ADAPTATIONS AND OUTCOMES OF A WALKING AND REMINISCENCE BRAIN HEALTH INTERVENTION FOR CAREGIVERS
Bibliographic record
Abstract
Abstract The SHARP-Caregiver (SHARP-CG) study evaluated the feasibility of adapting SHARP to family caregivers of care partners with mild cognitive impairment or early-stage dementia. SHARP-CG assigned 7 triads (n=21) to Group A or B. Triads had a family caregiver (age >40), their care partner (age >40), and a support person (age >18). Caregivers and support persons were healthy or mildly cognitively impaired. Triads walked 1-mile routes with images to prompt conversational reminiscence 3x a week for 16 weeks using the SHARP walking application. Caregivers and care partners contributed weekly health update data, and (optionally) sleep and step-count data. Group A participants walked immediately. Group B participants first completed 16-weeks of observation. Ages were 35-90 (mean 69.8); most were female (52%). Mean Montreal Cognitive Assessment score for caregivers and care partners was 21.7 (SD+4.6). Eighty-six percent (n=6) of caregivers and 71% of care partners (n=5) opted to engage in digital biomarker data collection (actigraphy watch and sleep sensor). Caregivers had greater mean total daily steps (2055; SD+686) than care partners (1684; SD+979). Mean sleep hours were similar for caregivers and care partners at 6.1 (SD+1.1) and 6.5 (SD+2.5). Recruiting caregivers was difficult because many family members did not recognize what they were doing as caregiving. To improve study accessibility for caregivers, adapted eligibility criteria included reducing minimum age, allowing mobility aids, and making some components optional. Increasing accessibility, while improving enrollment, did not necessarily impact adherence. Caregiver demands and sporadic health concerns limited participation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".