P4‐294: Relevance and application of mindfulness‐based interventions for older adults at risk for developing Alzheimer's disease
Bibliographic record
Abstract
In the last decade, pharmacological research has failed finding novel treatments for Alzheimer's disease (AD). This situation has stimulated the development of non-pharmacological approaches. However, several non-pharmacological interventions (e.g., cognitive training) do not target the diversity of symptoms that can be observed in the pre-dementia or dementia stage of AD. Approaches being more holistic, such as Mindfulness-Based Interventions (MBI), may show great potential to prevent or slow down cognitive decline in older adults at risk for AD. This presentation aims at formulating recommendations to overcome the limitations of previous studies on MBI in the context of AD and at presenting adaptations drawn from a pilot experiment. The recommendations are based on a comprehensive literature review of studies using interventions similar to MBI (Larouche et al., 2015). They are also based on a pilot study carried out with 11 elders presenting Subjective cognitive decline (SCD) or Mild cognitive impairment (MCI). In this pilot study, the instructors met every week in order to adapt the intervention according to the challenges met during each session. Based on our comprehensive literature review, clinical studies measuring the efficacy of MBI in elders at risk for AD must include larger samples and use randomized/controlled designs, with long-term outcome assessment. Moreover, the use of validated and structured interventions (such as the Mindfulness-based stress reduction program) is recommended. As regards the pilot experiment, it yielded adaptations of the program to elders at risk for AD in the following ways. The psychoeducative content of the intervention was made more concrete using Acceptance and Commitment Therapy strategies. Also, commitment to the program was fostered by explaining clearly during the first session how the program is thought to lead to memory improvements and by making weekly phone calls to the participants, in order to improve adherence and sustain motivation. The pilot study also revealed that it is recommended to exclude participants intending to miss one of the first four sessions of the intervention. These recommendations should help demonstrating the benefits of MBI and support clinicians who intend to administrate MBI to individuals at risk for developing AD.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".