Article Sidebar

Download
PDF
Statistic
Read Counter : 13 Download : 11

Main Article Content

Abstract

The proliferation of digital health information through short video platforms creates cognitive overload challenges for elderly hypertensive patients managing chronic conditions, compromising effective health information processing and decision-making capabilities. This research investigates the mechanisms of short video selection behavior among elderly hypertensive patients under health information overload, employing cognitive load theory integrated with artificial intelligence analytics to optimize content delivery strategies. A mixed-methods design involving 128 elderly participants (mean age, 71.3 years) from Jiangsu Province utilized behavioral tracking, physiological monitoring, and AI-powered content analysis over a two-week period. The study employed ensemble machine learning algorithms, integrated cognitive load assessment, and structural equation modeling to examine selection pathways and predictive mechanisms. Results demonstrate that cognitive load substantially impacts information processing efficiency, with performance declining from 89.4% accuracy under low cognitive load to 41.2% under high load scenarios. The artificial intelligence framework achieved exceptional predictive performance with 94.2% training accuracy, 92.8% validation accuracy, and 91.5% test accuracy. Feature importance analysis reveals that cognitive variables dominate prediction mechanisms, accounting for 63% of the total importance distribution, compared to behavioral features (23%) and demographic factors (14%). Working memory emerges as the most influential predictor (importance score: 0.847, contributing 18.3% to prediction accuracy), followed by processing speed (16.8%) and attention allocation (15.2%). The research establishes evidence-based guidelines for cognitive-centered health communication design, enabling personalized digital health interventions that optimize content complexity, delivery timing, and presentation modalities based on individual cognitive capacities, ultimately advancing therapeutic outcomes for vulnerable elderly populations through intelligent, adaptive content delivery systems.

Keywords

Cognitive load theory Artificial intelligence Health information processing Elderly hypertensive patients Digital health communication

Article Details

Author Biographies

Ruina Guo, Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia

Ruina Guo is currently pursuing her PhD at The National University of Malaysia. Her research interests are health communication and technology.

Arina Anis Azlan, Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia

Dr. Arina Anis Azlan is a lecturer at the Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia. Her research interests are in health communication, information management and communicative behaviour among publics. She is currently involved in several research projects focusing on health communication and strategic communication to publics.

Emma Mohamad, Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia; 2. Universiti Kebangsaan Malaysia, Komunikasi Kesihatan (Healthcomm) - UKM Research Group, Selangor, Malaysia

Associate Professor Dr. Emma Mohamad has 20 years of experience as an academic and a health communication researcher. She is passionate with the role communication to nurture positive health behaviours and strongly believes in the importance of health literacy to empower society in making informed health decisions. She is currently researching communication behaviours related to obesity, nutrition, COVID-19 and Knowlesi Malaria in Malaysia, where she works closely with UNICEF and The Ministry of Health to design effective behavior interventions. She was also a research consultant for the World Health Organisation where she led the development of a health literacy framework for Malaysia and is involved in the development of the National Health Literacy Policy. Emma was also a global UNICEF Think Tank member on Social Behaviour Change and Community Engagement. Emma led numerous research grants in the past including international grants from the WHO, UNICEF Malaysia and the Ministry of Higher Education LRGS Grant. She holds administrative position as the Deputy Dean (Research and Innovation) at the Faculty of Social Sciences and Humanities and the Director at UKM x UNICEF Communication For Development Centre in Health, also known as HEALTHCOMM.

