Cited by 264Digital Mental Health InterventionsMobile Health and mHealth ApplicationsBehavioral Health and Interventions

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.

Just-in-the-Moment Adaptive Interventions (JITAI): A Meta-Analytical Review

Wang, Liyuan, Miller, Lynn Carol·2020·Health Communication

DOI 10.1080/10410236.2019.1652388

Open this paper in CoChat — free account

Abstract

A just-in-time, adaptive intervention (JITAI) is an emerging type of intervention that provides tailored support at the exact time of need. It does so using enabling new technologies (e.g., mobile phones, sensors) that capture the changing states of individuals. Extracting effect sizes of primary outcomes produced by 33 empirical studies that used JITAIs, we found moderate to large effect sizes of JITAI treatments compared to (1) waitlist-control conditions (k = 9), Hedges's g = 1.65 and (2) non-JITAI treatments (k = 21), g = 0.89. Also, participants of JITAI interventions showed significant changes (k = 13) in the positive direction (g = 0.79). A series of sensitivity tests suggested that those effects persist. Those effects also persist despite differences in the behaviors of interests (e.g., blood glucose control, recovering alcoholics), duration of the treatments, and participants' age. Two aspects of tailoring, namely: (1) tailoring to what (i.e., both people's previous behavioral patterns and their current need states; with these effects additive) and (2) approach to tailoring (i.e., both using a human agent and an algorithm to decide tailored feedback; with these effects additive), are significantly associated with greater JITAI efficacy.

Integrity signals

Registry

Crossref · 10.1080/10410236.2019.1652388

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Introduction to Meta‐Analysis
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
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
Ecological Momentary Assessment (Ema) in Behavioral Medicine
Stone, A. A., & Shiffman, S. (1994) · Annals of Behavioral Medicine · doi:10.1093/abm/16.3.199 · Cited by 1,931
Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments
Heron, K. E., & Smyth, J. M. (2010) · British Journal of Health Psychology · doi:10.1348/135910709x466063 · Cited by 1,438
A meta-analysis of computer-tailored interventions for health behavior change
Krebs, P., Prochaska, J. O., & Rossi, J. S. (2010) · Preventive Medicine · doi:10.1016/j.ypmed.2010.06.004 · Cited by 854
Comparison of Strategies for Sustaining Weight Loss<subtitle>The Weight Loss Maintenance Randomized Controlled Trial</subtitle>
Svetkey, L. P. (2008) · JAMA · doi:10.1001/jama.299.10.1139 · Cited by 748
Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging
Rodgers, A., Corbett, T., Bramley, D., Riddell, T., Wills, M., Lin, R.-B., & Jones, M. (2005) · Tobacco Control · doi:10.1136/tc.2005.011577 · Cited by 732
Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial
Free, C., Knight, R., Robertson, S., Whittaker, R., Edwards, P., Zhou, W., Rodgers, A., Cairns, J., Kenward, M. G., & Roberts, I. (2011) · The Lancet · doi:10.1016/s0140-6736(11)60701-0 · Cited by 728
A 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

Show all 52 references

Research works printed but not linked: 5

Borenstein M., 2011, Introduction to m eta-analysis
10.2196/jmr.1005 · DOI
Lipsey M. W., 2001, Applied social research methods series; Vol. 49. Practical meta-analysis
*Miner, A., Kuhn, E., Hoffman, J. E., Owen, J. E., Ruzek, J. I., & Taylor, C. B., 2016, Psychological Trauma: Theory, Research, Practice, and Policy, 8
10.1111/acer.2012.36.issue-3 · DOI

