Patients have completed the PSQI for 2250 nights (98.2%). Figure s1 in the online supplement for the distribution of average sleep quality scores per patient). In 2291 nights, the 79 patients slept rather well with an average sleep quality of 77.5% (SD = 15.2% cf. PSQI score was standardized on a scale from 0 (worst possible sleep quality) to 1 (best possible sleep quality). Although devices for assessing sleep quality passively and with a higher temporal resolution are available, 19 we decided to use a questionnaire-based assessment to reduce the burden for patients during the study (ie, patients had to fill out questions on the smartphone every morning either way). Since the power analysis was based on conservative assumptions (ie, disregard of longitudinal measurements), the slightly smaller sample size likely sufficed for statistical analysis.Ī patient’s sleep quality was assessed daily via an adapted version of the Pittsburgh Sleep Quality Index (PSQI 18).
14 Data from 79 patients could be analyzed after accounting for dropouts, so that the targeted sample size was missed by 6 patients ( Figure 1). The study was powered to examine statistical associations between both markers and asthma control. This work has four objectives: (1) to examine the statistical associations of both markers (ie, nocturnal cough and sleep quality) with asthma control, in particular with regard to within-patient associations to investigate whether they enable detection of weeks, in which (2) patients had uncontrolled asthma or (3) clinically significant deteriorations in asthma control occurred and (4) to explore whether they can be used to predict asthma attacks in advance. In the time between study center appointments, questionnaire and sensor data were collected in situ with a smartphone (Samsung Galaxy A3 2017, SM-A320FL) application based on MobileCoach. On d 1 and d 29, medical assessments of patients were performed at the study centers. We then investigated whether both markers can enable the detection of periods with uncontrolled asthma or clinically meaningful deteriorations of asthma control, and prediction of asthma attacks. Expanding the findings of prior research, we first examined whether both markers are associated with asthma control in a longitudinal (ie, within-patient) setting. We investigated whether nocturnal cough and sleep quality may constitute useful markers for asthma. 9–11 Research in the domain of mobile sensing demonstrated their measurability by means of a smartphone’s built-in sensors. 8 Nocturnal cough and sleep quality, for example, have been reported to be statistically associated with asthma control in cross-sectional studies.
Novel objective markers, that can be measured without extra patient effort, could mitigate these shortcomings. 5 Furthermore, objective methods of asthma monitoring (eg, self-administered asthma control test (ACT 6) or peak flow monitoring) are considered impractical by patients 7 and are still poorly adopted in practice. 4 For patients with a longer duration of illness, in particular, subjective assessments are of limited reliability. 2 However, prior research has found that patients’ subjective self-assessments are biased towards an overestimation of asthma control. To prevent potentially life-threatening asthma attacks, patients need to be aware of their actual asthma control and early warning signs of deterioration. 1 Despite the availability of effective treatment options, suboptimal asthma control is still common. It affects an estimated 300 million people globally. Asthma is one of the most prevalent chronic respiratory diseases.
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