Advances in data collection enable the capture of rich patient-generated data: from passive sensing (e.g., wearables and smartphones) to active self-reports (e.g., cross-sectional surveys and ecological momentary assessments). Although prior research has demonstrated the utility of patient-generated data in mental healthcare, significant challenges remain in effectively presenting these data streams along with clinical data (e.g., clinical notes) for clinical decision-making. Through co-design sessions with five clinicians, we propose MIND, a large language model-powered dashboard designed to present clinically relevant multimodal data insights for mental healthcare. MIND presents multimodal insights through narrative text, complemented by charts communicating underlying data. Our user study (N=16) demonstrates that clinicians perceive MIND as a significant improvement over baseline methods, reporting improved performance to reveal hidden and clinically relevant data insights (p<.001) and support their decision-making (p=.004). Grounded in the study results, we discuss future research opportunities to integrate data narratives in broader clinical practices.

Social virtual reality (VR) avatars hold promise for allowing people to represent themselves as they want to be seen. But most social VR environments constrain avatar options in ways that limit the accurate presentation of age and promote the assumption of youth. Through individual interviews with ten women in midlife, we explore experiences representing age and other aspects of identity in social VR. Participants expressed a desire to show age and gender with increased nuance: they sought more gradations of color, texture, and body types, and disliked the hypersexualization that resulted from integrated clothing and body parts. As they customized their avatars, participants struggled to depict both physical and personal characteristics, taking into account how others might evaluate their self-perception. These findings highlight opportunities for nuanced representations of physical attributes as well as options for representing the self at other levels: psychological, social, and aesthetic.

During the COVID-19 pandemic, risk negotiation became an important precursor to in-person contact. For young adults, social planning generally occurs through computer-mediated communication. Given the importance of social connectedness for mental health and academic engagement, we sought to understand how young adults plan in-person meetups over computer-mediated communication in the context of the pandemic. We present a qualitative study that explores young adults’ risk negotiation during the COVID-19 pandemic, a period of conflicting public health guidance. Inspired by cultural probe studies, we invited participants to express their preferred precautions for one week as they planned in-person meetups. We interviewed and surveyed participants about their experiences. Through qualitative analysis, we identify strategies for risk negotiation, social complexities that impede risk negotiation, and emotional consequences of risk negotiation. Our findings have implications for AI-mediated support for risk negotiation and assertive communication more generally. We explore tensions between risks and potential benefits of such systems.

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users’ data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms’ generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

Indirect disclosure strategies include hinting about an experience or a facet of one's identity or relaying information explicitly but through another person. These strategies lend themselves to sharing stigmatized or sensitive experiences such as a pregnancy loss, mental illness, or abuse. Drawing on interviews with women in the U.S. who use social media and experienced pregnancy loss, we investigated factors guiding indirect disclosure decisions on social media. Our findings include 1) a typology of indirect disclosure strategies based on content explicitness, original content creator, and content sharer, and 2) an examination of indirect disclosure decision factors related to the self, audience, platform affordances, and temporality. We identify how people intentionally adapt social media and indirect disclosures to meet psychological (e.g., keeping a personal record) and social (e.g., feeling out the audience) needs associated with loss. We discuss implications for design and research, including features that support disclosures through proxy, and relevance for algorithmic detection and intervention. CAUTION: This paper includes quotes about pregnancy loss.

This pictorial illustrates how objects can illuminate people's experience of public space, an approach we call itinerant probes. Itinerant probes are not individualized, mediated artifacts, but instead dynamic events that en- liven people's personal and collective memories. Building on the probes literature and recent ecological perspectives, we describe three probes related to lighting that we investigated at three public sites. Our explorations with these probes highlight the intimate histories associated with public spaces.

To understand why and how people share health information online, we interviewed fourteen people with significant health concerns who participate in both online health communities and Facebook. Qualitative analysis of these interviews highlighted the ways that people think about with whom and how to share different types of information as they pursue social goals related to their personal health, including emotional support, motivation, accountability, and advice. Our study suggests that success in these goals depends on how well they develop their social networks and how effectively they communicate within those networks. Effective communication is made more challenging by the need to strike a balance between sharing information related to specific needs and the desire to manage self-presentation. Based on these observations, we outline a set of design opportunities for future systems to support health-oriented social interactions online, including tools to help users shape their social networks and communicate effectively within those.

