ArticleDigital biomarkers
Automate, Illuminate, Predict: A Universal Framework for Integrating Wearable Sensors in Healthcare.
Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- A systematic review of portable technologies for the early assessment of motor development in infants.NPJ digital medicine · 2025Article
- Enhanced gait tracking measures for individuals with stroke using leg-worn inertial sensors.Journal of neuroengineering and rehabilitation · 2024Article
- Quantifying Arm and Leg Movements in 3-Month-Old Infants Using Pose Estimation: Proof of Concept.Sensors (Basel, Switzerland) · 2024Article
- GPS and Smartphone Technology for Real-World Measurement of Community Mobility in Healthcare.Digital biomarkersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Background: Wearable sensors have been heralded as revolutionary tools for healthcare. However, while data are easily acquired from sensors, users still grapple with questions about how sensors can meaningfully inform everyday clinical practice and research. Summary: We propose a simple, comprehensive framework for utilizing sensor data in healthcare. The framework includes three key processes that are applied together or separately to (1) automate traditional clinical measures, (2) illuminate novel correlates of disease and impairment, and (3) predict current and future outcomes. We demonstrate applications of the Automate-Illuminate-Predict framework using examples from rehabilitation medicine. Key Messages: Automate-Illuminate-Predict provides a universal approach to extract clinically meaningful data from wearable sensors. This framework can be applied across the care continuum to enhance patient care and inform personalized medicine through accessible, noninvasive technology.
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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.