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Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations

Overview of attention for article published in Translational Behavioral Medicine, September 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#34 of 1,020)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
twitter
54 tweeters
patent
1 patent
facebook
3 Facebook pages

Citations

dimensions_citation
222 Dimensions

Readers on

mendeley
550 Mendeley
Summary Blogs Twitter Patents Facebook Dimensions citations
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Title
Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations
Published in
Translational Behavioral Medicine, September 2014
DOI 10.1007/s13142-014-0293-9
Pubmed ID
25584085
Authors

Edwin D Boudreaux, Molly E Waring, Rashelle B Hayes, Rajani S Sadasivam, Sean Mullen, Sherry Pagoto

Abstract

Mobile applications (apps) to improve health are proliferating, but before healthcare providers or organizations can recommend an app to the patients they serve, they need to be confident the app will be user-friendly and helpful for the target disease or behavior. This paper summarizes seven strategies for evaluating and selecting health-related apps: (1) Review the scientific literature, (2) Search app clearinghouse websites, (3) Search app stores, (4) Review app descriptions, user ratings, and reviews, (5) Conduct a social media query within professional and, if available, patient networks, (6) Pilot the apps, and (7) Elicit feedback from patients. The paper concludes with an illustrative case example. Because of the enormous range of quality among apps, strategies for evaluating them will be necessary for adoption to occur in a way that aligns with core values in healthcare, such as the Hippocratic principles of nonmaleficence and beneficence.

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Twitter Demographics

The data shown below were collected from the profiles of 54 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 550 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Portugal 1 <1%
Turkey 1 <1%
Hungary 1 <1%
Brazil 1 <1%
Netherlands 1 <1%
United Kingdom 1 <1%
Finland 1 <1%
Unknown 541 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 119 22%
Student > Ph. D. Student 73 13%
Student > Bachelor 62 11%
Researcher 54 10%
Student > Doctoral Student 32 6%
Other 114 21%
Unknown 96 17%
Readers by discipline Count As %
Medicine and Dentistry 108 20%
Computer Science 83 15%
Nursing and Health Professions 54 10%
Psychology 40 7%
Social Sciences 31 6%
Other 116 21%
Unknown 118 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 48. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 May 2022.
All research outputs
#783,795
of 23,655,983 outputs
Outputs from Translational Behavioral Medicine
#34
of 1,020 outputs
Outputs of similar age
#8,780
of 253,519 outputs
Outputs of similar age from Translational Behavioral Medicine
#1
of 23 outputs
Altmetric has tracked 23,655,983 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,020 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 253,519 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.

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