Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
UK Dementia Research Institute, Care Research and Technology Centre at Imperial College, London and the University of Surrey, Guildford, United Kingdom.
JMIR Mhealth Uhealth. 2023 Oct 25;11:e46338. doi: 10.2196/46338.
Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people.
This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population.
Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard.
The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r=0.0096, P=.58; Emfit: r=0.11, P=.21).
These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.
接触式睡眠技术（CSTs）有望在社区和大规模范围内进行长期、非侵入性的睡眠监测。它们在老年人中可能特别有用，因为睡眠障碍可能表明身心健康恶化，在老年人中非常普遍。然而，很少有 CSTs 在老年人中进行过评估。
本研究评估了 3 种 CSTs 与多导睡眠图（PSG）和活动记录仪在老年人中的性能比较。
共有 35 名年龄在 70.8 岁（标准差 4.9 岁）的男性和女性老年人（女性：n=14，40%）参与了这项研究，其中一些人患有睡眠呼吸暂停等合并症。在睡眠实验室中进行了 10 小时的卧床时间协议，同时使用床边雷达（Somnofy [Vital Things]：n=17）、2 个床垫下设备（Withings sleep analyzer [WSA；Withings Inc]：n=35；Emfit-QS [Emfit；Emfit Ltd]：n=17）、PSG（n=35）和活动记录仪（Actiwatch Spectrum [Philips Respironics]：n=18）同时记录睡眠。通过汇总指标和逐时分类的性能指标来评估设备。PSG 作为金标准。
该方案诱导了轻度睡眠障碍，总睡眠时间（TST）效率为 70.9%（标准差 10.4%；范围 52.27%-92.60%）。所有 3 种 CSTs 均高估了 TST（偏差：>90 分钟）和睡眠效率（偏差：>13%），低估了睡眠后觉醒时间（偏差：>50 分钟）。床边雷达准确检测到睡眠潜伏期（偏差：<6 分钟），但床垫下设备高估了睡眠潜伏期（偏差：>16 分钟）。CSTs 在估计整晚的睡眠汇总指标方面表现不如活动记录仪。在逐时一致性分析中，床边雷达在区分睡眠与觉醒方面表现优于床垫下设备（马修相关系数[MCC]：平均值 0.63，标准差 0.12，95%置信区间 0.57-0.69）（WSA 的 MCC：平均值 0.41，标准差 0.15，95%置信区间 0.36-0.46；Emfit 的 MCC：平均值 0.35，标准差 0.16，95%置信区间 0.26-0.43）。所有 CSTs 识别快速眼动和浅睡眠的准确性都很差，而深睡眠（即慢波睡眠）的预测准确性较高（MCC：>0.45），Somnofy 和 WSA 都可以预测。Somnofy 的深睡眠时间估计与 PSG 得出的脑电图慢波活动（0.75-4.5 Hz）相关（r=0.60；P<.01），而对于床垫下设备，这种相关性不显著（WSA：r=0.0096，P=.58；Emfit：r=0.11，P=.21）。
这些 CSTs 高估了 TST，在 SEFF 相对较低的这群老年人中，睡眠阶段预测并不理想。虽然之前已经表明 CSTs 提供了有关床位占用的有用信息，这在特定用例中可能有用，但在估计大多数睡眠变量方面，这些 CSTs 的 TST 和睡眠阶段估计性能需要改进，才能在估计老年人的 TST 和睡眠阶段方面替代 PSG。
