Project description:BackgroundGerm Defence ( www.germdefence.org ) is an evidence-based interactive website that promotes behaviour change for infection control within households. To maximise the potential of Germ Defence to effectively reduce the spread of COVID-19, the intervention needed to be implemented at scale rapidly.MethodsWith NHS England approval, we conducted an efficient two-arm (1:1 ratio) cluster randomised controlled trial (RCT) to examine the effectiveness of randomising implementation of Germ Defence via general practitioner (GP) practices across England, UK, compared with usual care to disseminate Germ Defence to patients. GP practices randomised to the intervention arm (n = 3292) were emailed and asked to disseminate Germ Defence to all adult patients via mobile phone text, email or social media. Usual care arm GP practices (n = 3287) maintained standard management for the 4-month trial period and then asked to share Germ Defence with their adult patients. The primary outcome was the rate of GP presentations for respiratory tract infections (RTI) per patient. Secondary outcomes comprised rates of acute RTIs, confirmed COVID-19 diagnoses and suspected COVID-19 diagnoses, COVID-19 symptoms, gastrointestinal infection diagnoses, antibiotic usage and hospital admissions. The impact of the intervention on outcome rates was assessed using negative binomial regression modelling within the OpenSAFELY platform. The uptake of the intervention by GP practice and by patients was measured via website analytics.ResultsGerm Defence was used 310,731 times. The average website satisfaction score was 7.52 (0-10 not at all to very satisfied, N = 9933). There was no evidence of a difference in the rate of RTIs between intervention and control practices (rate ratio (RR) 1.01, 95% CI 0.96, 1.06, p = 0.70). This was similar to all other eight health outcomes. Patient engagement within intervention arm practices ranged from 0 to 48% of a practice list.ConclusionsWhile the RCT did not demonstrate a difference in health outcomes, we demonstrated that rapid large-scale implementation of a digital behavioural intervention is possible and can be evaluated with a novel efficient prospective RCT methodology analysing routinely collected patient data entirely within a trusted research environment.Trial registrationThis trial was registered in the ISRCTN registry (14602359) on 12 August 2020.

Project description:BackgroundDecreasing ineffective or harmful healthcare practices (de-implementation) may require different approaches than those used to promote uptake of effective practices (implementation). Few psychological theories differentiate between processes involved in decreasing, versus increasing, behaviour. However, it is unknown whether implementation and de-implementation interventions already use different approaches. We used the behaviour change technique (BCT) taxonomy (version 1) (which includes 93 BCTs organised into 12 groupings) to investigate whether implementation and de-implementation interventions for clinician behaviour change use different BCTs.MethodsIntervention descriptions in 181 articles from three systematic reviews in the Cochrane Library were coded for (a) implementation versus de-implementation and (b) intervention content (BCTs) using the BCT taxonomy (v1). BCT frequencies were calculated and compared using Pearson's chi-squared (χ2), Yates' continuity correction and Fisher's exact test, where appropriate. Identified BCTs were ranked according to frequency and rankings for de-implementation versus implementation interventions were compared and described.ResultsTwenty-nine and 25 BCTs were identified in implementation and de-implementation interventions respectively. Feedback on behaviour was identified more frequently in implementation than de-implementation (Χ2(2, n=178) = 15.693, p = .000057). Three BCTs were identified more frequently in de-implementation than implementation: Behaviour substitution (Χ2(2, n=178) = 14.561, p = .0001; Yates' continuity correction); Monitoring of behaviour by others without feedback (Χ2(2, n=178) = 16.187, p = .000057; Yates' continuity correction); and Restructuring social environment (p = .000273; Fisher's 2-sided exact test).ConclusionsThere were some significant differences between BCTs reported in implementation and de-implementation interventions suggesting that researchers may have implicit theories about different BCTs required for de-implementation and implementation. These findings do not imply that the BCTs identified as targeting implementation or de-implementation are effective, rather simply that they were more frequently used. These findings require replication for a wider range of clinical behaviours. The continued accumulation of additional knowledge and evidence into whether implementation and de-implementation is different will serve to better inform researchers and, subsequently, improve methods for intervention design.

Project description:BackgroundDigital harm-reduction interventions typically focus on people with severe drug-use problems, yet these interventions have moderate effectiveness on drug-users with lower levels of risk of harm. The difference in effectiveness may be explained by differences in behavioural patterns between the two groupings. Harnessing behavioural theories to understand what is at the core of drug-use behaviours and mapping the content of new interventions, may improve upon the effectiveness of interventions for lower-risk drug-users. To the best of our knowledge, this is the first study to systematically apply the Behaviour Change Wheel (BCW) approach to understand the components, influencing capabilities, opportunities, and motivations (COM-B) of higher education students to change their drug-use behaviors. It is also the first study which identifies specific patterns of behaviours that are more responsive to harm reduction practices through the use of the Theoretical Domain Framework (TDF).MethodsWe employed an explanatory sequential mix-method design. We first conducted an on-line survey and a Delphi exercise to understand the factors influencing COM-B components of higher education students to change their drug-use. Subsequently, we mapped all evidence onto the COM-B components and the TDF domains to identify clusters of behaviours to target for change, using a pattern-based discourse analysis. Finally, a series of multidisciplinary group meetings identified the intervention functions-the means by which the intervention change targeted behaviours and the Behavioural Change Techniques (BCTs) involved using the behaviour change technique taxonomy (v.1).ResultsTwenty-nine BCTs relevant to harm-reduction practices were identified and mapped across five intervention functions (education, modelling, persuasion, incentivization, and training) and five policy categories (communication/marketing, guidelines, regulation, service provision, and environmental/social planning). These BCTs were distributed across eight identified saturated clusters of behaviours MyUSE intervention attempts to change.ConclusionsThe BCTs, identified, will inform the development of a digitally delivered behaviour change intervention that focuses on increasing mindful decision-making with respect to drug-use and promotes alternatives to drug-use activities. The findings can also inform implementation scientists in applying context-specific harm-reduction practices in higher education. We present examples of how the eight identified clusters of target behaviours are mapped across the COM-B components and the TDF, along with suggestions of implementation practices for harm reduction at student population level.

Project description: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.

Dataset Information

Primary Care implementation of Germ Defence, a digital behaviour change intervention to improve household infection control during the COVID-19 pandemic: A structured summary of a study protocol for a randomised controlled trial.

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