Center of Sleep Medicine, Villa Serena Hospital, Città S. Angelo, Pescara, Italy.
Villaserena Foundation for the Research, Città S. Angelo, Pescara, Italy.
J Alzheimers Dis. 2020;78(4):1707-1719. doi: 10.3233/JAD-200632.
Circadian and sleep disturbances are associated with increased risk of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Wearable activity trackers could provide a new approach in diagnosis and prevention.
To evaluate sleep and circadian rhythm parameters, through wearable activity trackers, in MCI and AD patients as compared to controls, focusing on sex dissimilarities.
Based on minute level data from consumer wearable devices, we analyzed actigraphic sleep parameters by applying an electromedical type I registered algorithm, and the corresponding circadian variables in 158 subjects: 86 females and 72 males (42 AD, 28 MCI, and 88 controls). Moreover, we used a confusion-matrix chart method to assess accuracy, precision, sensitivity, and specificity of two decision-tree models based on actigraphic data in predicting disease or health status.
Wake after sleep onset (WASO) was higher (p < 0.001) and sleep efficiency (SE) lower (p = 0.003) in MCI, and Sleep Regularity Index (SRI) was lower in AD patients compared to controls (p = 0.004). SE was lower in male AD compared to female AD (p = 0.038) and SRI lower in male AD compared to male controls (p = 0.008), male MCI (p = 0.047), but also female AD subjects (p = 0.046). Mesor was significantly lower in males in the overall population. Age reduced the dissimilarities for WASO and SE but demonstrated sex differences for amplitude (p = 0.009) in the overall population, controls (p = 0.005), and AD subjects (p = 0.034). The confusion-matrices showed good predictive power of actigraphic data.
Actigraphic data could help identify disease or health status. Sex (possibly gender) differences could impact on neurodegeneration and disease trajectory with potential clinical applications.
昼夜节律和睡眠紊乱与轻度认知障碍（MCI）和阿尔茨海默病（AD）的风险增加有关。可穿戴活动追踪器可以为诊断和预防提供新方法。
通过可穿戴活动追踪器评估 MCI 和 AD 患者与对照组相比的睡眠和昼夜节律参数，重点关注性别差异。
基于消费类可穿戴设备的分钟级数据，我们通过应用一种医疗器械 I 类注册算法分析了活动记录仪睡眠参数，并分析了 158 名受试者的相应昼夜节律变量：86 名女性和 72 名男性（42 名 AD、28 名 MCI 和 88 名对照组）。此外，我们使用混淆矩阵图表方法评估了基于活动记录仪数据的两种决策树模型在预测疾病或健康状况方面的准确性、精度、敏感性和特异性。
与对照组相比，MCI 患者的睡眠后觉醒时间（WASO）更高（p<0.001），睡眠效率（SE）更低（p=0.003），AD 患者的睡眠规律性指数（SRI）更低（p=0.004）。与女性 AD 相比，男性 AD 的 SE 更低（p=0.038），与男性 AD 相比，男性 AD 的 SRI 更低（p=0.008），男性 MCI（p=0.047），也与女性 AD 患者（p=0.046）相比。在整个人群中，男性的 Mesor 明显更低。年龄降低了 WASO 和 SE 的差异，但在整个人群、对照组（p=0.005）和 AD 组（p=0.034）中表现出性别对振幅的差异（p=0.009）。混淆矩阵显示了活动记录仪数据的良好预测能力。
活动记录仪数据可帮助识别疾病或健康状况。性别（可能是性别）差异可能会影响神经退行性变和疾病轨迹，具有潜在的临床应用价值。
