DESIGN PRINCIPLES FOR APP-BASED HEALTHCARE INTERVENTIONS: A MIXED METHOD APPROACH

Despite the ubiquity of mobile health applications (apps), the practical use and success of the apps have been questionable. Design Principles (DP) can affect chronic health app user satisfaction and have been studied for ensuring favorable app usage. However, there is no consensual definition of DP within the preceding literature, which has a technical rather than an end-user-centric focus and lacks a rigorous theoretical basis. Moreover, different levels of DPs’ application can lead to differential user satisfaction as influenced by the user-contextual environment, warranting a quantitative assessment. Accordingly, the overarching question to be addressed is which DP for the self-management of chronic conditions contributes to better user satisfaction outcomes. The research focuses on Multiple Sclerosis (MS) as a representative condition. This research uses a mixed methods, with a qualitative approach for DP identification and a quantitative approach for the studying the DP-Satisfaction relationship. The DP identification is achieved through - 1) An in depth review of foundational theory for greater validity, 2) A Systematic Literature Review (SLR), for DP themes grounded in theory, and 3) Manually coded user reviews for MS apps. The theoretical underpinnings of the empirical approach are established through a composite theoretical lens, based on technologically, behaviorally, and cognitively oriented frameworks. The DP extracted from theory, SLR, and manual coding methods are found to be largely consistent with each other, namely ‘Communication with Clinicians’, ‘Compatibility, ‘Education’, ‘Notifications’, ‘Tracking’, ‘Social Support’, ‘Ease of Use’, ‘Technical Support’, ‘Usefulness’, ‘Privacy and Security’, and Quality. An ordinal logistic regression analysis is conducted to understand the relationship between DP and User Satisfaction outcomes based on the manually coded DP scores of the user reviews. All DP have a significant impact on User Satisfaction. From a theoretical perspective, the research improves our understanding of key design principles for the self-management of chronic conditions such as MS and the impact of such principles on user satisfaction. From a practical perspective, the findings provide guidance to the user requirement elicitation process, potentially leading to the development of more successful, sustainable, and responsive healthcare interventions

Human Activity Recognition: A Comparison of Machine Learning Approaches

This study aims to investigate the performance of Machine Learning (ML) techniques used in Human Activity Recognition (HAR). Techniques considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Decision Tree with entropy, Random Forest, Gradient Boosting Decision Tree, and NGBoost algorithm. Following the activity recognition chain model for preprocessing, segmentation, feature extraction, and classification of human activities, we evaluate these ML techniques against classification performance metrics such as accuracy, precision, recall, F1 score, support, and run time on multiple HAR datasets. The findings highlight the importance to tailor the selection of ML technique based on the specific HAR requirements and the characteristics of the associated HAR dataset. Overall, this research helps in understanding the merits and shortcomings of ML techniques and guides the applicability of different ML techniques to various HAR datasets

Understanding the Influence of Digital Divide and Socio-Economic Factors on the Prevalence of Diabetes

https://scholar.dsu.edu/research-symposium/1010/thumbnail.jp

INFLUENCE OF THE DIGITAL DIVIDE AND SOCIO-ECONOMIC FACTORS ON PREVALENCE OF DIABETES

More than 100 million Americans have diabetes or prediabetes (29 and 84 million, respectively). Factors such as overweight, sedentary behavior, and history of diabetes in the family have been commonly associated with the onset of type 2 diabetes. Extant literature now points to the effect of socio-economic factors such as education, income, ethnicity, and physical location on the prevalence of the disease. This research aims to investigate the impact of social determinants on diabetes with a particular emphasis on the digital divide. We used data from the Centers for Disease Control and Prevention (CDC) for diagnosed diabetes prevalence, obesity prevalence, and leisure time physical inactivity data for the year 2013 by county. We contrasted the diabetes prevalence data against social factors such as race, educational attainment, income, poverty, unemployment, and digital divide obtained from the US Census Bureau data. Used bivariate, multivariate and regression analysis reveals a statistically significant relation between the prevalence of diabetes and digital divide, race, education, income and unemployment rate, obesity prevalence, and leisure time physical inactivity (P\u3c0.000). Overall, the results demonstrate the significant role of the digital divide in influencing chronic conditions such as diabetes

