Results show that our approach helped students by allowing moments of self-reflection and self-awareness. Next, we conducted a longitudinal study to validate the tool's effectiveness and analyse its acceptability. As a first case-study, we designed a self-tracking tool to help college students manage their wellbeing by increasing self-awareness and easing help-seeking behaviours. We propose to digitally augment a paper diary to allow both analogue and digital data, bridging the gap between qualitative and quantitative data tracking practices to support better awareness and reflection on health data. We discuss the suitability and challenges of discreet tangible self-report techniques, and highlight open research questions for future work.Īn uprising trend of Personal Informatics has leveraged mobile applications to help users track their wellbeing however, these digital solutions focus on quantitative data, lacking the insights provided by qualitative data in paper notebooks. Our results indicate that participant accuracy was highest using a slider, and lowest using a squeeze-based input. We assessed input accuracy and participant perceptions across devices through a controlled lab study (N=20), highlighting diverging limits to the accuracy of the input technique and possible explanations for the differences in resolution. Each of these wireless devices was designed in a similar form factor and intended to be operated discretely with one hand. We compared six input techniques, including slider, slider with resistance, capacitive touch slider, squeeze, rotary knob, and joystick, to understand their accuracy and resolution profile. However, the accuracy of these tangible devices has not been studied systematically. Tangible input has been explored as a means for participants to self-report experiences while minimising disruption and allowing for discrete data collection. Machine learning in digital health systems allows for a more comprehensive understanding of neurodegenerative diseases and their stages and may also depict new features that influence the ways digital health technology should be configured. However, individuals in advanced stages had elevated perceptions (1.57 x ) for executive and behavioral functions compared to early-stage populations. In early-stage PD was shown to be $$21.6\%$$ 21.6 % lower than sensor-based scores with notable perceived deficits in memory and executive function. Finally, this work depicts that perceived functionality of individuals with PD differed from sensor-based functionalities. For classification between early (H
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