BACKGROUND: The effects of air pollution on health are associated with the amount of pollutants inhaled which depends on the environmental concentration and the inhaled air volume. It has not been clear whether statistical models of the relationship between heart rate and ventilation obtained using laboratory cardiopulmonary exercise test (CPET) can be applied to an external group to estimate ventilation. OBJECTIVES: To develop and evaluate a model to estimate respiratory ventilation based on heart rate for inhaled load of pollutant assessment in field studies. METHODS: Sixty non-smoking men; 43 public street workers (public street group) and 17 employees of the Forest Institute (park group) performed a maximum cardiopulmonary exercise test (CPET). Regression equation models were constructed with the heart rate and natural logarithmic of minute ventilation data obtained on CPET. Ten individuals were chosen randomly (public street group) and were used for external validation of the models (test group). All subjects also underwent heart rate register, and particulate matter (PM2.5) monitoring for a 24-hour period. RESULTS: For the public street group, the median difference between estimated and observed data was 0.5 (CI 95% -0.2 to 1.4) l/min and for the park group was 0.2 (CI 95% -0.2 to 1.2) l/min. In the test group, estimated values were smaller than the ones observed in the CPET, with a median difference of -2.4 (CI 95% -4.2 to -1.8) l/min. The mixed model estimated values suggest that this model is suitable for situations in which heart rate is around 120-140bpm. CONCLUSION: The mixed effect model is suitable for ventilation estimate, with good accuracy when applied to homogeneous groups, suggesting that, in this case, the model could be used in field studies to estimate ventilation. A small but significant difference in the median of external validation estimates was observed, suggesting that the applicability of the model to external groups needs further evaluation.
BACKGROUND: The effects of air pollution on health are associated with the amount of pollutants inhaled which depends on the environmental concentration and the inhaled air volume. It has not been clear whether statistical models of the relationship between heart rate and ventilation obtained using laboratory cardiopulmonary exercise test (CPET) can be applied to an external group to estimate ventilation. OBJECTIVES: To develop and evaluate a model to estimate respiratory ventilation based on heart rate for inhaled load of pollutant assessment in field studies. METHODS: Sixty non-smoking men; 43 public street workers (public street group) and 17 employees of the Forest Institute (park group) performed a maximum cardiopulmonary exercise test (CPET). Regression equation models were constructed with the heart rate and natural logarithmic of minute ventilation data obtained on CPET. Ten individuals were chosen randomly (public street group) and were used for external validation of the models (test group). All subjects also underwent heart rate register, and particulate matter (PM2.5) monitoring for a 24-hour period. RESULTS: For the public street group, the median difference between estimated and observed data was 0.5 (CI 95% -0.2 to 1.4) l/min and for the park group was 0.2 (CI 95% -0.2 to 1.2) l/min. In the test group, estimated values were smaller than the ones observed in the CPET, with a median difference of -2.4 (CI 95% -4.2 to -1.8) l/min. The mixed model estimated values suggest that this model is suitable for situations in which heart rate is around 120-140bpm. CONCLUSION: The mixed effect model is suitable for ventilation estimate, with good accuracy when applied to homogeneous groups, suggesting that, in this case, the model could be used in field studies to estimate ventilation. A small but significant difference in the median of external validation estimates was observed, suggesting that the applicability of the model to external groups needs further evaluation.
Authors: Cara M Smith; Steven N Chillrud; Darby W Jack; Patrick Kinney; Qiang Yang; Aimee M Layton Journal: J Occup Environ Med Date: 2019-04 Impact factor: 2.162