Digital Health Interventions to Improve Engagement in Preventive Care
Keywords:
Digital health, Preventive care, Empathy, Personalization, PrivacyAbstract
Objective: This paper explores how these new digital health designs characteristics contribute to preventive care engagement, synthesizing behavioural, ethical, and emotional factors in one framework.
Methods: A cross-sectional design with validated constructs, and estimated with regression and moderation models by mean of EViews software.
Results: Results suggest that empathetic digital connections have the strongest impact on motivating preventive care usage, followed by ritualized nudges integrated in daily life, transparent consent processes, and calibrated friction signals. Crucially, all relationships are moderated by the Privacy personalization threshold, which indicates that personalization increases engagement up to the point beyond which the concerns over privacy over take the perceived value. Above this threshold, the gain from empathy, nudges, consent comfort, and care-related friction are increasingly eroded. This finding underscores the double-edged nature of personalization: while critical for engagement, it risks eroding trust if viewed as too intrusive.
Novelty: It leverages four new constructs Ritualised Micro-Nudge Fit, Friction-as-Care Signal, Consent comfort dynamics, and Agent Anticipatory Empathy and conceptualises privacy personalisation thresholds as moderation boundaries. The model innovatively combines nudge theory, privacy calculus, and affective computing in order to understand digital health engagement.
Implications for Research: The results contribute theoretically through the partial evidence that shows interaction of behavioural economics, emotional design and ethical governance in prevention. From a practical perspective, the study offers direction on developing empathetic, contextually embedded, privacy-sensitive as well as ethically-sound digital health interventions consisting of a blueprint for sustaining global long-term preventive health participation.
Downloads
References
Ahmad, R. (2025). Developing trustworthy and ethically-based healthcare systems. Applied Computing and Informatics, 1–13. https://doi.org/10.1108/ACI-05-2025-0203
Aigbonoga, D., Adewale, B., Igwilo, J., Adeyeye, V., Olajide, T., Olaniran, O., Akintayo, A., Aremu, P., Oluwadamilare, F., Popoola, O., & Ogunniyi, A. (2025). Efficacy of short message service (SMS) intervention on medication adherence and knowledge of stroke prevention among clinic attendees at risk of stroke: a randomized controlled trial. BMC Public Health, 25(1), 1070. https://doi.org/10.1186/s12889-025-22204-6
Aungst, T. D. (2025). Beyond the fill: Navigating pharmacy’s technological future in 2050. Journal of the American Pharmacists Association, 65(1), 102285. https://doi.org/https://doi.org/10.1016/j.japh.2024.102285
Bender, B. G. (2018). Technology Interventions for Nonadherence: New Approaches to an Old Problem. The Journal of Allergy and Clinical Immunology: In Practice, 6(3), 794–800. https://doi.org/https://doi.org/10.1016/j.jaip.2017.10.029
Bion, V., & Turner, G. (2025). A Scoping Review of Choice Architecture to Promote Healthy Nutrition in Health and Care Settings. Journal of Human Nutrition and Dietetics, 38(4), e70111. https://doi.org/https://doi.org/10.1111/jhn.70111
Chen, S., Banks, L. M., Carew, M. T., Kipchumba, E., Davey, C., Sulaiman, M., & Kuper, H. (2025). Disability-inclusive graduation programme intervention on social participation among ultra-poor people with disability in North Uganda: a cluster randomized trial. BMC Medicine, 23(1), 253. https://doi.org/10.1186/s12916-025-04100-3
