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Changing behaviour 'more or less'-do theories of behaviour inform strategies for implementation and de-implementation? A critical interpretive synthesis
BACKGROUND: Implementing evidence-based care requires healthcare practitioners to do less of some things (de-implementation) and more of others (implementation). Variations in effectiveness of behaviour change interventions may result from failure to consider a distinction between approaches by which behaviour increases and decreases in frequency. The distinction is not well represented in methods for designing interventions. This review aimed to identify whether there is a theoretical rationale to support this distinction. METHODS: Using Critical Interpretative Synthesis, this conceptual review included papers from a broad range of fields (biology, psychology, education, business) likely to report approaches for increasing or decreasing behaviour. Articles were identified from databases using search terms related to theory and behaviour change. Articles reporting changes in frequency of behaviour and explicit use of theory were included. Data extracted were direction of behaviour change, how theory was operationalised, and theory-based recommendations for behaviour change. Analyses of extracted data were conducted iteratively and involved inductive coding and critical exploration of ideas and purposive sampling of additional papers to explore theoretical concepts in greater detail. RESULTS: Critical analysis of 66 papers and their theoretical sources identified three key findings: (1) 9 of the 15 behavioural theories identified do not distinguish between implementation and de-implementation (5 theories were applied to only implementation or de-implementation, not both); (2) a common strategy for decreasing frequency was substituting one behaviour with another. No theoretical basis for this strategy was articulated, nor were methods proposed for selecting appropriate substitute behaviours; (3) Operant Learning Theory makes an explicit distinction between techniques for increasing and decreasing frequency. DISCUSSION: Behavioural theories provide little insight into the distinction between implementation and de-implementation. Operant Learning Theory identified different strategies for implementation and de-implementation, but these strategies may not be acceptable in health systems. Additionally, if behaviour substitution is an approach for de-implementation, further investigation may inform methods or rationale for selecting the substitute behaviour
Precision analysis of a quantitative CT liver surface nodularity score
To evaluate precision of a software-based liver surface nodularity (LSN) score derived from CT images.
An anthropomorphic CT phantom was constructed with simulated liver containing smooth and nodular segments at the surface and simulated visceral and subcutaneous fat components. The phantom was scanned multiple times on a single CT scanner with adjustment of image acquisition and reconstruction parameters (N = 34) and on 22 different CT scanners from 4 manufacturers at 12 imaging centers. LSN scores were obtained using a software-based method. Repeatability and reproducibility were evaluated by intraclass correlation (ICC) and coefficient of variation. Using abdominal CT images from 68 patients with various stages of chronic liver disease, inter-observer agreement and test-retest repeatability among 12 readers assessing LSN by software- vs. visual-based scoring methods were evaluated by ICC.
There was excellent repeatability of LSN scores (ICC:0.79-0.99) using the CT phantom and routine image acquisition and reconstruction parameters (kVp 100-140, mA 200-400, and auto-mA, section thickness 1.25-5.0Â mm, field of view 35-50Â cm, and smooth or standard kernels). There was excellent reproducibility (smooth ICC: 0.97; 95% CI 0.95, 0.99; CV: 7%; nodular ICC: 0.94; 95% CI 0.89, 0.97; CV: 8%) for LSN scores derived from CT images from 22 different scanners. Inter-observer agreement for the software-based LSN scoring method was excellent (ICC: 0.84; 95% CI 0.79, 0.88; CV: 28%) vs. good for the visual-based method (ICC: 0.61; 95% CI 0.51, 0.69; CV: 43%). Test-retest repeatability for the software-based LSN scoring method was excellent (ICC: 0.82; 95% CI 0.79, 0.84; CV: 12%).
The software-based LSN score is a quantitative CT imaging biomarker with excellent repeatability, reproducibility, inter-observer agreement, and test-retest repeatability