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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

Project description

Improved computation modelling for effective behaviour changes

Evidence shows that using a behaviour change theory to inform the development of intervention activities leads to a higher impact of the latter. In the case of tobacco smoking, which is still the leading cause of premature deaths in Europe, fighting dependency is mainly supported by pharmacotherapy and behavioural coaching strategies. Unfortunately, people often fail to complete such treatments because they cannot control their cravings and social pressures. The EU-funded COMPLAPSE project aims to enable dynamically tailored digital smoking cessation interventions through developing and validating a dynamic computational model of lapse risks. The new approach will improve the static behaviour change theories concerning observed complexities and bring more effective interventions in health, society and other fields.

Objective

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 Europes 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 researchers expertise and transferrable skills, enabling her to develop independence; valorise the researchers knowledge within the associated partner organisation and beneficiary; and disseminate the results to the scientific community, industry professionals, and the public.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2021-PF-01

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Coordinator

TAMPEREEN KORKEAKOULUSAATIO SR
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 302 331,36
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Partners (1)

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