Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study.
Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study.
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DOI：
10.2196/24666
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发表时间：
2021-06-11
影响因子：
5
通讯作者：
Nef T
中科院分区：
文献类型：
--
作者：
Schütz N;Saner H;Botros A;Pais B;Santschi V;Buluschek P;Gatica-Perez D;Urwyler P;Müri RM;Nef T
人口老龄化给社会带来了多重社会和经济挑战。其中一个挑战是，过早住院造成的医疗保健支出增加带来的社会和经济负担。现代普适计算技术的使用使人们能够在家中持续监测社区居住的老年人的健康状况。通过这些技术及早发现健康问题，可以减少治疗费用，并采取有针对性的预防措施，从而改善健康状况。睡眠是影响整体健康的一个关键因素，许多健康问题都与睡眠恶化有关。睡眠质量和睡眠障碍，如睡眠呼吸暂停综合征已被广泛研究使用各种可穿戴设备在家里或在睡眠实验室的设置。然而，很少有研究评估非接触式和连续睡眠监测在检测社区居住的老年人健康问题的早期迹象方面的潜力。在这项工作中，我们的目标是评估哪些接触式可测量的睡眠参数最适合监测老年人的感知和实际健康状况的变化。我们分析了来自37名社区居住的老年人的真实世界纵向（长达1年）数据，包括超过6000个夜晚的测量睡眠。睡眠参数由放置在床垫下的压力传感器记录，相应的健康状况信息通过每周的问卷调查和医护人员的报告获得。共分析了20个睡眠参数，包括常见的睡眠指标，如睡眠效率，睡眠开始延迟和睡眠阶段，以及心脏和呼吸频率形式的生命体征以及床上运动。采用个体线性混合效应模型定量评价了与自我报告健康状况的相关性，通过欧洲生活质量视觉模拟量表（EQ-VAS）评分进行评价。翻译为客观的，真实世界的健康事件进行了调查，通过手动回顾性个案分析。使用基于自我报告的健康感知的EQ-VAS评级，我们确定床上的身体运动-通过翻身事件的数量来测量-作为最具预测性的睡眠参数（t得分=-0.435，P值[adj]=<.001）。个案分析进一步证实了这一发现，表明身体运动次数的增加往往可以用报告的健康事件来解释。真实的世界事件包括心力衰竭、高血压、腹部肿瘤、季节性流感、胃肠道问题和尿路感染。我们的研究结果表明，夜间在床上的身体运动可能是一种高度相关的，易于解释和推导的数字生物标志物，以监测老年人的各种健康恶化。因此，它可以帮助早期检测健康恶化，并提供更及时，更个性化和精确的治疗方案。
Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.
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DOI：
10.1002/trc2.12079
发表时间：
2020-01-01
影响因子：
4.8
作者：
Au-Yeung, Wan-Tai M.;Miller, Lyndsey;Kaye, Jeffrey
通讯作者：
Kaye, Jeffrey
影响因子：
3.8
作者：
Giganti, Fiorenza;Ficca, Gianluca;Salzarulo, Piero
通讯作者：
Salzarulo, Piero
DOI：
10.1016/s0140-6736(14)61461-6
发表时间：
2015-02-14
期刊：
Lancet (London, England)
影响因子：
--
作者：
Beard JR;Bloom DE
通讯作者：
Bloom DE
影响因子：
6.3
作者：
Bloom HG;Ahmed I;Alessi CA;Ancoli-Israel S;Buysse DJ;Kryger MH;Phillips BA;Thorpy MJ;Vitiello MV;Zee PC
通讯作者：
Zee PC
影响因子：
--
作者：
Martinez-Alcala, Claudia I;Pliego-Pastrana, Patricia;Molina-Trinidad, Eva M
通讯作者：
Molina-Trinidad, Eva M