Antecedents of mHealth inequalities and mHealth equitable service model

The introduction of consumer MHealth technology is highly extolled for its potential to facilitate access to health, alleviate the shortage of health care resources, reduce hospitalization of patients, and mitigate health cost. The overwhelming endorsement shows the use of MHealth to complement existing healthcare infrastructure by targeting heterogeneous audience for specific health need. However, consumer MHealth innovation is traditionally considered for measures of coverage, efficacy, and cost-effectiveness with little discussion of the unintended consequences of escalating inequalities for underserved consumers of low socioeconomic populations. Furthermore, MHealth studies show that inequalities are fundamentally addressed as derivative of socioeconomic phenomenon without further explanation of how social and technology factors reinforce and aggravate its patterns. Therefore, the proliferation of consumer MHealth innovation and its concomitant health inequalities have important consequences. Researchers, managers, and other health information systems’ stakeholders increasingly face the dilemma of reconciling the perplexing, and often contradictory rise in health inequalities in their commitment to implement MHealth innovation. Existing studies reveal the paucity of empirical research and methodological limitation, including the lack of relevant theories to describe, explain or predict how sociotechnical mechanisms reinforce and aggravate inequalities in MHealth. Thus, the study of inequalities in consumer MHealth presents fundamental challenges relating to its substantive nature, its origin, and scope; as well as the methodological concern of how to address the anomalies. It is therefore the objective of this research to address these gaps by exploring the antecedents of inequalities in consumer MHealth, and to resolve the following challenges: (1) the lack of consensus on the theoretical concepts of the relevant factors, (2) the elaboration of the relationship between the antecedent factors, and (3) to develop IS framework which can be used to mitigate inequalities in consumer MHealth innovation for PAB. To achieve the above objective, the researcher adopted the interpretivist paradigm and qualitative approach as a reflective method to capture the emergent complexity of human sense making in a natural sociotechnical interaction between information technology, the people, and the context. Multiple case study and purposive sampling were also adopted to enable comparative selection of cases, and to intensify comprehensive data gathering that captures the richness of the cases. Accordingly, the prerequisite technology artefact was operationalised with MHealth for physical activity and fitness (PAF). Essentially, the aim was to document in detail the conduct of everyday events in the implementation and use of MHealth for PAF and to identify the meaning assigned to these experiences by participants. The research study was conducted in the Republic of Ireland (ROI); and the data collection occurred in the period between July 2019 and March 2020. Twenty-four individuals from twelve households of ethnic minority people of African background (PAB) participated individually in the data collection which involved demographic survey, observational data with think-aloud protocol (TAP), and role-play demonstration (RPD), as well as in-depth interviews. The lack of pre-existing notion of the MHealth phenomenon and the originality of this study necessitated the use of TAP and RPD, which were devised as templates to apprehend the true nature of the emerging phenomenon. The TAP and RPD are direct observational tools designed to illuminate human interactions which are situated in practice, to grasp knowledge that are mainly observed but absent from other documentation. The researcher reasoned that unless research participants are extremely insightful, they might not know or remember all the rationale for their behaviour. Thus, the researcher prepared and collected quantitative and qualitative data from each participant for eight weeks. Thereafter, the researcher organised all data with NVivo QDAS and concurrently conducted grounded theoretical analysis. The qualitative analysis resulted to categories and core categories which have explanatory and predictive powers and provide understanding of the inequalities in consumer MHealth. Thus, this research study has immense contribution to IS theory and practice, especially for its novel methodology which uncovers the nine antecedents for examining inequalities in MHealth. Similarly, the discovery of the formative factors of inequalities in MHealth provides useful taxonomy, and clearly reveals that socioeconomic factor is one part of the nine antecedents that impact MHealth. Furthermore, the researcher developed the MHES model, and a framework to mitigate inequalities in consumer MHealth innovation. Consequently, the IS stakeholders, the PAB and underserved populations can leverage the MHESF at individual, social or organisational level to mitigate inequalities in consumer MHealth innovation. However, the transdisciplinary nature of sociotechnical research such as this requires complementary representation from relevant IS reference disciplines, as well as greater involvement of MHealth stakeholders for richer insight. Furthermore, qualitative studies of this type are subjective, idiographic, and emic, with emphasis on relevance. Notwithstanding, this study paves way for mixed method research that combines relevance and theory verification