Harnessing digital health interventions to address the heterogeneity of depression: a systematic review.

Background: Depression affects over 229 million people worldwide and ranks among the leading causes of disability and death, particularly in young adults, where suicide is a top contributor to mortality. Standard diagnostic and treatment approaches often overlook the marked clinical and biological heterogeneity of depression, resulting in low first-line remission rates and prolonged trial-and-error care, underscoring an urgent need for precision strategies in mental health practice. Objective: This review explores the recent literature (January 2020-September 2025) on personalized digital health interventions for depression, with an emphasis on how these technologies address heterogeneity in symptomatology, biological underpinnings, and treatment response across diverse patient populations. Methods: The study followed PRISMA guidelines, searching Scopus, IEEE Xplore, and ClinicalTrials.gov for English-language peer-reviewed articles and trials published and registered between January 2020 and September 2025. Only studies relevant to depression heterogeneity and digital health were included, and studies focusing solely on generic digital health tools without a personalized or adaptive component were excluded. Findings were synthesized narratively. Findings: 29 publications were reviewed: 20 studies and 9 clinical trial reports, representing over 5,000 participants. Personalized machine-learning models using mobile sensing and ecological momentary assessments improved mood-forecasting accuracy by up to 25%. Randomized trials of just-in-time adaptive interventions (e.g., the Mello app) demonstrated moderate to large effect sizes for reductions in depression ( Conclusions: and Relevance: Digital mental health technologies exhibit substantial promise for delivering personalized interventions that accommodate inter-individual variability in depression. High-quality evidence supports their capacity to enhance prediction, engagement, and clinical outcomes. However, broader implementation requires standardized multidimensional outcome measures, equity-focused algorithm validation, and integration of established clinical phenotypes.