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 country’s 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

The effect of self-efficacy and outcome expectation on medication adherence behaviour

Medication adherence still ranks as a big challenge for clinicians and health workers. Based on a social learning theoretical framework, this study explores the adoption of patient adherence, medication adherence as a catalyst for improving the health and quality of life of individuals in Nigeria. Structural Equation Modelling technique was used to analyze the empirical data obtained. SLT variables including selfefficacy and outcome expectation were tested against medication adherence behavior. The constructs are related and positively correlated except definition which is contrary to previous researches. The research discusses these findings while also highlighting the implications for practice and policy

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 country's 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

User Reviews of Depression App Features: Sentiment Analysis

BackgroundMental health in general, and depression in particular, remain undertreated conditions. Mobile health (mHealth) apps offer tremendous potential to overcome the barriers to accessing mental health care and millions of depression apps have been installed and used. However, little is known about the effect of these apps on a potentially vulnerable user population and the emotional reactions that they generate, even though emotions are a key component of mental health. App reviews, spontaneously posted by the users on app stores, offer up-to-date insights into the experiences and emotions of this population and are increasingly decisive in influencing mHealth app adoption. ObjectiveThis study aims to investigate the emotional reactions of depression app users to different app features by systematically analyzing the sentiments expressed in app reviews. MethodsWe extracted 3261 user reviews of depression apps. The 61 corresponding apps were categorized by the features they offered (psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment). We then produced word clouds by features and analyzed the reviews using the Linguistic Inquiry Word Count 2015 (Pennebaker Conglomerates, Inc), a lexicon-based natural language analytical tool that analyzes the lexicons used and the valence of a text in 4 dimensions (authenticity, clout, analytic, and tone). We compared the language patterns associated with the different features of the underlying apps. ResultsThe analysis highlighted significant differences in the sentiments expressed for the different features offered. Psychoeducation apps exhibited more clout but less authenticity (ie, personal disclosure). Medical assessment apps stood out for the strong negative emotions and the relatively negative ratings that they generated. Therapeutic treatment app features generated more positive emotions, even though user feedback tended to be less authentic but more analytical (ie, more factual). Supportive resources (connecting users to physical services and people) and entertainment apps also generated fewer negative emotions and less anxiety. ConclusionsDevelopers should be careful in selecting the features they offer in their depression apps. Medical assessment features may be riskier as users receive potentially disturbing feedback on their condition and may react with strong negative emotions. In contrast, offering information, contacts, or even games may be safer starting points to engage people with depression at a distance. We highlight the necessity to differentiate how mHealth apps are assessed and vetted based on the features they offer. Methodologically, this study points to novel ways to investigate the impact of mHealth apps and app features on people with mental health issues. mHealth apps exist in a rapidly changing ecosystem that is driven by user satisfaction and adoption decisions. As such, user perceptions are essential and must be monitored to ensure adoption and avoid harm to a fragile population that may not benefit from traditional health care resources

Examining Users’ Concerns while Using Mobile Learning Apps

Mobile learning applications (apps) are increasingly and widely adopted for learning purposes and educational content delivery globally, especially with the massive means of accessing the internet done majorly on mobile handheld devices. Users often submit their feedback on use, experience and general satisfaction via the reviews and ratings given in the digital distribution platforms. With this massive information given through the reviews, it presents an opportunity to derives valuable insights which can be utilized for various reasons and by different stakeholders of these mobile learning apps. This large volume of online reviews creates significant information overload which presents a time-consuming task to read through all reviews. By combining text mining techniques of topic modeling using Latent Dirichlet Algorithm (LDA) and sentiment analysis using Linguistic Inquiry Word Count (LIWC), we analyze these user reviews. These techniques identify inherent topics in the reviews and identifies variables of user satisfaction of mobile learning apps. The thematic analysis done reveals different keywords which guide classification into the topics identified. Conclusively, the topics derived are important to app stakeholders for further modifications and evolution tasks

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