Conversational voice assistants are often imbued with personality and human-like characteristics (e.g., gender). While researchers have begun to examine and design for the downstream societal impacts of voice assistants encoding characteristics such as gender, we know little about other human-like characteristics such as age that are encoded in an artificial, yet, anthropomorphic voice. As older adults continue to adopt voice assistants, we brought older adults into an activity to customize human-like characteristics for their voice assistant. Our findings reveal the different stereotypes and assumptions individuals associated with voice assistant characteristics (e.g., age, gender, race). We also describe individuals' motivations behind customizing or not customizing these characteristics. We discuss how biases get encoded through our design process, marginalizing older adults and other non-dominant user groups and call for a need to examine the systemic, yet unspoken, power structures encoded in anthropomorphic technologies.

COVID Long Haul (CLH) is an emerging chronic illness with varied patient experiences. Our understanding of CLH is often limited to data from electronic health records (EHRs), such as diagnoses or problem lists, which do not capture the volatility and severity of symptoms or their impact. To better understand the unique presentation of CLH, we conducted a 3-month long cohort study with 14 CLH patients, collecting objective (EHR, daily Fitbit logs) and subjective (weekly surveys, interviews) data. Our findings reveal a complex presentation of symptoms, associated uncertainty, and the ensuing impact CLH has on patients’ personal and professional lives. We identify patient needs, practices, and challenges around adhering to clinical recommendations, engaging with health data, and establishing "new normals" post COVID. We reflect on the potential found at the intersection of these various data streams and the persuasive heuristics possible when designing for this new population and their specific needs.

Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months, including a total of 50 interview sessions with 10 patients working with 3 different clinicians. Patients were able to express multiple types of goals, evolve their goals over time, align tracking to their goals, personalize their tracking, reflect in the context of their goals, and gain insights that enabled understanding, communication, and action. We discuss how these results highlight the importance of accounting for distinct and concurrent goals in personal informatics together with implications for the design of future goal-directed personal informatics tools.

COVID Long Haul (CLH) is an emerging chronic illness for which the healthcare system continues to seek a common understanding of symptoms, diagnosis, and treatment. CLH experiences can differ drastically, necessitating personalized care plans. Because patients interact with different clinicians during their CLH journey, it becomes important to ensure interoperability and understand clinical relevance of different data that can support clinicians in making appropriate recommendations. We conducted qualitative research where we interviewed 13 patients, conducted a focus group with 8 clinicians, and analyzed care plan follow-up records. We report patient and clinician expectations from and interactions with clinic data. We uncover logistical challenges, personal contexts, and health barriers impacting patient compliance. As researchers embedded in the clinical system, we identify the potential of using multiple patient data streams to support personalized treatment and clinical decisions. We discuss technology design opportunities and provide actionable recommendations for improving clinical workflows and cross-provider collaboration.

Menopause is often overlooked or medicalized, consequently devaluing individual experiences and failing to support individuals experiencing this life event. Family dynamics, death, and taboo further mean that individuals often miss out on information that could help them contextualize their experiences. We examine participant experiences with menopause and explore designs of digital and non-digital legacies for sharing menopause experiences across generations. We conducted semi-structured interviews and design sessions with 17 participants who experienced or are experiencing menopause. We report participant information needs and sense-making practices, including what personalized information participants wish to pass down and preferred formats for intergenerational sharing. Findings highlight the potential of using storytelling and life-logging to create "holistic" memories of the menopause journey, to support self-reflection, and for using legacies to initiate conversations about marginalized health experiences. We identify future design and research opportunities for the HCI and CSCW communities to support intergenerational sharing of non-medicalized and stigmatized health experiences.

Polycystic Ovary Syndrome (PCOS) is a condition that causes hormonal imbalance and infertility in women and people with female reproductive organs. PCOS causes different symptoms for different people, with no singular or universal cure. Being a stigmatized and enigmatic condition, it is challenging to discover, diagnose, and manage PCOS. This work aims to inform the design of inclusive health technologies through an understanding of people’s lived experiences and challenges with PCOS. We conducted semi-structured interviews with 10 women diagnosed with PCOS and analyzed a PCOS-specific subreddit forum. We report people’s support-seeking, sense-making, and self-experimentation practices, and find uncertainty and stigma to be key in shaping their unique experiences of the condition. We further identify potential avenues for designing technology to support their diverse needs, such as personalized and contextual tracking, accelerated self-discovery, and co-management, contributing to a growing body of HCI literature on stigmatized topics in women’s health and well-being.

In cultural contexts where menstruation is a stigmatized health topic, daily management of menstrual hygiene comes with its set of challenges. Our research aims to identify and examine such challenges faced during menstruation in the urban environs of Delhi, India. Through participatory design activities and interviews conducted with 35 participants who identified as menstruating and female, and a survey with 139 responses, we investigate how participants deal with their periods on the go. We also examine participants' conceptualizations of safe spaces, where they are able to deal with their period on their own terms. Finally, we discuss how menstrual mobilities are being, and might be, supported through technology-based interventions for a third space, targeting the legibility, literacy, and legitimacy of surrounding environments.

Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets. Detecting sensing contexts in the wild is a challenging task and requires features from smartphone sensors that are not always available. In this paper, we propose three heuristic algorithms for detecting sensing contexts such as in/out-pocket, under/over-ground, and in/out-door for crowdsourced datasets that are destined for human mobility mining. These are unsupervised binary classifiers with a small memory footprint and execution time. Using a segment of the Ambiciti real dataset - a feature-limited crowdsourced dataset - we report that our algorithms perform equally well in terms of balanced accuracy (within 4.3%) when compared to machine learning (ML) models reported by an AutoML tool.

Menstruation has long remained a conversational taboo across India, resulting in inadequate dissemination of menstrual health education (MHE). Menstrupedia, a digital platform designed for an Indian audience, aims to bridge this information gap to impart MHE via its website and comic. We contribute a study of Menstrupedia---the information exchange on its website, the education it aims to provide, and the perceptions of its users. Using a combination of qualitative research methods, and engaging a feminist Human-Computer Interaction (HCI) lens, we critically analyze Menstrupedia's affordances and shortcomings. We also make recommendations for the design of technology-based dissemination of MHE, as well as additional sensitive and taboo topics.