The effects of a long-term care walking program on balance, falls and well-being
Bibliographic record
Abstract
BACKGROUND: The effects of a regular and graduated walking program as a stand-alone intervention for individuals in long-term care are unclear. Exercise and fall prevention programs typically studied in long-term care settings tend to involve more than one exercise mode, such as a combination of balance, aerobic, strengthening, and flexibility exercises; and, measures do not always include mental health symptoms and behaviors, although these may be of even greater significance than physical outcomes. METHODS/DESIGN: We are randomly assigning residents of long-term care facilities into one of three intervention groups: (1) Usual Care Group--individuals receive care as usual within their long-term care unit; (2) Interpersonal Interaction Group--individuals receive a comparable amount of one-on-one stationary interpersonal interaction time with study personnel administering the walking program; and, (3) Walking Program Group--individuals participate in a supervised, progressive walking program five days per week, for up to half an hour per day. Assessments completed at baseline, 2 and 4 months during intervention, and 2 and 4 months post-intervention include: gait parameters using the GAITRite® computerized system, grip strength, the Berg Balance Scale, the Senior Fitness Test, the Older Adult Resource Services Physical Activities of Daily Living, the Geriatric Depression Scale Short Form, the Cornell Scale for Depression in Dementia, the Revised Memory and Behavior Problems Checklist, the Short Portable Mental Status Questionnaire, the Coloured Analogue Scale, pain assessment scales, and the number and nature of falls. Sophisticated data analytic procedures taking into account both the longitudinal nature of the data and the potential for missing data points due to attrition, will be employed. DISCUSSION: Residents in long-term care have a very high number of comorbidities including physical, mental health, and cognitive. The presence of dementia in particular makes standardized research protocols difficult to follow, and staff shortages, along with inconsistencies related to shift changes may impact on the accuracy of caregiver-rated assessment scales. Practical challenges to data collection validity and maintenance of inter-rater reliability due to the large number of research staff required to implement the interventions at multiple sites are also posed. TRIAL REGISTRATION: ClinicalTrials.gov NCT01277809.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".