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Improving the precision of behaviour change theories: Development and validation of a computational model of lapse risk in smokers attempting to quit (COMPLAPSE)

Tampere University

Tobacco smoking remains the leading preventable cause of premature morbidity and mortality in Europe. Gold standard treatment for smoking cessation includes pharmacotherapy and behavioural support. However, smoking lapses – influenced by momentary fluctuations in cravings and social cues – are a key source of treatment failure. COMPLAPSE aims to advance the state-of-the-art by developing and validating a dynamic computational model of lapse risk, improving the precision of static behaviour change theories to account for observed complexities and laying the foundation for dynamically tailored, person-centred digital smoking cessation interventions for increased effectiveness. COMPLAPSE is interdisciplinary in scope – drawing on know-how from behavioural science, engineering, and computer science – and directly contributes to Europe’s Path to the Digital Decade and its Strategic Framework for the Prevention of Non-Communicable Diseases.

First, a conceptual model will be developed by articulating a diverse group of stakeholders’ (e.g., researchers, policymakers, smokers) dynamic predictions through participatory systems mapping. Next, a computational model will be developed through formulating mathematical equations for each model component, followed by a series of simulations to iteratively refine the model to align with stakeholders’ predictions. Finally, the model will be validated against temporally dense experience sampling and sensor data collected in smokers’ daily lives to critically examine whether the computational model outperforms static state-of-the-art theories.

The research objectives are linked to key training, knowledge transfer and communication activities to advance the researcher’s expertise and transferrable skills, enabling her to develop independence; valorise the researcher’s knowledge within the associated partner organisation and beneficiary; and disseminate the results to the scientific community, industry professionals, and the public.

Project lead: Dr Olga Perski, Marie Skłodowska-Curie Postdoctoral Research Fellow, Tampere University/UC San Diego (Website: ; Twitter: @OlgaPerski)

Mentors: Prof. Nelli Hankonen (Tampere University) and Prof. Eric Hekler (UC San Diego)