Evidence map›Paper›PMID 29592848›Full record

ReviewJMIR mHealth and uHealth2018

Consumer Mobile Apps for Potential Drug-Drug Interaction Check: Systematic Review and Content Analysis Using the Mobile App Rating Scale (MARS).

Ben Yb Kim, Anis Sharafoddini, Nam Tran, Emily Y Wen, Joon Lee

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 6 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.

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

49 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  3. Sticky apps, not sticky hands: A systematic review and content synthesis of hand hygiene mobile apps.Journal of the American Medical Informatics Association : JAMIA · 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

5 authors.

Ben Yb KimHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0002-6793-5551
Anis SharafoddiniHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0003-4231-8116
Nam TranHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0002-0223-9272
Emily Y WenHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0001-6328-8572
Joon LeeHealth Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID http://orcid.org/0000-0001-8593-9321

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGeneral consumers can now easily access drug information and quickly check for potential drug-drug interactions (PDDIs) through mobile health (mHealth) apps. With aging population in Canada, more people have chronic diseases and comorbidities leading to increasing numbers of medications. The use of mHealth apps for checking PDDIs can be helpful in ensuring patient safety and empowerment.

objectiveThe aim of this study was to review the characteristics and quality of publicly available mHealth apps that check for PDDIs.

methodsApple App Store and Google Play were searched to identify apps with PDDI functionality. The apps' general and feature characteristics were extracted. The Mobile App Rating Scale (MARS) was used to assess the quality.

resultsA total of 23 apps were included for the review-12 from Apple App Store and 11 from Google Play. Only 5 of these were paid apps, with an average price of $7.19 CAD. The mean MARS score was 3.23 out of 5 (interquartile range 1.34). The mean MARS scores for the apps from Google Play and Apple App Store were not statistically different (P=.84). The information dimension was associated with the highest score (3.63), whereas the engagement dimension resulted in the lowest score (2.75). The total number of features per app, average rating, and price were significantly associated with the total MARS score.

conclusionsSome apps provided accurate and comprehensive information about potential adverse drug effects from PDDIs. Given the potentially severe consequences of incorrect drug information, there is a need for oversight to eliminate low quality and potentially harmful apps. Because managing PDDIs is complex in the absence of complete information, secondary features such as medication reminder, refill reminder, medication history tracking, and pill identification could help enhance the effectiveness of PDDI apps.

Indexed as

consumer health informaticsconsumer health informationdrug interactionsmobile applicationssmartphonetelemedicine

Identifiers

PMID29592848
PMCPMC5895923

What OpenQuestion holds

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LicenceCC BY
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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.