METHODS: We used relevant key words to search electronic databases to identify studies published up to 2019 that used receiver operating characteristic (ROC) curves for assessing the cut-off points of anthropometric indices. We used a random-effects model to pool study results and assessed between-study heterogeneity by using the I2 statistic and Cochran's Q test.
RESULTS: This meta-analysis included 38 cross-sectional and 2 cohort studies with 105 to 137,256 participants aged 18 or older. The pooled area under the ROC curve (AUC) value for BMI was 0.66 (95% CI, 0.63-0.69) in both men and women. The pooled AUC values for WC were 0.69 (95% CI, 0.67-0.70) in men and 0.69 (95% CI, 0.64-0.74) in women, and the pooled AUC values for WHR were 0.69 (95% CI, 0.66-0.73) in men and 0.71 (95% CI, 0.68-0.73) in women.
CONCLUSION: Our findings indicated a slight difference between AUC values of these anthropometric indices. However, indices of abdominal obesity, especially WHR, can better predict CVD occurrence.
DESIGN: A cross sectional study.
METHOD: A self-administered online survey was used from August to October 2022, with a sample size of 417 nursing students selected through convenience sampling. Descriptive statistics, correlation analyses, and PROCESS macro v4.1 (Model 4) were used for data analysis.
RESULTS: The results revealed that virtual learning infrastructure, access to electronic facilities, and student collaboration, significantly predict student computer competency and e-learning outcomes. Virtual learning infrastructure and access to electronic facilities were found to be the strongest predictors of student computer competency, while student collaboration had a smaller but still significant effect. Student computer competency was found to mediate the relationship between virtual learning infrastructure, access to electronic facilities, student collaboration, and e-learning outcomes.