Reliability and validity of the habitual activity estimation scale (HAES) in patients with cystic fibrosis
Bibliographic record
Abstract
PURPOSE: To understand potential benefits of exercise in the cystic fibrosis (CF) population, there needs to be accurate methods to quantify it. The Habitual Activity Estimation Scale (HAES) questionnaire has been shown to be a feasible tool to measure physical activity however the reliability and validity have yet to be determined in the CF population. METHODS: Fourteen (seven male, seven female) patients aged 16.2 +/- 4.2 years with CF participated in this study. Participants were clinically stable at the time of the study and participating in their habitual physical activity. To assess reliability, patients completed the HAES and a validated 3-day activity diary, and wore an ActiGraph Accelerometer for two consecutive weeks. Validity was assessed by comparing the activity results of each of the three instruments over a single week time period. RESULTS: ICC estimates of reliability for the HAES, diary, and accelerometer were 0.72 (P < 0.0001), 0.76 (P < 0.0001), 0.63 (P < 0.0001), respectively. Validity analysis indicated that there were significant relationships between the participants' activity results as estimated by the HAES, diary and accelerometer. Further, significant relationships were detected between activity measures when broken into morning, afternoon, or evening periods, and between measures from weekday or weekend days. There were also significant relationships among the three instruments when recording different activity levels (somewhat inactive, somewhat active, and very active). CONCLUSION: The findings of this study suggest that the HAES questionnaire is a reliable and valid instrument that can be used to assess activities of varying intensity in patients with CF.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".