Design of a Mobile App and a Clinical Trial Management System for Cognitive Health and Dementia Risk Reduction: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: The rising prevalence of dementia is a major concern, with approximately 45% of cases linked to 14 modifiable risk factors. The European project LETHE aims to develop a personalized digital intervention model to delay or prevent cognitive decline through risk factor management. OBJECTIVE: The objective of our study was to design a clinical trial platform for older individuals at risk of cognitive decline, including a mobile app for study participants and a clinical trial management system (CTMS) for health professionals. METHODS: Using a user-centered design approach, workshops and feedback rounds involved potential participants representing the target group and professionals. The LETHE app's usability was assessed among 156 older adults enrolled in a 2-year multinational randomized controlled trial evaluating the feasibility of a digitally supported lifestyle program for dementia risk reduction. The randomized controlled trial is currently ongoing; the System Usability Scale (SUS) was administered 1 month after baseline to map first user experiences. Feedback on the LETHE CTMS was collected from 21 users. RESULTS: Of the 78 participants in the trial intervention group, 66 (85%) provided responses for the mobile app, with a median SUS score of 70 (IQR 55-82). Within the control group, 73% (57/78) of responses were received, with a median SUS score of 73 (IQR 63-90). For the CTMS, we received 71% (15/21) of responses, and the feedback was mostly positive. A ranking of the features that could be considered beyond state of the art showed that the integration of personalized activities (mean 2.23, SD 1.17) and real-time appointments (mean 2.46, SD 1.51) were considered the most novel ones. CONCLUSIONS: The LETHE app and CTMS were developed to support a personalized digital intervention method within a study involving 156 participants. Limitations include participants having digital literacy and internet access, potentially impacting the generalizability of the findings. Despite these limitations, positive feedback and high usability scores suggest promising potential for the LETHE app and CTMS in supporting personalized interventions to prevent cognitive decline in older adults.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".