Department of Medicine, University of California, San Diego, La Jolla, California.
Specialists in Global Health, Encinitas, California.
J Clin Sleep Med. 2024 Sep 1;20(9):1479-1488. doi: 10.5664/jcsm.11178.
We evaluated the accuracy and precision of continuous overnight oxygen saturation (SpO) measurement by a commercial wrist device (WD) incorporating high-grade sensors and investigated WD estimation of sleep-disordered breathing by quantifying overnight oxygen desaturation index compared to polysomnography (PSG) oxygen desaturation index and apnea-hypopnea index (AHI) with and without sleep questionnaire data to assess the WD's ability to detect obstructive sleep apnea and determine its severity.
Participants completed sleep questionnaires, had a WD (Samsung Galaxy Watch 4) placed on their wrist, and underwent attended, in-laboratory overnight PSG (Nihon Kohden) with a pulse oximetry probe secured either to a finger or an ear lobe. PSG data were scored by a single experienced registered PSG technologist. Statistical analysis included demographic characteristics, continuous SpO measurement WD vs PSG root-mean-square error with Bland-Altman plot and linear regression associations. Predictive models for PSG oxygen desaturation index and AHI severity were built using logistic regression with probability cutoffs determined via receiver operating curve characteristics.
The 51 participants analyzed had a median age of 49 (range, 22-78) years; 66.7% were male, with median body mass index of 28.1 (range, 20.1-47.3) kg/m with a race/ethnicity distribution of 49.0% Caucasian, 25.5% Hispanic, 9.8% African American, 9.8% Asian, and 5.9% Middle Eastern. WD vs PSG continuous SpO measurement in percentage points demonstrated a bias of 0.91 (95% confidence interval, 0.38, 1.45), standard deviation of 2.37 (95% confidence interval, 2.36, 2.38), and root-mean-square error of 2.54 (95% confidence interval, 2.34, 2.73). WD area under the curve receiver operating curve characteristics for predicting PSG were 0.882 oxygen desaturation index > 15 events/h, 0.894 AHI > 30 events/h, 0.800 AHI > 15 events/h, and 0.803 AHI > 5 events/h. WD plus select sleep questionnaire areas under the curve for predicting PSG were 0.943 AHI > 30 events/h, 0.868 AHI > 15 events/h, and 0.863 AHI > 5 events/h.
The WD conducted reliable overnight continuous SpO monitoring with root-mean-square error < 3% vs PSG. Predictive models of PSG AHI based on WD measurements alone, or plus sleep questionnaires, demonstrated excellent to outstanding discrimination for obstructive sleep apnea identification and severity. Longitudinal WD use should be evaluated promptly based on the WD's potential to improve accessibility and accuracy of obstructive sleep apnea testing, as well as support treatment follow-up.
Browne SH, Vaida F, Umlauf A, Kim J, DeYoung P, Owens RL. Performance of a commercial smart watch compared to polysomnography reference for overnight continuous oximetry measurement and sleep apnea evaluation. 2024;20(9):1479-1488.
我们评估了一款商业腕带设备(WD)连续整夜血氧饱和度(SpO2)测量的准确性和精密度,该设备采用了高级传感器,并通过定量比较整夜氧减指数(ODI),同时结合睡眠问卷数据评估睡眠呼吸紊乱,来评估 WD 对阻塞性睡眠呼吸暂停(OSA)的检测能力及其严重程度。方法:参与者完成睡眠问卷,将 WD(三星 Galaxy Watch 4)佩戴在手腕上,并进行了在实验室进行的、有监护的整夜 PSG(尼高力),脉搏血氧饱和度探头分别固定在手指或耳垂上。PSG 数据由一位有经验的注册 PSG 技师进行评分。统计分析包括人口统计学特征、WD 与 PSG 之间的连续 SpO2 测量均方根误差(RMSE),并采用 Bland-Altman 图和线性回归关联进行评估。使用逻辑回归建立 PSG 氧减指数和 AHI 严重程度的预测模型,并通过接受者操作特征曲线(ROC)特征确定概率截断值。结果:分析的 51 名参与者的中位年龄为 49 岁(范围,22-78 岁);66.7%为男性,中位体重指数为 28.1kg/m2(范围,20.1-47.3kg/m2),种族/民族分布为 49.0%白种人,25.5%西班牙裔,9.8%非裔美国人,9.8%亚裔,5.9%中东人。WD 与 PSG 连续 SpO2 测量的百分比点显示出 0.91(95%置信区间,0.38,1.45)的偏倚、2.37(95%置信区间,2.36,2.38)的标准差和 2.54(95%置信区间,2.34,2.73)的 RMSE。WD 对 PSG 的曲线下面积(AUC)接受者操作特征曲线特征的预测值为 0.882 ODI>15 次/h、0.894 AHI>30 次/h、0.800 AHI>15 次/h 和 0.803 AHI>5 次/h。WD 加上睡眠问卷的选择区域的 AUC 预测 PSG 为 0.943 AHI>30 次/h、0.868 AHI>15 次/h 和 0.863 AHI>5 次/h。结论:WD 进行可靠的整夜连续 SpO2 监测,RMSE<3%与 PSG 相比。基于 WD 测量值或加上睡眠问卷的 PSG AHI 预测模型,对 OSA 的识别和严重程度具有极好的区分度。应根据 WD 改善 OSA 检测的可及性和准确性以及支持治疗随访的潜力,及时评估 WD 的长期使用情况。