Modelling the Predictors of Mobile Health (mHealth) Adoption among Healthcare Professionals in Low-Resource Environments

Bibliographic Data

ID15509367
AuthorsMehreen Azam (0000-0003-0069-2249, Islamia University of Bahawalpur), Salman Bin Naeem (0000-0002-0153-1669, Islamia University of Bahawalpur), Maged N Kamel Boulos (0000-0003-2400-6303, University of Lisbon, corresponding author), Anthony Faiola (0000-0002-7733-5947, University of Kentucky)
Year2023
Volume20
Issue23
Pages7112-7112
Publication date2023-11-26
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph20237112
PMID38063542
OpenAlexW4389051538
LanguageEN
Citations received2
References cited67

This study was conducted with objectives to measure and validate the unified theory of the acceptance and use of technology (UTAUT) model as well as to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. A cross-sectional survey was conducted at the six public and private hospitals in the two districts (Lodhran and Multan) of Punjab, Pakistan. The participants of the study comprised healthcare professionals (registered doctors and nurses) working in the participating hospitals. The findings of the seven-factor measurement model showed that behavioral intention (BI) to mHealth adoption is significantly influenced by performance expectancy (β = 0.504, CR = 5.064, p p 2 (df = 259) = 3.207; p = 0.000; CFI = 0.891, IFI = 0.892, TLI = 0.874, RMSEA = 0.084). This study suggests that the adoption of mHealth can significantly help in improving people's access to quality healthcare resources and services as well as help in reducing costs and improving healthcare services. This study is significant in terms of identifying the predictors that play a determining role in the adoption of mHealth among healthcare professionals. This study presents an evidence-based model that provides an insight to policymakers, health organizations, governments, and political leaders in terms of facilitating, promoting, and implementing mHealth adoption plans in low-resource settings, which can significantly reduce health disparities and have a direct impact on health promotion

Business · Expectancy theory · Health care · Knowledge management · mHealth · Political science · Psychological intervention · Resource (disambiguation · Structural equation modeling · Unified theory of acceptance and use of technology · Applied Psychology · Computer Science · Medicine · Mobile Health and mHealth Applications · Nursing · Psychology · Social Psychology · Technology Adoption and User Behaviour · Technology Use by Older Adults