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Intervention planning for a digital intervention for self-management of hypertension: a theory-, evidence- and person-based approach

Overview of attention for article published in Implementation Science, February 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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41 X users
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1 Facebook page

Citations

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116 Dimensions

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354 Mendeley
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Title
Intervention planning for a digital intervention for self-management of hypertension: a theory-, evidence- and person-based approach
Published in
Implementation Science, February 2017
DOI 10.1186/s13012-017-0553-4
Pubmed ID
Authors

Rebecca Band, Katherine Bradbury, Katherine Morton, Carl May, Susan Michie, Frances S. Mair, Elizabeth Murray, Richard J. McManus, Paul Little, Lucy Yardley

Abstract

This paper describes the intervention planning process for the Home and Online Management and Evaluation of Blood Pressure (HOME BP), a digital intervention to promote hypertension self-management. It illustrates how a Person-Based Approach can be integrated with theory- and evidence-based approaches. The Person-Based Approach to intervention development emphasises the use of qualitative research to ensure that the intervention is acceptable, persuasive, engaging and easy to implement. Our intervention planning process comprised two parallel, integrated work streams, which combined theory-, evidence- and person-based elements. The first work stream involved collating evidence from a mixed methods feasibility study, a systematic review and a synthesis of qualitative research. This evidence was analysed to identify likely barriers and facilitators to uptake and implementation as well as design features that should be incorporated in the HOME BP intervention. The second work stream used three complementary approaches to theoretical modelling: developing brief guiding principles for intervention design, causal modelling to map behaviour change techniques in the intervention onto the Behaviour Change Wheel and Normalisation Process Theory frameworks, and developing a logic model. The different elements of our integrated approach to intervention planning yielded important, complementary insights into how to design the intervention to maximise acceptability and ease of implementation by both patients and health professionals. From the primary and secondary evidence, we identified key barriers to overcome (such as patient and health professional concerns about side effects of escalating medication) and effective intervention ingredients (such as providing in-person support for making healthy behaviour changes). Our guiding principles highlighted unique design features that could address these issues (such as online reassurance and procedures for managing concerns). Causal modelling ensured that all relevant behavioural determinants had been addressed, and provided a complete description of the intervention. Our logic model linked the hypothesised mechanisms of action of our intervention to existing psychological theory. Our integrated approach to intervention development, combining theory-, evidence- and person-based approaches, increased the clarity, comprehensiveness and confidence of our theoretical modelling and enabled us to ground our intervention in an in-depth understanding of the barriers and facilitators most relevant to this specific intervention and user population.

X Demographics

X Demographics

The data shown below were collected from the profiles of 41 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 354 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 354 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 53 15%
Researcher 52 15%
Student > Ph. D. Student 51 14%
Student > Bachelor 28 8%
Other 17 5%
Other 58 16%
Unknown 95 27%
Readers by discipline Count As %
Medicine and Dentistry 69 19%
Nursing and Health Professions 52 15%
Psychology 52 15%
Social Sciences 17 5%
Computer Science 12 3%
Other 39 11%
Unknown 113 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 07 March 2021.
All research outputs
#1,566,595
of 25,205,864 outputs
Outputs from Implementation Science
#276
of 1,791 outputs
Outputs of similar age
#30,211
of 317,289 outputs
Outputs of similar age from Implementation Science
#10
of 44 outputs
Altmetric has tracked 25,205,864 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,791 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one has done well, scoring higher than 84% 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 317,289 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 90% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.