Cited by 128Digital Mental Health InterventionsMobile Health and mHealth ApplicationsInnovative Human-Technology Interaction

The DOI matches its Crossref record; 31 of the 32 references it cites were found in a registry or an index.

Predicting user adherence to behavioral eHealth interventions in the real world: examining which aspects of intervention design matter most

Baumel, Amit, Yom-Tov, Elad·2018·Translational Behavioral Medicine

DOI 10.1093/tbm/ibx037

Open this paper in CoChat — free account

Abstract

Existing frameworks have identified a range of intervention design features that may facilitate adherence to eHealth interventions; however, empirical data are lacking on whether intervention design features can predict user adherence in the real world-where the public access available tools-and whether some design aspects of behavioral eHealth interventions are more important than others in predicting adherence. This study examined whether intervention design qualities predict user adherence to behavioral eHealth interventions in real-world use and which qualities matter the most. We correlated the online activities of users of 30 web-based behavioral interventions-collected from a proprietary data set of anonymized logs from consenting users of Microsoft Internet Explorer add-on-with interventions' quality ratings obtained by trained raters prior to empirical examination. The quality ratings included: Usability, Visual Design, User Engagement, Content, Therapeutic Persuasiveness (i.e., persuasive design and incorporation of behavior change techniques), and Therapeutic Alliance. We found Therapeutic Persuasiveness (i.e., the incorporation of persuasive design/behavior change principles) to be the most robust predictor of adherence (i.e., duration of use, number of unique sessions; 40 ≤ rs ≤ .58, ps ≤ .005), explaining 42% of the variance in user adherence in our regression model. Results indicated up to six times difference in the percentage of users utilizing the interventions for more than a minimum amount of time and sessions based on Therapeutic Persuasiveness. Findings suggest the importance of persuasive design and behavior change techniques incorporation during the design and evaluation of digital behavioral interventions.

Integrity signals

Registry

Crossref · 10.1093/tbm/ibx037

Cites

31 of 31 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?

The Law of Attrition
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524
Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy
Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1376 · Cited by 2,517
What is user engagement? A conceptual framework for defining user engagement with technology
O'Brien, H. L., & Toms, E. G. (2008) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20801 · Cited by 1,397
Persuasive System Design Does Matter: a Systematic Review of Adherence to Web-based Interventions
Kelders, S. M., Kok, R. N., Ossebaard, H. C., & Van Gemert-Pijnen, J. E. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2104 · Cited by 1,308
Adherence in Internet Interventions for Anxiety and Depression
Christensen, H., Griffiths, K. M., & Farrer, L. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1194 · Cited by 1,133
Health behavior models in the age of mobile interventions: are our theories up to the task?
Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., & Mermelstein, R. (2011) · Translational Behavioral Medicine · doi:10.1007/s13142-011-0021-7 · Cited by 978
Healthcare via Cell Phones: A Systematic Review
Krishna, S., Boren, S. A., & Balas, E. A. (2009) · Telemedicine and e-Health · doi:10.1089/tmj.2008.0099 · Cited by 882
A Review of eHealth Interventions for Physical Activity and Dietary Behavior Change
Norman, G. J., Zabinski, M. F., Adams, M. A., Rosenberg, D. E., Yaroch, A. L., & Atienza, A. A. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.05.007 · Cited by 605
A Behavior Change Model for Internet Interventions
Ritterband, L. M., Thorndike, F. P., Cox, D. J., Kovatchev, B. P., & Gonder-Frederick, L. A. (2009) · Annals of Behavioral Medicine · doi:10.1007/s12160-009-9133-4 · Cited by 591
Adherence to Internet-Based and Face-to-Face Cognitive Behavioural Therapy for Depression: A Meta-Analysis
van Ballegooijen, W., Cuijpers, P., van Straten, A., Karyotaki, E., Andersson, G., Smit, J. H., & Riper, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100674 · Cited by 409

