CP 1: Novel Use of mHealth Data to Identify States of Vulnerability and Receptivity to JITAIs

Collaborating Investigators:

Dr. Inbal Nahum-Shani, University of Michigan

Dr. David Wetter, University of Utah

Funding Status:

U01CA229437-01

NIH/NCI

9/1/18 – 8/31/23

Associated with:

TR&D1, TR&D2, TR&D3

Publications
Significance
Approach & Push Pull Relationship

TR&D1: Discovery

mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels

Authors:

Md Azim Ullah, Soujanya Chatterjee, Christopher P Fagundes, Cho Lam, Inbal Nahum-Shani, James M Rehg, David W Wetter, Santosh Kumar

Publication Venue:

ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT)

Publication Date:

September 7, 2022

Keywords:

behavioral intervention, human-centered computing, risk prediction, smoking cessation, ubiquitous and mobile computing design and evaluation methods, wearable sensors

View Full Paper

Related Projects:

CP 1, CP 5

Estimation of the continuous risk state may be critical for delivering temporally-precise interventions and treatment adaptations in cessation programs. Continuous sensor data collected from wearables and smartphones to capture risk factors of adverse behaviors in the natural environment are usually noisy and incomplete. For adverse behavioral events such as a smoking lapse, capturing the precise timing of each smoking lapse may not be feasible, as sensors may not be worn at the time of a lapse or the lapse events may not be accurately detected due to the imperfection of machine learning models that are used to detect smoking events via hand-to-mouth gestures. Therefore, only a few positive events (i.e., smoking lapse in a cessation attempt) are available. Confirmed negative labels can be assigned to a block of sensor data corresponding to a prediction window only if the entire time period is confirmed to have no high-risk moment. As not all high-risk moments may result in a lapse, labeling a block of sensor data to the negative class is difficult for such events. We addressed each of these challenges in developing the mRisk model. Specifically, we encoded sensor data as events to handle noise and missingness, modeled the historical influence of recent psychological, behavioral, and environmental events via deep learning model and addressed the issue of lack of negative labels and only a small subset of positive labels by using a positive-unlabeled framework with a novel loss function.

Abstract:

Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day.

TL;DR:

We present a model for identifying ideal moments for intervention by passively detecting risk of an imminent adverse behavior.

TR&D2: Optimization

Engagement in Digital Interventions

Authors:

Inbal Nahum-Shani, Steven Shaw, Stephanie Carpenter, Susan Murphy, Carolyn Yoon

Publication Venue:

American Psychologist

Publication Date:

March 17, 2022

Keywords:

engagement, digital interventions, affect, motivation, attention

View Full Paper

Related Project:

CP 1

Abstract:

The notion of “engagement,” which plays an important role in various domains of psychology, is gaining increased currency as a concept that is critical to the success of digital interventions. However, engagement remains an ill-defined construct, with different fields generating their own domain-specific definitions. Moreover, given that digital interactions in real-world settings are characterized by multiple demands and choice alternatives competing for an individual’s effort and attention, they involve fast and often impulsive decision making. Prior research seeking to uncover the mechanisms underlying engagement has nonetheless focused mainly on psychological factors and social influences and neglected to account for the role of neural mechanisms that shape individual choices. This paper aims to integrate theories and empirical evidence across multiple domains to define engagement and discuss opportunities and challenges to promoting effective engagement in digital interventions. We also propose the AIM-ACT framework, which is based on a neurophysiological account of engagement, to shed new light on how in-the-moment engagement unfolds in response to a digital stimulus. Building on this framework, we provide recommendations for designing strategies to promote engagement in digital interventions and highlight directions for future research.

TL;DR:

This paper focuses on defining and understanding engagement in digital interventions by combining various theories and evidence from different domains. It introduces the AIM-ACT framework, which explains how engagement happens in response to digital stimuli based on neurophysiological principles and offers suggestions for designing effective engagement strategies in digital interventions.

The Microrandomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations

Authors:

Tianchen Qian, Ashley Walton, Linda Collins, Predrag Klasnja, Stephanie Lanza, Inbal Nahum-Shani, Mashfiqui Rabbi, Michael Russell, Maureen Walton, Hyesun Yoo, Susan Murphy

Publication Venue:

Psychological Methods

Publication Date:

January 13, 2022

Keywords:

Micro-randomized trial (MRT), health behavior change, digital intervention, just-in-time adaptive intervention (JITAI), causal inference, intensive longitudinal data

View Full Paper

Related Project:

CP 1, CP 3,

Abstract:

Just-in-time adaptive interventions (JITAIs) are time-varying adaptive interventions that use frequent opportunities for the intervention to be adapted-weekly, daily, or even many times a day. The microrandomized trial (MRT) has emerged for use in informing the construction of JITAIs. MRTs can be used to address research questions about whether and under what circumstances JITAI components are effective, with the ultimate objective of developing effective and efficient JITAI.

