# Joint longitudinal and survival models for intensive longitudinal data from mobile health studies of smoking cessation

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $23,232

## Abstract

Project Summary/Abstract
 Increasing collection of intensive longitudinal data (ILD) through mobile health (mHealth)-based
approaches, such as ecological momentary assessment (EMA), present a rich source of information for
understanding temporal variations in psychological states key to smoking cessation. However, advances in
statistical methods are needed to fully leverage these rich data to assess interventions and inform the design
of future interventions. In Aims 1 and 2, this proposal seeks to develop a novel statistical model (joint
longitudinal recurrent-event model) and estimation method that will allow for analysis of EMA data from a
smoking cessation study using low-dimensional interpretable states to describe the behavioral phenomenon
and processes related to smoking cessation. By incorporating just-in-time adaptive interventions (JITAIs) into
the model in Aim 3, this project will facilitate assessment of the impact of time-varying adaptive interventions
on a subject’s risk of a future lapse in smoking cessation using data from the Mobile Assistance for Regulating
Smoking (MARS) micro-randomized trial (U01CA229437; PIs: Nahum-Shani, Wetter).
 Training goals, which were developed with the mentorship team, include: (i) advancing technical
training in statistical theory and computing, (ii) improving written and oral communication skills, (iii) building
collaborative relationships, and (iv) attending conferences, workshops, and professional development
activities. The proposed research and training will be conducted at the University of Michigan (UM) in the
Department of Biostatistics, which has close ties to the UM Institute for Social Research and a reputation for
excellence in research and training.
 Altogether, the statistical methodology proposed in this project will contribute to the science in two key
ways: (i) it will allow for the integration of many different items (e.g. emotions, urge, motivation) in a way that
facilitates interpretation when measuring vulnerability (e.g. risk of lapse) and (ii) its interpretability will
subsequently help inform the design of evidence-based adaptive interventions (e.g. JITAIs) through increased
understanding of the conditions that represent vulnerability. These scientific contributions directly promote the
National Institute on Drug Abuse’s strategic goal of developing “new and improved treatments to help people
with substance use disorders achieve and maintain a meaningful and sustained recovery”. Although presented
in the context of a smoking cessation study, this methodological framework is highly flexible with broad
applicability to mHealth studies of substance use disorders and in other health domains. This novel analytic
method will be freely available in a user-friendly R package, thus facilitating the potential impact on drug-use
research.

## Key facts

- **NIH application ID:** 10813012
- **Project number:** 5F31DA057048-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Madeline Abbott
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $23,232
- **Award type:** 5
- **Project period:** 2023-05-01 → 2024-08-25

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10813012

## Citation

> US National Institutes of Health, RePORTER application 10813012, Joint longitudinal and survival models for intensive longitudinal data from mobile health studies of smoking cessation (5F31DA057048-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10813012. Licensed CC0.

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