ArticleJournal of medical Internet research2019
Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Brief Digital Interventions for Psychological Distress: An AI-Enhanced Response-Adaptive Randomized Clinical Trial.JAMA network open · 2025Trial
- Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data.Contrast media & molecular imaging · 2021Trial
- Challenges and standardisation strategies for sensor-based data collection for digital phenotyping.Communications medicine · 2025Review
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- App-Based Ecological Momentary Assessment of Problematic Smartphone Use During Examination Weeks in University Students: 6-Week Observational Study.Journal of medical Internet research · 2025Observational
- Opportunities and challenges of lifestyle intervention-based digital therapeutics in LDL-C management: a scoping review.Therapeutic advances in chronic disease · 2025Article
- Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers.Cerebellum (London, England) · 2024Review
- The Future Proofing Study: Design, methods and baseline characteristics of a prospective cohort study of the mental health of Australian adolescents.International journal of methods in psychiatric research · 2023Article
- Development of Open Backend Structures for Health Care Professionals to Improve Participation in App Developments: Pilot Usability Study of a Medical App.JMIR formative research · 2023Article
- Indigenous population genome databases for India and South Asia: emerging need for health and social applications.Journal of genetics · 2023Review
- Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review.JMIR mHealth and uHealth · 2021Article
- Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study.JMIR mHealth and uHealth · 2021Article
- Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring.Frontiers in digital health · 2021Review
- Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review.Journal of personalized medicine · 2020Article
- Efficacy of a Smartphone App Intervention for Reducing Caregiver Stress: Randomized Controlled Trial.JMIR mental health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.
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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.