Measuring Exercise Self-Efficacy After Stroke: Validity and Reliability of Current Measures
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many physically capable stroke survivors are insufficiently active, with low self-efficacy considered an important contributor. However, validity and reliability of self-efficacy measures in stroke survivors have not been established. This research aims to evaluate the test-retest reliability and construct validity of 3 self-efficacy measures: Self-Efficacy for Exercise Scale (SEE), Spinal Cord Injury Exercise Self-Efficacy Scale (SCI-ESES), and Participation Strategies Self-Efficacy Scale (PS-SES). METHODS: A repeated measures study with community-dwelling, independently mobile adult stroke survivors (n = 51, mean age 74 years, 45% female, median 22 months poststroke) was completed. Test-retest reliability was assessed using intraclass correlation coefficients and Bland-Altman analyses. Construct validity was assessed using 8 pre-determined hypotheses concerning physical activity level (subjective and objective), comorbidities, work and volunteering, and measures of function. RESULTS: Retest reliability was established for the SEE (intraclass correlation coefficient, ICC = 0.77) and PS-SES (ICC = 0.78) but not for the SCI-ESES (ICC = 0.68). Bland-Altman analysis showed participants consistently scored higher on the second test for all measures. The SEE achieved construct validity by meeting 75% of hypotheses, whereas the PS-SES and SCI-ESES did not. Self-efficacy was positively related to steps/day, functional capacity, self-reported activity levels, and work or volunteering participation. DISCUSSION AND CONCLUSIONS: The SEE was found to be the most appropriate tool to measure exercise self-efficacy in independently mobile chronic stroke survivors in terms of retest reliability and validity. TRIAL REGISTRATION: N/A. VIDEO ABSTRACT AVAILABLE: for more insights from the authors (see the Video, Supplemental Digital Content 1 "Espernberger-JNPT-Video-Abstract," available at: http://links.lww.com/JNPT/A489 ).
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".