Choe Ju-Pil, Kang Minsoo
Health and Sport Analytics Laboratory, Department of Health, Exercise Science, and Recreation Management, The University of Mississippi, University, MS 38677, United States of America.
Physiol Meas. 2025 Apr 17;46(4). doi: 10.1088/1361-6579/adca82.
. Wearable technology like the Apple Watch is increasingly important for monitoring health metrics. Accurate measurement is crucial, as inaccuracies can impact health outcomes. Despite extensive research, findings on the Apple Watch's accuracy vary across different conditions. While previous reviews have summarized findings, few have utilized a meta-analytic approach. This study aims to quantitatively evaluate the accuracy of the Apple Watch in measuring health metrics. The accuracy of the Apple Watch was assessed in measuring energy expenditure (EE), heart rate (HR), and step counts (steps).. We searched Embase, PubMed, Scopus, and SPORTDiscus for studies on adults using the Apple Watch compared to reference measures. The Bland-Altman framework was applied to assess mean bias and limits of agreement (LoA), with robust variance estimation to address within-study correlations. Heterogeneity was assessed across variables such as age, health status, device series, activity intensity, and activity type. Additionally, the mean absolute percentage error (MAPE) reported in the included studies was summarized by subgroups.. This review included 56 studies, comprising 270 effect sizes on EE (71), HR (148), and steps (51). The meta-analysis showed a mean bias of 0.30 (LoA: -2.09-2.69) for EE (kcal min), -0.12 (LoA: -11.06-10.81) for HR (beats min), -1.83 (LoA: -9.08-5.41) for steps (steps min). The forest plots showed variability in LoA across subgroups. For MAPE, all subgroups for EE exceeded the 10% validity threshold, while none of the subgroups for HR exceeded this threshold. For steps, some subgroups exceeded 10%, highlighting variability in accuracy based on different conditions.. This study demonstrates that while the Apple Watch generally provides accurate HR and step measurements, its accuracy for EE is limited. Although HR and step measurements showed acceptable accuracy, variability was observed across different user characteristics and measurement conditions. These findings highlight the importance of considering such factors when evaluating validity.
像苹果手表这样的可穿戴技术对于监测健康指标越来越重要。准确测量至关重要，因为不准确的测量可能会影响健康结果。尽管进行了广泛的研究，但苹果手表在不同条件下的准确性研究结果各不相同。虽然之前的综述总结了研究结果，但很少有研究采用荟萃分析方法。本研究旨在定量评估苹果手表在测量健康指标方面的准确性。评估了苹果手表在测量能量消耗（EE）、心率（HR）和步数方面的准确性。我们在Embase、PubMed、Scopus和SPORTDiscus中搜索了关于使用苹果手表的成年人与参考测量方法对比的研究。应用Bland-Altman框架评估平均偏差和一致性界限（LoA），并采用稳健方差估计来处理研究内相关性。评估了年龄、健康状况、设备系列、活动强度和活动类型等变量之间的异质性。此外，纳入研究中报告的平均绝对百分比误差（MAPE）按亚组进行了总结。本综述纳入了56项研究，包括270个关于EE（71个）、HR（148个）和步数（51个）的效应量。荟萃分析显示，EE（千卡/分钟）的平均偏差为0.30（LoA：-2.09至2.69），HR（次/分钟）的平均偏差为-0.12（LoA：-11.06至10.81），步数（步/分钟）的平均偏差为-1.83（LoA：-9.08至5.41）。森林图显示各亚组的LoA存在差异。对于MAPE，EE的所有亚组均超过了10%的有效性阈值，而HR的亚组均未超过该阈值。对于步数，一些亚组超过了10%，突出了基于不同条件的准确性差异。本研究表明，虽然苹果手表通常能提供准确的HR和步数测量，但它在EE测量方面的准确性有限。尽管HR和步数测量显示出可接受的准确性，但在不同用户特征和测量条件下仍观察到了差异。这些发现凸显了在评估有效性时考虑这些因素的重要性。
