Do aging suits adequately simulate objective age-related decline in gait? A kinematic comparison of induced aging in young and middle-aged adults
Bibliographic record
Abstract
Background and Objectives Aging suits are widely used as arguably didactic tool to foster understanding for age-related challenges in healthcare training by mimicking physical impairments associated with aging. However, effects on functional levels are ambiguous and necessitating validation of their potential to simulate age-related walking impairments. We evaluated effects of wearing an aging suit on kinematic gait variables, in younger and middle-aged adults in different walking conditions. Available reference data were used to compare aging-suit induced effects to standard and dual-task walking in older adults.Research Design and Methods Whole-body kinematics (sagittal ankle-, knee-, hip-angles, arm-swing, trunk-bend) and spatiotemporal parameters (walking speed, stride length, step width) were measured in 14 young (20–34 years) and 15 middle-aged adults (40–63 years). SPM analysis and mixed ANOVA were conducted to evaluate the effects of the suit, age-group and their interaction.Results Overall, wearing the aging suit changed gait patterns, but kinematic parameters were hardly affected in both groups. During standard walking, arm-swing decreased by 17%, walking speed by 9%, and step width increased by 15% across both groups. Compared to reference data, changes in arm-swing corresponded to an instant aging effect of 45–55 years in young and 15–25 years in middle-aged adults.Discussion and Implications The aging suit changed gait patterns considerably making both groups walk more cautiously compared to reference values of older adults. However, performance deficits seen in individuals 80+ years were clearly not attained. Caution is advised when using aging suits as an educational tool to simulate age-related walking impairments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".