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Digital phenotyping for mental health of college students: a clinical review

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to men...

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Detalles Bibliográficos
Autores principales: Melcher, Jennifer, Hays, Ryan, Torous, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231503/
https://www.ncbi.nlm.nih.gov/pubmed/32998937
http://dx.doi.org/10.1136/ebmental-2020-300180
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author Melcher, Jennifer
Hays, Ryan
Torous, John
author_facet Melcher, Jennifer
Hays, Ryan
Torous, John
author_sort Melcher, Jennifer
collection PubMed
description Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help—the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.
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spelling pubmed-102315032023-09-12 Digital phenotyping for mental health of college students: a clinical review Melcher, Jennifer Hays, Ryan Torous, John Evid Based Ment Health Clinical Review Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help—the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students. BMJ Publishing Group 2020-11 2020-09-30 /pmc/articles/PMC10231503/ /pubmed/32998937 http://dx.doi.org/10.1136/ebmental-2020-300180 Text en © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ. https://bmj.com/coronavirus/usageThis article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.
spellingShingle Clinical Review
Melcher, Jennifer
Hays, Ryan
Torous, John
Digital phenotyping for mental health of college students: a clinical review
title Digital phenotyping for mental health of college students: a clinical review
title_full Digital phenotyping for mental health of college students: a clinical review
title_fullStr Digital phenotyping for mental health of college students: a clinical review
title_full_unstemmed Digital phenotyping for mental health of college students: a clinical review
title_short Digital phenotyping for mental health of college students: a clinical review
title_sort digital phenotyping for mental health of college students: a clinical review
topic Clinical Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231503/
https://www.ncbi.nlm.nih.gov/pubmed/32998937
http://dx.doi.org/10.1136/ebmental-2020-300180
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