Co-designing Interactive Digital Platforms for Promoting Physical Health in Older Adults
Bibliographic record
Abstract
With every passing day, each one of us is getting older. In Iceland the number of people aged 70 and older is conservatively estimated to double over the next 30 years and rise to become a quarter of the population. Given this information, there is great importance to the task of delaying disability as much as possible and for as long as possible (Samúelsson, 2021).\n\nThis study is intended to identify the needs and wants (requirements and wishes) of the target group of older adults when building a digital platform to help them find motivation to increase or maintain their physical strength with customized training programs. Árvakr is a platform created for the older adults that are not sure how to gain or maintain strength, keeping them motivated by responding to their success. A business version of Árvakr can be used by physiotherapists to communicate with their clients, to create personalized exercise programs and to follow up on their clients’ progress.\n\nAfter reviewing the relevant literature, I conducted a series of interviews to collect as much knowledge as possible. Analysis of the interviews helped to better understand behavior, experiences and needs, and to get knowledge and insights from professionals in the healthcare industry. The interviews helped shape the target group and showed how much of an effect a carefully designed digital platform can have. All \ncollected information was mapped with the use of interaction and service design methods and put into a prototype. This was done through workshops with co-designers and tested by the end users. \n\nThe final product, having taken into consideration all of the collected input from \nolder adults and physiotherapists, is a design suggestion for the digital platform named Árvakr.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".