SynthesisBMC psychiatry2022
Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence.
Synthesis in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 33 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 5 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review.JMIR mental health · 2025Pooled it
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review.Journal of medical Internet research · 2023Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- Mental health, personality, and cross-addictions as predictors of social media addiction: a machine learning longitudinal study.Addictive behaviors reports · 2026Article
- Design Preferences for Mental Health Apps Among Nondigitally Native Adults With Chronic Pain: Qualitative Analysis.JMIR mHealth and uHealth · 2026Article
- Mechanism-First Psychobiotics: Fermented Vegetables, Dairy, and Soy for Depression and Anxiety.International journal of molecular sciences · 2026Review
- Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review.Health science reports · 2026Review
- Article
- Gaming disorder in the ICD-11: the state of the game.BMC psychiatry · 2025Review
- A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.Npj mental health research · 2025Article
- Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study.Journal of medical Internet research · 2025Observational
- Machine Learning(s) in gaming disorder through the user-avatar bond: A step towards conceptual and methodological clarity.Journal of behavioral addictions · 2024Article
- Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.JMIR mental health · 2024Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life.Psychological medicine · 2024Article
- Personalized mood prediction from patterns of behavior collected with smartphones.NPJ digital medicine · 2024Article
- Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study.JMIR mental health · 2024Article
- What digital health technology types are used in mental health prevention and intervention? Review of systematic reviews for systematization of technologies.Journal of occupational health · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundThis PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.
aimsFour distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.
resultsOverall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.
conclusionsFindings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.