Other Titles
Rising Star Poster/Presentation
Abstract
Health systems face escalating burdens from physical inactivity, poor nutrition, and related noncommunicable diseases (NCDs), which disproportionately affect socioeconomically and clinically susceptible populations [1], [2]. Concurrently, artificial intelligence (AI), digital health technologies, and wearable devices are increasingly shaping health decision making and behavior change interventions [3], [4]. Nudge theory, grounded in behavioral economics, offers a noncoercive approach to influencing behavior through choice architecture while preserving individual autonomy [5], [6]. As AI enables nudges to become more personalized and scalable, conceptual clarity is needed to guide ethical, nurse-led applications that advance prevention and health equity [7], [8].
This work conceptually examines AI-enabled nudging from nursing and public health perspectives to clarify its defining attributes, antecedents, consequences, and relevance for nurse-led prevention and equity-centered practice.
Walker and Avant’s concept analysis method guided synthesis of nursing, public health, behavioral science, and digital health literature published between 2016 and 2026 [7]. Analysis focused on core nudge mechanisms, AI-enabled delivery modalities, prevention-oriented public health frameworks, and outcome domains related to physical activity, medication adherence, and NCD indicators [3], [9], [10]. Umbrella-level evidence mapping summarized author-reported quantitative ranges from review-level literature without pooling or re–analysis.
AI-enabled nudging is characterized by noncoercive, choice-preserving, and context-aware choice architecture supported by AI-driven personalization [11–13]. Antecedents include behavioral risk, cognitive burden, and digital infrastructure, while reported consequences include improved engagement, scalable prevention strategies, and clinically meaningful improvements in physical activity, adherence, and cardiometabolic outcomes [9], [14], [15]. Without explicit nurse leadership and ethical governance, however, AI-enabled nudges risk becoming opaque or inequitable [5], [16]. Nurses are uniquely positioned to lead the ethical design, implementation, and governance of AI-enabled nudging to support person-centered care, prevent NCDs, and advance health equity. Sustained exposure to well-designed nudges may also support habit formation and long–term prevention benefits among susceptible populations [6], [8], [17–20].
Notes
References:
[1] World Health Organization, “World health statistics 2025: monitoring health for the SDGs, sustainable development goals,” ISBN: 978-92-4-009470-3, May 2025. Accessed: Oct. 25, 2025. [Online]. Available: https://www.who.int/publications/i/item/9789240110496
[2] C. Hategeka et al., “Implementation research on noncommunicable disease prevention and control interventions in low- and middle-income countries: A systematic review,” PLOS Med., vol. 19, no. 7, p. e1004055, Jul. 2022, doi: 10.1371/journal.pmed.1004055.
[3] A. H. Sadeghian and A. Otarkhani, “Data-driven digital nudging: a systematic literature review and future agenda,” Behav. Inf. Technol., vol. 43, no. 15, pp. 3834–3862, Nov. 2024, doi: 10.1080/0144929X.2023.2286535.
[4] L. G. Jóhannsdóttir, S. G. Sigurõardóttir, M. Óskarsdóttir, and A. S. Islind, “Digital nudging in digital health technologies: a systematic review,” Health Technol., vol. 15, no. 6, pp. 1037–1051, Nov. 2025, doi: 10.1007/s12553-025-01000-7.
[5] H. Shang, L. Xiong, K. Chen, R. Tian, J. Tu, and X. Shang, “Ethical dimensions of healthcare nudges: a PRISMA-ScR-guided scoping review and framework for responsible behavioral governance,” Front. Public Health, vol. 13, p. 1716466, Nov. 2025, doi: 10.3389/fpubh.2025.1716466.
[6] Health Ethics & Governance, Ethics and governance of artificial intelligence for health. in WHO Guidance. World Health Organization, 2021. Accessed: Nov. 30, 2025. [Online]. Available: https://www.who.int/publications/i/item/9789240029200
[7] L. O. Walker and K. C. Avant, Strategies for Theory Construction in Nursing, 6th ed. VitalSource Bookshelf version: Pearson, 2018. doi: bned://9780134803548.
[8] A. I. Meleis, Theoretical Nursing Development and Progress, 6th ed. Wolters Kluwer, 2018. Accessed: Aug. 23, 2025. [Online]. Available: https://bookshelf.vitalsource.com/home/dashboard
[9] S. J. Alley et al., “The effectiveness of digital physical activity interventions in older adults: a systematic umbrella review and meta-meta-analysis,” Int. J. Behav. Nutr. Phys. Act., vol. 21, p. 144, Dec. 2024, doi: 10.1186/s12966-024-01694-4.
[10] R. A. Aftab, M. F. B. Ramlan, Z. Asim, and I. Shaik, “Umbrella review of systematic reviews analyzing the effectiveness of digital tools in improving medication adherence among diabetic patients,” J. Public Health, Mar. 2026, doi: 10.1007/s10389-026-02716-0.
[11] R. H. Thaler and C. R. Sunstein, Nudge: Improving decisions about health, wealth, and happiness. in Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT, US: Yale University Press, 2008, pp. x, 293.
