ABSTRACT
Context:
Asthma is interconnected with lower health-related quality of life along with lack of sleep.
Aims:
To assess the sleep quality in asthmatic patients. To examine the relationship between sleep quality, asthma control, and sociodemographic characteristics. To assess sleep quality and its association with asthma control and asthma quality.
Settings and Design:
The cross-sectional study included 45 normal control and 45 asthmatic patients.
Methods and Material:
We assessed sleep quality and daytime sleepiness using questionnaires. FEV1 prebronchodilators were recorded by spirometry. Questionnaires on asthma control and asthma quality of life, Gastroesophageal reflux disease impact scale, and sleep apnea scale were filled. Asthmatic subjects were categorized as having either nonsevere asthma or severe asthma.
Statistical Analysis Used:
Mann–Whitney U test and Spearman’s correlation.
Results:
The comparison of studied variables Pittsburgh sleep quality index (PSQI) global score, sleep quality, sleep latency, sleep disturbance, sleep medication, and daytime dysfunction between the control and study groups was significant (P value < 0.05). Poor sleep quality was associated with excessive sleepiness (r = 0.31, P < 0.05). The correlation between the asthma control questionnaire (ACQ) with sleep quality and Epworth sleepiness scale (ESS) was significant. Asthma quality of life questionnaire was negatively correlated with sleep quality, ACQ, and ESS. Severely impaired asthma quality of life was correlated with poor sleep quality, poor asthma control, and excessive sleepiness. The association between PSQI and asthma severity was not significant.
Conclusions:
Poor sleep quality was associated with not well-controlled asthma and severely impaired asthma quality of life.
Keywords: Asthma, daytime sleepiness, sleep quality
Introduction
Asthma control is a major management problem.[1] Uncontrolled asthma costs sleep quality.[2] Pathologic features like bronchial obstruction deteriorate sleep.[3] Interruption in sleep can affect physical and emotional wellbeing,[4] work efficiency,[5] and cognitive performance.[6] Asthma is interconnected with lower quality of life and poor sleep.[7] Therefore, sleep disturbance detection and monitoring can help to manage asthma.[8] Asthma is related to comorbidities like obesity, obstructive sleep apnea (OSA), and gastroesophageal reflux disease (GERD), and reciprocity between these comorbidities is not fully understood.[9] There is limited study about sleep quality of asthmatics in the Indian population.
Subjects and Methods
The study is approved by the Institutional Ethics Committee. Patient selection: The present cross-sectional study included voluntary subjects as normal control (NC), known asthmatic subjects, and asthmatic patients, who were recruited from the Pulmonary Medicine department. We recruited 45 NC and 45 asthmatic subjects as sample size calculated at 7.5% precision as per previous study showing 93% asthmatic patients were poor sleepers.[1] Participants will be categorized as NC, nonsevere asthma (NSA), and severe asthma (SA) depending on the inclusion criteria.
Inclusion criteria
NCs will be healthy individuals with normal lung function and without any previous history of asthma
Subjects having asthma will be classified and categorized according to the definition of refractory asthma given by American Thoracic Society.[10]
Subjects recruited from pulmonary medicine, who fall into any one of the two major criteria and minimum two out of the minor criteria (total seven), will be categorized as severe asthma.
Participants recruited from pulmonary medicine who do not meet criteria for severe asthma will be categorized as NSA.
Exclusion criteria
Smokers with five or more pack-years of tobacco use.
Subjects younger than 18 years age
Patients who had ear–nose–throat disorders or other respiratory disease.
The present study used the following tools after getting informed consent from subjects and permission of using the tool from concerned authority. We maintained records of sociodemographic data of gender, education, weight, height, age, history of hypertension and diabetes, and body mass index (BMI).
Asthma control questionnaire (ACQ)
It is a well-validated questionnaire. It records responses that describe how you have been during the past week. It includes six self-reported questions and contains one question (FEV1 prebronchodilator) that needed to be recorded and filled by medical staff.
FEV1 prebronchodilator will be recorded by spirometry (Medicaid pulmonary function test spiro excel machine) in the department of Physiology.
Procedure: The percentage of the vital capacity that can be forcibly expelled in the first, second, and third second of expiration is called FEV1, FEV2, and FEV3, respectively, which will be recorded by spirometry. The patient will be instructed to expel air forcibly after a normal quiet breathing.
Normally, more than 80% is expired in the first second, more than 90% in the second, and usually completed by the third second.
