The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Realizing the Potential of Behavioral Intervention Technologies
Abstract
Behavioral intervention technologies (BITs) apply behavioral and psychological intervention strategies by using digital media to target behaviors, cognitions, and emotions in support of physical and mental health. BITs offer promising opportunities to expand psychological practice. However, to realize the potential of BITs, psychologists must understand both the possibilities and the limitations associated with using technology to advance psychology. We review examples of the most cutting-edge BITs and discuss features that differentiate BITs from traditional modes of delivery. We highlight major challenges in designing BITs, including adherence, engagement, technological and interventional obsolescence, multidisciplinary collaboration, and a reliance on psychological skeuomorphisms. Psychologists who do not understand the conceptual and applied considerations involved in creating BITs risk being left out of the development of BITs in the future. Teaming psychologists with experts in technology will help to promote novel interventions rather than using technology to disseminate existing treatments.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Digital behavior change interventions, such as smartphone apps, can be effective and cost-effective tools to change a range of health-related behaviors [1,2].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [2]
In the citing paper
“The notion that mental health technologies should mimic existing evidence-based treatments has become a skeuomorph, limiting our vision of what is possible by maintaining a frame based on past conceptualizations (11).”
From Three Problems With Current Digital Mental Health Research . . . and Three Things We Can Do About Them (2017) · cites this paper as [11]
In the citing paper
“Internet-based interventions have been shown to work at least as effectively as face-to-face intervention in improving mental health across a range of conditions.”
Citations
Cites
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524
The neglected 95%: Why American psychology needs to become less American.
Arnett, J. J. (2008) · American Psychologist · doi:10.1037/0003-066x.63.7.602 · Cited by 2,178
Agile Software Development, Principles, Patterns, and Practices
Martin, R. (2002) · Cited by 1,466
The design of eco-feedback technology
Froehlich, J., Findlater, L., & Landay, J. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753629 · Cited by 813
Harnessing context sensing to develop a mobile intervention for depression
Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1838 · Cited by 646 · cited 1×
“Mobilyze!, a smartphoneapplication-based treatment for depression, collects information about one’s context using embedded sensors to cue relevant and timely intervention content (Burns et al., 2011).”
BackgroundSelf-citationTheory-driven design strategies for technologies that support behavior change in everyday life
Consolvo, S., McDonald, D. W., & Landay, J. A. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518766 · Cited by 594
Efficacy of an Internet-based behavioral intervention for adults with insomnia
Ritterband, L. M., Thorndike, F. P., Gonder-Frederick, L. A., Magee, J. C., Bailey, E. T., Saylor, D. K., & Morin, C. M. (2009) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2009.66 · Cited by 482 · cited 2×
“, 2010), insomnia (Ritterband et al., 2009), diet (Resnicow et al.”
BackgroundLow vs. high-fidelity prototyping debate
Rudd, J., Stern, K., & Isensee, S. (1996) · Interactions · doi:10.1145/223500.223514 · Cited by 422
How to evaluate technologies for health behavior change in HCI research
Klasnja, P., Consolvo, S., & Pratt, W. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979396 · Cited by 414
Research works printed but not linked: 2
- Bickmore T., 2009, Proceedings of the 27th ACM International Conference on Human Factors in Computing Systems, CHI 2009, Extended Abstracts Volume
- Morris R. R., 2012, Proceedings of Collective Intelligence
Cited by
Sorted by citations
Items 101–152 of 152
Chen, A., Swaminathan, A., Kearns, W. R., Alberts, N. M., Law, E. F., & Palermo, T. M. (2019) · Journal of Medical Internet Research · doi:10.2196/11756 · Cited by 10
Process‐based and principle‐guided approaches in youth psychotherapy
Weisz, J. R., Fitzpatrick, O. M., Venturo‐Conerly, K. E., & Cho, E. (2021) · World Psychiatry · doi:10.1002/wps.20887 · Cited by 9
Behavioral intervention technologies for positive psychology: Introduction to the special issue
