Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: Saleh Alarifi
DOI Link: https://doi.org/10.22214/ijraset.2026.79416
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Mobile health (mHealth) applications are increasingly used to support patient engagement and self-management; however, ensuring users remain actively engaged over the long term remains a challenge. This study investigates the factors influencing continuance intention and shared decision-making in the mHealth context. Drawing on patient-centered healthcare perspectives, the research examines the roles of digital patient agency, perceived personalization, and app usability in shaping patient empowerment and e-patient satisfaction. Data was collected through a survey of mHealth users and analyzed using structural equation modeling. The results indicate that digital patient agency, perceived personalization, and app usability significantly enhance patient empowerment and e-patient satisfaction. In turn, patient empowerment strongly predicts continuance intention and shared decision-making, while e-patient satisfaction positively influences shared decision-making. The findings highlight the central role of patient empowerment in sustaining mHealth engagement and fostering collaborative healthcare interactions, offering insights for researchers, healthcare providers, and digital health developers seeking to design more patient-centered mHealth solutions.
Healthcare is increasingly shifting toward patient-centered care through the adoption of mobile health (mHealth) technologies, including smartphone applications, wearable devices, and remote monitoring systems. These technologies improve disease management, patient monitoring, and communication with healthcare providers, contributing to better health outcomes. However, despite their benefits, long-term user engagement remains a major challenge, with many users discontinuing use due to limited perceived value, reliability concerns, or lack of satisfaction. Existing studies have primarily focused on initial adoption rather than continued usage.
This study addresses this gap by examining the factors influencing the continuance intention of mHealth applications and their role in promoting patient empowerment and shared decision-making. Based on technology acceptance and patient engagement theories, the proposed conceptual model investigates how digital patient agency, perceived personalization, and app usability enhance patient empowerment and e-satisfaction, which subsequently influence continuance intention and collaborative healthcare decisions. The study formulates hypotheses linking these constructs and employs a cross-sectional quantitative survey of mHealth users in Saudi Arabia. The collected data are analyzed using Structural Equation Modeling (SEM) to validate the proposed relationships and provide insights for designing patient-centered, sustainable digital healthcare services.
This study examined the factors that influence sustained engagement with mHealth applications and their implications for patient empowerment and shared decision-making. By integrating technological factors (perceived personalization and app usability) with patient-related capabilities (digital patient agency), the study developed and tested a model explaining how these elements shape patient empowerment, e-patient satisfaction, continuance intention, and collaborative healthcare decisions. The results show that digital patient agency, perceived personalization, and app usability significantly enhance patient empowerment and e-patient satisfaction. In turn, patient empowerment was found to strongly influence both continuance intention and shared decision-making, highlighting its critical role in sustaining mHealth engagement and enabling more collaborative healthcare interactions. Additionally, e-patient satisfaction was found to positively contribute to shared decision-making, indicating that positive digital health experiences encourage patients to participate more actively in healthcare decisions.These findings reinforce the growing importance of digital health technologies in supporting patient-centered care. mHealth applications can empower patients by providing accessible health information, self-monitoring tools, and communication channels that strengthen collaboration between patients and healthcare providers (Mesko et al., 2025; Wu et al., 2026). As healthcare systems increasingly integrate digital technologies into clinical practice, understanding the factors that promote sustained engagement with mHealth tools becomes essential. Overall, This study advances digital health research by emphasizing the pivotal role of patient empowerment in linking technology design features with long-term mHealth usage and shared decision-making. The findings provide valuable insights for researchers, healthcare providers, and digital health developers seeking to design more effective, user-centered mHealth solutions that enhance patient participation and improve healthcare outcomes.
