From: The CANadian Pediatric Weight Management Registry (CANPWR): Study protocol
Objective | Hypothesis | Outcome measure (C = Continuous; B = Binary) | Methods of analysis |
---|---|---|---|
Primary | Change in BMI z-score will be influenced by child/youth, family, and program characteristics consistent with our theoretical model | BMI z-score (C) | Hierarchical/multilevel modeling |
Document changes in anthropometric, lifestyle, behavioural, and obesity-related co-morbidities in children enrolled in Canadian pediatric weight management programs over a three-year period | |||
Secondary | Change in cardiometabolic health outcomes will be influenced by child/youth, family, and program characteristics consistent with our theoretical model | Systolic and diastolic blood pressure (C) | Hierarchical/multilevel modeling |
1) Document changes in anthropometric, lifestyle, behavioural, and obesity-related co-morbidities in children enrolled in Canadian pediatric weight management programs over a three-year period; | |||
Blood glucose (Fasting & 2Â hr post glucose load) (C) | |||
Total cholesterol/HDL-C ratio (C) | |||
Triglyceride (C) | |||
Fitness (C) | |||
Quality of Life (C) | |||
Lifestyle behaviours (C) | |||
2) Characterize the individual-, family-, and program-level determinants of change in anthropometric and obesity-related co-morbidities; | Individual-, family-, and program-level determinants will be identified that predict sustainability of change from years 1 – 3. | BMI z-score (C) | Hierarchical/multilevel modeling |
3) Examine the individual-, family-, and program-level determinants of program attrition. | Individual-, family-, and program-level determinants will differentiate those who dropped out of the program | Drop out from the program between enrollment and 1Â year (B) | Hierarchical/multilevel modeling |
Logistic regression | |||
Exploratory analyses | We will identify interaction terms between some individual, family and program determinants | All outcomes | Hierarchical/multilevel modeling |
Identify what works best for what groups of individuals or families | |||
Sensitivity analyses | As above | All outcomes | 1) Analysis with multiple imputation |
1) Imputation methods | |||
2) All outcomes analyzed simultaneously to account for correlation among them | 2) MANCOVA | ||
3) GEE | |||
3) Serial correlation of all outcomes over time | Â | Â | Â |