Evaluation of measurement properties of the Impact of Weight on Quality of Life-Lite (IWQOL-Lite) instrument among Chinese overweight and obese populations
Bibliographic record
Abstract
To evaluate measurement properties of the Impact of Weight on Quality of Life-Lite (IWQOL-Lite) instrument among Chinese overweight and obese populations. A representative sample of Chinese overweight and obese populations was recruited stratified by age, sex, residence and body mass index (BMI). Social-demographic characteristics, self-reported EQ-5D-5 L and IWQOL-Lite responses were collected through the online survey. Test-retest reliability was assessed using intraclass correlation coefficient (ICC) among a subgroup of the total sample. Structural validity was evaluated by confirmatory factor analysis (CFA). Convergent validity and known-group validity were examined using Spearman’s rank correlation and effect sizes, respectively. A total of 1000 respondents (48% female; mean age: 51.7 years; mean BMI: 27.4) were included in this study. Ceiling and floor effects of the IWQOL-Lite were 5.4% and 0.67%, respectively. The ICC between the two tests was 0.992 for IWQOL-Lite among the subgroup (N = 150). The results of the CFA suggested that the five-factor model had an acceptable structural validity (GFI = 0.894, CFI = 0.960, TLI = 0.957, RMSEA = 0.054 and SRMR = 0.033). The Spearman’s rank correlation (range: 0.413–0.611) indicated a satisfactory convergent validity. The effect sizes values of IWQOL-Lite total score and different dimensions were moderate. The IWQOL-Lite has been demonstrated to have satisfactory validity and reliability in measuring the HRQoL of Chinese overweight and obese populations. Further research is needed to confirm the sensitivity and responsiveness. Overweight and obesity are one of the major public health problems worldwide, with serious negative impacts on health-related quality of life (HRQoL). The impact of weight on quality of life-lite (IWQOL-Lite) is a widely used measure to evaluate HRQoL for overweight and obese populations. However, no study has validated the measurement properties of the IWQOL-Lite among Chinese population. Our study aimed to evaluate the measurement properties of the IWQOL-Lite in China. We evaluated several measurement properties, including ceiling and floor effects, test-retest reliability, structural validity, convergent validity and known-group validity, of the IWQOL-Lite in a representative sample of Chinese overweight and obese individuals. We found that the IWQOL-Lite is a valid and reliable tool for measuring the HRQoL of overweight and obese populations in China. This supports the use of the IWQOL-Lite in overweight and obese populations in the future.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".