Aging in 10 Minutes: Do Age Simulation Suits Mimic Physical Decline in Old Age? Comparing Experimental Data with Established Reference Data
Bibliographic record
Abstract
INTRODUCTION: Age simulation suits are increasingly used in health care education. However, empirical evidence that quantifies the simulated performance losses in established geriatric tests and compares those declines with reference data of older adults is scarce. METHODS: = 61 participants (46 middle-aged, 15 young adults) with and without age simulation suit, for example in the Timed Up and Go Test (+dual task), Short Physical Performance Battery, grip strength, and 30-Second-Chair- Standing Test. Additionally, we compared the results with suit to established reference values of older adults in different age groups. RESULTS: Reduced performance was observed in both groups when wearing the suit, yet to different degrees dependent on the assessment and user age. For one, larger declines were observed in more challenging and complex tasks across age groups. In addition, comparisons with reference values revealed age-differential "instant aging" effects. DISCUSSION: A simulated "fourth age," where frailty and impairments are accumulating, was not reached in the majority of assessments, especially not among younger participants. In conclusion, existing age simulation suits may have some educational and empathy potential, but so far, they fail in simulating the age period with most serious functional loss.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".