Sorted by citations

The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality
Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021) · World Psychiatry · doi:10.1002/wps.20883 · Cited by 1,199
A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916221134490 · Cited by 263
Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions
Coppersmith, D. D. L., Dempsey, W., Kleiman, E. M., Bentley, K. H., Murphy, S. A., & Nock, M. K. (2022) · Psychiatry · doi:10.1080/00332747.2022.2092828 · Cited by 136
Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study
Cohen, A., Naslund, J. A., Chang, S., Nagendra, S., Bhan, A., Rozatkar, A., Thirthalli, J., Bondre, A., Tugnawat, D., Reddy, P. V., et al. (2023) · Schizophrenia · doi:10.1038/s41537-023-00332-5 · Cited by 90
An interpretable machine learning approach to multimodal stress detection in a simulated office environment
Naegelin, M., Weibel, R. P., Kerr, J. I., Schinazi, V. R., Marca, R. L., von Wangenheim, F., Hölscher, C., & Ferrario, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104299 · Cited by 83
Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review
Kargarandehkordi, A., Li, S., Lin, K., Phillips, K. T., Benzo, R. M., & Washington, P. Y. (2025) · Biosensors · doi:10.3390/bios15040202 · Cited by 79
State of the Science: Using Digital Mental Health Interventions to Extend the Impact of Psychological Services
Ramos, G., Hernandez-Ramos, R., Taylor, M. E., & Schueller, S. M. (2024) · Behavior Therapy · doi:10.1016/j.beth.2024.04.004 · Cited by 64
Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.
Nemesure, M. D., Collins, A. C., Price, G. D., Griffin, T. Z., Pillai, A., Nepal, S. K., Heinz, M. V., Lekkas, D., Campbell, A. T., & Jacobson, N. C. (2024) · Journal of Psychopathology and Clinical Science · doi:10.1037/abn0000884 · Cited by 73
Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review
Teepe, G. W., Fonseca, A. D., Kleim, B., Jacobson, N. C., Salamanca-Sanabria, A., Car, L. T., Fleisch, E., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/29412 · Cited by 65
Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review
van Genugten, C., Thong, M. S. Y., van Ballegooijen, W., Kleiboer, A. M., Spruijt‐Metz, D., Smit, A. C., Sprangers, M. A. G., Terhorst, Y., & Riper, H. M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1460167 · Cited by 64
Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial
NeCamp, T., Sen, S., Frank, E., Walton, M. A., Ionides, E. L., Fang, Y., Tewari, A., & Wu, Z. (2020) · Journal of Medical Internet Research · doi:10.2196/15033 · Cited by 55
Alcohol Use Disorder in the Age of Technology: A Review of Wearable Biosensors in Alcohol Use Disorder Treatment
Davis-Martin, R. E., Alessi, S. M., & Boudreaux, E. D. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.642813 · Cited by 54
Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization
Czyz, E. K., Koo, H. J., Al‐Dajani, N., King, C. A., & Nahum-Shani, I. (2021) · Psychological Medicine · doi:10.1017/s0033291721005006 · Cited by 68
Personalized interventions for behaviour change: A scoping review of just‐in‐time adaptive interventions
Hsu, T. C., Whelan, P., Gandrup, J., Armitage, C. J., Cordingley, L., & McBeth, J. (2024) · British Journal of Health Psychology · doi:10.1111/bjhp.12766 · Cited by 57
Biocueing and ambulatory biofeedback to enhance emotion regulation: A review of studies investigating non-psychiatric and psychiatric populations
ter Harmsel, J. F., Noordzij, M. L., Goudriaan, A. E., Dekker, J. J. M., Swinkels, L. T. A., van der Pol, T. M., & Popma, A. (2021) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2020.11.009 · Cited by 47
A Randomized Controlled Trial to Address Consumer Food Waste with a Technology-aided Tailored Sustainability Intervention
