# Predicting Attrition from a Lifestyle Medicine Intervention

> **NIH ALLCDC R21** · STATE UNIVERSITY NEW YORK STONY BROOK · 2024 · $237,643

## Abstract

World Trade Center (WTC) related disorders inflict a major toll on the health and well-being of WTC
responders. These debilitating conditions can be ameliorated or even reversed with lifestyle medicine
programs targeting nutrition and diet, physical activity, sleep, and stress management. Despite their efficacy,
lifestyle medicine programs are constrained by high attrition rates. Adjunctive interventions to increase
retention can be deployed if accurate forecasting algorithms are developed to identify who is most likely to drop
out. Previous studies found a host of demographic and behavioral risk factors for attrition. However, none are
sufficiently strong individually to accurately predict future dropout, and they have not yet been combined into a
validated predictive algorithm. Furthermore, recent technological advances made it possible to identify new
powerful predictors of dropout using objective, passively sensed data (natural language, sleep, and
movement). However, the utility of these passively sensed data for predicting attrition has not been rigorously
proven. To address these gaps, the goal of the current project is to identify psychological and behavioral
factors assessed from self-report, medical exams, and passive sensing that predict attrition in a lifestyle
medicine program. We will use existing infrastructure within the WTC Health Program at Stony Brook
University to assess 800 WTC responders as they enroll in an established 3-month lifestyle medicine program.
At program intake, we will (a) assess established risk factors, (b) record the first treatment visit to obtain
natural language predictors, and (c) give participants a FitBit to wear for 1 week to measure physical activity
and sleep patterns via passive sensing. Our aims are to (1) identify individual predictors of attrition in a lifestyle
medicine program and (2) combine predictors to develop an algorithm to forecast attrition (using machine
learning methods). This study will improve our ability to identify patients at risk of attrition and also characterize
profile of dropout risk across many potential predictors, revealing pathways to attrition that can be targeted by
supportive interventions in future studies (e.g., motivational interviewing, just-in-time adaptive interventions
based on passive sensing data). The proposed project takes the first step to address the problem of high
attrition in lifestyle medicine programs among WTC responders and other patient populations. It will pave the
way for randomized clinical trials of supportive interventions with patients identified by the algorithm as likely to
drop out.

## Key facts

- **NIH application ID:** 10903705
- **Project number:** 5R21OH012614-02
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Roman I Kotov
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2024
- **Award amount:** $237,643
- **Award type:** 5
- **Project period:** 2023-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10903705

## Citation

> US National Institutes of Health, RePORTER application 10903705, Predicting Attrition from a Lifestyle Medicine Intervention (5R21OH012614-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10903705. Licensed CC0.

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