Surrey Sleep Research Centre, Guildford, United Kingdom.
UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London, United Kingdom, and the University of Surrey, Guildford, London, United Kingdom.
JMIR Mhealth Uhealth. 2024 Aug 27;12:e53643. doi: 10.2196/53643.
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.
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.
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.
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: r=0.76; P<.001; WSA apnea-hypopnea index: r=0.59; P<.001).
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.
生命体征的纵向监测为个体的整体健康变化提供了一种方法,尤其是在老年人中。夜间睡眠期为评估生命体征提供了一个方便的机会。可以嵌入卧室环境中的非接触技术不干扰、无负担,具有实现生命体征无缝监测的潜力。为了实现这一潜力,这些技术需要与黄金标准措施和相关人群进行评估。
我们旨在评估 3 种非接触技术(2 种床垫下追踪器,Withings Sleep Analyzer[WSA]和 Emfit QS[Emfit];以及床边雷达 Somnofy)在睡眠实验室环境中测量心率和呼吸率的准确性,并评估它们在真实环境中捕捉生命体征的潜力。
在睡眠实验室进行 1 晚的临床多导睡眠图(PSG)测试前,35 名年龄在 65 岁至 83 岁(平均 70.8,SD 4.9)之间的社区居住的老年人(男性:21 名,60%)进行了为期 7 至 14 天的家庭数据收集。一些参与者(20/35,57%)有健康状况,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%(n=10)有周期性肢体运动障碍。床垫下追踪器提供了心率和呼吸率的估计值,而床边雷达仅提供了呼吸率。设备估计的心率和呼吸率与 PSG 心电图衍生的心率(每分钟心跳)和呼吸感应体积描记法胸导衍生的呼吸率(每分钟呼吸周期)进行了比较。我们还评估了 WSA 提供的打鼾和呼吸暂停低通气指数的呼吸干扰指数。
所有 3 种非接触技术在 1 分钟分辨率下均能以可接受的精度估计心率(平均绝对误差<2.12 次/分钟和平均绝对百分比误差<5%)和呼吸率(平均绝对误差≤1.6 次/分钟和平均绝对百分比误差<12%)。所有 3 种非接触技术都能够捕捉到睡眠期间心率和呼吸率的变化。WSA 打鼾和呼吸干扰估计值与 PSG 估计值也具有可比性(WSA 打鼾:r=0.76;P<.001;WSA 呼吸暂停低通气指数:r=0.59;P<.001)。
非接触技术为社区居住的老年人提供了一种可靠的替代传统可穿戴技术的方法,可大规模监测心率、呼吸率和睡眠呼吸暂停。它们能够评估这些生命体征的夜间变化,这可能有助于识别健康的急性变化,以及纵向监测,这可能提供对健康轨迹的洞察。