Roe, B. E., Qi, D., Beyl, R. A., Neubig, K., Apolzan, J. W., & Martin, C. K. (2021) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2021.106121 · Cited by 51
Effects of digital Just-In-Time nudges on healthy food choice – A field experiment
van der Laan, L. N., & Orcholska, O. (2022) · Food Quality and Preference · doi:10.1016/j.foodqual.2022.104535 · Cited by 50
Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study
Stamatis, C. A., Meyerhoff, J., Meng, Y., Lin, Z. C. C., Cho, Y. M., Liu, T., Karr, C. J., Liu, T., Curtis, B. L., Ungar, L., et al. (2024) · npj Mental Health Research · doi:10.1038/s44184-023-00041-y · Cited by 56
Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis
Keller, R., Hartmann, S., Teepe, G. W., Lohse, K.-M., Alattas, A., Car, L. T., Müller‐Riemenschneider, F., von Wangenheim, F., Mair, J. L., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/33348 · Cited by 44
Effectiveness and Minimum Effective Dose of App-Based Mobile Health Interventions for Anxiety and Depression Symptom Reduction: Systematic Review and Meta-Analysis
Lu, S.-C., Xu, M., Wang, M., Hardi, A., Cheng, A. L., Chang, S.-H., & Yen, P.-Y. (2022) · JMIR Mental Health · doi:10.2196/39454 · Cited by 45
“A recent meta-analysis of 31 JITAI studies found significant effects of JITAI on improving health outcomes and enhancing study retention and intervention adherence [13,23,24].”
Causal Inference in Generalizable Environments: Systematic Representative Design
Miller, L. C., Shaikh, S. J., Jeong, D. C., Wang, L., Gillig, T. K., Godoy, C. G., Appleby, P. R., Corsbie-Massay, C. L., Marsella, S., Christensen, J. L., et al. (2019) · Psychological Inquiry · doi:10.1080/1047840x.2019.1693866 · Cited by 49
Self-citation
Digital chemsex support and care: The potential of just-in-time adaptive interventions
Platteau, T., Herrijgers, C., & de Wit, J. B. F. (2020) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2020.102927 · Cited by 48
Opening the Black Box of Daily Life in Nonsuicidal Self-injury Research: With Great Opportunity Comes Great Responsibility
Kiekens, G., Robinson, K., Tatnell, R., & Kirtley, O. J. (2021) · JMIR Mental Health · doi:10.2196/30915 · Cited by 49
Bridging Innovation and Equity: Advancing Public Health Through Just-in-Time Adaptive Interventions
Zainal, N. H., Liu, X., Leong, U., Yan, X., & Chakraborty, B. (2024) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-071723-103909 · Cited by 42
A Personalized, Transdiagnostic Smartphone Intervention (Mello) Targeting Repetitive Negative Thinking in Young People With Depression and Anxiety: Pilot Randomized Controlled Trial
Bell, I., Arnold, C., Gilbertson, T., D’Alfonso, S., Castagnini, E., Chen, N. A., Nicholas, J., O’Sullivan, S., Valentine, L., & Álvarez‐Jiménez, M. (2023) · Journal of Medical Internet Research · doi:10.2196/47860 · Cited by 40
Neurophysiological Measurements in Higher Education: A Systematic Literature Review
Darvishi, A., Khosravi, H., Sadiq, S. W., & Weber, B. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00256-0 · Cited by 39
Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health
Liu, X., Deliu, N., & Chakraborty, B. (2022) · American Journal of Public Health · doi:10.2105/ajph.2022.307150 · Cited by 39
Slow down and be critical before using early warning signals in psychopathology
Helmich, M. A., Schreuder, M. J., Bringmann, L. F., Riese, H., Snippe, E., & Smit, A. C. (2024) · Nature Reviews Psychology · doi:10.1038/s44159-024-00369-y · Cited by 39
Precision health in behaviour change interventions: A scoping review
Mauch, C. E., Edney, S. M., Viaña, J. N. M., Gondalia, S. V., Sellak, H., Boud, S. J., Nixon, D. D., & Ryan, J. C. (2022) · Preventive Medicine · doi:10.1016/j.ypmed.2022.107192 · Cited by 33
Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample
Price, G. D., Heinz, M. V., Collins, A. C., & Jacobson, N. C. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115693 · Cited by 37
Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing
Bhattacharjee, A., Williams, J. J., Meyerhoff, J., Kumar, H., Mariakakis, A. T., & Kornfield, R. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580774 · Cited by 38
Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study
Willms, A., & Liu, S. (2023) · JMIR Medical Education · doi:10.2196/51426 · Cited by 35
A Randomized Controlled Trial of CBT+: A Clinician-Controlled, Just-In-Time, Adjunctive Intervention for Bulimia-Spectrum Disorders
Juarascio, A. S., Presseller, E. K., Srivastava, P., Manasse, S. M., & Forman, E. M. (2022) · Behavior Modification · doi:10.1177/01454455221109434 · Cited by 34
Personalization and localization as key expectations of digital health intervention in women pre- to post-pregnancy
Lee, V. V., Vijayakumar, S., Ng, W. Y., Lau, N. Y., Leong, Q. Y., Ooi, D. S. Q., Su, L. L., Lee, Y. S., Chan, S., Blasiak, A., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00924-6 · Cited by 30
Effectiveness of just-in-time adaptive interventions for improving mental health and psychological well-being: a systematic review and meta-analysis
von Lützow, U., Neuendorf, N. L., & Scherr, S. (2025) · BMJ Mental Health · doi:10.1136/bmjment-2025-301641 · Cited by 29
Innovations to improve outcomes and uptake of psychotherapies for mental disorders: a state‐of‐the‐art review
Cuijpers, P. J., Harrer, M., & Furukawa, T. A. (2026) · World Psychiatry · doi:10.1002/wps.70002 · Cited by 34
A systematic scoping review of just-in-time, adaptive interventions finds limited automation and incomplete reporting
Oikonomidi, T., Ravaud, P., LeBeau, J., & Tran, V.-T. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2022.12.006 · Cited by 26
Approaches for tailoring between-session mental health therapy activities
Oewel, B., Areán, P. A., & Agapie, E. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642856 · Cited by 33
Toward Human-Centered Artificial Intelligence for Users’ Digital Well-Being: Systematic Review, Synthesis, and Future Directions
Shin, Y. (2025) · JMIR Human Factors · doi:10.2196/69533 · Cited by 42
From Precision Metapharmacology to Patient Empowerment: Delivery of Self-Care Practices for Epilepsy, Pain, Depression and Cancer Using Digital Health Technologies
Bulaj, G. W., Clark, J. E., Ebrahimi, M., & Bald, E. M. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.612602 · Cited by 31
Digital Therapeutics (DTx) Expand Multimodal Treatment Options for Chronic Low Back Pain: The Nexus of Precision Medicine, Patient Education, and Public Health
Rohaj, A., & Bulaj, G. W. (2023) · Healthcare · doi:10.3390/healthcare11101469 · Cited by 27
Assessment and Disruption of Ruminative Episodes to Enhance Mobile Cognitive Behavioral Therapy Just-in-Time Adaptive Interventions in Clinical Depression: Pilot Randomized Controlled Trial
Wang, L., & Miller, L. C. (2022) · JMIR Formative Research · doi:10.2196/37270 · Cited by 29
Self-citation
“Just-in-time” but a bit delayed: Personalizing digital nudges for healthier online food choices
de Vries, R., Bol, N., & van der Laan, L. N. (2025) · Appetite · doi:10.1016/j.appet.2025.107852 · Cited by 28
Adapting just-in-time interventions to vulnerability and receptivity: Conceptual and methodological considerations
Nahum-Shani, I. B., Wetter, D. W., & Murphy, S. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00012-5 · Cited by 30
Personalized Interventions for Online Moderation
Cresci, S., Trujillo, A., & Fagni, T. (2022) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3511095.3536369 · Cited by 25
Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study
de Vries, H., Pennings, H. J. M., van der Schans, C. P., Sanderman, R., Oldenhuis, H. K. E., & Kamphuis, W. (2022) · Sensors · doi:10.3390/s23010332 · Cited by 25
Social Comparison and Mental Health
