29 A Pilot Study of DataDay: Daily support for people with dementia
Bibliographic record
Abstract
Objectives/Goals: This pilot study aims to assess the implementation of the DataDay app in memory clinics for patients with MCI or dementia, focusing on usability, user satisfaction, and impact on health outcomes. We seek to identify barriers and facilitators to implementation and evaluate its effect on reducing unnecessary hospital stays. Methods/Study Population: This mixed-methods study will involve 50 participants, 25 diads of patients with MCI or mild-to-moderate dementia and their caregivers from the community. Participants will use DataDay for 12 weeks, receiving reminders to log daily activities such as nutrition, mood, cognition, and physical activity. Baseline demographic data will be collected from self-reported surveys. Participants will receive training on app use, with follow-up interviews at 4, 8, and 12 weeks to gather feedback. Quantitative data analysis will include repeated measures analysis of variance to compare pre- and post-intervention outcomes, such as medication use and ER visits. Thematic analysis will be conducted on interview transcripts to understand user experiences. Results/Anticipated Results: We anticipate the study will demonstrate the feasibility of the DataDay app for self-management in individuals with MCI or dementia. Expected outcomes include improved medication adherence, reduced emergency room visits, and increased user engagement with daily health monitoring. Qualitative feedback is expected to highlight user satisfaction with the app’s reminders and ease of integration into daily routine. We also expect potential challenges to be identified such as initial learning difficulties and technology-related frustration. The data will help refine the app for better usability and inform strategies for widespread implementation in memory assessment clinics. Discussion/Significance of Impact: The study will provide insights into the practicality of implementing DataDay in memory clinics. The results will highlight necessary adjustments and provide key factors for successful adoption in other clinics. DataDay aims to allow individuals with MCI or dementia to manage their condition at home and enhance their quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".