Construction and validation of a predictive model to improve the performance of non-wearable actigraphy in a psychiatric setting: A cross-sectional study

DOI
  • Takeshita Yuko
    Division of Health Sciences, The University of Osaka Graduate School of Medicine
  • Odachi Ryo
    Division of Health Sciences, The University of Osaka Graduate School of Medicine Department of Nursing, Faculty of Human Health Sciences, Shunan University
  • Nakashima Keisuke
    Department of Medical Informatics, The University of Osaka Graduate School of Medicine
  • Nishiyama Naoki
    Division of Health Sciences, The University of Osaka Graduate School of Medicine School of Nursing, Mukogawa Women’s University
  • Nozawa Kyosuke
    Division of Health Sciences, The University of Osaka Graduate School of Medicine
  • Matoba Kei
    Division of Health Sciences, The University of Osaka Graduate School of Medicine Faculty of Nursing, Kansai Medical University
  • Nakano Natsuko
    Department of Psychiatry, The University of Osaka Graduate School of Medicine
  • Mashita Midori
    Department of Psychiatry, The University of Osaka Graduate School of Medicine
  • Mamiya Yoshimasa
    Department of Psychiatry, The University of Osaka Graduate School of Medicine
  • Yamakawa Miyae
    Division of Health Sciences, The University of Osaka Graduate School of Medicine The Japan Centre for Evidence Based Practice, the Centre of Excellence of Joanna Briggs Institute
  • Buyo Momoko
    Division of Health Sciences, The University of Osaka Graduate School of Medicine
  • Adachi Hiroyoshi
    Department of Psychiatry, The University of Osaka Graduate School of Medicine
  • Ikeda Manabu
    Department of Psychiatry, The University of Osaka Graduate School of Medicine
  • Takeya Yasushi
    Division of Health Sciences, The University of Osaka Graduate School of Medicine

Bibliographic Information

Other Title
  • 精神科領域における非装着型アクチグラフの性能向上のための予測モデルの構築と検証：観察研究

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Description

Monitoring sleep status in psychiatric settings is crucial. However, psychiatric symptoms and cognitive impairments complicate traditional sleep assessments, such as polysomnography (PSG). To address this, we employed Nemuri SCAN (NSCAN, Paramount Bed Co. Ltd.), a contact-free patient sensor, and compared its performance with PSG in patients with psychiatric disorders. This cross-sectional study included 29 cases (median age: 61 years; 55.2% male) from August 2021 to January 2023. NSCAN showed lower specificity than PSG, often misclassifying still wakefulness as sleep. To improve this, we developed a logistic regression model named the Patient-Adjusted Cole Model (PAC Model), which incorporates 10 patient characteristics into the NSCAN decision algorithm based on the Cole–Kripke equation (Cole model). The agreement with PSG, sensitivity, and specificity were 77.8%, 97.3%, and 28.2% for the Cole model and 78.8%, 94.5%, and 38.9% for the PAC Model, respectively, where agreement represented the percentage of sleep/wake determinations by NSCAN that matched those by PSG. While sensitivity was slightly lower in the PAC Model, specificity improved notably, addressing a critical limitation of non-contact sensors. These findings highlight the importance of integrating patient characteristics into sleep monitoring algorithms to enhance the practicality and utility of NSCAN in psychiatric care.

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Details 詳細情報について

  • CRID
    1390585492992172928
  • DOI
    10.24462/jnse.12.0_184
  • ISSN
    24326283
    21884323
  • Text Lang
    en
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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