Replication Data for: Systematic Review of the Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate
Bibliographic record
Abstract
Introduction: Consumer-wearable activity trackers are small electronic devices engineered to monitor and record fitness and health-related measures. The purpose of this systematic review is to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Method: We extracted information about commercial wearable devices (e.g., price, size, battery life, sensors, measurements, algorithms) using an Internet search conducted from November 2016- January 2017. From this search we identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only included articles published in the English language up to May 2019. Studies were excluded if they did not identify the device used and if they did not examine the validity and/or reliability of a device. Studies including the general population and all special populations were included. We operationalized validity as criterion (as compared to other measures) and construct (degree to which device is measuring what it purports) validity. Reliability measures focused on intradevice and interdevice reliability. Results: We included 158 publications examining 9 different commercial wearable device brands. Fitbit was by far the most studied brand. In lab-based settings Fitbit, Apple, and Samsung appeared to measure steps accurately. Heart rate was more variable with Apple Watch, Garmin was the most accurate and Fitbit tended towards underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusion: Activity trackers are still an emerging market and the devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.337 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".