Senanu Okuboyejo

mHealth: Using Mobile Technology to Support Healthcare

Adherence to long-term therapy in outpatient setting is required to reduce the prevalence of chronic diseases such as HIV/AIDS, Diabetes, Tuberculosis and Malaria. This paper presents a mobile technology-based medical alert system for outpatient adherence in Nigeria. The system makes use of the SMS and voice features of mobile phones. The system has the potential of improving adherence to medication in outpatient setting by reminding patients of dosing schedules and attendance to scheduled appointments through SMS and voice calls. It will also inform patients of benefits and risks associated with adherence. Interventions aimed at improving adherence would provide significant positive return on investment through primary prevention (of risk factors) and secondary prevention of adverse health outcomes.

Non-Adherence to Medication in Outpatient Setting in Nigeria: The Effect of Employment Status

Background: Non-adherence to prescribed medication and health regimen has been identified as responsible for poor health outcomes. This study investigates the reasons for medication non-adherence in outpatient setting among malaria patients in Nigeria. Methods: This research adopted quantitative research methods. A well-structured questionnaire was completed by 440 respondents with minimum age of 18 years. The aim of the questionnaire was to get respondents’ reasons for non-adherence to medication. The demographic details of the respondents were also captured. Results: Age, gender, educational level, marital status and medication payment were found not to influence non-adherence while employment was a significant variable. Respondents also indicated fear of death, nauseating smell of drugs, religious beliefs, the side effects of medication, the fear of taking counterfeit drugs or drugs that are past their expiry dates as also responsible for non-adherence. Conclusion: The results highlighted reasons for poor adherence in southwest Nigeria. Interventions can be targeted towards these reasons.

Adoption of Mobile Technology by Farmers in Southwest-Nigeria: A Cross-Sectional Study

This study evaluates the behavioral intention of farmers in selected areas of Nigeria to adopt mobile technology for agricultural purposes. A theoretical framework was developed based on the technology acceptance model TAM and innovation diffusion theory IDT. Five variables were evaluated. Convenience sampling was employed in the investigation. A total of 202 responses were retrieved. Factor analysis was performed to test the reliability and validity of the measurement items. Correlation analysis was used to test the conformity of the empirical data with the presumed model using SPSS version 16. All five measurement items: perceived ease of use, perceived usefulness, relative advantage, compatibility, and attitude were found to be direct predictors of adoption behavior. The study provides evidence for the potential of mobile technology in agriculture.

Adoption and use of mobile learning in higher education: the UTAUT model

This paper evaluates the adoption of mobile learning in Nigerian Educational institution a non-Western country with the use of the UTAUT model. This study re-evaluates the relationships among the human factor measures of the UTAUT model in assessing its applicability to a cultural context of a different country. The data for this study were obtained through a self-administered survey of Nigerian University students and the model was estimated using structural equation modeling framework. The findings of this study confirmed and contradicted some UTAUT relationships. This shows that countrys level of cultural differences to a large extent moderates the interactions of the UTAUT effects as such direct application of information system models validated by other cultures might be detrimental as vital relationships determining the adoption of such of technology might not be revealed. The finding of this study provides policy makers of educational institutions and industry practitioners with an appropriate model that can be used to assess the level of adoption of mobile learning and other learning technologies in Nigeria and similar countries of the same cultural context.

An Artificial Neural Network Classification of Prescription Nonadherence

This study investigates the use of artificial neural networks ANNs to classify reasons for medication nonadherence. A survey method is used to collect individual reasons for nonadherence to treatment plans. Seven reasons for nonadherence are identified from the survey. ANNs using backpropagation learning are trained and validated to produce a nonadherence classification model. Most patients identified multiple reasons for nonadherence. The ANN models were able to accurately predict almost 63 percent of the reasons identified for each patient. After removal of two highly common nonadherence reasons, new ANN models are able to identify 73 percent of the remaining nonadherence reasons. ANN models of nonadherence are validated as a reliable medical informatics tool for assisting healthcare providers in identifying the most likely reasons for treatment nonadherence. Physicians may use the identified nonadherence reasons to help overcome the causes of nonadherence for each patient.