We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Adoption of Mobile Applications for Self-healthcare Monitoring by the Youth in South Africa | SpringerLink
Skip to main content
SpringerLink
Log in

Adoption of Mobile Applications for Self-healthcare Monitoring by the Youth in South Africa

  • Chapter
  • First Online:
Innovation Practices for Digital Transformation in the Global South

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 645))

Abstract

Despite growing recognition of the contribution and availability of mobile applications for self-healthcare monitoring to prevent non-communicable diseases (NCDs), NCDs remain a rising concern amongst the youth in sub-Saharan Africa and South Africa in particular. Indeed, self-healthcare monitoring mobile applications can be used to continuously educate and sensitize people about the need to adopt healthy lifestyles to prevent such diseases. However, the adoption of such applications is influenced by many factors which need to be identified in order to devise strategies to encourage people to adopt such applications. Thus, this study investigated the potential adoption of self-healthcare monitoring mobile applications by the youth in South Africa, using the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical lens. Data, gathered from a convenient sample size of 280 participants, were analysed using the Partial Least Square Structural Equation Modelling. The results revealed that performance expectancy and social influence explained 20.3% of the variance of youth’s behavioural intention to adopt self- healthcare monitoring mobile applications in the South African context, while effort expectancy and facilitating conditions did not have a significant effect. Taking the resulting model into account could lead to an increase in the adoption of self-healthcare monitoring applications in South Africa which could assist in the prevention of NCDs. It is recommended that further studies should test the established factors from this study in other contexts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. World Health Organization – Non-communicable Diseases (NCD) Country Profiles (2018). https://apps.who.int/iris/handle/10665/274512. Accessed 20 Jan 2022

  2. De Wet, N., Frade, S.: Non-communicable disease prevalence among youth in South Africa: the past, present and future. In 2017 XXVIII International Population Conference Proceedings, p. 90. International Union for the Scientific Study of Population (IUSSP), Cape town (2017)

    Google Scholar 

  3. Ndayizigamiye, P., Soni, T.C., Jere, N.: Factors motivating the adoption of self- healthcare monitoring mobile applications by the South African Youth. In: 2018 IST- Africa Week Conference (IST-Africa), pp. 1–7. IEEE, Gaborone (2018)

    Google Scholar 

  4. Hofman, K.: Non-communicable diseases in South Africa: a challenge to economic development. S. Afr. Med. J. 104(10), 647 (2014)

    Article  Google Scholar 

  5. Zhao, J., Freeman, B., Li, M.: Can mobile phone apps influence people’s health behavior change? An evidence review. J. Med. Internet Res. 18(11), e287 (2016)

    Article  Google Scholar 

  6. Kim, K.A., Shin, S.Y., Suh, J.W., Park, C., Cha, E.J., Bae, H.D.: Home healthcare self-monitoring system for chronic diseases. In: 2012 IEEE International Conference on Consumer Electronics (ICCE), pp. 486–487. IEEE, Las Vegas (2012)

    Google Scholar 

  7. Petersen, F., Brown, A., Pather, S., Tucker, W.D.: Challenges for the adoption of ICT for diabetes self- management in South Africa. Electron. J. Inf. Syst. Developing Countries 86, e12113 (2020)

    Google Scholar 

  8. Ndayizigamiye, P., Kante, M., Shingwenyana, S.: An adoption model of mHealth applications that promote physical activity. Cogent Psychol. 7(1), 1764703 (2020)

    Article  Google Scholar 

  9. Dwivedi, Y.K., Shareef, M.A., Simintiras, A.C., Lal, B., Weerakkody, V.: A generalized adoption model for services: a cross-country comparison of mobile health (m-health). Gov. Inf. Q. 33(1), 174–187 (2016)

    Article  Google Scholar 

  10. Laxman, K., Krishnan, S.B., Dhillon, J.S.: Barriers to adoption of consumer health informatics applications for health self-management. Health Sci. J. 9(5), 1–7 (2015)

    Google Scholar 

  11. Neill, D., Van Belle, J.P., Ophoff, J.: Understanding the adoption of wearable technology in South African organizations. In: International Conference on Information Resources Management (CONF-IRM), pp. 1–14. Association for Information Systems, Cape Town (2016)

    Google Scholar 

  12. Nojilana, B., et al.: Persistent burden from non-communicable diseases in South Africa needs strong action. South African Med. J. 106(5), 436–437 (2016)

