ArticlemHealth2019
Actionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcare.
Article in mHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Trial
- Mobile cognitive remote assessment of schizophrenia: a global multi-site pilot study.Schizophrenia (Heidelberg, Germany) · 2025Article
- Bridging Ontologies of Neurological Conditions: Towards Patient-centered Data Practices in Digital Phenotyping Research and Design.Proceedings of the ACM on human-computer interaction · 2025Article
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Digital health technologies in the accelerating medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- From Steps to Context: Optimizing Digital Phenotyping for Physical Activity Monitoring in Older Adults by Integrating Wearable Data and Ecological Momentary Assessment.Sensors (Basel, Switzerland) · 2025Article
- Digital health monitoring for adults with treatment-resistant depression: Observational feasibility study protocol.PloS one · 2025Article
- Mobility-Based Smartphone Digital Phenotypes for Unobtrusively Capturing Everyday Cognition, Mood, and Community Life-Space in Older Adults: Feasibility, Acceptability, and Preliminary Validity Study.JMIR human factors · 2024Article
- Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals-The OASIS Study.Biotech (Basel (Switzerland)) · 2023Article
- Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons.JMIR perioperative medicine · 2023Article
- Shaping the Screening, Behavioral Intervention, and Referral to Treatment (SBIRT) Model for Treatment of Alcohol Use Disorder in the COVID-19 Era.Mayo Clinic proceedings · 2022Article
- Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools.JMIR aging · 2022Article
- HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint.Journal of medical Internet research · 2022Article
- The Learning Loop: Conceptualizing Just-in-Time Faculty Development.AEM education and training · 2022Article
- Global Collaboration Around Digital Mental Health: The LAMP Consortium.Journal of technology in behavioral science · 2022Article
- Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study.JMIR mHealth and uHealth · 2021Article
- Empowering clinical research in a decentralized world.NPJ digital medicine · 2021Article
- Article
- Using health technology to capture digital phenotyping data in HIV-associated neurocognitive disorders.AIDS (London, England) · 2021Article
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
Designed to improve health, today numerous wearables and smartphone apps are used by millions across the world. Yet the wealth of data generated from the many sensors on these wearables and smartwatches has not yet transformed routine clinical care. One central reason for this gap between data and clinical insights is the lack of transparency and standards around data generated from mobile device that hinders interoperability and reproducibility. The clinical informatics community has offered solutions via the Fast Healthcare Interoperability Resources (FHIR) standard which facilities electronic health record interoperability but is less developed towards precision temporal contextually-tagged sensor measurements generated from today's ubiquitous mobile devices. In this paper we explore the opportunities and challenges of various theoretical approaches towards FHIR compatible digital phenotyping, and offer a concrete example implementing one such framework as an Application Programming Interface (API) for the open-source mindLAMP platform. We aim to build a community with contributions from statisticians, clinicians, patients, family members, researchers, designers, engineers, and more.
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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.