Show all 32 references

Research works printed but not linked: 1

Aitken, 2013, Patient apps for improved healthcare from novelty to mainstream

Sorted by citations

Objective user engagement with mental health apps: systematic search and panel-based usage analysis
Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019) · Journal of Medical Internet Research · doi:10.2196/14567 · Cited by 965
Self-citation
Beyond the trial: systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety
Fleming, T., Bavin, L., Lucassen, M., Stasiak, K., Hopkins, S., & Merry, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9275 · Cited by 670
“The dose or amount of exposure to digital mental health interventions for clinically significant effects has been considered in previous research, with greater adherence generally associated with greater clinical gains [6,7,9].”
BackgroundMethod
Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review
Jakob, R., Harperink, S., Rudolf, A. M., Fleisch, E., Haug, S., Mair, J. L., Salamanca-Sanabria, A., & Kowatsch, T. (2022) · Journal of Medical Internet Research · doi:10.2196/35371 · Cited by 446
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
Ringeval, F., Schuller, B., Valstar, M., Cummins, N., Cowie, R., Tavabi, L., Schmitt, M., Alisamir, S., Amiriparian, S., Messner, E.-M., et al. (2019) · Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop · doi:10.1145/3347320.3357688 · Cited by 409
The Handbook of Behavior Change
Hagger, M. S., Cameron, L. D., Hamilton, K., Hankonen, N. E., & Lintunen, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318 · Cited by 446
Changing Behavior Using the Theory of Planned Behavior
Ajzen, I., & Schmidt, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.002 · Cited by 346
Validation of the Mobile Application Rating Scale (MARS)
Terhorst, Y., Philippi, P., Sander, L., Schultchen, D., Paganini, S., Bardus, M., Santo, K., Knitza, J., Machado, G. C., Schöeppe, S., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241480 · Cited by 291
The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review
Tremain, H., McEnery, C., Fletcher, K., & Murray, G. (2020) · JMIR Mental Health · doi:10.2196/17204 · Cited by 193
By the numbers: ratings and utilization of behavioral health mobile applications
Carlo, A. D., Hosseini Ghomi, R., Renn, B. N., & Areán, P. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0129-6 · Cited by 164
Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement
Wu, A., Scult, M. A., Barnes, E. D., Betancourt, J. A., Falk, A. E., & Gunning, F. M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00386-8 · Cited by 174
The German Version of the Mobile App Rating Scale (MARS-G): Development and Validation Study
Messner, E.-M., Terhorst, Y., Barke, A., Baumeister, H., Stoyanov, S., Hides, L., Kavanagh, D., Pryss, R., Sander, L., & Probst, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/14479 · Cited by 156
Changing Behavior Using the Model of Action Phases
Keller, L., Gollwitzer, P. M., & Sheeran, P. (2020) · The Handbook of Behavior Change · doi:10.1017/9781108677318.006 · Cited by 147
Is there a trial bias impacting user engagement with unguided e-mental health interventions? A systematic comparison of published reports and real-world usage of the same programs
Baumel, A., Edan, S., & Kane, J. M. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz147 · Cited by 143
Self-citation
Examining Predictors of Real-World User Engagement with Self-Guided eHealth Interventions: Analysis of Mobile Apps and Websites Using a Novel Dataset
Baumel, A., & Kane, J. M. (2018) · Journal of Medical Internet Research · doi:10.2196/11491 · Cited by 131
Self-citation
Digital Micro Interventions for Behavioral and Mental Health Gains: Core Components and Conceptualization of Digital Micro Intervention Care
Baumel, A., Fleming, T. M., & Schueller, S. M. (2020) · Journal of Medical Internet Research · doi:10.2196/20631 · Cited by 116
Self-citation
Changing Behavior Using the Health Action Process Approach
Schwarzer, R., & Hamilton, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.007 · Cited by 114
Mobile Health and Inhaler-Based Monitoring Devices for Asthma Management
Himes, B. E., Leszinsky, L., Walsh, R. N., Hepner, H., & Wu, A. C. (2019) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2019.08.034 · Cited by 80
Habit Interventions
Gardner, B., Rebar, A. L., & Lally, P. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.041 · Cited by 73
The Model of Gamification Principles for Digital Health Interventions: Evaluation of Validity and Potential Utility
