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Usability of Commercially Available Mobile Applications for Diverse Patients

Overview of attention for article published in JGIM: Journal of General Internal Medicine, July 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
8 news outlets
policy
1 policy source
twitter
81 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
173 Mendeley
Title
Usability of Commercially Available Mobile Applications for Diverse Patients
Published in
JGIM: Journal of General Internal Medicine, July 2016
DOI 10.1007/s11606-016-3771-6
Pubmed ID
Authors

Urmimala Sarkar, Gato I. Gourley, Courtney R. Lyles, Lina Tieu, Cassidy Clarity, Lisa Newmark, Karandeep Singh, David W. Bates

Abstract

Mobile applications or 'apps' intended to help people manage their health and chronic conditions are widespread and gaining in popularity. However, little is known about their acceptability and usability for low-income, racially/ethnically diverse populations who experience a disproportionate burden of chronic disease and its complications. The objective of this study was to investigate the usability of existing mobile health applications ("apps") for diabetes, depression, and caregiving, in order to facilitate development and tailoring of patient-facing apps for diverse populations. Usability testing, a mixed-methods approach that includes interviewing and direct observation of participant technology use, was conducted with participants (n = 9 caregivers; n = 10 patients with depression; and n = 10 patients with diabetes) on a total of 11 of the most popular health apps (four diabetes apps, four depression apps, and three caregiver apps) on both iPad and Android tablets. The participants were diverse: 15 (58 %) African Americans, seven (27 %) Whites, two (8 %) Asians, two (8 %) Latinos with either diabetes, depression, or who were caregivers. Participants were given condition-specific tasks, such as entering a blood glucose value into a diabetes app. Participant interviews were video recorded and were coded using standard methods to evaluate attempts and completions of tasks. We performed inductive coding of participant comments to identify emergent themes. Participants completed 79 of 185 (43 %) tasks across 11 apps without assistance. Three themes emerged from participant comments: lack of confidence with technology, frustration with design features and navigation, and interest in having technology to support their self-management. App developers should employ participatory design strategies in order to have an impact on chronic conditions such as diabetes and depression that disproportionately affect vulnerable populations. While patients express interest in using technologies for self-management, current tools are not consistently usable for diverse patients.

Twitter Demographics

The data shown below were collected from the profiles of 81 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 173 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
United Kingdom 1 <1%
United States 1 <1%
Netherlands 1 <1%
Unknown 169 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 19%
Student > Master 30 17%
Researcher 26 15%
Unspecified 20 12%
Student > Bachelor 19 11%
Other 45 26%
Readers by discipline Count As %
Medicine and Dentistry 42 24%
Computer Science 29 17%
Unspecified 26 15%
Nursing and Health Professions 19 11%
Psychology 19 11%
Other 38 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 122. 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 21 July 2019.
All research outputs
#123,798
of 13,452,308 outputs
Outputs from JGIM: Journal of General Internal Medicine
#103
of 4,837 outputs
Outputs of similar age
#5,706
of 258,312 outputs
Outputs of similar age from JGIM: Journal of General Internal Medicine
#5
of 100 outputs
Altmetric has tracked 13,452,308 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,837 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has done particularly well, scoring higher than 97% 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 258,312 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 97% of its contemporaries.
We're also able to compare this research output to 100 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 95% of its contemporaries.