JMIR Publications

ABSTRACT

Background:

In 2017, an estimated 17.3 million adults in the US experienced at least one major depressive episode, with 35% of them not receiving any treatment. Under-diagnosis of depression has been attributed to many reasons including stigma surrounding mental health, limited access to medical care or barriers due to cost.

Objective:

To determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes.

Methods:

Here we present the development of PSYCHE-D (Prediction of SeveritY CHange - Depression), a predictive model developed using PGHD from more than 4000 individuals, that forecasts long-term increase in depression severity. PSYCHE-D uses a two-phase approach: the first phase supplements self-reports with intermediate generated labels; the second phase predicts changing status over a 3 month period, up to 2 months in advance. The two phases are implemented as a single pipeline in order to eliminate data leakage, and ensure results are generalizable.

Results:

PSYCHE-D is composed of two Light Gradient Boosting Machine (LightGBM) algorithm-based classifiers that use a range of PGHD input features, including objective activity and sleep, self reported changes in lifestyle and medication, as well as generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect increase in depression severity over a 3-month interval with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity, while maintaining specificity, versus a random model.

Conclusions:

These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual's mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals suffering from depression. Clinical Trial: Data used to develop the model was derived from the Digital Signals in Chronic Pain (DiSCover) Project (Clintrials.gov identifier: NCT03421223)