Co-Design Of Technology Driven Self-Management With Older Adults: An International Approach
Bibliographic record
Abstract
Abstract There has been an emergence in technology applications (apps) aimed at addressing needs amongst older adults and persons with cognitive impairment (PwCI). Despite the ubiquity of these apps, utilization is low, primarily due to a lack of involvement of PwCI and the perception that these apps have little motivational value. Engaging PwCI in creative processes such as co-design could lead to the creation of apps that better meets the needs of this population. The current study applies a user-centered, participatory approach to involve PwCI in the design of a new self-management and coaching app, RESILIEN-T. Co-design workshops were held with 12 PwCI across Italy, Netherlands and Canada; structured as four modules: (1) introduction and expectation setting, (2) user analysis, (3) storytelling, and (4) collaborative design. Based on interviews with PwCI, stories of individuals which reflect the target population were created (personas) and the solutions to the needs of these personas were discussed. Information about participant’s interests, computer proficiency and self-rated cognitive decline were collected. Participants were asked to try 10 existing apps and provided feedback on the design, usability and functionality. Lastly, participants were shown a prototype for RESILIEN-T and provided feedback based on the personas that they helped create. Co-design activities revealed that personalization is crucial for adherence. Aspects of physical and social activity, nutrition and cognition were most important to participants. Participants found many apps that are recommended for older adults do not appear age appropriate and seem condescending. These findings were common across PwCI from various nations.
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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".