Asthma quality of life questionnaire (AQLQ)
This questionnaire consists of 32 condition-specific questions.[11] Among the questions, it includes two items which are sleep-related.
Pittsburgh sleep quality index (PSQI)
PSQI is a well-validated tool.[12] It comprises seven components: latency during sleep, duration, sleeping medications, sleep quality, efficiency, disturbance, and daytime dysfunction. A global score that is ≥5 suggests poor quality of sleep.
Epworth sleepiness scale (ESS)
ESS helps to assess sleepiness during day.[13] The ESS figures out the likelihood of getting dozed off in eight circumstances, with scores ranging from zero (no chance of getting dozed off in the circumstance) to three (high probability of getting dozed off in the circumstance). A score of >10 indicates excessive sleepiness during daytime.
Sleep Apnea Scale of the Sleep Disorders Questionnaire (SA-SDQ)
The SA-SDQ consists of 12 items with eight OSA items related to symptoms, and the other four items include smoking status, age, weight, and BMI. To tag high OSA risk, SA-SDQ cutoff of ≥36 in males and ≥32 in females will be taken, This is validated in a good number of samples with polysomnography of sleep clinic patients.[14]
GERD Impact Scale (GIS)
The GIS is a validated tool that shows the frequency and severity of reflux symptoms.[15]
Subjects at high risk for OSA syndrome and GERD are separately noted for further exclusion in one part of analysis.
Implications
The study will show the predictor and impact of poor sleep quality and sleep disturbances in asthmatic patients so that the need to address poor sleep quality by physicians can be suggested.
Recognition of modifiable risk factors such as sleep has importance in asthma management in the Indian population.
Results
Participant characteristics
We included 45 NC and 45 asthmatic (study) subjects. One asthmatic subject was found to be at high risk for GERD, so one subject was separately noted for further exclusion in part of the analysis. The groups have no differences in race. Among 44 asthmatics, NSA were n = 14 and SA were n = 30. Control, severe asthma, and NSA groups had similar parameters. Over 36.36% NSA, 35.29% SA, and 8.6% NC reported having hypertension.
Table 1 compares the PSQI global score (as PSQI code), sleep latency, quality, efficiency, sleep medication, sleep disturbance, sleep duration and dysfunction during daytime in the study and control groups. A PSQI score less than or equal to five is represented as code 1, a PSQI global score more than five but less than eight is described as code 2, and a PSQI global score greater than or equal to eight is represented as code 3. It showed a significant comparison of studied variables (PSQI global score, sleep latency, sleep disturbance, medications for sleep, quality, and dysfunction during daytime) between the control and study groups. These parameters in case and control are significantly different. The mean values of the parameters mentioned above of the case (study group) are higher than the control group. The median values of the case (study group) parameters like PSQI code, duration of sleep, sleep latency, quality of sleep, and dysfunction during day were higher than the control group.
Table 1.
Comparison of PSQI global score (as PSQI code), sleep latency, sleep duration, sleep quality, sleep efficiency, sleep disturbance, sleep medication, and daytime dysfunction in study and control groups
| Parameters | Group | Median±QD | Mean±SD | P (Mann-Whitney U test) |
|---|---|---|---|---|
| PSQI Code | case | 2.00±0.5 | 1.77±0.803 | <0.001** |
| control | 1.00±0.00 | 1.11±0.438 | ||
| Sleep Quality | case | 1.00±0.5 | 1.30±0.632 | <0.001** |
| control | 0.00±0.5 | 0.51±0.549 | ||
| Sleep Latency | case | 1.00±0.5 | 1.36±0.892 | <0.001** |
| Control | 0.00±0.00 | 0.07±0.330 | ||
| Sleep Duration | case | 1.00±0.5 | 0.82±0.896 | 0.468 |
| control | 0.00±0.5 | 0.69±0.848 | ||
| Sleep Efficiency | case | 0.00±0.00 | 0.20±0.668 | 0.084 |
| control | 0.00±0.00 | 0.02±0.149 | ||
| Sleep disturbance | case | 1.00±0.375 | 1.20±0.509 | <0.001** |
| control | 1.00±0.5 | 0.60±0.495 | ||
| Sleep Medication | case | 0.00±0.00 | 0.25±0.651 | 0.011* |
| control | 0.00±0.00 | 0.00±0.00 | ||
| Daytime dysfunction | case | 1.00±0.5 | 0.84±0.608 | 0.001** |
| control | 0.00±0.5 | 0.40±0.580 |
*Significant (P value less than 0.05 but ≥ 0.01). **Highly significant (P value less than 0.01)
Table 2 represents the age comparison between NSA and severe asthma. It showed the age of the NSA group (43.64 ± 21.9) and the asthma group (41.70 ± 19.03). There is no significant difference between age in the NSA and severe asthma groups.