The purpose of this article is to clarify why, when, and how to use MRTs; to highlight elements that must be considered when designing and implementing an MRT; and to review primary and secondary analyses methods for MRTs. We briefly review key elements of JITAIs and discuss a variety of considerations that go into planning and designing an MRT. We provide a definition of causal excursion effects suitable for use in primary and secondary analyses of MRT data to inform JITAI development. We review the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as R (R Core Team, 2019). Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals. We supplement the HeartSteps MRT with two other MRTs, SARA and BariFit, each of which highlights different research questions that can be addressed using the MRT and experimental design considerations that might arise.

TL;DR:

Throughout we illustrate the MRT design and analyses using the HeartSteps MRT, for developing a JITAI to increase physical activity among sedentary individuals.

Translating Strategies for Promoting Engagement in Mobile Health: A Proof-of-Concept Microrandomized Trial

Authors:

Inbal Nahum-Shani, Mashfiqui Rabbi, Jamie Yap, Meredith Philyaw-Kotov, Predrag Klasnja, Erin Bonar, Rebecca Cunningham, Susan Murphy, Maureen Walton

Publication Venue:

Health Psychology

Publication Date:

December 2021

Keywords:

engagement, mobile health (mHealth), Micro-Randomized Trial (MRT), reciprocity, reinforcement

View Full Paper

Related Project:

CP 1

Abstract:

Objective: Mobile technologies allow for accessible and cost-effective health monitoring and intervention delivery. Despite these advantages, mobile health (mHealth) engagement is often insufficient. While monetary incentives may increase engagement, they can backfire, dampening intrinsic motivations and undermining intervention scalability. Theories from psychology and behavioral economics suggest useful nonmonetary strategies for promoting engagement; however, examinations of the applicability of these strategies to mHealth engagement are lacking. This proof-of-concept study evaluates the translation of theoretically-grounded engagement strategies into mHealth, by testing their potential utility in promoting daily self-reporting.

Method: A microrandomized trial (MRT) was conducted with adolescents and emerging adults with past-month substance use. Participants were randomized multiple times daily to receive theoretically-grounded strategies, namely reciprocity (the delivery of inspirational quote prior to self-reporting window) and nonmonetary reinforcers (e.g., the delivery of meme/gif following self-reporting completion) to improve proximal engagement in daily mHealth self-reporting.

Results: Daily self-reporting rates (62.3%; n = 68) were slightly lower than prior literature, albeit with much lower financial incentives. The utility of specific strategies was found to depend on contextual factors pertaining to the individual’s receptivity and risk for disengagement. For example, the effect of reciprocity significantly varied depending on whether this strategy was employed (vs. not employed) during the weekend. The nonmonetary reinforcement strategy resulted in different outcomes when operationalized in various ways.

Conclusions: While the results support the translation of the reciprocity strategy into this mHealth setting, the translation of nonmonetary reinforcement requires further consideration prior to inclusion in a full scale MRT.

TL;DR:

A microrandomized trial (MRT) was conducted with young adults with past-month substance use. Participants were randomized multiple times daily to receive theoretically-grounded strategies to improve proximal engagement in daily mHealth self-reporting.

The Mobile Assistance for Regulating Smoking (MARS) Micro-Randomized Trial Design Protocol

Authors:

Inbal Nahum-Shani, Lindsey Potter, Cho Lam, Jamie Yap, Alexander Moreno, Rebecca Stoffel, Zhenke Wu, Neng Wan, Walter Dempsey, Santosh Kumar, Emre Ertin, Susan Murphy, James Rehg, David Wetter

Publication Venue:

Contemporary Clinical Trials

Keywords:

engagement, Micro-randomized trial (MRT), mobile health (mHealth), self-regulatory strategies, smoking cessation

Publication Date:

November 2021

View Full Paper

Related Project:

CP 1

Abstract:

Smoking is the leading preventable cause of death and disability in the U.S. Empirical evidence suggests that engaging in evidence-based self-regulatory strategies (e.g., behavioral substitution, mindful attention) can improve smokers’ ability to resist craving and build self-regulatory skills. However, poor engagement represents a major barrier to maximizing the impact of self-regulatory strategies. This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) – a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies. The study will employ a 10-day Micro-Randomized Trial (MRT) enrolling 112 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either: (a) no intervention prompt; (b) a prompt recommending brief (low effort) cognitive and/or behavioral self-regulatory strategies; or (c) a prompt recommending more effortful cognitive or mindfulness-based strategies. Prompts will be delivered via push notifications from the MARS mobile app. The goal is to investigate whether, what type of, and under what conditions prompting the individual to engage in self-regulatory strategies increases engagement. The results will build the empirical foundation necessary to develop a mHealth intervention that effectively utilizes intensive longitudinal self-report and sensor-based assessments of emotions, context and other factors to engage an individual in the type of self-regulatory activity that would be most beneficial given their real-time, real-world circumstances. This type of mHealth intervention holds enormous potential to expand the reach and impact of smoking cessation treatments.

