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Reporting behaviour change interventions: do the behaviour change technique taxonomy v1, and training in its use, improve the quality of intervention descriptions?

Overview of attention for article published in Implementation Science, June 2016
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

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46 tweeters
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1 Facebook page

Citations

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30 Dimensions

Readers on

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164 Mendeley
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Title
Reporting behaviour change interventions: do the behaviour change technique taxonomy v1, and training in its use, improve the quality of intervention descriptions?
Published in
Implementation Science, June 2016
DOI 10.1186/s13012-016-0448-9
Pubmed ID
Authors

Caroline E. Wood, Wendy Hardeman, Marie Johnston, Jill Francis, Charles Abraham, Susan Michie

Abstract

Behaviour change interventions are likely to be reproducible only if reported clearly. We assessed whether the behaviour change technique taxonomy version 1 (BCTTv1), with and without training in identifying BCTs, improves the clarity and replicability of written reports of observed behaviour change interventions. Three studies assessed effects of using and training in the use of BCTTv1 on the clarity and replicability of intervention descriptions written after observing videos of smoking cessation interventions. Study 1 examined the effects of using and not using BCTTv1. Study 2 examined the effects of using BCTTv1 and training in use of BCTTv1 compared no use and no training. Study 3 employed a within-group design to assess change in descriptions written before and after training. One-hundred and 66 'writers' watched videos of behaviour change interventions and wrote descriptions of the active components delivered. In all studies, the participants' written descriptions were evaluated by (i) 12 'raters' (untrained in BCTTv1) for clarity and replicability and (ii) 12 'coders' (trained in BCTTv1) for reliability of BCT coding. Writers rated the usability and accessibility of using BCTTv1 to write descriptions. Ratings of clarity and replicability did not differ between groups in study 1 (all ps > 0.05), were poorer for trained users in study 2 (all ps < 0.01) and improved following training in study 3 (all ps < 0.05). BCT identification was more reliable from descriptions written by trained BCTTv1 users (p < 0.05; study 2) but not simple use of BCTTv1 (p = 0.93; study 1) or by writers who had written a description without BCTTv1, before training (p = 0.50; study 3). Writers reported that using BCTTv1 was difficult but 'useful', 'good' and 'desirable' and that their descriptions would be clear and replicable (all means above mid-point of the scale). Effects of training to use BCTTv1 on the quality of written reports of observed interventions were mixed, with some suggestion of improved clarity and replicability of reporting in the within- (study 3) but not the between-group studies (studies 1 and 2). Potential benefits of using BCTTv1 may have been limited by the artificial nature and time constraints of the task.

Twitter Demographics

The data shown below were collected from the profiles of 46 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 164 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Unknown 162 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 23%
Student > Ph. D. Student 30 18%
Student > Master 28 17%
Student > Doctoral Student 12 7%
Student > Bachelor 10 6%
Other 31 19%
Unknown 16 10%
Readers by discipline Count As %
Psychology 62 38%
Social Sciences 18 11%
Nursing and Health Professions 15 9%
Medicine and Dentistry 13 8%
Computer Science 7 4%
Other 27 16%
Unknown 22 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 05 January 2017.
All research outputs
#984,287
of 19,521,967 outputs
Outputs from Implementation Science
#207
of 1,641 outputs
Outputs of similar age
#19,918
of 277,101 outputs
Outputs of similar age from Implementation Science
#1
of 10 outputs
Altmetric has tracked 19,521,967 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,641 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one has done well, scoring higher than 87% 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 277,101 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 92% of its contemporaries.
We're also able to compare this research output to 10 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