Cited by 241Mobile Health and mHealth ApplicationsPhysical Activity and HealthDigital Mental Health Interventions

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps

Open access

Klasnja, Predrag, Smith, Shawna, Seewald, Nicholas J, Lee, Andy, Hall, Kelly, Luers, Brook, Hekler, Eric B, Murphy, Susan A·2019·Annals of Behavioral Medicine

DOI 10.1093/abm/kay067PMC PMC6401341

Open this paper in CoChat — free account

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/abm/kay067

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support
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-citation
Built 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
The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART)
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-citation

Flagged first, then by citations

Show all 32 references

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

Sorted by citations

Items 101–200 of 241

Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations
Potter, L. N., Yap, J. R. T., Dempsey, W., Wetter, D. W., & Nahum-Shani, I. B. (2023) · Prevention Science · doi:10.1007/s11121-023-01495-4 · Cited by 12
Personality psychology is getting personal: Advancing the field through personalization
Modersitzki, N., Phan, L. V., McDonald, S., Wright, A. G. C., & Renner, K. (2025) · European Journal of Personality · doi:10.1177/08902070251316915 · Cited by 13
Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data
Jung, G., Park, S., Ma, E.-Y., Kim, H., & Lee, U. (2024) · ACM Computing Surveys · doi:10.1145/3648356 · Cited by 11
Exploring the added effect of three recommender system techniques in mobile health interventions for physical activity: a longitudinal randomized controlled trial
Coppens, I., Pessemier, T. D., & Martens, L. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09407-z · Cited by 12
The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols
Chow, S., Nahum-Shani, I. B., Baker, J. T., Spruijt‐Metz, D., Allen, N. B., Auerbach, R. P., Dunton, G. F., Friedman, N. P., Intille, S. S., Klasnja, P., et al. (2022) · Translational Behavioral Medicine · doi:10.1093/tbm/ibac069 · Cited by 10
Self-citation
Mobile Acceptance and Commitment Therapy With Distressed First-Generation College Students: Microrandomized Trial
Thomas, E. B. K., Sagorac Gruichich, T., Maronge, J. M., Hoel, S., Victory, A., Stowe, Z. N., & Cochran, A. (2023) · JMIR Mental Health · doi:10.2196/43065 · Cited by 9
Just-In-Time Adaptive Intervention to Sit Less and Move More in People With Type 2 Diabetes: Protocol for a Microrandomized Trial
Daryabeygi‐Khotbehsara, R., Dunstan, D. W., Islam, S. M. S., Zhang, Y., Abdelrazek, M., & Maddison, R. (2023) · JMIR Research Protocols · doi:10.2196/41502 · Cited by 10
Smartphone-based study reminders can be a double-edged sword
Nobbe, L., Breitwieser, J., Biedermann, D., & Brod, G. (2024) · npj Science of Learning · doi:10.1038/s41539-024-00253-7 · Cited by 13
Effectiveness of 2 Just-in-Time Adaptive Interventions for Reducing Stress and Stabilizing Cardiac Autonomic Function: Microrandomized Trials
Schwerdtfeger, A. R., Tatschl, J. M., & Rominger, C. (2025) · Journal of Medical Internet Research · doi:10.2196/69582 · Cited by 13
Implementation of Behavior Change Theories and Techniques for Physical Activity Just-in-Time Adaptive Interventions: A Scoping Review
Cotie, P. N. R., Willms, A., & Liu, S. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22071133 · Cited by 11
Technical Challenges of a Mobile Application Supporting Intersession Processes in Psychotherapy
Stach, M., Vogel, C., Gablonski, T., Andreas, S., Probst, T., Reichert, M., Schickler, M., & Pryss, R. C. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.07.038 · Cited by 10