Table 2.
Age comparison between nonsevere and severe asthma
| NSA Group | SA | ||||
|---|---|---|---|---|---|
| Age | Age | ||||
| n | 14 | n | 30 | ||
| Mean | 43.64 | Mean | 41.70 | ||
| Median | 42.50 | Median | 38.00 | ||
| Std. Deviation | 21.946 | Std. Deviation | 19.037 | ||
| Minimum | 18 | Minimum | 16 | ||
| Maximum | 82 | Maximum | 78 | ||
| Percentiles | 25 | 21.25 | Percentiles | 25 | 24.25 |
| 50 | 42.50 | 50 | 38.00 | ||
| 75 | 65.00 | 75 | 54.50 | ||
P=0.890, Mann–Whitney U test
Correlation of sleep quality in asthma (study) group
Tables 3 and 4 represent the correlation of studied variables among study subjects. It was found that the correlation between PSQI global score and sleep quality was significantly correlated among study subjects. Subjects with poor PSQI global scores had poor sleep quality (r = 0.48, P < 0.001).
Table 3.
Correlation of studied variables among study subjects
| Variables | Spearman’s correlation | PSQI Code | Sleep Quality | ESS Code |
|---|---|---|---|---|
| PSQI code | P | 0.00 | 0.001 | 0.954 |
| r | 1.000 | 0.487** | - 0.009 | |
| Sleep quality | P | 0.001 | 0.000 | 0.036 |
| r | 0.487** | 1.000 | 0.318* | |
| ESS code | P | 0.954 | 0.036 | 0.00 |
| r | - 0.009 | 0.318* | 1.00 | |
| ACQ code | P | 0.100 | 0.001 | 0.016 |
| r | 0.251 | 0.479** | 0.362* | |
| FEV1 | P | 0.204 | 0.357 | 0.188 |
| r | - 0.195 | - 0.142 | 0.202 | |
| AQLQ Mean | P | 0.338 | 0.002 | 0.000 |
| r | - 0.148 | - 0.453* | - 0.540** | |
| Age | P | 0.541 | 0.767 | 0.251 |
| r | 0.095 | - 0.046 | 0.173 |
*Significant (P value less than 0.05 but ≥ 0.01. **Highly significant (P value less than 0.01). PSQI Code=Pittsburg sleep quality index code, ESS Code=Epworth sleepiness scale code, ACQ code=Asthma control questionnaire code, AQLQ Mean=Asthma quality of life questionnaire mean
Table 4.
Correlation of studied variables among study subjects
| Variables | Spearman’s correlation | ACQ CODE | FEV1 | AQLQ Mean |
|---|---|---|---|---|
| PSQI code | P | 0.1 | 0.204 | 0.338 |
| r | 0.251 | -0.195 | -0.148 | |
| Sleep Quality | P | 0.001 | 0.357 | 0.002 |
| r | 0.479** | -0.142 | -0.453* | |
| ESS code | P | 0.016 | 0.188 | 0 |
| r | 0.362* | 0.202 | -0.540** | |
| ACQ code | P | 0 | 0.474 | 0.006 |
| r | 1 | 0.111 | -0.404* | |
| FEV1 | P | 0.474 | 0 | 0.249 |
| r | 0.111 | 1 | -0.177 | |
| AQLQ mean | P | 0.006 | 0.249 | 0 |
| r | -0.404* | -0.177 | 1 | |
| Age | P | 0.542 | 0.542 | 0.277 |
| r | -0.095 | 0.094 | -0.167 |
*Significant (P value less than 0.05 but ≥ 0.01. **Highly significant (P value less than 0.01). Spearman’s Correlation. PSQI Code=Pittsburgh sleep quality index code, ESS Code=Epworth sleepiness scale code, ACQ code=Asthma control questionnaire code, AQLQ Mean=Asthma quality of life questionnaire code
The correlation between sleep quality and ESS code was significant. Poor sleep quality was associated with excessive sleepiness (r = 0.31, P < 0.05). ESS code 1 represents 0-7 ESS score, code 2 represents 8-9 ESS score, code 3 represents 10-15 ESS score, code 4 represents 16-24 ESS score. Additionally, ACQ code 0 represents no asthma, code 1 represents well controlled asthma, code 2 represents partially controlled asthma, code 3 represents not well controlled asthma. ACQ code was significantly correlated with sleep quality (r = 0.47, P < 0.001) and ESS code (r = 0.36, P < 0.05). Well-controlled asthma was associated with good sleep quality; the subjects were unlikely to be abnormally sleepy. AQLQ mean was negatively correlated with sleep quality (r = 0.45, P < 0.05), ACQ (r = 0.40, P < 0.05), and ESS codes (r = 0.54, P < 0.001). Severely impaired asthma quality of life was correlated with poor sleep quality, poor asthma control, and excessive sleepiness, and one should consider seeking medical attention.
