Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety (SWARTS-DA) observational study in Korea.

Journal: BMJ open
Published Date:

Abstract

INTRODUCTION: Depression and anxiety are highly prevalent mental health conditions that significantly affect quality of life and cause societal burdens. However, their detection and diagnosis rates remain low owing to the limitations of the current screening methods. With rapid technological advancements and the proliferation of consumer-grade wearable devices and smartphones, their integration into digital phenotyping research has enabled the unobtrusive screening for depression and anxiety in natural settings. The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. By collecting comprehensive data using smartphones and smartwatches, this study aims to facilitate the translation of artificial intelligence-based early detection research into clinical impact, thereby potentially enhancing patient care through more accurate and timely interventions.

Authors

  • Yu-Bin Shin
    Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
  • Ah Young Kim
    Medical Information Research Section, Electronics and Telecommunications Research Institute, Dajeon, Republic of Korea.
  • Seonmin Kim
    Department of Psychiatry, Korea University College of Medicine, Seoul, Korea.
  • Min-Sup Shin
    Department of Psychology, Korea University, Seoul, Republic of Korea.
  • Jinhwa Choi
    Batoners Inc, Daegu, Korea.
  • Kyung Lyun Lee
    Batoners Inc, Daegu, Korea.
  • Jisu Lee
    Department of Microbiology, School of Medicine, Kyungpook National University, Daegu, South Korea.
  • Sangwon Byun
    Department of Electronics Engineering, Incheon National University, Incheon, Republic of Korea.
  • Sujin Kim
    Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
  • Heon-Jeong Lee
    Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
  • Chul-Hyun Cho
    Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.

Keywords

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