Skip to main content

COVID-19 Resources

What people with cancer should know: https://www.cancer.gov/coronavirus

Guidance for cancer researchers: https://www.cancer.gov/coronavirus-researchers

Get the latest public health information from CDC: https://www.cdc.gov/coronavirus

Get the latest research information from NIH: https://www.covid19.nih.gov

Grant Details

Grant Number: 5U01CA229437-04 Interpret this number
Primary Investigator: Nahum-Shani, Inbal
Organization: University Of Michigan At Ann Arbor
Project Title: Novel Use of Mhealth Data to Identify States of Vulnerability and Receptivity to Jitais
Fiscal Year: 2021


Abstract

Abstract: Smoking cessation decreases morbidity and mortality and is a cornerstone of cancer prevention. The ability to impact current and future vulnerability (e.g., high risk for a lapse) in real-time via engagement in self-regulatory activities (e.g., behavioral substitution, mindful attention) is considered an important pathway to quitting success. However, poor engagement represents a major barrier to maximizing the impact of self- regulatory activities. Hence, enhancing real-time, real-world engagement in evidence-based self-regulatory activities has the potential to improve the effectiveness of smoking cessation interventions. Just-In-Time Adaptive Interventions (JITAIs) delivered via mobile devices have been developed for preventing and treating addictions. JITAIs adapt over time to an individual’s changing status and are optimized to provide appropriate intervention strategies based on real time, real world context. Organizing frameworks on JITAIs emphasize minimizing disruptions to the daily lives and routines of the individual, by tailoring strategies not only to vulnerability, but also to receptivity (i.e., an individual’s ability and willingness to utilize a particular intervention). Although both vulnerability and receptivity are considered latent states that are dynamically and constantly changing based on the constellation and temporal dynamics of emotions, context, and other factors, no attempt has been made to systematically investigate the nature of these states, as well as how knowledge of these states can be used to optimize real-time engagement in self-regulatory activities. To close this gap, the proposed project will apply innovative computational approaches to one of the most extensive and racially/ethnically diverse collection of real time, real world data on health behavior change (smoking cessation). Intensive longitudinal self-reported and sensor data from 5 studies (3 completed and 2 ongoing) of ~1,500 smokers attempting to quit will be analyzed with advanced probabilistic latent variable models and machine learning to investigate how the temporal dynamics and interactions of emotions, self-regulatory capacity (SRC), context, and other factors can be used to detect (Aim 1) states of vulnerability to a lapse and (Aim 2) states of receptivity to engaging in self-regulatory activities. We will also investigate (Aim 3) how knowledge of these states can be used to optimize real-time engagement in self-regulatory activities by conducting a Micro-Randomized Trial (MRT) enrolling 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. The proposed research will be the first to yield a comprehensive conceptual, technical, and empirical foundation necessary to develop effective JITAIs based on dynamic models of vulnerability and receptivity.



Publications

Estimating time-varying causal excursion effect in mobile health with binary outcomes.
Authors: Qian T. , Yoo H. , Klasnja P. , Almirall D. , Murphy S.A. .
Source: Biometrika, 2021 Sep; 108(3), p. 507-527.
EPub date: 2020-09-04.
PMID: 34629476
Related Citations

IntelligentPooling: Practical Thompson Sampling for mHealth.
Authors: Tomkins S. , Liao P. , Klasnja P. , Murphy S. .
Source: Machine learning, 2021 Sep; 110(9), p. 2685-2727.
EPub date: 2021-06-21.
PMID: 34621105
Related Citations

Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention.
Authors: Battalio S.L. , Conroy D.E. , Dempsey W. , Liao P. , Menictas M. , Murphy S. , Nahum-Shani I. , Qian T. , Kumar S. , Spring B. .
Source: Contemporary clinical trials, 2021-08-08; 109, p. 106534.
EPub date: 2021-08-08.
PMID: 34375749
Related Citations

The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol.
Authors: Nahum-Shani I. , Potter L.N. , Lam C.Y. , Yap J. , Moreno A. , Stoffel R. , Wu Z. , Wan N. , Dempsey W. , Kumar S. , et al. .
Source: Contemporary clinical trials, 2021-07-24; , p. 106513.
EPub date: 2021-07-24.
PMID: 34314855
Related Citations

Smoking Cessation Using Wearable Sensors: Protocol for a Microrandomized Trial.
Authors: Hernandez L.M. , Wetter D.W. , Kumar S. , Sutton S.K. , Vinci C. .
Source: JMIR research protocols, 2021-02-24; 10(2), p. e22877.
EPub date: 2021-02-24.
PMID: 33625366
Related Citations

