Harnessing Smartphone Data and AI to Predict Depression and Treatment Response in Teens
In a world where smartphones are nearly ubiquitous, a group of researchers led by Jae Sung Kim have found a novel way to use these devices to help adolescents struggling with depression. Their study, the first of its kind, uses a specially designed smartphone app to collect data on teens' social and behavioral activities, such as the amount of time they spend on their phones, how far they move physically, and the number of calls and texts they send and receive. This data is then analyzed using deep learning techniques, a type of artificial intelligence, to predict whether a teen has depression and how they might respond to treatment.
The study involved 24 adolescents diagnosed with Major Depressive Disorder (MDD), a mental health disorder characterized by persistent feelings of sadness and loss of interest in activities, and 10 healthy controls. After a week of baseline data collection, the teens with MDD were treated with escitalopram, a common antidepressant, for eight weeks. The researchers found that they could predict a depression diagnosis with an accuracy of 96.3% and treatment response with an accuracy of 94.2%. Interestingly, they found that teens with MDD tended to move longer distances and use their smartphones for longer periods compared to the control group.
This research represents a significant step forward in the use of digital biomarkers - measurable indicators of a biological state or condition collected via digital devices - for diagnosing and treating depression in adolescents. It's a promising development in the field of mental health, offering a new way to identify and help teens who may be struggling with depression. The use of smartphones and AI in this context not only provides a non-invasive and objective method for diagnosis and treatment prediction, but also opens up new possibilities for remote monitoring and intervention in mental health care.
Article Information
Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study
Published on JMIR Formative Research. Jae Sung Kim et al.
Abstract
Background: Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem.
Objective: We sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app.
Methods: We developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week's baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children's Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection.
Results: We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD.
Conclusions: Our smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.
Bring preventive care to your clinic
© Longevity Artificial Intelligence. All rights reserved.