Improving the precision of behaviour change theories: Development and validation of a dynamic COMPutational model of LAPSE risk in smokers attempting to stop (COMPLAPSE)
COMPLAPSE is funded by the European Commission under the Horizon Europe Framework Programme (HORIZON), Grant No. 101065293 (PI: Dr. Olga Perski; Mentors: Prof. Nelli Hankonen and Prof. Eric Hekler). This website will be used for the dissemination of the projcet findings, including linkes to scientific publications, talks and blogs.
Aims and objectives
Cigarette smoking remains the leading preventable cause of premature morbidity and mortality in Europe and beyond. Gold standard treatment for smoking cessation includes pharmacotherapy and behavioural support. However, smoking lapses – influenced by momentary fluctuations in cigarette availability, stress, and cravings – are a key source of treatment failure. COMPLAPSE aims to advance the state-of-the-art by developing and validating a formal, dynamical systems 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, systems science and dynamical systems modelling – 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).
ROI4. To propose a set of ‘best practice’ recommendations for how to use formal and computational modelling in health psychology research (WP4).
WP1
Theories are needed to predict, explain, and influence phenomena of interest. Popular relapse prevention theories are vaguely specified using natural language descriptions and lack temporal information about how key phenomena (i.e., ‘relapse’, ‘prolapse’, ‘abstinence’) are dynamically caused over time and within individuals. Formal modelling involves the translation of a theory’s structure into a mathematical framework and can help mitigate psychology’s ‘theory crisis’, which contributes to the wider reproducibility crisis.
The aim of WP1 was to develop a conceptual model of lapse risk in smokers attempting to stop. However, due to the lack of fit-for-purpose methodological guidance, we first conducted a scoping review 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, expert-derived ‘best practice’ modelling recommendations, which informed the next steps of our research.
After conducting the scoping review, we drew on the Theory Construction Methodology to develop a dynamic model of smoking lapse and relapse. We used a participatory, iterative, multi-method approach to iteratively develop the conceptual model (also referred to as a ‘prototheory’), which included: i) an informal theory review of popular lapse and relapse theories; and ii) two linked stakeholder interviews with researchers, people with lived experience, stop smoking practitioners, and policymakers (N = 15).
WP2
Next, we translated the prototheory into a series of difference equations which were implemented in R code and an R Shiny application, which enables interested users to explore the dynamic model on their own. 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’). The stakeholders were invited back to provide feedback on the simulated model outputs. Their feedback was incorporated into an updated model version. 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.
WP3
During the final stage of project COMPLAPSE, we will collect EMA and sensor data to validate the dynamic model of lapse and relapse. Eligibility criteria will include being an adult, daily smoker who i) owns a smartphone, ii) is willing to make a quit attempt, and iii) is willing to respond to regular EMAs and wear a smartwatch for the duration of the study. We will fit the formal model to the data from each participant and goodness-of-fit statistics will be calculated.
WP4
In WP4, we will draw on the knowledge generated within project COMPLAPSE and other, related modelling projects to write a tutorial on how to develop formal models in health psychology research. The scoping review in WP1 used a bottom-up approach to identify modelling practices in the published literature and then used this to propose a set of initial, expert-derived modelling recommendations. In WP4, however, we aim to take a broader view by reviewing and synthesising interdisciplinary modelling guidance from several disciplines (e.g., psychology, engineering, biology, physics). We will use this to provide in-depth modelling recommendations.