OBJECTIVES: To quantify intraindividual variability in peak expiratory flow (PEF) measured with peak flow-meters and to define factors affecting PEF variability. METHODS: Three hundred one healthy subjects (aged 4 to 84 years) were recruited from sites at sea level (n = 220) and at 1,400 m altitude (n = 81). All testing was done with the same model peak flowmeter. Each subject was actively coached through five to eight successive PEF maneuvers. Three meters of the same model were tested using a mechanical waveform simulator at three different flows at both testing altitudes (sea level and at 1,400 m). RESULTS: Excluding outliers, the mean PEF was 523 L/min, mean standard deviation (SD) was 22 L/min, and mean coefficient of variation (CV) was 4.6%. The upper 95th percentile for CV was 8% for adults and 10% for youths. Analyzing only the three highest peak flows for each subject, the mean PEF was 539 L/min, mean SD was 12 L/min, and mean CV was 2.4%. The upper 95th percentile for CV was 6% for adults and 9% for youths. Linear regression analysis revealed a small but statistically significant correlation (p < 0.01) between mean peak flow and CV. In adults, SD correlated with sex (p < 0.01) but neither CV nor SD was correlated with age, height, weight, or altitude. Meter variability defined with the mechanical waveform simulator was small. Standard deviation varied from 1.5 to 4.2 L/min and CV from 0.4 to 1.6%. When the three largest peak flows for each subject were used, 5.5% of intraindividual variance was explained by meter variance. CONCLUSIONS: These estimates of intraindividual variability in healthy subjects are generally lower than those previously reported. Meter variability accounts for only a small part of total intraindividual variability. The 95th percentile data suggest that a fall in PEF of 6 to 8% in adults and 9 to 10% in youths would be statistically significant.
OBJECTIVES: To quantify intraindividual variability in peak expiratory flow (PEF) measured with peak flow-meters and to define factors affecting PEF variability. METHODS: Three hundred one healthy subjects (aged 4 to 84 years) were recruited from sites at sea level (n = 220) and at 1,400 m altitude (n = 81). All testing was done with the same model peak flowmeter. Each subject was actively coached through five to eight successive PEF maneuvers. Three meters of the same model were tested using a mechanical waveform simulator at three different flows at both testing altitudes (sea level and at 1,400 m). RESULTS: Excluding outliers, the mean PEF was 523 L/min, mean standard deviation (SD) was 22 L/min, and mean coefficient of variation (CV) was 4.6%. The upper 95th percentile for CV was 8% for adults and 10% for youths. Analyzing only the three highest peak flows for each subject, the mean PEF was 539 L/min, mean SD was 12 L/min, and mean CV was 2.4%. The upper 95th percentile for CV was 6% for adults and 9% for youths. Linear regression analysis revealed a small but statistically significant correlation (p < 0.01) between mean peak flow and CV. In adults, SD correlated with sex (p < 0.01) but neither CV nor SD was correlated with age, height, weight, or altitude. Meter variability defined with the mechanical waveform simulator was small. Standard deviation varied from 1.5 to 4.2 L/min and CV from 0.4 to 1.6%. When the three largest peak flows for each subject were used, 5.5% of intraindividual variance was explained by meter variance. CONCLUSIONS: These estimates of intraindividual variability in healthy subjects are generally lower than those previously reported. Meter variability accounts for only a small part of total intraindividual variability. The 95th percentile data suggest that a fall in PEF of 6 to 8% in adults and 9 to 10% in youths would be statistically significant.