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Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps
Open accessAbstract
BACKGROUND: HeartSteps is an mHealth intervention that encourages regular walking via activity suggestions tailored to the individuals' current context. PURPOSE: We conducted a micro-randomized trial (MRT) to evaluate the efficacy of HeartSteps' activity suggestions to optimize the intervention. METHODS: We conducted a 6-week MRT with 44 adults. Contextually tailored suggestions could be delivered up to five times per day at user-selected times. At each of these five times, for each participant on each day of the study, HeartSteps randomized whether to provide an activity suggestion, and, if so, whether to provide a walking or an antisedentary suggestion. We used a centered and weighted least squares method to analyze the effect of suggestions on the 30-min step count following suggestion randomization. RESULTS: Averaging over study days and types of activity suggestions, delivering a suggestion versus no suggestion increased the 30-min step count by 14% (p = .06), 35 additional steps over the 253-step average. The effect was not evenly distributed in time. Providing any type of suggestion versus no suggestion initially increased the step count by 66% (167 steps; p < .01), but this effect diminished over time. Averaging over study days, delivering a walking suggestion versus no suggestion increased the average step count by 24% (59 steps; p = .02). This increase was greater at the start of study (107% or 271 additional steps; p < .01), but decreased over time. Antisedentary suggestions had no detectable effect on the 30-min step count. CONCLUSION: Contextually tailored walking suggestions are a promising way of initiating bouts of walking throughout the day. CLINICAL TRIAL INFORMATION: This study was registered on ClinicalTrials.gov number NCT03225521.
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“this method can evaluate the moderating role of contextual factors (eg, current mood or the time of day) on the intervention effect, which is especially helpful when optimizing rules for intervention delivery [50,51]”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [51]
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“The 60:40 ratio was chosen to ensure a higher chance of receiving an intervention at every decision point. This strategy has been used in previous MRT studies too [51,52].”
From Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial (2023) · cites this paper as [51]
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“These factors seem to play a role in intervention functioning [51,55,56].”
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Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Implementation intentions: Strong effects of simple plans.
Gollwitzer, P. M. (1999) · American Psychologist · doi:10.1037/0003-066x.54.7.493 · Cited by 5,478
Handbook of self-regulation: research, theory, and applications
(2005) · Choice Reviews Online · doi:10.5860/choice.42-3719 · Cited by 3,646
Health Belief Model and Personal Health Behavior
Becker, M. H. (1976) · Medical Entomology and Zoology · Cited by 3,065
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · cited 1×
“From the perspective of JITAI construction, such analyses of how intervention effects are moderated by time and context can be particularly important for optimizing rules for intervention delivery [3].”
BackgroundSelf-citationBuilt Environment Correlates of Walking: A Review
SAELENS, B. E., & HANDY, S. L. (2008) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31817c67a4 · Cited by 1,912
Novelty, complexity, and hedonic value
Berlyne, D. E. (1970) · Perception & Psychophysics · doi:10.3758/bf03212593 · Cited by 1,489
Understanding environmental influences on walking
Owen, N. G., Humpel, N., Leslie, E. R., Bauman, A. E., & Sallis, J. F. (2004) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2004.03.006 · Cited by 1,348
Collins, L. M., Murphy, S. A., & Strecher, V. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.01.022 · Cited by 1,082
Self-citationA Smartphone Application to Support Recovery From Alcoholism: A Randomized Clinical Trial
Gustafson, D. H., McTavish, F. M., Chih, M.-Y., Atwood, A. K., Johnson, R. A., Boyle, M. G., Levy, M. S., Driscoll, H., Chisholm, S. M., Dillenburg, L., et al. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.4642 · Cited by 692
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
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 660
The Importance of Walking to Public Health
Lee, I.-M. M., & Büchner, D. M. (2008) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31817c65d0 · Cited by 624
Nahum-Shani, I., Hekler, E. B., & Spruijt-Metz, D. (2015) · Health Psychology · doi:10.1037/hea0000306 · Cited by 568
Self-citationA strategy for optimizing and evaluating behavioral interventions
Collins, L. M., Murphy, S. A., Nair, V. N., & Strecher, V. J. (2005) · Annals of Behavioral Medicine · doi:10.1207/s15324796abm3001_8 · Cited by 492
Self-citationInterventions to Increase Physical Activity Among Healthy Adults: Meta-Analysis of Outcomes
