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Development of an electronic health message system to support recovery after stroke: Inspiring Virtual Enabled Resources following Vascular Events (iVERVE)

Overview of attention for article published in Patient preference and adherence, July 2018
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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 (#23 of 1,100)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
40 tweeters

Readers on

mendeley
41 Mendeley
Title
Development of an electronic health message system to support recovery after stroke: Inspiring Virtual Enabled Resources following Vascular Events (iVERVE)
Published in
Patient preference and adherence, July 2018
DOI 10.2147/ppa.s154581
Pubmed ID
Authors

Dominique A Cadilhac, Doreen Busingye, Jonathan C Li, Nadine E Andrew, Monique F Kilkenny, Amanda G Thrift, Vincent Thijs, Maree L Hackett, Ian Kneebone, Natasha A Lannin, Alana Stewart, Ida Dempsey, Jan Cameron

Abstract

Worldwide, stroke is a leading cause of disease burden. Many survivors have unmet needs after discharge from hospital. Electronic communication technology to support post-discharge care has not been used for patients with stroke. In this paper, we describe the development of a novel electronic messaging system designed for survivors of stroke to support their goals of recovery and secondary prevention after hospital discharge. This was a formative evaluation study. The design was informed by a literature search, existing data from survivors of stroke, and behavior change theories. We established two working groups; one for developing the electronic infrastructure and the other (comprising researchers, clinical experts and consumer representatives) for establishing the patient-centered program. Following agreement on the categories for the goal-setting menu, we drafted relevant messages to support and educate patients. These messages were then independently reviewed by multiple topic experts. Concurrently, we established an online database to capture participant characteristics and then integrated this database with a purpose-built messaging system. We conducted alpha testing of the approach using the first 60 messages. The initial goal-setting menu comprised 26 subcategories. Following expert review, another 8 goal subcategories were added to the secondary prevention category: managing cholesterol; smoking; physical activity; alcohol consumption; weight management; medication management; access to health professionals, and self-care. Initially, 455 health messages were created by members of working group 2. Following refinement and mapping to different goals by the project team, 980 health messages across the health goals and 69 general motivational messages were formulated. Seventeen independent reviewers assessed the messages and suggested adding 73 messages and removing 16 (2%). Overall, 1,233 messages (18 administrative, 69 general motivation and 1,146 health-related) were created. This novel electronic self-management support system is ready to be pilot tested in a randomized controlled trial in patients with stroke.

Twitter Demographics

The data shown below were collected from the profiles of 40 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 20%
Student > Master 7 17%
Professor 5 12%
Unspecified 5 12%
Student > Postgraduate 3 7%
Other 12 29%
Unknown 1 2%
Readers by discipline Count As %
Nursing and Health Professions 9 22%
Unspecified 6 15%
Medicine and Dentistry 5 12%
Psychology 4 10%
Chemistry 4 10%
Other 12 29%
Unknown 1 2%

Attention Score in Context

This research output has an Altmetric Attention Score of 27. 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 10 August 2018.
All research outputs
#602,641
of 13,350,484 outputs
Outputs from Patient preference and adherence
#23
of 1,100 outputs
Outputs of similar age
#23,076
of 265,992 outputs
Outputs of similar age from Patient preference and adherence
#2
of 33 outputs
Altmetric has tracked 13,350,484 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,100 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 265,992 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 91% of its contemporaries.
We're also able to compare this research output to 33 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 93% of its contemporaries.