ReviewDialogues in clinical neuroscience2016
Mobile technology for mental health assessment.
Review in Dialogues in clinical neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06556251 (Evaluating the Effectiveness of a Digital Platform in the Engagement and Prognosis of Patients in the Early Intervention Programme in Psychosis), which is not on this map. Cited by 45 papers, 2 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.
Evaluating the Effectiveness of a Digital Platform in the Engagement and Prognosis of Patients in the Early Intervention Programme in Psychosis
Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Remote Monitoring Systems for Patients With Chronic Diseases in Primary Health Care: Systematic Review.JMIR mHealth and uHealth · 2021Pooled it
- Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations.JMIR mHealth and uHealth · 2019Pooled it
- Real-time cognitive performance metrics derived from a digital therapeutic for inattention predict ADHD-related clinical outcomes: Replication across three independent trials of AKL-T01.Translational psychiatry · 2024Trial
- The accuracy of passive phone sensors in predicting daily mood.Depression and anxiety · 2019Trial
- Exploring the Interpretive Clarity of the TCCNI-RePract and Identifying Conceptual Barriers Encountered by Japanese Psychiatric Nurses: A Concurrent Mixed-Methods Study.Nursing reports (Pavia, Italy) · 2026Article
- Digital phenotyping of affect and stress in emerging adults.Frontiers in digital health · 2026Article
- TrailMap: Pheromone-Based Adaptive Peer Matching for Sustainable Online Support Communities.Biomimetics (Basel, Switzerland) · 2025Article
- Smartphone-Based Self-Monitoring in First Episode Psychosis: Mixed-Methods Study of Barriers and Facilitators to Engagement.Journal of medical Internet research · 2025Article
- Perception of Apps for Mental Health Assessment With Recommendations for Future Design: United Kingdom Semistructured Interview Study.JMIR formative research · 2024Article
- Experience of primary healthcare workers in using the mobile app-based WHO mhGAP intervention guide in detection and treatment of people with mental disorders: A qualitative study in Nepal.SSM. Mental health · 2023Article
- Tablet-Based Puzzle Game Intervention for Cognitive Function and Well-Being in Healthy Adults: Pilot Feasibility Randomized Controlled Trial.JMIR aging · 2023Article
- Exploring User Perspectives of and Ethical Experiences With Teletherapy Apps: Qualitative Analysis of User Reviews.JMIR mental health · 2023Article
- An exploration of actigraphy in the context of ketamine and treatment-resistant depression.International journal of methods in psychiatric research · 2023Article
- Development and usability evaluation of a mHealth application for albinism self-management.BMC medical informatics and decision making · 2023Article
- Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students.Bioengineering (Basel, Switzerland) · 2023Article
- Impact of COVID-19 on mental health in China: analysis based on sentiment knowledge enhanced pre-training and XGBoost algorithm.Frontiers in public health · 2023Article
- A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Empirical and Theoretical Framework, Intervention Design, and Study Protocol for a Randomized Controlled Trial.JMIR research protocols · 2022Article
- Prediction Modeling of Mental Well-Being Using Health Behavior Data of College Students.Research square · 2022Article
- Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review.PeerJ. Computer science · 2022Article
- Assessing Cognitive Function in Multiple Sclerosis With Digital Tools: Observational Study.Journal of medical Internet research · 2021Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment and outcome monitoring are critical for the effective detection and treatment of mental illness. Traditional methods of capturing social, functional, and behavioral data are limited to the information that patients report back to their health care provider at selected points in time. As a result, these data are not accurate accounts of day-to-day functioning, as they are often influenced by biases in self-report. Mobile technology (mobile applications on smartphones, activity bracelets) has the potential to overcome such problems with traditional assessment and provide information about patient symptoms, behavior, and functioning in real time. Although the use of sensors and apps are widespread, several questions remain in the field regarding the reliability of off-the-shelf apps and sensors, use of these tools by consumers, and provider use of these data in clinical decision-making.
Indexed as
Identifiers
27489456PMC4969703What 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.