Bridging Ontologies of Neurological Conditions: Towards Patient-centered Data Practices in Digital Phenotyping Research and DesignAmidst the increasing datafication of healthcare, deep digital phenotyping is being explored in clinical research to gather comprehensive data that can improve understanding of neurological conditions. However, participants currently do not have access to this data due to researchers' apprehension around whether such data is interpretable or useful. This study focuses on patient perspectives on the potential of deep digital phenotyping data to benefit people with neurodegenerative diseases, such as ataxias, Parkinson's disease, and multiple system atrophy. We present an interview study (n=12) to understand how people with these conditions currently track their symptoms and how they envision interacting with their deep digital phenotyping data. We describe how participants envision the utility of this deep digital phenotyping data in relation to multiple stages of disease and stakeholders, especially its potential to bridge different and sometimes conflicting understandings of their condition. Looking towards a future in which patients have increased agency over their data and can use it to inform their care, we contribute implications for shaping patient-driven clinical research practices and deep digital phenotyping tools that serve a multiplicity of patient needs.2025JSJianna So et al.Beyond AI: Additional Considerations for Enhancing HealthcareCSCW
Stairway to Heaven: A Gamified VR Journey for Breath AwarenessGamification and virtual reality (VR) are increasingly being explored for their potential to enhance mindful practices and well-being. We further explore the potential of gamification and VR for breath awareness and mindfulness, and contribute Stairway to Heaven, a VR artifact that combines gamification with respiratory sensor biofeedback to cultivate mindful awareness of breathing. In our mixed-method study with 21 participants, we evaluated the usability and effectiveness of our artifact in promoting breathing frequencies between 4 and 10 breaths per minute (BPM). We integrate breath-driven teleportation as a virtual locomotion technique (VLT) using respiratory biofeedback to gamify progression through a virtual wilderness. Additionally, we supplement our design with a mindfulness audio guide. The results of our user study showcase the potential of combining actionable gamification and VR, guided mindfulness, and breath-driven VLT to foster slow breathing self-regulation successfully.2024NMNathan Miner et al.Northeastern UniversitySerious & Functional GamesMental Health Apps & Online Support CommunitiesCHI
MuscleRehab: Improving Unsupervised Physical Rehabilitation by Monitoring and Visualizing Muscle Engagement Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor the muscle engagement and visualize the current state on a virtual muscle-skeleton avatar. We use additional optical motion tracking to also monitor the user's movement. We run a user study with 10 participants that compares exercise execution while seeing muscle + motion data vs. motion data only, and also present the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy for users during rehabilitation, and post-rehabilitation evaluation for physical therapists.2022JZYunyi Zhu et al.Surgical Assistance & Medical TrainingBiosensors & Physiological MonitoringComputational Methods in HCIUIST
EIT-kit: An Electrical Impedance Tomography Toolkit for Health and Motion SensingIn this paper, we propose EIT-kit, an electrical impedance tomography toolkit for designing and fabricating health and motion sensing devices. EIT-kit contains (1) an extension to a 3D editor for personalizing the form factor of electrode arrays and electrode distribution, (2) a customized EIT sensing motherboard for performing the measurements, (3) a microcontroller library that automates signal calibration and facilitates data collection, and (4) an image reconstruction library for mobile devices for interpolating and visualizing the measured data. Together, these EIT-kit components allow for applications that require 2- or 4-terminal setups, up to 64 electrodes, and single or multiple (up to four) electrode arrays simultaneously. We motivate the design of each component of EIT-kit with a formative study, and conduct a technical evaluation of the data fidelity of our EIT measurements. We demonstrate the design space that EIT-kit enables by showing various applications in health as well as motion sensing and control.2021JZJunyi Zhu et al.Fitness Tracking & Physical Activity MonitoringBiosensors & Physiological MonitoringUIST
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens Major depressive disorder is a debilitating disease affecting 264 million people worldwide. While many antidepressant medications are available, few clinical guidelines support choosing among them. Decision support tools (DSTs) embodying machine learning models may help improve the treatment selection process, but often fail in clinical practice due to poor system integration. We use an iterative, co-design process to investigate clinicians’ perceptions of using DSTs in antidepressant treatment decisions. We identify ways in which DSTs need to engage with the healthcare sociotechnical system, including clinical processes, patient preferences, resource constraints, and domain knowledge. Our results suggest that clinical DSTs should be designed as multi-user systems that support patient-provider collaboration and offer on-demand explanations that address discrepancies between predictions and current standards of care. Through this work, we demonstrate how current trends in explainable AI may be inappropriate for clinical environments and consider paths towards designing these tools for real-world medical systems.2021MJMaia Jacobs et al.Northwestern UniversityExplainable AI (XAI)AI-Assisted Decision-Making & AutomationCHI
"I think we know more than our doctors": How primary caregivers manage care teams with limited disease-related expertiseHealthcare providers play a critical role in the management of a chronic illness by providing education about the disease, recommending treatment options, and developing care plans. However, when managing a rare disease, patients and their primary caregivers often work with healthcare systems that lack the infrastructure to diagnosis, treat, or provide education on the disease. Little research has explored care coordination practices between patients, family members, and healthcare providers under these circumstances. With the goal of identifying opportunities for technological support, we conducted qualitative interviews with the primary caregivers of children with a rare neurodegenerative disorder, ataxia-telangiectasia. We report on the responsibilities that the primary caregivers take on in response to care teams' lack of experience with the illness, and the ways in which an online health community supports this care coordination work. We also describe barriers that limited participants' use of the online health community, including the emotional consequences of participation and information overload. Based on these findings, we discuss two promising research agendas for supporting rare disease management: facilitating primary caregivers' care coordination tasks and increasing access to online community knowledge.2019MJMaia Jacobs et al.Health and CaregivingCSCW
Insert Needle Here! A Custom Display for Optimized Biopsy Needle PlacementNeedle-guiding templates are used for a variety of minimally invasive medical interventions. While physically supporting needle placement with a grid of holes, they lack integrated information where needles need to be inserted. Physicians must manually determine the correct holes based on the output of planning software - a workflow that is error-prone and lengthy. We address these issues by embedding a display into the template using electroluminescence (EL) screen printing. The EL display is connected to planning software and illuminates the correct hole. In an empirical evaluation with physicians and researchers from the medical domain, we compare the illuminated against the conventional template as used in magnetic resonance imaging (MRI) guided prostate biopsies. Our results show that the EL display significantly improves task completion time by 51 %, task load by 47 % and usability by 30 %.2018ARAnke Verena Reinschluessel et al.University of BremenFitness Tracking & Physical Activity MonitoringSurgical Assistance & Medical TrainingCHI