Health-related Cognitive Factors and Intention to Adopt mHealth: The Mediating Influence of Attitude
Abstract
Mobile health (mHealth) is an important service that has remarkable effects on the development of the health care system. Health-related cognitive factors, such as perceived susceptibility (PSU), perceived severity (PSE), and health consciousness (HC), are associated with health-related technology adoption behavior. However, the underlying mechanisms of these associations have not been studied sufficiently. Attitude toward technology is a key construct in health psychology because it has a central role in motivating and changing behavior. Individuals’ attitude toward a particular behavior is expected to have a strong association with the behavior. This study aimed to examine how attitude toward mHealth plays a mediating role in the relationship between health-related cognitive factors (i.e., PSU, PSE, HC) and behavioral intention (BI) to adopt mHealth. A convenient sample of 374 Malaysian adults composed of 229 females and 149 males was recruited. These respondents completed a survey that measured PSU, PSE, HC, attitude toward mHealth, and BI to adopt mHealth. PSU and HC were significantly associated with BI to use mHealth, whereas PSE had no significant relationship with BI to use mHealth. Attitude toward mHealth mediated the relationship between two health-related cognitive factors (i.e., PSU and HC) and BI to adopt mHealth. The mediation results suggest that although BI to use mHealth is driven by health-related factors, it is facilitated by positive feelings toward health technology. Implications and recommendations for future research are presented.
Bahasa Abstract
Mobil Health (mHealth) adalah salah satu layanan yang paling menonjol dengan dampak luar biasa terhadap perkembangan sistem perawatan kesehatan. Faktor kognitif terkait kesehatan seperti persepsi kerentanan (PSU), persepsi keparahan (PSE) dan kesadaran kesehatan (HC) ditemukan berhubungan dengan perilaku penggunaan teknologi terkait kesehatan. Namun, mekanisme dasar hubungan ini tidak banyak dikaji. Sikap terhadap teknologi adalah konstruk kunci dalam psikologi kesehatan karena peran utamanya dalam memotivasi dan mengubah perilaku. Sikap individu terhadap perilaku tertentu diharapkan menunjukkan hubungan yang kuat dengan perilaku tersebut. Oleh karena itu, studi ini bertujuan untuk mengkaji peran mediasi sikap terhadap mHealth dalam hubungan antara faktor kognitif terkait kesehatan (yaitu PSU, PSE, HC) dan niat tingkah laku (BI) untuk menggunakan mHealth. Sampel sebanyak 374 orang dewasa Malaysia terdiri dari 229 perempuan dan 149 lelaki telah dikumpulkan melalui teknik convenient sampling. Responden melengkapi survei yang mengukur PSU, PSE, HC, sikap terhadap mHealth, dan BI penggunaan mHealth. PSU dan HC berhubungan secara signifikan dengan BI penggunaan mHealth sedangkan PSE tidak berhubungan signifikan. Sikap terhadap mHealth memediasi hubungan antara dua faktor kognitif terkait kesehatan (yaitu PSU dan HC) dengan BI penggunaan mHealth. Hasil efek mediasi menunjukkan bahwa meskipun niat perilaku untuk menggunakan mHealth adalah proses yang didorong oleh faktor-faktor terkait kesehatan, hubungan ini juga didukung oleh perasaan positif terhadap teknologi kesehatan. Artikel ini diakhiri dengan implikasi dan rekomendasi untuk penelitian masa depan.
References
Abu Seman, R. A., Md Syed, M. A., Aziz, A. A., Mahmood Zuhdi, A. S., & Mohd Zamin, R. (2020). Fixing the communication gap through mhealth: The effects of attitude, perceived usefulness, and risks of Mhealth on prescribed self-care among coronary heart disease patients in Malaysia. SEARCH, 12(3), 37-69. https://fslmjournals.taylors.edu.my/fixing-the-communication-gap-through-mhealth-the-effects-of-attitude-perceived-usefulness-and-risks-of-mhealth-on-prescribed-self-care-among-coronary-heart-disease-patients-in-malaysia/
Ahadzadeh, A. S., Wu, S. L., Ong, F. S., & Deng, R. (2021). The mediating influence of the Unified Theory of Acceptance and Use of Technology on the relationship between internal health locus of control and mobile health adoption: Cross-sectional study. Journal of Medical Internet Research, 23(12), e28086. https://doi/org/10.2196/28086
Ahadzadeh, A. S., & Pahlevan Sharif, S. (2017). Online health information seeking among Malaysian women: Technology acceptance model perspective. Search, 9(1), 47-70. https://fslmjournals.taylors.edu.my/wp-content/uploads/SEARCH/SEARCH-2017-9-1/SEARCH-2017-P3-9-1.pdf
Ahadzadeh, A. S., Pahlevan Sharif, S., & Ong, F. S. (2018). Online health information seeking among women: The moderating role of health consciousness. Online Information Review, 42(1), 58-72. https://doi.org/10.1108/OIR-02-2016-0066
Ahadzadeh, A. S., Pahlevan Sharif, S., Ong, F. S., & Khong, K. W. (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.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T.
