Differential Effects of Haptic Biofeedback on Gait Performance in Older Adults
Bibliographic record
Abstract
Older adults have poorer gait performance, leading to diminished mobility and increased risk of mortality.They walk with shorter stride length and lower gait speed, which are key determinants of gait performance.They also have reduced hip range of motion which leads to shorter steps and higher cadence.Wearable biofeedback systems are a potential solution to enhance walking ability in older adults, however, providing biofeedback to multiple gait variables is challenging.Yet, it is unknown if by providing biofeedback to one parameter only, all users display the same movement strategies resulting in the same gait pattern.This pilot study investigated if healthy older adults presented different gait patterns when a wearable biofeedback system prompted users to increase their swing time only.Four participants aged over 65 years used the device in an outdoor flat surface and received haptic biofeedback during 10-minutes.Two conditions were evaluated, with (Biofeedback) and without biofeedback (Control).Gait trials analysis suggested that in this pilot test, all participants increased swing time and reduced cadence, however, two walking patterns were characterized among participants.Participant 3 (P3) and participant 4 (P4) increased their stride length and speed, whereas participant 1 (P1) and participant 2 (P2) behaved the opposite.While P3 and P4 used their hip extension to produce larger strides to propel the body forward, P1 and P2 increased their knee flexion but lacked substantial increments in hip extension.This pilot test demonstrated that while all users followed the clues from the biofeedback, their entire gait could be very different.This pilot study provides insights into differential gait patterns and serves as a foundation to guide further experiments aiming to improve gait performance in healthy older adults.
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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".