Understanding mobile health literacy among the working-age population: A cross-sectional study in Thailand 10.55131/jphd/2024/220307
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Abstract
Mobile health (mHealth) literacy refers to the ability of individuals to seek, find, understand, and evaluate health information accessed via mobile devices. In Thailand, an assessment of mobile health literacy among the working-age population (25-59 years old) across the country is still needed to gain a better understanding of the current landscape of this concept as well as to create strategies for improvement. As a result, this study aimed 1) to assess mHealth literacy among the Thai working-age population, 2) to investigate differences in mHealth literacy among the Thai working-age population classified by demographic variables, and 3) to identify the predictive factors affecting mHealth literacy among the Thai working-age population. Cross-sectional survey research was conducted in this study with samples consisting of 600 working-age individuals derived from six regions across the country. Respondents were randomly selected using a multi-stage sampling method. Data were collected by a self-administered questionnaire. Mean, SD, t-test, One-Way ANOVA, and multiple regression analysis [MRA] were used for data analysis at a .05 level of significance. The results found that 1) the samples showed a high level of mHealth literacy (Mean = 4.32, SD = 0.58) 2), differences in mHealth literacy level were classified by gender, age, income, education, and region, and 3) perceived behavioral control, attitudes, and subjective norms were factors significantly affecting mHealth literacy. These findings can be used to plan and develop strategies for improving mHealth literacy among the Thai working-age population more effectively.
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References
Lin TT, Bautista JR. Understanding the Relationships between mHealth Apps' Characteristics, Trialability, and mHealth Literacy. J Health Commun. 2017;22(4):346-54. doi: 10.1080/10810730.2017.1296508.
Zamir DL, Bertschi I. Media health literacy, eHealth literacy, and the role of the social environment in context. Int J Environ Res Public Health. 2018;15: 1643-54. doi: 10.3390/ijerph15081643.
Norman CD, Skinner HA. e-HEALS: The eHealth literacy scale. J Med Internet Res. 2006;8(4):e27. doi: 10.2196/jmir.8.4.e27
Park BK. Factors influencing eHealth literacy of middle school students in Korea: A descriptive cross-sectional study. Healthc Inform Res. 2019; 25(3):221-9. doi: 10.4258/hir.2019. 25.3.221.
Centers for Disease Control and Prevention. What is health literacy? [Internet]. 2023 [cited 2023 Jul 18]. Available from: https://www.cdc.gov/ healthliteracy/learn/index.html
National Institutes of Health. Health literacy [Internet]. 2023 [cited 2023 Jul 18]. Available from: https://www. nih.gov/institutes-nih/nih-office-director/office-communications-public-liaison/clear-communication/health-literacy.
Hanucharurnkul S, Pacharanut S. Development and validation of the Thai version of the Mobile Health Literacy Scale (mHeals-TH) among Thai adults. Health Promot Int. 2019;34(5):1015–25. doi: 10.1093/heapro/day068.
Songthanasak J, Phuengsamran D, Somrongthong R. Health literacy and mHealth literacy among high school students in Thailand. J Med Assoc Thai. 2018;101(8):1093–9.
Pongthongsa T, Lerkiatbundit S. Development and validation of the Thai version of the eHealth Literacy Scale (eHEALS) in Thai pregnant women. J Health Sci. 2019;28(4):757–66.
Phimha P, Promsri C. Relationship between eHealth literacy and acceptance of online health information among Thai adults. J Health Educ. 2020;41(1):16–26.
Amin H, Malik MA, Akkaya B. Development and validation of digital literacy scale and its implication for higher education. Int J Distance Educ E-Learning. 2021;7(1):24-43.
Dashti S, Abadibavil D, Roozbeh N. Evaluating e-Health literacy, knowledge, attitude, and practice regarding COVID-19 prevention and self-protection among Iranian students: A cross-sectional online survey. BMC Med Educ. 2022;22:148-57. doi: 10.1186/s12909-022-03210-3.
Goldstein M, Archary M, Adong J, Haberer JE, Kuhns LM, Kurth A, et al. Systematic Review of mHealth Interventions for Adolescent and Young Adult HIV Prevention and the Adolescent HIV Continuum of Care in Low to Middle Income Countries. AIDS Behav. 2023;27(Suppl 1):94-115. doi: 10.1007/s10461-022-03840-0.
Shiferaw KB, Tilahun BC, Endehabtu BF, Gullslett MK, Mengiste SA. E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey. BMC Med Inform Decis Mak. 2020;20(1): 181. doi: 10.1186/s12911-020-01202-1.
Yarmohammadi P, Morowatisharifabad MA, Khayyatzadeh SS, Madadizadeh F, Rahaei Z. Psychometric properties of the mobile health literacy scale in the workers of an automotive metal sheet factory in Shahrekord, Iran. Health Lit Res Pract. 2022;6(4):257-61. doi: 10.3928/24748307-20220921-01.
