The development of an instrument to predict patients’ adoption of mHealth in the developing world

INTRODUCTION: There are many tools for measuring patient’s potential adoption of mHealth (i.e. mobile health) in the developed world, but none of these instruments provides a comprehensive means for measuring critical issues affecting the adoption of mHealth by patients in the developing world. The aim of this paper was to develop a valid and reliable assessment instrument for predicting mHealth adoption by patients in the developing world. METHOD: A Patients mHealth Technology Adoption Questionnaire (PmTAQ) was developed based on themes identified through a prior published structured literature review of factors affecting patients’ mHealth adoption in the developing world, from which eight constructs evolved. Face and content validity was confirmed by 15 mothers who had used mHealth (the Mobile Technology for Community Health (MoTeCH) service) for maternal care, and the findings were used to improve the instrument. To assess the validity and reliability of the instrument at least 64 mothers who used MoTeCH were randomly selected from each of nine clusters of health posts in one district in Ghana. The assessment instrument consisted of 39 items, categorised under eight components: Cost and ownership, user characteristics, language and literacy, infrastructure, collaboration and funding, governance, system utility, and intention to adopt. Exploratory and confirmatory factor analysis were performed. RESULTS: The data from 585 mothers were analysed. Exploratory factor analysis showed the eigenvalue of all eight components to be significant (cumulative total greater than 1.0). Bartlett’s test of sphericity was significant, the Kaiser-Meyer-Olkin value was 0.84 and the mean Cronbach’s α value was 0.82 (range 0.81–0.83). The components were found to be valid. Confirmatory factor analysis showed that all indices for the measurement model were within acceptable limit leading to the use of structural equation modelling to show the causal relationship between components, resulting in the development of the mHealth Adoption Impact Model (mAIM). The mAIM shows a strong relationship between latent constructs for patients’ mHealth adoption. CONCLUSION: The study presents an evidence-based, reliable and valid instrument and model for application in future research, policy development, and implementations related to patient mHealth adoption in the developing world.