Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning
Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light e...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844020301195 |
| _version_ | 1818991033334104064 |
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| author | Yuuki Tazawa Kuo-ching Liang Michitaka Yoshimura Momoko Kitazawa Yuriko Kaise Akihiro Takamiya Aiko Kishi Toshiro Horigome Yasue Mitsukura Masaru Mimura Taishiro Kishimoto |
| author_facet | Yuuki Tazawa Kuo-ching Liang Michitaka Yoshimura Momoko Kitazawa Yuriko Kaise Akihiro Takamiya Aiko Kishi Toshiro Horigome Yasue Mitsukura Masaru Mimura Taishiro Kishimoto |
| author_sort | Yuuki Tazawa |
| collection | DOAJ |
| description | Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity. |
| first_indexed | 2024-12-20T20:03:50Z |
| format | Article |
| id | doaj.art-1775885465bd4f2ca13e2b84f406d733 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-20T20:03:50Z |
| publishDate | 2020-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-1775885465bd4f2ca13e2b84f406d7332022-12-21T19:27:59ZengElsevierHeliyon2405-84402020-02-0162e03274Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learningYuuki Tazawa0Kuo-ching Liang1Michitaka Yoshimura2Momoko Kitazawa3Yuriko Kaise4Akihiro Takamiya5Aiko Kishi6Toshiro Horigome7Yasue Mitsukura8Masaru Mimura9Taishiro Kishimoto10Keio University School of Medicine, Tokyo, JapanKeio University School of Medicine, Tokyo, JapanKeio University School of Medicine, Tokyo, JapanKeio University School of Medicine, Tokyo, JapanKeio University School of Medicine, Tokyo, JapanKeio University School of Medicine, Tokyo, JapanFaculty of Science and Technology, Keio University, Kanagawa, JapanKeio University School of Medicine, Tokyo, JapanFaculty of Science and Technology, Keio University, Kanagawa, JapanKeio University School of Medicine, Tokyo, JapanKeio University School of Medicine, Tokyo, Japan; Corresponding author.Objective: We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices. Methods: We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation. Results: Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning. Limitations: The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted. Conclusion: The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.http://www.sciencedirect.com/science/article/pii/S2405844020301195PsychiatryBiological psychiatryDepressionClinical researchHealth informaticsHealth technology |
| spellingShingle | Yuuki Tazawa Kuo-ching Liang Michitaka Yoshimura Momoko Kitazawa Yuriko Kaise Akihiro Takamiya Aiko Kishi Toshiro Horigome Yasue Mitsukura Masaru Mimura Taishiro Kishimoto Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning Heliyon Psychiatry Biological psychiatry Depression Clinical research Health informatics Health technology |
| title | Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning |
| title_full | Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning |
| title_fullStr | Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning |
| title_full_unstemmed | Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning |
| title_short | Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning |
| title_sort | evaluating depression with multimodal wristband type wearable device screening and assessing patient severity utilizing machine learning |
| topic | Psychiatry Biological psychiatry Depression Clinical research Health informatics Health technology |
| url | http://www.sciencedirect.com/science/article/pii/S2405844020301195 |
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