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277. https://doi.org/10.1037/h0076477
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall.
Ajzen, I., & Timko, C. (1986). Correspondence between health attitudes and behavior. Basic and Applied Social Psychology, 7(4), 259-276. https://doi.org/10.1207/s15324834basp0704_2.
Basu, A., & Dutta, M. J. (2008). The relationship between health information seeking and community participation: The roles of health information orientation and efficacy. Health Communication, 23(1), 70-79. https://doi.org/10.1080/10410230701807121.
Bhuvan, K. C., Alrasheedy, A. A., Goh, B. H., Blebil, A., Bangash, N. S. A., Ibrahim, M. I. M., & Rehman, I. U. (2021). The Types and Pattern of Use of Mobile Health Applications Among the General Population: A Cross-Sectional Study from Selangor, Malaysia. Patient Preference and Adherence, 15, 1755. https://doi.org/10.2147/PPA.S325851
Bourassa, K. J., Sbarra, D. A., Caspi, A., & Moffitt, T. E. (2020). Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors. Annals of Behavioral Medicine, 54(8), 548-556. https://doi.org/10.1093/abm/kaaa049
Cabuk, S., Tanrikulu, C., & Gelibolu, L. (2014). Understanding organic food consumption: attitude as a mediator. International Journal of Consumer Studies, 38(4), 337-345. https://doi.org/10.1111/ijcs.12094
Ceci, L. (2021). Global health and fitness app downloads as of Q2 2020. Statista. https://www.statista.com/statistics/1127248/health-fitness-apps-downloads-worldwide/
Chen, M.F., & Lin, N.P. (2018). Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 28(2), 351-373. https://doi.org/10.1108/IntR-03-2017-0099.
Chen, M. F. (2009). Attitude toward organic foods among Taiwanese as related to health consciousness, environmental attitudes, and the mediating effects of a healthy lifestyle. British Food Journal, 111, 165–178. https://doi.org/10.1108/00070700910931986
Cho, J., Park, D., & Lee, H. E. (2014a). Cognitive factors of using health apps: systematic analysis of relationships among health consciousness, health information orientation, eHealth literacy, and health app use efficacy. Journal of Medical Internet Research, 16(5), e125. https://doi.org/10.2196/jmir.3283.
Cho, J., Quinlan, M. M., Park, D., & Noh, G. Y. (2014b). Determinants of adoption of smartphone health apps among college students. American Journal of Health Behavior, 38(6), 860-870. https://doi.org/10.5993/AJHB.38.6.8
Chu, K. M. (2018). Mediating influences of attitude on internal and external factors influencing consumers’ intention to purchase organic foods in China. Sustainability, 10(12), 4690. https://doi.org/10.3390/su10124690.
Darmayanti, K. K. H., Winata, E. Y., & Anggraini, E. (2020). “Why Can Other People Live Normally While I Cannot?”: An Application of Telecounseling Due to COVID-19. Makara Human Behavior Studies in Asia, 24(2), 109-117. https://doi.org/10.7454/hubs.asia.1140920
Dutta-Bergman, M. J. (2004a). Health attitudes, health cognitions, and health behaviors among Internet health information seekers: population-based survey. Journal of Medical Internet Research, 6(2), e15. https://doi.org/10.2196/jmir.6.2.e15.
Dutta-Bergman, M. J. (2004b). Primary sources of health information: Comparisons in the domain of health attitudes, health cognitions, and health behaviors. Health Communication, 16(3), 273-288. https://doi.org/10.1207/S15327027HC1603_1.
Dutta-Bergman, M. J. (2006). A formative approach to strategic message targeting through soap operas: Using selective processing theories. Health Communication, 19(1), 11-18. https://doi.org/10.1207/s15327027hc1901_2.
Freeman, R., Maizels, J., Wyllie, M., & Sheiham, A. (1993). The relationship between health related knowledge, attitudes and dental health behaviours in 14-16-year-old adolescents. Community Dental Health, 10(4), 397-404.
Guo, X., Han, X., Zhang, X., Dang, Y., & Chen, C. (2015). Investigating m-health acceptance from a protection motivation theory perspective: gender and age differences. Telemedicine and e-Health, 21(8), 661-669. https://doi.org/10.1089/tmj.2014.0166
Hamine, S., Gerth-Guyette, E., Faulx, D., Green, B. B., & Ginsburg, A. S. (2015). Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. Journal of Medical Internet Research, 17(2), e52. https://doi.org/10.2196/jmir.3951.
