An adoption model of mHealth applications that promote physical activity

Abstract

Physical activity is one of the ways to promote a healthy and balanced life. There is growing evidence that physical activity can be promoted through the use of mHealth applications. However, the adoption of such applications is influenced by many factors. This study investigated these factors and the relationship among them to propose a model for the adoption of mHealth applications that promote physical activity. The study adopted two theoretical lenses as the guiding frameworks, namely the Diffusion of Innovation Theory and the Unified Theory of Acceptance and Use of Technology. Data were collected from a convenient sample of 140 respondents from South Africa, using a survey questionnaire. The Partial Least Square Structural Equation Modelling (PLS-SEM) was used to assess the proposed model. The results have revealed that awareness, effort expectancy, social influence and behavioural intention account for 35.3% of the variance of the use behaviour towards adopting mHealth applications that promote physical activity in South Africa. Hence, this study recommends that any intervention that ABOUT THE AUTHORS Dr Patrick Ndayizigamiye holds a PhD in Information Systems and Technology, and is currently a senior lecturer in the Department of Applied Information Systems at the University of Johannesburg in South Africa. He has extensive experience in research and has published many papers in international peer-reviewed journals and conference proceedings. His current research interests are technology-enhanced healthcare, ICT4D and cyber security.Dr Macire Kante holds a PhD in Information Systems from the University of Nairobi. Dr Kante has served as a research associate at the Centre National de la Recherche Scientifique et Technologique (Bamako, Mali). Currently, he is a postdoctoral fellow at the University of Johannesburg in South Africa. His recent work is largely in the area of Information and Communication Technology for Development (ICT4D) in the following thematic areas: ICT for agriculture, health informatics and knowledge management.Shalati Shingwenyana holds a BCom Honours degree in Information Systems and Technology from the University of Johannesburg in South Africa.

The effect of logistic service quality on customer satisfaction and loyalty using kansei engineering during the COVID-19 pandemic

“…The reliability test testing is used to determine the level of consistency of answers given by respondents. The questionnaire is reliable if the Cronbach alpha value is > 0.6 (Ndayizigamiye et al, 2020). Based on Table 12, it is known that all items have a "reliable" value, meaning that the measurements made in this questionnaire are reliable because they can consistently measure even though they are repeated in the same conditions.…”

The Intention to Use Fitness and Physical Activity Apps: A Systematic Review

“…Analyzing the sample used in the different studies, there is a total of 16,025 subjects with an average sample of 843.42 subjects per study, with the Ndayizigamiye; Kante, and Shingwenyana study [54] having the smallest sample (n = 139) and Wei, Vinnikova, Lu and Xu study [55] having the largest sample with a total of 8840 subjects. Approximately a half of the studies (n = 8) used university students as a sample, followed by studies that considered users of sports applications (n = 4) and other studies took as their general population [54][55][56], a population of sports consumers [45], employees of a sports organization [57] and members of a fitness community [44,58]. Most studies had a higher proportion of females than males (n = 9), followed by studies that had parity in the sample (n = 5), four studies had a higher proportion of males while one study did not indicate the gender distribution of the sample [34].…”

“…Li, Liu, Ma and Zhang [46] sampled subjects over 25 years of age, while Huang and Ren [60] and Mohammadi and Isanejad [57] were at least 30 years old. Regarding the type of App evaluated, six studies evaluated the intention to use diet and fitness applications [30,44,51,52,55,59], another five studies evaluated sports information Apps [42,43,53,57,62], two studies measured the intention of fans to use the sports team app [1,45], health and fitness app [56,61], or fitness [58,60] and one study evaluated a social fitness-tracking app [63] and mHealth related to promote physical activity [54]. The most widely used theory for the design and use of the mobile sports app intent of use assessment instrument was TAM (n = 10).…”

“…The most widely used theory for the design and use of the mobile sports app intent of use assessment instrument was TAM (n = 10). Chen and Lin [44] and Chiu and Cho [61] used a variant of the TAM, Theory of Readiness and Acceptance Model (TRAM), three studies used UTAUT [54,55,62] and UTAUT2 [30,58,63], and an article with the Expectation-Confirmation Model (ECM) [56]. The most common method of analysis used was a content analysis by structural equations using the AMOS statistical package (n = 9) and the rest of the Partial Least Square studies.…”

Key Potentials of mHealth for Community-Based Early Detection of Cardiovascular Disease During the COVID-19 Pandemic

“…The mHealth applications can be classi ed into two categories: applications designed for disease management and applications that can support changes in the user's health behavior (24). Currently, the mHealth application is increasingly being used as a tool to promote changes in user behavior to prevent NCDs (25). Besides being designed to detect CVD risk, the mHealth application is also designed to provide information on health conditions and follow-up recommendations for CVD prevention and control for its users.…”