Table 5 represents the correlation of age and studied variables among study subjects. It was observed that age was not significantly correlated with studied variables.
Table 5.
Correlation of age and studied variables among study subjects
| Variables | Spearman’s correlation | AGE |
|---|---|---|
| PSQI code | P | 0.541 |
| r | 0.095 | |
| Sleep Quality | P | 0.767 |
| r | - 0.046 | |
| ESScode | P | 0.261 |
| r | 0.173 | |
| ACQ code | P | 0.542 |
| r | - 0.095 | |
| FEV1 | P | 0.542 |
| r | 0.094 | |
| AQLQ mean | P | 0.277 |
| r | - 0.167 |
PSQI Code=Pittsburgh sleep quality index, ESS Code=Epworth sleepiness scale, ACQ code=Asthma control questionnaire code, AQLQ Mean=Asthma quality of life questionnaire mean
Association of sleep quality with severity of asthma
Table 6 represents the relation between the PSQI code and severity code of asthma among study subjects. Since two cells (33.3%) have an expected count less than five, instead of the Pearson Chi-Square test, the Likelihood Ratio was used for the association. The association between PSQI code and asthma severity was not significant. There was no association between PSQI global score and asthma severity.
Table 6.
Relation between PSQI code and severity code of asthma among study subjects
| Association between PSQI code and Severity | |||||
|---|---|---|---|---|---|
|
| |||||
| Severity code | Total | ||||
|
| |||||
| NSA | SA | ||||
| PSQI code | 1 | Count | 5 | 15 | 20 |
| % within PSQI code | 25.0% | 75.0% | 100.0% | ||
| % within Severity_Code | 35.7% | 50.0% | 45.5% | ||
| % of Total | 11.4% | 34.1% | 45.5% | ||
| 2 | Count | 4 | 10 | 14 | |
| % within PSQI code | 28.6% | 71.4% | 100.0% | ||
| % within Severity_Code | 28.6% | 33.3% | 31.8% | ||
| % of Total | 9.1% | 22.7% | 31.8% | ||
| 3 | Count | 5 | 5 | 10 | |
| % within PSQI code | 50.0% | 50.0% | 100.0% | ||
| % within Severity_Code | 35.7% | 16.7% | 22.7% | ||
| % of Total | 11.4% | 11.4% | 22.7% | ||
| Total | Count | 14 | 30 | 44 | |
| % within PSQI code | 31.8% | 68.2% | 100.0% | ||
| % within Severity_Code | 100.0% | 100.0% | 100.0% | ||
| % of Total | 31.8% | 68.2% | 100.0% | ||
Likelihood Ratio. P=0.380
Discussion
Our study assessed the association between asthma control, asthma quality of life, and sleep quality, which were not dependent on GERD and other relevant factors. Findings imply that disrupted sleep in the sample may indicate problems in sleep that are not dependent on the effects of GERD. Disturbance at night time among asthma subjects, like being woken by the need to use an inhaler at night time, indicates poorly controlled asthma, leading to poor sleep quality and disturbances in sleep. A research study suggested that activation of inflammatory cells during night and a decrease in lung capacity is the reason for poor sleep quality in asthmatic subjects.[1] Insomnia can imply many etiological factors, such as precipitating, predisposing, and perpetuating factors. These can lead to insomnia in asthmatic patients. Predisposing factors make the patient vulnerable to developing insomnia. They include personality traits, anxiety, aging, and female sex being a genetic factor. Precipitating events such as life events which are stressful can activate the onset of sleep disturbances, as nocturnal asthma attacks, and night time inhaler use disturbs sleep; it acts as a precipitating factor. Disruption in sleep caused by inciting events generally resolves after the resolution of the event. At the same time, nonadaptive or defective sleep habits and attitudes that are followed to deal with the starting phase of problems in sleep can eventually induce chronic insomnia. In participants with asthma, the anxiety of attacks at night can contribute to chronic insomnia. Through behavioral therapy, most perpetuating factors can be targeted.
