top of page

Efficacy of Precision Text Messaging to Increase Physical Activity

in Insufficiently-Active Young Adults

 

 

Multiple Principal Investigators

 

R61 HL164868
NIH Reporter listing
ClinicalTrials.gov registration
Click here if you are interested in participating

 

 

Physical activity and weight management are key components of ideal cardiovascular health but many young adults are insufficiently active and gaining weight rapidly (0.5-1.0 kg/year on average). Physical activity is a proven strategy for attenuating weight gain so there is a pressing need for effective interventions that reduce cardiovascular risk by motivating people to move more, sit less, and reduce weight gain. Mobile and wearable technologies are nearly ubiquitous among young adults and provide access to dense digital data about the person and their environment. This technology also provides a means of delivering interventions on a just-in-time basis so each person can receive the right intervention content at the right time. Inspired by the vision of precision behavioral interventions, we have applied system identification tools from control systems engineering to develop computational models and algorithms that optimize person-specific dosing of text messages to promote physical activity. In a series of preliminary studies, we (a) identified weather indices consistently linked with physical activity, (b) demonstrated the feasibility of long-term activity tracker wear and the acceptability of text messages among young adults, (c) enriched our computational model of physical activity using momentary weather data to characterize weather-graded responses to messages, and (d) designed a controller that uses person-specific parameters from the computational model and data on recent behavior and forecasted weather conditions to optimize the selection and timing of text message delivery. This controller is the basis for the Precision Adaptive Intervention Messaging (Precision AIM) intervention. In this application, we propose a single-site clinical trial to answer question, “Does Precision AIM increase insufficiently-active young adults’ physical activity and reduce weight gain more than randomly-assigned intervention messages (Random AIM) from the same message library or an activity tracker with no assigned intervention messages (No AIM)?” The specific aims of this project are (1) to evaluate the efficacy of Precision AIM compared to Random AIM and No AIM for increasing physical activity and reducing weight gain in insufficiently-active young adults, and (2) to identify characteristics of participants who respond more to Precision AIM than Random AIM or No AIM. We propose a three-arm randomized controlled trial to achieve these aims. Insufficiently-active young adults will receive a consumer activity tracker and be randomly assigned to one of three groups: Precision AIM, Random AIM, or No AIM. The intervention period will last for 12 months with a maintenance assessment at 18 months. The primary outcome is average daily step counts based on accumulating evidence that increasing this readily-interpretable and widely-available metric reduces cardiovascular risk in adulthood (assessed at baseline, 3, 6, 12, and 18 months). If successful, the underlying algorithmic analyses informed by dense data from wearable devices will provide a new and scalable approach for using consumer wearables in primordial and primary prevention of cardiovascular risk in young adults.

​Phase 1 Clinical Trial to Develop a Personalized Adaptive Text Message Intervention Using Control Systems Engineering Tools to Increase Physical Activity in Early Adulthood

 

Multiple Principal Investigators

  • David E. Conroy

  • Constantino M. Lagoa

    •  

    R01 HL 142732
    NIH Reporter listing
    ClinicalTrials.gov registration

     

         

    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 relevant for preventing weight gain, preserving cardiovascular health, and reducing chronic disease risk.

    Random Aim

    Precision Aim

    Collaborative Research: Data-driven control of switched systems with

    applications to human behavioral modification

     

    Principal Investigator: Constantino M. Lagoa
    Co-Principal Investigators:

     

    NSF ECCS 1808266 (in collaboration with NSF ECCS 1808381)
    NSF Award Database listing

     

    Dramatically increasing health care costs threaten the nation's economy. Over 80% of those costs are due to chronic illnesses which can be prevented or mitigated through lifestyle change. Physical activity is also a key behavioral component of ideal cardiovascular health. This suggests that promoting physical activity through the personalized virtual health advisors can lead to substantial health improvements across a broad spectrum of the population. Motivated by these observations, this proposal seeks to develop a tractable, practical framework for designing personalized behavior monitoring systems, aimed at maintaining optimal levels of physical activity. This is accomplished by embedding the problem into a more general, systems-theoretic one: design of controllers with provable performance for systems characterized by a collection of models where neither the number of models nor their parameters are a priori known and must be obtained from experimental data, collected from multiple sensors with large variations in quality. Education is proactively integrated into this project, starting with STEM summer camps projects for urban middle school students on data driven modeling and continuing at the college level with a multi-disciplinary program that uses personalized medicine to link a full range of distinct subjects ranging from machine learning to systems theory and optimization. At the graduate level, these activities are complemented by recruitment efforts that leverage the resources of Penn State's McNair Scholars Program and Northeastern University's Program in Multicultural Engineering to broaden the participation of underrepresented groups in research.

