BackgroundRespiratory rate is a basic clinical measurement used for illness assessment. Errors in measuring respiratory rate are attributed to observer and equipment problems. Previous studies commonly report rate differences ranging from 2 to 6 breaths·min−1 between observers.MethodsTo study why repeated observations should vary so much, we conducted a virtual experiment, using continuous recordings of breathing from acutely ill patients. These records allowed each breathing cycle to be precisely timed. We made repeated random measures of respiratory rate using different sample durations of 30, 60 and 120 s. We express the variation in these repeated rate measurements for the different sample durations as the interquartile range of the values obtained for each subject. We predicted what values would be found if a single measure, taken from any patient, were repeated and inspected boundary values of 12, 20 or 25 breaths·min−1, used by the UK National Early Warning Score, for possible mis-scoring.ResultsWhen the sample duration was nominally 30 s, the mean interquartile range of repeated estimates was 3.4 breaths·min−1. For the 60 s samples, the mean interquartile range was 3 breaths·min−1, and for the 120 s samples it was 2.5 breaths·min−1. Thus, repeat clinical counts of respiratory rate often differ by >3 breaths·min−1. For 30 s samples, up to 40% of National Early Warning Scores could be misclassified.ConclusionsEarly warning scores will be unreliable when short sample durations are used to measure respiratory rate. Precision improves with longer sample duration, but this may be impractical unless better measurement methods are used.
BackgroundAutomatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal, and variation of the breathing cycle means that accurate observation for at least 60 s is needed for adequate precision.MethodsWe studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a tri-axial accelerometer attached to the chest wall, and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula.ResultsIn the test set of patients, machine classification of the respiratory signal reduced the absolute difference from 1.25 (0.56 to 2.18) to 0.48 (0.30 to 0.78) breaths/min (median, interquartile range). 50% of the recording periods were rejected as unreliable, and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6 min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time.ConclusionSignals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.
Clinical trials employing manual processes for data collection and administering of questionnaires are time-consuming, expensive to run and result in noisy data. Wireless body-worn sensors coupled with mobile applications can be harnessed to automate the data collection process during clinical trials. This paper describes the use of the Respeck monitor, worn as a plaster on the chest, for characterising breathing and physical activity patterns in the general population during their normal everyday lives. Respeck data collected from 93 subjects for periods ranging between 24 to 72 hours, amounting to a total of 106 days of continuous Respeck data. Analysis of the data revealed new insights, such as the respiratory rate levels dropped by 4.39 breaths per minute (BrPM) on average during sleeping periods, compared to the preceding daytime periods. This change is higher than typically reported levels when normally measured directly before the subjects fall asleep. Previous research in activity patterns in the general population were based on high-level activities logged using questionnaires. A method is presented for clustering simple, yet highdimensional, activity patterns based on the Respeck data, by first extracting relevant features for each day. The results reveal four distinct groups in the cohort corresponding to different identifiable lifestyles: "Sedentary", "Moderately active", "Active walkers" and "Active movers".
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