Project Title

🎯 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

• Evaluate validity of specific metrics estimation features in 4 popular wearables

• Assess reliability of repeated measurements during identical activities.

• Identify inconsistencies based on device placement (e.g., dominant vs. non-dominant hand).

• 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.

• Devices were worn on both dominant and non-dominant arms across trials

• Sensor data was collected during circuit

• 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:

• Real-time trust in device readings

• Confusion or usability concerns

• 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

• All four devices demonstrated high validity and reliability for metric detection across placements.

• Energy Expenditure:

  • Only one device was both valid and reliable.

  • One was reliable but systematically overestimated.

  • Two devices produced inconsistent and invalid data, particularly on the non-dominant arm.

• A/B Placement:

  • Devices showed notable variation in calorie estimates depending on which arm they were worn on, suggesting sensor calibration and motion sensitivity differed between units.

• 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

• Two devices overestimated metrics 20–35% in most cases — leading to potential user frustration or overtraining if used for recovery tracking.

• Dominant wrist placement improved metric accuracy on certain devices

• Participants expected seamless consistency between readings from both arms — and were confused by fluctuations between device outputs

• 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:

• Sensor placement diagrams showing accuracy thresholds

• Visualizations of discrepancies over time 

• UX flags for product teams to consider in algorithm refinement and onboarding content

• Design insights to support clearer user expectations for different sports contexts

 

 

 

 

 

 

 

 

 

 

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🎯 Deliverables

• Raw data exports + coded usability intercepts

• Sensor diagram + device heatmaps

• 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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