Previous research on the effectiveness of wellness mHealth apps focused on the design and features of such apps and paid insufficient attention to how the whole relationship between the apps and users impact use. Using affordance theory, we investigated what wellness mHealth apps afford to users and why these affordances are not actualized by all users. We conducted a qualitative study, collecting data from apps' reviews and from fifteen participants who used multiple wellness mHealth apps. Our grounded theory analysis revealed four shared affordances (promoting goals, comparing oneself to others, coaching, and nurturing) related to the use of wellness mHealth apps and three immediate concrete outcomes (habit formation, self-awareness, and goal attainment) reached after the affordances were actualized. Nonetheless, factors such as information overload, aesthetic appreciation, and users' characteristics may impact users' actualizations of the shared affordances and prevent some users from reaching their immediate concrete outcomes.
Rare diseases, affecting approximately 30 million Americans, are often poorly understood by clinicians due to lack of familiarity with the disease and proper research. Patients with rare diseases are often unfavorably treated, especially those with extremely painful chronic orofacial rare disorders. In the absence of structured knowledge, such patients often choose social media to seek help from peers within patient-oriented social media communities thereby generating tremendous amounts of unstructured data daily. We investigate whether we can organize this unstructured data using machine learning to help members of rare communities find relevant information more efficiently in real-time. We chose Trigeminal Neuralgia (TN), an extremely painful rare disorder, as our case study and collected 20,000 social media TN posts. We categorized TN posts into Twitter (very short), and Facebook (short, medium, long) datasets based on message length and performed three clustering experiments. Results revealed GSDMM outperformed both K-means and Spherical K-means in clustering Facebook especially for short messages in terms of speed. For long messages, MDS reduction outperformed the PCA when both were used with K-means and Spherical K-means. Our study demonstrated the need for further topic modeling to utilize among high level clusters based on semantic analysis of posts within each cluster.
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