Evaluating the Modified Patient Health Questionnaire-2 and Insomnia Severity Index-2 for Daily Digital Screening of Depression and Insomnia: Validation Study.

BACKGROUND: The Patient Health Questionnaire-2 (PHQ-2) and Insomnia Severity Index-2 (ISI-2) are screening assessments that reflect the past 2-week experience of depression and insomnia, respectively. Retrospective assessment has been associated with reduced accuracy owing to recall bias. OBJECTIVE: This study aimed to increase the reliability of responses by validating the use of the PHQ-2 and ISI-2 for daily screening. METHODS: A total of 167 outpatients from the psychiatric department at the Yongin Severance Hospital participated in this study, of which 63 (37.7%) were male and 104 (62.3%) were female with a mean age of 35.1 (SD 12.1) years. Participants used a mobile app ("Mental Protector") for 4 weeks and rated their depressive and insomnia symptoms daily on the modified PHQ-2 and ISI-2 scales. The validation assessments were conducted in 2 blocks, each with a fortnight response from the participants. The modified version of the PHQ-2 was evaluated against the conventional scales of the Patient Health Questionnaire-9 and the Korean version of the Center for Epidemiologic Studies Depression Scale-Revised. RESULTS: According to the sensitivity and specificity analyses, an average score of 3.29 on the modified PHQ-2 was considered valid for screening for depressive symptoms. Similarly, the ISI-2 was evaluated against the conventional scale, Insomnia Severity Index, and a mean score of 3.50 was determined to be a valid threshold for insomnia symptoms when rated daily. CONCLUSIONS: This study is one of the first to propose a daily digital screening measure for depression and insomnia delivered through a mobile app. The modified PHQ-2 and ISI-2 were strong candidates for daily screening of depression and insomnia, respectively.

Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone.

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a "Mental Health Protector" application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system's prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis.