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Register a new userPatterns of Patient and Caregiver Mutual Support Connections in an Online Health Community
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Added by
Zachary Levonian
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a users role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.
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Chatbots systems, despite their popularity in todays HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole communitys engagement.
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Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good nights sleep is important for job performance. However, both real-world sleep behavior and job performance are hard to measure at scale. In this work, we show that peoples everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep behavior and job performance of salespeople (N = 15) and athletes (N = 19) for 18 months, using a mattress sensor and online mobile app. We first demonstrate that cumulative sleep measures are correlated with job performance metrics, showing that an hour of daily sleep loss for a week was associated with a 9.0% and 9.5% reduction in performance of salespeople and athletes, respectively. We then examine the utility of online app interaction time as a passively collectible and scalable performance indicator. We show that app interaction time is correlated with the performance of the athletes, but not the salespeople. To support that our app-based performance indicator captures meaningful variation in psychomotor function and is robust against potential confounds, we conducted a second study to evaluate the relationship between sleep behavior and app interaction time in a cohort of 274 participants. Using a generalized additive model to control for per-participant random effects, we demonstrate that participants who lost one hour of daily sleep for a week exhibited 5.0% slower app interaction times. We also find that app interaction time exhibits meaningful chronobiologically consistent correlations with sleep history, time awake, and circadian rhythms. Our findings reveal an opportunity for online app developers to generate new insights regarding cognition and productivity.
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