For people diagnosed with a mental illness, psychiatric hospitalization is one step in a long journey, consisting of clinical recovery such as removal of symptoms, and social reintegration involving resuming social roles and responsibilities, overcoming stigma and self-maintenance of the condition. Both clinical recovery and social reintegration need to go hand-in-hand for the overall well-being of individuals. However, research exploring social media for mental health has considered narrower, disjoint conceptualizations of people with mental illness – either as a patient or as a support-seeker. In this paper, we combine medical records with social media data of 254 consented individuals who have experienced a psychiatric hospitalization to address this gap. Adopting a theory-driven, Gaussian Mixture modeling approach, we provide a taxonomy of six heterogeneous behavioral patterns characterizing peoples’ mental health status transitions around hospitalizations. Then we present an empirically derived framework, based on feedback from clinical researchers, to understand peoples’ trajectories around clinical recovery and social reintegration. Finally, to demonstrate the utility of this taxonomy and the empirical framework, we assess social media signals that are indicative of individuals’ reintegration trajectories post-hospitalization. We discuss the implications of combining peoples’ clinical and social experiences in mental health care and the opportunities this intersection presents to post-discharge support and technology-based interventions for mental health.
https://doi.org/10.1145/3449229
Technology bears important relationships to our health and wellness and has been utilized over the past two decades as an aid to support both self-management goals as well as collaboration among treatment teams. However, when chronic illnesses such as eating disorders (ED) are managed outside of institutionalized care settings, designing effective technology to support collaboration in treatment necessitates that we understand the relationships between patients, clinicians, and support networks. We conducted in-depth, semi-structured, interviews with 9 ED patients and 10 clinicians to understand the ED journey through the lens of collaborative efforts, technology use, and potential detriments. Based on our analysis of these 19 interviews, we present novel findings on various underlying disconnects within the collaborative ED treatment process – disconnects among clinicians, between treatment foci, among preferences in tracking, within support networks, and in patients’ own identities. Our findings highlight how these various disconnects are concomitant with and gaps can stem from a lack of collaboration between different stakeholders in the ED journey. We also identify methods of facilitating collaboration in these disconnects through technological mediators.
https://doi.org/10.1145/3449105
Numerous studies have highlighted a range of potential benefits of teletherapy for clients. Nonetheless, researchers have found that many therapists are reluctant to adopt teletherapy in their work practice. There is a dearth of research about how therapists have appropriated telehealth platforms, either to understand teletherapy practice or to understand the challenges and opportunities for system design. The COVID-19 pandemic offers an unfortunate but unique opportunity to learn more about the experiences of therapists who use a range of therapeutic interventions with a range of client populations. In this work, we explore the following research question: in what ways do telehealth platforms support and challenge the work of teletherapy? We present results of semi-structured interviews conducted with 14 mental health therapists during the first six months of the pandemic in the United States. We present a descriptive account of their experiences as well as a discussion of the ways in which the multi-layered and interdependent nature of two facets of therapeutic work---the therapeutic alliance and the therapeutic interventions---made the transition to computer-supported cooperative work particularly challenging. We then offer a suite of design implications for systems that better support the nuanced and unique work of teletherapy.
https://doi.org/10.1145/3479508
Online technologies offer great promise to expand models of delivery for therapeutic interventions to help users cope with increasingly common mental illnesses like anxiety and depression. For example, "cognitive reappraisal" is a skill that involves changing one's perspective on negative thoughts in order to improve one's emotional state. In this work, we present Flip*Doubt, a novel crowd-powered web application that provides users with cognitive reappraisals ("reframes") of negative thoughts. A one-month field deployment of Flip*Doubt with 13 graduate students yielded a data set of negative thoughts paired with positive reframes, as well as rich interview data about how participants interacted with the system. Through this deployment, our work contributes: (1) an in-depth qualitative understanding of how participants used a crowd-powered cognitive reappraisal system in the wild; and (2) detailed codebooks that capture informative context about negative input thoughts and reframes. Our results surface data-derived hypotheses that may help to explain what types of reframes are helpful for users, while also providing guidance to future researchers and developers interested in building collaborative systems for mental health. In our discussion, we outline implications for systems research to leverage peer training and support, as well as opportunities to integrate AI/ML-based algorithms to support the cognitive reappraisal task. (Note: This paper includes potentially triggering mentions of mental health issues and suicide.)
People with mental distress are increasingly turning to one-to-one synchronous communication websites to receive peer support from other members. Though some research has identified benefits and challenges of online peer-support, there is a limited understanding of how to best prepare and scaffold for untrained peer supporters as they attempt to become skillful in an online setting. We recruited 30 (15 pairs) participants to engage in an online support conversation about procrastination problems, gave one member of each pair minimal training in the principles and strategies of motivational interviewing, and used interviews and conversation transcripts to examine challenges novice helpers faced when providing support and learning new conversational skills. We presented the helpers with two conversation goals to achieve with the conversation: building understanding, and promoting readiness for change. The research identified the common strategies the helpers used to achieve these goals and the challenges they faced. We also discuss theoretical and design implications for platform designers to better scaffold this experience.
https://doi.org/10.1145/3479510
People with multiple chronic conditions (MCC) need support to understand and articulate how their personal values relate to their health and health care. We developed three prototypes for supporting reflection values and health and tested them in a qualitative study involving 12 people with MCC. We identified benefits and limitations to building on patients’ existing visit-preparation practices; revealed varying levels of comfort with deep, exploratory reflection involving a facilitator; and found that reflection oriented toward the future could elicit hopeful attitudes and plans for change, while reflection on the past elicited strong resistance. We translated these findings into design guidelines for supporting collaborative reflection on values and health. We also discussed these findings in relation to previous literature on designing for reflection in three areas: shifting between self-guided and facilitator-guided reflection, balancing between outcome-oriented and exploratory reflection, and exploring temporality in reflection.
https://doi.org/10.1145/3476040
Co-design is a widely applied design process with well-documented benefits, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through retrospective interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group design activities; HCI and AI researchers described challenges collaborating to apply co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross-organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.
Nutrition is a key determinant of long-term health, and social influence has long been theorized to be a key determinant of nutrition. It has been difficult to quantify the postulated role of social influence on nutrition using traditional methods such as surveys, due to the typically small scale and short duration of studies. To overcome these limitations, we leverage a novel source of data: logs of 38 million food purchases made over an 8-year period on a major university campus, linked to anonymized individuals via the smartcards used to make on-campus purchases. In a longitudinal observational study, we ask: How is a person's food choice affected by eating with someone else whose own food choice is healthy vs. unhealthy? To estimate causal effects from the passively observed log data, we control confounds in a matched quasi-experimental design: we identify focal users who at first do not have any regular eating partners but then start eating with a fixed partner regularly, and we match focal users into comparison pairs such that paired users are nearly identical with respect to covariates measured before acquiring the partner, but the two focal users' new eating partners diverge in the healthiness of their respective food choice. A difference-in-differences analysis of the paired data yields clear evidence of social influence: focal users acquiring a healthy-eating partner change their habits significantly more toward healthy foods than focal users acquiring an unhealthy-eating partner. We further identify foods whose purchase frequency is impacted significantly by the eating partner's healthiness of food choice. Overall, this work demonstrates the utility of passively sensed food purchase logs for deriving insights with the potential of informing how food is offered, especially on university campuses.
https://doi.org/10.1145/3449297