Measurement Properties of a Virtually Administered 30-Second Chair Stand Test in People With Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Muscle strength is important for functional independence after a stroke. Given the rise in telerehabilitation, there is a need to study the measurement properties of virtually administered performance-based measures. The purpose of this study was to assess the validity and responsiveness of a virtually administered 30-second chair stand test (30sCST-Virtual) in people with stroke. METHODS: Thirty-two hypotheses were generated about construct validity and responsiveness using several outcome measures (Stroke Impact Scale, strength domain [SIS-S], Timed Up and Go [TUG], Activities-Specific Balance Confidence Scale, Fugl-Meyer Lower Extremity Assessment, Functional Reach Test, and SIS cognition domain). Hypotheses were tested using Spearman's correlations. Scores on the 30sCST-Virtual were compared between higher- and lower-functioning participants using the modified Rankin Scale (mRS) and NIH Stroke Scale (NIHSS) with Wilcoxon rank-sum tests to assess known-groups validity. RESULTS: Sixty-seven participants ( n = 19 female, 9.3 months post-stroke) with mild to moderate stroke were included. The 30sCST-Virtual demonstrated acceptable construct validity and responsiveness, as 14 (82%) and 12 (80%) hypotheses were confirmed, respectively. Its baseline scores were most highly correlated with the TUG ( r = - 0.64) and change scores with the SIS-S ( r = 0.35). The 30sCST-Virtual scores were also lower in those with lower function using the mRS (median difference [MD] = 4.0 repetitions, P < 0.001) and NIHSS (MD = 3.5 repetitions, P = 0.003), meeting our hypotheses for known-groups validity. DISCUSSION AND CONCLUSIONS: The 30sCST-Virtual demonstrated acceptable construct validity and responsiveness, as well as adequate known-groups validity. It was also moderately correlated with other measures of physical function, indicating that the 30sCST-Virtual may measure the construct of functional strength. VIDEO ABSTRACT AVAILABLE: For more insights from the authors (see the Video, Supplemental Digital Content available at http://links.lww.com/JNPT/A526 ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".