Breathing rate is critical for the user's respiratory health and is hard to track outside the clinical context, requiring specialized devices. Earables could provide a convenient solution to track the breathing rate anywhere by leveraging the user's breathing-related motion and sound captured through the earables' motion sensors and microphones. However, small non-breathing head movements or background noises during the assessment affect the estimation accuracy. While noise filtering improves accuracy, it can discard valid measurements. This paper presents a multimodal approach to tracking the user's breathing rate using a signal-processing-based algorithm on motion sensors and a lightweight machine-learning algorithm on acoustic sensors from the earables that balances the accuracy and data retention. A user study with 30 participants shows that the system can accurately calculate breathing rate (Mean Absolute Error < 2 breaths per minute) while retaining most breathing sessions (75\%) performed in real-world settings. This work provides an essential direction for remote breathing rate monitoring.
Upper extremity (UE) health issues are a common concern among wheelchair users and have a large impact on their independence, social participation, and quality of life. However, despite the well-documented prevalence and negative impacts, these issues remain unresolved. Existing solutions (e.g. surgical repair, conservative treatments) often fail to promote sustained UE health improvement in wheelchair users' day-to-day lives.
Recent HCI research has shown the effectiveness of health tracking technologies in supporting patients' self-care for different health conditions (e.g. chronic diseases, mental health). In this work, we explore how health tracking technologies could support wheelchair users' UE health self-care. We conducted semi-structured interviews with 12 wheelchair users and 5 therapists to understand their practices and challenges in UE health management, as well as the potential benefits of integrating health tracking technologies into self-care routines. We discuss design implications for UE health tracking technologies and outline opportunities for future investigation.
We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.
Epilepsy is a common chronic neurological disease. People with epilepsy (PWE) and their caregivers face several challenges related to their epilepsy management, including quality of care, care coordination, side effects, and stigma management. The sociotechnical issues of the information management contexts and challenges for epilepsy care may be mitigated through effective information management. We conducted 4 focus groups with 5 PWE and 7 caregivers to explore how they manage epilepsy-related information and the challenges they encountered. Primary issues include challenges of finding the right information, complexities of tracking and monitoring data, and limited information sharing. We provide a framework that encompasses three attributes --- individual epilepsy symptoms and health conditions, information complexity, and circumstantial constraints. We suggest future design implications to mitigate these challenges and improve epilepsy information management and care coordination.
People engage in self-tracking with diverse data collection and visualisation needs and preferences. Customisable self-tracking tools offer the potential to support individualized preferences by letting people make changes to the aesthetics and functionality of tracker displays. In this paper, we use the customisation options offered by the displays of commercial fitness smartwatches as a lens to investigate when, why and how 386 self-trackers engage in customisations in their daily lives. We find that people largely customise their trackers' display frequently, multiple times a day, or not at all, with frequent customisations reflecting situational data, aesthetic and personal meaning needs. We discuss implications for the design of tracking tools aiming to support customisation and discuss the utility of customisations towards goal scaffolding and maintaining interest in tracking.
Patient-generated data from commercially available self-tracking devices has the potential to enhance support for people transitioning from hospitalization to self-care. However, studies have revealed significant barriers to the routine use of such data in clinical settings. This paper explores the use of patient-generated data in the context of cardiac rehabilitation. We describe a two-stage investigation: (1) a co-design study with clinicians to design a data system that combines objective and subjective patient data; and (2) an 18-week field-study where this system was deployed as part of a hospital-based rehabilitation program. Our findings suggest the system is feasible, supported clinicians’ workflow, and helped patients to bridge the gap between supervised and self-managed care. Subjective data contextualized objective data and a structured approach data collection helped generate actionable information. The paper also provides insight on patients' attitudes towards peer data sharing and demonstrates the importance of timing when introducing self-tracking technology.