Evidence map›Paper›PMID 39347625›Full record

ArticleJMIR human factors2024

Italian Version of the mHealth App Usability Questionnaire (Ita-MAUQ): Translation and Validation Study in People With Multiple Sclerosis.

Jessica Podda, Erica Grange, Alessia Susini, Andrea Tacchino, Federica Di Antonio, Ludovico Pedullà, Giampaolo Brichetto, Michela Ponzio

Abstract read
In one paragraph

Article in JMIR human factors, 2024. 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

8 authors.

Jessica PoddaScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0000-0002-9327-9148
Erica GrangeScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0000-0001-5114-9276
Alessia SusiniScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0009-0005-2905-3936
Andrea TacchinoScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0000-0002-2263-7315
Federica Di AntonioScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0009-0007-1552-0737
Ludovico PedullàScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0000-0002-2547-382X
Giampaolo BrichettoScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0000-0003-2026-3572
Michela PonzioScientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.ORCID 0000-0001-5245-0474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Telemedicine and mobile health (mHealth) apps have emerged as powerful tools in health care, offering convenient access to services and empowering participants in managing their health. Among populations with chronic and progressive disease such as multiple sclerosis (MS), mHealth apps hold promise for enhancing self-management and care. To be used in clinical practice, the validity and usability of mHealth tools should be tested. The most commonly used method for assessing the usability of electronic technologies are questionnaires. Objective: This study aimed to translate and validate the English version of the mHealth App Usability Questionnaire into Italian (ita-MAUQ) in a sample of people with MS. Methods: The 18-item mHealth App Usability Questionnaire was forward- and back-translated from English into Italian by an expert panel, following scientific guidelines for translation and cross-cultural adaptation. The ita-MAUQ (patient version for stand-alone apps) comprises 3 subscales, which are ease of use, interface and satisfaction, and usefulness. After interacting with DIGICOG-MS (Digital Assessment of Cognitive Impairment in Multiple Sclerosis), a novel mHealth app for cognitive self-assessment in MS, people completed the ita-MAUQ and the System Usability Scale, included to test construct validity of the translated questionnaire. Confirmatory factor analysis, internal consistency, test-retest reliability, and construct validity were assessed. Known-groups validity was examined based on disability levels as indicated by the Expanded Disability Status Scale (EDSS) score and gender. Results: In total, 116 people with MS (female n=74; mean age 47.2, SD 14 years; mean EDSS 3.32, SD 1.72) were enrolled. The ita-MAUQ demonstrated acceptable model fit, good internal consistency (Cronbach α=0.92), and moderate test-retest reliability (intraclass coefficient correlation 0.84). Spearman coefficients revealed significant correlations between the ita-MAUQ total score; the ease of use (5 items), interface and satisfaction (7 items), and usefulness subscales; and the System Usability Scale (all P values <.05). Known-group analysis found no difference between people with MS with mild and moderate EDSS (all P values >.05), suggesting that ambulation ability, mainly detected by the EDSS, did not affect the ita-MAUQ scores. Interestingly, a statistical difference between female and male participants concerning the ease of use ita-MAUQ subscale was found (P=.02). Conclusions: The ita-MAUQ demonstrated high reliability and validity and it might be used to evaluate the usability, utility, and acceptability of mHealth apps in people with MS.

Indexed as

Mobile ApplicationsMultiple SclerosisTelemedicineAdultFemaleHumansItalyMaleMiddle AgedPsychometricsReproducibility of ResultsSurveys and QuestionnairesTranslatingTranslationsapp usabilitycognitive assessmentdisabilityMAUQmHealthmHealth appmHealth applicationmobile healthmultiple sclerosisquestionnaire validationtelemedicineusabilityusability questionnairevalidation study

Identifiers

PMID39347625
PMCPMC11457702

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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.