Floryan, M., Chow, P. I., Schueller, S. M., & Ritterband, L. M. (2020) · Journal of Medical Internet Research · doi:10.2196/16506 · Cited by 65
Changing Behavior Using Social Cognitive Theory
Łuszczyńska, A., & Schwarzer, R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.003 · Cited by 81
Persuasive E-Health Design for Behavior Change
Baumeister, H., Kraft, R., Baumel, A., Pryss, R., & Messner, E.-M. (2019) · Studies in Neuroscience, Psychology and Behavioral Economics · doi:10.1007/978-3-030-31620-4_17 · Cited by 53
Self-citation
Self-Efficacy Interventions
Warner, L. M., & French, D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.032 · Cited by 62
Changing Behavior Using Ecological Models
Salmon, J., Hesketh, K. D., Arundell, L., Downing, K. L., & Biddle, S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.017 · Cited by 57
Informal Caregivers’ Use of Internet-Based Health Resources: An Analysis of the Health Information National Trends Survey
Shaffer, K. M., Chow, P. I., Cohn, W. F., Ingersoll, K. S., & Ritterband, L. M. (2018) · JMIR Aging · doi:10.2196/11051 · Cited by 50
Changing Behavior Using Self-Determination Theory
Hagger, M. S., Hankonen, N. E., Chatzisarantis, N. L. D., & Ryan, R. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.008 · Cited by 67
Systematic evaluation of content and quality of English and German pain apps in European app stores
Terhorst, Y., Meßner, E.-M., Schultchen, D., Paganini, S., Portenhauser, A. A., Eder, A.-S., Bauer, M., Papenhoff, M. C., Baumeister, H., & Sander, L. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100376 · Cited by 42
Quality of Physical Activity Apps: Systematic Search in App Stores and Content Analysis
Paganini, S., Terhorst, Y., Sander, L., Catic, S., Balci, S., Küchler, A., Schultchen, D., Plaumann, K., Sturmbauer, S. C., Krämer, L. V., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/22587 · Cited by 41
There is a non-evidence-based app for that: A systematic review and mixed methods analysis of depression- and anxiety-related apps that incorporate unrecognized techniques
Baumel, A., Torous, J. B., Edan, S., & Kane, J. M. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.011 · Cited by 40
Self-citation
Adhärenz digitaler Interventionen im Gesundheitswesen: Definitionen, Methoden und offene Fragen
Kernebeck, S., Busse, T. S., Ehlers, J. P., & Vollmar, H. C. (2021) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-021-03415-9 · Cited by 41
Design and Delivery Features That May Improve the Use of Internet-Based Cognitive Behavioral Therapy for Children and Adolescents With Anxiety: A Realist Literature Synthesis With a Persuasive Systems Design Perspective
Radomski, A. D., Wozney, L., McGrath, P., Huguet, A., Hartling, L., Dyson, M. P., Bennett, K., & Newton, A. S. (2019) · Journal of Medical Internet Research · doi:10.2196/11128 · Cited by 40
“The extent of iCBT program use may be a fundamental indication of its persuasiveness and its potential to assist the user with their goals of the program [15,88-91].”
Background
Evaluation of Digital Technologies Tailored to Support Young People’s Self-Management of Musculoskeletal Pain: Mixed Methods Study
Slater, H., Stinson, J., Jordan, J. M., Chua, J., Low, B., Lalloo, C., Phạm, Q., Cafazzo, J. A., & Briggs, A. M. (2020) · Journal of Medical Internet Research · doi:10.2196/18315 · Cited by 45
Planning and Implementation Intention Interventions
Rhodes, R. E., Grant, S. J., & de Bruijn, G. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.039 · Cited by 46
Attitudes and Persuasive Communication Interventions
Hamilton, K., & Johnson, B. T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.031 · Cited by 44
Changing Behavior Using Habit Theory
Orbell, S., & Verplanken, B. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.013 · Cited by 50
Changing Behavior: A Theory- and Evidence-Based Approach
Hagger, M. S., Cameron, L. D., Hamilton, K., Hankonen, N. E., & Lintunen, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.001 · Cited by 47
User feedback and usability testing of an online training and support program for dementia carers
Teles, S., Paúl, C., Lima, P., Chilro, R., & Ferreira, A. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100412 · Cited by 39
Social Identity Interventions
Tarrant, M., Haslam, C., Carter, M., Calitri, R., & Haslam, S. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.044 · Cited by 38
Changing Behavior Using Integrated Theories
Hagger, M. S., & Hamilton, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.015 · Cited by 44
Changing Behavior Using the Transtheoretical Model
DiClemente, C. C., & Graydon, M. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.010 · Cited by 35
