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Towards an individualized protocol for workload increments in cardiopulmonary exercise testing in children and
H J Hulzebos1, M S Werkman, M van Brussel
1Child Development & Exercise Center, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands. H.Hulzebos@umcutrecht.nl
Insights
A new model predicts peak workload for children with cystic fibrosis (CF) during exercise tests. This individualized approach helps tailor exercise protocols for better CF patient care.
Area of Science:
- Pediatric Exercise Physiology
- Pulmonary Rehabilitation
- Clinical Exercise Testing
Background:
- Exercise testing protocols for pediatric cystic fibrosis (CF) patients lack standardization due to wide variations in age and disease severity.
- Developing a CF-specific, individualized approach for cycle ergometry workload increments is crucial for accurate assessment.
Purpose of the Study:
- To develop and validate a CF-specific regression model for predicting peak workload (Wpeak) in children and adolescents.
- To establish individualized workload increments for cycle ergometry testing in CF patients.
Main Methods:
- Utilized a database of 409 assessments from 160 pediatric CF patients, including maximal exercise data, anthropometrics, and lung function.
- Employed backward linear regression analysis on 90% of the data to develop the predictive model for Wpeak.
- Validated the model's predictive capacity and the protocol's time frame (10±2 minutes) in separate validation groups.
Main Results:
- The developed regression model accurately predicted peak workload (R²=0.79), with no significant bias compared to actual measurements.
- The model equation incorporates age, sex, height, and FEV1 to predict Wpeak.
- Protocol validation showed that most adolescents achieved maximal effort within the target time frame.
Conclusions:
- A novel, CF-specific linear regression model was successfully developed to predict peak workload.
- This model enables individualized workload calculations for cycle ergometry protocols in pediatric CF patients.
- The findings support the use of this individualized approach for optimizing exercise testing and management in CF.
Background:
There is no single optimal exercise testing protocol for children and adolescents with cystic fibrosis (CF) that differs widely in age and disease status. The aim of this study was to develop a CF-specific, individualized approach to determine workload increments for a cycle ergometry testing protocol.
Methods:
A total of 409 assessments consisting of maximal exercise data, anthropometric parameters, and lung function measures from 160 children and adolescents with CF were examined. 90% of the database was analyzed with backward linear regression with peak workload (W(peak)) as the dependent variable. Afterwards, we [1] used the remaining 10% of the database (model validation group) to validate the model's capacity to predict W(peak) and [2] validated the protocol's ability to provide a maximal effort within a 10±2 minute time frame in 14 adolescents with CF who were tested using this new protocol (protocol validation group).
Results:
No significant differences were seen in W(peak) and predicted W(peak) in the model validation group or in the protocol validation group. Eight of 14 adolescents with CF in the protocol validation group performed a maximal effort, and seven of them terminated the test within the 10±2 minute time frame. Backward linear regression analysis resulted in the following equation: W(peak) (W)=-142.865+2.998×Age (years)-19.206×Sex (0=male; 1=female)+1.328×Height (cm)+23.362×FEV(1) (L) (R=.89; R(2)=.79; SEE=21). Bland-Altman analysis showed no systematic bias between the actual and predicted W(peak).
Conclusion:
We developed a CF-specific linear regression model to predict peak workload based on standard measures of anthropometry and FEV(1), which could be used to calculate individualized workload increments for a cycle ergometry testing protocol.
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