How to Cite
Guo, R., Anis Azlan, A., & Mohamad, E. (2025). Mechanisms of short video selection behavior in elderly hypertensives under health information overload: a cognitive load theory. Future Technology, 4(3), 107–118. Retrieved from https://fupubco.com/futech/article/view/378
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. W. X. Lai, A. Visaria, T. Østbye, and R. Malhotra, "Prevalence and correlates of use of digital technology for managing hypertension among older adults," Journal of Human Hypertension, vol. 37, no. 1, pp. 80-87, 2023. DOI: https://doi.org/10.1038/s41371-022-00654-4
  2. M. E. Katz et al., "Digital health interventions for hypertension management in US populations experiencing health disparities: a systematic review and meta-analysis," JAMA Network open, vol. 7, no. 2, pp. e2356070-e2356070, 2024. DOI: 10.1001/jamanetworkopen.2023.56070
  3. K. Kario, "Management of hypertension in the digital era: small wearable monitoring devices for remote blood pressure monitoring," Hypertension, vol. 76, no. 3, pp. 640-650, 2020. DOI: https://doi.org/10.1161/HYPERTENSIONAHA.120.147
  4. S. Muli, C. Meisinger, M. Heier, B. Thorand, A. Peters, and U. Amann, "Prevalence, awareness, treatment, and control of hypertension in older people: results from the population-based KORA-age 1 study," BMC Public Health, vol. 20, pp. 1-10, 2020. DOI: https://doi.org/10.1186/s12889-020-09165-8
  5. A. Benetos, M. Petrovic, and T. Strandberg, "Hypertension management in older and frail older patients," Circulation research, vol. 124, no. 7, pp. 1045-1060, 2019. DOI: https://doi.org/10.1161/CIRCRESAHA.118.3132
  6. E. Oliveros et al., "Hypertension in older adults: Assessment, management, and challenges," Clinical cardiology, vol. 43, no. 2, pp. 99-107, 2020. DOI: https://doi.org/10.1161/CIRCRESAHA.118.313236
  7. T. Fujiwara, J. P. Sheppard, S. Hoshide, K. Kario, and R. J. McManus, "Medical telemonitoring for the management of hypertension in older patients in Japan," International Journal of Environmental Research and Public Health, vol. 20, no. 3, p. 2227, 2023. DOI: https://doi.org/10.3390/ijerph20032227
  8. B. M. Shanab et al., "Closing the Gap: Digital Innovations to Address Hypertension Disparities," Current Cardiology Reports, vol. 27, no. 1, pp. 1-14, 2025. DOI: https://doi.org/10.1007/s11886-024-02171-x
  9. J. J. Van Merriënboer and J. Sweller, "Cognitive load theory in health professional education: design principles and strategies," Medical education, vol. 44, no. 1, pp. 85-93, 2010. DOI: https://doi.org/10.1111/j.1365-2923.2009.03498.x
  10. W. Schnotz and C. Kürschner, "A reconsideration of cognitive load theory," Educational psychology review, vol. 19, pp. 469-508, 2007. DOI: https://doi.org/10.1007/s10648-007-9053-4
  11. A. Skulmowski and K. M. Xu, "Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load," Educational psychology review, vol. 34, no. 1, pp. 171-196, 2022. DOI: https://doi.org/10.1007/s10648-021-09624-7
  12. Z. Ali, J. Janarthanan, and P. Mohan, "Understanding digital dementia and cognitive impact in the current era of the internet: a review," Cureus, vol. 16, no. 9, 2024. DOI: https://doi.org/10.7759/cureus.70029
  13. Z. Fang, Y. Liu, and B. Peng, "Empowering older adults: bridging the digital divide in online health information seeking," Humanities and Social Sciences Communications, vol. 11, no. 1, pp. 1-11, 2024. DOI: https://doi.org/10.1057/s41599-024-04312-7
  14. F. Paas, T. Van Gog, and J. Sweller, "Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives," Educational psychology review, vol. 22, pp. 115-121, 2010. DOI: https://doi.org/10.1007/s10648-010-9133-8
  15. V. L. Patel, N. A. Yoskowitz, J. F. Arocha, and E. H. Shortliffe, "Cognitive and learning sciences in biomedical and health instructional design: A review with lessons for biomedical informatics education," Journal of biomedical informatics, vol. 42, no. 1, pp. 176-197, 2009. DOI: https://doi.org/10.1016/j.jbi.2008.12.002
  16. J. F. Benge et al., "Technology use and subjective cognitive concerns in older adults," Archives of gerontology and geriatrics, vol. 106, p. 104877, 2023. DOI: https://doi.org/10.1016/j.archger.2022.104877