[12] D. Kahneman and A. Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, vol. 47, no. 2, pp. 263–291, 1979, doi: 10.2307/1914185.
[13] A. Giarlotta and A. Petralia, “Simon’s bounded rationality,” Decis. Econ. Finance, vol. 47, no. 1, pp. 327–346, Jun. 2024, doi: 10.1007/s10203-024-00436-2.
[14] P. M. Ho et al., “Personalized Patient Data and Behavioral Nudges to Improve Adherence to Chronic Cardiovascular Medications: A Randomized Pragmatic Trial,” JAMA, vol. 333, no. 1, pp. 49–59, Jan. 2025, doi: 10.1001/jama.2024.21739.
[15] D. Gallardo-Gómez et al., “Optimal Dose and Type of Physical Activity to Improve Glycemic Control in People Diagnosed With Type 2 Diabetes: A Systematic Review and Meta-analysis,” Diabetes Care, vol. 47, no. 2, pp. 295–303, Jan. 2024, doi: 10.2337/dc23-0800.
[16] E. Roy-Highley, K. Körner, C. Mulrenan, and M. Petticrew, “Dark patterns, dark nudges, sludge and misinformation: alcohol industry apps and digital tools,” Health Promot. Int., vol. 39, no. 5, p. daae037, Oct. 2024, doi: 10.1093/heapro/daae037.
[17] American Nurses Association, “Nursing Scope of Practice,” American Nurses Association. Accessed: Oct. 25, 2025. [Online]. Available: https://www.nursingworld.org/practice-policy/scope-of-practice/
[18] International Council of Nurses, “The ICN Code of Ethics for Nurses,” ICN - International Council of Nurses. Accessed: Mar. 16, 2026. [Online]. Available: https://www.icn.ch/resources/publications-and-reports/icn-code-ethics-nurses
[19] R. H. Thaler and C. R. Sunstein, Nudge: The final edition, Final edition. New Haven: Yale University Press, 2021.
[20] J. W. Davis, “Physical activity habit formation through a technology based program,” JAANP: Journal of the American Association of Nurse Practitioners, vol. 32, no. 7, pp.540-546. 2020. Available: https://doi.org/10.1097/JXX.0000000000000385
Sigma Membership
Phi Gamma (Virtual), Theta Epsilon
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Qualitative Research
Keywords:
Nudge Theory, Artificial Intelligence, AI, Human Behavior, Health Behavior
Recommended Citation
Ulrich, Pattama S.; Pallas, Katherine M.; Dino, Michael Joseph; and Davis, Jean W., "AI-Enabled Nudging for Nursing-Led Prevention and Health Equity" (2026). International Nursing Research Congress (INRC). 90.
https://www.sigmarepository.org/inrc/2026/posters_2026/90
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-09-05
AI-Enabled Nudging for Nursing-Led Prevention and Health Equity
Toronto, Ontario, Canada
Health systems face escalating burdens from physical inactivity, poor nutrition, and related noncommunicable diseases (NCDs), which disproportionately affect socioeconomically and clinically susceptible populations [1], [2]. Concurrently, artificial intelligence (AI), digital health technologies, and wearable devices are increasingly shaping health decision making and behavior change interventions [3], [4]. Nudge theory, grounded in behavioral economics, offers a noncoercive approach to influencing behavior through choice architecture while preserving individual autonomy [5], [6]. As AI enables nudges to become more personalized and scalable, conceptual clarity is needed to guide ethical, nurse-led applications that advance prevention and health equity [7], [8].
This work conceptually examines AI-enabled nudging from nursing and public health perspectives to clarify its defining attributes, antecedents, consequences, and relevance for nurse-led prevention and equity-centered practice.
Walker and Avant’s concept analysis method guided synthesis of nursing, public health, behavioral science, and digital health literature published between 2016 and 2026 [7]. Analysis focused on core nudge mechanisms, AI-enabled delivery modalities, prevention-oriented public health frameworks, and outcome domains related to physical activity, medication adherence, and NCD indicators [3], [9], [10]. Umbrella-level evidence mapping summarized author-reported quantitative ranges from review-level literature without pooling or re–analysis.
AI-enabled nudging is characterized by noncoercive, choice-preserving, and context-aware choice architecture supported by AI-driven personalization [11–13]. Antecedents include behavioral risk, cognitive burden, and digital infrastructure, while reported consequences include improved engagement, scalable prevention strategies, and clinically meaningful improvements in physical activity, adherence, and cardiometabolic outcomes [9], [14], [15]. Without explicit nurse leadership and ethical governance, however, AI-enabled nudges risk becoming opaque or inequitable [5], [16]. Nurses are uniquely positioned to lead the ethical design, implementation, and governance of AI-enabled nudging to support person-centered care, prevent NCDs, and advance health equity. Sustained exposure to well-designed nudges may also support habit formation and long–term prevention benefits among susceptible populations [6], [8], [17–20].
Description
This session explores AI–enabled nudging from a nursing and public health perspective, emphasizing ethical, noncoercive strategies to support prevention and health equity. Participants will learn how digital nudges can improve physical activity, medication adherence, and NCD prevention.