Co-Creating a Digital Life-Integrated Self-Assessment for Older Adults: User Experience Study
Bibliographic record
Abstract
BACKGROUND: Older adults are at increased risk of developing health disorders and functional decline. However, owing to time constraints and considerable effort, physicians rarely conduct comprehensive assessments to detect early signs of negative trajectories. If designed properly, digital technologies could identify health risks already at a preclinical stage, thereby facilitating preventive efforts and targeted intervention. For this purpose, a Life-integrated Self-Assessment (LiSA) tablet system will be developed through a structured co-creation process. OBJECTIVE: This study aims to investigate older adults' perceptions of different self-assessment domains, components affecting user experience, risks and benefits associated with LiSA, characteristics of potential LiSA users, and the LiSA concept in general. METHODS: A total of 10 community-dwelling older adults aged ≥70 years were recruited. In total, 6 co-creation workshops were held and started with expert input followed by semistructured discussion rounds. Participants performed hands-on activities with a tablet, including testing of preinstalled self-assessment apps. All workshops were audio recorded and additionally documented by the researchers using flipcharts, notes, and photos. Qualitative content analysis was used to analyze the data following a deductive-inductive approach guided by the Optimized Honeycomb Model for user experience. RESULTS: The group (mean age 77.8, SD 5.1 years) was heterogeneous in terms of previous technology experience and health status. The mean workshop duration was 2 hours (122.5, SD 4.43 min), and an average of 8 (SD 1.15) participants attended each workshop. A total of 11 thematic categories were identified, covering results on all research questions. Participants emphasized a strong interest in conducting a digital self-assessment of physical activity and function and sensory and cognitive functions and requested additional features such as recommendations for actions or reminders. LiSA was perceived as empowering and a motivator to engage in active health care planning as well as enabling shared and informed decision-making. Concerns and barriers included the lack of technical competence, feelings of frustration, and fear of being left alone, with negative assessment results. In essence, participants expressed a positive attitude toward using LiSA repeatedly and identified it as an option to increase the chances of maintaining independence when growing older. CONCLUSIONS: The co-creation participants supported the LiSA approach and were interested in performing regular self-assessments on a long-term basis. In their opinion, LiSA should include relevant assessments capturing physical activity and function and sensory and cognitive functions as well as recommendations for actions. It should be customizable to individual needs. These results will form the basis for a prototype. Iterative development and validation will aim to make LiSA accessible in the public domain as a reliable tablet-based system for self-assessment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".