Measurement properties of the SenseWear armband in adults with chronic obstructive pulmonary disease
Bibliographic record
Abstract
RATIONALE: The SenseWear armband (SAB) is designed to measure energy expenditure (EE). In people with chronic obstructive pulmonary disease (COPD), EE estimated using the SAB (EE(SAB)) is a popular outcome measure. However, a detailed analysis of the measurement properties of the SAB in COPD is lacking. OBJECTIVE: To examine the sensitivity of EE(SAB), agreement between EE(SAB) and EE measured via indirect calorimetry (EE(IC)), and its repeatability in COPD. METHODS: 26 people with COPD (forced expiratory volume in 1 s (FEV(1))=49+/-18% predicted; 15 males) spent 6 min in five standardised tasks that comprised supine, sitting, standing and two walking speeds. A subgroup (n=12) walked using a rollator. Throughout each task, measurements of EE(SAB) and EE(IC) were collected. The protocol was repeated on a second day. RESULTS: EE(SAB) increased between standing and slow walking (2.4, metabolic equivalents (METs) 95% CI 2.2 to 2.7) as well as slow and fast walking (0.5 METs, 95% CI 0.3 to 0.7). Considering all tasks together, the difference between EE(SAB) and EE(IC) was -0.2 METs (p=0.21) with a limit of agreement of 1.3 METs. The difference between days in EE(SAB) was 0.0 METs with a coefficient of repeatability of 0.4 METs. Rollator use increased the variability in EE(SAB), compromising its repeatability and agreement with EE(IC). CONCLUSIONS: EE(SAB) was sensitive to small but important changes. There was fair agreement between EE(SAB) and EE(IC), and measurements of EE(SAB) were repeatable. These observations suggest that the SAB is useful for the evaluation of EE in patients with COPD who walk without a rollator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".