# Phase 1 clinical trial to develop a personalized adaptive text message intervention using control systems engineering tools to increase physical activity in early adulthood

> **NIH NIH R01** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $592,065

## Abstract

Project Summary
Physical inactivity is part of a constellation of lifestyle factors – with smoking and diet – that contribute to weight
gain in early adulthood. Risk factors that compromise cardiovascular health begin to accumulate during the
transition into adulthood. Interventions that prevent decreases in physical activity (PA) during this period can
reduce long-term chronic disease risk. Text message interventions have shown a consistent positive effect on
PA but efforts to increase those intervention effects via tailoring, targeting or personalizing have not realized
their potential. New approaches have emerged for tailoring interventions based on treatment responses or
contextual factors (e.g., stepped care, just-in-time adaptive interventions) but they apply a single decision rule
uniformly for all participants. Behavior is complex and multiply determined so it is possible that treatment
responses are idiosyncratic, necessitating personalized decision rules. Building on interest in precision
medicine, we propose a method to develop personalized adaptive messaging interventions using intensive
longitudinal data (from wearable sensors and momentary weather indices) and tools from control systems
engineering (system identification and robust control synthesis). In preliminary work, we developed a
computational model of physical activity responses to individual text messages. The greatest barrier to
implementing that approach in interventions is that the computational models required for predictive modeling
of PA dynamics have a high degree of uncertainty and are too complex to run efficiently on smartphones and
other wearable devices. We propose to solve that problem by (1) developing a dynamical model of physical
activity based on historical responses to messages, recent behavior, location-specific weather, and temporal
features, and (2) evaluating the acceptability and feasibility of more versus less aggressive adaptation
strategies for personalizing an intervention controller. To accomplish these aims, we will recruit young adults to
participate in a PA messaging intervention and develop a computational model of responses to different
messages under different conditions. A model-based controller will be developed to (a) optimize message
timing, frequency, and content selection, and (b) achieve specified behavior change goals under varying
conditions. We will then deploy that controller with an independent sample of young adults to determine how
more versus less aggressive adaptation strategies over the next six months impact user experience. This study
will contribute a model-based intervention controller and an acceptable adaptation strategy to use in a
personalized adaptive messaging intervention for increasing PA. If successful, it will increase both PA and user
engagement by selecting and timing messages to maximize effects and minimize burden. This approach can
be applied to develop personalized interventions for other behaviors relev...

## Key facts

- **NIH application ID:** 9922375
- **Project number:** 5R01HL142732-03
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** DAVID E. CONROY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $592,065
- **Award type:** 5
- **Project period:** 2018-07-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9922375

## Citation

> US National Institutes of Health, RePORTER application 9922375, Phase 1 clinical trial to develop a personalized adaptive text message intervention using control systems engineering tools to increase physical activity in early adulthood (5R01HL142732-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9922375. Licensed CC0.

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