Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors
© Copyright © 2020 Pedrelli, Fedor, Ghandeharioun, Howe, Ionescu, Bhathena, Fisher, Cusin, Nyer, Yeung, Sangermano, Mischoulon, Alpert and Picard. Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be lever...
| Үндсэн зохиолчид: | , , , , , , , , , , , , , |
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| Бусад зохиолчид: | |
| Формат: | Өгүүллэг |
| Хэл сонгох: | English |
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Frontiers Media SA
2021
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| Онлайн хандалт: | https://hdl.handle.net/1721.1/134388 |
| _version_ | 1826215403040276480 |
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| author | Pedrelli, Paola Fedor, Szymon Ghandeharioun, Asma Howe, Esther Ionescu, Dawn F Bhathena, Darian Fisher, Lauren B Cusin, Cristina Nyer, Maren Yeung, Albert Sangermano, Lisa Mischoulon, David Alpert, Johnathan E Picard, Rosalind W. |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Pedrelli, Paola Fedor, Szymon Ghandeharioun, Asma Howe, Esther Ionescu, Dawn F Bhathena, Darian Fisher, Lauren B Cusin, Cristina Nyer, Maren Yeung, Albert Sangermano, Lisa Mischoulon, David Alpert, Johnathan E Picard, Rosalind W. |
| author_sort | Pedrelli, Paola |
| collection | MIT |
| description | © Copyright © 2020 Pedrelli, Fedor, Ghandeharioun, Howe, Ionescu, Bhathena, Fisher, Cusin, Nyer, Yeung, Sangermano, Mischoulon, Alpert and Picard. Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed–one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors–and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy. |
| first_indexed | 2024-09-23T16:27:28Z |
| format | Article |
| id | mit-1721.1/134388 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:27:28Z |
| publishDate | 2021 |
| publisher | Frontiers Media SA |
| record_format | dspace |
| spelling | mit-1721.1/1343882024-08-09T19:42:53Z Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors Pedrelli, Paola Fedor, Szymon Ghandeharioun, Asma Howe, Esther Ionescu, Dawn F Bhathena, Darian Fisher, Lauren B Cusin, Cristina Nyer, Maren Yeung, Albert Sangermano, Lisa Mischoulon, David Alpert, Johnathan E Picard, Rosalind W. Massachusetts Institute of Technology. Media Laboratory © Copyright © 2020 Pedrelli, Fedor, Ghandeharioun, Howe, Ionescu, Bhathena, Fisher, Cusin, Nyer, Yeung, Sangermano, Mischoulon, Alpert and Picard. Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed–one combining features from smartphone and wearable sensors, one including only features from the smartphones, and one including features from wrist sensors–and evaluated in two different scenarios. Correlations between the models' estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI: 0.42, 0.74] to 0.7 [CI: 0.66, 0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88 ± 0.18 and 4.74 ± 1.24. The time-split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the model combining physiological and mobile features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptom severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy. 2021-10-27T20:04:46Z 2021-10-27T20:04:46Z 2020 2021-06-28T18:04:21Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134388 en 10.3389/fpsyt.2020.584711 Frontiers in Psychiatry Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Media SA Frontiers |
| spellingShingle | Pedrelli, Paola Fedor, Szymon Ghandeharioun, Asma Howe, Esther Ionescu, Dawn F Bhathena, Darian Fisher, Lauren B Cusin, Cristina Nyer, Maren Yeung, Albert Sangermano, Lisa Mischoulon, David Alpert, Johnathan E Picard, Rosalind W. Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors |
| title | Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors |
| title_full | Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors |
| title_fullStr | Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors |
| title_full_unstemmed | Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors |
| title_short | Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors |
| title_sort | monitoring changes in depression severity using wearable and mobile sensors |
| url | https://hdl.handle.net/1721.1/134388 |
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