Changing Behavior Using the Health Belief Model and Protection Motivation Theory
Orbell, S., Zahid, H., & Henderson, C. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.004 · Cited by 35
Feasibility of a Software agent providing a brief Intervention for Self-help to Uplift psychological wellbeing (“SISU”). A single-group pretest-posttest trial investigating the potential of SISU to act as therapeutic agent
Bendig, E., Erb, B., Meißner, E., Bauereiß, N., & Baumeister, H. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100377 · Cited by 24
“However, the scope of IMIs seems to be limited by a low uptake and intervention adherence of individuals (Baumel and Yom-Tov, 2018; Baumel et al., 2017).”
Changing Behavior by Changing Environments
Marteau, T. M., Fletcher, P. C., Hollands, G. J., & Munafo, M. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.014 · Cited by 32
Design, Implementation, and Evaluation of Behavior Change Interventions: A Ten-Task Guide
Abraham, C., & Denford, S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.019 · Cited by 27
Changing Behavior Using the Reflective-Impulsive Model
Deutsch, R., & Strack, F. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.012 · Cited by 30
Mobile 5P-Medicine Approach for Cardiovascular Patients
Pires, I. M., Denysyuk, H. V., Villasana, M. V., Sá, J., Lameski, P., Chorbev, I., Zdravevski, E., Trajkovik, V., Morgado, J. F., & García, N. M. (2021) · Sensors · doi:10.3390/s21216986 · Cited by 26
Adoption of Digital Therapeutics in Europe
Fassbender, A., Donde, S. S., Silva, M., Friganović, A., Stievano, A., Costa, E. M., Winders, T. A., & van Vugt, J. (2024) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s489873 · Cited by 25
Dyadic Behavior Change Interventions
Scholz, U., Berli, C., Lüscher, J., & Knoll, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.043 · Cited by 26
Developing Behavior Change Interventions
Hankonen, N. E., & Hardeman, W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.021 · Cited by 28
Intervention Use and Symptom Change With Unguided Internet-Based Cognitive Behavioral Therapy for Depression During the COVID-19 Pandemic: Log Data Analysis of a Convenience Sample
Oehler, C., Scholze, K., Reich, H., Sander, C., & Hegerl, U. (2021) · JMIR Mental Health · doi:10.2196/28321 · Cited by 23
Moving from Theoretical Principles to Intervention Strategies: Applying the Experimental Medicine Approach
Rothman, A. J., Klein, W. M. P., & Sheeran, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.020 · Cited by 26
Imagery, Visualization, and Mental Simulation Interventions
Hagger, M. S., & Conroy, D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.033 · Cited by 23
Changing Behavior Using the Common-Sense Model of Self-Regulation
Cameron, L. D., Fleszar‐Pavlović, S. E., & Khachikian, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.005 · Cited by 29
Engagement of Stakeholders in the Design, Evaluation, and Implementation of Complex Interventions
Hudson, J. L., Moon, Z., Hughes, L. D., & Moss-Morris, R. E. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.024 · Cited by 20
Incentive-Based Interventions
Uri Gneezy, B., Kajackaite, A., & Meier, S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.036 · Cited by 20
Psychometric evaluation of the Mobile Application Rating Scale (MARS) (Preprint)Preprint
Terhorst, Y., Philippi, P., Sander, L., Schultchen, D., Paganini, S., Bardus, M., Santo, K., Knitza, J., Machado, G. C., Schöeppe, S., et al. (2020) · doi:10.2196/preprints.17689 · Cited by 19
Changing Behavior Using Theories at the Interpersonal, Organizational, Community, and Societal Levels
Ruiter, R. A. C., Crutzen, R., de Leeuw, E., & Kok, G. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.018 · Cited by 20
The impact of therapeutic persuasiveness on engagement and outcomes in unguided interventions: A randomized pilot trial of a digital parent training program for child behavior problems
Baumel, A., Brandes, O., Brendryen, H., Muench, F. J., Kane, J. M., & Saar, C. R. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100680 · Cited by 17
Self-citation
Changing Behavior Using Social Identity Processes
Reynolds, K. J., Branscombe, N. R., Subašić, E., & Willis, L. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.016 · Cited by 21
The development of a theory-based eHealth app prototype to promote oral health during prenatal care visits
Vamos, C. A., Griner, S. B., Kirchharr, C., Green, S. M., DeBate, R. D., Daley, E. M., Quiñonez, R. B., Boggess, K., Jacobs, T. G., & Christiansen, S. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz047 · Cited by 15
Persuasive e-Health Design for Behavior Change
