Living with Parkinson’s Disease introduces a range of significant challenges into one’s daily life. While medical interventions exist to overcome some of these challenges, patient self-care techniques often form an essential complement to the treatments recommended by medical doctors. Knowledge on these self-care techniques often originates from those living with Parkinson’s themselves or their close caregivers, as they have the knowledge and experience required to assess self-care techniques. This so-called ‘patient knowledge’ is usually exchanged in peer meetings or discussion forums. Although vital to the Parkinson’s Disease community, this information is often difficult to access due to its unstructured format and the difficulty of navigating through online forums. We present an online tool that allows for contributing, assessing, and finally discovering Parkinson’s Disease self-care techniques. The custom discovery tool was populated with self-care knowledge by over 300 people with Parkinson’s and dozens of their carers, spanning areas such as daily well-being and using assistive equipment. Then, we invited patients to explore the discover features in a smaller scale trial. While well-received, our deployment highlighted several challenges that we further discuss in this paper. Overall, our study contributes to crowdsourced digital health solutions and provides both design and research implications to this challenging domain with a vulnerable user group.

A new generation of wearable devices now enable end-users to keep track of their sleep patterns. This paper reports on a longitudinal study of 82 participants who used a state-of-the-art sleep tracking ring for an average of 65 days. We conducted interviews and questionnaires to understand changes to their lifestyle, their perceptions of the tracked information and sleep, and the overall experience of using an unobtrusive sleep tracking device. Our results indicate that such a device is suitable for long-term sleep tracking and helpful in identifying detrimental lifestyle elements that hinder sleep quality. However, tracking one's sleep can also introduce stress or physical discomfort, potentially leading to adverse outcomes. We discuss these findings in light of related work and highlight the near-term research directions that the rapid commoditisation of sleep tracking technology enables.

Feedback is an important aspect of design education, and crowdsourcing has emerged as a convenient way to obtain feedback at scale. In this paper, we investigate how crowdsourced design feedback compares to peer design feedback within a design-oriented HCI class and across two metrics: perceived quality and perceived fairness. We also examine the perceived monetary value of crowdsourced feedback, which provides an interesting contrast to the typical requester-centric view of the value of labor on crowdsourcing platforms. Our results reveal that the students (N = 106) perceived the crowdsourced design feedback as inferior to peer design feedback in multiple ways. However, they also identified various positive aspects of the online crowds that peers cannot provide. We discuss the meaning of the findings and provide suggestions for teachers in HCI and other researchers interested in crowd feedback systems on using crowds as a potential complement to peers.

Spiral drawing has been utilized for years as a clinical tool to observe tremors and other abnormal movements in the assessment of different movement disorders. Specifically, in Parkinson's Disease (PD), patients' motor functionalities are measured by various tests, and spiral drawing is one of the proven techniques for assessing the severity of PD motor symptoms. Traditionally, this test is performed on pen and paper, and visually assessed by a clinician. There have been successful efforts for digitizing this test on tablets. Here, we describe a smartphone-based digitized version of the spiral drawing test. Moreover, we introduce a square-shaped drawing to solve an identified challenge of a smaller screen estate: finger occlusion while drawing. Both approaches are evaluated with 8 Parkinson's Disease patients and 6 age-matching control participants. Based on earlier studies and our data, we select suitable motion parameters for quantifying the task. Our results show an observable, statistically difference in performance between users with Parkinson's Disease and the control group in drawing accuracy.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP for tracking hand's motor symptoms, and a medication journal for recording medication intake. We followed 13 PD patients from two countries for a 1-month long real-world deployment. We found that PD patients are willing to use digital tools, such as STOP, to track their medication intake and symptoms, and are also willing to share such data with their caregivers and medical personnel to improve their own care.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. It is commonly accepted that improving medication adherence alleviates symptoms and maintains motor capabilities. Not following the medication regimen (e.g., skipping or over-medicating) may worsen side-effects, which mislead clinicians and patients. We developed and evaluated a mobile application, STOP, for screening the PD symptoms and medication intake. It contains a game for tracking the PD symptoms, and a medication journal for recording medical intake and adherence. We conducted a 1-month long real-world deployment with 13 PD patients from two countries. We found that the application medication adherence tracking provides non-bias information, and users are receptive to share such data with their care and medical personnel.

The connection between stress and smartphone usage behavior has been investigated extensively. While the prediction results using machine learning are encouraging, the challenge of how to cope with data loss remains. Addressing this problem, we propose an Intelligent Recommender System for logging stress based on adding a subjective user data-based validation to predictions made by intelligent algorithms. In a user study involving 731 daily stress self-reports from 30 participants we found discrepancies between subjective and smartphone usage data, i.e. battery, call information, or network usage. Despite the good prediction accuracy of 65% using a Random Forest classifier, combining both information would be beneficial for avoiding data and improving prediction accuracy. For realizing such a system (i.e., a mobile application), we propose three design recommendations, based on the capabilities of frequently used machine learning classifiers, enabling users to annotate their daily stress levels with a predict-and-validate methodology.