Abstract
BACKGROUND: Physical activity (PA) is a key component in the management of type 2 diabetes. However, this population have low rates of PA engagement. Electrically assisted cycling has been identified as a means through which to increase PA by incorporating activity into daily life, while overcoming some of the barriers to conventional cycling. The determinants of e-cycling among people living with chronic disease are largely unknown. The aim of this research was to explore the determinants of e-cycling among individuals with type 2 diabetes using the Theoretical Domains Framework (TDF) and the Capability, Opportunity and Motivation for Behaviour change model (COM-B). This information is important for determining the suitability of future e-cycling initiatives and, if appropriate, inform future e-cycling interventions. METHOD: One-to-one semi structured interviews were conducted with 16 participants from the e-cycling arm of a pilot randomised controlled trial between September 2019 and April 2020. The TDF was used to develop the interview guide. The framework method of analysis was used, incorporating both deductive and inductive reasoning. A conceptual model of the factors that influence e-cycling in this population was created. RESULTS: The most commonly reported TDF domains were skills, knowledge, belief about capabilities, belief about consequences and environmental context and resources. Specifically, e-bike training facilitated e-cycling engagement by providing participants with the skills, knowledge, and confidence needed to ride the e-bike and ride on the road. In addition, the enjoyment of e-cycling was a key facilitator to engagement. Participants engaged in e-cycling to improve their health rather than for environmental reasons. Most barriers to e-cycling related to the natural or physical environment. CONCLUSION: This study provides insight into the personal, social, and environmental factors associated with e-cycling in this population. The findings of this study can be used to develop a more refined e-cycling intervention targeting the factors identified as influencing e-cycling engagement. In addition, this information will help in the selection of mechanistic outcome measures for evaluation.