# Microtemporal Processes Underlying Health Behavior Adoption and Maintenance

> **NIH NIH U01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2021 · $648,337

## Abstract

SUMMARY
Emerging adulthood (ages 18-24 years) is marked by substantial weight gain, leading to increased
lifetime risks of cancer and other chronic diseases. Engaging in sufficient levels of physical activity and
sleep, and limiting sedentary time are important contributors to the prevention of weight gain. However,
engaging in these healthy behaviors peaks during the childhood and adolescent years, and steeply
deteriorates into emerging adulthood. Interventions promoting physical activity, reduced sedentary time, and
sufficient sleep typically focus on the adoption of these behaviors. Yet, when these interventions are
successful, new patterns of behavior are not maintained and typically regress back to baseline levels.
Traditional health behavior theories provide limited guidance regarding factors underlying behavior
maintenance. To address this gap, our work suggests that dual-process models of decision-making and
behavior can shed light on differences in the mechanisms underlying adoption versus maintenance.
Reflective processes (e.g., efficacy, deliberations, self-control) may be activated to a greater extent during
behavior adoption. In contrast, reactive processes (e.g., contextual cues, automaticity, habits) may play a
greater role in behavior maintenance. However, reactive processes are difficult to measure using retrospective
methods because they can unfold on a micro-timescale (i.e., change across minutes or hours). To solve this
problem, we propose to use real-time mobile technologies to collect intensive longitudinal data
examining differences in the micro-temporal processes underlying the adoption and maintenance of
physical activity, low sedentary time, and sufficient sleep duration. We will conduct a prospective within-
subject case-crossover observational study across a 12-month period. Ethnically-diverse, emerging adults
(ages 18-24, N=300) will be recruited from the Happiness & Health Cohort (R01DA033296). We will conduct
intermittent self-report (i.e., ecological momentary assessment) of reflective variables; and continuous, sensor-
based passive monitoring of reactive variables (e.g., location, social proximity, voice/text communication) and
behaviors (i.e., physical activity, sedentary time, sleep) using smartwatches and smartphones. These data will
be used to predict within-subject variation (within-days, between-days) in the likelihood of behavior “episodes”
(e.g., ≥10 min of physical activity, ≥120 min sedentary time, ≥7 hr sleep) and “lapses” (i.e., failure to achieve
recommended levels ≥7 days). The specific aims are to (1) idiographically use machine learning to identify
person-specific combinations of time-varying reflective and reactive factors that predict behavior episodes and
lapse; and (2) nomothetically determine whether there are general, group-level patterns of time-varying
predictors, and whether those patterns predict successful behavior maintenance outcomes. The data and
methods from this project will contri...

## Key facts

- **NIH application ID:** 10224874
- **Project number:** 5U01HL146327-04
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Genevieve Fridlund Dunton
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $648,337
- **Award type:** 5
- **Project period:** 2018-08-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224874

## Citation

> US National Institutes of Health, RePORTER application 10224874, Microtemporal Processes Underlying Health Behavior Adoption and Maintenance (5U01HL146327-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10224874. Licensed CC0.

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