Arigo, D., Bercovitz, I., Lapitan, E., & Gular, S. (2024) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-024-00313-0 · Cited by 32
Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation
Suh, J., Howe, E. S., Lewis, R., Hernandez, J., Saha, K., Althoff, T., & Czerwinski, M. P. (2024) · JMIR Mental Health · doi:10.2196/48974 · Cited by 26
mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial
Větrovský, T., Král, N., Pfeiferová, M., Kühnová, J., Novák, J., Wahlich, C., Jaklova, A., Jurková, K., Janek, M., Omcirk, D., et al. (2023) · BMC Public Health · doi:10.1186/s12889-023-15513-1 · Cited by 25
Leveraging Vagally Mediated Heart Rate Variability as an Actionable, Noninvasive Biomarker for Self-Regulation: Assessment, Intervention, and Evaluation
Laborde, S., Ackermann, S., Borges, U., D’Agostini, M., Giraudier, M., Iskra, M., Mosley, E., Ottaviani, C., Salvotti, C., Schmaußer, M., et al. (2023) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231196789 · Cited by 23
Sensitivity, specificity, and tolerability of the BACTrack Skyn compared to other alcohol monitoring approaches among young adults in a field‐based setting
Ash, G. I., Gueorguieva, R. V., Barnett, N. P., Wang, W., Robledo, D. S., DeMartini, K. S., Pittman, B. P., Redeker, N. S., O’Malley, S. S., & Fucito, L. M. (2022) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.14804 · Cited by 20
Dynamic structural equation modelling evaluating the progressively lowered stress threshold as an explanation for behavioural symptoms of dementia
Pickering, C. E. Z., Yefimova, M., Wang, D., Maxwell, C. D., & Jablonski, R. A. (2022) · Journal of Advanced Nursing · doi:10.1111/jan.15173 · Cited by 21
Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance
Duckworth, C., Cliffe, B., Pickering, B., Ainsworth, B., Blythin, A. M., Kirk, A., Wilkinson, T. M. A., & Boniface, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01063-2 · Cited by 22
Detecting Idiographic Personality Change
Beck, E. D., & Jackson, J. J. (2021) · Journal of Personality Assessment · doi:10.1080/00223891.2021.1984246 · Cited by 27
The State of Digital Biomarkers in Mental Health
McGinnis, E. W., Cherian, J., & McGinnis, R. S. (2024) · Digital Biomarkers · doi:10.1159/000542320 · Cited by 25
A Theory-Informed, Personalized mHealth Intervention for Adolescents (Mobile App for Physical Activity): Development and Pilot Study
Domin, A., Uslu, A. S., Schulz, A., Ouzzahra, Y., & Vögele, C. (2022) · JMIR Formative Research · doi:10.2196/35118 · Cited by 24
Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis
Xu, Z., & Smit, E. S. (2023) · Digital Health · doi:10.1177/20552076231183543 · Cited by 18
Investigating two mobile just-in-time adaptive interventions to foster psychological resilience: research protocol of the DynaM-INT study
Bögemann, S. A., Riepenhausen, A., Puhlmann, L. M. C., Bar, S., Hermsen, E. J. C., Mituniewicz, J., Reppmann, Z. C., Uściƚko, A., van Leeuwen, J. M. C., Wackerhagen, C., et al. (2023) · BMC Psychology · doi:10.1186/s40359-023-01249-5 · Cited by 23
Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data
Siepe, B. S., Sander, C., Schultze, M., Kliem, A., Ludwig, S., Hegerl, U., & Reich, H. (2024) · JMIR Mental Health · doi:10.2196/50136 · Cited by 21
Health behaviour change: Theories, progress, and recommendations for the next generation of physical activity research
Simpson, A., Beauchamp, M. R., Dimmock, J. A., Willis, C., & Jackson, B. (2025) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2025.102918 · Cited by 23
A Gambling Just-In-Time Adaptive Intervention (GamblingLess: In-The-Moment): Protocol for a Microrandomized Trial
Dowling, N. A., Merkouris, S., Youssef, G. J., Lubman, D. I., Bagot, K. L., Hawker, C. O., Portogallo, H. J., Thomas, A. C., & Rodda, S. N. (2022) · JMIR Research Protocols · doi:10.2196/38958 · Cited by 20