[1] Al Amin, M., Razib Alam, M., & Alam, M. Z. (2023). Antecedents of students’e-learning continuance intention during COVID-19: An empirical study. E-learning and Digital Media, 20(3), 224-254. [2] Alluhaidan, A. S., Chatterjee, S., Drew, D. E., Ractham, P., &Kaewkitipong, L. (2023). Empowerment enabled by information and communications technology and intention to sustain a healthy behavior: survey of general users. JMIR Human Factors, 10, e47103. [3] Almunawar, M. N., Anshari, M., & Younis, M. Z. (2015). Incorporating customer empowerment in mobile health. Health Policy and Technology, 4(4), 312-319. [4] Aranha, M., James, K., Deasy, C., & Heavin, C. (2021). Exploring the barriers and facilitators which influence mHealth adoption among older adults: A literature review. Gerontechnology, 20(2). [5] Bahar, A., Prananta, A. W., Afifudin, M., & Rahman, R. T. (2024). Mobile App Usability, Data Privacy, Brand Loyalty, and Customer Retention in Indonesia\'s Online Transportation Services Industry. International Journal of Business, Law, and Education, 5(2), 2562-2572. [6] Baudier, P., Kondrateva, G., Ammi, C., Chang, V., & Schiavone, F. (2023). Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs. Technovation, 120, 102547. [7] Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370. [8] Bok, A., Noone, D., & Skouw-Rasmussen, N. (2022). Patient agency: key questions and challenges–A report from the 1st workshop of the EHC Think Tank Workstream on Patient Agency. The Journal of Haemophilia Practice, 9(1), 27-35. [9] Caston, S., Greenfield, B., Piemonte, N., & Jensen, G. (2024). Turning toward suffering: Rethinking the patient-clinician relationship in physical therapy practice. Physiotherapy theory and practice, 40(11), 2630-2640. [10] Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. [11] De Groot, J. I. M. (2022). The personalization paradox in Facebook advertising: The mediating effect of relevance on the personalization–brand attitude relationship and the moderating effect of intrusiveness. Journal of Interactive Advertising, 22(1), 57-74. [12] Dennison Himmelfarb, C. R., Beckie, T. M., Allen, L. A., Commodore-Mensah, Y., Davidson, P. M., Lin, G., ... & American Heart Association Council on Cardiovascular and Stroke Nursing. (2023). Shared decision-making and cardiovascular health: a scientific statement from the American Heart Association. Circulation, 148(11), 912-931. [13] Davidson, K. W., Mangione, C. M., Barry, M. J., Nicholson, W. K., Cabana, M. D., ... & Wong, J. B. (2022). Collaboration and shared decision-making between patients and clinicians in preventive health care decisions and US preventive services task force recommendations. Jama, 327(12), 1171-1176. [14] Driever, E. M., Stiggelbout, A. M., & Brand, P. L. (2020). Shared decision making: physicians’ preferred role, usual role and their perception of its key components. Patient education and counseling, 103(1), 77-82. [15] Emerson, M. R., Buckland, S., Lawlor, M. A., Dinkel, D., Johnson, D. J., Mickles, M. S., ... & Watanabe-Galloway, S. (2022). Addressing and evaluating health literacy in mHealth: a scoping review. Mhealth, 8, 33. [16] Erturkmen, G. B. L., Juul, N. K., Redondo, I. E., Gil, A. O., Berastegui, D. V., de Manuel, E., ... & ADLIFE study group. (2024). Design, implementation and usability analysis of patient empowerment in ADLIFE project via patient reported outcome measures and shared decision making. BMC Medical Informatics and Decision Making, 24(1), 185. [17] Fang, Z., Liu, Y., & Peng, B. (2024). Empowering older adults: bridging the digital divide in online health information seeking. Humanities and Social Sciences Communications, 11(1), 1-11. [18] Franque, F. B., Oliveira, T., & Tam, C. (2021). Understanding the factors of mobile payment continuance intention: empirical test in an African context. Heliyon, 7(8). [19] Ghose, A., Guo, X., Li, B., & Dang, Y. (2022). Empowering patients