Scholarlink

<p>Isolated REM Sleep Behavior Disorder (iRBD) is a strong predictor of neurodegenerative diseases, particularly synucleinopathies. Current diagnosis requires overnight video-polysomnography (vPSG) in sleep laboratories. Limited access to vPSG and differences in sleep habits result in diagnostic challenges. Here we aimed to evaluate the feasibility of identifying iRBD from a lumbar-mounted wearable sensor in the home setting and explored night-to-night variability. Seventy-three participants (15 iRBD, 58 controls) underwent vPSG, followed by six nights of wearing a lower-back inertial measurement unit at home. iRBD participants showed distinct mobility patterns compared to controls. Machine learning models were trained on mobility features and classified iRBD with high sensitivity and moderate specificity. Performance improved with increased nights, plateauing at five nights recorded at home. Principal component analysis identified substantial differences between lab and home data. Our findings suggest that lumbar-mounted wearables can support sensitive, multi-night home-based detection of nocturnal motor patterns associated with iRBD, with potential utility as part of a staged screening approach and for enriching cohorts for further evaluation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Detecting isolated REM sleep behavior disorder at home using a lower-back wearable sensor

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

查看全文
  • Tal Tzfoni,
  • Riva Tauman,
  • Jeffrey M. Hausdorff,
  • Yael Hanein,
  • Anat Mirelman

摘要

Isolated REM Sleep Behavior Disorder (iRBD) is a strong predictor of neurodegenerative diseases, particularly synucleinopathies. Current diagnosis requires overnight video-polysomnography (vPSG) in sleep laboratories. Limited access to vPSG and differences in sleep habits result in diagnostic challenges. Here we aimed to evaluate the feasibility of identifying iRBD from a lumbar-mounted wearable sensor in the home setting and explored night-to-night variability. Seventy-three participants (15 iRBD, 58 controls) underwent vPSG, followed by six nights of wearing a lower-back inertial measurement unit at home. iRBD participants showed distinct mobility patterns compared to controls. Machine learning models were trained on mobility features and classified iRBD with high sensitivity and moderate specificity. Performance improved with increased nights, plateauing at five nights recorded at home. Principal component analysis identified substantial differences between lab and home data. Our findings suggest that lumbar-mounted wearables can support sensitive, multi-night home-based detection of nocturnal motor patterns associated with iRBD, with potential utility as part of a staged screening approach and for enriching cohorts for further evaluation.