Co-creation of a video feedback tool for managing self-care at home in pairs of older adults: a remote experience-based co-design study (Preprint)
Bibliographic record
Abstract
BACKGROUND Involving older adults in co-design studies is essential for the development of digital technologies and healthcare solutions to enhance self-care management at home. The use of remote co-design approaches, particularly with a focus on video feedback tools for self-care management, could provide technologically sustainable solutions that address the personal needs of older adults with chronic illness and their companions. OBJECTIVE The aim of this study was three-fold: (1) to identify the factors influencing self-care management among end users, (2) to co-create and refine the design and contents of a video feedback tool, and (3) to test usability together with pairs of older adults at home. METHODS This study was conducted using an experience-based co-design involving collaboration with older adults, researchers, and service designers. Six workshops, of which five were held remotely and one in-person, were conducted to co-create contents for a video feedback tool with four pairs of older adults. Thereafter, the newly developed self-care exercises underwent usability testing with the pairs of older adults in their homes. Collected data were analyzed inductively through thematic analysis and deductively using the U.S.A.B.I.L.I.T.Y. framework. RESULTS The workshop sessions revealed that support and learning within a promoting self-care environment had a positive influence on self-care management for the pairs. The influencing factors and needs were used to co-create two self-care exercises within the video feedback tool: “Breathing exercises” and “Picking up from the floor.” The usability test revealed that the pairs found it difficult to analyze their self-care exercises and indicated a need for a support structure around the video feedback tool. CONCLUSIONS This study emphasizes that video feedback promotes understanding and facilitates learning by highlighting differences between one's own performance of self-care exercises and that of others. The video feedback tool can be beneficial for pairs of older adults managing self-care at home or as a complement to traditional healthcare services. However, its effectiveness and integration into existing healthcare services need to be assessed through careful design and structured support is still needed.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".