Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement,Sports Medicine

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Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement
Sports Medicine ( IF 9.3 ) Pub Date : 2024-07-30 , DOI: 10.1007/s40279-024-02077-2
Cailbhe Doherty _{1,

2} , Maximus Baldwin _{1,

2,

3} , Alison Keogh _{2,

4} , Brian Caulfield _{1,

2} , Rob Argent _{2,

5}

Affiliation

Background

Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear.

Objective

To conduct a ‘living’ (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes.

Methods

A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors’ results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences.

Results

Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of ± 3%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO_2max by ± 15.24% during resting tests and ± 9.83% during exercise tests. Physical activity intensity measurements had a mean absolute error ranging from 29 to 80%, depending on the intensity of the activity being undertaken. Wearables mostly underestimated step counts (mean absolute percentage errors ranging from − 9 to 12%) and energy expenditure (mean bias = − 3 kcal per minute, or − 3%, with error ranging from − 21.27 to 14.76%). For blood oxygen saturation, wearables showed a mean absolute difference of up to 2.0%. Sleep measurement showed a tendency to overestimate total sleep time (mean absolute percentage error typically > 10%).

Conclusions

While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments.

Prospero ID

CRD42023402703.

中文翻译：

与可穿戴设备保持同步：评估消费类可穿戴技术在健康测量中准确性的系统评价的活伞回顾

背景

消费类可穿戴技术已经变得无处不在，临床和非临床人群利用各种设备来量化健康和保健的各个方面。然而，这些设备测量心率、睡眠和身体活动等生物识别结果的准确性仍不清楚。

目的

对消费类可穿戴技术在测量各种生理结果方面的准确性进行“活生生的”（即持续）评估。

方法

在以下科学数据库中对文献进行了系统检索：MEDLINE via PubMed、Embase、Cinahl 和 SPORTDiscus via EBSCO。纳入标准需要系统评价或荟萃分析，根据公认的参考标准评估消费类可穿戴设备的验证。除了发表详情、综述方案、设备细节和作者结果总结外，我们还提取了平均绝对百分比误差（MAPE）、汇总绝对偏倚、类内相关系数（ICCs）和平均绝对差异的数据。

结果

在通过初步检索确定的 904 项研究中，有 24 项系统评价符合我们的纳入标准;这些系统评价包括 249 项消费类可穿戴设备的非重复验证研究，涉及 430,465 名参与者（43% 为女性）。在迄今为止发布的市售可穿戴设备中，大约 11% 已针对至少一种生物识别结果进行了验证。然而，由于典型的设备可以测量多种生物识别结果，因此进行的验证研究数量仅占对这些设备进行全面评估所需的总数的 3.5%。对于心率，可穿戴设备的平均偏差为 ± 3%。在心律失常检测中，可穿戴设备的汇总灵敏度和特异性分别为 100% 和 95%。在有氧能力方面，可穿戴设备在静息测试期间显着高估了_{VO 2max} ± 15.24%，在运动测试期间± 9.83%。身体活动强度测量的平均绝对误差在 29% 到 80% 之间，具体取决于所进行活动的强度。可穿戴设备大多低估了步数（平均绝对百分比误差范围为 -9 至 12%）和能量消耗（平均偏差 = -3 kcal/分钟，或 -3%，误差范围为 -21.27 至 14.76%）。对于血氧饱和度，可穿戴设备的平均绝对差异高达 2.0%。睡眠测量显示有高估总睡眠时间的趋势（平均绝对百分比误差通常为 > 10%）。