Dual tasking with MS: The role of attentional switching on gait parameters
Bibliographic record
Abstract
Dual tasking with MS: The role of attentional switching on gait parameters Daniel T*, B.S.; Martin S*, B.S.; Symonds J*, B.S.; Soltero J*, B.S.; Warshaw A*, B.S.; Kasser S, Ph.D. *= first author Introduction: Multiple Sclerosis (MS) is a neurodegenerative disease presenting in a range of physical and cognitive impairments that negatively impact mobility and cause frequent falls. Persons with MS are at an even higher risk of falls when simultaneously performing a cognitive and motor activity (i.e., walking and talking) which increase attentional demand and resources. Given that individuals spend a majority of their daily lives dual tasking, understanding how attentional focus affects walking is imperative. Objective: The purpose of this pilot study is to examine the impact of attentional focus and gait speed on walking in MS . Methods: All participants in this study were diagnosed with MS and were ambulatory. Background and baseline data were collected by self-report questionnaires including Modified Fatigue Impact Scale (MFIS), Falls Efficacy Scale (FES-I), 12-Item MS Walking Scale (MSWS-12), and International Physical Activity Questionnaire (IPAQ). Cognitive speed and function was assessed using the Symbol Digit Modalities Test (SDMT) and Montreal Cognitive Assessment (MoCA). Variables of gait were assessed across eight dual task conditions related to focus of attention (internal, external, or switching) and gait speed (normal pace or fact walking). Dual task cost will be calculated for each variable in each condition and data will be analyzed using SPSS statistical software and one sample T-tests upon the completion of data collection. Results: Results are pending completion of data collection and subsequent analyses. Conclusion: The results of this study may help to expand our understanding of attentional focus and dual task cost in order to enhance targeted physical therapy interventions and reduce the risk of falls in individuals with MS.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".