A model for continuous monitoring of patients with major depression in short and long term periods

Implementing Digital Cognitive-Behavioral Therapy for Major Depressive Disorder in Routine Psychiatric Appointments: A Pilot Project in a Rural Population

Psychotherapy is well-established as an effective treatment for major depressive disorder (MDD). However, many individuals with MDD in rural areas of the United States do not have access to psychotherapy. Self-management (SM) strategies are now the standard of care for chronic medical conditions and may be a viable alternative for individuals without access to psychotherapy. The current article describes the implementation of a 13-week pilot project to integrate digital cognitive-behavioral therapy (dCBT) SM programs into routine psychiatric advanced practice nurse (APN) telehealth appointments in the rural United States. Eight participants completed the project. The project did not meet the benchmark of 20 participants to improve access to treatment; however, one half (n = 4) of participants reported clinically significant improvement in MDD symptoms over 6 weeks. For clients without access to psychotherapy, dCBT SM programs can be effective in improving individual client outcomes when implemented by APNs in routine telehealth appointments. [Journal of Psychosocial Nursing and Mental Health Services, 61(10), 44-51.].

Analysis of depression in social media texts through the Patient Health Questionnaire-9 and natural language processing

Objective

Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users’ depression based on their results.

Methods

First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whether a user’s sentence is related to depression, and the 0–9 sentence classifier, which further categorizes the user sentence based on the depression symptomology of the Patient Health Questionnaire-9. Then the depression classifier, which is a logistic regression model, was generated to classify the sentence writer’s depression. These trained sentence classifiers and the depression classifier were used to analyze the social media textual data of users and establish their depression.

Results

Our experimental results showed that the proposed depression classifier showed 68.3% average accuracy, which was better than the baseline depression classifier that used only the Y/N sentence classifier and had 53.3% average accuracy.

Conclusions

This study is significant in that it demonstrates the possibility of determining depression from only social media users’ textual data.