Virtual reality‐based assessment of cognitive‐locomotor interference in healthy young adults
Bibliographic record
Abstract
BACKGROUND: A recent literature review emphasized the importance of assessing dual-task (DT) abilities with tasks that are representative of community ambulation. Assessing DT ability in real-life activities using standardized protocols remains difficult. Virtual reality (VR) may represent an interesting alternative enabling the exposure to different scenarios simulating community walking. To better understand dual-task abilities in everyday life activities, the aims of this study were (1) to assess locomotor and cognitive dual-task cost (DTC) during representative daily living activities, using VR, in healthy adults; and 2) to explore the influence of the nature and complexity of locomotor and cognitive tasks on DTC. METHODS: Fifteen healthy young adults (24.9 ± 2.7 years old, 8 women) were recruited to walk in a virtual 100 m shopping mall corridor, while remembering a 5-item list (DT condition), using an omnidirectional platform and a VR headset. Two levels of difficulty were proposed for the locomotor task (with vs. without virtual agent avoidance) and for the cognitive task (with vs. without items modification). These tasks were also performed in single task (ST) condition. Locomotor and cognitive DTC were measured by comparing performances in ST and DT conditions. Locomotor performance was characterized using walking speed, walking fluidity, and minimal distance between the participant and the virtual agent during avoidance. Cognitive performance was assessed with the number of items correctly recalled. Presence of DTC were determined with one-sample Wilcoxon signed-rank tests. To explore the influence of the tasks' complexity and nature on DTC, a nonparametric two-way repeated measure ANOVA was performed. RESULTS: No locomotor interference was measured for any of the outcomes. A cognitive DTC of 6.67% was measured (p = .017) while participants performed simultaneously both complex locomotor and cognitive tasks. A significant interaction between locomotor task complexity and cognitive task nature (p = .002) was identified on cognitive DTC. CONCLUSIONS: In challenging locomotor and cognitive conditions, healthy young adults present DTC in cognitive accuracy, which was influenced by the locomotor task complexity task and the cognitive task nature. A similar VR-based protocol might be used to investigate DT abilities in older adults and individuals with a stroke.
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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 | 0.001 |
| 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 | 0.000 | 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".