Research


My research investigates health technology design and personal data use from three perspectives:

  1. i) Human-centered AI for and personal data use in planning, tracking, reflecting, and acting around personal health
  2. ii) Integration of patient-generated data for clinical care and collaboration
  3. iii) Inclusive and community-centered design for sensitive health and wellbeing contexts

I have examined a range of health and wellbeing contexts (e.g., gendered health, chronic health management, AI for personal and community health), including those marked by uncertainty, stigma, and structural inequities.

Human-Centered AI and Personal Data Use for Health

Generative AI for Personal Health Data

Generative AI for Operationalizing Health Goals to Support Planning, Tracking, Reflecting, and Acting around Personal Health Data

Generative AI (GAI)-based health agents demonstrate potential to support reflection, motivate behavior change, and provide personalized recommendations. Many commercial tools (e.g., Whoop, Oura, Onvy) have started adding GAI support for analyzing data and generating insights around sleep, recovery, and activity but remain limited in the data they integrate and insights they can provide. Extending GAI capabilities within individual stages of self-tracking, my research investigated how GAI can support people in navigating complexities introduced by heterogeneous tracking data, goals, expertise, and context across the full arc of personal health informatics: planning, tracking, reflecting, and acting. Through an empirical examination of how people engage with GAI around their personal health data across diverse goals and contexts, I identified where GAI could add genuine value (e.g., supporting people translate vague or high-level health goals into concrete tracking plans), where it breaks down (e.g., people struggle to craft effective prompts toward self-tracking support or to interpret GAI responses), and other considerations and design decisions for making GAI health agents more context-appropriate and trustworthy in practice.

Building on these findings, I am developing LLM-enabled self-tracking support for health, specifically for supporting people: (i) define and operationalize hypotheses based on their individual health goals and contexts and (ii) translate those into personalized and actionable self-tracking plans. Our tool aims to support the range of and variability in goals and contexts people bring to their personal health data.

Bayesian Analysis of Self-Tracked Health Data

Bayesian Analysis of Self-Tracked Health Data

People self-track for health, often to answer questions using their data. However, heterogeneity in tracking goals, data, and questions makes it challenging to support effective integration and reflection. Extending theoretical considerations around Bayesian modeling of trigger and symptom relationships in self-tracked health data, we empirically investigated self-tracker experiences using Bayesian analysis to examine their real-world data around a range of questions. We interviewed and observed 8 participants reflect on self-tracked data using a technology probe that applied Bayesian analysis, and identified considerations for designing Bayesian analysis to support exploration and reflection on relationships across a range of health goals, data, and questions. This work is currently under review.

Integration of Patient-Generated Data for Clinical Care and Collaboration

COVID Long Haul Cohort Study

Cohort Study of Long-COVID Patients & Data Needs to Support Clinical Decisions

Covid long haul (CLH) is an emerging chronic illness with drastically differing experiences that necessitate personalized care. Because patients interact with different clinicians during diagnosis of and ongoing medical treatment for CLH symptoms, ensuring interoperability and clinical relevance of different data is key to supporting personalized care recommendations. Collaborating with the Parkview Post-COVID Clinic, I conducted clinically-embedded and longitudinal research to develop a holistic understanding of CLH using multimodal patient-generated data (wearable tracking data, weekly surveys, EHR, patient interviews) and to inform designs supporting clinical care workflows — including clinical dashboards and holistic patient profiles integrating patient-reported and self-tracked data. My designs remain a part of and continue to have a sustained impact in Parkview's health ecosystem.

Migraine Self-Tracking

Implementing Goal-Directed Self-Tracking for Migraine Management

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. I contributed to research on goal-directed self-tracking of migraine, conducting a longitudinal examination of patient-clinician collaboration to set up, evolve, and align self-tracking plans with patient migraine management goals through a 12+ month deployment study.

Inclusive and Community-Centered Design for Sensitive Health and Wellbeing Contexts

Self-Tracking and Sense-Making for Gendered Health and Socially-Informed Bodily Experiences

Menopause Experience Design

Designing to Record and Share Experiences of Menopause Across Generations

Menopause is often overlooked or medicalized, devaluing individual experiences and failing to support individuals experiencing this life event. Family dynamics, death, and taboo mean that individuals often miss out on information that could help them contextualize their experiences. We conducted interviews and co-design sessions with people who have experienced or are currently experiencing menopause to investigate social and familial aspects of menopause and design in support of intergenerational sharing of menopause experiences.

PCOS Tracking & Management

Designing for Tracking & Managing PCOS

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. Our work aims to inform the design of inclusive health technologies through an understanding of people's lived experiences and challenges with PCOS. We conducted interviews with people diagnosed with PCOS and qualitatively analyzed a PCOS-specific subreddit forum. We reported people's support-seeking, sense-making, and self-experimentation practices, and found uncertainty and stigma to be key in shaping their experiences. We identified avenues for designing technology to support diverse needs such as personalized and contextual tracking, accelerated self-discovery, and co-management.

Designing Digital and AI-Enabled Health Tools with Communities

Community-Informed mHealth Equity

Community-Informed Considerations for Advancing Equity in AI-Enabled Mobile Health Tools

Hispanic and Latino communities in the U.S. bear a disproportionate burden of chronic conditions (e.g., asthma), with access barriers and structural injustices having historically excluded them from medical research and health technology design. Our team developed a case scenario storyboard depicting use of AI-enabled health tools for pediatric asthma and conducted focus groups to uncover community perspectives on preferences, benefits, barriers, and concerns. Participants discussed usability, individualized support, affordability, prediction accuracy, overreliance, data sharing, and privacy. I further translated project-specific insights into a translational resource for supporting designers in engaging communities to create inclusive and culturally-responsive health technologies — contributing methodological guidance for community-based, human-centered approaches to health technology design and evaluation.

Human Rights-Based Design for Dementia

A Human Rights-Based Approach for Designing for & with People with Dementia

User-centered design is typically framed around meeting the preferences and needs of populations involved in the design process. However, when designing technology for people with disabilities — in particular dementia — there is also a moral imperative to ensure their human rights are consciously integrated into the process and included in the product. Our work introduced a human rights-based user-centered design process informed by the United Nations Convention on the Rights of Persons with Disabilities (CRPD). We conducted design workshops with undergraduate students and dementia advocates to design technology for people with dementia, actively involving people with dementia throughout the design process.

Menstrual Health in India

Culturally-Responsive Technology Design for Menstrual Health Management and Education in the Indian Context

Menstruation remains stigmatized in many parts of India, shaping how people access information and manage their periods in everyday life. I contributed to research examining how sociocultural norms influence both menstrual health education and the lived experience of menstruation in public and private spaces. Using qualitative approaches and design-based exploration, we investigated how people manage their periods on the go and conceptualize and access "safe spaces" for menstrual management. In other work, we also used a Feminist HCI lens to analyze a digital menstrual health education platform designed for Indian audiences, identifying its affordances and limitations for addressing menstrual stigma and delivering culturally-responsive menstrual health education. Both these works contributed recommendations for designing culturally-responsive technologies for sensitive health topics.