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‘Around the edges’: using behaviour change techniques to characterise a multilevel implementation strategy for a fall prevention programme

Overview of attention for article published in Implementation Science, August 2018
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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 (89th percentile)

Mentioned by

30 tweeters


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49 Mendeley
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‘Around the edges’: using behaviour change techniques to characterise a multilevel implementation strategy for a fall prevention programme
Published in
Implementation Science, August 2018
DOI 10.1186/s13012-018-0798-6
Pubmed ID

S. McHugh, C. Sinnott, E. Racine, S. Timmons, M. Byrne, P. M. Kearney


Implementation strategies are needed to ensure that evidence-based healthcare interventions are adopted successfully. However, strategies are generally poorly described and those used in everyday practice are seldom reported formally or fully understood. Characterising the active ingredients of existing strategies is necessary to test and refine implementation. We examined whether an implementation strategy, delivered across multiple settings targeting different stakeholders to support a fall prevention programme, could be characterised using the Behaviour Change Technique (BCT) Taxonomy. Data sources included project plans, promotional material, interviews with a purposive sample of stakeholders involved in the strategy's design and delivery and observations of staff training and information meetings. Data were analysed using TIDieR to describe the strategy and determine the levels at which it operated (organisational, professional, patient). The BCT Taxonomy identified BCTs which were mapped to intervention functions. Data were coded by three researchers and finalised through consensus. We analysed 22 documents, 6 interviews and 4 observation sessions. Overall, 21 out a possible 93 BCTs were identified across the three levels. At an organisational level, identifiable techniques tended to be broadly defined; the most common BCT was restructuring the social environment. While some activities were intended to encourage implementation, they did not have an immediate behavioural target and could not be coded using BCTs. The largest number and variety of BCTs were used at the professional level to target the multidisciplinary teams delivering the programme and professionals referring to the programme. The main BCTs targeting the multidisciplinary team were instruction on how to perform the (assessment) behaviour and demonstration of (assessment) behaviour; the main BCT targeting referrers was adding objects to the environment. At the patient level, few BCTs were used to target attendance. In this study, several behaviour change techniques were evident at the individual professional level; however, fewer techniques were identifiable at an organisational level. The BCT Taxonomy was useful for describing components of a multilevel implementation strategy that specifically target behaviour change. To fully and completely describe an implementation strategy, including components that involve organisational or systems level change, other frameworks may be needed.

Twitter Demographics

The data shown below were collected from the profiles of 30 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 18%
Student > Doctoral Student 6 12%
Researcher 5 10%
Student > Ph. D. Student 5 10%
Lecturer > Senior Lecturer 4 8%
Other 12 24%
Unknown 8 16%
Readers by discipline Count As %
Nursing and Health Professions 10 20%
Psychology 10 20%
Medicine and Dentistry 6 12%
Social Sciences 3 6%
Design 2 4%
Other 6 12%
Unknown 12 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 31 August 2018.
All research outputs
of 13,451,706 outputs
Outputs from Implementation Science
of 1,389 outputs
Outputs of similar age
of 268,633 outputs
Outputs of similar age from Implementation Science
of 3 outputs
Altmetric has tracked 13,451,706 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 1,389 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done well, scoring higher than 81% 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 268,633 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 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them