Probabilistic cause-of-disease assignment using case-control diagnostic tests: A latent variable regression approach.
Authors: Wu Z. , Chen I. .
Source: Statistics in medicine, 2021-02-20; 40(4), p. 823-841.
EPub date: 2020-11-06.
PMID: 33159360
Related Citations

The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.
Authors: Kwon S. , Wan N. , Burns R.D. , Brusseau T.A. , Kim Y. , Kumar S. , Ertin E. , Wetter D.W. , Lam C.Y. , Wen M. , et al. .
Source: Sensors (Basel, Switzerland), 2021-02-18; 21(4), .
EPub date: 2021-02-18.
PMID: 33670507
Related Citations

Intersectionality and Smoking Cessation: Exploring Various Approaches for Understanding Health Inequities.
Authors: Potter L.N. , Lam C.Y. , Cinciripini P.M. , Wetter D.W. .
Source: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, 2021-01-07; 23(1), p. 115-123.
PMID: 32208484
Related Citations

Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health.
Authors: Liao P. , Klasnja P. , Murphy S. .
Source: Journal of the American Statistical Association, 2021; 116(533), p. 382-391.
EPub date: 2020-10-01.
PMID: 33814653
Related Citations

Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.
Authors: Bell L. , Garnett C. , Qian T. , Perski O. , Williamson E. , Potts H.W. .
Source: Journal of medical Internet research, 2020-12-11; 22(12), p. e23369.
EPub date: 2020-12-11.
PMID: 33306026
Related Citations

Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors.
Authors: Bari R. , Rahman M.M. , Saleheen N. , Parsons M.B. , Buder E.H. , Kumar S. .
Source: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2020 Dec; 4(4), .
PMID: 34099995
Related Citations

Optimizing an Acceptance and Commitment Therapy Microintervention Via a Mobile App With Two Cohorts: Protocol for Micro-Randomized Trials.
Authors: Kroska E.B. , Hoel S. , Victory A. , Murphy S.A. , McInnis M.G. , Stowe Z.N. , Cochran A. .
Source: JMIR research protocols, 2020-09-23; 9(9), p. e17086.
EPub date: 2020-09-23.
PMID: 32965227
Related Citations

Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science.
Authors: Carpenter S.M. , Menictas M. , Nahum-Shani I. , Wetter D.W. , Murphy S.A. .
Source: Current addiction reports, 2020 Sep; 7(3), p. 280-290.
EPub date: 2020-06-27.
PMID: 33747711
Related Citations

Notifications to Improve Engagement With an Alcohol Reduction App: Protocol for a Micro-Randomized Trial.
Authors: Bell L. , Garnett C. , Qian T. , Perski O. , Potts H.W.W. , Williamson E. .
Source: JMIR research protocols, 2020-08-07; 9(8), p. e18690.
EPub date: 2020-08-07.
PMID: 32763878
Related Citations

THE STRATIFIED MICRO-RANDOMIZED TRIAL DESIGN: SAMPLE SIZE CONSIDERATIONS FOR TESTING NESTED CAUSAL EFFECTS OF TIME-VARYING TREATMENTS.
Authors: Dempsey W. , Liao P. , Kumar S. , Murphy S.A. .
Source: The annals of applied statistics, 2020 Jun; 14(2), p. 661-684.
EPub date: 2020-06-29.
PMID: 33868539
Related Citations

Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example.
Authors: Li S. , Psihogios A.M. , McKelvey E.R. , Ahmed A. , Rabbi M. , Murphy S. .
Source: Current opinion in systems biology, 2020 Jun; 21, p. 1-8.
EPub date: 2020-07-07.
PMID: 32832738
Related Citations

An Individualized, Data-Driven Digital Approach for Precision Behavior Change.
Authors: Wongvibulsin S. , Martin S.S. , Saria S. , Zeger S.L. , Murphy S.A. .
Source: American journal of lifestyle medicine, 2020 May-Jun; 14(3), p. 289-293.
EPub date: 2019-04-25.
PMID: 32477031
Related Citations

Socioeconomic status, mindfulness, and momentary associations between stress and smoking lapse during a quit attempt.
Authors: Cambron C. , Hopkins P. , Burningham C. , Lam C. , Cinciripini P. , Wetter D.W. .
Source: Drug and alcohol dependence, 2020-04-01; 209, p. 107840.
EPub date: 2020-01-30.
PMID: 32058242
Related Citations

Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity.
Authors: Liao P. , Greenewald K. , Klasnja P. , Murphy S. .
Source: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2020 Mar; 4(1), .
PMID: 34527853
Related Citations

Socioeconomic Status, Social Context, and Smoking Lapse During a Quit Attempt: An Ecological Momentary Assessment Study.
Authors: Cambron C. , Lam C.Y. , Cinciripini P. , Li L. , Wetter D.W. .
Source: Annals of behavioral medicine : a publication of the Society of Behavioral Medicine, 2020-02-21; 54(3), p. 141-150.
PMID: 31612218
Related Citations

Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.
Authors: Qian T. , Klasnja P. , Murphy S.A. .
Source: Statistical science : a review journal of the Institute of Mathematical Statistics, 2020; 35(3), p. 375-390.
EPub date: 2020-09-11.
PMID: 33132496
Related Citations

ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence.
Authors: Rabbi M. , Li K. , Yan H.Y. , Hall K. , Klasnja P. , Murphy S. .
Source: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2019 Dec; 3(4), p. 1-27.
EPub date: 2019-12-11.
PMID: 34164595
Related Citations

Momentary precipitants connecting stress and smoking lapse during a quit attempt.
Authors: Cambron C. , Haslam A.K. , Baucom B.R.W. , Lam C. , Vinci C. , Cinciripini P. , Li L. , Wetter D.W. .
Source: Health psychology : official journal of the Division of Health Psychology, American Psychological Association, 2019 Dec; 38(12), p. 1049-1058.
EPub date: 2019-09-26.
PMID: 31556660
Related Citations

Artificial intelligence decision-making in mobile health.
Authors: Menictas M. , Rabbi M. , Klasnja P. , Murphy S. .
Source: The biochemist, 2019 Oct; 41(5), p. 20-24.
EPub date: 2019-10-18.
PMID: 33828355
Related Citations

Mindfulness-Based Smoking Cessation Enhanced With Mobile Technology (iQuit Mindfully): Pilot Randomized Controlled Trial.
Authors: Spears C.A. , Abroms L.C. , Glass C.R. , Hedeker D. , Eriksen M.P. , Cottrell-Daniels C. , Tran B.Q. , Wetter D.W. .
Source: JMIR mHealth and uHealth, 2019-06-24; 7(6), p. e13059.
EPub date: 2019-06-24.
PMID: 31237242
Related Citations

Mechanisms linking mindfulness and early smoking abstinence: An ecological momentary assessment study.
Authors: Spears C.A. , Li L. , Wu C. , Vinci C. , Heppner W.L. , Hoover D.S. , Lam C. , Wetter D.W. .
Source: Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2019 May; 33(3), p. 197-207.
EPub date: 2019-03-04.
PMID: 30829517
Related Citations

Text Messaging to Enhance Mindfulness-Based Smoking Cessation Treatment: Program Development Through Qualitative Research.
Authors: Spears C.A. , Bell S.A. , Scarlett C.A. , Anderson N.K. , Cottrell-Daniels C. , Lotfalian S. , Bandlamudi M. , Grant A. , Sigurdardottir A. , Carter B.P. , et al. .
Source: JMIR mHealth and uHealth, 2019-01-07; 7(1), p. e11246.
EPub date: 2019-01-07.
PMID: 30617043
Related Citations

Anhedonia and smoking cessation among Spanish-speaking Mexican-Americans.
Authors: Haslam A.K. , Correa-Fernández V. , Hoover D.S. , Li L. , Lam C. , Wetter D.W. .
Source: Health psychology : official journal of the Division of Health Psychology, American Psychological Association, 2018 Sep; 37(9), p. 814-819.
EPub date: 2018-07-26.
PMID: 30047750
Related Citations

The use of ambulatory assessment in smoking cessation.
Authors: Vinci C. , Haslam A. , Lam C.Y. , Kumar S. , Wetter D.W. .
Source: Addictive behaviors, 2018 08; 83, p. 18-24.
EPub date: 2018-02-03.
PMID: 29398067
Related Citations

Rationale, design and pilot feasibility results of a smartphone-assisted, mindfulness-based intervention for smokers with mood disorders: Project mSMART MIND.
Authors: Minami H. , Brinkman H.R. , Nahvi S. , Arnsten J.H. , Rivera-Mindt M. , Wetter D.W. , Bloom E.L. , Price L.H. , Vieira C. , Donnelly R. , et al. .
Source: Contemporary clinical trials, 2018 03; 66, p. 36-44.
EPub date: 2017-12-27.
PMID: 29288740
Related Citations




Back to Top