Baumeister, H., Kraft, R., Baumel, A., Pryss, R. C., & Meßner, E.-M. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_20 · Cited by 16
Self-citation
The development of a smartphone app to enhance post-traumatic stress disorder treatment in high-risk workers
Deady, M., Collins, D., Gayed, A., Harvey, S. B., & Bryant, R. A. (2023) · Digital Health · doi:10.1177/20552076231155680 · Cited by 14
Monitoring Interventions
Webb, T. L., & de Bruin, M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.037 · Cited by 16
Naturalistic Evaluation of HeadGear: A Smartphone App to Reduce Depressive Symptoms in Workers
Deady, M., Collins, D., Glozier, N., Gardiner, E., Arena, A. F. A., Gayed, A., Bryant, R. A., Calvo, R. A., & Harvey, S. B. (2024) · Behavior Therapy · doi:10.1016/j.beth.2024.01.001 · Cited by 12
Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations
Torkamaan, H., & Ziegler, J. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511118 · Cited by 13
Mobile Health for Asthma
Kouri, A., & Gupta, S. (2023) · CHEST Pulmonary · doi:10.1016/j.chpulm.2023.100002 · Cited by 14
Critical and Qualitative Approaches to Behavior Change
Chamberlain, K., & Lyons, A. C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.030 · Cited by 13
Beyond the trial: Systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety
Fleming, T. M., Bavin, L. M., Lucassen, M., Stasiak, K., Hopkins, S., & Merry, S. (2020) · doi:10.26686/wgtn.12331259 · Cited by 14
Maximizing User Engagement with Behavior Change Interventions
Yardley, L., Morrison, L. G., Müller, I., & Bradbury, K. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.025 · Cited by 11
Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial
Terhorst, Y., Weilbacher, N., Suda, C., Simon, L., Messner, E.-M., Sander, L. B., & Baumeister, H. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1075266 · Cited by 10
Satisfaction and Acceptability Ratings of a Web-Based Self-help Intervention for Depression: Retrospective Cross-sectional Study From a Resource-Limited Country
Lara, M. A., Patiño, P., Tiburcio, M., & Navarrete, L. (2021) · JMIR Formative Research · doi:10.2196/29566 · Cited by 10
Application of a Sociotechnical Framework to Uncover Factors That Influence Effective User Engagement With Digital Mental Health Tools in Clinical Care Contexts: Scoping Review
Lo, B., Durocher, K., Charow, R., Kimball, S., Phạm, Q., Sockalingam, S., Wiljer, D., & Strudwick, G. (2025) · Journal of Medical Internet Research · doi:10.2196/67820 · Cited by 11
Improving the Safety of Medicines via Digital Technology: An Assessment of the Scope and Quality of Risk Minimization Websites in the United States and United Kingdom
Smith, M. Y., Frise, S., Feron, J., & Marshall, R. (2022) · Drug Safety · doi:10.1007/s40264-022-01165-4 · Cited by 10
The Science of Behavior Change: The Road Ahead
Hagger, M. S., Cameron, L. D., Hamilton, K., Hankonen, N. E., & Lintunen, T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.046 · Cited by 13
Physiotherapists’ clinical use and acceptance of a telemonitoring platform during anterior cruciate ligament rehabilitation: a prospective clinical study
Rothgangel, A. S., Kanera, I. M., van den Heuvel, R., Wientgen, M., Jamin, G., Lenssen, A. F., & Braun, S. M. (2020) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2020.1774810 · Cited by 9
Autonomy-Supportive Interventions
Reeve, J., & Cheon, S. H. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.035 · Cited by 13
Wirkfaktoren und Veränderungsmechanismen digitaler psychotherapeutischer Interventionen
Domhardt, M., & Baumeister, H. (2021) · Psychotherapeut · doi:10.1007/s00278-021-00525-2 · Cited by 9
Implementation Science and Translation in Behavior Change
Łuszczyńska, A., Łobczowska, K., & Horodyska, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.023 · Cited by 10
An Open Trial of the Effectiveness, Program Usage, and User Experience of Internet-based Cognitive Behavioural Therapy for Mixed Anxiety and Depression for Healthcare Workers on Disability Leave
Miki, A., Lau, M. A., & Moradian, H. (2021) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000002248 · Cited by 8
Development of a Digital Behavioral Intervention to Reduce the Consumption of Sugar-Sweetened Beverages Among Rural Appalachian Adults: Multiphased, Human-Centered Design Approach
Zoellner, J., Reid, A., Porter, K., Frederick, C., Hilgart, M. M., & Ritterband, L. M. (2022) · JMIR Human Factors · doi:10.2196/41262 · Cited by 7
Evaluating the Contribution of New Factors in the Assessment of eHealth Therapeutic Alliance
Ashur, O., Muench, F. J., & Baumel, A. (2023) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-023-00336-z · Cited by 8
