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Optimisation of complex health interventions prior to a randomised controlled trial: a scoping review of strategies used

Overview of attention for article published in Pilot and Feasibility Studies, March 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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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Title
Optimisation of complex health interventions prior to a randomised controlled trial: a scoping review of strategies used
Published in
Pilot and Feasibility Studies, March 2016
DOI 10.1186/s40814-016-0058-y
Pubmed ID
Authors

Sara Levati, Pauline Campbell, Rachael Frost, Nadine Dougall, Mary Wells, Cam Donaldson, Suzanne Hagen

Abstract

Many complex intervention trials fail to show an intervention effect. Although this may be due to genuine ineffectiveness, it may also be the result of sub-optimal intervention design, implementation failure or a combination of these. Given current financial constraints and the pressure to reduce waste and increase value in health services research, pre-trial strategies are needed to reduce the likelihood of design or implementation failure and to maximise the intervention's potential for effectiveness. In this scoping review, we aimed to identify and synthesise the available evidence relating to the strategies and methods used to 'optimise' complex interventions at the pre-trial stage. We searched MEDLINE, CINAHL, AMED, PsycINFO and ProQuest Nursing & Allied Health Source for papers published between January 2000 and March 2015. We included intervention development and optimisation studies that explored potential intervention weaknesses and limitations before moving to a definitive randomised controlled trial (RCT). Two reviewers independently applied selection criteria and systematically extracted information relating to the following: study characteristics; intervention under development; framework used to guide the development process; areas of focus of the optimisation process, methods used and outcomes of the optimisation process. Data were tabulated and summarised in a narrative format. We screened 3968 titles and 231 abstracts for eligibility. Eighty-nine full-text papers were retrieved; 27 studies met our selection criteria. Optimisation strategies were used for a range of reasons: to explore the feasibility and acceptability of the intervention to patients and healthcare professionals; to estimate the effectiveness and cost-effectiveness of different combinations of intervention components; and to identify potential barriers to implementation. Methods varied widely across studies, from interviews and focus groups to economic modelling and probability analysis. The review identifies a range of optimisation strategies currently used. Although a preliminary classification of these strategies can be proposed, a series of questions remain as to which methods to use for different interventions and how to determine when the intervention is ready or 'optimised enough' to be tested in a RCT. Future research should explore potential answers to the questions raised, to guide researchers in the development and evaluation of more effective interventions.

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X Demographics

The data shown below were collected from the profiles of 34 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 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 190 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 23%
Researcher 32 17%
Student > Master 17 9%
Other 16 8%
Student > Doctoral Student 11 6%
Other 29 15%
Unknown 42 22%
Readers by discipline Count As %
Medicine and Dentistry 45 24%
Psychology 29 15%
Nursing and Health Professions 24 13%
Social Sciences 8 4%
Computer Science 4 2%
Other 22 12%
Unknown 59 31%
Attention Score in Context

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 12 October 2018.
All research outputs
#1,816,959
of 25,080,267 outputs
Outputs from Pilot and Feasibility Studies
#75
of 1,200 outputs
Outputs of similar age
#29,116
of 305,703 outputs
Outputs of similar age from Pilot and Feasibility Studies
#2
of 13 outputs
Altmetric has tracked 25,080,267 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,200 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done particularly well, scoring higher than 93% 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 305,703 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 13 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 92% of its contemporaries.