ArticleJournal of medical Internet research2025
A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
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Who cites it
3 citing papers in PubMed.
- Digital assessment in myasthenia gravis: evidence, validation, and a proposed framework for autoimmune heterogeneity, remote monitoring, and clinical endpoints.Frontiers in immunology · 2026Review
- Patient-centered gastrointestinal function assessment technologies: a paradigm shift from traditional approaches to non-invasive innovations.Frontiers in physiology · 2026Review
- Methods for analytical validation of novel digital clinical measures: A simulation study.PloS one · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs-evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable-is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development.
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Registered trials
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