Chu, Z.-X., Jin, X., Ye, Z.-H., Zhu, Y.-Y., Huang, X.-J., Wang, H., Chen, Y.-K., An, Y.-J., Wu, Z.-H., Jiang, Y.-J., Hu, Q.-H., & Shang, H. (2025). Real-time digital intervention on oral pre-exposure prophylaxis adherence among MSM: randomized controlled trial. Npj Digital Medicine, 8(1), 349. https://doi.org/10.1038/s41746-025-01743-7
Chung, S., Giuffrè, M., Rajashekar, N., Pu, Y., Shin, Y. E., Kresevic, S., Chan, C., Nakamura-Sakai, S., You, K., Saarinen, T., Hsiao, A., Wong, A. H., Evans, L., McCall, T., Kizilcec, R. F., Sekhon, J., Laine, L., & Shung, D. L. (2025). Usability and adoption in a randomized trial of GutGPT a GenAI tool for gastrointestinal bleeding. Npj Digital Medicine, 8(1), 527. https://doi.org/10.1038/s41746-025-01896-5
Desoky, A. A., Mostafa, N. M., AbdEllah-Alawi, M. H. M., & Hashem, E. M. (2025). Telehealth and challenges of statin adherence in patients with diabetes: a randomized controlled trial. BMC Health Services Research, 25(1), 1150. https://doi.org/10.1186/s12913-025-13295-3
Dinev, T., & Hart, P. (2006). Privacy Concerns and Levels of Information Exchange: An Empirical Investigation of Intended e-Services Use. E-Service Journal, 4(3), 25–60. https://doi.org/10.2979/esj.2006.4.3.25
Gavilan, D., & Martinez-Navarro, G. (2022). Exploring user’s experience of push notifications: a grounded theory approach. Qualitative Market Research: An International Journal, 25(2), 233–255. https://doi.org/10.1108/QMR-05-2021-0061
Huang, Jessica A, Hartanti, Intan R, Colin, Michelle N, & Pitaloka, Dian A E. (2022). Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges. DIGITAL HEALTH, 8, 20552076221100630. https://doi.org/10.1177/20552076221100634
Jung, Y., Bao, J. A., Norman, M. P., & Sundar, S. S. (2025). Privacy concerns in mobile technology: Can interactivity reduce friction? Computers in Human Behavior, 162, 108421. https://doi.org/https://doi.org/10.1016/j.chb.2024.108421
Karwowski, W., Salvendy, G., Endsley, M., Rouse, W., Salmon, P., Stanney, K., Thatcher, A., Andre, T., Yang, J., Ayaz, H., Cakir, A., Duffy, V., Drury, C., Gao, Q., Guo, Y., Hancock, P., Marras, W. S., Rau, P., Sawyer, B., & Stanton, N. (2025). Grand challenges for human factors and ergonomics. Theoretical Issues in Ergonomics Science, 26(4), 361–456. https://doi.org/10.1080/1463922X.2024.2431336
Kim, J., Zo, H., & Jun, J. (2025). How dataveillance shapes user behavior: The role of perceived value in disclosure and discontinuation. Computers in Human Behavior, 168, 108655. https://doi.org/https://doi.org/10.1016/j.chb.2025.108655
Krishna, B., Krishnan, S., & Sebastian, M. P. (2023). Understanding the process of building institutional trust among digital payment users through national cybersecurity commitment trustworthiness cues: a critical realist perspective. Information Technology & People, 38(2), 714–756. https://doi.org/10.1108/ITP-05-2023-0434
Liu, L., Chen, Z., Al-Hiyari, A., & Nassani, A. (2024). Sustainable growth in mineral rich BRI countries: Linking institutional performance, Fintech, and green finance to environmental impact. Resources Policy, 96, 105159. https://doi.org/https://doi.org/10.1016/j.resourpol.2024.105159
Matos Fialho, P. M., Wenig, V., Heumann, E., Müller, M., Stock, C., & Pischke, C. R. (2025). Digital public health interventions for the promotion of mental well-being and health behaviors among university students: a rapid review. BMC Public Health, 25(1), 2500. https://doi.org/10.1186/s12889-025-23669-1
Morey, B. N., Michelen, M., Phan, M., Cárdenas, S., Foo, M. A., Cantero, P. J., Peralta, S., Chirinos, N., Salazar, R., Montiel, G. I., Tanjasiri, S. P., Billimek, J., & LeBrón, A. M. W. (2025). Structural supports and challenges for community health worker models: Lessons from the COVID-19 response in Orange County, California. SSM - Qualitative Research in Health, 7, 100510. https://doi.org/https://doi.org/10.1016/j.ssmqr.2024.100510