The Service of Research Analytics to Optimize Digital Health Evidence Generation: Multilevel Case Study
Phạm, Q., Shaw, J., Morita, P. P., Seto, E., Stinson, J., & Cafazzo, J. A. (2019) · Journal of Medical Internet Research · doi:10.2196/14849 · Cited by 12
A Community Participatory Approach to Creating Contextually Tailored mHealth Notifications: myBPmyLife Project
Hellem, A. K., Casetti, A., Bowie, K., Golbus, J. R., Merid, B., Nallamothu, B. K., Dorsch, M. P., Newman, M., & Skolarus, L. E. (2023) · Health Promotion Practice · doi:10.1177/15248399221141687 · Cited by 9
mHealth-Based Just-in-Time Adaptive Intervention to Improve the Physical Activity Levels of Individuals With Spinal Cord Injury: Protocol for a Randomized Controlled Trial
Carey, R. L., Le, H., Coffman, D. L., Nahum-Shani, I. B., Thirumalai, M., Hagen, C., Baehr, L. A., Schmidt-Read, M., Lamboy, M. R., Kolakowsky‐Hayner, S. A., et al. (2024) · JMIR Research Protocols · doi:10.2196/57699 · Cited by 12
PERFECT: Personalized Exercise Recommendation Framework and architECTure
Mehrabadi, M. A., Khatibi, E., Jimah, T., Labbaf, S., Borg, H., Narvaez, L., Pimentel, P. S., Turner, A., Dutt, N. D., Guo, Y., et al. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3696425 · Cited by 9
mHealth and Applications
Choe, E. K., Klasnja, P., & Pratt, W. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_19 · Cited by 12
Self-citation
Physical activity and diet just-in-time adaptive intervention to reduce blood pressure: a randomized controlled trial
Dorsch, M. P., Golbus, J. R., Stevens, R., Trumpower, B., Basu, T., Luff, E., Warden, K., Giacalone, M., Bailey, S., Rubick, G. V., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01844-3 · Cited by 11
Self-citation
Beyond the cross‐section: Rethinking the intention–behaviour gap through a conceptual and methodological lens
Jekauc, D., Voelkle, M. C., Sniehotta, F., & Nigg, C. R. (2025) · British Journal of Health Psychology · doi:10.1111/bjhp.70046 · Cited by 9
The future of precision health is data‐driven decision support
Sperger, J., Freeman, N. L. B., Jiang, X., Bang, D., de Marchi, D., & Kosorok, M. R. (2020) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11475 · Cited by 10
Receptivity to mobile health interventions
Keller, R., von Wangenheim, F., Mair, J. L., & Kowatsch, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00006-x · Cited by 7
Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review
Leong, U., & Chakraborty, B. (2023) · JMIR mhealth and uhealth · doi:10.2196/44685 · Cited by 7
Causal message-passing for experiments with unknown and general network interference
Faradonbeh, M. S. S., & Bayati, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322232121 · Cited by 12
Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006
Cho, S. E., Saha, E., Matabuena, M., Wei, J., & Ghosal, R. (2024) · Annals of Epidemiology · doi:10.1016/j.annepidem.2024.10.001 · Cited by 7
Optimization of a Mobile Transdiagnostic Emotion Regulation Intervention for University Students: A Micro‐Randomized Trial
Laure, T., Remmerswaal, D., Konigorski, S., Engels, R. C. M. E., & Boffo, M. (2024) · Stress and Health · doi:10.1002/smi.3507 · Cited by 7
Attitudes and barriers to mobile mental health interventions among first-year college students: a mixed-methods study
McCarthy, K., & Horwitz, A. G. (2025) · Journal of American College Health · doi:10.1080/07448481.2025.2458085 · Cited by 7
Intelligent Pooling in Thompson Sampling for Rapid Personalization in Mobile Health
Tomkins, S., Liao, P., Yeung, S. Y., Klasnja, P., & Murphy, S. A. (2019) · Cited by 7
Self-citation
Exploring Opportunities to Improve Physical Activity in Individuals with Spinal Cord Injury Using Context-Aware Messaging
