The co‐adaptation of a lifestyle program to improve sleep in persons living with dementia
Bibliographic record
Abstract
BACKGROUND: Sleep disturbance is a common experience in persons with Alzheimer's disease (PWAD) and can be stressful for care partners to manage. However, few programs have been designed to support sleep among PWAD. The few existing programs may not be appropriate for all contexts (e.g., geography, weather). Using co-design to develop or adapt interventions can help create programs that identify and overcome local barriers to programming. METHOD: The Nighttime Insomnia and Treatment Education for Alzheimer's Disease (NITE-AD) by McCurry et al. (2005) is a program for care partners, designed to improve sleep of PWAD. Previous offerings of the NITE-AD program have effectively reduced total awake time at night among PWAD. However, adaptation of program materials and physical activity recommendations was needed for winter use in the Canadian context. The co-adaptation of the NITE-AD program (to the Nighttime Insomnia and Treatment Education for Canadians Alzheimer's Disease, NITE-CAD program) took place in 5 stages: 1) understanding the barriers, facilitators, and supports needed for winter physical activity among PWAD, 2) engaging a co-advisory team, 3) co-adapting the program; and 4) assessing the feasibility of NITE-CAD. RESULT: Winter conditions were a barrier to physical activity, with driving in snow or ice and the extra time needed to prepare for outdoor activities as notable challenges. Some overcame these challenges through specialized equipment and/or accessible facilities. Three persons with lived experience (a care partner, a dementia exercise provider, an Alzheimer Society staff) were recruited to the co-adaptation team. Program adaptations included: having multiple physical activity options, increasing accessibility of the manual and using Canadian specific resources, and encouraging PWAD to participate in the program. Unfortunately, we were unable to assess the feasibility of NITE-CAD due to recruitment challenges. Among those who expressed interest, people were not eligible due to high risk of sleep apnea or having dementias other than Alzheimer's disease. CONCLUSION: The co-adaptation of NITE-CAD identified and implemented changes important to the Canadian, and especially winter, context. However, more work is needed to co-design recruitment processes and eligibility criteria to ensure that the program is relevant and accessible.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".