Scalable Health Labs

Mobile Bio-Behavioral Sensing

SOLVD-Teen

SOLVD-Teen: Tracking and predicting depressive symptoms of adolescents using smartphone-based daily self-reports, parental evaluations, and passive phone sensor data

Jian Cao, Anh Lan Truong, Sophia Banu, Asim A Shah, Ashutosh Sabharwal and Nidal Moukaddam, SOLVD-Teen: Tracking and predicting depressive symptoms of adolescents using smartphone-based daily self-reports, parental evaluations, and passive phone sensor data. Journal of Medical Internet Research #14045.

TEAM MEMBERS:

Jian Cao, Graduate Student, ECE
Dr. Ashutosh Sabharwal, Professor and Chair, ECE

Overview: Depression carries significant financial, medical and emotional burden on modern society. Various proof of concept studies have highlighted how apps can link dynamic activity changes to fluctuations in smartphone usage in adult patients with major depressive disorder. The application of such apps to adolescents remains a more challenging field.

 

 

Our Solution: Smartphone apps such as SOLVD represent a useful way to monitor depressive symptoms in clinically depressed adolescents, and correlate well with current gold-standard psychometric instruments. This is a first-of-its-kind study that was conducted on the adolescent population, and it included inputs from both the teens and their parents as observers. The results are preliminary because of small sample size and the authors plan to expand the study to a larger population.