    Article  Google Scholar 

  13. National Development Plan 2030. https://www.gov.za/sites/default/files/Executive%20Summary-NDP%202030%20-%20Our%20future%20%20make%20it%20work.pdf. Accessed 19 Jan 2022

  14. Han, S., Harkke, V., Mustonen, P., Seppanen, M., Kallio, M.: Mobilizing medical information and knowledge: some insights from a survey. In: Proceedings of the 13th European Conference on Information Systems, pp.1–13. Association for Information Systems, Turku (2004)

    Google Scholar 

  15. Meng, F., Guo, X., Peng, Z., Lai, K., Vogel, D.: The routine use of mobile health services in the presence of health consciousness. Electron. Commer. Res. Appl. 35, 1–10 (2019)

    Article  Google Scholar 

  16. Kante, M., Ndayizigamiye, P.: A systematic mapping of the adoption of Internet of Things to provide healthcare services in developing countries. In: Ndayizigamiye, P., Barlow-Jones, G., Brink, R., Bvuma, S., Minty, R., Mhlongo, S. (eds.) Perspectives on ICT4D and Socio-Economic Growth Opportunities in Developing Countries, pp. 99–126. IGI Global, Hershey (2020)

    Google Scholar 

  17. Kante, M., Ndayizigamiye, P.: An analysis of the current status of Internet of Medical Things policies in developing countries. NPG Neurologie - Psychiatrie - Gériatrie 20(119), 259–265 (2020)

    Article  Google Scholar 

  18. Kante, M., Ndayizigamiye, P.: Internet of medical things, policies and geriatrics: an analysis of the national digital health strategy for South Africa 2019–2024 from the policy triangle framework perspective. Sci. Afr. 12(2021), e00759 (2021)

    Google Scholar 

  19. Ndayizigamiye, P., Maharaj, M.: A systematic review of mHealth interventions for public healthcare in East Africa. In: 24th Americas Conference on Information Systems, p. 77. Association for Information Systems, New Orleans (2018)

    Google Scholar 

  20. Mechael, P.N.: The case for mHealth in developing countries. Innov. Technol. Governance Globalization 4(1), 103–118 (2009)

    Google Scholar 

  21. Kenny, G., Connolly, R.: Towards an inclusive world: exploring m-health adoption across generations. In: Proceedings of the 25th European Conference on Information Systems (ECIS), pp. 1129–1144. Association for Information Systems, Guimarães (2017)

    Google Scholar 

  22. Mobius MD. https://mobius.md/2021/10/25/11-mobile-health-statistics/. Accessed 20 Jan 2022

  23. Whittaker, R.: Issues in mHealth: findings from key informant interviews. J. Med. Internet Res. 14(5), e129 (2012)

    Article  Google Scholar 

  24. Cho, J., Park, D., Lee, H.E.: Cognitive factors of using health apps: systematic analysis of relationships among health consciousness, health information orientation, eHealth literacy, and health app use efficacy. J. Med. Internet Res. 16(5), e125 (2014)

    Google Scholar 

  25. Botha, A., Booi, V.: mHealth implementation in South Africa. In: IST-Africa Conference, pp. 1–13. IEEE, Durban (2016)

    Google Scholar 

  26. O’Connor, Y., O’Sullivan, T., Gallagher, J., Heavin, C., Hardy, V., O’Donoghue, J.: A mobile health technology intervention for addressing the critical public health issue of child mortality. Electron. J. Inf. Syst. Dev. Countries 84(1), 1–11 (2017)

    Google Scholar 

  27. Ndayizigamiye, P.: Potential adoption of mobile health technologies for public healthcare in Burundi. Doctoral thesis. University of KwaZulu-Natal (2016)

    Google Scholar 

  28. Ndayizigamiye, P., Maharaj, M.: Applying bridges framework to investigate challenges to the adoption of mHealth in Burundi. In: IST-Africa Conference, pp. 1–8. IEEE, Durban (2016)

    Google Scholar 

  29. Niemöller, C., Metzger, D., Berkemeier, L., Zobel, B., Thomas, O.: Designing mHealth applications for developing countries. In: Twenty-Fourth European Conference on Information Systems, pp. 1–14. Association for Information Systems, İstanbul (2016)

    Google Scholar 

  30. Deng, Z., Zhang, J., Zhang, L.: Applying technology acceptance model to explore the determinants of mobile health service: from the perspective of public user. In: Eleventh Wuhan International Conference on e-Business, pp. 406–411. Association for Information Systems, Wuhan (2012)

