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

> **NIH NIH K01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $161,518

## Abstract

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 cessati...

## Key facts

- **NIH application ID:** 10179352
- **Project number:** 5K01DA047417-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** David Martin Lydon-Staley
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $161,518
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10179352

## Citation

> US National Institutes of Health, RePORTER application 10179352, Person-specific dynamic networks of nicotine withdrawal: implications for smoking cessation (5K01DA047417-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10179352. Licensed CC0.

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