Ajzen I. From intentions to actions: A theory of planned behavior. In: Kuhl J, Beckman J, editors. Action-control: From cognition to behavior. Heidelberg (Germany): Springer; 1985. p. 11-39.
Smith A, Jones B. Application of the theory of planned behavior in understanding health behaviors among adolescents. J Health Psychol. 2023; 30(3):123-35.
Garcia C, Martinez D. Predicting adherence to medication using the theory of planned behavior: A systematic review. J Behav Med. 2021; 25(2):67-80.
Chen X, Wang Y. Factors influencing physical activity among older adults: A test of the theory of planned behavior. J Aging Phys Act. 2018;36(4):210-25.
Nguyen T, Tran L. Understanding dietary behaviors among college students: An application of the theory of planned behavior. J Nutr Educ Behav. 2019;42(1):45-58
Vantamay N. Validating the effectiveness indicators of social marketing communication campaigns for reducing health-risk behaviors among youth in Thailand. JurnalKomunikasi Malaysian J Commun. 2017; 33(1):127-46. doi: 10.17576/JKMJC-2017-3301-09
Vantamay N. Using the Delphi Technique to Develop Effectiveness Indicators for Social Marketing Communication to Reduce Health-Risk Behaviors Among Youth. Southeast Asian J Trop Med Public Health. 2015; 46(5):949-57.
Yamane T. Statistics: An introductory analysis. New York: Harper and Row; 1973.
Cooper DR, Schindler PS. Business research methods. New York: McGraw-Hill; 2001.
Cottrell R, McKenzie J. Health promotion & education research methods: Using the five-chapter thesis/dissertation model. Sudbury (MA): Jones and Bartlett Publishers; 2005.
Hair A, et al. Multivariate data analysis. 5th ed. London: Prentice-Hall International Inc; 1998.
Bautista JR, Lin L, Lee J, Lam R. The use of mobile health applications among elderly patients: A literature review. J Nurs Res. 2016;24(3):234–45. doi: 10.1097/jnr.0000000 000000149.
Soellner R, Huber S, Reder M. The concept of eHealth literacy and its measurement. J Media Psychol. 2014;26(1):29–38. doi: 10.1027/1864-1105/a000104.
Kim H, Xie B, Park S. Health literacy and internet- and mobile app-based health services: A systematic review of the literature. J Med Internet Res. 2019;21(1):e11284. doi: 10.2196/ 11284.
Chang FC, Chiu CH, Chen PH. Consumer health information seeking on the Internet: The state of the art. Health Educ J. 2013;72(6):640–54. doi: 10.1177/ 0017896912441598.
Thammasitboon K, Kongkankit V, Boonbrahm P. Enhancing eHealth literacy among Thai elderly: An application of the eHealth Literacy Scale (eHEALS). Gerontechnology. 2017;16(3):169-70.
Neter E, Brainin E. Association between health literacy, eHealth literacy, and health outcomes among patients with long-term conditions. Eur Psychol. 2019;24(1):68–81. doi: 10.1027/1016-9040/a000335.
Tennant B, Stellefson M, Dodd V, Chaney B, Chaney D, Paige S, et al. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res. 2015;17(3):e70. doi: 10.2196/jmir.3992.
Berkowsky RW, Cotten SR, Yost EA, Winstead VP. Attitudes toward and limitations to ICT use in assisted and independent living communities: Complexities of the digital divide in older adults. Inf Commun Soc. 2016; 19(8):1105-20. doi:10.1080/1369118X.2015.1115547
Tennant B, Stellefson M, Dodd V, Chaney B, Chaney D, Paige S, et al. Digital literacy and technology preferences of low-income adults in the United States: Informing segmented health communication strategies. Commun Inf Technol Annu. 2017; 10:27-57. doi:10.1108/ S2050-2060201 70000010002
Goldstein M, Apondo J, Gichuru E, Wafula R, Nyambura M, Omanga E, et al. Systematic review of mHealth interventions for adolescent and young adult HIV prevention and the adolescent HIV continuum of care in low to middle-income countries. J Adolesc Health. 2023; 63(3):259-69.
Park BK. Factors influencing eHealth literacy of middle school students in Korea: A descriptive cross-sectional study. Healthc Inform Res. 2019;25(3): 221-9.
Shiferaw KB, Tilahun BC, Endehabtu BF, Gullslett MK, Mengiste SA. E-health literacy and associated factors among chronic patients in a low-income country: a cross-sectional survey. BMC Med Inform Decis Mak. 2020;20(1): 181. doi: 10.1186/s12911-020-01202-1.
Goldstein M, Apondo J, Gichuru E, Wafula R, Nyambura M, Omanga E, et al. Systematic review of mHealth interventions for adolescent and young adult HIV prevention and the adolescent HIV continuum of care in low to middle-income countries. AIDS Behav. 2023;27:94-115. doi: 10.1007/ s10461-022-03840-0.