Co-designing technology for ageing in place: A systematic review
Bibliographic record
Abstract
Abstract Background: Co-design in healthcare has become mainstream. Co-design with end-users can improve patient satisfaction, outcomes and reduce the cost of care. As populations age, there is a growing interest to involve the elderly in the co-design of health technology to maintain their well-being and independence. However, it is less clear if co-designed technology improves health and well-being outcomes. The aim of this study is to evaluate co-designed technology that supports elders to age in place. Methods: We conducted a systematic review to: i) investigate the health and well-being outcomes of co-designed technology for elders (≥ 60 years); ii) to identify co-design approaches and contexts where they are applied and; iii) to identify barriers and facilitators of the co-design process with elders. Searches were conducted in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), Scopus, OpenGrey and Business Source Premiere databases using MeSH terms and key words. Results: We identified 14,649 articles of which 34 studies were included. Studies were from Europe (n=28), Australia (n=4), America (n=1) and Canada (n=1). Twenty of the 32 studies targeted older adults (≥ 60 years old) and 14 targeted specific medical conditions or elder-related issues. Technological solutions included robots, online applications and software, smart televisions, computer games for exercise, global positioning solutions, smart home systems and design of care pathways. Five studies reported health and well-being outcomes and were extracted. The health and well-being impact of co-designed technology was inconsistent. Co-design processes varied greatly and in their intensity of elder involvement. Common facilitators of and barriers to the co-design process included the building of relationships between stakeholders, stakeholder knowledge of problems and solutions, as well as expertise in the co-design methodology.Conclusions: The co-design approach was applied in the design of a diverse set of technologies. The effect of co-designed technology on health and well-being was rarely studied and it was difficult to ascertain its impact. Future co-design efforts need to address barriers unique to the elderly population. More evaluation of the impact of co-designed technologies’ is needed and standardisation of the definition of co-design would be helpful to researchers and designers.
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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".