Lakshminarayanan, R., Canori, A., Ponnada, A., Nunn, M., Read, M. S., Hiremath, S. V., & Intille, S. S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555628 · Cited by 7
Rejoinder: ‘Estimating time-varying causal excursion effects in mobile health with binary outcomes’
Qian, T., Yoo, H., Klasnja, P., Almirall, D., & Murphy, S. A. (2021) · Biometrika · doi:10.1093/biomet/asab033 · Cited by 7
Self-citation
Keep Moving! A Systematic Review of App-Based Behavior Change Techniques and Visualizations for Promoting Everyday Physical Activity
Ulmer, T., & Baldauf, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05412-9_31 · Cited by 7
Off-policy evaluation in partially observed Markov decision processes under sequential ignorability
Hu, Y., & Wager, S. (2023) · The Annals of Statistics · doi:10.1214/23-aos2287 · Cited by 14
Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example
Li, S., Psihogios, A. M., McKelvey, E. R., Ahmed, A., Rabbi, M., & Murphy, S. A. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.002 · Cited by 9
Self-citation
Off-Policy Estimation of Long-Term Average Outcomes With Applications to Mobile HealthPreprint
Liao, P., Klasnja, P., & Murphy, S. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.13088 · Cited by 7
Self-citation
Optimization of a Transdiagnostic Mobile Emotion Regulation Intervention for University Students: Protocol for a Microrandomized Trial
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 4
“In addition, 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].”
BackgroundMethodInfluential
Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives
Pichon, A., Urteaga, I., Mamykina, L., & Elhadad, N. (2025) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3717063 · Cited by 7
The Positive Effect of pro‐Environmental Behavior on Eudaimonic Well‐Being in Young Adults: A Daily Diary Study Using the Within‐Person Encouragement Design
Caldaroni, S., Gerbino, M., Schmiedek, F., Neubauer, A., Manfredi, L., Gregori, F., Pastorelli, C., Corbelli, G., & Zuffianò, A. (2025) · Journal of Personality · doi:10.1111/jopy.13021 · Cited by 7
Digital Twins for Just-in-Time Adaptive Interventions (JITAIs): Framework for Optimizing and Continually Improving JITAIs
Gazi, A. H., Gao, D., Ghosh, S., Xu, Z., Trella, A. L., Klasnja, P., & Murphy, S. A. (2025) · Journal of Medical Internet Research · doi:10.2196/72830 · Cited by 8
Self-citation
Digital solutions for shaping mood and behavior among individuals with mood disorders
Victory, A. M., Letkiewicz, A. M., & Cochran, A. L. (2020) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2020.07.008 · Cited by 6
Precision prevention in worksite health–A scoping review on research trends and gaps
Mess, F., Blaschke, S., Schick, T. S., & Friedrich, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304951 · Cited by 9
Bayesian Structural Time Series for Biomedical Sensor Data: A Flexible Modeling Framework for Evaluating InterventionsPreprint
Liu, J. J., Spakowicz, D. J., Ash, G. I., Hoyd, R., Zhang, A., Lou, S., Lee, D., Zhang, J., Presley, C., Greene, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.02.973677 · Cited by 6
Design of a Temporally Augmented Text Messaging Bot to Improve Adolescents’ Physical Activity and Engagement: Proof-of-Concept Study
Ortega, A., & Cushing, C. C. (2024) · JMIR Formative Research · doi:10.2196/60171 · Cited by 5
Like Adding a Small Weight to a Scale About to Tip: Personalizing Micro-Financial Incentives for Digital Wellbeing
Jang, S., Seo, Y., Choi, W., & Lee, U. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714208 · Cited by 6
Digital Therapies for Substance Use Disorders: Recent Advances and Engagement Strategies
