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 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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".