Periodic Reporting for period 1 - COMPLAPSE (Improving the precision of behaviour change theories: Development and validation of a computational model of lapse risk in smokers attempting to quit)
Période du rapport: 2022-10-01 au 2024-09-30
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. COMPLAPSE aims to address the following Research and Innovation Objectives (ROIs), with each ROI associated with a dedicated Work Package (WP):
ROI1. To develop a conceptual model of lapse risk, drawing on stakeholder input and the researcher’s knowledge of the available literature via participatory systems mapping (WP1).
ROI2. To formulate mathematical equations for each component in the conceptual model and perform a series of computer simulations (WP2).
ROI3. To collect and analyse EMA and sensor data and use this to validate the expert-derived dynamic computational model (WP3).
ROI1. To develop a conceptual model of lapse risk, drawing on stakeholder input and the researcher’s knowledge of the available literature via participatory systems mapping (WP1).
ROI2. To formulate mathematical equations for each component in the conceptual model and perform a series of computer simulations (WP2).
ROI3. To collect and analyse EMA and sensor data and use this to validate the expert-derived dynamic computational model (WP3).
WP1 and WP2 were completed during the outgoing phase at the associated partner organisation. To achieve WP1, a scoping review was first undertaken to examine the extent and nature of research activities pertaining to the use of formal and computational modelling to predict, explain and influence health behaviours unfolding at the within-person level. We synthesised methodological steps in the published literature and generated a set of initial ‘best practice’ modelling recommendations, which informed the next steps of the research. The scoping review has been published in the journal Health Psychology Review.
Next, we conducted an informal theory review and a series of linked stakeholder interviews with researchers, policymakers and stop smoking practitioners, and people with lived experience. We drew on these diverse knowledge sources to iteratively develop a conceptual model of lapse risk in smokers attempting to stop (also referred to as a ‘prototheory’). In WP2, we translated the prototheory into a series of difference equations which were implemented in R code. We conducted a series of computer simulations to examine if the formal and computational model could produce the empirical phenomena which it set out to explain (i.e. relapse, prolapse, abstinence). A paper describing the development and initial evaluation of the formal and computational model of lapse risk has been submitted for publication and is available as a pre-print. The R code underpinning the formal and computational model is openly available via GitHub.
During the incoming phase at the beneficiary, WP3 will be completed. We will collect survey and wearable sensor data in smokers’ daily lives and fit the formal model to the empirical data to examine its predictive success.
Next, we conducted an informal theory review and a series of linked stakeholder interviews with researchers, policymakers and stop smoking practitioners, and people with lived experience. We drew on these diverse knowledge sources to iteratively develop a conceptual model of lapse risk in smokers attempting to stop (also referred to as a ‘prototheory’). In WP2, we translated the prototheory into a series of difference equations which were implemented in R code. We conducted a series of computer simulations to examine if the formal and computational model could produce the empirical phenomena which it set out to explain (i.e. relapse, prolapse, abstinence). A paper describing the development and initial evaluation of the formal and computational model of lapse risk has been submitted for publication and is available as a pre-print. The R code underpinning the formal and computational model is openly available via GitHub.
During the incoming phase at the beneficiary, WP3 will be completed. We will collect survey and wearable sensor data in smokers’ daily lives and fit the formal model to the empirical data to examine its predictive success.
To develop potent just-in-time adaptive interventions with potential for population-level impact, we need theories that account for the inherent complexity and dynamic nature of lapse risk in smokers attempting to quit. The limitations of static theories have already been recognised across a wide range of fields, including biology, physics, and engineering, which rely primarily on computational modelling to formulate theories about phenomena of interest. The development of a dynamic computational model of lapse risk in project COMPLAPSE opens up new possibilities for dynamically tailored smoking cessation interventions. For example, the dynamic, computational model developed as part of project COMPLAPSE can be directly incorporated within a reinforcement learning algorithm or ‘controller’ (i.e. an algorithm type which is widely used in control systems engineering), which dynamically adapts the intervention delivery to each individual’s needs.