Oesterle, T., & Bormann, N. L. (2026) · Substance Abuse and Rehabilitation · doi:10.2147/sar.s560350 · Cited by 6
Gender difference in the effects of chronic diseases on daily physical activity patterns in older adults: analysis of objectively measured physical activity in NHATS 2021
Saha, E., & Ghosal, R. (2023) · Annals of Epidemiology · doi:10.1016/j.annepidem.2023.08.004 · Cited by 5
A flexible micro-randomized trial design and sample size considerations
Xu, J., Yan, X., Figueroa, C. A., Williams, J. J., & Chakraborty, B. (2023) · Statistical Methods in Medical Research · doi:10.1177/09622802231188513 · Cited by 6
Realizing the potential of mobile interventions for education
Breitwieser, J., Neubauer, A., Schmiedek, F., & Brod, G. (2024) · npj Science of Learning · doi:10.1038/s41539-024-00289-9 · Cited by 7
The Digital Therapeutics Real World Evidence Framework: An approach for guiding evidence-based DTx design, development, testing, and monitoringPreprint
Kim, M., Patrick, K., Nebeker, C., Godino, J., Stein, S., Klasnja, P., Perski, O., Viglione, C., Coleman, A., & Hekler, E. B. (2022) · doi:10.31219/osf.io/8t5xy · Cited by 5
Self-citation
Outcomes of a pilot microrandomized trial of just-in-time adaptive intervention messages nudging weight-related behaviors in young adults
Valle, C. G., Nezami, B. T., O’Shea, N. G., Hatley, K. E., & Tate, D. F. (2025) · DIGITAL HEALTH · doi:10.1177/20552076251353267 · Cited by 4
Improving Affective Associations With Physical Activity via a Message-Based mHealth Intervention (WalkToJoy): Proof-of-Concept Study
Choi, S. J., Hung, P.-Y., Liu, M. Z., Dempsey, W., Newman, M., & Klasnja, P. (2025) · Journal of Medical Internet Research · doi:10.2196/75792 · Cited by 4
Self-citation
A Smartphone Physical Activity App for Patients in Alcohol Treatment: Single-Arm Feasibility Trial
Abrantes, A. M., Meshesha, L. Z., Blevins, C. E., Battle, C. L., Lindsay, C., Marsh, E. L., Feltus, S. R., Buman, M. P., Agu, E., & Stein, M. D. (2022) · JMIR Formative Research · doi:10.2196/35926 · Cited by 3
Pay, Pat, and Clawback: Incentivizing Service Providers’ Participation in On-Demand Digital Platforms
Yaraghi, N., Ramesh, R., & Tayi, G. K. (2024) · Management Science · doi:10.1287/mnsc.2023.02603 · Cited by 4
Investigating Motivations and Patient Profiles for Personalization of Health Applications for Behaviour Change
Bonneux, C., Dendale, P., & Coninx, K. (2022) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3529190.3529201 · Cited by 4
Data-driven Interpretable Policy Construction for Personalized Mobile Health
Bertsimas, D., Klasnja, P., Murphy, S. A., & Na, L. (2022) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh55609.2022.00010 · Cited by 4
Self-citation
Testing stationarity and change point detection in reinforcement learning
Li, M., Shi, C., Wu, Z., & Fryźlewicz, P. (2025) · The Annals of Statistics · doi:10.1214/25-aos2501 · Cited by 4
Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study
Ikegaya, M., Foo, J. C., Murata, T., Oshima, K., & Kim, J. (2025) · JMIR Human Factors · doi:10.2196/66750 · Cited by 5
A just-in-time adaptive mobile application intervention to reduce sodium intake and blood pressure in patients with hypertension: Rationale and design of the LowSalt4Life 2 trial
Dorsch, M. P., Dempsey, W., Krambrink, A., Ganai, S., Greer, K. M., Ali, M. S., Gesierich, C., O’Neill, M., Nallamothu, B. K., & Hummel, S. L. (2025) · American Heart Journal · doi:10.1016/j.ahj.2025.107294 · Cited by 3
A pilot effectiveness study of a just-in-time micro-randomized controlled trial on the physical activity and sedentary time of young children and their parents: The active family m-health intervention
