Person-specific dynamic networks of nicotine withdrawal: implications for smoking cessation

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PROJECT SUMMARY The overall aim of this K01 submission is to provide Dr. David Lydon with the knowledge and skills to achieve his long-term goal of establishing an independent research career that will inform cigarette-smoking cessation interventions. Cigarette-smoking remains a leading cause of morbidity and mortality worldwide. Upon smoking cessation, withdrawal symptoms emerge that are primary determinants of smoking reuptake. The majority of intervention-guided cessation attempts fail, despite showing the ability to target withdrawal symptoms. This proposal consists of a training and research plan that will lead to the development and testing of a novel, network conceptualization of smoking withdrawal that focuses on person-specific patterns of moment-to-moment interplay among withdrawal symptoms. The proposed training plan under the guidance of Dr. Danielle Bassett (Mentor), Dr. Emily Falk (Co-Mentor), Dr. Robert Schnoll (Co-Mentor), Dr. Ian Barnett (Consultant), and Dr. Michael Rovine (Consultant) will build on Dr. Lydon's training to date, leading to the acquisition of expertise in 1) network science analysis of ecological momentary assessment data, 2) the science of behavior change in addiction , and 3) the use of ecological momentary assessment in behavior-change interventions. The proposed research project entails an ecological momentary assessment study during which smokers (n=250) will undergo 2 counterbalanced ecological momentary assessment bursts during which they will report on the intensity of withdrawal symptoms multiple times a day for 10 days. Smokers will undergo one burst while smoking as usual and one burst while abstaining from smoking. In Aim 1, withdrawal experiences will be modeled as person- specific, dynamic networks that indicate the interplay among symptoms across time. Graph theory will be applied to the constructed networks to demonstrate the ability to combine a dynamic network perspective of withdrawal and network science techniques to identify person-specific leverage points for intervention in the form of individual withdrawal symptoms that exert the most effects on other withdrawal symptoms. Aim 2 will test the extent to which self-perpetuating symptom networks act as a risk factor for smoking cessation failure. Aim 3 will examine changes in withdrawal symptom networks across levels of smoking satiety to test the feasibility of using pre-cessation experience-sampling data to tailor smoking cessation interventions. The unique project capitalizes on a multidisciplinary research team to develop a novel perspective of withdrawal that will culminate in an R01 submission to examine the use of person-specific withdrawal networks as smoking intervention tools. The project will provide Dr. Lydon with skills necessary to become a leader in research on the coupling of sophisticated graph theory techniques, ecological momentary assessment data, and behavior change theories to inform the personalization of smoking cessation interventions.