Cerebral Palsy Clinical Trial
Official title:
Predictors of Physical Activity Levels in Children and Adolescents With Cerebral Palsy - a Clinical Cohort Study
NCT number | NCT04614207 |
Other study ID # | 1-Fonvig |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | November 3, 2020 |
Est. completion date | January 31, 2022 |
Verified date | June 2022 |
Source | University of Southern Denmark |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational [Patient Registry] |
Children and adolescents with cerebral palsy may be trapped in a vicious circle of low physical fitness, early fatigue in daily activities, resulting in deconditioning and a further decrease in physical activity. From this perspective, establishing a healthy and active lifestyle during childhood is even more important for individuals with a disability, who are at higher risk for functional limitations and general fatigue in addition with developing secondary' conditions such as cardiovascular disease, diabetes, and obesity. Furthermore, physical activity has a positive impact on quality of life, but it remains unknown which factors that influence high levels of habitual physical activity in children and adolescents with cerebral palsy. The present research project will investigate predictors of habitual physical activity in children and adolescents with cerebral palsy with the perspective of providing evidence on optimizing physical activity and consequently improved overall health.
Status | Completed |
Enrollment | 123 |
Est. completion date | January 31, 2022 |
Est. primary completion date | January 31, 2022 |
Accepts healthy volunteers | |
Gender | All |
Age group | 8 Years to 15 Years |
Eligibility | Inclusion Criteria: - Children born in the timeperiod 2003 - 2013 - Diagnosed with cerebral palsy (according to current classifications) - Gross Motor Function Classification System (GMFCS) level I - III - Registered in the Cerebral Palsy Follow-up Registry (CPUP), Denmark (As explained in the Statistical Analysis Plan dated June 6, 2022, CPUP data will be used were available. However, being registred in the CPUP database is no longer a requirement). Exclusion criteria: - Parents/guardians unable to read and understand Danish. |
Country | Name | City | State |
---|---|---|---|
Denmark | University of Southern Denmark | Odense C |
Lead Sponsor | Collaborator |
---|---|
University of Southern Denmark | AJ Andersen og Hustrus Fond, Dagmar Marshalls Fond, Elsass Foundation, Familien Hede Nielsens Fond, Fonden til Lægevidenskabens Fremme, Region of Southern Denmark |
Denmark,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Model 1a: Prediction of habitual physical activity using data from the CPUP registry | Habitual physical activity will be assessed objectively using accelerometer counts.
Multiple linear regression analyzes between accelerometer counts (response variable) and all CPUP variables within each ICF (International Classification of Functioning, Disability and Health) component as predictive variables, using the latest CPUP registration prior to accelerometer data collection. |
Response variable is measured between 0 and 39 months post registry data (predictive variables). | |
Secondary | Model 1b: Prediction of habitual physical activity using data from questionnaire variables | Habitual physical activity will be assessed objectively using accelerometer counts.
Multiple linear regression analysis between accelerometer counts (response variable) and all questionnaire variables within each ICF component (predictive variables), using the questionnaire data. |
Response variable is measured between 1 and 15 months post registry data (predictive variables). | |
Secondary | Model 2a: Prediction of habitual physical activity using data from the CPUP registry (penalized regression) | Penalized regression (lasso) with accelerometer counts as the response variable and covariates as chosen by lasso to obtain an optimal fit from model 1a as predictive variables to determine which variables to retain in the model. | Response variable is measured between 0 and 39 months post registry data (predictive variables). | |
Secondary | Model 2b: Prediction of habitual physical activity using data from questionnaire variables (penalized regression) | Penalized regression (lasso) with accelerometer counts as the response variable and covariates as chosen by lasso to obtain an optimal fit from model 1b as predictive variables to determine which variables to retain in the model. | Accelerometer data is collected 1-15 months post collection of questionnaire data. |
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