Abstract：
OBJECTIVE: We determined the cough counts and their variability in subjects with persistent cough for 30 days.
METHODS: The Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analyzed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.
RESULTS: The mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS) have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as the percentage of days when cough frequencies fall within that day\'s 95% confidence interval. Overall Predictability was the mean of the 30-One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).
CONCLUSIONS: There is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. If confirmed in future studies, the clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.
METHODS: The Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analyzed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.
RESULTS: The mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS) have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as the percentage of days when cough frequencies fall within that day\'s 95% confidence interval. Overall Predictability was the mean of the 30-One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).
CONCLUSIONS: There is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. If confirmed in future studies, the clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.
摘要：
目的：我们测定了持续咳嗽30天的受试者的咳嗽计数及其变异性。
方法：Hyfe咳嗽跟踪器应用程序使用手机麦克风监控声音，并通过启用人工智能的算法识别咳嗽。我们分析了每天的咳嗽计数，包括每天监测咳嗽超过30天的97个人的每日可预测率，每天咳嗽率至少为每小时5次。
结果：平均（中位数）每日咳嗽率从每小时6.5至182（6.2至160）咳嗽不等，在所有受试者中，每小时咳嗽的标准偏差（四分位数范围）从0.99到124（1.30到207）不等。咳嗽率和变异性之间存在正相关，因为平均咳嗽率（OLS）较高的受试者具有较大的标准偏差。预测所有30天的任何给定日期的准确性是该日期的一天可预测性，定义为咳嗽频率落在当天95%置信区间内的天数百分比。总体可预测性是30-1天可预测性百分比的平均值,范围从95%(最佳可预测性)到30%(最低可预测性)。
结论：在30天内记录的持续性咳嗽的每个受试者在一天内和每天之间都有很大的变化。如果在未来的研究中得到证实，需要评估这种变异性的临床意义以及使用咳嗽计数作为咳嗽干预的主要终点的影响.
方法：Hyfe咳嗽跟踪器应用程序使用手机麦克风监控声音，并通过启用人工智能的算法识别咳嗽。我们分析了每天的咳嗽计数，包括每天监测咳嗽超过30天的97个人的每日可预测率，每天咳嗽率至少为每小时5次。
结果：平均（中位数）每日咳嗽率从每小时6.5至182（6.2至160）咳嗽不等，在所有受试者中，每小时咳嗽的标准偏差（四分位数范围）从0.99到124（1.30到207）不等。咳嗽率和变异性之间存在正相关，因为平均咳嗽率（OLS）较高的受试者具有较大的标准偏差。预测所有30天的任何给定日期的准确性是该日期的一天可预测性，定义为咳嗽频率落在当天95%置信区间内的天数百分比。总体可预测性是30-1天可预测性百分比的平均值,范围从95%(最佳可预测性)到30%(最低可预测性)。
结论：在30天内记录的持续性咳嗽的每个受试者在一天内和每天之间都有很大的变化。如果在未来的研究中得到证实，需要评估这种变异性的临床意义以及使用咳嗽计数作为咳嗽干预的主要终点的影响.