E-Mental Health for People with Personality Disorders: A Systematic Review
Xie, Q., Torous, J. B., & Goldberg, S. B. (2022) · Current Psychiatry Reports · doi:10.1007/s11920-022-01360-1 · Cited by 21
Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study
Kerr, J. I., Naegelin, M., Benk, M., von Wangenheim, F., Meins, E., Viganò, E., & Ferrario, A. (2023) · Journal of Medical Internet Research · doi:10.2196/44131 · Cited by 19
Comparison Conditions in Research on Persuasive Message Effects: Aligning Evidence and Claims About Persuasiveness
O’Keefe, D. J. (2023) · Communication Methods and Measures · doi:10.1080/19312458.2023.2214949 · Cited by 17
Applying the Just-In-Time Adaptive Intervention Framework to the Development of Gambling Interventions
Dowling, N. A., Rodda, S. N., & Merkouris, S. (2023) · Journal of Gambling Studies · doi:10.1007/s10899-023-10250-x · Cited by 21
The Promise of Just-in-Time Adaptive Interventions for Organizational Scholarship and Practice: Conceptual Development and Research Agenda
Zhu, Z., Aitken, J. A., Dalal, R. S., & Kaplan, S. A. (2023) · Organizational Research Methods · doi:10.1177/10944281231202740 · Cited by 18
Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses
Ehrmann, D., Hermanns, N., SCHMITT, A. J., Klinker, L. Y., Haak, T., & Kulzer, B. (2024) · Diabetologia · doi:10.1007/s00125-024-06239-9 · Cited by 20
Emotional change in its “natural habitat”: Measuring everyday emotion regulation with passive and active ambulatory assessment methods.
Kaplan, D. M., Hughes, C. D., Schatten, H. T., Mehl, M. R., Armey, M. F., & Nugent, N. R. (2022) · Journal of Psychotherapy Integration · doi:10.1037/int0000291 · Cited by 16
The effect of self‐management techniques on relevant outcomes in chronic low back pain: A systematic review and meta‐analysis
Scholz, C., Schmigalle, P., Plessen, C. Y., Liegl, G., Vajkoczy, P., Praßer, F., Rose, M., & Obbarius, A. (2023) · European Journal of Pain · doi:10.1002/ejp.2221 · Cited by 18
Connected Yet Cognitively Drained? A Mixed-Methods Study Examining Whether Online Vigilance and Availability Pressure Promote Mental Fatigue
Gaeveren, K. V., Murphy, S. L., Vicente, D. D. S., & Abeele, M. V. (2024) · Communication Research · doi:10.1177/00936502241248494 · Cited by 29
Heart Rate Variability Measurement to Assess Acute Work-Content-Related Stress of Workers in Industrial Manufacturing Environment—A Systematic Scoping Review
Tran, T.-A., Péntek, M., Motahari-Nezhad, H., Abonyi, J., Kovács, L., Gulàcsi, L., Eigner, G., Zrubka, Z., & Ruppert, T. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3282141 · Cited by 15
Use of geofencing interventions in population health research: a scoping review
Tobin, K. E., Heidari, O., Volpi, C. R., Sodder, S., & Duncan, D. T. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-069374 · Cited by 15
The future of the eating disorder field: Inclusive, aware of systems, and personalized
Levinson, C. A., Cusack, C. E., Hunt, R. A., Fitterman-Harris, H. F., Ralph‐Nearman, C., & Hooper, S. C. (2024) · Behaviour Research and Therapy · doi:10.1016/j.brat.2024.104648 · Cited by 15
Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports
Cunha, B. C. R., Rodrigues, K. R. D. H., Zaine, I., da Silva, E. A. N., Viel, C. C., & Pimentel, M. D. G. C. (2021) · Journal of Medical Internet Research · doi:10.2196/24278 · Cited by 18
Visual Analytics for Decision-Making During Pandemics
Reinert, A., Snyder, L. S., Zhao, J. L., Fox, A., Hougen, D. F., Nicholson, C., & Ebert, D. S. (2020) · Computing in Science & Engineering · doi:10.1109/mcse.2020.3023288 · Cited by 15
Ecological Momentary Assessment of the Relationship between Positive Outcome Expectancies and Gambling Behaviour
Dowling, N. A., Merkouris, S., & Spence, K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10081709 · Cited by 14
