Minimal Detectable Change Thresholds and Responsiveness of Zephyr Bioharness and Fitbit Charge Devices
Bibliographic record
Abstract
Nazari, G and MacDermid, JC. Minimal detectable change thresholds and responsiveness of zephyr bioharness and Fitbit Charge devices. J Strength Cond Res 34(1): 257-263, 2020-The aim of the current study was to define the minimal detectable change (MDC) thresholds for Zephyr Bioharness (ZB) and Fitbit Charge Heart Rate (FC-HR) variables and to establish the internal responsiveness of ZB and FC-HR devices in terms of their HR, respiratory rate variables, and number of steps taken variables, in a healthy sample of men and women at resting, submaximal activity and throughout recovery phases. A total of sixty participants (30 women, 48 ± 15) and (30 men, 48 ± 15) were recruited using stratified convenience and snowball sampling approaches from our university student, staff, and faculty population. At rest, MDC90 values of ±4.89 and ±5.15 were noted for ZB and FC-HR devices, respectively. Throughout the recovery, thresholds of ±8.14 for ZB and 8.74 for FC-HR were reported. During the submaximal phases, scores of ±8.18 and ±9.35 were established for ZB and FC-HR, respectively. From a resting to a submaximal state, the standardized response means (SRMs) for ZB and FC-HR variables exhibited large responsiveness of SRMZB-HR = 4.42; SRMFC-HR = 4.04. In addition, the ZB respiratory rate variable showed the largest effect size of SRMZB-RR = 2.55. Similarly, the large effect size of SRMFC-ST = 1.76 was reported for the FC-HR steps taken variable. From a submaximal to a recovery state, the SRMs for ZB and FC-HR variables demonstrated effect sizes of SRMZB-HR = -3.10; SRMFC-HR = -3.12. Furthermore, the ZB respiratory rate variable displayed similar internal responsiveness measures of SRMZB-RR = -2.45. To conclude, Similar MDC HR thresholds can be expected using the ZB and FC-HR devices and that the two devices demonstrated large internal responsiveness in our healthy male and female cohort when transitioning from resting to submaximal states and vice-versa. Our findings suggest that the ZB and FC-HR devices can be used in monitoring performance improvements or deterioration over time and would allow qualified personnel in assessing the effectiveness of exercise programs in healthy individuals.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".