Apple watch accuracy in monitoring health metrics: a systematic review and meta-analysis.

Abstract

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 to 2.69) for EE (kcal/min), -0.12 (LoA: -11.06 to 10.81) for HR (beats/min), -1.83 (LoA: -9.08 to 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.