Self-citation
Self-Control Interventions
de Ridder, D. T. D., Gillebaart, M., & Friese, M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.040 · Cited by 9
Goal Setting Interventions
Epton, T., & Armitage, C. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.038 · Cited by 8
Usability guidelines for developing and evaluating web-based mental health interventions: Establishing a practical frameworkPreprint
Petersen, G. B., Nielsen, J. H., Olsen, J. V., & Kok, R. N. (2019) · doi:10.31234/osf.io/3ewz4 · Cited by 8
Development of Therapeutic Alliance and Social Presence in a Digital Intervention for Pediatric Concussion: Qualitative Exploratory Study
O'Kane, K. M. K., Otamendi, T., Silverberg, N. D., Choi, E., Sicard, V., Zemek, R., Healey, K., Brown, O., Butterfield, L., Smith, A., et al. (2024) · JMIR Formative Research · doi:10.2196/49133 · Cited by 7
Precision computerised cognitive behavioural therapy (cCBT) for adolescents with depression: a pilot and feasibility randomised controlled trial protocol for SPARX-UK
Khan, K., Hall, C. L., Babbage, C., Dodzo, S., Greenhalgh, C., Lucassen, M. F. G., Merry, S., Sayal, K. S., Sprange, K., Stasiak, K., et al. (2024) · Pilot and Feasibility Studies · doi:10.1186/s40814-024-01475-7 · Cited by 8
Evaluation of Behavior Change Interventions
Matthews, L., & Simpson, S. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.022 · Cited by 8
Motivational Interviewing Interventions
Berman, A. H., Beckman, M., & Lindqvist, H. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.045 · Cited by 12
Lessons learned from a multimodal sensor-based eHealth approach for treating pediatric obsessive-compulsive disorder
Klein, C. S., Hollmann, K., Kühnhausen, J., Alt, A. K., Pascher, A., Seizer, L., Primbs, J., Ilg, W., Thierfelder, A., Severitt, B., et al. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1384540 · Cited by 6
Economic and Behavioral Economic Approaches to Behavior Change
Brown, P. M., Cameron, L. D., Wilkinson, M., & Taylor, D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.042 · Cited by 7
Changing Behavior Using Integrative Self-Control Theory
Hofmann, W., Dohle, S., & Diel, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.011 · Cited by 7
Effectiveness and Feasibility of a Self-guided Mobile App Targeting Emotional Well-being in Healthy Adults: 4-Week Randomized Controlled Trial
Brindal, E., Kakoschke, N., Golley, S., Rebuli, M. A., & Baird, D. (2023) · JMIR Mental Health · doi:10.2196/44925 · Cited by 6
Affect-Based Interventions
Conner, M. T., Williams, D. M., & Rhodes, R. E. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.034 · Cited by 6
Evaluating the Acceptance and Usability of an Independent, Noncommercial Search Engine for Medical Information: Cross-Sectional Questionnaire Study and User Behavior Tracking Analysis
Specht, L., Scheible, R., Boeker, M., Farin‐Glattacker, E., Kampel, N., Schmölz, M., Schöpf-Lazzarino, A. C., Schulz, S., Schlett, C., Thomczyk, F., et al. (2024) · JMIR Human Factors · doi:10.2196/56941 · Cited by 6
Addressing Underserved Populations and Disparities in Behavior Change
Schüz, B., & Hooper, M. W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.027 · Cited by 5
Changing Behavior in the Digital Age
Kavanagh, D. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.029 · Cited by 4
Identifying Active Ingredients that Cause Change in Digital Parent Training Programs for Child Behavior Problems: A Qualitative Exploration
Saar, C. R., Brandes, O., & Baumel, A. (2024) · Child Psychiatry & Human Development · doi:10.1007/s10578-024-01803-1 · Cited by 3
Self-citation
Persuasive software features as antecedents to perceived compliance within physical activity behavior change support systems
Ekpezu, A. O., Wiafe, I., & B, H. O.-K. A. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2421448 · Cited by 3
Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial
Knauer, J., Baumeister, H., Schmitt, A., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1352762 · Cited by 2
“Within this context, user engagement and usability play a crucial role, warranting further investigation into personal habits that may facilitate the utilization of smart sensing (85, 86).”
Background
The Impact of Mechanisms of Action on Adherence and Outcomes in Self-Guided Digital Mental Health Interventions: Protocol for a Randomized Controlled Trial With Mediation Analysis
Baumel, A., & Aderka, I. M. (2025) · JMIR Research Protocols · doi:10.2196/71238 · Cited by 2
Self-citation
Changing Behavior Using Control Theory
Mansell, W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108677318.009 · Cited by 11

