Evidence map›Paper›PMID 33733234›Full record

ArticleFrontiers in sports and active living2021

Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability.

Jason D Stone, Hana K Ulman, Kaylee Tran, Andrew G Thompson, Manuel D Halter, Jad H Ramadan, Mark Stephenson, Victor S Finomore, Scott M Galster, Ali R Rezai and 1 more

Abstract read
In one paragraph

Article in Frontiers in sports and active living, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Observational
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jason D StoneRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Hana K UlmanRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Kaylee TranRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Andrew G ThompsonRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Manuel D HalterRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Jad H RamadanRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Mark StephensonRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Victor S FinomoreRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Scott M GalsterRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Ali R RezaiRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.
Joshua A HagenRockefeller Neuroscience Institute, West Virginia University, Morgantown, WV, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices.

Indexed as

electrocardiogramheart rateheart rate variabilityphotoplethysmographyroot mean square of successive differencesvalidationwearables

Identifiers

PMID33733234
PMCPMC7956986

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.