Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol
Open accessAbstract
Background: Recent advances in mobile and wearable technologies have led to new forms of interventions, called “Just-in-Time Adaptive Interventions” (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology. Methods: The study involves a year-long deployment of HeartSteps, a JITAI for physical activity and sedentary behavior, with 96 sedentary, overweight, but otherwise healthy adults. The central purpose is twofold: (1) to support the development of modeling approaches for operationalizing dynamic, mathematically rigorous theories of health behavior; and (2) to serve as a testbed for the development of learning algorithms that JITAIs can use to individualize intervention provision in real time at multiple timescales. Discussion and Conclusions: We outline an innovative modeling paradigm to model and use ILD in real- or near-time to individually tailor JITIAs.
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An MRT can address the limitations of traditional research methods, such as RCTs, which have shaped current behavior change theories.
“However, an MRT can address the limitations of traditional research methods, such as RCTs, which have shaped current behavior change theories.”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [115]
The Introduction says conventional behavior-change theories often rely on temporally sparse cross-sectional data, while MRTs use intensive longitudinal data to examine effects over time and across contexts. The Discussion and Conclusions proposes using MRT data to help build more dynamic theories. The paper does not identify RCTs as the traditional method that shaped current theories, so that specific attribution is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAn MRT offers a deeper understanding of the dynamics involved in health behaviors and behavior change, which are influenced by a myriad of contextual factors, including emotional states, physical context, social norms, and expectations.
“Specifically, an MRT offers a deeper understanding of the dynamics involved in health behaviors and behavior change, which are influenced by a myriad of contextual factors, including emotional states, physical context, social norms, and expectations [115].”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [115]
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Cites
A Global Measure of Perceived Stress
Cohen, S., Kamarck, T., & Mermelstein, R. (1983) · Journal of Health and Social Behavior · doi:10.2307/2136404 · Cited by 32,807 · see citing works
A very brief measure of the Big-Five personality domains
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003) · Journal of Research in Personality · doi:10.1016/s0092-6566(03)00046-1 · Cited by 8,307 · see citing works
Deci, E. L., & Ryan, R. M. (2012) · Handbook of Theories of Social Psychology: Volume 1 · doi:10.4135/9781446249215.n21 · Cited by 3,161 · see citing works
A taxonomy of behavior change techniques used in interventions
Abraham, C., & Michie, S. (2008) · Health Psychology · doi:10.1037/0278-6133.27.3.379 · Cited by 2,525 · see citing works
The Health Belief Model and Sick Role Behavior
Becker, M. H. (1974) · Health Education Monographs · doi:10.1177/109019817400200407 · Cited by 2,327 · see citing works
Mobile app rating scale: a new tool for assessing the quality of health mobile apps
Stoyanov, S. R., Hides, L., Kavanagh, D. J., Zelenko, O., Tjondronegoro, D., & Mani, M. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3422 · Cited by 2,233 · see citing works
Shaping perceptions to motivate healthy behavior: The role of message framing.
Rothman, A. J., & Salovey, P. (1997) · Psychological Bulletin · doi:10.1037/0033-2909.121.1.3 · Cited by 1,932 · see citing works
Michie, S., Ashford, S., Sniehotta, F. F., Dombrowski, S. U., Bishop, A., & French, D. P. (2011) · Psychology & Health · doi:10.1080/08870446.2010.540664 · Cited by 1,756 · see citing works
Using Generalized Estimating Equations for Longitudinal Data Analysis
Ballinger, G. A. (2004) · Organizational Research Methods · doi:10.1177/1094428104263672 · Cited by 1,567 · see citing works
Markland, D., & Tobin, V. (2004) · Journal of Sport and Exercise Psychology · doi:10.1123/jsep.26.2.191 · Cited by 1,444 · cited 4× · see citing works
“Klasnja, P.; Smith, S.; Seewald, N.; Lee, A.J.; Hall, K.; Luers, B.; Hekler, E.B.; Murphy, S.A. Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps.”
BackgroundInfluentialLoneliness, social isolation, and behavioral and biological health indicators in older adults.
Shankar, A., McMunn, A., Banks, J., & Steptoe, A. (2011) · Health Psychology · doi:10.1037/a0022826 · Cited by 1,100 · see citing works
Neighborhood Environment Walkability Scale
Cerin, E., Saelens, B. E., Sallis, J. F., & Frank, L. D. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000227639.83607.4d · Cited by 822 · see citing works
Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.
Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2015) · Health Psychology · doi:10.1037/hea0000305 · Cited by 671 · see citing works
Self-citationThe Health Belief Model: Origins and Correlates in Psychological Theory
Maiman, L. A., & Becker, M. H. (1974) · Health Education Monographs · doi:10.1177/109019817400200404 · Cited by 667 · see citing works
Hagger, M. S., & Łuszczyńska, A. (2013) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.12017 · Cited by 588 · see citing works
Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015) · Health Psychology · doi:10.1037/hea0000306 · Cited by 568 · see citing works
Self-citationThe Oxford Handbook of Human Motivation
(2012) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780195399820.001.0001 · Cited by 498 · see citing works
Implementation Intentions and Shielding Goal Striving From Unwanted Thoughts and Feelings
Achtziger, A., Gollwitzer, P. M., & Sheeran, P. (2008) · Personality and Social Psychology Bulletin · doi:10.1177/0146167207311201 · Cited by 348 · see citing works
Marcus, B. H., Eaton, C. A., Rossi, J. S., & Harlow, L. L. (1994) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1994.tb00595.x · Cited by 263 · see citing works
Klasnja, P., Smith, S., Seewald, N. J., Lee, A., Hall, K., Luers, B., Hekler, E. B., & Murphy, S. A. (2019) · Annals of Behavioral Medicine · doi:10.1093/abm/kay067 · Cited by 241 · see citing works
Self-citationThe Relation of the Chronic Disease Epidemic to the Health Care Crisis
Holman, H. R. (2020) · ACR Open Rheumatology · doi:10.1002/acr2.11114 · Cited by 212 · see citing works
Spruijt‐Metz, D., Hekler, E. B., Saranummi, N., Intille, S. S., Korhonen, I., Nilsen, W., Rivera, D. E., Spring, B. J., Michie, S., Asch, D. A., et al. (2015) · Translational Behavioral Medicine · doi:10.1007/s13142-015-0324-1 · Cited by 210 · see citing works
Self-citationSpring, B., Schneider, K., McFadden, H. G., Vaughn, J., Kozak, A. T., Smith, M., Moller, A. C., Epstein, L. H., DeMott, A., Hedeker, D., et al. (2012) · Archives of Internal Medicine · doi:10.1001/archinternmed.2012.1044 · Cited by 208 · see citing works
Buckingham, S. A., Williams, A. J., Morrissey, K., Price, L., & Harrison, J. (2019) · Digital Health · doi:10.1177/2055207619839883 · Cited by 182 · see citing works
Advancing Models and Theories for Digital Behavior Change Interventions
Hekler, E. B., Michie, S., Pavel, M., Rivera, D. E., Collins, L. M., Jimison, H. B., Garnett, C., Parral, S., & Spruijt-Metz, D. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.013 · Cited by 174 · see citing works
Self-citationConceptualizing and Defining the Intention Construct for Future Physical Activity Research
Rhodes, R. E., & Rebar, A. L. (2017) · Exercise and Sport Sciences Reviews · doi:10.1249/jes.0000000000000127 · Cited by 168 · see citing works
Assessing time-varying causal effect moderation in mobile health
Boruvka, A., Almirall, D., Witkiewitz, K., & Murphy, S. A. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1305274 · Cited by 152 · see citing works
Liao, P., Greenewald, K. H., Klasnja, P., & Murphy, S. A. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3381007 · Cited by 152 · see citing works
Self-citationRivera, D. E., Pew, M. D., & Collins, L. M. (2007) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2006.10.020 · Cited by 151 · see citing works
Self-citationPrecision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision
Hekler, E. B., Tiro, J. A., Hunter, C. M., & Nebeker, C. (2020) · Annals of Behavioral Medicine · doi:10.1093/abm/kaaa018 · Cited by 147 · see citing works
Self-citationTutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions
Hekler, E. B., Rivera, D. E., Martín, C. A., Phatak, S. S., Freigoun, M. T., Korinek, E. V., Klasnja, P., Adams, M. A., & Buman, M. P. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8622 · Cited by 137 · see citing works
Self-citationHealth Message Framing: Moderators, Mediators, and Mysteries
Updegraff, J. A., & Rothman, A. J. (2013) · Social and Personality Psychology Compass · doi:10.1111/spc3.12056 · Cited by 116 · see citing works
Korinek, E. V., Phatak, S. S., Martín, C. A., Freigoun, M. T., Rivera, D. E., Adams, M. A., Klasnja, P., Buman, M. P., & Hekler, E. B. (2017) · Journal of Behavioral Medicine · doi:10.1007/s10865-017-9878-3 · Cited by 97 · see citing works
Self-citationA 'Model-on-Demand' identification methodology for non-linear process systems
Braun, M., Rivera, D. E., & Stenman, A. (2001) · International Journal of Control · doi:10.1080/00207170110089734 · Cited by 90 · see citing works
