Evidence map›Paper›PMID 33118944›Full record

ArticleJMIR mHealth and uHealth2020

Evaluating Asthma Mobile Apps to Improve Asthma Self-Management: User Ratings and Sentiment Analysis of Publicly Available Apps.

Marlene Camacho-Rivera, Huy Vo, Xueqi Huang, Julia Lau, Adeola Lawal, Akira Kawaguchi

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Digital tools in allergy and respiratory care.The World Allergy Organization journal · 2022
    Review
  5. Article
  6. Article
  7. Article
  8. Article
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

6 authors.

Marlene Camacho-RiveraDepartment of Community Health Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, United States.ORCID 0000-0003-2343-2941
Huy VoDepartment of Computer Science, Grove School of Engineering, City College of New York, New York, NY, United States.ORCID 0000-0002-5963-6615
Xueqi HuangDepartment of Computer Science, Grove School of Engineering, City College of New York, New York, NY, United States.ORCID 0000-0002-7962-5980
Julia LauDepartment of Computer Science, Grove School of Engineering, City College of New York, New York, NY, United States.ORCID 0000-0002-5872-8665
Adeola LawalDepartment of Community Health and Social Medicine, CUNY School of Medicine, New York, NY, United States.ORCID 0000-0003-1457-3638
Akira KawaguchiDepartment of Computer Science, Grove School of Engineering, City College of New York, New York, NY, United States.ORCID 0000-0002-8798-4713

Funding

TRANSPORT - the TRANSlational Program Of health disparities Research TrainingS21MD012474 · NIMHD · SUNY DOWNSTATE MEDICAL CENTER · PI Carla Boutin-Foster, William Marcus Lambert · 2017 to 2026
$18.0M
NIMHD NIH HHS S21 MD012474
6 · The paper itself

Abstract

backgroundThe development and use of mobile health (mHealth) apps for asthma management have risen dramatically over the past two decades. Asthma apps vary widely in their content and features; however, prior research has rarely examined preferences of users of publicly available apps.

objectiveThe goals of this study were to provide a descriptive overview of asthma mobile apps that are publicly available and to assess the usability of asthma apps currently available on the market to identify content and features of apps associated with positive and negative user ratings.

methodsReviews were collected on June 23, 2020, and included publicly posted reviews until June 21, 2020. To characterize features associated with high or low app ratings, we first dichotomized the average user rating of the asthma app into 2 categories: a high average rating and a low average rating. Asthma apps with average ratings of 4 and above were categorized as having a high average rating. Asthma apps with average ratings of less than 4 were categorized as having a low average rating. For the sentiment analysis, we modeled both 2-word (bi-gram) and 3-word (tri-gram) phrases which commonly appeared across highly rated and lowly rated apps.

resultsOf the 10 apps that met the inclusion criteria, a total of 373 reviews were examined across all apps. Among apps reviewed, 53.4% (199/373) received high ratings (average ratings of 4 or 5) and 47.2% (176/373) received low ratings (average ratings of 3 or less). The number of ratings across all apps ranged from 188 (AsthmaMD) to 10 (My Asthma App); 30% (3/10) of apps were available on both Android and iOS. From the sentiment analysis, key features of asthma management that were common among highly rated apps included the tracking of peak flow readings (n=48), asthma symptom monitoring (n=11), and action plans (n=10). Key features related to functionality that were common among highly rated apps included ease of use (n=5). Users most commonly reported loss of data (n=14) and crashing of app (n=12) as functionality issues among poorly rated asthma apps.

conclusionsOur study results demonstrate that asthma app quality, maintenance, and updates vary widely across apps and platforms. These findings may call into question the long-term engagement with asthma apps, a crucial factor for determining their potential to improve asthma self-management and asthma clinical outcomes.

Indexed as

AsthmaMobile ApplicationsHealth BehaviorHumansMonitoring, Physiologicasthma appsmHealthmobile phonesentiment analysissmartphoneuser ratings

Identifiers

PMID33118944
PMCPMC7661227

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LicenceCC BY
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Registered trials

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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.