Complex interventions - Exploring the application of behaviour change theory to doctoral supervisor training.

Rationale: The student-supervisor relationship is an important factor impacting on doctoral student satisfaction and successful completion rates (e.g., Hodsdon & Buckley 2011; Kulej & Park 2008). Good supervision affects the student experience, student wellbeing and happiness (e.g., Cowling, 2017). Given the complex nature of effective supervision and the many specific behaviours it consists of (e.g., Debowski, 2016; Hyatt, 2017; Lee, 2008; Peelo, 2011), a key question is whether desired supervisory behaviours can be created by staff development trainings. Aims: The Com-B model (e.g., Michie et al. 2011) was used as a framework with the aim to i) define capabilities, opportunities and motivations that underpin supervisor behaviours towards their doctoral students, ii) design a research supervisor training programme and iii) develop criteria for measuring and evaluating the effectiveness of such trainings. Methodology: The Com-B framework has been tested over a period of seven years by applying it to the development, implementation and evaluation of a supervisor development training at a UK university. The training, delivered by a team of experienced researchers and supervisors, is aimed at academics new to the role of doctoral supervisor. It was designed to build new supervisors’ practical skills, knowledge of regulatory requirements and critical awareness of pedagogical literature required to engage in effective supervisory behaviour. The training consists of three, three-hour long sessions spread over three months. Questionnaires were handed out to 87 new supervisors from a range of subject areas and types of doctoral degree at the end of their training programme. 61 staff (70%) returned completed questionnaires. The questionnaire consisted of open-ended questions about participants’ motivations to do the training, confidence in newly learned skills and knowledge, most useful aspects of the training received and areas for further training. Analysis: Responses were analysed thematically and frequencies of common types of responses were compared. Results: The great majority of supervisors reported an increase in their knowledge, capabilities and confidence as a result of the training, whilst a minority expressed a desire for more exposure to actual supervisory practice as part of the training. Many candidates mentioned exchange and discussion with colleagues from different subject areas as useful and motivational. Only very few specific suggestions for what else to include in the training were made, asking for more opportunities aimed at bridging a perceived knowledge-practice gap. Conclusions: The findings suggest that the behaviour change framework provides a promising strategy for creating, implementing and evaluating doctoral supervisor trainings. Desired supervisory behaviours can be created by improving staff capabilities (their knowledge, skills) and confidence through training, in line with previous research (e.g., Kiley, 2011; McCulloch & Loeser, 2016; Peelo, 2011). Future interventions need to include further activities to bridge the practice-knowledge gap experienced by new supervisors, and extend discussion with a fuller range of stakeholders. Future research should establish the long-term effects of supervisory training on supervisory behaviours and investigate how opportunities provided by institutional and wider contexts affect supervisor behaviour and the health and wellbeing of doctoral students throughout their doctoral journey.University of Derby, Research, Innovation & Academic Enterpris

Engagement with Digital Behaviour Change Interventions: Conceptualisation, Measurement and Promotion

Digital behaviour change interventions (DBCIs) can help people change various health behaviours; however, engagement is low on average and there is a positive association of engagement with intervention effectiveness. The extent to which this relationship is confounded or subject to reverse causality is unclear, and evidence-based models of how to promote engagement are lacking. Progress is hindered by the existence of multiple definitions and measures of engagement; this hampers attempts to aggregate data in meta-analyses. Using smartphone applications (apps) for smoking cessation and alcohol reduction as case studies, this thesis investigated how to conceptualise and measure engagement and identified factors that influence engagement with DBCIs in general, and with apps for smoking cessation and alcohol reduction in particular. Six studies using qualitative and quantitative methods were conducted. Study 1 was a systematic, interdisciplinary literature review, which synthesised existing conceptualisations and generated an integrative definition of engagement with behavioural and experiential dimensions, and a conceptual framework of factors that influence engagement with DBCIs. Studies 3 and 4 involved the development and evaluation of a self-report measure of the behavioural and experiential dimensions of engagement. Studies 2, 5 and 6 used mixed-methods to identify factors that influence engagement with apps for smoking cessation and alcohol reduction. Engagement with DBCIs can usefully be defined in both behavioural and experiential terms: the self-report measure demonstrated promising psychometric properties and was underpinned by two distinct factors, labelled ‘Experiential Engagement’ and ‘Behavioural Engagement’. Design features that support users’ motivation to change, foster their beliefs about the perceived usefulness and relevance of the technology, and spark their interest were found to be most important in the promotion of engagement with apps for smoking cessation and alcohol reduction. These findings can be used to inform the design of new, or modification of existing, apps for these behaviours