Evidence map›Paper›PMID 32379052›Full record

ArticleJMIR research protocols2020

Patient-Reported Outcome Measures of Utilizing Person-Generated Health Data in the Case of Simulated Stroke Rehabilitation: Development Method.

Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2020. 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
1.0field-weighted citation impact, top 24% of its field
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, 7 citations in OpenAlex.

  1. The Association of Learning Health System Practicing Hospitals and other Health Information Interested Hospitals with Patient-Generated Health Data Uptake.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
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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

3 authors at 1 institution in 1 country.

Gerardo Luis DimaguilaSchool of Computing and Information Systems, University of Melbourne, Melbourne, Australia.ORCID https://orcid.org/0000-0002-3498-6256
Kathleen GrayCentre for Digital Transformation of Health, University of Melbourne, Melbourne, Australia.ORCID https://orcid.org/0000-0003-1616-2170
Mark MerolliCentre for Digital Transformation of Health, University of Melbourne, Melbourne, Australia.ORCID https://orcid.org/0000-0003-4273-1816
The University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPerson-generated health data (PGHD) are health data that people generate, record, and analyze for themselves. Although the health benefits of PGHD use have been reported, there is no systematic way for patients to measure and report the health effects they experience from using their PGHD. Patient-reported outcome measures (PROMs) allow patients to systematically self-report their outcomes of a health care service. They generate first-hand evidence of the impact of health care services and are able to reflect the real-world diversity of actual patients and management approaches. Therefore, this paper argues that a PROM of utilizing PGHD, or PROM-PGHD, is necessary to help build evidence-based practice in clinical work with PGHD.

objectiveThis paper aims to describe a method for developing PROMs for people who are using PGHD in conjunction with their clinical care-PROM-PGHD, and the method is illustrated through a case study.

methodsThe five-step qualitative item review (QIR) method was augmented to guide the development of a PROM-PGHD. However, using QIR as a guide to develop a PROM-PGHD requires additional socio-technical consideration of the PGHD and the health technologies from which they are produced. Therefore, the QIR method is augmented for developing a PROM-PGHD, resulting in the PROM-PGHD development method.

resultsA worked example was used to illustrate how the PROM-PGHD development method may be used systematically to develop PROMs applicable across a range of PGHD technology types used in relation to various health conditions.

conclusionsThis paper describes and illustrates a method for developing a PROM-PGHD, which may be applied to many different cases of health conditions and technology categories. When applied to other cases of health conditions and technology categories, the method could have broad relevance for evidence-based practice in clinical work with PGHD.

Indexed as

patient generated health datapatient monitoringpatient reported outcome measuresperson generated health dataquestionnaire designtelemedicine

Identifiers

PMID32379052
PMCPMC7243131
OpenAlexW3000784180

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.