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
IntroductionFew 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, physiolo...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1025517/full |
| _version_ | 1811195800492965888 |
|---|---|
| author | Taishiro Kishimoto Taishiro Kishimoto Shotaro Kinoshita Shotaro Kinoshita Toshiaki Kikuchi Shogyoku Bun Shogyoku Bun Momoko Kitazawa Toshiro Horigome Toshiro Horigome Yuki Tazawa Yuki Tazawa Akihiro Takamiya Akihiro Takamiya Jinichi Hirano Masaru Mimura Kuo-ching Liang Kuo-ching Liang Norihiro Koga Yasushi Ochiai Hiromi Ito Yumiko Miyamae Yuiko Tsujimoto Kei Sakuma Hisashi Kida Hisashi Kida Gentaro Miura Yuko Kawade Yuko Kawade Akiko Goto Akiko Goto Fumihiro Yoshino Fumihiro Yoshino |
| author_facet | Taishiro Kishimoto Taishiro Kishimoto Shotaro Kinoshita Shotaro Kinoshita Toshiaki Kikuchi Shogyoku Bun Shogyoku Bun Momoko Kitazawa Toshiro Horigome Toshiro Horigome Yuki Tazawa Yuki Tazawa Akihiro Takamiya Akihiro Takamiya Jinichi Hirano Masaru Mimura Kuo-ching Liang Kuo-ching Liang Norihiro Koga Yasushi Ochiai Hiromi Ito Yumiko Miyamae Yuiko Tsujimoto Kei Sakuma Hisashi Kida Hisashi Kida Gentaro Miura Yuko Kawade Yuko Kawade Akiko Goto Akiko Goto Fumihiro Yoshino Fumihiro Yoshino |
| author_sort | Taishiro Kishimoto |
| collection | DOAJ |
| description | IntroductionFew 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.Methods and analysisPatients 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.DiscussionOur machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.Clinical trial registration[https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478]. |
| first_indexed | 2024-04-12T00:48:47Z |
| format | Article |
| id | doaj.art-f49aad8e9cf1448d8470e1c4dce93fc7 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-12T00:48:47Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-f49aad8e9cf1448d8470e1c4dce93fc72022-12-22T03:54:48ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402022-12-011310.3389/fpsyt.2022.10255171025517Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocolTaishiro Kishimoto0Taishiro Kishimoto1Shotaro Kinoshita2Shotaro Kinoshita3Toshiaki Kikuchi4Shogyoku Bun5Shogyoku Bun6Momoko Kitazawa7Toshiro Horigome8Toshiro Horigome9Yuki Tazawa10Yuki Tazawa11Akihiro Takamiya12Akihiro Takamiya13Jinichi Hirano14Masaru Mimura15Kuo-ching Liang16Kuo-ching Liang17Norihiro Koga18Yasushi Ochiai19Hiromi Ito20Yumiko Miyamae21Yuiko Tsujimoto22Kei Sakuma23Hisashi Kida24Hisashi Kida25Gentaro Miura26Yuko Kawade27Yuko Kawade28Akiko Goto29Akiko Goto30Fumihiro Yoshino31Fumihiro Yoshino32Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japani2medical LLC, Kawasaki, JapanHills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, JapanGraduate School of Interdisciplinary Information Studies, The University of Tokyo, Tokyo, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, JapanSato Hospital, Yamagata, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japani2medical LLC, Kawasaki, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japani2medical LLC, Kawasaki, JapanOffice for Open Innovation, Keio University, Tokyo, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, JapanAkasaka Clinic, Tokyo, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japani2medical LLC, Kawasaki, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japani2medical LLC, Kawasaki, JapanFrontier Business Office, Sumitomo Pharma Co., Ltd., Tokyo, JapanFrontier Business Office, Sumitomo Pharma Co., Ltd., Tokyo, JapanFrontier Business Office, Sumitomo Pharma Co., Ltd., Tokyo, JapanFrontier Business Office, Sumitomo Pharma Co., Ltd., Tokyo, JapanAsaka Hospital, Koriyama, JapanDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, JapanAsaka Hospital, Koriyama, Japan0Oizumi Hospital, Tokyo, Japan1Department of Psychiatry, Tsurugaoka Garden Hospital, Tokyo, Japan2Nagatsuta Ikoinomori Clinic, Yokohama, Japan1Department of Psychiatry, Tsurugaoka Garden Hospital, Tokyo, Japan2Nagatsuta Ikoinomori Clinic, Yokohama, Japan1Department of Psychiatry, Tsurugaoka Garden Hospital, Tokyo, Japan2Nagatsuta Ikoinomori Clinic, Yokohama, JapanIntroductionFew 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.Methods and analysisPatients 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.DiscussionOur machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.Clinical trial registration[https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1025517/fullmachine learningdepressionwearablespersonalized medicinedigital health |
| spellingShingle | Taishiro Kishimoto Taishiro Kishimoto Shotaro Kinoshita Shotaro Kinoshita Toshiaki Kikuchi Shogyoku Bun Shogyoku Bun Momoko Kitazawa Toshiro Horigome Toshiro Horigome Yuki Tazawa Yuki Tazawa Akihiro Takamiya Akihiro Takamiya Jinichi Hirano Masaru Mimura Kuo-ching Liang Kuo-ching Liang Norihiro Koga Yasushi Ochiai Hiromi Ito Yumiko Miyamae Yuiko Tsujimoto Kei Sakuma Hisashi Kida Hisashi Kida Gentaro Miura Yuko Kawade Yuko Kawade Akiko Goto Akiko Goto Fumihiro Yoshino Fumihiro Yoshino 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 Frontiers in Psychiatry machine learning depression wearables personalized medicine digital health |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | machine learning depression wearables personalized medicine digital health |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1025517/full |
| work_keys_str_mv | AT taishirokishimoto developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT taishirokishimoto developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT shotarokinoshita developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT shotarokinoshita developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT toshiakikikuchi developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT shogyokubun developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT shogyokubun developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT momokokitazawa developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT toshirohorigome developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT toshirohorigome developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yukitazawa developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yukitazawa developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT akihirotakamiya developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT akihirotakamiya developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT jinichihirano developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT masarumimura developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT kuochingliang developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT kuochingliang developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT norihirokoga developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yasushiochiai developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT hiromiito developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yumikomiyamae developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yuikotsujimoto developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT keisakuma developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT hisashikida developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT hisashikida developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT gentaromiura developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yukokawade developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT yukokawade developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT akikogoto developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT akikogoto developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT fumihiroyoshino developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol AT fumihiroyoshino developmentofmedicaldevicesoftwareforthescreeningandassessmentofdepressionseverityusingdatacollectedfromawristbandtypewearabledeviceswiftstudyprotocol |