In the present study, we excluded high-risk OSA with the subjective method; we aimed to rule out OSA as a contributor to poor sleep. We observed that there is a positive correlation between asthma control and sleep quality and subjective measure of sleepiness using the ESS. We also observed that severely impaired asthma quality of life was associated with poor sleep quality, poor asthma control, and excessive sleepiness. Moreover, age did not have a significant correlation with the studied variable. The association of sleep quality with asthma severity was not found to be significant. The finding is consistent with a study by Luyster FS et al.[1] They observed that poor sleep quality was associated with asthma quality of life and greater daytime sleepiness. They found no association of sleep quality with age and BMI, while FEV1 was weakly correlated, whereas in difference to our study, they found that in participants with NSA, sleep quality was a significant predictor of asthma control after balancing BMI, age, and GERD, and in SA, sleep quality was a significant predictor of asthma control after adjusting for BMI, daytime sleepiness, GERD, and use of oral corticosteroids. A study conducted by Mastronade et al.[16] with 487 participants also found similar results. They observed that there was a significant correlation between improvement in asthma control with ESS and PSQI scores. Similarly, Braido F et al.[2] found that sleep quality and asthma control were significantly correlated after excluding OSA and GERD.
We observed that no subjects were at high risk for OSA in our study. In variance to our study, Teodorescu M et al.[17] compared the participants with high risk for OSA with those without high OSA risk. They found that high OSA risk subjects had higher scores on the asthma control questionnaire, which indicates not well-controlled asthma.
A research study was conducted by Eriksson et al.;[18] they studied risk factors for uncontrolled asthma and insufficient quality of life with mild asthma. The International Global Initiative for Asthma (GINA) guidelines define mild asthma as asthma that is well controlled at pharmacological treatment step 1 or step 2. They observed that 20% had both uncontrolled asthma and insufficient Quality of life (QoL) and age greater than 60, obesity, daily smoking, rhinitis, and inadequate knowledge of asthma self-management were independently associated with not achieving asthma control. In our study, we also found a similar result, that is, severely impaired asthma quality of life was correlated with poor asthma control, while we observed that age was not significantly correlated with studied variables.
The limitation of our study is that we had a small sample size as our study duration was less. Our study was cross-sectional, so we could not draw causal inferences about the direction of the relationship between sleep quality and asthma control. OSA was excluded using the subjective method, and we did not perform polysomnography to exclude objectively. Future perspectives of similar studies should include objective parameters of sleep and physician-diagnosed exclusion of comorbidities.
List of abbreviations
| Abbreviation | Definition |
|---|---|
| OSA | Obstructive sleep apnea |
| GERD | Gastroesophageal reflux disease |
| SA and NSA | Severe asthma and nonsevere asthma |
| FEV | Fractional Expiratory Volume |
| PSQI | Pittsburgh Sleep Quality Index |
| ACQ | Asthma Control Questionnaire |
| ESS | Epworth Sleepiness Scale |
| AQLQ | Asthma Quality of Life Questionnaire |
| NC | Normal Controls |
| BMI | Body Mass Index |
| SA-SDQ | Sleep Apnea Scale of the Sleep Disorders Questionnaire |
| GIS | GERD Impact Scale |
Ethical policy and institutional review board statement
This study has been approved by the Institutional Ethical Committee and the Review Board (No. IEC/AIIMS/BTI/419). The study was conducted in accordance with ethical principles mentioned in the Declaration of Helsinki.
Conflicts of interest
There are no conflicts of interest.
Acknowledgement
| Name | Role |
|---|---|
| Soham Modak | Technical help |
| Kulwinder Singh | Technical help |
Funding Statement
Nil.
