Evidence map›Paper›PMID 41068528›Full record

ArticleJournal of general internal medicine2026

Integrating Consumer-Grade Wearable Devices and Patient-Generated Health Data into Clinical Care: Perspectives from Healthcare Professionals at a Learning Health System.

Selene S Mak, Rebecca L Kinney, Anne L Bailey, Allison E Gaffey, Mark R Relyea, Elias K Spanakis, Garrett I Ash

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

7 authors.

Selene S MakVeterans Health Administration at the Greater Los Angeles Healthcare System, Los Angeles, CA, USA. Selene.Mak@va.gov.ORCID http://orcid.org/0000-0002-3722-4361
Rebecca L KinneyDepartment of Population and Quantitative Health Sciences, Division of Preventive and Behavioral Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Anne L BaileyDepartment of Veteran Affairs, Veterans Health Administration Strategic Initiatives Lab, Washington, DC, USA.
Allison E GaffeyDepartment of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA.
Mark R RelyeaVeterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
Elias K SpanakisDivision of Endocrinology, Baltimore Veterans Affairs Medical Center, Baltimore, MD, USA.
Garrett I AshDepartment of Internal Medicine, Section of General Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Funding

Informatics-Based Digital Application to Promote Safe Exercise in Middle-Aged Adults with Type 1 DiabetesK01DK129441 · NIDDK · YALE UNIVERSITY · PI Garrett Igo Ash · 2022 to 2026
$950k
Social Vulnerability, Sleep, and Early Hypertension Risk in Younger AdultsK23HL168233 · NHLBI · YALE UNIVERSITY · PI Allison Gaffey · 2023 to 2026
$517k
NHLBI NIH HHS K23 HL168233NIDDK NIH HHS K01 DK129441
6 · The paper itself

Abstract

backgroundThe increasing popularity of consumer-grade wearable (CGW) devices for everyday use has prompted discussions within learning healthcare systems (LHSs) about the integration of patient-generated health data (PGHD) into clinical workflow.

objectiveTo report findings from interviews with healthcare professionals (HCPs) about the potential role of CGWs and PGHD in clinical workflow, which in turn informed recommendations for health systems to consider when integrating PGHD from CGWs.

designMixed methods.

participantsClinical care providers and holistic/wellness providers with various training and expertise from the largest integrated health system in the USA (i.e., Veterans Health Administration). APPROACH: A purposive sample of 29 individuals who were knowledgeable informants were invited to complete a survey focused on CGW feasibility and usability of PGHD. Those who completed the survey were invited to participate in a semi-structured interview to elicit their perceptions of the barriers and facilitators for PGHD utilization. Survey data were analyzed descriptively and rapid qualitative analysis techniques were used to identify themes from interview data. KEY

resultsTwenty-one HCPs were included in this study. Quantitative and qualitative findings revealed significant interest in PGHD integration in clinical workflow and patient interactions. Clinical care providers focused on the value of PGHD as a complement to traditional clinical data, whereas holistic/wellness providers emphasized using CGWs and PGHD for behavioral change. Their interest levels were tempered by concerns about workload implications and apprehension about having to distill the vast amount of PGHD of varying validity into clinically actionable information.

conclusionsIntegrating CGWs and PGHD can enable an LHS to improve patient outcomes, particularly to promote health access and equity. Yet, concerns were raised regarding staffing and workload issues, data interpretation, expectations, access, privacy, and educational needs. Therefore, harnessing the benefits of CGWs will require investing in health systems' infrastructure to optimally integrate PGHD into clinical workflow.

Indexed as

Attitude of Health PersonnelHealth PersonnelLearning Health SystemPatient Generated Health DataWearable Electronic DevicesDigital HealthFemaleHumansMaleUnited Statesclinical workflowdigital healthlearning health systempatient-generated health data

Identifiers

PMID41068528
PMCPMC12954729

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.