Daniel Diethei

Daniel Diethei, Ashley Colley, Julian Wienert et al.

Online health communities provide a knowledge exchange platform for a wide range of diseases and health conditions. Informational and emotional support helps forum participants orient around health issues beyond in-person doctor visits. So far, little is known about the relation between the level of participation and participants' contributions in online health communities. To gain insights on the issue, we analyzed 456 posts in 56 threads from the Dermatology sub-forum of an online health community. While low participation threads (short threads) revolved around solving an individual's health issue through diagnosis suggestions and medical advice, participants in high participation threads (long threads) built collective knowledge and a sense of community, typically discussing chronic and rare conditions that medical professionals were unfamiliar with or could not treat effectively. Our results suggest that in short threads an individual's health issue is addressed, while in long threads, sub-communities about specific rare and chronic diseases emerge. This has implications for the user interface design of health forums, which could be developed to better support community building elements, even in short threads.

Daniel Diethei, Ashley Colley, Lisa Dannenberg et al.

The majority of blindness is preventable, and is located in developing countries. While mHealth applications for retinal imaging in combination with affordable smartphone lens adaptors are a step towards better eye care access, the expert knowledge and additional hardware needed are often unavailable in developing countries. Eye screening apps without lens adaptors exist, but we do not know much about the experience of guiding users to take medical eye images. Additionally, when an AI based diagnosis is provided, trust plays an important role in ensuring in the adoption. This work addresses factors that impact the usability and trustworthiness dimensions of mHealth applications. We present the design, development and evaluation of EyeGuide, a mobile app that assists users in taking medical eye images using only their smartphone camera. In a study (n=28) we observed that users of an interactive tutorial captured images faster compared to audible tone based guidance. In a second study (n=40) we found out that providing disease-specific background information was the most effective factor to increase trustworthiness in the AI based diagnosis. Application areas of EyeGuide are AI based disease detection and telemedicine examinations.