References
- 1.Luyster FS, Teodorescu M, Bleecker E, Busse W, Calhoun W, Castro M, et al. Sleep quality and asthma control and quality of life in non-severe and severe asthma. Sleep Breath. 2012;16:1129–37. doi: 10.1007/s11325-011-0616-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Braido F, Baiardini I, Ferrando M, Scichilone N, Santus P, Petrone A, et al. The prevalence of sleep impairments and predictors of sleep quality among patients with asthma. J Asthma. 2021;58:481–7. doi: 10.1080/02770903.2019.1711391. [DOI] [PubMed] [Google Scholar]
- 3.Ferrando M, Bagnasco D, Roustan V, Canonica GW, Braido F, Baiardini I. Sleep complaints and sleep breathing disorders in upper and lower obstructive lung diseases. J Thorac Dis. 2016;8:E716–25. doi: 10.21037/jtd.2016.07.82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Morphy H, Dunn KM, Lewis M, Boardman HF, Croft PR. Epidemiology of Insomnia: A longitudinal study in a UK population. Sleep. 2007;30:274–80. [PubMed] [Google Scholar]
- 5.Lian Y, Xiao J, Liu Y, Ning L, Guan S, Ge H, et al. Associations between insomnia, sleep duration and poor work ability. J Psychosom Res. 2015;78:45–51. doi: 10.1016/j.jpsychores.2014.09.009. [DOI] [PubMed] [Google Scholar]
- 6.Fortier-Brochu E, Beaulieu-Bonneau S, Ivers H, Morin CM. Insomnia and daytime cognitive performance: A meta-analysis. Sleep Med Rev. 2012;16:83–94. doi: 10.1016/j.smrv.2011.03.008. [DOI] [PubMed] [Google Scholar]
- 7.Rask-Andersen A, Leander M, Sundbom F, Lampa E, Oudin A, Leynaert B, et al. Health-related quality of life as associated with asthma control, psychological status and insomnia. Ups J Med Sci. 2022:127. doi: 10.48101/ujms.v127.8967. doi: 10.48101/ujms.v127.8967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sorscher AJ. How is your sleep: A neglected topic for health care screening. J Am Board Fam Med. 2008;21:141–8. doi: 10.3122/jabfm.2008.02.070167. [DOI] [PubMed] [Google Scholar]
- 9.Althoff MD, Ghincea A, Wood LG, Holguin F, Sharma S. Asthma and three colinear comorbidities: Obesity, OSA, and GERD. J Allergy Clin Immunol Pract. 2021;9:3877–84. doi: 10.1016/j.jaip.2021.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Proceedings of the ATS workshop on refractory asthma: Current understanding, recommendations, and unanswered questions. American Thoracic Society. Am J Respir Crit Care Med. 2000;162:2341–51. doi: 10.1164/ajrccm.162.6.ats9-00. [DOI] [PubMed] [Google Scholar]
- 11.Juniper EF, Guyatt GH, Epstein RS, Ferrie PJ, Jaeschke R, Hiller TK. Evaluation of impairment of health related quality of life in asthma: Development of a questionnaire for use in clinical trials. Thorax. 1992;47:76–83. doi: 10.1136/thx.47.2.76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Buysse DJ, Reynolds CF, 3rd, Monk TH, Berman SR, Kupfer DJ. The pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
- 13.Johns MW. A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep. 1991;14:540–5. doi: 10.1093/sleep/14.6.540. [DOI] [PubMed] [Google Scholar]
- 14.Douglass AB, Bornstein R, Nino-Murcia G, Keenan S, Miles L, Zarcone VP, Jr, et al. The sleep disorders questionnaire. I: Creation and multivariate structure of SDQ. Sleep. 1994;17:160–7. doi: 10.1093/sleep/17.2.160. [DOI] [PubMed] [Google Scholar]
- 15.Jones R, Coyne K, Wiklund I. The gastro-oesophageal reflux disease impact scale: A patient management tool for primary care. Aliment Pharmacol Ther. 2007;25:1451–9. doi: 10.1111/j.1365-2036.2007.03343.x. [DOI] [PubMed] [Google Scholar]
- 16.Mastronarde JG, Wise RA, Shade DM, Olopade CO, Scharf SM American Lung Association Asthma Clinical Research Centers. Sleep quality in Asthma: Results of a large prospective clinical trial. J Asthma. 2008;45:183–9. doi: 10.1080/02770900801890224. [DOI] [PubMed] [Google Scholar]
- 17.Teodorescu M, Polomis DA, Hall SV, Teodorescu MC, Gangnon RE, Peterson AG, et al. Association of obstructive sleep apnea risk with asthma control in adults. Chest. 2010;138:543–50. doi: 10.1378/chest.09-3066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Eriksson S, Giezeman M, Hasselgren M, Janson C, Kisiel MA, Montgomery S, et al. Risk factors associated with asthma control and quality of life in patients with mild asthma without preventer treatment, a cross-sectional study. J Asthma Allergy. 2024;17:621–32. doi: 10.2147/JAA.S460051. [DOI] [PMC free article] [PubMed] [Google Scholar]