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- All Subjects: Wireless communication systems in medical care
- Creators: Pina, Armando
Description
In the last decade, the number of people who own a mobile phone or portable electronic communication device has grown exponentially. Recent advances in smartphone technology have enabled mobile devices to provide applications (“mHealth apps”) to support delivering interventions, tracking health treatments, or involving a healthcare team into the treatment process and symptom monitoring. Although the popularity of mHealth apps is increasing, few lessons have been shared regarding user experience design and evaluation for such innovations as they relate to clinical outcomes. Studies assessing usability for mobile apps primarily rely on survey instruments. Though surveys are effective in determining user perception of usability and positive attitudes towards an app, they do not directly assess app feature usage, and whether feature usage and related aspects of app design are indicative of whether intended tasks are completed by users. This is significant in the area of mHealth apps, as proper utilization of the app determines compliance to a clinical study protocol. Therefore it is important to understand how design directly impacts compliance, specifically what design factors are prevalent in non-compliant users. This research studies the impact of usability features on clinical protocol compliance by applying a mixed methods approach to usability assessment, combining traditional surveys, log analysis, and clickstream analysis to determine the connection of design to outcomes. This research is novel in its construction of the mixed methods approach and in its attempt to tie usability results to impacts on clinical protocol compliance. The validation is a case study approach, applying the methods to an mHealth app developed for early prevention of anxiety in middle school students. The results of three empirical studies are shared that support the construction of the mixed methods approach.
ContributorsPatwardhan, Mandar (Author) / Gary, Kevin A (Thesis advisor) / Pina, Armando (Committee member) / Amresh, Ashish (Committee member) / Arizona State University (Publisher)
Created2016
Description
Mobile health (mHealth) applications (apps) hold tremendous potential for addressing chronic health conditions. Smartphones are now the most popular form of computing, and the ubiquitous “always with us, always on” nature of mobile technology makes them amenable to interventions aimed and managing chronic disease. Several challenges exist, however, such as the difficulty in determining mHealth effects due to the rapidly changing nature of the technology and the challenges presented to existing methods of evaluation, and the ability to ensure end users consistently use the technology in order to achieve the desired effects. The latter challenge is in adherence, defined as the extent to which a patient conducts the activities defined in a clinical protocol (i.e. an intervention plan). Further, higher levels of adherence should lead to greater effects of the intervention (the greater fidelity to the protocol, the more benefit one should receive from the protocol). mHealth has limitations in these areas; the ability to have patients sustainably adhere to a protocol, and the ability to drive intervention effect sizes. My research considers personalized interventions, a new approach of study in the mHealth community, as a potential remedy to these limitations. Specifically, in the context of a pediatric preventative anxiety protocol, I introduce algorithms to drive greater levels of adherence and greater effect sizes by incorporating per-patient (personalized) information. These algorithms have been implemented within an existing mHealth app for middle school that has been successfully deployed in a school in the Phoenix Arizona metropolitan area. The number of users is small (n=3) so a case-by-case analysis of app usage is presented. In addition simulated user behaviors based on models of adherence and effects sizes over time are presented as a means to demonstrate the potential impact of personalized deployments on a larger scale.
ContributorsSingal, Vishakha (Author) / Gary, Kevin (Thesis advisor) / Pina, Armando (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2019