Accuracy of Wearable Technology & Smart Watches

Report Highlights. Wearable devices have as much as 20% error when measuring heart rate, and caloric expenditure measurements can be off by as much as 100%. Most wearable fitness devices overestimate total sleep time and underestimate wakefulness after sleep onset.

Key TAKEAWAYS

44% of people use wearable fitness devices to measure heart rate.
42% of people use wearable fitness devices to measure caloric expenditure (calories burned).
On average, wearable fitness devices have a slight tendency to underestimate heart rate.
On average, there is a tendency for wearable fitness devices to underestimate caloric expenditure.
Exercise intensity, motion of extremities during exercise, wrist position, interference between skin and sensors (sweat or dirt on the skin), and skin pigmentation have been shown to decrease the accuracy of wearable devices.
60% of people use wearable fitness devices to track step count.
On average, wearable fitness devices underestimate step count by 9%.

Contents

For further analysis, we broke down the data by wearable device:

Contents

For further analysis, we broke down the data by wearable device:

Table 1.

Note: Swipe left and right to view table.

	Expert Advice	Expert Rating
Heart Rate	Accuracy decreases with increasing intensity. Helpful to track resting heart rate over time to monitor for sickness or over training. Helpful to monitor heart rate over time doing similar activities to see if there are any deviations from the norm.	★★★★☆
Caloric Expenditure	There is a chance of over/underestimation. With options for goal setting, reminders to move, and social support, clinicians may find wearables useful to help patients initially monitor calories consumed and physical activity completed to determine behavioral changes needed.	★★★☆☆
Step Count	There are differences in the accuracy of step count measurement depending on the device being utilized. It can be useful to track steps over time to improve activity. Tracking step count could demotivate people if goals are not being achieved.	★★★☆☆
Sleep Tracking	There are concerns from sleep specialists regarding the use of wearable devices to measure sleep. Concerns mount over what they call orthosomnia- the desire to perfect sleep. (typically through monitoring using wearable devices) In their opinion, some users rely too heavily on their devices to measure sleep, and overestimate the validity of said devices.	★★☆☆☆

Table 1 highlights specific advice and ratings given by experts on the capabilities of wearable technology to measure heart rate, caloric expenditure, step count, and sleep tracking.

‍

Apple Watch

Apple watches have been shown to underestimate heart rate by an average of 1.3 beats per minute during exercise.
Contrary to other wearable fitness devices, during graded exercise testing Apple watch accuracy improves as heart rate increases.
During graded exercise testing Apple watches have been shown to miscalculate caloric expenditure by as much as 115%.
On average, the Apple watches have a 0.9-3.4% error when measuring total step count.
Apple watches correctly identify when a person is sleeping 97% of the time when monitoring sleep.
Apple watches correctly identify when someone wakes up during sleep only 26% of the time.
On average, Apple Watches underestimate heart rate variability by 9.6 ms.

‍

Oura Ring

The Oura ring has been shown to accurately measure resting heart rate.
On average, the device demonstrated just a 3% error when measuring resting heart rate.
On average, the Oura ring tended to underreport resting heart rate by 1 beat per minute.
On average, the device demonstrated a 13% error when measuring caloric expenditure.
The Oura ring correctly identifies when a person is sleeping 94% of the time when monitoring sleep.
Oura ring’s sleep algorithm calculates total sleep time with 96% accuracy.
Oura ring’s sleep algorithm correctly identifies time spent in light, deep, wake, and rem sleep 79% of the time.
The Oura ring correctly identifies when someone wakes up during sleep 57% of the time.
On Average, Oura ring underestimated heart rate variability by 10.2 ms.
On average, the Oura ring demonstrates a 4.8% error when me when measuring step count.

‍

Whoop

On average heart rate measurements performed by WHOOP are 99.7% accurate.
WHOOP is 99% accurate when measuring heart rate variability, with an average underestimated of just 4.5 ms.
Research has labeled WHOOP’s ability to Identify specific stages of sleep as “excellent.”
Other research has shown WHOOP to have a high degree of consistency when measuring heart rate and heart rate variability.
WHOOP correctly identifies when a person is sleeping 90% of the time when monitoring sleep.
WHOOP correctly identifies when someone wakes up during sleep 56% of the time.
WHOOP claims that their product does not count steps because step count because it ignores intensity and other movements. Their preferred method is “strain,” which takes into account simultaneous heart rate with physical activity or activities of daily living.

‍

Garmin

On average Garmin has been shown to have a 1.16-1.39% error when measuring heart rate.
Literature has shown that Garmin has a 6.1-42.9% error when measuring caloric expenditure.
When measuring step count, literature demonstrates that Garmin has an average measurement error of 23.7%.
Garmin correctly identifies when a person is sleeping 98% of the time when monitoring sleep.
Garmin correctly identifies when someone wakes up during sleep only 27% of the time.
On average, Garmin underestimated heart rate variability by 22.4 ms.

‍

Fitbit

Fitbit has been shown to underestimate heart rate by an average of 9.3 beats per minute during exercise.
On average Fitbit displays a 14.8% error when measuring caloric expenditure.
On average, Fitbit miscalculates step count by 9.1-21.9%.
Fitbit devices tend to overestimate total sleep time by 7-67 minutes.

‍

Samsung

Samsung devices have been shown to underestimate heart rate by an average of 7.1 beats per minute during exercise.
Samsung devices display a 1.08-6.30% error when measuring step count.
On average, Samsung devices have been shown to have a 9.1-20.8% error when measuring energy expenditure.
On average, Samsung devices underreport heart rate variability by 18.24 ms.

‍

Polar (Wrist & Arm)

On average, Polar wearable fitness devices placed on the upper arm had a 2.2% error when measuring heart rate.
Polar wrist worn devices correctly identify when a person is sleeping 92% of the time when monitoring sleep.
Polar wrist worn devices correctly identify when someone wakes up during sleep 51% of the time.
On average, Polar wrist worn devices underestimate heart rate variability by 8.7 ms.
On average, wrist worn polar devices have been shown to have a 16.7% error when measuring caloric expenditure during moderate intensity exercise.

‍

Comparison of Error by Metric for Wearable devices

‎🟩 ≤ 10 % error

🟨 10.1-25% error

🟥 ≥ 25.1% error

Note: Swipe left and right to view table.

	Apple Watch	Oura Ring	WHOOP	Garmin	Fitbit	Samsung	Polar
Calorie Expenditure	115%	13%		42.9%	14.8%	20.8%	16.7%
Heart Rate	10.7%	3%	0.3%	1.39%		1.1%	2.2%
Steps	3.4%	4.8%		23.7%	21.9%	6.3%
Sleep	3%	6%	10%	2%	13%		8%

Caloric Expenditure: % Error of Caloric Expenditure when compared to the gold standard measurement.
Heart rate: % Error of heart rate when compared to the gold standard measurement.
Steps: % Error of step count when compared to the gold standard measurement.
Sleep: % Error of identifying sleep versus wakefulness compared to the gold standard measurement.

‍

Conclusion

The information presented in this blog is sourced from the references cited . The values reported represent the ‘worst case scenario’ from the literature reviewed. The relative error of each device may differ from the reported values during real world application because the values reported are an average of a sample collected in a particular study. Overall, each of these devices is relatively inconsistent when measuring biometric data. The measurement capability of wearable fitness devices has improved tremendously and with further improvements in technology, it is expected to get even better.

‍

References:

Cite this page:

Korem, Erik. “Accuracy of Consumer Wearable Technology” AIM7.com, March 8, 2023, https://aim7.com/post/accuracy-of-consumer-wearable-technology

Contents

For further analysis, we broke down the data by wearable device:

Cite this page:

Brownell, A., Korem, E., and Morris, C. “Accuracy of Wearable Technology & Smart Watches” AIM7.com, March 23, 2023, https://aim7.com/blog/smartwatch-wearable-technology-accuracy

Key TAKEAWAYS

44% of people use wearable fitness devices to measure heart rate.
42% of people use wearable fitness devices to measure caloric expenditure (calories burned).
On average, wearable fitness devices have a slight tendency to underestimate heart rate.
On average, there is a tendency for wearable fitness devices to underestimate caloric expenditure.
Exercise intensity, motion of extremities during exercise, wrist position, interference between skin and sensors (sweat or dirt on the skin), and skin pigmentation have been shown to decrease the accuracy of wearable devices.
60% of people use wearable fitness devices to track step count.
On average, wearable fitness devices underestimate step count by 9%.

Contents

For further analysis, we broke down the data by wearable device:

Table 1.

Note: Swipe left and right to view table.

	Expert Advice	Expert Rating
Heart Rate	Accuracy decreases with increasing intensity. Helpful to track resting heart rate over time to monitor for sickness or over training. Helpful to monitor heart rate over time doing similar activities to see if there are any deviations from the norm.	★★★★☆
Caloric Expenditure	There is a chance of over/underestimation. With options for goal setting, reminders to move, and social support, clinicians may find wearables useful to help patients initially monitor calories consumed and physical activity completed to determine behavioral changes needed.	★★★☆☆
Step Count	There are differences in the accuracy of step count measurement depending on the device being utilized. It can be useful to track steps over time to improve activity. Tracking step count could demotivate people if goals are not being achieved.	★★★☆☆
Sleep Tracking	There are concerns from sleep specialists regarding the use of wearable devices to measure sleep. Concerns mount over what they call orthosomnia- the desire to perfect sleep. (typically through monitoring using wearable devices) In their opinion, some users rely too heavily on their devices to measure sleep, and overestimate the validity of said devices.	★★☆☆☆

Table 1 highlights specific advice and ratings given by experts on the capabilities of wearable technology to measure heart rate, caloric expenditure, step count, and sleep tracking.

