DESIGN: Food choice was assessed using the validated New Zealand Adolescent FFQ. Principal components analysis was used to determine dietary patterns. Trained research assistants measured participants' height and body mass. Cardiorespiratory fitness was assessed in a subset of participants using the multistage 20 m shuttle run. The level and stage were recorded, and the corresponding VO2max was calculated. Differences in mean VO2max according to sex and BMI were assessed using t tests, while associations between cardiorespiratory fitness and dietary patterns were examined using linear regression analyses adjusted for age, sex, school attended, socio-economic deprivation and BMI.
SETTING: Secondary schools in Otago, New Zealand.
SUBJECTS: Students (n 279) aged 14-18 years who completed an online lifestyle survey during a class period.
RESULTS: Principal components analysis produced three dietary patterns: 'Treat Foods', 'Fruits and Vegetables' and 'Basic Foods'. The 279 participants who provided questionnaire data and completed cardiorespiratory fitness testing had a mean age of 15·7 (sd 0·9) years. Mean VO2max was 45·8 (sd 6·9) ml/kg per min. The 'Fruits and Vegetables' pattern was positively associated with VO2max in the total sample (β=0·04; 95%CI 0·02, 0·07), girls (β=0·06; 95% CI 0·03, 0·10) and boys (β=0·03; 95% CI 0·01, 0·05).
CONCLUSIONS: These results indicate that increase in cardiorespiratory fitness was associated with a healthier dietary pattern, suggesting both should be targeted as part of a global lifestyle approach. Longitudinal studies are needed to confirm this association in relation to health outcomes in New Zealand adolescents.
METHODS: A total of 401 whole blood samples with a fresh HbA1c measurement were randomly selected from The Malaysian Cohort's (TMC) biobank. The HbA1c measurements of fresh and frozen (stored for 7-8 years) samples were assayed using different high-performance liquid chromatography (HPLC) systems. The HbA1c values of the fresh samples were then calculated and corrected according to the later system. The reproducibility of HbA1c measurements between calculated-fresh and frozen samples was assessed using a Passing-Bablok linear regression model. The Bland-Altman plot was then used to evaluate the concordance of HbA1c values.
RESULTS: The different HPLC systems highly correlated (r = 0.99) and agreed (ICC = 0.96) with each other. Furthermore, the HbA1c measurements for frozen samples strongly correlate with the corrected HbA1c values of the fresh samples (r = 0.875) with a mean difference of -0.02 (SD: -0.38 to 0.38). Although the mean difference is small, discrepancies were observed within the diabetic and non-diabetic samples.
CONCLUSION: These data demonstrate that the HbA1c measurements between fresh and frozen samples are highly correlated and reproducible.
METHODS: A dataset of 1576 online survey responses yielded subsamples for anorexia nervosa (n = 155), bulimia nervosa (n = 55), binge eating disorder (n = 33), other specified feeding or eating disorder (n = 93), and healthy participants (n = 505). The hierarchical linear regression analysis included Eating Disorder Examination Questionnaire 6.0 Global Score as the dependent variable; Young Positive Schema Questionnaire, Emotional Regulation Questionnaire, and Cognitive Flexibility Inventory subscale scores as the independent variables; and demographic measures as the covariates.
RESULTS: The number of significant predictors varied considerably by ED sub-group. Amongst the anorexia nervosa, bulimia nervosa, and healthy subsamples, the adaptive schema Self-Compassion and Realistic Expectations was associated with lower ED symptom severity. In comparison, age and body mass index were the strongest predictors for binge eating disorder, whilst the Expressive Suppression (a subscale of the Emotional Regulation Questionnaire) was the strongest predictor for other specified feeding or eating disorders.
CONCLUSION: Early adaptive schemas, cognitive flexibility, and emotional regulation vary across ED subtype, suggesting the need for tailored treatment that disrupts the self-reinforcing cycle of ED psychopathology. Future research investigating how early adaptive schemas may predict or be associated with treatment response across diagnostic subtypes is needed.
LEVEL OF EVIDENCE: Level IV, evidence obtained from multiple time-series with or without the intervention, such as case studies.
METHODS: A dataset containing medical records of 809 patients suspected to suffer from ACS was used. For each subject, 266 clinical factors were collected. At first, a feature selection was performed based on interviews with 20 cardiologists; thereby 40 seminal features for classifying ACS were selected. Next, a feature selection algorithm was also applied to detect a subset of the features with the best classification accuracy. As a result, the feature numbers considerably reduced to only seven. Lastly, based on the seven selected features, eight various common pattern recognition tools for classification of ACS were used.
RESULTS: The performance of the aforementioned classifiers was compared based on their accuracy computed from their confusion matrices. Among these methods, the multi-layer perceptron showed the best performance with the 83.2% accuracy.
CONCLUSION: The results reveal that an integrated AI-based feature selection and classification approach is an effective method for the early and accurate classification of ACS and ultimately a timely diagnosis and treatment of this disease.
Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE.
Results: Adjusting for controls, both MPI (β =-0.245, P<0.001) and IPG at 3.1 USD (β=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (β=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (β=-0.210, P<0.001) than male (β=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity.
Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.
Methods: A quasi-experimental study with 328 obese and overweight low socio- economic status housewives aged 18-59 years old who met the screening criteria participated in the study. They were recruited into an intervention group (N = 169) or control group (N = 159). The intervention group received a lifestyle intervention consisting of a diet, physical activity and self-monitoring behavior package. The control group (delayed intervention group) received a women's health seminar package. Both groups were followed up for six months. Weight, body mass index (BMI), and blood pressure were evaluated both pre- and post-intervention.
Results: A total of 124 participants from the intervention group and 93 participants from the control group completed the study. Mean weight loss was 1.13 ± 2.70 kg (P < 0.05) in the intervention group and 0.97 ± 2.60 kg (P < 0.05) in the control group. Systolic blood pressure (SBP) reductions in the intervention group were 5.84 ± 18.10 mmHg (P < 0.05). The control group showed reduction in SBP 6.04 ± 14.52 mmHg (P < 0.05). Both group had non-significant DBP reduction. Multivariate analysis via General Linear Model Repeated Measures observed no significant differences in terms of parameter changes with time in both groups for all parameters.
Conclusions: The results indicate that the lifestyle interventions in this study resulted in modest weight loss and thus decreased BMI and blood pressure (SBP) within six months of intervention.