Hussein, Z., Oon, S. W., & Fikry, A. (2017). Consumer attitude: does it influencing the intention to use mHealth?. Procedia Computer Science, 105, 340-344. https://doi.org/10.1016/j.procs.2017.01.231
Jeihooni, A. K., Dindarloo, S. F., & Harsini, P. A. (2019). Effectiveness of health belief model on oral cancer prevention in smoker men. Journal of Cancer Education, 34(5), 920-927. https://doi.org/10.1007/s13187-018-1396-7.
Jeihooni, A. K., & Rakhshani, T. (2019). The effect of educational intervention based on health belief model and social support on promoting skin cancer preventive behaviors in a sample of Iranian farmers. Journal of Cancer Education, 34(2), 392-401. https://doi.org/10.1007/s13187-017-1317-1.
Jembai, J.V.J., Lin, Y., Wong, C., Amir Bakhtiar, N.A.M., Md Lazim, S.N., Ling, H.S., Kuan, P.X., Chua, P.F. (2022). Mobile health applications: A cross-sectional study of awareness, attitudes, and practices among medical students in Malaysia. https://doi.org/10.21203/rs.3.rs-1493720/v1. [cited 2022 April 23]; Available from: https://assets.researchsquare.com/files/rs-1493720/v1/6cf6ea62-1dfd-4508-b5a2-0e58c3c09181.pdf?c=1650033056
Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: an empirical study. MIS quarterly, 549-566. https://doi.org/10.2307/25750691
Juárez-García, D. M., Valenciano-Salas, I. A., de Jesús García-Solís, M., & Téllez, A. (2021). Development and validation of a mexican version of the Champion’s Health Belief Model Scale for breast cancer screening. Journal of Cancer Education, 36(1),1-6. https://doi.org/10.1007/s13187-019-01603-5.
Khazaee-Pool, M., Zarei, F., Pashaei, T., & Shojaeizadeh, D. (2017). The effect of an educational intervention based on health belief model on improving smoking preventive behaviors among students. Iranian Journal of Health Education and Health Promotion, 4(4), 300-308. https://doi.org/10.18869/acadpub.ihepsaj.4.4.300
Kim, J., & Park, H. (2012). Development of a health information technology acceptance model using consumers' health behavior intention. Journal of Media Internet Research, 14(5), e133. https://doi.org/10.2196/jmir.2143.
Kim, S., Lee, K.-H., Hwang, H., & Yoo, S. (2015). Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Medical Informatics and Decision Making, 16(1)s. https://doi.org/10.1186/s12911-016-0249-8.
Lee, J. Y., Wong, C. P., & Lee, S. W. H. (2020). m-Health views and perception among Malaysian: findings from a survey among individuals living in Selangor. Mhealth, 6. 1-11. https://doi.org/10.21037/mhealth.2019.09.16
Leichman, E. S., Gould, R. A., Williamson, A. A., Walters, R. M., & Mindell, J. A. (2020). Effectiveness of an mHealth intervention for infant sleep disturbances. Behavior Therapy, 51(4), 548- 558. https://doi.org/10.1016/j.beth.2019.12.011
Lin, S.P. (2011). Determinants of adoption of mobile healthcare service. International Journal of Mobile Communications, 9(3), 298-315. https://doi.org/10.1504/IJMC.2011.040608
Lostao, L., Joiner, T. E., Pettit, J. W., Chorot, P., & Sandin, B. (2001). Health beliefs and illness attitudes as predictors of breast cancer screening attendance. The European Journal of Public Health, 11(3), 274-279. https://doi.org/10.1093/eurpub/11.3.274.
Lowe, R., & Norman, P. (2013). Attitudinal approaches to health behavior: Integrating expectancy‐value and automaticity accounts. Social and Personality Psychology Compass, 7(8), 572-584. https://doi.org/10.1111/spc3.12046
Malaysia Ministry of Health. Malaysia's Telemedicine Blueprint: Leading Healthcare into the Information Age (1997). https://www.moh.gov.my/moh/resources/auto%20download%20images/5ca1b20928065.pdf
Marcolino, M. S., Oliveira, J. A. Q., D'Agostino, M., Ribeiro, A. L., Alkmim, M. B. M., & Novillo-Ortiz, D. (2018). The impact of mHealth interventions: systematic review of systematic reviews. JMIR mHealth and uHealth, 6(1), e23. https://doi.org/10.2196/mhealth.8873
Meng, F., Guo, X., Peng, Z., Zhang, X., & Vogel, D. (2019). The routine use of mobile health services in the presence of health consciousness. Electronic Commerce Research and Applications, 35, 1-10. https://doi.org/10.1016/j.elerap.2019.100847
Müller, J. (2021). Smartphone penetration rate as share of the population in Malaysia from 2010 to 2020 and a forecast up to 2025. https://www.statista.com/statistics/625418/smartphone-user-penetration-in-malaysia/.