Early Public Outlook on the Coronavirus Disease (COVID-19): A Social Media Study

The recent outbreak of the coronavirus (COVID-19) brought with its public concerns and fears about a global epidemic. With the increase in the popularity, usage, and reach of social media, this research examined the early public outlook on COVID-19 using SM-Platform, Twitter.com. The current study employed a mixed-method approach in collecting and analyzing public tweets by combining quantitative sentiment analysis with a qualitative thematic analysis. Our results revealed positive sentiment prior to the spread of the disease. The sentiment then turned negative as the disease spread, accompanied by a large amount of fear as rumors. In a thematic analysis we also uncovered nine key topics on the disease including, but not limited to, prevention, symptoms and spread of disease. Our study will provide an understanding of social media and public health outbreak surveillance. The findings of the research revealed the usefulness of twitter mining to illuminate public health education

Social Media for Exploring Adverse Drug Events Associated with Multiple Sclerosis

Multiple Sclerosis (MS) affects 400,000 people in the USA and almost 2.5 million people worldwide. There is no cure for MS. A variety of disease-modifying therapies are currently available. They aim to reduce disease activity that ultimately leads to disability. However, such drugs have adverse effects that vary widely among patients making the choice of a suitable drug particularly challenging. With the proliferation of social media, this research aims to understand the perspective of people with MS on social media (Twitter) in regard to Adverse Drug Events (ADEs) and to analyze ADEs as perceived by MS patients. This study helps in understanding ADEs associated with MS drugs and can further inform future medical research by highlighting and prioritizing additional clinical trials needed to better assess such adverse drug effects

A Novel Framework for Crop Pests and Disease Identification Using Social Media

Pest and disease determination are among significant issues emerging in agriculture since they influence the generation of agribusiness. Persistent change of the daily costs of Agri-products is destabilizing to a nation’s economy and will impact the nation in the long-run. One factor that influences the persistent changes in daily costs is changes in production. Early identification of crop pests and diseases using smart technology will generously improve production. This paper discusses the existing methods/techniques used to identify crop pests and disease and proposes a novel framework using social media and expert systems. The practical contribution of this research is a conceptual framework for pests and disease identification in agriculture using social media

A Comparative Study of Machine Learning Approaches for Human Activity Recognition

The goal of this project is to study the performance of Machine Learning (ML) techniques used in Human Activity Recognition (HAR). Specifically, we aim to 1) evaluate and benchmark the performance of various ML techniques used for HAR against established ML performance metrics using multiple datasets, and 2) map the characteristics of various HAR datasets to appropriate ML techniques. From a theoretical perspective, the research will shed light into the strengths and weaknesses of various ML techniques that can provide insights into future research aimed at improving these techniques for HAR. From a practical perspective, the research provides guidance into the applicability of various ML techniques to HAR datasets. Overall, studies into improving HAR performance could lead to a significant improvement in the self-care and self-management interventions. These improvements would open doors for creative innovations in healthcare and other commercial applications that require the detection of human activity

Health Information systems capabilities and Hospital performance – An SEM analysis

The evaluation of the value generated by IT applications in general and hospital IT/IS, in particular, is an essential aspect of the research within the IS discipline. IT investment and IS capability as its functional manifestation can help improve the performance of hospitals. However, the estimated and expected efficiencies and improvement in care quality remain evasive and have yielded mixed evidence. One key finding that emerges from the research is that IT does bring value to organizations, but not in isolated cases. Instead, IT can create a synergic relationship that improves business performance by creating a process in an organization when coupled with organizational factors. We introduce hospital function efficiency as an intermediate business process that mediates HIT and hospital performance. Thus, our research investigates the impact of HIT capabilities on hospital quality of care through the hospital functional efficiency as a mediating variable using a structural equational modeling approach

Design Principles for Multiple Sclerosis Mobile Self-Management Applications: A Patient-Centric Perspective

This research aims to explore the users’ reactions and perception towards mobile applications for Multiple Sclerosis (MS) self-management mobile applications. The emphasis is on identifying design principles that can inform the development of successful and responsive MS self-management interventions. We employ a grounded theory approach to analyze user reviews of MS mobile applications available in the Apple and Google Play app stores. A total of 33 MS mobile applications and 1,378 user reviews and ratings were extracted from these stores. Using the results from grounded theory approach, as building blocks, we generated a domain ontology for design principles in the case of MS mobile applications. This research sheds light into design principles from a patient perspective and provides design recommendations for the usability of the MS mobile applications. The findings could extend to other conditions that share characteristics with MS