Robert West University College London November 2015 Applying behavioural science to the development of digital aids to smoking cessation Robert West University College London November 2015

Digital behaviour change interventions Are good for Dynamic tailoring Providing immediate reward Displaying images Social networking Monitoring and feedback Data gathering Data sharing Cumulative development

The development process

Activity map Context Concept Knowledge Goals Opportunities Development Testing Constraints Activity map Context Stakeholders Implementation Abandonment Collaborators West R, Michie S (2015) Developing and Evaluating Digital Behaviour Change Interventions. In Preparation. Risks Promotion

Knowledge

COM-B model of behaviour change Michie S, M van Stralen, West R (2011) The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42..

Top level framework for understanding behaviour change interventions Intervention/ comparator Mechanism of action Behaviour change Usage metrics Context Intervention-behaviour complex Effect West R, Michie S (2015) Developing and Evaluating Digital Behaviour Change Interventions. In Preparation. denotes ‘Influences’

Mechanisms of action

For smoking cessation the focus is momentary motivation to smoke and not to smoke

West R (2009). The multiple facets of cigarette addiction and what they mean for encouraging and helping smokers to stop. COPD: The Journal of Chronic Obstructive Disease, 6, 277-83.

Intervention components

Focus on behaviour change techniques that can be delivered by smartphones and have evidence of effectiveness when delivered by other modalities

Promote effective medication use Increase uptake and engagement, manage expectations Enhance coping Promote relaxation, provide distraction, increase self-control, promote mindfulness Enable avoidance or escape Negotiate strategies, train behaviour, provide triggers Provide reward for not smoking Congratulate, provide incentives, increase enjoyment and satisfaction Boost resolve not to smoke Build confidence, provide social support, boost morale, maintain commitment Boost concern about smoking Create disincentives, create negative imagery, increase salience of harms

Examples from team at UCL

StopAdvisor Website designed to aid cessation in smokers across the social spectrum Found in a large RCT to be effective in lower income smokers; currently being developed further by Public Health England Brown J, Michie S, Geraghty A, Yardley L, Gardner B, Shahab L, Stapleton J, West R, (2014) Internet-based intervention for smoking cessation (StopAdvisor) in people with low and high socioeconomic status: a randomised controlled trial. Lancet Respiratory Medicine, 12, 997-1006.

SF28 Sets goal of managing 28 days smoke-free and provides advice and tools to achieve this Promising results from an uncontrolled observational study; usage data being studied Ubhi HK, Michie S, Kotz D, Wong WC, West R, (2015). A mobile app to aid smoking cessation: Preliminary evaluation of SmokeFree28. Journal of Medical Internet Research, (17)1:e17

SmokeFree Most popular smoking cessation app available with 3000 new users per day, developed by Dave Crane RCT of earlier version being written up; currently studying usage patterns

BupaQuit Smoking cessation app based on SF28 with added craving management tools Currently undergoing RCT to evaluate usage patterns and effectiveness of the craving management components

SmokeFree Baby Smoking cessation and reduction app for pregnant smokers, aims to maximise number of smoke-free days Currently being evaluated in a ‘factorial’ experiment to optimise content

NRT2Quit Smoking cessation app developed to promote effective NRT use Currently being evaluated in an RCT

Challenges

Building Competing with 100s of other applications Maintaining engagement Keeping up with an ever-changing user environment Adapting to changes in platforms and operating systems Evaluating Attracting enough users into evaluations Collecting robust outcome data Choosing or building the right comparator in evaluations

Conclusions

Research has yielded a robust model of the processes leading to lapse and relapse Digital interventions could be well-equipped to target those processes to help smokers to stop This includes components aimed at promoting effective medication use There are 100s of digital interventions on the market but most do not deliver behaviour change techniques that would be expected to be effective Research is just beginning to build and evaluate effective interventions based on behavioural science There are major challenges to this but some promising results are emerging

Questions?