对老年人睡眠期间的心率、呼吸频率和呼吸紊乱进行可靠的非接触式监测:数字健康技术评估研究》。

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES JMIR mHealth and uHealth Pub Date : 2024-08-27 DOI:10.2196/53643
Kiran K G Ravindran, Ciro Della Monica, Giuseppe Atzori, Damion Lambert, Hana Hassanin, Victoria Revell, Derk-Jan Dijk
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引用次数: 0

摘要

背景:对生命体征的纵向监测为确定个人总体健康状况的变化提供了一种方法,尤其是对老年人而言。夜间睡眠时间为评估生命体征提供了便利。可嵌入卧室环境的非接触式技术无干扰、无负担,有可能实现对生命体征的无缝监测。要实现这一潜力,需要在相关人群中根据金标准测量方法对这些技术进行评估:我们的目的是在睡眠实验室环境中评估 3 种非接触式技术(2 种压下追踪器,Withings Sleep Analyzer [WSA] 和 Emfit QS [Emfit];以及一种床旁雷达,Somnofy)测量心率和呼吸频率的准确性,并评估它们在真实世界环境中捕捉生命体征的潜力:在睡眠实验室进行为期一晚的临床多导睡眠图(PSG)测试期间,收集了 35 名年龄介于 65 至 83 岁(平均 70.8 岁,标准差 4.9 岁)的社区老年人(男性:21 人,占 60%)的数据,在此之前还在家中进行了 7 至 14 天的数据收集。一些参与者(20/35,57%)有健康问题,包括 2 型糖尿病、高血压、肥胖和关节炎,49%(17 人)有中度至重度睡眠呼吸暂停,29%(10 人)有周期性腿部运动障碍。床下压力跟踪器提供心率和呼吸频率的估计值,而床边雷达只提供呼吸频率。我们将这些设备估算的心率和呼吸频率的准确性分别与 PSG 心电图得出的心率(每分钟跳动次数)和呼吸电感胸透法得出的呼吸频率(每分钟循环次数)进行了比较。我们还评估了WSA提供的打鼾呼吸紊乱指数和呼吸暂停-低通气指数:结果:所有 3 种非接触式技术在估计心率方面都提供了可接受的准确性(平均绝对误差 2=0.76;P2=0.59;PC 结论:非接触式技术可提供无侵入性的心率估计:非接触式技术可替代传统的可穿戴技术,对社区老年人的心率、呼吸频率和睡眠呼吸暂停进行可靠的大规模监测。非接触式技术可评估这些生命体征的夜间变化,从而识别健康状况的急性变化;还可进行纵向监测,从而了解健康状况的变化轨迹:RR2-10.3390/clockssleep6010010.
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Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.

Background: Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.

Objective: We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting.

Methods: Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA.

Results: All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001).

Conclusions: Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories.

International registered report identifier (irrid): RR2-10.3390/clockssleep6010010.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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