using smart mobile health platforms: Evidence from a randomized field experiment. MIS Quarterly, 46(1), 151-192. [20] Guo, X., Zhang, X., & Sun, Y. (2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55-65. [21] Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer international publishing. [22] Huang, Z., & Benyoucef, M. (2023). An empirical study of mobile application usability: A unified hierarchical approach. International Journal of Human–Computer Interaction, 39(13), 2624-2643. [23] Huang, C., Plummer, V., Lam, L., & Cross, W. (2020). Shared decision-making in serious mental illness: a comparative study. Patient Education and Counseling, 103(8), 1637-1644. [24] Hung, C. L., Wu, J. H., Chen, P. Y., Xu, X., Hsu, W. L., Lin, L. M., & Hsieh, M. C. (2023). Enhancing healthcare services and brand engagement through social media marketing: Integration of Kotler\'s 5A framework with IDEA process. Information Processing & Management, 60(4), 103379. [25] Hunsaker, A., &Hargittai, E. (2018). A review of Internet use among older adults. New media & society, 20(10), 3937-3954. [26] Islam, M. N., Karim, M. M., Inan, T. T., & Islam, A. N. (2020). Investigating usability of mobile health applications in Bangladesh. BMC medical informatics and decision making, 20(1), 19. [27] ISO9241-11. Ergonomics of human–system interaction—Part 11: usabil ity: definitions and concepts 2018. Available from: https://www.iso.org/ obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en. [28] Jameel, A. S., Hamdi, S. S., Karem, M. A., Raewf, M. B., & Ahmad, A. R. (2021, February). E-Satisfaction based on E-service Quality among university students. In Journal of physics: Conference series (Vol. 1804, No. 1, p. 012039). IOP Publishing. [29] Johnson, M. O., Rose, C. D., Dilworth, S. E., &Neilands, T. B. (2012). Advances in the conceptualization and measurement of health care empowerment: development and validation of the health care empowerment inventory. [30] Kidman, P. G., Curtis, R. G., Watson, A., & Maher, C. A. (2024). When and why adults abandon lifestyle behavior and mental health mobile apps: Scoping review. Journal of Medical Internet Research, 26, e56897. [31] Kim, H., Schnall, R., Yoon, N., Koh, S. J., Lee, J., & Cheon, J. H. (2024). Development and validation of a Mobile-Centered Digital Health Readiness Scale (mDiHERS): health literacy and equity scale. Journal of medical Internet research, 26, e58497. [32] Klossner, Sara, Hadi Ghanbari, Matti Rossi, and Lill Sarv. \"Personalization-privacy paradox in using mobile health services.\" In European conference on information systems, p. 346. Association for Information Systems, 2023. [33] Kriston, L., Scholl, I., Hölzel, L., Simon, D., Loh, A., &Härter, M. (2010). The 9-item Shared Decision Making Questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample. Patient education and counseling, 80(1), 94-99. [34] Li, C. (2016). When does web-based personalization really work? The distinction between actual personalization and perceived personalization. Computers in human behavior, 54, 25-33. [35] Li, X., Yang, D., Meng, M., Zhao, J., Yin, Y., Wang, H., ... & Hao, Y. (2023). Shared decision-making in healthcare in mainland China: a scoping review. Frontiers in Public Health, 11, 1162993. [36] Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026. [37] Liu, H., Zhang, Y., Li, Y., & Albright, K. (2023). Better interaction performance attracts more chronic patients? Evidence from an online health platform. Information Processing & Management, 60(4), 103413. [38] Liu, Y., Zhang, Y., & Fang, Z. (2025). Leveraging mobile fitness apps for healthier lifestyles: How affordances drive sustained engagement. Digital Health, 11, 20552076251389058. [39] Ma, X., Li, Y., & Suo, A. (2025). Reveal the dynamics of mobile health services continuance intention: effects of expectation, confirmation, and chronic disease. Frontiers in Public Health, 13, 