TL;DR:

This paper describes the protocol for Mobile Assistance for Regulating Smoking (MARS) – a research study designed to inform the development of a mobile health (mHealth) intervention for promoting real-time, real-world engagement in evidence-based self-regulatory strategies.

CP1 is analyzing dense longitudinal sensor and self-report data collected in 5 field studies (3 completed and 2 ongoing) of ~1,500 smokers attempting to quit. Specifically, the project is investigating how the temporal dynamics and interactions of emotion, self-regulatory capacity (SRC), context, and other factors can be used to detect states of vulnerability and receptivity to just-in-time interventions, both of which are considered dynamically evolving latent states. Self-reported and sensor-based measures will be used to identify empirically-based and theoretically-grounded features across multiple time scales that are most predictive of engagement (i.e., usage of self-regulatory activities) and smoking lapse. CP1 hypothesizes that current receptivity is best represented by high positive activating emotions (e.g., happy, grateful), low negative deactivating emotions (e.g., sad, boredom), low craving, high self-efficacy, high self-regulatory capacity, and low risk contexts (e.g., specific locations, such as home). CP1 also hypothesizes that current vulnerability is represented by current and/or recent high negative emotions, low positive emotions, high craving, low self-efficacy, low self-regulatory capacity, and risky context (e.g., seeing others smoking).

Additionally, CP1 aims to investigate how knowledge of these latent states can be used to optimize real-time engagement in self-regulatory activities by conducting a Micro-Randomized Trial (MRT) of 150 smokers attempting to quit. Utilizing a mobile smoking cessation app, the MRT will randomize each individual multiple times per day to either (a) no intervention prompt; (b) a prompt recommending engagement in brief (low effort) strategies; or (c) a prompt recommending a more effortful practice of self-regulation strategies. CP1 aims to investigate whether, what type, and under what conditions (e.g., current state of vulnerability and/or receptivity) a prompt to engage the individual in self-regulatory activities increases engagement, hence reduces vulnerability. CP1 will be the first to yield a comprehensive conceptual, technical, and empirical foundation to develop effective just-in-time adaptive interventions based on dynamic models of vulnerability and receptivity.

CP1 will closely collaborate with all three TR&D’s. From sensor and self-report data of 1500 daily smokers, CP1 is developing a novel biomarker of self-efficacy. Initial biomarker development will not account for uncertainty, but a push activity with Aim 1 of TR&D1 will utilize uncertainty modeling tools to represent and quantity uncertainty in the self-efficacy biomarker derived from sensor-data. After validation of the uncertainty model using the collected data, Aim 2 will work with CP1 to train deep feature learning methods on large quantity of available mHealth biomarker data of stress, craving, self-efficacy, location, and other contexts, to improve the accuracy of predictions of risk and receptivity that incorporate measures of uncertainty. Given predictors of risk and receptivity, CP1 will work with TR&D1 to develop composite scores of vulnerability and receptivity which are predictive of risk of smoking lapse and opportunity to engage in self-regulatory activities, respectively. Aim 2 of TR&D1 will work with CP1 to characterize the time-varying relationships between biomarkers for stress, craving, self-efficacy, location, and other contexts and the risk of lapse and receptivity to engage in self-regulatory activities. Subsequently, CP1 will utilize the refined composite scores from Aim 2 and temporal dynamics of risk drivers from Aim 3 in an MRT, which will provide opportunities for evaluation and iterative refinement of the methods developed in TR&D1.

CP1 will work with TR&D2 to optimize decision rules to learn what type and under what conditions (e.g., current state of vulnerability and/or receptivity) a prompt to engage the individual in self-regulatory activities reduces vulnerability. Here, the treatment actions are different types of prompts and the near time reward will be measures of engagement in the self-regulatory activity as well as vulnerability. The MRT will provide data for TR&D2 to develop useful simulation testbeds for early evaluations of the methods under Aims 1,2. CP1 is interested in conducting feasibility studies that involve a TR&D2 RL personalization algorithm (based on work under Aims 1,2) that uses the above decision rules as a warm start to personalize these decision rules as an individual experiences the prompts. These studies will be used to iteratively refine methods under Aims 1,2 and this collaboration will lay the foundation for successful future research projects.

TR&D3 Aim 1 will work with CP1 to develop, test, iteratively refine, and deploy computationally efficient biomarkers of stress, craving, and self-efficacy derived from wrist-worn PPG sensors that can run in real-time and be used in the MRT trial. High rate multispectral PPG and motion sensor architecture design from Aim 2 will enable reliable beat-to-beat heart rate estimation to support real-time computation of biomarkers. CP1 will test and provide iterative feedback for improvement of both the sensor and biomarkers computed in real-time.

The mDOT Center

CP 1: Novel Use of mHealth Data to Identify States of Vulnerability and Receptivity to JITAIs