Background
In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost.
Objective
This study aimed 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, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable.
Results
PSYCHE-D is composed of 2 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, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an 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 when compared with 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 experiencing depression.
The objective of the authors is to make a reflection about the causes of tianeptine abuse and dependence. In the scientific literature we can find case studies of anti-depressive dependence, which show amphetaminergic effects. In what concerns other anti-depressives, the information is rare, specifically about tianeptine. The few case studies reported until now, focus the psychostimulant effect as being the cause of the abuse and dependence. Though, a study case is described of a female patient, with 40 years old, previous history of alcohol abuse, who takes approximately 40 cigarettes per day and 10 cofee per day. She presents an history of tianeptine abuse for several years, which has become more severe in the last six months (1286 mg/day) and resulted in the third psychiatric hospitalization. The patient experiences and seeks for a psychostimulant effect and physically energizing through the excessive consumption of the drug. This tianeptine abuse is also accompanied by an excessive consumption of benzodiazepines (30 mg/day of bromazepam). Over the course of the hospitalization, we did not find physical symptoms and signs of withdrawal. Hepatic parameters were not affected. The authors conclued that the abuse and dependence of tianeptine seems to be an important problem in patients with history of abuse and/or dependence of other substances. Thus, this treatment and the implications that it may have in this population need more investigation.
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