A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study
BackgroundInternalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders u...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2021-10-01
|
| Series: | JMIR mHealth and uHealth |
| Online Access: | https://mhealth.jmir.org/2021/10/e20638 |
| _version_ | 1797735598248689664 |
|---|---|
| author | Lucy MacLeod Banuchitra Suruliraj Dominik Gall Kitti Bessenyei Sara Hamm Isaac Romkey Alexa Bagnell Manuel Mattheisen Viswanath Muthukumaraswamy Rita Orji Sandra Meier |
| author_facet | Lucy MacLeod Banuchitra Suruliraj Dominik Gall Kitti Bessenyei Sara Hamm Isaac Romkey Alexa Bagnell Manuel Mattheisen Viswanath Muthukumaraswamy Rita Orji Sandra Meier |
| author_sort | Lucy MacLeod |
| collection | DOAJ |
| description |
BackgroundInternalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier.
ObjectiveThis study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada.
MethodsIn this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants.
ResultsMore severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=−0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=−0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit.
ConclusionsPassively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on. |
| first_indexed | 2024-03-12T13:01:23Z |
| format | Article |
| id | doaj.art-5c0ee43a53f74876a71cb9e10428141e |
| institution | Directory Open Access Journal |
| issn | 2291-5222 |
| language | English |
| last_indexed | 2024-03-12T13:01:23Z |
| publishDate | 2021-10-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR mHealth and uHealth |
| spelling | doaj.art-5c0ee43a53f74876a71cb9e10428141e2023-08-28T19:42:04ZengJMIR PublicationsJMIR mHealth and uHealth2291-52222021-10-01910e2063810.2196/20638A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot StudyLucy MacLeodhttps://orcid.org/0000-0002-2180-7028Banuchitra Surulirajhttps://orcid.org/0000-0001-8016-6016Dominik Gallhttps://orcid.org/0000-0002-5634-764XKitti Bessenyeihttps://orcid.org/0000-0002-9989-4811Sara Hammhttps://orcid.org/0000-0003-3244-2543Isaac Romkeyhttps://orcid.org/0000-0001-5996-1269Alexa Bagnellhttps://orcid.org/0000-0002-1484-9676Manuel Mattheisenhttps://orcid.org/0000-0002-8442-493XViswanath Muthukumaraswamyhttps://orcid.org/0000-0002-9898-0929Rita Orjihttps://orcid.org/0000-0001-6152-8034Sandra Meierhttps://orcid.org/0000-0002-3287-5894 BackgroundInternalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier. ObjectiveThis study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada. MethodsIn this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants. ResultsMore severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=−0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=−0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit. ConclusionsPassively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on.https://mhealth.jmir.org/2021/10/e20638 |
| spellingShingle | Lucy MacLeod Banuchitra Suruliraj Dominik Gall Kitti Bessenyei Sara Hamm Isaac Romkey Alexa Bagnell Manuel Mattheisen Viswanath Muthukumaraswamy Rita Orji Sandra Meier A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study JMIR mHealth and uHealth |
| title | A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study |
| title_full | A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study |
| title_fullStr | A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study |
| title_full_unstemmed | A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study |
| title_short | A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study |
| title_sort | mobile sensing app to monitor youth mental health observational pilot study |
| url | https://mhealth.jmir.org/2021/10/e20638 |
| work_keys_str_mv | AT lucymacleod amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT banuchitrasuruliraj amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT dominikgall amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT kittibessenyei amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT sarahamm amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT isaacromkey amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT alexabagnell amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT manuelmattheisen amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT viswanathmuthukumaraswamy amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT ritaorji amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT sandrameier amobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT lucymacleod mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT banuchitrasuruliraj mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT dominikgall mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT kittibessenyei mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT sarahamm mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT isaacromkey mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT alexabagnell mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT manuelmattheisen mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT viswanathmuthukumaraswamy mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT ritaorji mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy AT sandrameier mobilesensingapptomonitoryouthmentalhealthobservationalpilotstudy |