The Accuracy of Tidal Volume Measured With a Smart Shirt During Tasks of Daily Living in Healthy Subjects: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The Hexoskin is a smart shirt that can take continuous and objective measurements and could be part of a potential telemonitoring system. OBJECTIVE: The aim of this study was to determine the accuracy of the calibrated Hexoskin in measuring tidal volumes (TVs) in comparison to spirometry during various tasks. METHODS: In a cross-sectional study, the TV of 15 healthy subjects was measured while performing seven tasks using spirometry and the Hexoskin. These tasks were performed during two sessions; between sessions, all equipment was removed. A one-time spirometer-based calibration per task was determined in session 1 and applied to the corresponding task in both sessions. Bland-Altman analysis was used to determine the agreement between TV that was measured with the Hexoskin and that measured with spirometry. A priori, we determined that the bias had to be less than ±5%, with limits of agreement (LOA) of less than ±15%. Lung volumes were measured and had to have LOA of less than ±0.150 L. RESULTS: In the first session, all tasks had a median bias within the criteria (±0.6%). In the second session, biases were ±8.9%; only two tasks met the criteria. In both sessions, LOA were within the criteria in six out of seven tasks (±14.7%). LOA of lung volumes were greater than 0.150 L. CONCLUSIONS: The Hexoskin was able to correctly measure TV in healthy subjects during various tasks. However, after reapplication of the equipment, calibration factors were not able to be reused to obtain results within the determined boundaries. TRIAL REGISTRATION: Netherlands Trial Register NL6934; https://www.trialregister.nl/trial/6934.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".