Narayan, S. M., Chung, M. K., Adedinsewo, D., Brant, L. C. C., Davis, L. L., Duncker, D., Hall, J. L., Han, J. K., Lam, C. S. P., Lewis, E., Loscalzo, J., Márquez, M. F., Rahimzadeh, V., Rodriguez, F., Sanders, P., Svennberg, E., Stein, K., Turakhia, M., Yancy, C., & Armoundas, A. A. (2025). Access to digital health technologies: personalized framework and global perspectives. Nature Reviews Cardiology. https://doi.org/10.1038/s41569-025-01184-5
Obeng, H. A., Atan, T., & Arhinful, R. (2025). Exploring organizational politics, psychological well-being, work-life balance, and turnover intentions in Ghanaian hospitals: a conservation of resource theory perspective. BMC Health Services Research, 25(1), 1053. https://doi.org/10.1186/s12913-025-13056-2
Paltin, D., Prescott, M., Ma, J., Yeager, S., Ham, L., Serrano, S., Narez, J., Delgado, J., Burke, L., Gouaux, B., Beckwith, M., Morris, S. R., Moore, D. J., & Montoya, J. L. (2025). Barriers and Facilitators to PrEP Adherence among Transgender and Non-binary Individuals: A Mixed-Methods Analysis of Psychosocial Factors and Health Belief Model Constructs. AIDS and Behavior. https://doi.org/10.1007/s10461-025-04810-y
Perski, O., Kale, D., Leppin, C., Okpako, T., Simons, D., Goldstein, S. P., Hekler, E., & Brown, J. (2024). Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development. PLOS Digital Health, 3(8), e0000594. https://doi.org/10.1371/journal.pdig.0000594
Picard, R. W. (2003). Affective computing: challenges. International Journal of Human-Computer Studies, 59(1), 55–64. https://doi.org/https://doi.org/10.1016/S1071-5819(03)00052-1
Prior, E., Dorstyn, D., Taylor, A., & Rose, A. (2024). Attrition in Psychological mHealth Interventions for Young People: A Meta-Analysis. Journal of Technology in Behavioral Science, 9(4), 639–651. https://doi.org/10.1007/s41347-023-00362-x
Probst, F., Ratcliffe, J., Molteni, E., Mexia, N., Rees, J., Matcham, F., Antonelli, M., Tinker, A., Shi, Y., Ourselin, S., & Liu, W. (2024). A scoping review on human-centered design approaches and considerations in the design of technologies for loneliness and social isolation in older adults. Design Science, 10, e39. https://doi.org/DOI: 10.1017/dsj.2024.22
Reynolds 3rd, C. F., Jeste, D. V, Sachdev, P. S., & Blazer, D. G. (2022). Mental health care for older adults: recent advances and new directions in clinical practice and research. World Psychiatry, 21(3), 336–363. https://doi.org/https://doi.org/10.1002/wps.20996
Sivakumar, C. L. V, Mone, V., & Abdumukhtor, R. (2024). Addressing privacy concerns with wearable health monitoring technology. WIREs Data Mining and Knowledge Discovery, 14(3), e1535. https://doi.org/https://doi.org/10.1002/widm.1535
Strauss, R. (2024). Data Readiness and Data Strategies … Without Data, You Are Just Another Person with an Opinion BT - Data-Driven Customer Engagement: Mastering MarTech Strategies for Success (R. Strauss (ed.); pp. 61–103). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64295-1_5
Tan, L. D., Nguyen, N., Lopez, E., Peverini, D., Shedd, M., Alismail, A., & Nguyen, H. B. (2025). Artificial Intelligence in the Management of Asthma: A Review of a New Frontier in Patient Care. Journal of Asthma and Allergy, 18(null), 1179–1191. https://doi.org/10.2147/JAA.S535264
Unlu, A., Truong, S., Sawhney, N., Sivelä, J., & Tammi, T. (2025). Tracing the dynamics of misinformation and vaccine stance in Finland amid COVID-19. Information, Communication & Society, 28(2), 193–217. https://doi.org/10.1080/1369118X.2024.2331756
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Aprillia Nurhayati, Zakiyah Yasinea (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Applied Health Promotion Science (AHPROCE) © 2024 by PT. Inovasi Analisis Data is licensed under CC BY-SA 4.0


