Self-citationTracking the Effect of the COVID-19 Pandemic on the Lives of American Households
Kapteyn, A., Angrisani, M., Bennett, D., de Bruin, W. B., Darling, J. E., Gutsche, T., Liu, Y., Meijer, E., Pérez‐Arce, F., Schaner, S. G., et al. (2020) · Survey research methods · doi:10.18148/srm/2020.v14i2.7737 · Cited by 86 · see citing works
Development of a dynamic computational model of social cognitive theory
Riley, W. T., Martín, C. A., Rivera, D. E., Hekler, E. B., Adams, M. A., Buman, M. P., Misha Pavel, C., & King, A. C. (2015) · Translational Behavioral Medicine · doi:10.1007/s13142-015-0356-6 · Cited by 78 · see citing works
Self-citationMurawski, B., Plotnikoff, R. C., Rayward, A. T., Oldmeadow, C. J., Vandelanotte, C., Brown, W. J., & Duncan, M. J. (2019) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2019.05.009 · Cited by 66 · see citing works
Martín, C. A., Rivera, D. E., Hekler, E. B., Riley, W. T., Buman, M. P., Adams, M. A., & Magann, A. B. (2018) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2018.2873538 · Cited by 54 · see citing works
Self-citationLiao, P., Dempsey, W., Sarker, H., Hossain, S. M., Al’Absi, M., Klasnja, P., & Murphy, S. A. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287057 · Cited by 50 · see citing works
Self-citationIntensively Adaptive Interventions Using Control Systems Engineering: Two Illustrative Examples
Rivera, D. E., Hekler, E. B., Savage, J. S., & Downs, D. S. (2018) · Statistics for social and behavioral sciences · doi:10.1007/978-3-319-91776-4_5 · Cited by 28 · see citing works
Self-citationDiscrete simultaneous perturbation stochastic approximation for resource allocation in public health
Wang, Q., & Spall, J. C. (2014) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2014.6859387 · Cited by 5 · see citing works
Toward an open mechanistic science of behavior change.
Hekler, E. B., & King, A. C. (2020) · Health Psychology · doi:10.1037/hea0000924 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [5]Glanz K. Rimer B.K. Viswanath K. Health Behavior: Theory, Research, and Practice John Wiley and Sons Hoboken, NJ, USA 2015
- [37]Goldstein H. Multilevel Models in Education and Social Research Oxford University Press Oxford UK 1987
- [39]Murphy K.P. Machine Learning: A Probabilistic Perspective MIT Press Cambridge, MA, USA 2012
Cited by
Sorted by citations
Laure, T., Engels, R. C. M. E., Remmerswaal, D., Spruijt-Metz, D., Konigorski, S., & Boffo, M. (2023) · JMIR Research Protocols · doi:10.2196/46603 · Cited by 6 · see citing works
“Specifically, an MRT offers a deeper understanding of the dynamics involved in health behaviors and behavior change, which are influenced by a myriad of contextual factors, including emotional states, physical context, social norms, and expectations [115].”
BackgroundSelf-citation
- Authors
- Spruijt-Metz, D., Marlin, B. M., Pavel, M., Rivera, D. E., Hekler, E., De La Torre, S., El Mistiri, M., Golaszweski, N. M., Li, C., Braga De Braganca, R., Tung, K., Kha, R., & Klasnja, P.
- Affiliations
- Center for Economic and Social Research, Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
- University of Massachusetts Amherst (US)
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
- Arizona State University (US)
- University of California San Diego (US)
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
- Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, Energy, Arizona State University, Tempe, AZ 85287, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA 92093, USA
- University of Southern California (US)
- University of Michigan (US)
- ORCID
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2022-02-17
- Volume
- 19
- Issue
- 4
- Pages
- 2267
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC8872509
- OpenAlex
- W4212803117
- Cited by
- 30
- References
- 42
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 5 · 2023: 5 · 2022: 3
- FWCI
- 6.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (U01CA229445)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Keywords
- just-in-time adaptive interventions, mobile health, wearable devices, intensive longitudinal data, micro-randomized trial, physical activity, sedentary behavior, overweight adults, theory development, personalized interventions, learning algorithms
- MeSH terms
- Adult, Behavior Therapy, Humans, Health Behavior, Exercise, Randomized Controlled Trials as Topic, Telemedicine, Sedentary Behavior
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