International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

line decor
  
line decor

 Volume 9, Issue 1 (January 2022), Pages: 117-127

----------------------------------------------

 Original Research Paper

 Title: Integrative adoption model for personal health records: A structural equation modeling approach

 Author(s): Riad Alharbey *

 Affiliation(s):

 Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4968-950X

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.01.014

 Abstract:

The adoption of a personal health record (PHR) is a crucial element in quality healthcare, allowing patients to permit the storage of their health information to create a more inclusive, reliable health record. However, the embracing of PHRs has been slow compared to other healthcare-related systems due to the poor design and behavioral aspects. The objective of this research is to study user acceptance factors to identify a better design for PHR systems and to promote healthy behaviors that support individuals' performance. The study proposes an integrative adoption model for PHRs that integrates theoretical factors from the health belief model with the user acceptance determinants from the technology acceptance model and innovation diffusion theory. Using structural equation modeling with the R “Lavaan” package, the study tested the hypothesis relationships of the constructs. The data were captured from individuals through Amazon’s MTurk. Among the nine relationships studied, the research revealed six significant relationships that inform the final PHR integrative adoption model. The research provides great insights into the factors that influence individuals’ PHR adoption. The results introduce a novel integration model to the current body of knowledge. This model will contribute to a better theoretical understanding of the actual use of healthcare-related technologies and bring greater estimates of patient engagement in healthy activities. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Personal health records, Health informatics, Integrative models, Health beliefs

 Article History: Received 10 September 2021, Received in revised form 22 November 2021, Accepted 22 November 2021

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Alharbey R (2022). Integrative adoption model for personal health records: A structural equation modeling approach. International Journal of Advanced and Applied Sciences, 9(1): 117-127

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4   

----------------------------------------------    

 References (44)