Mamun, A. A., Hayat, N., & Zainol, N. R. B. (2020). Healthy eating determinants: A study among Malaysian young adults. Foods, 9(8), 974. https://doi.org/10.3390/foods9080974
Omboni, S., Caserini, M., & Coronetti, C. (2016). Telemedicine and m-health in hypertension management: technologies, applications and clinical evidence. High Blood Pressure & Cardiovascular Prevention, 23(3), 187-196. https://doi.org/10.1007/s40292-016-0143-6
Oude Ophuis, P. A. M. (1989). Measuring health orientation and health consciousness as determinants of food choice behavior: development and implementation of various attitudinal scales. In Marketing Thought and Practice in the 1990’s. EMAC XVIII, ed. G. J. Avlonitis, N. K. Papavasiliou and A. G. Kouremenos, pp. 1723-1725. Athens School of Economics and Business, Athens, Greece.
Park, A., Eckert, T. L., Zaso, M. J., Scott‐Sheldon, L. A., Vanable, P. A., Carey, K. B., ... & Carey, M. P. (2017). Associations between health literacy and health behaviors among urban high school students. Journal of School Health, 87(12), 885-893. https://doi.org/10.1111/josh.12567
Richardson, M., McCabe, R., & Priebe, S. (2013). Are attitudes towards medication adherence associated with medication adherence behaviours among patients with psychosis? A systematic review and meta analysis. Social Psychiatry and Psychiatric Epidemiology, 48(4), 649-657. https://doi.org/10.1007/s00127-012-0570-1.
Robinson, J. G., Fox, K. M., Grandy, S., & Group, S. S. (2009). Attitudes about health and health-related behaviors in patients with cardiovascular disease or at elevated risk for cardiovascular disease. Preventive Cardiology, 12(3), 136-143. https://doi.org/10.1111/j.1751-7141.2009.00037.x
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change1. The Journal of Psychology, 91(1), 93-114. https://doi.org/10.1080/00223980.1975.9915803.
Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education Monographs, 2, 328-335. https://doi.org/10.1177/109019817400200403.
VanDyke, S. D., & Shell, M. D. (2017). Health beliefs and breast cancer screening in rural Appalachia: an evaluation of the health belief model. The Journal of Rural Health, 33(4), 350-360. https://doi.org/10.1111/jrh.12204
Wisuantari, N. P. P., & Sekarasih, L. (2020). Health literacy program to reduce the consumption of sugary drinks by middle school students in Jakarta. Makara Human Behavior Studies in Asia, 24(2), 129-140. https://doi.org/10.7454/hubs.asia.1071019
World Health Organization. (2016). Monitoring and evaluating digital health intervention: A practical guide to conducting research and assessment. https://www.who.int/reproductivehealth/publications/mhealth/digital-health-interventions/en/
World Health Organization (2011). mHealth: New horizons for health through mobile technologies. https://apps.who.int/iris/handle/10665/44607
Xiao, N., Sharman, R., Rao, H. R., & Upadhyaya, S. (2014). Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decision Support Systems, 57, 417-427. https://doi.org/10.1016/j.dss.2012.10.047.
Yun, E. K., & Park, H. A. (2010). Consumers' disease information–seeking behaviour on the Internet in Korea. Journal of Clinical Nursing, 19(19‐20), 2860-2868. https://doi.org/10.1111/j.1365-2702.2009.03187.x.
Zavareh, M. F., Hezaveh, A. M., & Nordfjærn, T. (2018). Intention to use bicycle helmet as explained by the Health Belief Model, comparative optimism and risk perception in an Iranian sample. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 248-263. https://doi.org/10.1016/j.trf.2018.02.003.
Zhang, X., Guo, X., Guo, F., & Lai, K.-H. (2014). Nonlinearities in personalization-privacy paradox in mHealth adoption: the mediating role of perceived usefulness and attitude. Technology and Health Care, 22(4), 515-529. https://doi.org/10.3233/THC-140811.
Zhao, J., & Wang, J. (2020). Health advertising on short-video social media: A study on user attitudes based on the extended technology acceptance model. International Journal of Environmental Research and Public Health, 17(5), 1501. https://doi.org/10.3390/ijerph17051501
Zhu, Z., Liu, Y., Che, X., & Chen, X. (2018). Moderating factors influencing adoption of a mobile chronic disease management system in China. Informatics for Health and Social Care, 43(1), 22-41. https://doi.org/10.1080/17538157.2016.1255631.
Recommended Citation
Ahadzadeh, A., Ong, F., & Wu, S. (2023). Health-related Cognitive Factors and Intention to Adopt mHealth: The Mediating Influence of Attitude. Makara Human Behavior Studies in Asia, 27(1), 8-18. https://doi.org/10.7454/hubs.asia.1280422