Novel Digital Gait Kinematic Solution to Improve Frailty
Bibliographic record
Abstract
Introduction: Frailty effects a person’s health and correlates with mobility and falls. Intervention studies that focus on exercise have demonstrated improved mobility and functional ability in some frailty groups. This study tested a personalised intervention programme automatically generated from digital gait data on frail older people under the care of the North East London Foundation Trust, Community Hospital setting. Methods: One hundred and twenty one people, average age 79, who suffered an injurious fall and were under the care of the Community Hospital, completed the personalised intervention programme. Objective gait kinematic data, obtained using GaitSmartTM automatically generated a personalised exercise programme. Each participant received four tests, approximately 3 weeks apart and was provided with a copy of their report plus personalised exercises. Frailty was measured using the Edmonton Frailty Scale (EFS), fear of falling was measured using the Falls Efficacy Scale-International (FES-I) and speed was determined from the gait data (GS). Results: Five parameters were analysed for all 121 participants at the start and end of the intervention: EFS; FES-I; GS; speed; walking aid. There was a statistically significance between the start and end (p<0.001) for all the parameters. Conclusion: The results demonstrate that addressing frailty using a digital gait solution that sets exercises based on the gait kinematic data, did reverse frailty. This four session programme has shown to improve frailty levels and fear of falling. It also reduced the reliance on walking aids and increased average walking speed from 0.46 to 0.62 m/s.
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 | 0.001 |
| 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) | 0.004 | 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".