Schueller, S. M. (2014) · The Journal of Positive Psychology · doi:10.1080/17439760.2014.943802 · Cited by 9
Self-citationA digital therapeutic alliance in digital mental health
Kaveladze, B., & Schueller, S. M. (2023) · Digital Therapeutics for Mental Health and Addiction · doi:10.1016/b978-0-323-90045-4.00009-5 · Cited by 8
Self-citationSelf-Control in Cyberspace: Applying Dual Systems Theory to Digital Self-Control ToolsPreprint
Lyngs, U., Lukoff, K., Slovák, P., Binns, R., Slack, A., Inzlicht, M., Kleek, M. G. V., & Shadbolt, N. (2019) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/685tm · Cited by 8
Murphy, K. M., Glock, R., Victorson, D. E., Reddy, M. C., Birken, S. A., & Salsman, J. M. (2025) · JMIR Formative Research · doi:10.2196/67175 · Cited by 8
O’Dell, S. M., Fisher, H. R., Schlieder, V., Klinger, T., Kininger, R. L., Cosottile, M., Cummings, S., & DeHart, K. (2021) · JMIR Pediatrics and Parenting · doi:10.2196/27551 · Cited by 7
Ranney, M. L., Pittman, S. K., Riese, A., Ybarra, M. L., Huang, J. J. S., Spirito, A., & Rosen, R. K. (2018) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2018.422 · Cited by 7
Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity
Theilig, M.-M., Korbel, J. J., Mayer, G., Hoffmann, C., & Zarnekow, R. (2019) · IEEE Access · doi:10.1109/access.2019.2958474 · Cited by 10
Liu, I., Xiao, Y., Liu, F., Wu, S., & Ni, S. (2022) · International Symposium of Chinese CHI (Chinese CHI) · doi:10.1145/3565698.3565789 · Cited by 6
Higinbotham, M. K., Emmert‐Aronson, B. O., & Bunge, E. L. (2020) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-020-00139-6 · Cited by 6
Using technology to engage boys and men in the prevention of sexual assault
Oesterle, D. W., Schipani‐McLaughlin, A. M., Salazar, L. F., & Gilmore, A. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-819202-3.00006-7 · Cited by 6
AdaptAI: A Personalized Solution to Sense Your Stress, Fix Your Mess, and Boost Productivity
Gadhvi, R., Petkar, S., Desai, P. P., Ramachandran, S., & Siddharth, S. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720284 · Cited by 6
Watkins, E. R., Phillips, D., Cranston, T., Choueiri, H., Newton, M., Cook, H., & Taylor, G. J. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-06079-2 · Cited by 6
Mobile mental health interventions for trauma survivors
Ruzek, J. I., & Yeager, C. M. (2019) · mHealth · doi:10.21037/mhealth.2019.02.03 · Cited by 5
Cha, B. S., Eikey, E. V., Mukamel, D. B., Palomares, K., Schueller, S. M., Sorkin, D. H., Stadnick, N. A., Stoeckl, S. E., Zheng, K., & Schneider, M. L. (2025) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-025-01441-2 · Cited by 3
Self-citationBeh, E., Lin, J., Leong, G., Loh, P. Y., Poh, E. J., & Chan, D. (2026) · JMIR mhealth and uhealth · doi:10.2196/79338 · Cited by 4
Li, D. H., Zamantakis, A., Zapata, J. P., Danielson, E. C., Saber, R., Benbow, N., Smith, J. D., Swann, G., Macapagal, K. R., & Mustanski, B. S. (2024) · Implementation Science Communications · doi:10.1186/s43058-024-00628-2 · Cited by 4
Formation of information medical environment in different levels
Kozak, L., Kovalenko, A. S., Romanyuk, O. A., Аntomonov, М. Y., Omiotek, Z., & Kalimoldayev, M. (2019) · Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA) · doi:10.1117/12.2536412 · Cited by 3
Habit Formation and Automaticity: Psychoneurobiological Correlates of Gamma Activity
Leaf, C., Wasserman, C. S., Leaf, A., Kopooshian, N., Turner, R., & Paulson, R. M. (2024) · NeuroRegulation · doi:10.15540/nr.11.1.2 · Cited by 5
A single vs. multi-sensor approach to enhanced detection of smartphone placement
Guiry, J. J., Karr, C. J., van de Ven, P. W. J., Nelson, J., & Begale, M. J. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944424 · Cited by 3
Learning based visual engagement and self-efficacy
Dhamija, S. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273659 · Cited by 3
Digital healthcare public health
Gulliford, M. C., Jessop, E., & Yardley, L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780198837206.003.0015 · Cited by 3
Internet-Delivered Dialectical Behavior Therapy Skills Training for Suicidal and Heavy Drinkers
Wilks, C. R. (2018) · ResearchWorks at the University of Washington (University of Washington) · Cited by 3
Moltrecht, B., Patalay, P., Bear, H. A., Deighton, J., & Edbrooke-Childs, J. H. (2021) · doi:10.31234/osf.io/ks7ag · Cited by 3
Shin, Y., Kim, C., & Yoon, J. (2022) · [ ] With Design: Reinventing Design Modes · doi:10.1007/978-981-19-4472-7_31 · Cited by 3
Wish or Truth: Can Digital Interventions Stop the Obesity Crisis?
