Psychometric properties of the Zephyr bioharness device: a systematic review
Bibliographic record
Abstract
BACKGROUND: Technological development and improvements in Wearable Physiological Monitoring devices, have facilitated the wireless and continuous field-based monitoring/capturing of physiologic measures in healthy, clinical or athletic populations. These devices have many applications for prevention and rehabilitation of musculoskeletal disorders, assuming reliable and valid data is collected. The purpose of this study was to appraise the quality and synthesize findings from published studies on psychometric properties of heart rate measurements taken with the Zephyr Bioharness device. METHODS: We searched the Embase, Medline, PsycInfo, PuMed and Google Scholar databases to identify articles. Articles were appraised for quality using a structured clinical measurement specific appraisal tool. Two raters evaluated the quality and conducted data extraction. We extracted data on the reliability (intra-class correlation coefficients and standard error of measurement) and validity measures (Pearson/Spearman's correlation coefficients) along with mean differences. Agreement parameters were summarised by the average biases and 95% limits of agreement. RESULTS: A total of ten studies were included: quality ratings ranged from 54 to 92%. The intra-class correlation coefficients reported ranged from 0.85-0.98. The construct validity coefficients compared against gold standard calibrations or other commercially used devices, ranged from 0.74-0.99 and 0.67-0.98 respectively. Zephyr Bioharness agreement error ranged from - 4.81 (under-estimation) to 3.00 (over-estimation) beats per minute, with varying 95% limits of agreement, when compared with gold standard measures. CONCLUSION: Good to excellent quality evidence from ten studies suggested that the Zephyr Bioharness device can provide reliable and valid measurements of heart rate across multiple contexts, and that it displayed good agreements vs. gold standard comparators - supporting criterion validity.
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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 | 0.036 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".