Conn, V. S., Hafdahl, A. R., & Mehr, D. R. (2011) · American Journal of Public Health · doi:10.2105/ajph.2010.194381 · Cited by 486
Nielsen, L., Riddle, M., King, J. W., Aklin, W. M., Chen, W., Clark, D., Collier, E., Czajkowski, S., Esposito, L., Ferrer, R., et al. (2018) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.07.002 · Cited by 446
Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review
Böcker, L., Dijst, M., & Prillwitz, J. (2012) · Transport Reviews · doi:10.1080/01441647.2012.747114 · Cited by 417
Hovsepian, K., Al’Absi, M., Ertin, E., Kamarck, T. W., Nakajima, M., & Kumar, S. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2807526 · Cited by 357
Collins, L. M., Dziak, J. J., Kugler, K. C., & Trail, J. B. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.06.021 · Cited by 335
Interventions to Increase Physical Activity Among Older Adults: A Meta-Analysis
Chase, J. D. (2014) · The Gerontologist · doi:10.1093/geront/gnu090 · Cited by 215
Dynamic Models of Behavior for Just-in-Time Adaptive Interventions
Spruijt‐Metz, D., & Nilsen, W. (2014) · IEEE Pervasive Computing · doi:10.1109/mprv.2014.46 · Cited by 186
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
Self-citationSample size calculations for micro‐randomized trials in mHealth
Liao, P., Klasnja, P., Tewari, A., & Murphy, S. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6847 · Cited by 147
Self-citationPlanning and the Implementation of Goals
Gollwitzer, P. M., Fujita, K., & Oettingen, G. (2004) · KOPS (University of Konstanz) · Cited by 136
Collins, L. M., Trail, J. B., Kugler, K. C., Baker, T. B., Piper, M. E., & Mermelstein, R. J. (2014) · Translational Behavioral Medicine · doi:10.1007/s13142-013-0239-7 · Cited by 119
Thomas, J. G., & Bond, D. S. (2015) · Health Psychology · doi:10.1037/hea0000304 · Cited by 117
Developing multicomponent interventions using fractional factorial designs
Chakraborty, B., Collins, L. M., Strecher, V. J., & Murphy, S. A. (2009) · Statistics in Medicine · doi:10.1002/sim.3643 · Cited by 107
Self-citationNews from the NIH: using an experimental medicine approach to facilitate translational research
Science of Behavior Change Working Group, & Riddle, M. (2015) · Translational Behavioral Medicine · doi:10.1007/s13142-015-0333-0 · Cited by 79
Standardized Effect Sizes for Preventive Mobile Health Interventions in Micro-randomized Trials
Luers, B., Klasnja, P., & Murphy, S. A. (2018) · Prevention Science · doi:10.1007/s11121-017-0862-5 · Cited by 21
Self-citationSedentary behaviour and health: Strengthening the evidence base
Owen, N. G., Salmon, J., Trost, S. G., Dunstan, D. W., Eakin, E., Healy, G. N., Kingwell, B. A., Lambert, G. W., Timperio, A. F., & Shilton, T. (2014) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2014.11.114 · Cited by 1
Research works printed but not linked: 2
- Hovsepian, 2015, cStress: Towards a gold standard for continuous stress assessment in the mobile environment, Proc ACM Int Conf Ubiquitous Comput, 2015, 493
- R Core Team, 2017, R: A language and environment for statistical computing. R Foundation for Statistical Computing
Cited by
Sorted by citations
Guo, C., Ashrafian, H., Ghafur, S., Fontana, G., Gardner, C., & Prime, M. S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00314-2 · Cited by 345
The history and future of digital health in the field of behavioral medicine
Arigo, D., Jake‐Schoffman, D. E., Wolin, K. Y., Beckjord, E. B., Hekler, E. B., & Pagoto, S. (2019) · Journal of Behavioral Medicine · doi:10.1007/s10865-018-9966-z · Cited by 207
Self-citationLiao, 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 188
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 154
Self-citationTong, H. L., Quiroz, J. C., Kocaballi, A. B., Fat, S. C. M., Dao, K. P., Gehringer, H., Chow, C. K., & Laranjo, L. (2021) · Preventive Medicine · doi:10.1016/j.ypmed.2021.106532 · Cited by 136
Metadata
- Authors
- Klasnja, P., Smith, S., Seewald, N. J., Lee, A., Hall, K., Luers, B., Hekler, E. B., & Murphy, S. A.
- Affiliations
- Kaiser Permanente Washington Health Research Institute (US)
- University of Michigan (US)
- School of Information, University of Michigan, Ann Arbor, MI, USA
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
- University of California San Diego (US)
- Harvard University (US)
- Corresponding author
- Klasnja, Predrag
- Venue
- Annals of Behavioral Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-05
- Volume
- 53
- Issue
- 6
- Pages
- 573-582
- ISSN
- 0742-5554 (ISSN-L)
- 0883-6612
- 1532-4796
- PMC
- PMC6401341
- OpenAlex
- W2890071448
- Cited by
- 241
- References
- 32
- Citations by year
- 2026: 40 · 2025: 25 · 2024: 39 · 2023: 33 · 2022: 31 · 2021: 24 · 2020: 30 · 2019: 16 · 2018: 2
- FWCI
- 27.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Annals of Behavioral Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01AA023187, P50DA039838, U54EB020404)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health intervention, walking, physical activity, step count, sedentary behavior, micro-randomized trial, intervention optimization
- MeSH terms
- Adult, Female, Health Promotion, Humans, Male, Middle Aged, Outcome and Process Assessment, Health Care, Walking, Telemedicine
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