Investigating the Role of Situational Disruptors in Engagement with Digital Mental Health ToolsChallenges in engagement with digital mental health (DMH) tools are commonly addressed through technical enhancements and algorithmic interventions. This paper shifts the focus towards the role of users' broader social context as a significant factor in engagement. Through an eight-week text messaging program aimed at enhancing psychological wellbeing, we recruited 20 participants to help us identify situational engagement disruptors (SEDs), including personal responsibilities, professional obligations, and unexpected health issues. In follow-up design workshops with 25 participants, we explored potential solutions that address such SEDs: prioritizing self-care through structured goal-setting, alternative framings for disengagement, and utilization of external resources. Our findings challenge conventional perspectives on engagement and offer actionable design implications for future DMH tools.2025ABAnanya Bhattacharjee et al.Designing for Mental Health SupportCSCW
Perfectly to a Tee: Understanding User Perceptions of Personalized LLM-Enhanced Narrative InterventionsStories about overcoming personal struggles can effectively illustrate the application of psychological theories in real life, yet they may fail to resonate with individuals' experiences. In this work, we employ large language models (LLMs) to create tailored narratives that acknowledge and address unique challenging thoughts and situations faced by individuals. Our study, involving 346 young adults across two settings, demonstrates that personalized LLM-enhanced stories were perceived to be better than human-written ones in conveying key takeaways, promoting reflection, and reducing belief in negative thoughts. These stories were not only seen as more relatable but also similarly authentic to human-written ones, highlighting the potential of LLMs in helping young adults manage their struggles. The findings of this work provide crucial design considerations for future narrative-based digital mental health interventions, such as the need to maintain relatability without veering into implausibility and refining the wording and tone of AI-enhanced content.2025ABAnanya Bhattacharjee et al.Human-LLM CollaborationMental Health Apps & Online Support CommunitiesDIS
A Comparative Analysis of Information Gathering by Chatbots, Questionnaires, and Humans in Clinical Pre-ConsultationInformation gathering is an important capability that allows chatbots to understand and respond to users' needs, yet the effectiveness of LLM-powered chatbots at this task remains underexplored. Our work investigates this question in the context of clinical pre-consultation, wherein patients provide information to an intermediary before meeting with a physician to facilitate communication and reduce consultation inefficiencies. We conducted a study at a walk-in clinic with 45 patients who interacted with one of three conversational agents: a chatbot, a questionnaire, and a Wizard-of-Oz. We analyzed patients' messages using metrics adapted from Grice's maxims to assess the quality of information gathered at each conversation turn. We found that the Wizard and LLM were more successful than the questionnaire because they modified questions and asked follow-ups when participants provided unsatisfactory answers. However, the LLM did not ask nearly as many follow-up questions as the Wizard, particularly when participants provided unclear answers.2025BLBrenna Li et al.University of Toronto, Computer ScienceMid-Air Haptics (Ultrasonic)Conversational ChatbotsHuman-LLM CollaborationCHI
Promoting Engagement in Remote Patient Monitoring Using Asynchronous MessagingRemote patient monitoring is becoming increasingly instrumental to healthcare delivery but can substantially hamper the interpersonal communication that underlies standard clinical practice. In this work, we explore the benefits imparted to patients, clinicians, and researchers by an asynchronous messaging feature within a platform called COVIDFree@Home. We created COVIDFree@Home to assist the healthcare system in a large metropolitan city in North America during the COVID-19 pandemic. Clinicians used COVIDFree@Home to monitor the self-reported symptoms and vital signs of over 350 COVID-19 patients post-infection. Using thematic analysis of user-initiated messages, we found the messaging feature helped maintain protocol adherence while allowing patients to ask questions about their health and clinicians to convey empathetic care. This feedback cycle also led to higher quality data for hospitalization prediction, as the revisions significantly improved the AUROC of a machine learning model trained on demographic variables, vital signs data, and self-reported symptoms from 0.53 to 0.59.2024SLSalaar Liaqat et al.University of TorontoChronic Disease Self-Management (Diabetes, Hypertension, etc.)Telemedicine & Remote Patient MonitoringSleep & Stress MonitoringCHI
Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic ProcrastinationTraditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.2024ABAnanya Bhattacharjee et al.University of TorontoHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Leveraging Idle Games to Incentivize Intermittent and Frequent Practice of Deep BreathingThe need for frequent and brief practice in deep breathing presents challenges in maintaining motivation and consistency. While persuasive technologies have been shown to improve engagement in therapeutic exercises, there is a lack of insight into specific motivational strategies for such intermittent activities. We investigate how idle games can incentivize behaviors like deep breathing and identify specific mechanics for fostering an optimal practice cycle. We illustrate this approach in a game called \textit{BreathPurr-suade}. After validating the physiological efficacy of the embedded breathing guide, our four-week study revealed idle games are more effective in maintaining deep breathing adherence than a standard breathing guide. Our work highlights the capacity of idle games to foster deep breathing, revealing their efficacy in subtle persuasive game designs that encourage intermittent therapeutic practices.2024BSBook Sadprasid et al.University of TorontoSerious & Functional GamesGamification DesignMental Health Apps & Online Support CommunitiesCHI
Functional Design Requirements to Facilitate Menstrual Health Data ExplorationMenstrual trackers currently lack the affordances required to help individuals achieve their goals beyond menstrual event predictions and symptom logging. Taking an initial step towards this aspiration, we propose, validate, and refine five functional design requirements for future interface designs that facilitate menstrual data exploration. We interviewed 30 individuals who menstruate and collected their feedback on the practical application of these requirements. To elicit ideas and impressions, we designed two proof-of-concept interfaces to use as design probes with similar core functionalities but different presentations of phase timing predictions and signal arrangement. Our analysis revealed participants' feedback regarding the presentation of predictions for menstrual-related events, the visualization of future signal patterns, personalization abilities for viewing signals relevant to their menstrual experience, the availability of resources to understand the underlying biological connections between signals, and the ability to compare multiple cycles side-by-side with context.2024GLGeorgianna Lin et al.University of TorontoReproductive & Women's HealthDiet Tracking & Nutrition ManagementCHI
ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled ExpressionsSilent speech interaction (SSI) allows users to discreetly input text without using their hands. Existing wearable SSI systems typically require custom devices and are limited to a small lexicon, limiting their utility to a small set of command words. This work proposes ReHearSSE, an earbud-based ultrasonic SSI system capable of generalizing to words that do not appear in its training dataset, providing support for nearly an entire dictionary's worth of words. As a user silently spells words, ReHearSSE uses autoregressive features to identify subtle changes in ear canal shape. ReHearSSE infers words using a deep learning model trained to optimize connectionist temporal classification (CTC) loss with an intermediate embedding that accounts for different letters and transitions between them. We find that ReHearSSE recognizes 100 unseen words with an accuracy of 89.3%.2024XDXuefu Dong et al.The University of TokyoElectrical Muscle Stimulation (EMS)Augmentative & Alternative Communication (AAC)CHI
PulmoListener: Continuous Acoustic Monitoring of Chronic Obstructive Pulmonary Disease in the Wild"Prior work has shown the utility of acoustic analysis in controlled settings for assessing chronic obstructive pulmonary disease (COPD) --- one of the most common respiratory diseases that impacts millions of people worldwide. However, such assessments require active user input and may not represent the true characteristics of a patient's voice. We propose PulmoListener, an end-to-end speech processing pipeline that identifies segments of the patient's speech from smartwatch audio collected during daily living and analyzes them to classify COPD symptom severity. To evaluate our approach, we conducted a study with 8 COPD patients over 164 ± 92 days on average. We found that PulmoListener achieved an average sensitivity of 0.79 ± 0.03 and a specificity of 0.83 ± 0.05 per patient when classifying their symptom severity on the same day. PulmoListener can also predict the severity level up to 4 days in advance with an average sensitivity of 0.75 ± 0.02 and a specificity of 0.74 ± 0.07. The results of our study demonstrate the feasibility of leveraging natural speech for monitoring COPD in real-world settings, offering a promising solution for disease management and even diagnosis." https://doi.org/10.1145/36108892023SBSejal Bhalla et al.Telemedicine & Remote Patient MonitoringBiosensors & Physiological MonitoringUbiComp