Phillips, S. M., Bourke, M., Inniss, B., Ahluwalia, M., & Tucker, P. (2026) · PLoS ONE · doi:10.1371/journal.pone.0340687 · Cited by 3
Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management
Fox, I. (2020) · Deep Blue (University of Michigan) · Cited by 3
Identifying Design Opportunities for Adaptive mHealth Interventions That Target General Well-Being: Interview Study With Informal Care Partners
Yan, X., Newman, M., Park, S. Y., Sander, A. M., Choi, S. W., Miner, J. A., Wu, Z., & Carlozzi, N. E. (2023) · JMIR Formative Research · doi:10.2196/47813 · Cited by 3
Recurrent Event Analysis in the Presence of Real-Time High Frequency Data via Random Subsampling
Dempsey, W. (2023) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2023.2276114 · Cited by 3
Motivated information avoidance in an mHealth weight loss intervention: Associations between unmet behavioral goals and likelihood of viewing program messages
Hurley, L., Nezami, B. T., Valle, C. G., & Tate, D. F. (2024) · Digital Health · doi:10.1177/20552076241287365 · Cited by 3
Online learning in bandits with predicted context
Guo, Y., Xu, Z., & Murphy, S. A. (2023) · PubMed · doi:10.48550/arxiv.2307.13916 · Cited by 3
Self-citation
Smart Start — Designing Powerful Clinical Trials Using Pilot Study Data
Ferstad, J. O., Prahalad, P., Maahs, D. M., Zaharieva, D. P., Fox, E. B., Desai, M., Johari, R., & Scheinker, D. (2024) · NEJM Evidence · doi:10.1056/evidoa2300164 · Cited by 4
The pleasure of moving: A compositional data analysis of the association between replacing sedentary time with physical activity on affective valence in daily life
Bourke, M., Phillips, S. M., Gilchrist, J. D., & Pila, E. (2024) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2024.102724 · Cited by 3
Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary OutcomesPreprint
Qian, T., Yoo, H., Klasnja, P., Almirall, D., & Murphy, S. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00528 · Cited by 3
Self-citation
Self-Tracking Technology for mHealth: A Systematic Review and the PAST SELF Framework.Preprint
Yfantidou, S., Sermpezis, P., & Vakali, A. I. (2021) · arXiv (Cornell University) · Cited by 3
Fluctuations in mHealth engagement following receipt of goal-discrepant feedback messages
Hurley, L., Gottfredson, N. C., Nezami, B. T., Valle, C. G., & Tate, D. F. (2025) · Digital Health · doi:10.1177/20552076241312569 · Cited by 2
Evaluating an Incentive-Based mHealth App for Physical Activity Promotion Using the Obesity-Related Behavioral Intervention Trial Model: Small Cohort Study
Salmani, B., Hiemstra, M. S., Harry Prapavessis, A., Vanderloo, L. M., & Mitchell, M. (2026) · JMIR Formative Research · doi:10.2196/85484 · Cited by 3
Micro-randomized trials in Information Systems research: An experimental method for advancing knowledge about our dynamic and digitalized world
Pieper, M., Fallon, M., & Heinzl, A. (2024) · Journal of Information Technology · doi:10.1177/02683962241280287 · Cited by 3
An analysis of physical activity coping plans: mapping barriers and coping strategies based on user ratings
Braun, M., Crombez, G., Backere, F. D., Tack, E., & Paepe, A. L. D. (2024) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2024.2434140 · Cited by 3
Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study
Park, J.-H., Norman, G. J., Klasnja, P., Rivera, D. E., & Hekler, E. B. (2023) · JMIR mhealth and uhealth · doi:10.2196/44296 · Cited by 3
Self-citation
Effectiveness of gamified team competition in the context of mHealth intervention for medical interns: a micro-randomized trialPreprint
Wang, J., Fang, Y., Frank, E., Walton, M. A., Burmeister, M., Tewari, A., Dempsey, W., NeCamp, T., Sen, S., & Wu, Z. (2022) · medRxiv · doi:10.1101/2022.03.11.22272278 · Cited by 2
