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Engineering a mobile health tool for resource-poor settings to assess and manage cardiovascular disease risk: SMARThealth study

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2015
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

policy
2 policy sources
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14 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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57 Dimensions

Readers on

mendeley
334 Mendeley
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Title
Engineering a mobile health tool for resource-poor settings to assess and manage cardiovascular disease risk: SMARThealth study
Published in
BMC Medical Informatics and Decision Making, April 2015
DOI 10.1186/s12911-015-0148-4
Pubmed ID
Authors

Arvind Raghu, Devarsetty Praveen, David Peiris, Lionel Tarassenko, Gari Clifford

Abstract

The incidence of chronic diseases in low- and middle-income countries is rapidly increasing both in urban and rural regions. A major challenge for health systems globally is to develop innovative solutions for the prevention and control of these diseases. This paper discusses the development and pilot testing of SMARTHealth, a mobile-based, point-of-care Clinical Decision Support (CDS) tool to assess and manage cardiovascular disease (CVD) risk in resource-constrained settings. Through pilot testing, the preliminary acceptability, utility, and efficiency of the CDS tool was obtained. The CDS tool was part of an mHealth system comprising a mobile application that consisted of an evidence-based risk prediction and management algorithm, and a server-side electronic medical record system. Through an agile development process and user-centred design approach, key features of the mobile application that fitted the requirements of the end users and environment were obtained. A comprehensive analytics framework facilitated a data-driven approach to investigate four areas, namely, system efficiency, end-user variability, manual data entry errors, and usefulness of point-of-care management recommendations to the healthcare worker. A four-point Likert scale was used at the end of every risk assessment to gauge ease-of-use of the system. The system was field-tested with eleven village healthcare workers and three Primary Health Centre doctors, who screened a total of 292 adults aged 40 years and above. 34% of participants screened by health workers were identified by the CDS tool to be high CVD risk and referred to a doctor. In-depth analysis of user interactions found the CDS tool feasible for use and easily integrable into the workflow of healthcare workers. Following completion of the pilot, further technical enhancements were implemented to improve uptake of the mHealth platform. It will then be evaluated for effectiveness and cost-effectiveness in a cluster randomized controlled trial involving 54 southern Indian villages and over 16000 individuals at high CVD risk. An evidence-based CVD risk prediction and management tool was used to develop an mHealth platform in rural India for CVD screening and management with proper engagement of health care providers and local communities. With over a third of screened participants being high risk, there is a need to demonstrate the clinical impact of the mHealth platform so that it could contribute to improved CVD detection in high risk low resource settings.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 334 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Brazil 1 <1%
Switzerland 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Unknown 328 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 59 18%
Student > Ph. D. Student 50 15%
Researcher 42 13%
Student > Postgraduate 21 6%
Student > Doctoral Student 21 6%
Other 67 20%
Unknown 74 22%
Readers by discipline Count As %
Medicine and Dentistry 73 22%
Nursing and Health Professions 49 15%
Computer Science 27 8%
Engineering 23 7%
Social Sciences 14 4%
Other 55 16%
Unknown 93 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 11 March 2021.
All research outputs
#2,113,999
of 24,008,549 outputs
Outputs from BMC Medical Informatics and Decision Making
#121
of 2,054 outputs
Outputs of similar age
#27,520
of 267,742 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 38 outputs
Altmetric has tracked 24,008,549 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,054 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 94% 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 267,742 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 89% of its contemporaries.
We're also able to compare this research output to 38 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 94% of its contemporaries.