Electronic Memory Support System (eMSS): An Innovative Digital Calendar and Training Program for Older Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Individuals with mild cognitive impairment (MCI) experience challenges in maintaining independence in their instrumental activities of daily living (IADLs). The memory support system (MSS) is a paper-based planner intervention to train individuals with MCI to complete personal goals and IADLs independently. As more individuals use electronic calendar systems, the MSS will need to adapt. This project aimed to develop and test an electronic prototype of the MSS (eMSS). METHOD: Drawing on the MSS, an eMSS prototype intended for phone/tablet use was developed in Axure, allowing participants to interact with the planner and its sections (calendar, notes, to do list). The prototype was evaluated with MSS trainers and individuals with MCI and their partners using a computer or tablet over a virtual platform. Participants were asked to go through fictional 'planning' scenarios which were assessed using cognitive task analysis (CTA). The scenarios were designed to evaluate overall ease of use, and specifics such as navigation between pages/sections and developing schedules and task lists. During sessions, participants were encouraged to 'think aloud,' providing insight into their eMSS experience. RESULT: To date, CTA has been conducted with 6 MSS trainers, and 2 patient/partner dyads that completed the MSS training program. Early results provide insight on strengths, challenges and opportunities to optimize the eMSS. Participants could identify and use key features (e.g., menus) and found the symbols and language consistent. However, participants didn't notice some features (e.g., interactive arrows to navigate across dates) and requested more 'cueing' or 'interactive prompts.' Participants would prefer to see all information entered on individual pages (which is possible with the MSS) to support better situation awareness. CONCLUSION: To respond to the everyday planning needs of individuals with MCI in a digital age, an eMSS was developed, drawing on a clinically trialed hardcopy version (MSS). Positive feedback from MSS trainers and patients with MCI and their partners suggests promise for the overall design. Improvements for the next iteration include designing cues to help users know actions have been completed or when to attend to important information, as well as providing more fulsome information on pages to support awareness of daily tasks.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".