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Understanding the barriers to successful adoption and use of a mobile health information system in a community health center in São Paulo, Brazil: a cohort study

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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18 tweeters

Citations

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19 Dimensions

Readers on

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168 Mendeley
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Title
Understanding the barriers to successful adoption and use of a mobile health information system in a community health center in São Paulo, Brazil: a cohort study
Published in
BMC Medical Informatics and Decision Making, November 2016
DOI 10.1186/s12911-016-0385-1
Pubmed ID
Authors

Jayant V. Rajan, Juliana Moura, Gato Gourley, Karina Kiso, Alexandre Sizilio, Ana Maria Cortez, Lee W. Riley, Maria Amelia Veras, Urmimala Sarkar

Abstract

Mobile technology to support community health has surged in popularity, yet few studies have systematically examined usability of mobile platforms for this setting. We conducted a mixed-methods study of 14 community healthcare workers at a public healthcare clinic in São Paulo, Brazil. We held focus groups with community healthcare workers to elicit their ideas about a mobile health application and used this input to build a prototype app. A pre-use test survey was administered to all participants, who subsequently use-tested the app on three different devices (iPhone, iPad mini, iPad Air). Usability was assessed by objectively scored data entry errors and through a post-use focus group held to gather open-ended feedback on end-user satisfaction. All of the participants were women, ranging from 18-64 years old. A large percentage (85.7%) of participants had at least a high school education. Internet (92.8%), computer (85.7%) and cell phone (71.4%) use rates were high. Data entry error rates were also high, particularly in free text fields, ranging from 92.3 to 100%. Error rates were comparable across device type. In a post-use focus group, participants reported that they found the app easy to use and felt that its design was consistent with their vision. The participants raised several concerns, including that they did not find filling out the forms in the app to be a useful task. They also were concerned about an app potentially creating more work for them and personal security issues related to carrying a mobile device in low-income areas. In a cohort of formally educated community healthcare workers with high levels of personal computer and cell phone use, we identified no technological barriers to adapting their existing work to a mobile device based system. Transferring current data entry work into a mobile platform, however, uncovered underlying dissatisfaction with some data entry tasks. This dissatisfaction may be a more significant barrier than the data entry errors our testing revealed. Our results highlight the fact that without a deep understanding of local process to optimize usability, technology-based solutions in health may fail. Developing such an understanding must be a central component in the design of any mHealth solution in global health.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 168 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 34 20%
Student > Ph. D. Student 24 14%
Student > Bachelor 22 13%
Researcher 16 10%
Student > Doctoral Student 11 7%
Other 22 13%
Unknown 39 23%
Readers by discipline Count As %
Medicine and Dentistry 32 19%
Nursing and Health Professions 19 11%
Computer Science 16 10%
Social Sciences 11 7%
Business, Management and Accounting 9 5%
Other 33 20%
Unknown 48 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 18 June 2017.
All research outputs
#2,322,937
of 21,008,389 outputs
Outputs from BMC Medical Informatics and Decision Making
#171
of 1,839 outputs
Outputs of similar age
#57,282
of 420,015 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#10
of 94 outputs
Altmetric has tracked 21,008,389 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,839 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 90% 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 420,015 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 94 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 90% of its contemporaries.