ReviewNPJ digital medicine2024
Advancing digital sensing in mental health research.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Towards Human-Centered Digital Health Interventions.The Psychiatric clinics of North America · 2026Review
- Rethinking Ethics for an Era of Trusted Computational Tools.The Psychiatric clinics of North America · 2026Review
- Neural Reward Processing in Digital Addiction: A Dynamical Systems Theory of Reward Instability.Brain sciences · 2026Article
- FedMIR: Multimodal Federated Learning with Missing Modality Imputation and Distribution-Aware Routing.Sensors (Basel, Switzerland) · 2026Article
- Interindividual differences in digital phenotypes of major depressive disorder: A passive sensing study using smartphone and wearable sensor data.Behaviour research and therapy · 2026Article
- Sensors Fusion in Digital Healthcare Applications.Sensors (Basel, Switzerland) · 2026Article
- Personalized Insights Derived from Wearable Device Data and Large Language Models to Improve Well-Being.medRxiv : the preprint server for health sciences · 2026Article
- Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review.Health science reports · 2026Review
- Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Health Care: Qualitative Study.JMIR mental health · 2026Article
- The Digital Mental Health Study assessed feasibility of large-scale digital sensing for depression and anxiety.npj digital public health · 2026Article
- Anxiety and depression in emerging adults: The STAND program as a model of scalable screening and intervention.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Information quality, readability, and empathy of AI-generated public mental health information: a comparative evaluation of eight large language models.Frontiers in public health · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Digital sensing tools, like smartphones and wearables, offer transformative potential for mental health research by enabling scalable, longitudinal data collection. Realizing this promise requires overcoming significant challenges including limited data standards, underpowered studies, and a disconnect between research aims and community needs. This report, based on the 2023 Workshop on Advancing Digital Sensing Tools for Mental Health, articulates strategies to address these challenges to ensure rigorous, equitable, and impactful research.
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.