FeverPhone: Accessible Core-Body Temperature Sensing for Fever Monitoring Using Commodity SmartphonesSmartphones contain thermistors that ordinarily monitor the temperature of the device's internal components; however, these sensors are also sensitive to warm entities in contact with the device, presenting opportunities for measuring human body temperature and detecting fevers. We present FeverPhone --- a smartphone app that estimates a person's core body temperature by having the user place the capacitive touchscreen of the phone against their forehead. During the assessment, the phone logs the temperature sensed by a thermistor and the raw capacitance sensed by the touchscreen to capture features describing the rate of heat transfer from the body to the device. These features are then used in a machine learning model to infer the user's core body temperature. We validate FeverPhone through both a lab simulation with a skin-like controllable heat source and a clinical study with real patients. We found that FeverPhone's temperature estimates are comparable to commercial off-the-shelf peripheral and tympanic thermometers. In a clinical study with 37 participants, FeverPhone readings achieved a mean absolute error of 0.229 °C, a limit of agreement of ±0.731 °C, and a Pearson's correlation coefficient of 0.763. Using these results for fever classification results in a sensitivity of 0.813 and a specificity of 0.904. https://dl.acm.org/doi/10.1145/35808502023JBJoseph Breda et al.Biosensors & Physiological MonitoringUbiComp
Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological WellbeingWithout a nuanced understanding of users’ perspectives and contexts, text messaging tools for supporting psychological wellbeing risk delivering interventions that are mismatched to users' dynamic needs. We investigated the contextual factors that influence young adults' day-to-day experiences when interacting with such tools. Through interviews and focus group discussions with 36 participants, we identified that people's daily schedules and affective states were dominant factors that shape their messaging preferences. We developed two messaging dialogues centered around these factors, which we deployed to 42 participants to test and extend our initial understanding of users' needs. Across both studies, participants provided diverse opinions of how they could be best supported by messages, particularly around when to engage users in more passive versus active ways. They also proposed ways of adjusting message length and content during periods of low mood. Our findings provide design implications and opportunities for context-aware mental health management systems.2023ABAnanya Bhattacharjee et al.University of TorontoMental Health Apps & Online Support CommunitiesContext-Aware ComputingCHI
Constraints and Workarounds to Support Clinical Consultations in Synchronous Text-based PlatformsMedical consultations over synchronous text-based platforms are becoming increasingly popular for virtual care, yet little is known about how physicians translate their training to this healthcare medium. We report the constraints, workarounds, and opportunities highlighted by eight primary care physicians who used such a platform in simulated medical scenarios with standardized patients. We found that due to the perceived inefficiency of communicating over text, the physicians made subconscious use of double-barreled questions and action multiplexing to streamline the conversation. In addition, the physicians overcame the lack of missing verbal and visual cues by adding explicit messages to convey empathy and active listening. We also identify several affordances of text-based platforms, such as the ability for users to reference the conversation history and for patients to feel a sense of privacy during sensitive disclosure. From these findings, we propose design opportunities for how future synchronous text-based platforms can better support medical consultations.2023BLBrenna Li et al.University of TorontoMid-Air Haptics (Ultrasonic)Mental Health Apps & Online Support CommunitiesCHI
“I Kind of Bounce off It”: Translating Mental Health Principles into Real Life Through Story-Based Text MessagesAdopting new psychological strategies to improve mental wellness can be challenging since people are often unable to anticipate how new habits are applicable to their circumstances. Narrative-based interventions have the potential to alleviate this burden by illustrating psychological principles in an applied context. In this work, we explore how stories can be delivered via the ubiquitous and scalable medium of text messaging. Through formative work consisting of interviews and focus group discussions with 15 participants, we identified desirable elements of stories about mental health, including authenticity and relatability. We then deployed story-based text messages to 42 participants to explore challenges regarding both the stories' content (e.g., specific versus generalized) and format (e.g., story length). We observed that our stories helped participants reflect on and identify flaws in their thinking patterns. Our findings highlight design implications and opportunities for mental wellness interventions that utilize stories in text messaging services.2022ABAnanya Bhattacharjee et al.Mental Health; Mental HealthCSCW