I design thoughtful experiences by translating research into clear interaction patterns and real-world product decisions.
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I design thoughtful experiences by translating research into clear interaction patterns and real-world product decisions.
Resume
Work
Ai-Powered Activity Trackers
Ai - Powered Activity Trackers
University of Washington
2025
University of Washington
2025
overview
With wearable activity trackers on the rise, an increasing number of family members, friends, and peers are adopting this technology. As researchers and designers immersed in this space, we aim to critically examine how wearables influence community members and to explore opportunities for improvement, ultimately enhancing community health and well-being.
research questions
- How do people who use wearable activity trackers (smartwatches) use these devices to meet their health goals?
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How receptive are people who use wearable activity trackers (smartwatches) to AI-based functionalities within these devices to meet their healthy goals?
methodolgy
Dairy Study
Participant’s dairy study entry at the gym
Interview + Cognitive walkthrough
Interviews and data were synthesized in Atlas.ti.
Design Probe
We showed participants images of four AI-based smartwatch functionalities and asked them to share their thoughts and perceived value of these functionalities for their health goals.
insights
RQ1: How do people who use wearable activity trackers use these devices to track their goals?
Insight 1: Users found value in having their exercise activities recorded.
- Motivational Boost: Users experience a heightened sense of achievement from seeing their activity data, which motivates continued exercise.
- Enhanced Accuracy: Recorded data helps fill memory gaps about untracked activities, ensuring a complete and accurate fitness log.
- Proof of Effort: Having tangible evidence of workout efforts, like a "receipt" for exercise, validates the time and effort spent, enhancing user satisfaction.
- Reliance on Objective Data: Access to precise workout data reduces reliance on subjective perception, increasing trust in the effectiveness of exercise routines.
Insight 2: Users exhibit negative emotional responses associated with smartwatch use.
- Addictive Behaviors: Users are motivated by accomplishing goals, begins to feel like a “chore.”
- Anxiety: Users experience anxiety due to frequent monitoring and dependency on using their smartwatch.
- Notifications: Users feel overwhelmed with smartwatch notifications, some entirely avoid looking at smartwatch data due to expectations of emotional responses.
RQ2: How receptive are people who use wearable activity trackers to AI-based functionalities within these devices to using these functionalities to meet their health goals?
Insight 3: Users exhibit negative emotional responses associated with smartwatch use.
- Personalized Goal Tracking: Users appreciate AI's ability to recognize and track their daily activities to effectively plan their goals and stay motivated.
- Customizable and Realistic Goals: AI's recognition capabilities can set realistic and personalized health goals based on users' daily routines.
- Flexible Scheduling: Users find value in AI's ability to identify barriers and adjust their schedules to make achieving health goals easier.
- Better Health Management: Users believe that AI can provide an overall view of their health by integrating various data points, which helps them manage their health goals more effectively.
Insight 4: Users dislike AI “decision-making” because of control and inaccuracy.
- Control: Some users expressed wanting to do things on their own but just needing help from AI, instead of AI doing it for them.
- Wake Time: Some users feel that sleep is a sensitive, personal thing and that they would prefer to determine their own wake time.
- Inaccuracy: Some users expressed concern over what data would be used to wake them up, if it is accurate, and that they’d be frustrated if it isn’t accurate.
refelection
Through this study, I gained valuable insights into how users experience smartwatches in relation to their health goals. I discovered that while users appreciate certain features—such as activity tracking—smartwatches can also introduce unexpected challenges, including increased anxiety.
The research revealed nuanced attitudes toward AI-based functionalities: users were generally receptive to features like data recognition and personalized recommendations, but expressed hesitation toward predictive analytics and automated decision-making. On a practical level, I strengthened my skills in qualitative analysis using Atlas.ti Web.
If I could do this again, recruiting additional backup participants would have been beneficial, as we experienced notable participant attrition throughout the study. Given more time, I would have conducted a deeper analysis of the participant-submitted photos to better understand the environmental and contextual factors that influence smartwatch usage patterns.
HCDE Project - University of Washington