Breaking Barriers: Co-Designing Physical Activity Promoting Technologies with Older Adults Living Alone
Bibliographic record
Abstract
Many older adults wish to be more physically active but encounter a complex multitude of barriers that impede their efforts to maintain routines aligned with their aspirations. Despite recognized potential, current physical activity promoting technologies have been described as poorly aligned with older adults’ unique needs. In this article, we present our work following a multi-stage design process consisting of diaries and interviews with 17 inactive older adults living alone wanting to become more physically active and a follow-on co-design workshop with eight of them to envision ways of better supporting exercise engagement. From the resulting design artifacts and a thematic analysis of workshop transcripts, we provided a deeper understanding of why existing technologies are poorly aligned with older adults’ needs and values as well as how older adults weigh the benefits against the costs of using those technologies. Our findings surface potential solutions to addressing the barriers older adults encounter to remaining physically active, identifying enhancing social support as a critical first step. By revealing how older adults’ needs for physical activity support exceed what current technologies can provide, we advocate a holistic network of support across healthcare, social, and information services.
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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 | 0.022 | 0.042 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".