Developing personalized algorithms for sensing mental health symptoms in daily life
Abstract The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting...
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | npj Mental Health Research |
| Online Access: | https://doi.org/10.1038/s44184-025-00147-5 |
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| author | Adela C. Timmons Abdullah Aman Tutul Kleanthis Avramidis Jacqueline B. Duong Kayla E. Carta Sierra N. Walters Grace A. Jumonville Alyssa S. Carrasco Gabrielle F. Freitag Daniela N. Romero Matthew W. Ahle Jonathan S. Comer Shrikanth S. Narayanan Ishita P. Khurd Theodora Chaspari |
| author_facet | Adela C. Timmons Abdullah Aman Tutul Kleanthis Avramidis Jacqueline B. Duong Kayla E. Carta Sierra N. Walters Grace A. Jumonville Alyssa S. Carrasco Gabrielle F. Freitag Daniela N. Romero Matthew W. Ahle Jonathan S. Comer Shrikanth S. Narayanan Ishita P. Khurd Theodora Chaspari |
| author_sort | Adela C. Timmons |
| collection | DOAJ |
| description | Abstract The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting individual and family mental health symptoms as a foundational step toward JITAI development, using data collected through the Colliga app on smart devices. Over a 60-day period, data from 35 families resulted in approximately 14 million data points across 52 data streams. Findings showed that personalized models consistently outperformed generalized models. Model performance varied significantly based on individual factors and symptom profiles, underscoring the need for tailored approaches. These preliminary findings suggest that successful implementation of passive sensing technologies for mental health will require accounting for users’ unique characteristics. Further research with larger samples is needed to refine the models, address data heterogeneity, and develop scalable systems for personalized mental health interventions. |
| format | Article |
| id | doaj-art-bb7740c6f1ce454aacc4ac4b6d6bc9fb |
| institution | Kabale University |
| issn | 2731-4251 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Mental Health Research |
| spelling | doaj-art-bb7740c6f1ce454aacc4ac4b6d6bc9fb2025-08-20T03:46:25ZengNature Portfolionpj Mental Health Research2731-42512025-08-014111910.1038/s44184-025-00147-5Developing personalized algorithms for sensing mental health symptoms in daily lifeAdela C. Timmons0Abdullah Aman Tutul1Kleanthis Avramidis2Jacqueline B. Duong3Kayla E. Carta4Sierra N. Walters5Grace A. Jumonville6Alyssa S. Carrasco7Gabrielle F. Freitag8Daniela N. Romero9Matthew W. Ahle10Jonathan S. Comer11Shrikanth S. Narayanan12Ishita P. Khurd13Theodora Chaspari14University of Texas at AustinTexas A&M UniversityUniversity of Southern CaliforniaUniversity of Texas at AustinUniversity of Texas at AustinUniversity of Texas at AustinUniversity of Texas at AustinUniversity of Texas at AustinFlorida International UniversityUniversity of Texas at AustinColliga AppsFlorida International UniversityUniversity of Southern CaliforniaUniversity of Texas at AustinUniversity of Colorado BoulderAbstract The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting individual and family mental health symptoms as a foundational step toward JITAI development, using data collected through the Colliga app on smart devices. Over a 60-day period, data from 35 families resulted in approximately 14 million data points across 52 data streams. Findings showed that personalized models consistently outperformed generalized models. Model performance varied significantly based on individual factors and symptom profiles, underscoring the need for tailored approaches. These preliminary findings suggest that successful implementation of passive sensing technologies for mental health will require accounting for users’ unique characteristics. Further research with larger samples is needed to refine the models, address data heterogeneity, and develop scalable systems for personalized mental health interventions.https://doi.org/10.1038/s44184-025-00147-5 |
| spellingShingle | Adela C. Timmons Abdullah Aman Tutul Kleanthis Avramidis Jacqueline B. Duong Kayla E. Carta Sierra N. Walters Grace A. Jumonville Alyssa S. Carrasco Gabrielle F. Freitag Daniela N. Romero Matthew W. Ahle Jonathan S. Comer Shrikanth S. Narayanan Ishita P. Khurd Theodora Chaspari Developing personalized algorithms for sensing mental health symptoms in daily life npj Mental Health Research |
| title | Developing personalized algorithms for sensing mental health symptoms in daily life |
| title_full | Developing personalized algorithms for sensing mental health symptoms in daily life |
| title_fullStr | Developing personalized algorithms for sensing mental health symptoms in daily life |
| title_full_unstemmed | Developing personalized algorithms for sensing mental health symptoms in daily life |
| title_short | Developing personalized algorithms for sensing mental health symptoms in daily life |
| title_sort | developing personalized algorithms for sensing mental health symptoms in daily life |
| url | https://doi.org/10.1038/s44184-025-00147-5 |
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