Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchIn the mental health domain, where AI technologies will impact their treatment and daily lives, patient engagement can be a key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who produce the data and who will be impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.2024DYDong Whi Yoo et al.Session 3b: Bridging Technology and TherapyCSCW
Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and TreatmentEarly detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.2024DYDong Whi Yoo et al.Kent State UniversityAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityMental Health Apps & Online Support CommunitiesCHI
Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident PhysiciansRecent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers’ well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.S. resident physician program. Residents and their supervising attending physicians were presented with medium-fidelity mockups of a dashboard providing behavioral data on residents’ sleep, activity and time working; self-reported data on residents’ levels of burnout; and a free text box where residents could further contextualize their well-being. Our findings uncover tensions around how best to measure workplace well-being, who within a workplace is accountable for worker stress, and how the introduction of such tools remakes the boundaries of appropriate information flows between worker and workplace. We conclude by charting future work confronting these tensions, to ensure personal sensing is leveraged to truly improve worker well-being.2022DADaniel A. Adler et al.Remote Work, Motivation, and Burnout; Remote Work, Motivation, and BurnoutCSCW
A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric HospitalizationsFor 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.2021SESindhu Kiranmai Ernala et al.Personal and Mental HealthCSCW
Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone SensingImpaired social functioning is a symptom of mental illness (e.g., depression, schizophrenia) and a wide range of other conditions (e.g., cognitive decline in the elderly, dementia). Today, assessing social functioning relies on subjective evaluations and self assessments. We propose a different approach and collect detailed social functioning measures and objective mobile sensing data from N=55 outpatients living with schizophrenia to study new methods of passively accessing social functioning. We identify a number of behavioral patterns from sensing data, and discuss important correlations between social function sub-scales and mobile sensing features. We show we can accurately predict the social functioning of outpatients in our study including the following sub-scales: prosocial activities (MAE = 7.79, r = 0.53), which indicates engagement in common social activities; interpersonal behavior (MAE = 3.39, r = 0.57), which represents the number of friends and quality of communications; and employment/occupation (MAE = 2.17, r = 0.62), which relates to engagement in productive employment or a structured program of daily activity. Our work on automatically inferring social functioning opens the way to new forms of assessment and intervention across a number of areas including mental health and aging in place.2020WWWeichen Wang et al.Dartmouth CollegeMental Health Apps & Online Support CommunitiesTelemedicine & Remote Patient MonitoringCHI
Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic SignalsA growing body of research is combining social media data with machine learning to predict mental health states of individuals. An implication of this research lies in informing evidence-based diagnosis and treatment. However, obtaining clinically valid diagnostic information from sensitive patient populations is challenging. Consequently, researchers have operationalized characteristic online behaviors as "proxy diagnostic signals" for building these models. This paper posits a challenge in using these diagnostic signals, purported to support clinical decision-making. Focusing on three commonly used proxy diagnostic signals derived from social media, we find that predictive models built on these data, although offer strong internal validity, suffer from poor external validity when tested on mental health patients. A deeper dive reveals issues of population and sampling bias, as well as of uncertainty in construct validity inherent in these proxies. We discuss the methodological and clinical implications of these gaps and provide remedial guidelines for future research.2019SESindhu Kiranmai Ernala et al.Georgia Institute of TechnologyExplainable AI (XAI)Mental Health Apps & Online Support CommunitiesCHI
Linguistic Markers Indicating Therapeutic Outcomes of Social Media Disclosures of SchizophreniaSelf-disclosure of stigmatized conditions is known to yield therapeutic benefits. Social media sites are emerging as promising platforms enabling disclosure around a variety of stigmatized concerns, including mental illness. What kind of behavioral changes precede and follow such disclosures? Do the therapeutic benefits of ``opening up'' manifest in these changes? In this paper, we address these questions by focusing on disclosures of schizophrenia diagnoses made on Twitter. We adopt a clinically grounded quantitative approach to first identify temporal phases around disclosure during which symptoms of schizophrenia are likely to be significant. Then, to quantify behaviors before and after disclosures, we define linguistic measures drawing from literature on psycholinguistics and the socio-cognitive model of schizophrenia. Along with significant linguistic differences before and after disclosures, we find indications of therapeutic outcomes following disclosures, including improved readability and coherence in language, future orientation, lower self preoccupation, and reduced discussion of symptoms and stigma perceptions. We discuss the implications of social media as a new therapeutic tool in supporting disclosures of stigmatized conditions.2018SESindhu Kiranmai Ernala et al.Language and LinguisticsCSCW