A feasibility study investigating the use of a thigh-worn accelerometer to measure physical activity in patients recovering from critical illness
Bibliographic record
Abstract
Background: \nRehabilitation of patients recovering from critical illness has been a focus of clinical research, however heterogeneity exists in the measurement of physical recovery. Wearable technology offers a simple, unobtrusive method of continuous activity monitoring. However, studies have reported issues with placement of wrist and ankle devices in critically ill patients. \n \nAim: \nTo evaluate the feasibility of a thigh-worn accelerometer to measure the physical activity of patients recovering from critical illness. \n \nMethods: \nA prospective observational feasibility study was conducted within a 33-bedded critical care unit over nine weeks. Thigh-worn activPALTM accelerometers were applied to patients for the duration of their critical care admission. \n \nResults: \nA total of 12 participants were recruited. Median (IQR) device wear time was 268.35 (299.15) hours or 99.58% of their critical care admission. A priori feasibility success criteria of three days of wear time for more than ten hours were achieved in every participant. Device removal primarily occurred for recharging. \n \nConclusion: \nThe practical application of a thigh-worn accelerometer may be feasible in this population for the duration of admission. As this was a small, single centre study additional research is necessary to further inform and determine the feasibility of this device.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".