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Accepted for/Published in: JMIR Mental Health

Date Submitted: Mar 19, 2019
Open Peer Review Period: Mar 22, 2019 - May 17, 2019
Date Accepted: Sep 24, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study

Cao J, Truong AL, Banu S, Shah AA, Sabharwal A, Moukaddam N

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study

JMIR Ment Health 2020;7(1):e14045

DOI: 10.2196/14045

PMID: 32012072

PMCID: 7007590

SOLVD-TEEN: Findings from Smartphone- and OnLine-usage-based eValuation for Depression (SOLVD) Study in Adolescents. Can we predict depressive symptoms with parental input?

  • Jian Cao; 
  • Anh Lan Truong; 
  • Sophia Banu; 
  • Asim A Shah; 
  • Ashutosh Sabharwal; 
  • Nidal Moukaddam

ABSTRACT

Background:

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.

Objective:

This study aims to investigate whether smartphone applications are useful in evaluating and monitoring depression symptoms in a clinically depressed adolescent population compared to gold-standard clinical psychometric instruments (PHQ-9, HAM-D and HAM-A).

Methods:

The authors recruited 13 families with adolescent patients diagnosed with major depressive disorder with or without comorbid anxiety disorder. Over an eight-week period, daily self-reported moods and smartphone sensor data were collected by the SOLVD App. The evaluations of teens’ parents were also collected. Baseline depression and anxiety symptoms were measured biweekly using PHQ-9, HAM-D, and HAM-A.

Results:

The authors observed a significant correlation between the self-evaluated mood averaged over a 2-week period and the biweekly psychometric scores (0.45≤|r|≤0.63, all P < .05). The daily steps taken, SMS frequency and average call duration were also highly correlated with clinical scores (0.44≤|r|≤0.72, all P < .05). By combining self-evaluations and smartphone sensor data of the teens, the authors could predict the PHQ-9 score with an accuracy of 74.8%. When including the evaluations from the teens’ parents, the prediction accuracy could be further increased to 79.2%.

Conclusions:

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. Clinical Trial: N/A


 Citation

Please cite as:

Cao J, Truong AL, Banu S, Shah AA, Sabharwal A, Moukaddam N

Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study

JMIR Ment Health 2020;7(1):e14045

DOI: 10.2196/14045

PMID: 32012072

PMCID: 7007590

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