  1. Abd-Alrazaq A, Bewick BM, Farragher T, and Gardner P (2019). Factors affecting patients’ use of electronic personal health records in England: Cross-sectional study. Journal of Medical Internet Research, 21(7): e12373. https://doi.org/10.2196/12373   [Google Scholar] PMid:31368442 PMCid:PMC6693305
  2. Adam AM (2020). Sample size determination in survey research. Journal of Scientific Research and Reports, 26(5): 90-97. https://doi.org/10.9734/jsrr/2020/v26i530263   [Google Scholar]
  3. Ahadzadeh AS, Sharif SP, Ong FS, and Khong KW (2015). Integrating health belief model and technology acceptance model: an investigation of health-related internet use. Journal of Medical Internet Research, 17(2): e45. https://doi.org/10.2196/jmir.3564   [Google Scholar] PMid:25700481 PMCid:PMC4376166
  4. Ahmad A, Rasul T, Yousaf A, and Zaman U (2020). Understanding factors influencing elderly diabetic patients’ continuance intention to use digital health wearables: Extending the technology acceptance model (TAM). Journal of Open Innovation: Technology, Market, and Complexity, 6(3): 81. https://doi.org/10.3390/joitmc6030081   [Google Scholar]
  5. Alharbey R and Chatterjee S (2019). An mHealth assistive system “MyLung” to empower patients with chronic obstructive pulmonary disease: Design science research. JMIR Formative Research, 3(1): e12489. https://doi.org/10.2196/12489   [Google Scholar] PMid:30888329 PMCid:PMC6444216
  6. Archer N, Fevrier-Thomas U, Lokker C, McKibbon KA, and Straus SE (2011). Personal health records: A scoping review. Journal of the American Medical Informatics Association, 18(4): 515-522. https://doi.org/10.1136/amiajnl-2011-000105   [Google Scholar] PMid:21672914 PMCid:PMC3128401
  7. Brunner M, Mussmann A, and Breu R (2018). Introduction of a tool-based continuous information security management system: An exploratory case study. In the IEEE International Conference on Software Quality, Reliability and Security Companion, IEEE, Lisbon, Portugal: 483-490. https://doi.org/10.1109/QRS-C.2018.00088   [Google Scholar]
  8. Champion VL, Monahan PO, Springston JK, Russell K, Zollinger TW, Saywell RM, and Maraj M (2008). Measuring mammography and breast cancer beliefs in African American women. Journal of Health Psychology, 13(6): 827-837. https://doi.org/10.1177/1359105308093867   [Google Scholar] PMid:18697896 PMCid:PMC2902247
  9. Davis FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13: 319-340. https://doi.org/10.2307/249008   [Google Scholar]
  10. Fornell C and Larcker DF (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. https://doi.org/10.1177/002224378101800104   [Google Scholar]
  11. Fuji KT, Abbott AA, Galt KA, Drincic A, Kraft M, and Kasha T (2012). Standalone personal health records in the United States: meeting patient desires. Health and Technology, 2(3): 197-205. https://doi.org/10.1007/s12553-012-0028-1   [Google Scholar]
  12. Gow CX, Wong SC, and Lim CS (2019). Effect of output quality and result demonstrability on generation y’s behavioural intention in adopting mobile health applications. Asia-Pacific Journal of Management Research and Innovation, 15(3): 111-121. https://doi.org/10.1177/2319510X19872597   [Google Scholar]
  13. Harrigan P, Daly TM, Coussement K, Lee JA, Soutar GN, and Evers U (2021). Identifying influencers on social media. International Journal of Information Management, 56: 102246. https://doi.org/10.1016/j.ijinfomgt.2020.102246   [Google Scholar]
  14. Heath M, Porter TH, and Dunegan K (2020). Obstacles to continued use of personal health records. Behaviour and Information Technology. https://doi.org/10.1080/0144929X.2020.1829051   [Google Scholar]
  15. Hsieh HL and Tsai CH (2013). An empirical study to explore the adoption of telehealth: Health belief model perspective. Journal of Engineering Science and Technology Review, 6(2): 1-5. https://doi.org/10.25103/jestr.062.01   [Google Scholar]
  16. Hsieh PJ and Lai HM (2020). Exploring peoples intentions to use the health passbook in self-management: An extension of the technology acceptance and health behavior theoretical perspectives in health literacy. Technological Forecasting and Social Change, 161: 120328. https://doi.org/10.1016/j.techfore.2020.120328   [Google Scholar]
  17. Jayanti RK and Burns AC (1998). The antecedents of preventive health care behavior: An empirical study. Journal of the Academy of Marketing Science, 26(1): 6-15. https://doi.org/10.1177/0092070398261002   [Google Scholar]
  18. Jenkins JL, Anderson BB, Vance A, Kirwan CB, and Eargle D (2016). More harm than good? How messages that interrupt can make us vulnerable. Information Systems Research, 27(4): 880-896. https://doi.org/10.1287/isre.2016.0644   [Google Scholar]
  19. Jones DA, Shipman JP, Plaut DA, and Selden CR (2010). Characteristics of personal health records: Findings of the medical library association/national library of medicine joint electronic personal health record task force. Journal of the Medical Library Association, 98(3): 243-249. https://doi.org/10.3163/1536-5050.98.3.013   [Google Scholar] PMid:20648259 PMCid:PMC2900995
  20. Kaelber D, Jha A, Johnston D, Middleton BD, and Bates DW (2008). A research agenda for personal health records (PHRs). Journal of the American Medical Informatics Association, 15 (6): 729–736. https://doi.org/10.1197/jamia.M2547   [Google Scholar] PMid:18756002 PMCid:PMC2585530
  21. Kang Y, Choi N, and Kim S (2021). Searching for new model of digital informatics for human–computer interaction: Testing the institution-based technology acceptance model (ITAM). International Journal of Environmental Research and Public Health, 18(11): 5593. https://doi.org/10.3390/ijerph18115593   [Google Scholar] PMid:34073786 PMCid:PMC8197211
