INTRODUCTION This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared to a research-grade accelerometry. METHODS Seventy-five 5-12-year-olds (58% male, 63% White) participated in a 60-minute protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (Cosmed K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity[MVPA]) were estimated via indirect calorimetry (criterion) and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95CI = 67.1%, 69.3%), 73.0% (95CI = 71.8%, 74.3%), and 66.6% (95CI = 65.7%, 67.5%), respectively; and weighted specificity = 84.4% (95CI = 83.6%, 85.2%), 82.0% (95CI = 80.6%, 83.4%), and 75.3% (95CI = 74.7%, 75.9%), respectively. Apple Watch produced the lowest mean bias (inactive = -4.0 ± 4.5, light activity = 2.1 ± 4.0) and absolute error (inactive = 4.9 ± 3.4, light activity = 3.6 ± 2.7) for inactive and light physical activity minutes. For MVPA, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than a research-grade device, when compared to indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

中文翻译：

---

根据消费者可穿戴加速计数据预测儿童活动的与设备无关的方法：概念验证研究。

简介 这项研究探讨了与设备无关的方法通过消费者可穿戴加速度测量与研究级加速度测量预测身体活动的潜力。方法 75 名 5-12 岁儿童（58% 为男性，63% 为白人）参加了 60 分钟的实验。孩子们佩戴腕式消费类可穿戴设备（Apple Watch Series 7 和 Garmin Vivoactive 4）和研究级设备 (ActiGraph GT9X)，同时佩戴间接热量计 (Cosmed K5)。通过间接量热法（标准）估算活动强度（即不活动、轻度、中度至剧烈体力活动 [MVPA]），并将希尔德布兰德阈值应用于来自消费类可穿戴设备和研究级设备的原始加速度计数据。逐个历元（例如，加权灵敏度、特异性）和差异（例如，平均偏差、绝对误差）分析评估了加速度计得出的估计值和标准估计值之间的一致性。等效性测试评估了消费者可穿戴设备和 ActiGraph 产生的估计值的等效性。结果 ActiGraph、Apple 和 Garmin 的原始加速度测量数据产生的估计值产生了类似的标准一致性，加权灵敏度 = 68.2%（95CI = 67.1%、69.3%）、73.0%（95CI = 71.8%、74.3%）和 66.6% （95CI = 65.7%、67.5%）；加权特异性分别为 84.4%（95CI = 83.6%、85.2%）、82.0%（95CI = 80.6%、83.4%）和 75.3%（95CI = 74.7%、75.9%）。对于不活动和轻度体力活动分钟数，Apple Watch 产生最低的平均偏差（不活动 = -4.0 ± 4.5，轻度活动 = 2.1 ± 4.0）和绝对误差（不活动 = 4.9 ± 3.4，轻度活动 = 3.6 ± 2.7）。对于 MVPA，ActiGraph 产生最低的平均偏差 (1.0 ± 2.9) 和绝对误差 (2.8 ± 2.4)。ActiGraph 和消费者可穿戴设备的估计在统计上没有显着相同。结论 与间接量热法相比，原始加速度测量法估计腕部消费可穿戴设备的非活动和轻度活动表现与研究级设备相似，甚至更好。这项概念验证研究强调了与设备无关的方法通过消费者可穿戴设备量化身体活动强度的潜力。