Abstract This study investigates the use of speech as an indicator of the onset of cognitive decline in the elderly. The analysis found features that correlate with the results of a clinical measure of cognitive function. Using a combination of temporal language features, such as pause and utterance duration, 76% classification accuracy was achieved. While no significant results were found for ASR experiments, vowel duration, on average, increased by 17% for subjects with cognitive impairment compared to those without. The results from this study introduce the concept of longitudinal studies into aging using speech as a window into cognitive function. Index Terms: cognitive decline; elderly, temporal features 1. Introduction The world’s population is growing older. In the next 50 years, the number of older people is forecast to quadruple, growing from about 600 million to almost 2 billion people [1]. With the prospect of people living longer there has been concerted efforts to provide healthcare at appropriate times to allow the elderly to live as independently as possible, for as long as possible [2], [3]. Remote monitoring of older people in their homes may enable early detection of some of the problems associated with old age, such as propensity to falls and social isolation along with decline in cognitive function. Once Mild Cognitive Impairment (MCI) has set in it is usually too late to implement any cognitive training schema that could help. The biggest limiting factor to independence in the elderly is impaired cognitive function and its consequences. Such consequences include: accident proneness (falls, burns, bruising and cuts), self-neglect (missed medication, poor nutrition, poor hygiene), loss of initiative, diminished repertoire of activities and low mood. The assessment of cognitive function is expensive and labour intensive and hence non-viable for all but a tiny fraction of the elderly. In addition the elderly is a population traditionally highly averse to using new technology, which makes technical solutions to the assessment of cognitive function more difficult. Speech may provide a cost effective, easily implementable means of monitoring and assessing cognitive function. The disproportionate vulnerability of the brain’s frontal lobe to aging means that attention and executive function predominate in cognitive impairment having major detrimental effects on memory, attention, planning, initiative, mood vigilance as well as self-, safety-, and environmental awareness. One of the standard clinic tools used by psychologists for assessing cognitive function is the Mini Mental State Examination (MMSE) [5]. This consists of a questionnaire that rates cognitive function out of 30. MMSE scores of less than 27 are generally considered as possibly cognitively impaired by clinicians. Previous studies have found spoken language markers to be indicative of Mild Cognitive Impairment (MCI) [6]. Roark et al found standardized pause rate (the ratio of words uttered to the number of pauses uttered) to be statistically different for two sets of elderly speakers; using a Clinical Dementia Rating (CDR), the cohort was separated into those with MCI and those without. While other speech duration parameters were investigated in the study including verbal rate (number of words per second), phonation rate (proportion of total time spent speaking) and mean duration of pauses, none of these were found be significantly different for the two populations. Speech has also been shown to be an indicator of other neuro-degenerative diseases, such as schizophrenia [7]. Stassen et al looked at temporal parameters extracted from read speech from known sufferers of the disease and matched controls. In this case the correlation with this type of cognitive impairment and temporal features such as mean pause duration is attributed to the negative symptoms of the disease. These include affective flattening, blunted affect emotional dullness, poverty of speech and psycho-motor retardation. A recent study carried out by Lieberman et al [8], investigated audio recorded from climbers ascending Mount Everest. The effect of altitude on the cognitive function of climbers was used to simulate that experienced by astronauts in space. Astronauts' cognition is impacted by hypoxic and cosmic ray-induced insult to the brain, as well as degraded cognitive performance resulting from task difficulty. The authors found acoustic measures of temporal characteristics of speech can be used to monitor cognitive impairment. This study focuses on identifying the onset of cognitive decline from features that can readily be extracted from speech. Experimentation includes investigating speech from a temporal feature perspective and from an acoustic perspective. Speech is a non-intrusive means of data collection and speech centred applications, particularly telephony based, can be implemented using technology that is familiar to the potential users.