    Google Scholar 

  31. Karuri, J.W.: Determinants of acceptance and use of routine HIS in developing countries: the case of DHIS2 in Kenya. Doctoral thesis. University of Nairobi (2015)

    Google Scholar 

  32. Watkins, J.O.T.A., Goudge, J., Gómez- Olivé, F.X., Griffiths, F.: Mobile phone use among patients and health workers to enhance primary healthcare: a qualitative study in rural South Africa. Soc. Sci. Med. 198, 139–147 (2018)

    Google Scholar 

  33. Ndayizigamiye, P., Hangulu, L., Akintola, O.: A design of a mobile health intervention to enhance homecarers’ disposal of medical waste in South Africa. In: IEEE Global Humanitarian Technology Conference, pp. 1–6. IEEE, San Jose (2017)

    Google Scholar 

  34. Ndayizigamiye, P., Maharaj, M.: Determinants of mobile health adoption in Burundi. Afr. J. Inf. Syst. 9(3), 171–191 (2017)

    Google Scholar 

  35. Nchise, C., Boateng, R., Shu, I., Mbarika, V.: Mobile phones in health care in Uganda: the AppLab study. Electron. J. Inf. Syst. Dev. Countries 52(1), 1–15 (2012)

    Article  Google Scholar 

  36. Kiwanuka, A., Kimaro, H.C., Senyoni, W.: Analysis of the acceptance process of District Health Information Systems (DHIS) for vertical health programmes: a case study of TB, HIV/AIDS and Malaria Programmes in Tanzania. Electron. J. Inf. Syst. Dev. Countries 70(1), 1–14 (2015)

    Article  Google Scholar 

  37. Imaja, I.M., Ndayizigamiye, P., Maharaj , M.: A design of a mobile health intervention for the prevention and treatment of Cholera in South Kivu in the Democratic Republic of Congo. In: 2017 IEEE Global Humanitarian Technology Conference, pp. 277–281. IEEE, San Jose (2017)

    Google Scholar 

  38. Albirini, A.: Cultural perceptions: the missing element in the implementation of ICT in developing countries. Int. J. Educ. Dev. Using ICT 2(1), 49–65 (2006)

    Google Scholar 

  39. Stults-Kolehmainen, M.A., Sinha, R.: The effects of stress on physical activity and exercise. Sports Med. 44(1), 81–121 (2014)

    Article  Google Scholar 

  40. Woosley, J.M., Ashia, K.: Comparison of con- temporary technology acceptance models and evaluation of the best fit for health industry organizations. Int. J. Comput. Sci. Eng. Technol. 1(11), 709–717 (2011)

    Google Scholar 

  41. Taiwo, A., Downe, A.G.: The theory of user acceptance and use of technology (UTAUT): a meta-analytic review of empirical findings. J. Theor. Appl. Inf. Technol. 49(1), 48–58 (2013)

    Google Scholar 

  42. Ami-Narh, J.T., Williams, P.A.H.: A revised UTAUT model to investigate E-health acceptance of health professionals in Africa. J. Emerging Trends Comput. Inf. Sci. 3(10), 1383–1391 (2012)

    Google Scholar 

  43. Ndayizigamiye, P., Maharaj, M.: Mobile health adoption in burundi: a UTAUT perspective. In: 2016 Global Humanitarian Technology Conference, pp.1–11. IEEE, Seattle (2016)

    Google Scholar 

  44. Bervell, B., Umar, I.N.: Validation of the UTAUT model: re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. EURASIA J. Math. Sci. Technol. Educ. 8223(10), 6471–6490 (2017)

    Google Scholar 

  45. Venkatesh, M., Morris, G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27(3), 425–478 (2003)

    Article  Google Scholar 

  46. Nanyombi, A., Habinka, A.: Factors influencing the adoption of mobile health in Uganda health facilities: a case study of mobile tracking system in Kayunga. Am. Sci. Res. J. Eng. Technol. Sci. 23(1), 131–145 (2016)

    Google Scholar 

  47. Jeon, E., Park, H.: Factors affecting acceptance of smartphone application for management of obesity. Healthc. Inf. Res. 21(2), 74–82 (2015)

    Article  Google Scholar 

  48. Albabtain, A.F., Almulhim, D.A., Yunus, F., Househ, M.S.: The role of mobile health in the developing world: a review of current knowledge and future trends. J. Sel. Areas Health Inf. 4(2), 1–6 (2014)

    Google Scholar 

  49. Benitez, J., Henseler, J., Castillo, A., Schuberth, F.: How to perform and report an impactful analysis using partial least squares: guidelines for confirmatory and explanatory IS research. Inf. Manag. 57(2), 1–16 (2020)

    Article  Google Scholar 

  50. Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M.: When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31(1), 2–24 (2019)

    Article  Google Scholar 

  51. Kante, M., Chepken, C., Oboko, R.: Partial least square structural equation modelling’ use in information systems: an updated guideline of practices in exploratory settings. Kabarak J. Res. Innov. 6(1), 49–67 (2018)

    Google Scholar 

  52. National Youth Commission Act. https://www.gov.za/sites/default/files/gcis_document/201409/a19-96.pdf. Accessed 20 Jan 2022

  53. Garson, G.D.: Partial Least Squares: Regression and Structural Equation Models. Statistical Associates Publishing, Asheboro (2016)

    Google Scholar 

  54. Henseler, J., Hubona, G., Ray, P.A.: Using PLS path modeling in new technology research: updated guidelines. Ind. Manag. Data Syst. 116(1), 2–20 (2016)

    Article  Google Scholar 

  55. Ogundaini, O.O., de la Harpe, R.: Integration of mHealth technologies to support service interaction moments in tertiary healthcare of Western Cape, South Africa. In: 2019 International Conference on Information Resources Management, pp. 1–18. Association for Information Systems, Auckland (2019)

    Google Scholar 

  56. Heeks, R.: Information and Communication for Development (ICT4D), 1st edn. Routledge, London (2018)

    Google Scholar 

  57. Opoku, D., Scott, P., Quentin, W.: Healthcare Professionals’ perceptions of the benefits and challenges of a teleconsultation service in the Amansie-West district of Ghana. Telemedicine e-Health 21(9), 748–755 (2015)

    Article  Google Scholar 

Download references

Acknowledgement

The authors would like to thank Tom Cyprian Soni for his involvement in the collection of data for this study.

Author information

Authors and Affiliations

  1. University of Johannesburg, Johannesburg, 2092, South Africa

    Macire Kante & Patrick Ndayizigamiye

Authors
  1. Macire Kante
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Ndayizigamiye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Ndayizigamiye .

Editor information

Editors and Affiliations

  1. University of West London, London, UK

    José Abdelnour-Nocera

  2. University of Nairobi, Nairobi, Kenya

    Elisha Ondieki Makori

  3. Esan University, Lima, Peru

    Jose Antonio Robles-Flores

  4. University of Fort Hare, Alice, South Africa

    Constance Bitso

Ethics declarations

Definition of Terms

mHealth: mHealth is a term that describes the use of wireless technology and mobile devices for medical care.

Youth: In this study, the term “youth” refers to people who are within the age range of 18 and 35.

PLS-SEM: The Partial Least Squares Structural Equation Modelling (PLS-SEM) is a statistical method that helps determine cause-effect relationships.

UTAUT: The unified theory of Acceptance and Use of Technology (UTAUT) is a theoretical framework used in the field of Information Systems (IS) to explain user intentions to use an information system and subsequent usage behaviour.

Self-Healthcare Monitoring: Self-Healthcare Monitoring refers to the use of mobile applications and wearable devices to monitor and manage one’s health.

Heterotrait-Monotrait Ratio (HTMT): HTMT is a statistical technique used to measure discriminant validity in Partial Least Squares-Structural Equation Modelling.

Convergent validity: Convergent validity helps determine whether measures of a construct are related.

Discriminant validity: Discriminant validity helps to determine whether measures that should not be related are indeed not related.

Standardized Root Mean Square Residual (SRMR): Root Mean Square Residual (SRMR) helps ascertain the mean size of residual correlations.

The Coefficient of Determination (R2): R2 helps determine the variation (s) within the dependent variable caused by independent variables.

Average Variance Extracted (AVE): The AVE helps to estimate, on average, the proportion of variation in a construct’s items can be explained by the construct or latent variable.

Path coefficient (β): In PLS-SEM, path coefficient (β) helps determine how an independent variable affects a dependent variable in the path model.

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kante, M., Ndayizigamiye, P. (2022). Adoption of Mobile Applications for Self-healthcare Monitoring by the Youth in South Africa. In: Abdelnour-Nocera, J., Makori, E.O., Robles-Flores, J.A., Bitso, C. (eds) Innovation Practices for Digital Transformation in the Global South. IFIP Advances in Information and Communication Technology, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-031-12825-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-12825-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12824-0

  • Online ISBN: 978-3-031-12825-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation