Data Sheet 1_Energy expenditure estimation during activities of daily living in middle-aged and older adults using an accelerometer integrated into a hearing aid.docx

BackgroundAccelerometers 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.
MethodsSixty 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.
ResultsThe 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).
DiscussionThis 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.

研究背景：
传统上，加速度计佩戴于髋部以估算运动过程中的能量消耗(energy expenditure, EE)，但为提升佩戴依从性，腕部佩戴设备正逐渐取代髋部加速度计，尽管这可能会降低EE估算的准确性。在听力损失患病率更高的老年人群中，集成式的新型方案或许可行。因此，本研究旨在探究集成于助听器的加速度计的EE估算准确性与精密度，并将其性能与同时佩戴于腕部和髋部的传感器进行对比。

研究方法：
本研究共纳入60名中老年人（平均年龄64.0±8.0岁，女性占比48%）。受试者先完成20分钟的静息能量消耗测量（隔夜禁食后进行），随后进食标准化早餐，并完成13项不同的日常活动，其中12项从35项预设活动中个体化选取，活动类型涵盖久坐低强度至高强度动态体力活动。本研究以间接测热法(indirect calorimetry)作为任务代谢当量(metabolic equivalent of task, MET)的金标准，将集成于助听器的设备（Audéo）的EE估算结果，与髋部佩戴的科研级设备（ZurichMove）以及腕部佩戴的消费级设备（Garmin和Fitbit）的估算结果进行对比。采用分类估计模型与分类已知模型，通过Bland-Altman分析(Bland-Altman analyses)评估EE估算的准确性与精密度。

研究结果：
结果显示，采用分类估计模型时，Audéo设备的平均偏倚及95%一致性界限为-0.23±3.33 METs，略优于腕部佩戴的消费级设备（Garmin：-0.64±3.53 METs；Fitbit：-0.67±3.40 METs）。采用分类已知模型时，Audéo（-0.21±2.51 METs）与ZurichMove（-0.13±2.49 METs）的性能相当。亚组分析显示，不同活动的估算准确性存在显著差异，但将典型单日10小时的活动数据进行平均后，估算准确性良好（偏差为+61±302 kcal）。

讨论：
本研究证实，集成于助听器的加速度计可在目标人群中准确估算多种活动场景下的EE，同时也指出，鉴于消费级与科研级设备均存在精密度局限性，仍需开展持续的优化工作。