Evidence map›Paper›PMID 28448497›Full record

ArticlePloS one2017

Examination of an eHealth literacy scale and a health literacy scale in a population with moderate to high cardiovascular risk: Rasch analyses.

Sarah S Richtering, Rebecca Morris, Sze-Ee Soh, Anna Barker, Fiona Bampi, Lis Neubeck, Genevieve Coorey, John Mulley, John Chalmers, Tim Usherwood and 3 more

2 registry-linked trialsAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 58 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 3 pooled it
–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.

NCT04810260 nacompletednot on this mapstarted 2021, after this paper: background citation

Implementing, Validating, and Disseminating a Patient-facing Mobile Health Application for HIV Infected and Uninfected Women With Co-morbid Conditions in the Outpatient Setting

TypeinterventionalSponsorMontefiore Medical CenterRan2021 to 2023Enrolled42ConditionsMobile Health, Asthma, Diabetes MellitusArmsUse mobile health app (WellXcel) to increase knowledge
NCT05180604 nacompletednot on this mapstarted 2019, after this paper: background citation

Improving Patients With Chronic Disease to Engage in the eHealth Care Environment: the Application and Evaluation of eHealth Literacy Concept

TypeinterventionalSponsorNational Yang Ming Chiao Tung UniversityRan2019 to 2021Enrolled92ConditionsType 2 Diabetes Mellitus, Chronic Kidney Diseases, Cardiovascular DiseasesArmsexperience learning programs of eHealth care
3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  2. Health literacy in cancer caregivers: a systematic review.Journal of cancer survivorship : research and practice · 2021
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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

13 authors.

Sarah S RichteringThe George Institute for Global Health, Sydney, NSW, Australia.
Rebecca MorrisSchool of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Sze-Ee SohSchool of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Anna BarkerSchool of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Fiona BampiThe George Institute for Global Health, Sydney, NSW, Australia.
Lis NeubeckThe George Institute for Global Health, Sydney, NSW, Australia.
Genevieve CooreyThe George Institute for Global Health, Sydney, NSW, Australia.
John MulleyThe George Institute for Global Health, Sydney, NSW, Australia.
John ChalmersThe George Institute for Global Health, Sydney, NSW, Australia.
Tim UsherwoodThe George Institute for Global Health, Sydney, NSW, Australia.
David PeirisThe George Institute for Global Health, Sydney, NSW, Australia.
Clara K ChowThe George Institute for Global Health, Sydney, NSW, Australia.
Julie RedfernThe George Institute for Global Health, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionElectronic health (eHealth) strategies are evolving making it important to have valid scales to assess eHealth and health literacy. Item response theory methods, such as the Rasch measurement model, are increasingly used for the psychometric evaluation of scales. This paper aims to examine the internal construct validity of an eHealth and health literacy scale using Rasch analysis in a population with moderate to high cardiovascular disease risk.

methodsThe first 397 participants of the CONNECT study completed the electronic health Literacy Scale (eHEALS) and the Health Literacy Questionnaire (HLQ). Overall Rasch model fit as well as five key psychometric properties were analysed: unidimensionality, response thresholds, targeting, differential item functioning and internal consistency.

resultsThe eHEALS had good overall model fit (χ2 = 54.8, p = 0.06), ordered response thresholds, reasonable targeting and good internal consistency (person separation index (PSI) 0.90). It did, however, appear to measure two constructs of eHealth literacy. The HLQ subscales (except subscale 5) did not fit the Rasch model (χ2: 18.18-60.60, p: 0.00-0.58) and had suboptimal targeting for most subscales. Subscales 6 to 9 displayed disordered thresholds indicating participants had difficulty distinguishing between response options. All subscales did, nonetheless, demonstrate moderate to good internal consistency (PSI: 0.62-0.82).

conclusionRasch analyses demonstrated that the eHEALS has good measures of internal construct validity although it appears to capture different aspects of eHealth literacy (e.g. using eHealth and understanding eHealth). Whilst further studies are required to confirm this finding, it may be necessary for these constructs of the eHEALS to be scored separately. The nine HLQ subscales were shown to measure a single construct of health literacy. However, participants' scores may not represent their actual level of ability, as distinction between response categories was unclear for the last four subscales. Reducing the response categories of these subscales may improve the ability of the HLQ to distinguish between different levels of health literacy.

Indexed as

AgedCardiovascular DiseasesFemaleHealth LiteracyHumansMaleMiddle AgedRisk

Identifiers

PMID28448497
PMCPMC5407817

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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 OpenQuestion graph.