Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol

Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological change...

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Published in:	Frontiers in psychiatry Vol. 13; p. 1025517
Main Authors:	Kishimoto, Taishiro, Kinoshita, Shotaro, Kikuchi, Toshiaki, Bun, Shogyoku, Kitazawa, Momoko, Horigome, Toshiro, Tazawa, Yuki, Takamiya, Akihiro, Hirano, Jinichi, Mimura, Masaru, Liang, Kuo-Ching, Koga, Norihiro, Ochiai, Yasushi, Ito, Hiromi, Miyamae, Yumiko, Tsujimoto, Yuiko, Sakuma, Kei, Kida, Hisashi, Miura, Gentaro, Kawade, Yuko, Goto, Akiko, Yoshino, Fumihiro
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 21-12-2022
Subjects:	depression digital health machine learning personalized medicine Psychiatry wearables wearables digital health depression machine learning personalized medicine
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Summary:	Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices. Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set. Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device. [https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Computational Psychiatry, a section of the journal Frontiers in Psychiatry Reviewed by: Dachun Chen, Beijing Huilongguan Hospital, Peking University, China; Callum Luke Stewart, NIHR Maudsley Biomedical Research Centre (BRC), United Kingdom Edited by: Taolin Chen, Sichuan University, China
ISSN:	1664-0640 1664-0640
DOI:	10.3389/fpsyt.2022.1025517