Ding Huitong, Madan Sanskruti, Searls Edward, McNulty Matthew, Low Spencer, Li Zexu, Ho Kristi, Rahman Salman, Igwe Akwaugo, Popp Zachary, Hwang Phillip H, De Anda-Duran Ileana, Kolachalama Vijaya B, Mez Jesse, Alosco Michael L, Thomas Robert J, Au Rhoda, Lin Honghuang
Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA; The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Sleep Med. 2025 Jul;131:106532. doi: 10.1016/j.sleep.2025.106532. Epub 2025 Apr 22.
Digital technology offers a convenient way to continuously monitor sleep and assess night-to-night variability, particularly in aging populations where traditional self-reported sleep assessments may be limited.
This study aimed to investigate nightly variability in sleep measures obtained via a ring oximeter sensor in older adults and to explore the influence of demographic and cognitive factors on the stability of these metrics.
The study included 62 participants (mean age 74, 67.7 % women, 90.3 % White) from the Boston University Alzheimer's Disease Research Center (BU ADRC) cohort. Each participant wore a SleepImage Ring for at least three consecutive nights. Thirty-four continuous sleep measures, such as mean SpO2 and apnea-hypopnea index within unstable sleep, were analyzed. Night-to-night variability was assessed using intraclass correlation coefficients (ICC) based on a two-way random-effects model. Subgroup analyses examined variability by sex, age, and cognitive status. Group-level changes were assessed using one-way repeated measures ANOVA.
Seven sleep measures demonstrated high stability across nights (ICC: 0.70-0.88), with average heart rate being the most stable, followed by mean SpO and apnea-hypopnea indices. Sleep latency exhibited the highest variability. Stability improved between the second and third nights compared to the first and second nights. Women and participants under 75 years old showed greater stability in several metrics, while cognitively intact individuals exhibited more consistent breathing-related measures.
At least three nights of monitoring are required for reliable estimates of key sleep metrics. Expanding studies with larger samples and extended monitoring periods could further elucidate sleep variability as a potential non-invasive marker for general health.
数字技术提供了一种便捷的方式来持续监测睡眠并评估夜间变化，特别是在老年人群中，传统的自我报告睡眠评估可能存在局限性。
本研究旨在调查通过指环血氧计传感器获得的老年人睡眠指标的夜间变化，并探讨人口统计学和认知因素对这些指标稳定性的影响。
该研究纳入了来自波士顿大学阿尔茨海默病研究中心（BU ADRC）队列的62名参与者（平均年龄74岁，67.7%为女性，90.3%为白人）。每位参与者连续佩戴SleepImage指环至少三晚。分析了34项连续睡眠指标，如不稳定睡眠期间的平均血氧饱和度（SpO2）和呼吸暂停低通气指数。基于双向随机效应模型，使用组内相关系数（ICC）评估夜间变化。亚组分析按性别、年龄和认知状态检查变化情况。使用单向重复测量方差分析评估组水平的变化。
七项睡眠指标在各夜间表现出高度稳定性（ICC：0.70 - 0.88），平均心率最稳定，其次是平均SpO2和呼吸暂停低通气指数。睡眠潜伏期的变异性最高。与第一晚和第二晚相比，第二晚和第三晚的稳定性有所提高。女性和75岁以下的参与者在几个指标上表现出更高的稳定性，而认知功能正常的个体在与呼吸相关的指标上表现更一致。
可靠估计关键睡眠指标需要至少三晚的监测。扩大样本量和延长监测期的研究可以进一步阐明睡眠变化作为一般健康潜在非侵入性标志物的情况。