A Digital Lifestyle Coach (E-Supporter 1.0) to Support People With Type 2 Diabetes: Participatory Development Study
Hietbrink, E. A. G., Middelweerd, A., van Empelen, P., Preuhs, K., Konijnendijk, A., Nijeweme-d’Hollosy, W. O., Schrijver, L. K., Laverman, G. D., & Vollenbroek-Hutten, M. M. R. (2023) · JMIR Human Factors · doi:10.2196/40017 · Cited by 16
Research Review: The internalizing paradox – youth anxiety and depression symptoms, psychotherapy outcomes, and implications for research and practice
Weisz, J. R., Fitzpatrick, O. M., Venturo‐Conerly, K. E., Sternberg, A., Steinberg, J. S., & Ng, M. Y. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13820 · Cited by 20
Systematic review of dynamically tailored eHealth interventions targeting physical activity and healthy diet in chronic disease
Hietbrink, E. A. G., Lansink, C., Laverman, G. D., Vollenbroek-Hutten, M. M. R., Middelweerd, A., & Tabak, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02054-7 · Cited by 20
Time-Lagged Prediction of Food Craving With Qualitative Distinct Predictor Types: An Application of BISCWIT
Kaiser, T., Butter, B., Arzt, S., Pannicke, B., Reichenberger, J., Ginzinger, S., & Blechert, J. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.694233 · Cited by 15
Designing digital health interventions with causal inference and multi-armed bandits: a review
Švihrová, R., Rossi, A. D., Marzorati, D., Tzovara, A., & Faraci, F. D. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1435917 · Cited by 17
A complex systems view on physical activity with actionable insights for behaviour change
Schüler, J., Heino, M. T. J., Balagué, N., Chater, A., Grüber, M., Kanning, M. K., Keim, D. A., Mier, D. U., Moreno‐Villanueva, M., Nußbeck, F. W., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02279-2 · Cited by 13
Correlates of Engagement Within an Online HIV Prevention Intervention for Single Young Men Who Have Sex With Men: Randomized Controlled Trial
Choi, S. K., Golinkoff, J., Michna, M., Connochie, D. M., & Bauermeister, J. A. (2022) · JMIR Public Health and Surveillance · doi:10.2196/33867 · Cited by 12
Is There a Place for Cognitive Restructuring in Brief, Self-Guided Interventions? Randomized Controlled Trial of a Single-Session, Digital Program for Adolescents
Steinberg, J. S., Fitzpatrick, O. M., Khurana, S., Kim, M. Y., Mair, P., Schleider, J. L., Hatzenbuehler, M. L., & Weisz, J. R. (2024) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2024.2384026 · Cited by 19
Just-In-Time Adaptive Interventions to Promote Behavioral Health: Protocol for a Systematic Review
Henry, L. M., Blay-Tofey, M., Haeffner, C. E., Raymond, C. N., Tandilashvili, E., Terry, N. L., Kiderman, M., Metcalf, O., Brotman, M. A., & López-Guzmán, S. (2024) · JMIR Research Protocols · doi:10.2196/58917 · Cited by 14
A Social Support Just-in-Time Adaptive Intervention for Individuals With Depressive Symptoms: Feasibility Study With a Microrandomized Trial Design
Elmer, T., Wolf, M., Snippe, E., & Scholz, U. (2025) · JMIR Mental Health · doi:10.2196/74103 · Cited by 16
Does Wearable-Measured Heart Rate Variability During Sleep Predict Perceived Morning Mental and Physical Fitness?
de Vries, H., Oldenhuis, H. K. E., van der Schans, C. P., Sanderman, R., & Kamphuis, W. (2023) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-022-09578-8 · Cited by 12
Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety
Stamatis, C. A., Liu, T., Meyerhoff, J., Meng, Y., Cho, Y. M., Karr, C. J., Curtis, B. L., Ungar, L., & Mohr, D. C. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100683 · Cited by 12
Person-specific and pooled prediction models for binge eating, alcohol use and binge drinking in bulimia nervosa and alcohol use disorder
Leenaerts, N., Soyster, P. D., Ceccarini, J., Sunaert, S. G., Fisher, A. J., & Vrieze, E. (2024) · Psychological Medicine · doi:10.1017/s0033291724000862 · Cited by 16
