# Methodological and data-driven approach to infer durable behavior change from mHealth data

> **NIH NIH R01** · NORTHWESTERN UNIVERSITY · 2021 · $510,240

## Abstract

Abstract
Poor
cancers,
lengthy,
achieve
diet and physical activity (PA) behaviors, the most prevalent risk factors for cardiometabolic diseases and
can be treated to prevent disease. However, most diet, PA, and weight loss interventions are costly,
and burdensome. Theseinterventions could be more cost-efficient if we could tell when people
a sustainable pattern of health behavior change so that treatment could be tapered and then stopped
without behavioral relapse. Theories of habit formation might be assumed to address this problem, but they
have not proved actionable to guide treatment decisions because they do not specify measurable criteria to
reliably detect acquisition of a durable behavior pattern. Hence, we propose to identify behavior patterns that
precede and predict maintenance of target-level behavioral improvement that persist after an intervention
ends. The measurements needed to tell whether an intervention has durably entrained behavioral
improvement are collected as part of diet, PA, and weight loss interventions. Specifically, participants
continuously self-monitor their behavior digitally while assessments are relayed back to inform them about
progress toward goals. We will analyze self-monitoring measures collected in 6 mHealth trials, conducted over
14 years among over 1,600 participants and more than 147,000 daily observations, to assess when an
intervention has durably entrained targeted behaviors, as validated by their reliable persistence post-
intervention. We will use location scale modeling to quantify change not only in the absolute level (location) of
a behavior but also in its within-person variability (scale). We posit that the induction of durable behavior
change requires both improvement in location (increases for healthy behaviors; decreases for unhealthy ones)
and decrease in scale (i.e., increased behavioral consistency). Aim 1 will apply existing location scale
methods to test the hypothesis that effective interventions will improve the location and reduce the scale of
targeted behaviors across all trials. Because existing methods only measure scale at the group level and
cannot measure the change in an individual's behavioral consistency that we need to personalize treatment
adaptation, Aim 2 will extend location scale methods to enable individual estimation of the rate of change in
behavioral consistency. Estimates derived from the new method will be analyzed to learn which parameters of
behavior change during intervention are most associated with maintenance post-treatment. Finally, Aim 3 will
apply machine learning to estimates from the extended location-scale mixed models to establish ranges and
behavioral patterns that predict behavioral maintenance post-treatment. These resultswill inform behaviorinterventionscience and improve treatment efficiency by guiding real-timedecisions about the needed dosage
and duration of behavioral treatments.

## Key facts

- **NIH application ID:** 10218158
- **Project number:** 5R01DK125414-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Donald Hedeker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $510,240
- **Award type:** 5
- **Project period:** 2020-07-17 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10218158

## Citation

> US National Institutes of Health, RePORTER application 10218158, Methodological and data-driven approach to infer durable behavior change from mHealth data (5R01DK125414-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10218158. Licensed CC0.

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