Park Jung In, Aqajari Seyed Amir Hossein, Rahmani Amir M, Lee Jung-Ah
Author Affiliations: Sue & Bill Gross School of Nursing (Drs Park, Rahmani, and Lee) and Department of Electrical Engineering and Computer Science (Mr Aqajari and Dr Rahmani), University of California, Irvine.
Comput Inform Nurs. 2025 Feb 1;43(2):e01192. doi: 10.1097/CIN.0000000000001192.
This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness. The study spanned 529 days and analyzed data using 70 features. Three machine learning algorithms-random forest, k nearest neighbor, and XGBoost classifiers-were developed for this purpose. The random forest classifier was shown to be the most effective, boasting an area under the curve of 0.86, an F1 score of 0.87, and a precision of 0.84. Key findings revealed that factors such as wake-up stress, wake-up heart rate, sedentary seconds, total distance traveled, and sleep duration significantly correlated with the caregivers' sleep quality. This research highlights the potential of wearable technology in assessing and predicting sleep quality, offering a pathway to creating targeted support measures for dementia caregivers from underserved groups. The study suggests that such technology can be instrumental in enhancing the well-being of these caregivers across diverse populations.
本研究旨在利用可穿戴技术预测代表性不足群体中痴呆症患者家庭护理人员的睡眠质量。痴呆症患者的护理人员经常承受高水平的压力且睡眠质量差，而来自代表性不足社区的护理人员还面临额外的负担，如语言障碍和文化适应挑战。由29名来自代表性不足人群的痴呆症护理人员组成的参与者佩戴了智能手表，这些手表可追踪各种生理和行为指标，包括压力水平、心率、步数、睡眠时间和阶段以及整体每日健康状况。该研究历时529天，并使用70个特征分析数据。为此开发了三种机器学习算法——随机森林、k近邻和XGBoost分类器。结果表明，随机森林分类器最为有效，曲线下面积为0.86，F1分数为0.87，精度为0.84。主要研究结果显示，诸如醒来时的压力、醒来时的心率、久坐时间、总行进距离和睡眠时间等因素与护理人员的睡眠质量显著相关。这项研究突出了可穿戴技术在评估和预测睡眠质量方面的潜力，为为服务不足群体的痴呆症护理人员制定有针对性的支持措施提供了一条途径。该研究表明，此类技术有助于提高不同人群中这些护理人员的福祉。
