Development of the intelligent knee osteoarthritis lifestyle app: a person-based approach.

BACKGROUND: Knee osteoarthritis is one of the most prevalent long term health conditions globally. Exercise and physical activity are now widely recognised to significantly reduce joint pain, improve physical function and quality of life in patients with knee osteoarthritis. However, prescribed exercise without regular contact with a healthcare professional often results in lower adherence and poorer health outcomes. Digital mobile health (mHealth) technologies offer great potential to support people with long-term conditions such as knee osteoarthritis more efficiently and effectively and with relatively lower cost than existing interventions. However, there are currently very few mHealth interventions for the self-management of knee osteoarthritis. The aim of the present study was to describe the development process of a mHealth app to extend the support for physical activity and musculoskeletal health beyond short-term, structured rehabilitation through self-management, personalised physical activity, education, and social support. METHODS: The development of the intelligent knee osteoarthritis lifestyle application intervention involved an iterative and interconnected process comprising intervention 'planning' and 'optimisation' informed by the person-based approach framework for the development of digital health interventions. The planning phase involved a literature review and collection of qualitative data obtained from focus groups with individuals with knee osteoarthritis (n = 26) and interviews with relevant physiotherapists (n = 5) to generate 'guiding principles' for the intervention. The optimisation phase involved usability testing (n = 7) and qualitative 'think aloud' sessions (n = 6) with potential beneficiaries to refine the development of the intervention. RESULTS: Key themes that emerged from the qualitative data included the need for educational material, modifying activities to suit individual abilities and preferences as well as the inclusion of key features such as rehabilitation exercises. Following a user-trial further changes were made to improve the usability of the application. CONCLUSIONS: Using a systematic person-based, development approach, we have developed the intelligent knee osteoarthritis lifestyle application to help people maintain physical activity behaviour. The app extends the support for physical activity and musculoskeletal health beyond short-term, structured rehabilitation through personalised physical activity guidance, education, and social support.

The evolution of online ideological communities.

Online communities are virtual spaces for users to share interests, support others, and to exchange knowledge and information. Understanding user behavior is valuable to organizations and has applications from marketing to security, for instance, identifying leaders within a community or predicting future behavior. In the present research, we seek to understand the various roles that users adopt in online communities-for instance, who leads the conversation? Who are the supporters? We examine user role changes over time and the pathways that users follow. This allows us to explore the differences between users who progress to leadership positions and users who fail to develop influence. We also reflect on how user role proportions impact the overall health of the community. Here, we examine two online ideological communities, RevLeft and Islamic Awakening (N = 1631; N = 849), and provide a novel approach to identify various types of users. Finally, we study user role trajectories over time and identify community "leaders" from meta-data alone. Study One examined both communities using K-MEANS cluster analysis of behavioral meta-data, which revealed seven user roles. We then mapped these roles against Preece and Schneiderman's (2009) Reader-to-Leader Framework (RtLF). Both communities aligned with the RtLF, where most users were "contributors", many were "collaborators", and few were "leaders". Study Two looked at one community over a two-year period and found that, despite a high churn rate of users, roles were stable over time. We built a model of user role transitions over the two years. This can be used to predict user role changes in the future, which will have implications for community managers and security focused contexts (e.g., analyzing behavioral meta-data from forums and websites known to be associated with illicit activity).