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Project Title
Project Type
Photography
Date
April 2023
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🎯 Challenge
Consumers rely on wearables to track performance and recovery — but are those readings accurate during dynamic, intermittent sports? The challenge was to validate whether wrist-based devices could deliver reliable heart rate and energy expenditure data under sport-specific movement demands. Our job was to identify blind spots in accuracy, potential UX risks, and limitations in sensor design or placement.
🎯 UX Research Goals
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Evaluate validity of specific metrics estimation features in 4 popular wearables
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Assess reliability of repeated measurements during identical activities.
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Identify inconsistencies based on device placement (e.g., dominant vs. non-dominant hand).
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Provide actionable insights on device performance to influence future design and development
🔬 Research Methodology
This project involved the use of mixed methodologies
A/B Testing Conditions:
To examine the influence of device placement on output, we performed A/B testing with wearables worn on the left vs. right arm in repeated trials. This controlled for side-dominance, motion artifacts, and contact variability.
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Devices were worn on both dominant and non-dominant arms across trials
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Sensor data was collected during circuit
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Quantitative measures were taken
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Supplemental Intercepts:
To layer in contextual UX insights, we conducted short intercepts with test participants after each trial to capture:
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Real-time trust in device readings
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Confusion or usability concerns
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Preferences in sensor placement and feedback accuracy
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Intercept Interviews: Users shared experiences of device accuracy during testing and perceived usefulness of the data.
Key Insights
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All four devices demonstrated high validity and reliability for metric detection across placements.
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Energy Expenditure:
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Only one device was both valid and reliable.
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One was reliable but systematically overestimated.
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Two devices produced inconsistent and invalid data, particularly on the non-dominant arm.
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A/B Placement:
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Devices showed notable variation in calorie estimates depending on which arm they were worn on, suggesting sensor calibration and motion sensitivity differed between units.
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User Trust: In intercepts, users reported low confidence in devices with conflicting data across arms or sessions, raising concerns about perceived data integrity.
Key UX Insights
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Two devices overestimated metrics 20–35% in most cases — leading to potential user frustration or overtraining if used for recovery tracking.
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Dominant wrist placement improved metric accuracy on certain devices
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Participants expected seamless consistency between readings from both arms — and were confused by fluctuations between device outputs
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Several users mistrusted data entirely when their effort perception didn’t match the readout. This was noted as a future UX concern around user feedback loops and belief in the product.
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🛠Solution & Recommendations
We delivered a comprehensive report with:
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Sensor placement diagrams showing accuracy thresholds
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Visualizations of discrepancies over time
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UX flags for product teams to consider in algorithm refinement and onboarding content
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Design insights to support clearer user expectations for different sports contexts
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🎯 Deliverables
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Raw data exports + coded usability intercepts
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Sensor diagram + device heatmaps
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Executive summary for product & marketing
Impact on UX Research
This study underscored the value of early-stage field testing and usability-focused validation in hardware UX. It highlighted how data discrepancies—especially from body placement—can erode user trust and lead to misinformed training or health decisions. As a UX researcher, this reinforced the importance of building inclusive testing protocols that mirror how products are used in the real world.
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Partial device set-up

