20-min resting energy expenditure measurement (after overnight fast) followed by a standardized breakfast and 13 different activities of daily living, 12 of them were individually selected from a set of 35 activities, ranging from sedentary and low intensity to more dynamic and physically demanding activities. Using indirect calorimetry as a reference for the metabolic equivalent of task (MET), we compared the EE estimations made using a hearing aid integrated device (Audéo) against those of a research device worn on the hip (ZurichMove) and consumer devices positioned on the wrist (Garmin and Fitbit). Class-estimated and class-known models were used to evaluate the accuracy and precision of EE estimates via Bland-Altman analyses.

Results: The findings reveal a mean bias and 95% limit of agreement for Audéo (class-estimated model) of -0.23 ± 3.33 METs, indicating a slight advantage over wrist-worn consumer devices (Garmin: -0.64 ± 3.53 METs and Fitbit: -0.67 ± 3.40 METs). Class-know models reveal a comparable performance between Audéo (-0.21 ± 2.51 METs) and ZurichMove (-0.13 ± 2.49 METs). Sub-analyses show substantial variability in accuracy for different activities and good accuracy when activities are averaged over a typical day's usage of 10 h (+61 ± 302 kcal).

Discussion: This study shows the potential of hearing aid-integrated accelerometers in accurately estimating EE across a wide range of activities in the target demographic, while also highlighting the necessity for ongoing optimization efforts considering precision limitations observed across both consumer and research devices.

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