This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Tuesday, January 24 • 4:30pm - 6:00pm
Poster: SOLVD: Smartphone- and OnLine-usage-based eValuation for Depression

Sign up or log in to save this to your schedule and see who's attending!

Feedback form is now closed.
Depression is one of the most common mental disorders that carries significant emotional and financial burden for modern society. To ensure successful prevention and treatment, the early diagnosis and continuous monitoring of one’s depression state are critical. In recent years, the smartphone has shown its potential as a wearable device to track and manage the mental health condition, yet very limited studies have considered clinically depressed patients, or included the ground truth of depression for comparison.

In this project, we developed the Smartphone- and OnLine-usage-based eValuation for Depression (SOLVD), which is a new tool for continuous tracking of patients’ depression state. We built the SOLVD App and cloud platform, for data collection, analysis and sharing with physicians. We also conducted a 1-year clinical trial of 25 depression patients. Three types of data were collected via the App and bi-weekly clinical visits: 1. Smartphone sensor and usage data, including accelerometer, GPS, steps, screen status, call log, text messages, and apps; 2. Self-reported mood and activity level; 3. Psychometric data including PHQ-9, HamiltonD and HamitonA.

The results showed that the adherence rate to the daily self-reported mood input was about
82%, and the attendance rate for clinic visits was 95%. The correlation between self-reported mood level and PHQ-9 score was 0.73 in the moderate/severe group and 0.36 in the normal/mild group. The passive phone sensor and usage data, including their number of steps, quantity of text messages and the amount of time spent messaging, also correlated with clinical assessments. For example, when depression worsened, the number of calls and text messages dropped. We also trained a SVM classifier which achieved around 80% accuracy categorizing the patients into mild/moderate/severe groups. The preliminary findings indicate that the SOLVD app could be a reliable approach to tracking moderate-to-severe depression.

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)