Clinical Measures of Balance and Walking Ability in People with Stroke for Assessment via Videoconferencing
Bibliographic record
Abstract
Purpose: This study modified established clinical balance and walking measures and estimated the reliability, validity, and feasibility of using these measures to assess people post-stroke via videoconferencing. Method: Twenty-eight people with chronic stroke were recruited and completed the in-person balance and mobility tests. Five clinical measures were modified as virtual assessments over videoconferencing. Feasibility was evaluated by task completion rate, occurrence of adverse events, and technical difficulties. Test–retest reliability and agreement were examined by intra-class correlations and standard error of measurement between two testing days. Convergent validity was examined by the magnitude of associations between in-person and virtual assessments using Pearson or Spearman rank correlation. Results: Twenty-one participants (52% female) participated in both in-person and virtual assessments. No adverse events occurred. Technical challenges were experienced by eight participants. Test–retest reliability for timed up and go test, 30-seconds sit-to-stand, five-times sit-to-stand, functional reach test, and tandem stance resulted in intra-class coefficients of 0.97, 0.90, 0.77, 0.54, and 0.50 respectively. The standard error of measurement was low across all virtual assessments. The timed up and go test, five-times sit-to-stand, and 30-seconds sit-to-stand showed relationship with in-person assessments ( r = −0.55 to −0.81). Conclusions: Virtual assessment of walking and balance function in ambulatory people post-stroke is feasible; however, technical challenges were experienced. The test–retest reliability of virtual assessments of timed up and go test and sit-to-stand tasks for people with stroke, together with strong convergent validity of the measures compared to in-person assessments is promising.
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 | 0.008 |
| 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".