1637264. [40] Maramba, I., Chatterjee, A., & Newman, C. (2019). Methods of usability testing in the development of eHealth applications: a scoping review. International journal of medical informatics, 126, 95-104. [41] Mamakou, X. J., Zaharias, P., & Milesi, M. (2024). Measuring customer satisfaction in electronic commerce: The impact of e-service quality and user experience. International Journal of Quality & Reliability Management, 41(3), 915-943. [42] Meskó, B., Radó, N., & Gy?rffy, Z. (2019). Opinion leader empowered patients about the era of digital health: a qualitative study. BMJ open, 9(3), e025267. [43] Mesko, B., DeBronkart, D., Dhunnoo, P., Arvai, N., Katonai, G., &Riggare, S. (2025). The Evolution of Patient Empowerment and Its Impact on Health Care’s Future. Journal of Medical Internet Research, 27, e60562. [44] Mirabootalebi, N., Holl, F., Meidani, Z., Jeddi, F. R., Tagharrobi, Z., Akbari, H., & Swoboda, W. (2025). Determinants of Nurses’ Continuance Intention to Use Mobile Health Apps in Clinical Nursing Practice: Structural Equation Modeling to Extend the Expectation-Confirmation Model. JMIR nursing, 8(1), e68048. [45] Minheere, A., Lambrechts, W., Mampaey, J., Stough, T., Caniëls, M. C., &Semeijn, J. (2023). Patient power and empowerment: mitigating elements of valuable patient participation in healthcare collaboratives. Behavioral Sciences, 13(4), 347. [46] Muscat, D. M., Shepherd, H. L., Nutbeam, D., Trevena, L., & McCaffery, K. J. (2021). Health literacy and shared decision-making: exploring the relationship to enable meaningful patient engagement in healthcare. Journal of general internal medicine, 36(2), 521-524. [47] Nie, L., Oldenburg, B., Cao, Y., & Ren, W. (2023). Continuous usage intention of mobile health services: model construction and validation. BMC Health Services Research, 23(1), 442. [48] Ouyang, P., Yao, M., & Liu, J. (2025). How can users effectively locate helpful non-interactive health information in online health communities? Information Processing & Management, 62(6), 104271. [49] Passey, D., Shonfeld, M., Appleby, L., Judge, M., Saito, T., & Smits, A. (2018). Digital agency: Empowering Equity in and through education. Technology, Knowledge and Learning, 23(3), 425-439. [50] Qian, Y., Wang, X. H., Chen, Y. J., Zhang, S. Z., Yu, C. Y., & Gu, Y. (2015). Studying on the progress of research in patients’ satisfaction on medical services and its problems. Chinese Health Service Management, 32(2), 105-107. [51] Raleigh, M. F., Nelson, M. D., & Nguyen, D. R. (2022). Shared decision-making: guidelines from the National Institute for Health and Care Excellence. American Family Physician, 106(2), 205-207. [52] Resnicow, K., Catley, D., Goggin, K., Hawley, S., & Williams, G. C. (2022). Shared decision making in health care: theoretical perspectives for why it works and for whom. Medical Decision Making, 42(6), 755-764. [53] Risnia, Z. N., &Solekah, N. A. (2023). E-Satisfaction as a mediating variable the influence of e-service quality on E-WOM in Linkaja Syariah users. MALIA: Jurnal Ekonomi Islam, 14(2), 171-188. [54] Rodolico, A., Cutrufelli, P., Maccarone, G., Avincola, G., Concerto, C., Cunsolo, A. L., ... & Signorelli, M. S. (2024). Exploring patient empowerment in major depressive disorder: Correlations of trust, active role in shared decision-making, and symptomatology in a sample of Italian patients. Journal of Clinical Medicine, 13(20), 6282. [55] Serlachius, A., Schache, K., Kieser, A., Arroll, B., Petrie, K., &Dalbeth, N. (2019). Association between user engagement of a mobile health app for gout and improvements in self-care behaviors: randomized controlled trial. JMIR mHealth and uHealth, 7(8), e15021. [56] Scherrenberg, M., Falter, M., Kaihara, T., Xu, L., van Leunen, M., Kemps, H., ... &Dendale, P. (2023). Development and internal validation of the digital health readiness questionnaire: prospective single-center survey study. Journal