Background: mHealth applications provide health practitioners with platforms that enable disease management, facilitate drug adherence, facilitate drug adherence, speed up diagnosis, monitor outbreaks, take and transfer medical images, and provide advice. Many developing economies are investing more in mobile telecommunication infrastructure than in road transport and electric power generation. Despite this, mHealth has not seen widespread adoption by healthcare workers in the developing world. This study reports a scoping review of factors that impact the adoption of mHealth by healthcare workers in the developing world, and based on these findings, a framework is developed for enhancing mHealth adoption by healthcare workers in the developing world. Methods: A structured literature search was performed using PubMed and Scopus, supplemented by hand searching. The searches were restricted to articles in English during the period January 2009 to December 2019 and relevant to the developing world that addressed: mobile phone use by healthcare workers and identified factors impacting the adoption of mHealth implementations. All authors reviewed selected papers, with final inclusion by consensus. Data abstraction was performed by all authors. The results were used to develop the conceptual framework using inductive iterative content analysis. Results and Discussion: Of 919 articles, 181 met the inclusion criteria and, following a review of full papers, 85 reported factors that impact (promote or impede) healthcare worker adoption of mHealth applications. These factors were categorised into 18 themes and, after continued iterative review and discussion were reduced to 7 primary categories (engagement/funding, infrastructure, training/technical support, healthcare workers’ mobile—cost/ownership, system utility, motivation/staffing, patients’ mobile—cost/ownership), with 17 sub-categories. These were used to design the proposed framework. Conclusions: Successful adoption of mHealth by healthcare workers in the developing world will depend on addressing the factors identified in the proposed framework. They must be assessed in each specific setting prior to mHealth implementation. Application of the proposed framework will help shape future policy and practice of mHealth implementation in the developing world and increase adoption by health workers.
Introduction Healthcare 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. Method A 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. Results Exploratory 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. Conclusion The 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.
Background: Patient perceptions and experiences of mobile health (m-health) systems have been recognised as an important element to consider in the adoption of m-health based technologies. Though much research supports this, published studies that identify m-health use by patients appear to highlight these issues in an indirect rather than holistic manner. Consequently, there is no encompassing framework that serves as a guide for effective implementation and maximum adoption of m-health from the perspective of patients in the developing world. This review documents patient adoption issues specifically and uses these to develop a proposed framework of patient adoption issues for m-health in the developing world. Methods: A structured literature search was conducted using PubMed and Scopus. For PubMed a consolidated search string combined ‘MeSH’ terms and ‘All Fields’ terms for selected keywords. For Scopus an equally consolidated search string was used. The searches were restricted to articles in English during the period January 2000 to December 2019 and relevant to the developing world. Duplicate articles were removed. Titles and abstracts were screened by all authors for inclusion, and those studies that met the inclusion criteria were selected for full-text review. Review and data abstraction was performed by two authors.Results: Fifty four (54) articles reported factors that impact patient adoption. Review and data abstraction identified specific factors, initially classified under 22 categories, that promote or impede m-health adoption in the developing world. Continued iterative review and discussion reduced these to 7 primary categories, with 21 sub-categories, which were used to design the proposed framework.Conclusions: The review showed: great inconsistency in the approach and tools used in published studies; multiple factors impact patient adoption of m-health in the developing world; the specific factors vary from setting to setting (e.g., country, rurality, mobile device type) and by recency of findings. Successful adoption of m-health by patients in the developing world critically depends on addressing the factors identified in the proposed framework and assessing them prior to implementation of m-health initiatives in any specific setting. The proposed framework will serve to increase the consistency of patient adoption studies, form the basis for informed policy decisions by stakeholders, and provide the foundation for greater success of future m-health implementations for patients in the developing world.
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