ReviewDepression and anxiety2017
Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies.
Review in Depression and anxiety, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Feasibility of mHealth interventions for depressive symptoms in Latin America: a systematic review.International review of psychiatry (Abingdon, England) · 2021Pooled it
- Responsible data selection method for algorithmic personalization of health apps: a case study on promoting mental health.Frontiers in digital health · 2026Article
- Social Media Detox and Youth Mental Health.JAMA network open · 2025Observational
- The digital intelligent precise nursing framework: theory development in health recommender system.BMC nursing · 2025Article
- Leveraging Swipe Gesture Interactions From Mobile Games as Indicators of Anxiety and Depression: Exploratory Study.JMIR mental health · 2025Article
- Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders.Scientific reports · 2025Article
- Factors influencing perceived benefits and behavioral intention to use mental health chatbots among professional employees: an empirical study.Frontiers in digital health · 2025Article
- Integration of passive sensing technology to enhance delivery of psychological interventions for mothers with depression: the StandStrong study.Scientific reports · 2024Article
- Article
- The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review.Cureus · 2024Review
- Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework.Internet interventions · 2023Article
- Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study.JMIR AI · 2023Article
- The Perceived Utility of Smartphone and Wearable Sensor Data in Digital Self-tracking Technologies for Mental Health.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms.Frontiers in digital health · 2023Review
- E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures.Sensors (Basel, Switzerland) · 2022Article
- Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention.ACM transactions on computing for healthcare · 2022Article
- Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features.International journal of environmental research and public health · 2021Article
- Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study.JMIR formative research · 2021Article
- Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study.JMIR research protocols · 2021Article
- Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments.Frontiers in psychiatry · 2021Review
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
Abstract
Unlike most other health conditions, the treatment of mental illness relies on subjective measurement. In addition, the criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behavior in situ. This promises to lead to more precise assessment of human behavior and ultimately individual mental health. More refined modeling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare. In this paper, we provide a conceptual review of such techniques for measuring, modeling, and treating mental illness and maintaining mental health.
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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.