‍

Apple Watch

Apple watches have been shown to underestimate heart rate by an average of 1.3 beats per minute during exercise.
Contrary to other wearable fitness devices, during graded exercise testing Apple watch accuracy improves as heart rate increases.
During graded exercise testing Apple watches have been shown to miscalculate caloric expenditure by as much as 115%.
On average, the Apple watches have a 0.9-3.4% error when measuring total step count.
Apple watches correctly identify when a person is sleeping 97% of the time when monitoring sleep.
Apple watches correctly identify when someone wakes up during sleep only 26% of the time.
On average, Apple Watches underestimate heart rate variability by 9.6 ms.

‍

Oura Ring

The Oura ring has been shown to accurately measure resting heart rate.
On average, the device demonstrated just a 3% error when measuring resting heart rate.
On average, the Oura ring tended to underreport resting heart rate by 1 beat per minute.
On average, the device demonstrated a 13% error when measuring caloric expenditure.
The Oura ring correctly identifies when a person is sleeping 94% of the time when monitoring sleep.
Oura ring’s sleep algorithm calculates total sleep time with 96% accuracy.
Oura ring’s sleep algorithm correctly identifies time spent in light, deep, wake, and rem sleep 79% of the time.
The Oura ring correctly identifies when someone wakes up during sleep 57% of the time.
On Average, Oura ring underestimated heart rate variability by 10.2 ms.
On average, the Oura ring demonstrates a 4.8% error when me when measuring step count.

‍

Whoop

On average heart rate measurements performed by WHOOP are 99.7% accurate.
WHOOP is 99% accurate when measuring heart rate variability, with an average underestimated of just 4.5 ms.
Research has labeled WHOOP’s ability to Identify specific stages of sleep as “excellent.”
Other research has shown WHOOP to have a high degree of consistency when measuring heart rate and heart rate variability.
WHOOP correctly identifies when a person is sleeping 90% of the time when monitoring sleep.
WHOOP correctly identifies when someone wakes up during sleep 56% of the time.
WHOOP claims that their product does not count steps because step count because it ignores intensity and other movements. Their preferred method is “strain,” which takes into account simultaneous heart rate with physical activity or activities of daily living.

‍

Garmin

On average Garmin has been shown to have a 1.16-1.39% error when measuring heart rate.
Literature has shown that Garmin has a 6.1-42.9% error when measuring caloric expenditure.
When measuring step count, literature demonstrates that Garmin has an average measurement error of 23.7%.
Garmin correctly identifies when a person is sleeping 98% of the time when monitoring sleep.
Garmin correctly identifies when someone wakes up during sleep only 27% of the time.
On average, Garmin underestimated heart rate variability by 22.4 ms.

‍

Fitbit

Fitbit has been shown to underestimate heart rate by an average of 9.3 beats per minute during exercise.
On average Fitbit displays a 14.8% error when measuring caloric expenditure.
On average, Fitbit miscalculates step count by 9.1-21.9%.
Fitbit devices tend to overestimate total sleep time by 7-67 minutes.

‍

Samsung

Samsung devices have been shown to underestimate heart rate by an average of 7.1 beats per minute during exercise.
Samsung devices display a 1.08-6.30% error when measuring step count.
On average, Samsung devices have been shown to have a 9.1-20.8% error when measuring energy expenditure.
On average, Samsung devices underreport heart rate variability by 18.24 ms.

‍

Polar (Wrist & Arm)

On average, Polar wearable fitness devices placed on the upper arm had a 2.2% error when measuring heart rate.
Polar wrist worn devices correctly identify when a person is sleeping 92% of the time when monitoring sleep.
Polar wrist worn devices correctly identify when someone wakes up during sleep 51% of the time.
On average, Polar wrist worn devices underestimate heart rate variability by 8.7 ms.
On average, wrist worn polar devices have been shown to have a 16.7% error when measuring caloric expenditure during moderate intensity exercise.

‍

Comparison of Error by Metric for Wearable devices

‎🟩 ≤ 10 % error

🟨 10.1-25% error

🟥 ≥ 25.1% error

Note: Swipe left and right to view table.

	Apple Watch	Oura Ring	WHOOP	Garmin	Fitbit	Samsung	Polar
Calorie Expenditure	115%	13%		42.9%	14.8%	20.8%	16.7%
Heart Rate	10.7%	3%	0.3%	1.39%		1.1%	2.2%
Steps	3.4%	4.8%		23.7%	21.9%	6.3%
Sleep	3%	6%	10%	2%	13%		8%

‍

Conclusion

‍

References:

Accuracy of Wearable Technology & Smart Watches

Accuracy of Wearable Technology & Smart Watches

Expert Advice

Expert Rating

Apple Watch

Oura Ring

Whoop

Garmin

Fitbit

Samsung

Polar (Wrist & Arm)

Comparison of Error by Metric for Wearable devices

Apple Watch

Oura Ring

WHOOP

Garmin

Fitbit

Samsung

Polar

Conclusion

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Expert Advice

Expert Rating

Apple Watch

Oura Ring

Whoop

Garmin

Fitbit

Samsung

Polar (Wrist & Arm)

Comparison of Error by Metric for Wearable devices

Apple Watch

Oura Ring

WHOOP

Garmin

Fitbit

Samsung

Polar

Conclusion

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