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

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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.

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Acknowledgement

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

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Authors and Affiliations

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

    Macire Kante & Patrick Ndayizigamiye

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  1. Macire Kante
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Correspondence to Patrick Ndayizigamiye .

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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

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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.

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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

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

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