Editorial: Use of smartphone applications to increase physical activity and fitness, volume II
Dourado, V. Z., & Kim, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1190566 · Cited by 4
Identifying classes of barriers to performing physical activity
Kunicki, Z. J., Stevenson, B. L., Popescu, D. L., Abrantes, A. M., Jones, R. N., & Harlow, L. L. (2023) · Sport Sciences for Health · doi:10.1007/s11332-023-01141-0 · Cited by 1
Estimating causal effects for binary outcomes using per-decision inverse probability weighting
Bao, Y., Bell, L., Williamson, E., Garnett, C. V., & Qian, T. (2024) · Biostatistics · doi:10.1093/biostatistics/kxae025 · Cited by 3
CausalCFF: Causal Analysis between User Stress Level and Contextually Filtered Features Extracted from Mobile Sensor Data
Zhang, P., Jung, G., Ahmed, U., & Lee, U. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719776 · Cited by 1
Emotion Regulation at Work: A Micro-Randomized Trial Comparing Three Cognitive Reappraisal Interventions
Aitken, J. A., Zhu, Z., Baines, J. I., Kim, J., Kaplan, S. A., Dalal, R. S., Hassani, J., & Gibson, J. L. (2025) · Occupational Health Science · doi:10.1007/s41542-025-00232-3 · Cited by 1
Mobile-Based Ecological Momentary Intervention for Improving Physical Activity in Adults Without Regular Physical Activity: Pilot Randomized Controlled Trial
Oba, T., Moriishi, C., Takano, K., Katahira, K., & Kimura, K. (2025) · JMIR Formative Research · doi:10.2196/79360 · Cited by 1
Intentional Strategies for Changing Character Traits
Ratchford, J. L., Schueller, S. M., & Jayawickreme, E. (2026) · Cambridge University Press eBooks · doi:10.1017/9781009707060 · Cited by 1
Reminder Strategies to Improve Meal-Logging Adherence: Protocol for a Microrandomized Trial
Fuchs, M., Brügger, V., Jin, Q., Wirth, B., Bilz, S., von Wangenheim, F., Brändle, M., Kowatsch, T., & Jovanova, M. (2026) · JMIR Research Protocols · doi:10.2196/100239 · Cited by 1
Personalization Matters: Benchmarking ML, DL and LLMs for Mental Health Forecasting from Smartphone Sensing Data
Feng, K., Sun, Z., Lee, R. K.-W., Jiang, X., Theng, Y., & Ding, Y. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3812715 · Cited by 1
Ecological Momentary Interventions in the Management of Eating Disorders and Obesity: A Systematic Review
Mao, W., Schmidt, R., Presseller, E. K., Juarascio, A. S., & Hilbert, A. (2026) · International Journal of Eating Disorders · doi:10.1002/eat.70140 · Cited by 2
From Detection to Forecasting: An Empirical Study of College Student Mental Health Using Smartphone Sensing
Gala, J. K., Sprigler, A., Malaviya, P. R., Shen, E., Zhao, Y., Ding, Y., & Shen, X. (2026) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase69719.2026.00011 · Cited by 1
Just-in-Time Adaptive Interventions for physical activity: a systematic review of their public health impact and methodological quality
Yang, S., Li, M., Li, Y., Chang, Y., Hu, S., Kou, X., & Liu, X. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1934024 · Cited by 1
Testing the Effects of App-Based Motivational Messages on Physical Activity and Resting Heart Rate Through Smartphone App Compliance in Patients With Vulnerable Coronary Artery Plaques: Protocol for a Microrandomized Trial
Mitra, S., Kroeger, C. M., Xu, J., Avery, L., Masedunskas, A., Cassidy, S., Wang, T., Hunyor, I., Wilcox, I., Huang, R., et al. (2023) · JMIR Research Protocols · doi:10.2196/46082 · Cited by 1
Engagement in mHealth-Prompted Self-Measured Blood Pressure Monitoring Among Participants Recruited From a Safety-Net Emergency Department: Secondary Analysis of the Reach Out Trial
Skolarus, L. E., Lin, C. C., Mishra, S. R., Meurer, W. J., Dinh, M., Whitfield, C., Bi, R., Brown, D. L., Oteng, R. A., Buis, L. R., et al. (2024) · JMIR mhealth and uhealth · doi:10.2196/54946 · Cited by 2
Handling of outcome missing data dependent on measured or unmeasured background factors in micro-randomized trial: Simulation and application study
Kondo, M., & Oba, K. (2024) · Digital Health · doi:10.1177/20552076241249631 · Cited by 1
Online model selection by learning how compositional kernels evolve.
Shin, E., Klasnja, P., Murphy, S. A., & Doshi‐Velez, F. (2023) · PubMed · Cited by 1
Self-citation
Predictors and Outcomes of Digital Weighing and Activity Tracking Lapses Among Young Adults During Weight Gain Prevention
Nezami, B. T., Valle, C. G., Brinson, A., Espeland, M. A., Wing, R. R., & Tate, D. F. (2021) · Obesity · doi:10.1002/oby.23123 · Cited by 1
Malthusian Trajectory for Heart Failure and Novel Translational Ambulatory Technologies
Iyngkaran, P., Bidargaddi, N. P., de Courten, M., & Battersby, M. (2022) · Current Cardiology Reviews · doi:10.2174/1573403x18666220524145646 · Cited by 1
Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile HealthPreprint
Menictas, M., Tomkins, S., & Murphy, S. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11646 · Cited by 1
Self-citation
Adaptive Sequential Design for a Single Time-SeriesPreprint
Malenica, I., Bibaut, A., & van der Laan, M. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00102 · Cited by 9
Transformers for prompt-level EMA non-response predictionPreprint
Nagesh, S., Moreno, A., Carpenter, S. M., Yap, J. R. T., Chatterjee, S., Lizotte, S. L., Wan, N., Kumar, S., Lam, C. Y., Wetter, D. W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.01193 · Cited by 3
Early Warning Signals Observed in Psycho-Behavioral Intensive Longitudinal Data for the Onset and Pathological Phase Transition of Psychiatric Disorders and Their Applicability to Very Early Intervention
志村, 広., 中村, 亨., 山口, 郁., & 山本, 義. (2019) · 計測と制御 · doi:10.11499/sicejl.58.102
Multi-Level Micro-Randomized Trial: Detecting the Proximal Effect of Messages on Physical ActivityPreprint
Xu, J., Yan, X., Figueroa, C. A., Williams, J. J., & Chakraborty, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13741 · Cited by 1
Assessing Time-Varying Causal Effect Moderation in the Presence of Cluster-Level Treatment Effect HeterogeneityPreprint
Shi, J., Wu, Z., & Dempsey, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01681
Online structural kernel selection for mobile healthPreprint
Shin, E., Klasnja, P., Murphy, S. A., & Doshi‐Velez, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09949
Self-citation
Navigating through digital health outcome evaluation methodologies: an algorithm based on a scoping review and emerging methodologies. (Preprint)Preprint
Perrin, C., Randriambelonoro, M., & Geissbühler, A. (2019) · doi:10.2196/preprints.15611
Personalization in digital interventions for behavior change (Preprint)Preprint
Tong, H. L., Quiroz, J. C., Kocaballi, A. B., Kennedy, G., Coiera, E. W., & Laranjo, L. (2020) · doi:10.2196/preprints.20562
The Effects of Personalized Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: Mobile Health Intervention Trial using a Healthcare IoT System (Preprint)Preprint
Takeuchi, H., Suwa, K., Kishi, A., Nakamura, T., Yoshiuchi, K., & Yamamoto, Y. (2022) · doi:10.2196/preprints.39150
Effect-Invariant Mechanisms for Policy Generalization
Saengkyongam, S., Pfister, N., Klasnja, P., Murphy, S. A., & Peters, J. (2023) · PubMed · doi:10.3929/ethz-b-000670427
Self-citation

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
ORCID: Klasnja, Predrag
Smith, Shawna
Seewald, Nicholas J
Luers, Brook
Hekler, Eric B
Murphy, Susan A
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
DOI: 10.1093/abm/kay067
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks