ArticleNPJ digital medicine2026
Sensor-based digital health technologies to capture endpoints in recent clinical trials: a scoping review.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensor-based digital health technologies (DHTs) enable continuous collection of physiological data, sensor-based functional outcomes, and performance outcomes in clinical and real-world settings. However, cross-therapeutic reviews examining sensor-based DHTs as outcome measurement tools rather than interventions in recent pharmaceutical and device trials are lacking, limiting understanding of practical implementation and utility in COA development for use in clinical trials. To address this gap, a scoping review was conducted that encompassed clinical studies that used sensor-based DHTs and were published in MEDLINE, MEDLINE In-Process, and PsycINFO databases from January 2021-December 2023. In total, 48 studies were included, and most (n = 38; 79%) collected sensor-based physiological data, with continuous glucose monitoring (CGM) being the most frequent. Additionally, 12 studies (25%) described sensor-based outcomes, such as physical activity and sleep; 2 studies collected both sensor-based physiological data and clinical outcomes. Our findings highlight the use of sensor-based DHTs in clinical research to measure patient outcomes and describe challenges in implementing these technologies in clinical trials.
Identifiers
What Socratic 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 Socratic graph.