  17. J. Q. Young, J. Van Merrienboer, S. Durning, and O. Ten Cate, "Cognitive load theory: implications for medical education: AMEE Guide No. 86," Medical teacher, vol. 36, no. 5, pp. 371-384, 2014. DOI: https://doi.org/10.3109/0142159X.2014.889290
  18. F. G. P. WM Van Gerven, Jeroen JG Van Merriënboer, Henk G. Schmidt, Pascal, "Cognitive load theory and the acquisition of complex cognitive skills in the elderly: Towards an integrative framework," Educational gerontology, vol. 26, no. 6, pp. 503-521, 2000. DOI: https://doi.org/10.1080/03601270050133874
  19. S. S. Tabatabaee, S. Jambarsang, and F. Keshmiri, "Cognitive load theory in workplace-based learning from the viewpoint of nursing students: application of a path analysis," BMC Medical Education, vol. 24, no. 1, p. 678, 2024. DOI: https://doi.org/10.1186/s12909-024-05664-z
  20. A. K. C. Wong, J. H. T. Lee, Y. Zhao, Q. Lu, S. Yang, and V. C. C. Hui, "Exploring Older Adults’ Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study," JMIR aging, vol. 8, p. e66778, 2025. DOI: https://doi.org/10.2196/66778
  21. A. Ho, "Are we ready for artificial intelligence health monitoring in elder care?," BMC geriatrics, vol. 20, no. 1, p. 358, 2020. DOI: https://doi.org/10.1186/s12877-020-01764-9
  22. B. Ma et al., "Artificial intelligence in elderly healthcare: A scoping review," Ageing Research Reviews, vol. 83, p. 101808, 2023. DOI: https://doi.org/10.1016/j.arr.2022.101808
  23. T. Shiwani et al., "New Horizons in artificial intelligence in the healthcare of older people," Age and Ageing, vol. 52, no. 12, p. afad219, 2023. DOI: https://doi.org/10.1093/ageing/afad219
  24. S. Iqbal, "Artificial Intelligence tools and applications for elderly healthcare-review," in Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence, 2023, pp. 394-397. DOI: https://doi.org/10.1145/3594315.359434
  25. M. Chowdhury, E. G. Cervantes, W.-Y. Chan, and D. P. Seitz, "Use of machine learning and artificial intelligence methods in geriatric mental health research involving electronic health record or administrative claims data: a systematic review," Frontiers in psychiatry, vol. 12, p. 738466, 2021. DOI: https://doi.org/10.3389/fpsyt.2021.738466
  26. J. C. Castro-Alonso, P. Ayres, and J. Sweller, "Instructional visualizations, cognitive load theory, and visuospatial processing," Visuospatial processing for education in health and natural sciences, pp. 111-143, 2019. DOI://doi.org/10.1007/978-3-030-20969-8_5
  27. P. A. Kirschner, "Cognitive load theory: Implications of cognitive load theory on the design of learning," vol. 12, ed: Elsevier, 2002, pp. 1-10. DOI: https://doi.org/10.1016/S0959-4752(01)00014-7
  28. J. Leppink, "Cognitive load theory: Practical implications and an important challenge," Journal of Taibah University Medical Sciences, vol. 12, no. 5, pp. 385-391, 2017. DOI: https://doi.org/10.1016/j.jtumed.2017.05.003
  29. B. d. O. O. Sigolo and H. d. C. S. Casarin, "Contributions of cognitive load theory to understanding information overload: a literature review," RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação, vol. 22, p. e024027, 2024. DOI: https://doi.org/10.20396/rdbci.v22i00.8677359/en
  30. Y. Gao, J. Liang, and Z. Xu, "Digital social media expression and social adaptability of the older adult driven by artificial intelligence," Frontiers in Public Health, vol. 12, p. 1424898, 2024. DOI: https://doi.org/10.3389/fpubh.2024.1424898
  31. G. B. Reedy, "Using cognitive load theory to inform simulation design and practice," Clinical Simulation in Nursing, vol. 11, no. 8, pp. 355-360, 2015. DOI: https://doi.org/10.1016/j.ecns.2015.05.004
  32. C. H. Chu et al., "Digital ageism: challenges and opportunities in artificial intelligence for older adults," The Gerontologist, vol. 62, no. 7, pp. 947-955, 2022. DOI: https://doi.org/10.1093/geront/gnab167
  33. R. Zhang, Y. Su, Z. Lin, and X. Hu, "The impact of short video usage on the mental health of elderly people," BMC psychology, vol. 12, no. 1, pp. 1-15, 2024. DOI: https://doi.org/10.1186/s40359-024-02125-6
  34. C. Wu, S. Chen, S. Wang, S. Peng, and J. Cao, "Short-Form Video Exposure and Its Two-Sided Effect on the Physical Activity of Older Community Women in China: Secondary Data Analysis," JMIR mHealth and uHealth, vol. 11, p. e45091, 2023. DOI: https://doi.org/10.2196/45091