ArticleBMC health services research2022
A healthcare workers' mHealth adoption instrument for the developing world.
Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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0 citing papers in PubMed, 8 citations in OpenAlex.
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Authors and funding
3 authors at 3 institutions in 3 countries.
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Abstract
introductionHealthcare workers' adoption of mHealth is critical to the success or failure of clinician based mHealth services in the developing world. mHealth adoption is affected or promoted by certain factors, some of which are peculiar to the developing world. Identifying these factors and evaluating them will help develop a valid and reliable measuring instrument for more successful prediction of mHealth adoption in the future. The aim of this study was to design and develop such an instrument.
methodA Healthcare workers' mHealth Adoption Questionnaire (HmAQ) was developed based on five constructs identified through a prior literature review: multi-sectorial engagement and ownership; staffing and technical support; reliable infrastructure; usefulness and stewardship; and intention to adopt. After testing face and content validity, the questionnaire was administered to 104 nurses and midwives in the Ewutu-Senya district of the Central Region of Ghana who used a maternal mHealth intervention. After data collection confirmatory factor analysis and structural equation modelling were applied and the Healthcare Worker mHealth Adoption Impact Model (HmAIM) developed.
resultsExploratory factor analysis showed the eigenvalue of all five components to be significant (cumulative total greater than 1.0). Bartlett's Test of Sphericity was significant, the Kaiser-Meyer-Olkin value was 0.777, and the mean Cronbach's α value was 0.82 (range 0.81-0.83). Confirmatory factor analysis showed that constructs for the HmAQ were within acceptable limits and valid. Structural equation modelling showed the causal relationships between components. This resulted in development of the HmAIM. A modified model was then developed using the averages of individual construct items. This model showed strong correlation among the constructs. Further research will be required to understand new dimensions of mHealth adoption as a result of emerging technology needs, new complexities in the healthcare work environment, and how different cadres of healthcare workers respond to it.
conclusionThe study presents a valid and reliable instrument, the HmAIM, to serve as a tool for assessment of healthcare workers' mHealth adoption in the developing world. Use of the instrument will enhance the likelihood of successful adoption of mHealth implementations.
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