Portius, D. (2024) · Management for professionals · doi:10.1007/978-3-031-47768-3_8 · Cited by 2
Chan, P., Furlonger, B., Leif, E., D’Souza, L., Phillips, K. J., & Mattia, M. D. (2022) · The Educational and Developmental Psychologist · doi:10.1080/20590776.2022.2066994 · Cited by 2
Opportunities and Challenges in Educator Development
Nicolaou, C. (2023) · Advances in higher education and professional development book series · doi:10.4018/978-1-6684-5518-0.ch002 · Cited by 2
Benbow, N., Li Dennis, H., Macapagal, K. R., Krystal, M., Rana, S., Zamantakis, A., Emma, R., Smith Justin, D., & Mustanski, B. S. (2025) · Implementation Science Communications · doi:10.1186/s43058-025-00777-y · Cited by 1
Chien, W.-C., & Chen, Y.-J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-12657-3_20 · Cited by 1
State of the Science in Clinical Practice: Digital Mental Health Interventions
Ramos, G., Taylor, M. E., Tabak, N. T., Poola, N., & Schueller, S. M. (2026) · Cognitive and Behavioral Practice · doi:10.1016/j.cbpra.2026.03.001 · Cited by 1
Self-citationTallon, D., Thomas, L., Brabyn, S., Ching, B. C. F., Fox, F. E., Hahn, J. S., Jude, B., Li, J., López-López, J. A., Stawarz, K., et al. (2025) · Programme Grants for Applied Research · doi:10.3310/gjnw3912 · Cited by 1
Tallon, D., Thomas, L., Brabyn, S., Ching, B. C. F., Hahn, J. S., Jude, B., Li, J., Logan, M. X., Burrage, A., Gilbody, S. M., et al. (2026) · The Lancet Primary Care · doi:10.1016/j.lanprc.2026.100164 · Cited by 1
Thinking Outside the Visit: Digitally Extending the Reach of Behavioral Health
Satterfield, J. M. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3736-9 · Cited by 1
Exploring User Perspectives on Brief Reflective Questioning Activities for Stress ManagementPreprint
Bhattacharjee, A., Chen, P., Mandal, A., Zhou, L., Aggarwal, J., Hsu, A., O'Leary, K., Mariakakis, A. T., & Williams, J. J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4057942 · Cited by 1
Yin, Y., & Yu, Y. (2021) · Proceedings of the Design Society · doi:10.1017/pds.2021.539
Moreno-Blanco, D. (2021) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.69176
Using Self-report to Study How Application affect Motivation of Behavior Change (Preprint)Preprint
Yin, Y., & Yu, Y. (2020) · doi:10.2196/preprints.21992
Faulk, J., Oluwadairo, O., & Yoon, J. (2022) · Proceedings of DRS · doi:10.21606/drs.2022.682
Building Virtue Through App Cultures
Schnitker, S. A. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197549803.013.21
Translation and Prevention in Family Science
Kogan, S. M., & Barton, A. W. (2022) · Springer eBooks · doi:10.1007/978-3-030-92002-9_52
Cushing, C. C., Ortega, A., Fedele, D. A., Carlson, J., Davis, A. M., & Smyth, J. M. (2024) · doi:10.2196/preprints.59334
Soto‐Ruiz, N., Escalada‐Hernández, P., de Murieta, A. B.-S., Leite, A. C. A. B., Pimentel-Parra, G. A., & García‐Vivar, C. (2025) · doi:10.2196/preprints.72654
Moderators of a mHealth Intervention for Adolescent Physical Activity: Intervention Refinement Study
Cushing, C. C., Ortega, A., Fedele, D. A., Leslie‐Miller, C. J., Carlson, J., Davis, A. M., & Smyth, J. M. (2025) · JMIR Pediatrics and Parenting · doi:10.2196/59334
Rosen, K., Austin, J., & Bode, C. (2025) · Psychology and Health · doi:10.1080/08870446.2025.2574349
Zamantakis, A., Danielson, E. C., Rudd, E., Zapata, J. P., Benbow, N., Saber, R., Knapp, A. A., & Mustanski, B. S. (2025) · Journal of Medical Internet Research · doi:10.2196/63199 · Cited by 1
Cha, B. S., Kim, J., Borghouts, J. W., Eikey, E. V., Schneider, M. L., Schueller, S. M., Stadnick, N. A., Zheng, K., Mukamel, D. B., & Sorkin, D. H. (2025) · Psychiatric Quarterly · doi:10.1007/s11126-025-10225-8
Self-citationChen, W. (2026) · Internet Interventions · doi:10.1016/j.invent.2026.100903 · Cited by 1
Gkintoni, E., & Vantarakis, A. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16040215
Huberty, J., Baez, L. M., McAlister, K. L., & Kerppola, M. (2026) · JMIR Formative Research · doi:10.2196/85642
Design and usability of the CUMACA-M mobile app for long-term breast cancer survivors
Soto‐Ruiz, N., Escalada‐Hernández, P., de Murieta, A. B.-S., Leite, A. C. A. B., Pimentel-Parra, G. A., & García‐Vivar, C. (2026) · Health Policy and Technology · doi:10.1016/j.hlpt.2026.101313
Metadata
- Authors
- Schueller, S. M., Muñoz, R. F., & Mohr, D. C.
- Affiliations
- Northwestern University (US)
- Palo Alto University (US)
- Venue
- Current Directions in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2013-12
- Volume
- 22
- Issue
- 6
- Pages
- 478-483
- ISSN
- 0963-7214 (ISSN-L)
- 1467-8721
- OpenAlex
- W2074111622
- Cited by
- 152
- References
- 32
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 8 · 2023: 6 · 2022: 17 · 2021: 14 · 2020: 14 · 2019: 16 · 2018: 13 · 2017: 14 · 2016: 10 · 2015: 9 · 2014: 11 · 2013: 1
- FWCI
- 15.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Directions in Psychological Science (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
- digital mental health interventions, adherence, user engagement, technological obsolescence, multidisciplinary collaboration, skeuomorphism, digital media, psychological practice
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