Digital therapeutics and behavioral chronic pain management: closing the gap between innovation and effective use
Eccleston, C., Fisher, E., Keefe, F. J., Palermo, T. M., & Toelle, T. R. (2024) · Pain · doi:10.1097/j.pain.0000000000003348 · Cited by 11
Measuring Affective State: Subject-Dependent and -Independent Prediction Based on Longitudinal Multimodal Sensing
Berkemeier, L., Kamphuis, W., Brouwer, A.-M., de Vries, H., Schadd, M. P. D., van Baardewijk, J. U., Oldenhuis, H. K. E., Verdaasdonk, R. M., & van Gemert‐Pijnen, J. E. W. C. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3474098 · Cited by 15
Daily Contributors of Tinnitus Loudness and Distress: An Ecological Momentary Assessment Study
Simoes, J. P., Bulla, J., Neff, P. K. A., Pryss, R. C., Marcrum, S. C., Langguth, B., & Schlee, W. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.883665 · Cited by 12
Development of a behaviour change intervention using a theory-based approach, Behaviour Centred Design, to increase nurses’ hand hygiene compliance in the US hospitals
Sands, M., & Aunger, R. V. (2021) · Implementation Science Communications · doi:10.1186/s43058-021-00124-x · Cited by 9
Just-in-Time: A Caregiver-Mediated Intervention for Toddlers With Autism
Bailey, K. M., Rodgers, M. E., Quinn, E. D., Thompson, S., Nietfeld, J. P., & Kaiser, A. P. (2024) · American Journal of Speech-Language Pathology · doi:10.1044/2023_ajslp-23-00279 · Cited by 10
A Personalized, Transdiagnostic Smartphone App (Mello) Targeting Repetitive Negative Thinking for Depression and Anxiety: Qualitative Analysis of Young People’s Experience
Valentine, L., Arnold, C., Nicholas, J., Castagnini, E., Malouf, J., Álvarez‐Jiménez, M., & Bell, I. (2024) · Journal of Medical Internet Research · doi:10.2196/63732 · Cited by 9
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
A systematic review on personalization of treatment components in IBIs for mental disorders
Schaeuffele, C., Zagorscak, P., Langerwisch, V., Wilke, J., Medvedeva, Y., & Knaevelsrud, C. (2025) · Internet Interventions · doi:10.1016/j.invent.2025.100840 · Cited by 11
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
Multidimensional analysis of human outdoor comfort: Integrating just-in-time adaptive interventions (JITAIs) in urban digital twins
Lei, B., Liu, P., Fujiwara, K., Frei, M., Miller, C., Chua, Y. X., & Biljecki, F. (2025) · Cities · doi:10.1016/j.cities.2025.106443 · Cited by 11
Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium
Lucassen, D. A., Lasschuijt, M. P., Camps, G., Loo, E. J. V., Fischer, A. R. H., de Vries, R. A. J., Haarman, J. A. M., Simons, M., de Vet, E., Vos, M. B.-D., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18157877 · Cited by 12

Authors: Wang, L., & Miller, L. C.
Affiliations: University of Southern California (US)
Corresponding author: Wang, Liyuan
ORCID: Wang, Liyuan
Miller, Lynn Carol
Venue: Health Communication
Publisher: Informa UK Limited
Published: 2019-09-05
Volume: 35
Issue: 12
Pages: 1531-1544
ISSN: 1041-0236 (ISSN-L)
1532-7027
DOI: 10.1080/10410236.2019.1652388
OpenAlex: W2971759879
Cited by: 264
References: 52
Citations by year: 2026: 53 · 2025: 58 · 2024: 47 · 2023: 43 · 2022: 35 · 2021: 21 · 2020: 6 · 2019: 1
FWCI: 17.84
Type: journal article
Language: en
Open access: closed
Locations: Health Communication (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
Keywords: just-in-time adaptive interventions, personalized feedback, tailored support, mobile health, wearable sensors, treatment efficacy, effect size, need states, glycemic control, alcohol recovery, waitlist control
MeSH terms: Behavior Therapy, Humans, Cell Phone

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

Just-in-the-Moment Adaptive Interventions (JITAI): A Meta-Analytical Review

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks