'A circuit breaker' - Interrupting the alcohol autopilot: A qualitative exploration of participants' experiences of a personalised mHealth approach bias modification intervention for alcohol use.

OBJECTIVE: There is a need for low-cost, wide-reaching interventions to enhance accessibility of support for people with hazardous alcohol consumption. We assessed participant experiences of using a novel, personalised mHealth intervention offering approach bias modification (ApBM) for alcohol use in a community sample drinking at harmful levels to enable a deeper understanding of the end user and engagement.

METHODS: Eighteen semi-structured telephone interviews were conducted with adults in the community drinking at harmful/hazardous levels. A reflexive thematic analysis approach was used and data analysis followed iterative categorisation.

RESULTS: Engagement/Motivation and Clinical Value were overarching themes. The useable, accessible, customisable design described by participants enabled training to be readily integrated into routines, enhancing autonomy and self-efficacy beliefs, and facilitating engagement/motivation. Where autonomy or perceived self-efficacy were threatened by a rigid training schedule or lack of clarity/reminders, engagement was reduced. Training increased awareness of drinking behaviours, and encouraged participants to consider alternate goal-directed behaviours with feedback suggesting training may function as a 'circuit breaker', increasing time between alcohol craving and seeking, and enabling reflective processing, at least in the short term.

CONCLUSIONS: This novel smartphone intervention for alcohol use may be a useful, accessible, 'just in time' adjunctive support tool for non-treatment seekers, meeting an important gap in the field. Findings have implications for the implementation of subsequent digital interventions, suggesting participants may stand to gain more from an intervention which enables autonomy and improves self-efficacy beliefs. Theoretically, findings speak to the role of inferential processing in behaviour change, but further research is needed to clearly elucidate ApBM training mechanisms. Practical recommendations for subsequent app iterations are suggested, along with additional opportunities worthy of consideration for future initiatives.