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Conformity of Diabetes Mobile apps with the Chronic Care Model

Overview of attention for article published in BMJ Health & Care Informatics, April 2019
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  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
Conformity of Diabetes Mobile apps with the Chronic Care Model
Published in
BMJ Health & Care Informatics, April 2019
DOI 10.1136/bmjhci-2019-000017
Pubmed ID
Authors

Raheleh Salari, Sharareh R Niakan Kalhori, Marjan Ghazisaeidi, Farhad Fatehi

Abstract

Despite the growing use of mobile applications (apps) for chronic disease management, the evidence on the effectiveness of this technology on clinical and behavioural outcomes of the patients is scant. Many studies highlight the importance of the theoretical foundations of mobile-based interventions. One of the most widely accepted models for the management of chronic diseases, such as diabetes, is the Chronic Care Model (CCM). In this study, we investigated the conformity of the selected diabetes mobile apps with CCM. We searched online journal databases related to diabetes mobile apps to find common features. Then considering the components of the CCM as a reference model, features of some popular and top-ranking apps were compared with CCM. Among 23 studied apps, 34 per cent of them had medium conformity and 66 per cent of these apps were in weak conformity. The self-management support component is covered by 100 per cent of them. Ninety-five per cent of apps have covered the proactive follow-up component. App conformance with CCM is generally weak. App developers are recommended to give greater consideration to established theoretical models in their design and implementation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 11%
Researcher 9 11%
Student > Ph. D. Student 8 10%
Student > Bachelor 7 9%
Lecturer 5 6%
Other 13 16%
Unknown 29 36%
Readers by discipline Count As %
Medicine and Dentistry 14 18%
Nursing and Health Professions 12 15%
Computer Science 7 9%
Psychology 4 5%
Engineering 3 4%
Other 8 10%
Unknown 32 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 22 July 2019.
All research outputs
#8,406,705
of 25,932,719 outputs
Outputs from BMJ Health & Care Informatics
#215
of 505 outputs
Outputs of similar age
#143,223
of 366,805 outputs
Outputs of similar age from BMJ Health & Care Informatics
#6
of 15 outputs
Altmetric has tracked 25,932,719 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 505 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.6. This one has gotten more attention than average, scoring higher than 56% 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 366,805 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.