Interrupting bedtime to reverse frailty levels in acute care: a study protocol for the Breaking Bad Rest randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Hospitalized older patients spend most of the waking hours in bed, even if they can walk independently. Excessive bedrest contributes to the development of frailty and worse hospital outcomes. We describe the study protocol for the Breaking Bad Rest Study, a randomized clinical trial aimed to promoting more movement in acute care using a novel device-based approach that could mitigate the impact of too much bedrest on frailty. METHODS: Fifty patients in a geriatric unit will be randomized into an intervention or usual care control group. Both groups will be equipped with an activPAL (a measure of posture) and StepWatch (a measure of step counts) to wear throughout their entire hospital stay to capture their physical activity levels and posture. Frailty will be assessed via a multi-item questionnaire assessing health deficits at admission, weekly for the first month, then monthly thereafter, and at 1-month post-discharge. Secondary measures including geriatric assessments, cognitive function, falls, and hospital re-admissions will be assessed. Mixed models for repeated measures will determine whether daily activity differed between groups, changed over the course of their hospital stay, and impacted frailty levels. DISCUSSION: This randomized clinical trial will add to the evidence base on addressing frailty in older adults in acute care settings through a devices-based movement intervention. The findings of this trial may inform guidelines for limiting time spent sedentary or in bed during a patient's stay in geriatric units, with the intention of scaling up this study model to other acute care sites if successful. TRIAL REGISTRATION: The protocol has been registered at clinicaltrials.gov (identifier: NCT03682523).
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".