F35使用fitbit对亨廷顿氏病患者的睡眠监测与多导睡眠图的比较

E. Doheny, Klavs Renerts, C. Baumann, P. Morgan-Jones, M. Busse, M. Lowery, H. Jung
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Time in bed (TIB), total sleep time (TST), wake time (TWT), time in REM sleep (REM), stage N3 (Deep) and stages N1 and N2 (Light), sleep onset latency (SOL) and wake after sleep onset (WASO) were examined on each night. Fitbit sensitivity and specificity to each sleep stage was examined. Home sleep metrics were compared to PSG. Results Data for 1 male with early-stage HD (UHDRS motor score 5) are reported. Using PSG, TIB was 474.5 min, TST was 373 min, TWT was 101.5 min, REM was 96 min, Deep was 100 min, Light was 177 min, SOL was 95 min, and WASO was 6.5 min. Compared to PSG, Fitbit overestimated TST by 23 mins, Light by 37.5 min, REM by 11.5 min, WASO by 38.5 min, and underestimated SOL by 67 min, TWT by 28.5 min, Deep by 12.5 min, TIB by 5.5 min. Fitbit sensitivity and specificity to wake was 98% and 54%, REM was 97% and 100%, and Deep sleep was 96% and 74%. PSG TST and sleep stage times were within the range observed at home, but PSG TWT was greater than observed at home. 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引用次数: 0

摘要

睡眠受亨廷顿氏病(HD)的影响,睡眠质量与神经变性有关。Fitbit等可穿戴设备使用惯性传感器和光电容积脉搏波来估计睡眠阶段,为家庭睡眠监测提供了一种低成本的解决方案。虽然Fitbit设备已经在健康人群中通过黄金标准——多导睡眠描记仪(PSG)进行了验证,但尚未在高清人群中得到验证。目的建立Fitbit睡眠指标与PSG的准确性,并量化在家7晚的睡眠质量。方法:参与者在夜间PSG期间佩戴Fitbit Charge 4,随后在家休息7晚。睡眠生理学专家对睡眠阶段进行了PSG评分。Fitbit睡眠数据每30秒提取一次。每晚检查卧床时间(TIB)、总睡眠时间(TST)、清醒时间(TWT)、快速眼动睡眠时间(REM)、N3阶段(深度)和N1、N2阶段(轻度)、睡眠开始潜伏期(SOL)和睡眠开始后醒来时间(WASO)。检查Fitbit对每个睡眠阶段的敏感性和特异性。将家庭睡眠指标与PSG进行比较。结果报告1例男性早期HD患者(UHDRS运动评分5分)。使用PSG,矿是474.5分钟,结核菌素是373分钟,行波管是101.5分钟,快速眼动是96分钟,深度是100分钟,光为177分钟,索尔是95分钟,和沃是6.5分钟。与PSG相比,Fitbit高估了TST 23分钟,轻了37.5分钟,快速眼动了11.5分钟,WASO了38.5分钟,而低估了索尔67分钟,行了28.5分钟,深12.5分钟,TIB的5.5分钟。Fitbit敏感性和特异性后是98%和54%,快速眼动是97%和100%,深度睡眠是96%和74%。PSG TST和睡眠阶段时间在家中观察到的范围内,但PSG TWT大于家中观察到的。一名参与者的初步结果表明,Fitbit Charge 4适用于监测高清患者的睡眠阶段,特别是快速眼动睡眠。在这种情况下,观察到区分清醒和浅睡眠的问题。数据收集正在进行中。
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F35 Sleep monitoring in huntington’s disease using fitbit compared to polysomnography
Background Sleep is affected by Huntington’s disease (HD), and sleep quality is linked to neurodegeneration. Wearable devices, such as Fitbit, use inertial sensors and photoplethysmography to estimate sleep stages during sleep, providing a low-cost solution for home-based sleep monitoring. While Fitbit devices have been validated in healthy individuals against the gold standard, polysomnography (PSG), they have not yet been validated in HD. Aims To establish the accuracy of Fitbit sleep metrics against PSG, and to quantify sleep quality over 7 nights at home. Methods Participants wore a Fitbit Charge 4 during overnight PSG, followed by 7 nights at home. PSG sleep stages were scored by an expert sleep physiologist. Fitbit sleep data were extracted every 30 s. Time in bed (TIB), total sleep time (TST), wake time (TWT), time in REM sleep (REM), stage N3 (Deep) and stages N1 and N2 (Light), sleep onset latency (SOL) and wake after sleep onset (WASO) were examined on each night. Fitbit sensitivity and specificity to each sleep stage was examined. Home sleep metrics were compared to PSG. Results Data for 1 male with early-stage HD (UHDRS motor score 5) are reported. Using PSG, TIB was 474.5 min, TST was 373 min, TWT was 101.5 min, REM was 96 min, Deep was 100 min, Light was 177 min, SOL was 95 min, and WASO was 6.5 min. Compared to PSG, Fitbit overestimated TST by 23 mins, Light by 37.5 min, REM by 11.5 min, WASO by 38.5 min, and underestimated SOL by 67 min, TWT by 28.5 min, Deep by 12.5 min, TIB by 5.5 min. Fitbit sensitivity and specificity to wake was 98% and 54%, REM was 97% and 100%, and Deep sleep was 96% and 74%. PSG TST and sleep stage times were within the range observed at home, but PSG TWT was greater than observed at home. Conclusion Initial results in one participant indicate that Fitbit Charge 4 is suitable to monitor sleep stages in HD, particularly REM. Issues distinguishing wake and light sleep were observed in this case. Data collection is ongoing.
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F38 Skill-based dysphagia training as an intervention for individuals with huntington’s disease F32 Exploring the feasibility of a novel and efficient trial design for the evaluation of long-term physical activity and exercise outcomes in people with huntington’s disease F52 Enroll-HD platform biosample resources F15 Visual-cognitive impairment in asymptomatic and symptomatic carriers of huntington’s disease (HD) F09 Late onset huntington’s disease phenotype progression. 2 years follow-up in 220 patients from enroll-HD PDS4
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