    Motivated by the problem of designing effective behavioral interventions, this proposal seeks to develop a comprehensive, computationally tractable framework for synthesizing data driven control laws for a class of systems described by switched difference inclusions. These models arise in a broad class of domains, ranging from resilient infrastructures to health care, characterized by large amounts of uncertainty and abruptly changing dynamics. The research addresses both the identification and control design problems in a unified framework based on polynomial optimization and its connections to the problem of moments. Contributions to the field of identification include the development of a tractable framework for robust identification of uncertain switched systems that exploits the underlying structure of the problem to substantially reduce the computational complexity and can handle both worst case and risk-adjusted descriptions. Contributions to control include a new framework for chance constrained control of uncertain switched systems that maximizes the probability of achieving a desired final state, while, at the same time, minimizing the probability of entering bad sets. As a proof-of-principle, the resulting framework is applied to the problem of designing smartphone based virtual health advisors capable of providing individualized optimal physical activity strategies.

     

         

    ​

    Feasibility of an adapted multicomponent physical activity intervention to reduce psychosocial distress in rural adults following cancer diagnosis


    Investigators:

  • Principal Investigator: Scherezade K. Mama (MD Anderson Cancer Center)

  • Co-investigators:

    •  

    American Institute for Cancer Research #634598

    ​

    Adults diagnosed with cancer who are physically active are less likely to experience adverse side-effects of treatment, such as fatigue, psychosocial distress, and loss of physical functioning. Yet, rural adults diagnosed with cancer are more likely to be inactive, report elevated psychosocial distress, and die from their cancer. Supportive care interventions are needed to help rural adults sit less and move more. However, these services are less likely to exist in rural settings. This study will assess the efficacy of an adapted multicomponent intervention to reduce sitting time and improve psychosocial distress and quality of life in rural adults receiving treatment following a cancer diagnosis. Components include a face-to-face light intensity physical activity intervention and an adaptive text messaging program for promoting lifestyle physical activity. This study provides a model for integrating lifestyle interventions in the treatment setting and will provide valuable information on whether a multicomponent intervention adapted for rural cancer survivors and to the treatment setting can be effective at reducing sedentary behavior and psychosocial distress in rural cancer survivors.

     

    ​
     

         

    ​

    PUBLICATIONS

    Click on image to gain access to the paper establishing specific responses to different message content.

    2023 paper establishing person-specific responses to different message content

    This is the Health Psychology Journal. Click on the image to gain access to the 2021 paper on person-specific, context-sensitive dose-finding for a digital message intervention to promote physical activity.

    2021 paper on person-specific, context-sensitive dose-finding for a digital message intervention to promote physical activity

    This is the Exercise and Sport Science Reviews Journal. Click on the image to access the 2020 paper on engineering person-specific behavioral interventions to promote physical activity.

    ​2020 paper on engineering person-specific behavioral interventions to promote physical activity

    Journal of Psychology & Health. Click on the image to access the in press paper investigating the broad and narrow bandwidth measures of automaticity for physical activity.

    2022 paper investigating broad and narrow bandwidth measures of automaticity for physical activity

    Click on the image to access the publication for a 2021 paper on person-specific dynamical models of stress-smoking systems.

    2021 paper on person-specific dynamical models of stress-smoking systems

    This is the Journal of Medical Internet Research. Click on the image to access the 2020 paper on message receipt rates over a 4-month intervention.

    2020 paper on message receipt rates over a 4-month intervention

    This is the Annals of Behavioral Medicine Journal. Click on the image to gain access to the 2022 paper on how the dynamics of physical activity intervention responses changed after the COVID-19 pandemic declaration.

    2022 paper on how the dynamics of physical activity intervention responses changed after the COVID-19 pandemic declaration

    This is the British Journal of Sports Medicine. Click on the image to gain access to the 2021 editorial about digital tools for personalized physical activity promotion.

    2021 editorial about digital tools for personalized physical activity promotion

    This is the Psychology of Sport and Exercise Journal. Click on the image to gain access to the 2019 paper with proof-of-concept using physical activity data.

    2019 paper with proof-of-concept using physical activity data

    This is the Psychology and Health Journal. Click on the image to gain access to the 2022 paper investigating relations between physical activity, affective judgments, environmental factors, and their interaction.

    2022 paper investigating relations between physical activity, affective judgments, environmental factors, and their interaction

    This is the International Journal of Behavioral Nutrition and Physical Activity. Click on the image to gain access to the 2021 review on natural environment correlates of physical activity & sedentary behavior.

    2021 review on natural environment correlates of physical activity & sedentary behavior

    This is the Institute of Electrical and Electronics Engineers. Click on the image to gain access to the 2016 paper modeling sedentary behavior dynamics in response to messages.

    2016 paper modeling sedentary behavior dynamics in response to messages

    bottom of page