Evidence map›Paper›PMID 31401587›Full record

ArticleBMJ health & care informatics2019

Measuring the outcomes of using person-generated health data: a case study of developing a PROM item bank.

Gerardo Luis Dimaguila, Kathleen Gray, Mark Merolli

Abstract readCase Reports
In one paragraph

Article in BMJ health & care informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  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.

Gerardo Luis DimaguilaHealth and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Parkville, Victoria, Australia dgl@student.unimelb.edu.au.ORCID http://orcid.org/0000-0002-3498-6256
Kathleen GrayHealth and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Parkville, Victoria, Australia.
Mark MerolliHealth and Biomedical Informatics Centre, The University of Melbourne, Melbourne, Parkville, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPatient-reported outcome measures (PROMs) allow patients to self-report the status of their health condition or experience independently. A key area for PROMs to contribute in building the evidence base is in understanding the effects of using person-generated health data (PGHD), and using PROMs to measure outcomes of using PGHD has been suggested in the literature. Key considerations inherent in the stroke rehabilitation context makes the measurement of PGHD outcomes in home-based poststroke rehabilitation, which uses body-tracking technologies, an important use case.

objectiveThis paper describes the development of a preliminary item bank of a PROM-PGHD for Kinect-based stroke rehabilitation systems (K-SRS), or PROM-PGHD for K-SRS.

methodsThe authors designed a method to develop PROMs of using PGHD, or PROM-PGHD. The PROM-PGHD Development Method was designed by augmenting a key PROM development process, the Qualitative Item Review, and follows PROM development best practice. It has five steps, namely, literature review; binning and winnowing; initial item revision; eliciting patient input and final item Revision.

resultsA preliminary item bank of the PROM-PGHD for K-SRS is presented. This is the result of implementing the first three steps of the PROM-PGHD Development Method within the domains of interest, that is, stroke and Kinect-based simulated rehabilitation.

conclusionsThis paper has set out a case study of our method, showing what needs to be done to ensure that the PROM-PGHD items are suited to the health condition and technology category. We described it as a case study because we argue that it is possible for the PROM-PGHD method to be used by others to measure effects of PGHD utilisation in other cases of health conditions and technology categories. Hence, it offers generalisability and has broader clinical relevance for evidence-based practice with PGHD. This paper is the first to offer a case study of developing a PROM-PGHD.

Indexed as

Medical InformaticsPatient Reported Outcome MeasuresStroke RehabilitationHumansQuality of LifeSelf ReportSurveys and QuestionnairesTreatment Outcomepatient monitoringpatient-reported outcome measuresperson-generated health dataquestionnaire designtelemedicine

Identifiers

PMID31401587
PMCPMC7062343

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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.