of medical Internet research, 25, e41615. [57] Shen, X. L., Yang, Y., Sun, Y., Li, Y. J., & Liu, H. (2020). Intermittent continuance of smart health devices: A zone-of-tolerance perspective. In 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 (pp. 3346-3355). IEEE Computer Society. [58] Stampe, K., Kishik, S., & Müller, S. D. (2021). Mobile health in chronic disease management and patient empowerment: exploratory qualitative investigation into patient-physician consultations. Journal of medical Internet research, 23(6), e26991. [59] Strategy 6I: Shared Decisionmaking. Content last reviewed April 2023. Agency for Healthcare Research and Quality, Rockville, MD. https://www.ahrq.gov/cahps/quality-improvement/improvement-guide/6-strategies-for-improving/communication/strategy6i-shared-decisionmaking.html [60] Tian, X. F., & Wu, R. Z. (2022). Determinants of the mobile health continuance intention of elders with chronic diseases: An integrated framework of ECM-ISC and UTAUT. International Journal of Environmental Research and Public Health, 19(16), 9980. [61] Tran, T. P., Lin, C. W., Baalbaki, S., & Guzmán, F. (2020). How personalized advertising affects equity of brands advertised on Facebook? A mediation mechanism. Journal of Business Research, 120, 1-15. [62] Wang, Y. T., & Lin, K. Y. (2021). Understanding continuance usage of mobile learning applications: The moderating role of habit. Frontiers in Psychology, 12, 736051. [63] Wang, Haixia, Jie Jia, Yafeng Fan, Hanlin Chen, Yi Lou, Xiaohe Wang, and Xianhong Huang. \"Impact of inpatient self-efficacy and trust in physicians on inpatient satisfaction with medical services: the mediating role of patient participation in medical decision-making.\" Frontiers in Psychology 15 (2024): 1364319. [64] Wittkowski, K., Klein, J. F., Falk, T., Schepers, J. J., Aspara, J., & Bergner, K. N. (2020). What gets measured gets done: can self-tracking technologies enhance advice compliance?. Journal of Service Research, 23(3), 281-298. [65] Wong, A. K. C., Bayuo, J., Wong, F. K. Y., Chow, K. K. S., Wong, S. M., Wong, B. B., & Law, K. H. Y. (2024). Experiences of receiving an mHealth application with proactive nursing support among community-dwelling older adults: a mixed-methods study. BMC nursing, 23(1), 232. [66] Wu, L., Chiu, M. L., & Chen, K. W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52, 102099. [67] Wu, C., Liu, W., Zhang, C., Zhou, H., & Zhang, H. (2026). From engagement to empowerment: How patient agency affects doctors’ shared decision-making intentions in mHealth. Information Processing & Management, 63(1), 104347. [68] Wu, P., Zhang, R., Zhu, X., & Liu, M. (2022). Factors influencing continued usage behavior on mobile health applications. In Healthcare (Vol. 10, No. 2, p. 208). MDPI. [69] Zhang, L., & Li, P. (2022). Problem-based mHealth literacy scale (PB-mHLS): Development and validation. JMIR mHealth and uHealth, 10(4), e31459. [70] Zhou, T., & Ma, X. (2025). Examining generative AI user continuance intention based on the SOR model. Aslib Journal of Information Management. [71] Zhu, Y., Wang, R., & Pu, C. (2022). “I am chatbot, your virtual mental health adviser.” What drives citizens’ satisfaction and continuance intention toward mental health chatbots during the COVID-19 pandemic? An empirical study in China. Digital Health, 8, 20552076221090031. [72] Yoon, C., & Rolland, E. (2015). Understanding continuance use in social networking services. Journal of Computer Information Systems, 55(2), 1-8. [73] Zhang, S., Han, B., & Fan, M. (2025). How the affordance and psychological empowerment promoting AI-based medical consultation usage: A mixed-methods approach. Digital Health, 11, 20552076251350001.
Copyright © 2026 Saleh Alarifi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET79416
Publish Date : 2026-04-04
ISSN : 2321-9653
Publisher Name : IJRASET
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