IMPACT OF DUAL-TASKING AND DEMENTIA DISEASE SEVERITY ON POSTURE, GAIT, AND FUNCTIONAL MOBILITY IN RESIDENTIAL CARE
Bibliographic record
Abstract
Abstract Dual-tasking may be a useful tool for fall-risk screening among people living with dementia (PWD), but more evidence is needed. Our aims were to compare single- and dual-task posture, gait and functional mobility; and examine the relationship between dementia severity and dual-task interference among PWD. Thirty PWD (Age:81.8±7.3years; 35% Female; Montreal Cognitive Assessment:10.5±6.1points) from two residential care facilities wore six APDM inertial sensors and completed two trials of single- (feet apart) and dual-task posture (feet apart while counting backwards by 1’s), single- (walk 4m) and dual-task gait (walk 4m while naming words), and single- (timed-up-and-go (TUG)); and dual-task functional mobility (TUG while completing a category task). Results revealed that greater frequency, jerk, and area were observed during dual- than single-task posture (ps< 0.05), with no differences in velocity. Slower gait speed, greater double limb support, shorter stride length, and reduced elevation at mid-swing were detected during dual- relative to single-task gait (ps< 0.05), with no differences in medial-lateral trunk sway. Dual-tasking resulted in a longer duration, reduced turn angle, and slower turn velocity than single-tasking for the TUG (ps< 0.05), with no differences in sit-to-stand lean angle. Dual-task interference (greater jerk, faster gait speed) was related to moderate-to-severe compared to mild dementia (ps< 0.05). Dual-tasking was more sensitive to detect impairments in posture, gait, and functional mobility than single-tasking. Moderate to severe PWD had poorer dynamic stability and a reduced ability to appropriately select cautious gait during dual-tasking than those with mild dementia, which should be considered in fall-risk screening.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".