ArticleNeuropsychiatric disease and treatment2021
Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods.
Article in Neuropsychiatric disease and treatment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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12 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- A systematic review and meta-analysis of diagnostic test accuracy of mental health screening tools applicable to adolescents in sub-Saharan Africa.Frontiers in psychiatry · 2026Pooled it
- Disparities in Distress Symptoms Among Cancer Inpatients, Outpatients and Relatives Through Introducing and Evaluating Digital Distress Screening.Psycho-oncology · 2026Trial
- Interpretable machine learning for depression symptom classification in NHANES: Performance in a curated high-confidence corpus and the full survey population.IBRO neuroscience reports · 2026Article
- Fidelity in the context of adapting a digital intervention for depression from an evidence-based in-person format in Vietnam.BMC health services research · 2026Article
- Disrupted brain functional networks in adolescents and young adults with gaming disorder during social interaction: An fNIRS study.Psychological medicine · 2026Article
- Feasibility of smartphone app-based neuropsychological tasks for screening people with subclinical depression and anxiety: a preliminary validation study.Frontiers in psychiatry · 2026Article
- Challenges and Mitigation Strategies in the Development and Feasibility Assessment of a Digital Mental Health Intervention for Depression (VMood): Mixed Methods Feasibility Study.JMIR formative research · 2025Article
- Emotion Forecasting: A Transformer-Based Approach.Journal of medical Internet research · 2025Article
- mHealth-Augmented Care for Reducing Depression Symptom Severity Among Patients With Chronic Pain: Exploratory, Retrospective Cohort Study.JMIR mHealth and uHealth · 2025Article
- Prevalence and factors associated with antenatal depressive symptoms across trimesters: a study of 110,584 pregnant women covered by a mobile app-based screening programme in Shenzhen, China.BMC pregnancy and childbirth · 2024Article
- Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression.NPJ digital medicine · 2023Article
- The relationship between loneliness and depression among college students: Mining data derived from passive sensing.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDepression is a symptom commonly encountered in primary care; however, it is often not detected by doctors. Recently, disease diagnosis and treatment approaches have been attempted using smart devices. In this study, instrumental effectiveness was confirmed with the diagnostic meta-analysis of studies that demonstrated the diagnostic effectiveness of PHQ-9 for depression using mobile devices. PATIENTS AND
methodsWe found all published and unpublished studies through EMBASE, MEDLINE, MEDLINE In-Process, and PsychINFO up to March 26, 2021. We performed a meta-analysis by including 1099 subjects in four studies. We performed a diagnostic meta-analysis according to the PHQ-9 cut-off score and machine learning algorithm techniques. Quality assessment was conducted using the QUADAS-2 tool. Data on the sensitivity and specificity of the studies included in the meta-analysis were extracted in a standardized format. Bivariate and summary receiver operating characteristic (SROC) curve were constructed using the metandi, midas, metabias, and metareg functions of the Stata algorithm meta-analysis words.
resultsUsing four studies out of the 5476 papers searched, a diagnostic meta-analysis of the PHQ-9 scores of 1099 people diagnosed with depression was performed. The pooled sensitivity and specificity were 0.797 (95% CI = 0.642-0.895) and 0.85 (95% CI = 0.780-0.900), respectively. The diagnostic odds ratio was 22.16 (95% CI = 7.273-67.499). Overall, a good balance was maintained, and no heterogeneity or publication bias was presented.
conclusionThrough various machine learning algorithm techniques, it was possible to confirm that PHQ-9 depression screening in mobiles is an effective diagnostic tool when integrated into a diagnostic meta-analysis.
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