Authors: Baumel, A., & Yom-Tov, E.
Affiliations: Northwell Health (US)
Microsoft Research, Herzeliya, Israel
Corresponding author: Baumel, Amit
ORCID: Baumel, Amit
Venue: Translational Behavioral Medicine
Publisher: Oxford University Press (OUP)
Published: 2018-02-17
Volume: 8
Issue: 5
Pages: 793-798
ISSN: 1613-9860 (ISSN-L)
1869-6716
DOI: 10.1093/tbm/ibx037
OpenAlex: W2790152727
Cited by: 128
References: 32
Citations by year: 2026: 6 · 2025: 8 · 2024: 14 · 2023: 8 · 2022: 6 · 2021: 14 · 2020: 59 · 2019: 10 · 2018: 3
FWCI: 27.15
Type: journal article
Language: en
Open access: closed
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Translational Behavioral Medicine (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
Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords: user adherence, intervention design, persuasive design, behaviour change techniques, therapeutic relationship, usability, user engagement, online intervention, real-world applications
MeSH terms: Therapeutic Alliance, Adult, Behavior Therapy, Mental Disorders, Humans, Outcome and Process Assessment, Health Care, Patient Compliance, Persuasive Communication, Telemedicine, Internet

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

Predicting user adherence to behavioral eHealth interventions in the real world: examining which aspects of intervention design matter most

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