ArticleInformatics in medicine unlocked2022
The development of an instrument to predict patients' adoption of mHealth in the developing world.
Article in Informatics in medicine unlocked, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Patient perceptions and predictors of intention to use telehealth for follow-up care: a mixed methods study among adults living with HIV in Kampala, Uganda.BMC health services research · 2025Article
- Determining information system end-user satisfaction and continuance intension with a unified modeling approach.Scientific reports · 2024Article
- Understanding the factors of wearable devices among the patients with thyroid cancer: A modified UTAUT2 model.PloS one · 2024Article
- Psychological antecedents of telehealth acceptance: A technology readiness perspective.International journal of disaster risk reduction : IJDRR · 2023Article
- A healthcare workers' mHealth adoption instrument for the developing world.BMC health services research · 2022Article
- Determinants of the Mobile Health Continuance Intention of Elders with Chronic Diseases: An Integrated Framework of ECM-ISC and UTAUT.International journal of environmental research and public health · 2022Article
- Article
- A public value-based model to understand patients' adoption of eHealth: Theoretical underpinnings and empirical application.Digital healthArticle
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.