Evidence map›Paper›PMID 39491883›Full record

ReviewClinical and translational science2024

Implementing sensor-based digital health technologies in clinical trials: Key considerations from the eCOA Consortium.

Elena S Izmailova, Danielle Middleton, Reem Yunis, Julia Lakeland, Kristen Sowalsky, Julia Kling, Alison Ritchie, Christine C Guo, Bill Byrom, Scottie Kern

Abstract readReview
In one paragraph

Review in Clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Continuous Monitoring of Head Turns: Compliance, Kinematics, and Reliability of Wearable Sensing.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025
    Article
  7. Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Elena S IzmailovaKoneksa Health, New York, New York, USA.ORCID 0000-0002-7150-1748
Danielle MiddletonAstraZeneca PLC, Cambridge, UK.ORCID 0009-0007-1897-7804
Reem YunisMedable, Inc, Palo Alto, California, USA.ORCID 0000-0001-5439-1607
Julia LakelanduMotif Ltd, London, UK.
Kristen SowalskyAPDM Wearable Technologies, a Clario Company, Portland, Oregon, USA.ORCID 0009-0003-3463-4394
Julia KlingKoneksa Health, New York, New York, USA.
Alison RitchieParexel International, Sheffield, UK.
Christine C GuoActiGraph LLC, Pensacola, Florida, USA.ORCID 0000-0003-1530-0172
Bill ByromSignant Health, London, UK.ORCID 0000-0002-9186-4271
Scottie KernCritical Path Institute, Tucson, Arizona, USA.ORCID 0000-0003-0135-6466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increased use of sensor-based digital health technologies (DHTs) in clinical trials brought to light concerns about implementation practices that might introduce burden on trial participants, resulting in suboptimal compliance and become an additional complicating factor in clinical trial conduct. These concerns may contribute to the lower-than-anticipated uptake of DHT deployment and data use for regulatory decision-making, despite well-articulated benefits. The Electronic Clinical Outcome Assessment (eCOA) Consortium gathered collective experience on deploying sensor-based DHTs and supplemented this with relevant literature focusing on mechanisms that may enhance participant compliance. The process for DHT implementation starts with identifying a clinical concept of interest followed by a digital measure selection, defining active or passive data capture and their sources, the number of sensors with respective body location, plus the duration and frequency of use in the context of perceived participant burden. Roundtable discussions among patient groups, physicians, and technology providers prior to protocol development can be very impactful for optimizing trial design. While diversity and inclusion are essential for any clinical trial, patient populations should be considered carefully in the context of trial-specific aims, requirements, and anticipated patient burden. Minimizing site burden includes assessment of training, research engagement, and logistical burden which needs to be triaged differently for early and late-stage clinical trials. Additional considerations include sharing trial results with study participants and leveraging publicly available data for compliance modeling. To the best of our knowledge, this report provides holistic considerations for sensor-based DHT implementation that may optimize participant compliance.

Indexed as

Clinical Trials as TopicBiomedical TechnologyDigital HealthDigital TechnologyHumansPatient ComplianceResearch Design

Identifiers

PMID39491883
PMCPMC11532371

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.