  22. Khan MI, Saleh MA, and Quazi A (2021). Social media adoption by health professionals: A TAM-based study. Informatics, 8(1): 6. https://doi.org/10.3390/informatics8010006   [Google Scholar]
  23. Lee JH and Lee CF (2019). Extension of TAM by perceived interactivity to understand usage behaviors on ACG social media sites. Sustainability, 11(20): 5723. https://doi.org/10.3390/su11205723   [Google Scholar]
  24. Lee WI, Fu HP, Mendoza N, and Liu TY (2021). Determinants impacting user behavior towards emergency use intentions of m-health services in Taiwan. Healthcare, 9(5): 535. https://doi.org/10.3390/healthcare9050535   [Google Scholar] PMid:34063637 PMCid:PMC8147645
  25. Lowry PB, Zhang J, Wang C, and Siponen M (2016). Why do adults engage in cyberbullying on social media? An integration of online disinhibition and deindividuation effects with the social structure and social learning model. Information Systems Research, 27(4): 962-986. https://doi.org/10.1287/isre.2016.0671   [Google Scholar]
  26. Moore G and Benbasat I (1991). Adoption of IT innovation. Information Systems Research, 9: 192-222. https://doi.org/10.1287/isre.2.3.192   [Google Scholar]
  27. Nam CS, Bahn S, and Lee R (2013). Acceptance of assistive technology by special education teachers: A structural equation model approach. International Journal of Human-Computer Interaction, 29(5): 365-377. https://doi.org/10.1080/10447318.2012.711990   [Google Scholar]
  28. Ogbanufe O and Gerhart N (2020). The mediating influence of smartwatch identity on deep use and innovative individual performance. Information Systems Journal, 30(6): 977-1009. https://doi.org/10.1111/isj.12288   [Google Scholar]
  29. Oinas-Kukkonen H (2013). A foundation for the study of behavior change support systems. Personal and Ubiquitous Computing, 17(6): 1223-1235. https://doi.org/10.1007/s00779-012-0591-5   [Google Scholar]
  30. Okediran OO, Wahab WB, and Oyediran MO (2020). Factors determining the intention to use electronic health records: An extension of the technology acceptance model. Journal of Scientific Research and Reports, 26(7): 119-133. https://doi.org/10.9734/jsrr/2020/v26i730290   [Google Scholar]
  31. Orji R, Oyibo K, Lomotey RK, and Orji FA (2019). Socially-driven persuasive health intervention design: Competition, social comparison, and cooperation. Health Informatics Journal, 25(4): 1451-1484. https://doi.org/10.1177/1460458218766570   [Google Scholar] PMid:29801426
  32. Ozok AA, Wu H, and Gurses AP (2017). Exploring patients’ use intention of personal health record systems: Implications for design. International Journal of Human–Computer Interaction, 33(4): 265-279. https://doi.org/10.1080/10447318.2016.1277637   [Google Scholar]
  33. Paccoud I, Baumann M, Le Bihan E, Pétré B, Breinbauer M, Böhme P, and Leist AK (2021). Socioeconomic and behavioural factors associated with access to and use of Personal Health Records. BMC Medical Informatics and Decision Making, 21: 18. https://doi.org/10.1186/s12911-020-01383-9   [Google Scholar] PMid:33435970 PMCid:PMC7805047
  34. Portz JD, Miller A, Foster B, and Laudeman L (2016). Persuasive features in health information technology interventions for older adults with chronic diseases: A systematic review. Health and Technology, 6(2): 89-99. https://doi.org/10.1007/s12553-016-0130-x   [Google Scholar]
  35. Pottas D and Mostert-Phipps N (2013). Citizens and personal health records–The case of Nelson Mandela Bay. In the MEDINFO 2013: 14th World Congress on Medical and Health Informatics, IOS Press, Copenhagen, Denmark: 501-504.   [Google Scholar]
  36. Rahimi B, Nadri H, Afshar HL, and Timpka T (2018). A systematic review of the technology acceptance model in health informatics. Applied Clinical Informatics, 9(03): 604-634. https://doi.org/10.1055/s-0038-1668091   [Google Scholar] PMid:30112741 PMCid:PMC6094026
  37. Razmak J and Bélanger C (2018). Using the technology acceptance model to predict patient attitude toward personal health records in regional communities. Information Technology and People, 31(2): 306-326. https://doi.org/10.1108/ITP-07-2016-0160   [Google Scholar]
  38. Rosseel Y (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5–12 (BETA). Journal of Statistical Software, 48(2): 1-36. https://doi.org/10.18637/jss.v048.i02   [Google Scholar]
  39. Soror AA, Hammer BI, Steelman ZR, Davis FD, and Limayem MM (2015). Good habits gone bad: Explaining negative consequences associated with the use of mobile phones from a dual‐systems perspective. Information Systems Journal, 25(4): 403-427. https://doi.org/10.1111/isj.12065   [Google Scholar]
  40. Steelman ZR, Hammer BI, and Limayem M (2014). Data collection in the digital age. MIS Quarterly, 38(2): 355-378. https://doi.org/10.25300/MISQ/2014/38.2.02   [Google Scholar]
  41. Tavakol M and Dennick R (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2: 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd   [Google Scholar] PMid:28029643 PMCid:PMC4205511
  42. Urbach N and Ahlemann F (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2): 5-40.   [Google Scholar]
  43. Venkatesh V and Davis FD (2000). Theoretical acceptance extension model: Field four studies of the technology longitudinal. Management Science, 46(2): 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926   [Google Scholar]
  44. Weston R, Gore PA, Chan F, and Catalano D (2008). An introduction to using structural equation models in rehabilitation psychology. Rehabilitation Psychology, 53(3): 340-356. https://doi.org/10.1037/a0013039   [Google Scholar]