Factors Affecting Adherence to Continuous Positive Airway Pressure in Thai Patients With Obstructive Sleep Apnea
Article information
Abstract
Background and Objective
Continuous positive airway pressure (CPAP) compliance significantly affects clinical outcomes in patients with obstructive sleep apnea (OSA). This study aimed to determine the prevalence of CPAP adherence and factors affecting non-adherence in OSA patients.
Methods
A cross-sectional study was conducted in adult OSA patients undergoing CPAP treatment. CPAP adherence (defined as usage ≥4 hours/night for ≥70% of nights) at 2 weeks, 4 weeks, 3 months, and 6 months was recorded. Patients were categorized into two groups: adherence and non-adherence.
Results
A total of 210 patients (60.5% male) were included, with mean age of 53.5±14.8 years and mean body mass index of 30.7±6.9 kg/m2. Comorbidities included hypertension (63%) and coronary heart disease (18%). Polysomnographic data showed apnea-hypopnea index of 48.9±32.0 events/hour and nadir saturation of 79.3%±11.4%. Severe OSA was present in 68% of cases. The proportion of CPAP adherence in 2 weeks, 4 weeks, 3 months, and 6 months was 59.1%, 60.0%, 57.6%, and 56.8%, respectively. The factors associated with CPAP non-adherence included removing the mask at night, irregular sleep time, lack of time to use CPAP, dry mouth, and doctor follow-up schedule of more than 6 months. Furthermore, patients who exhibited good adherence at 2 weeks also demonstrated significantly good compliance at 4 weeks, 3 months, and 6 months.
Conclusions
Only about half of OSA patients had good adherence to CPAP. Various factors influenced CPAP adherence. Patients who exhibited good compliance in the short term were likely to maintain it over the long term.
INTRODUCTION
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repeated episodes of partial or complete blockage of the upper airway during sleep due to relaxation of muscles in the throat. These episodes result in decreased airflow or complete cessation of breathing [1-3]. The risk factors of OSA include obesity, aging, male sex, family history of sleep apnea, and certain physical characteristics such as a small jaw or a large neck circumference [4,5]. The symptoms of OSA include loud snoring, choking or gasping during sleep, excessive daytime sleepiness, difficulty concentrating, and morning headache [1,6,7]. OSA is associated with an increased risk of various health problems, including cardiovascular disease, hypertension, type 2 diabetes mellitus, atrial fibrillation, and stroke [3,8,9]. It is essential for OSA to be diagnosed promptly to initiate optimal treatment. A sleep study, or polysomnography (PSG), is the standard diagnostic test for OSA, which assesses the severity of the disease [10,11]. Treatment options for OSA include continuous positive airway pressure (CPAP), lifestyle changes such as weight loss and positional therapy, oral appliances, and surgery [1,12-14].
CPAP is an effective treatment for OSA and other sleep-disordered breathing conditions. The air pressure from this device helps to keep the airway open during sleep and prevents collapse or blockage. CPAP therapy is considered the gold standard for treating moderate to severe OSA [1,13]. It can alleviate symptoms such as snoring, excessive daytime sleepiness, and interruptions in breathing during sleep. When patients adhere well to CPAP usage, it can improve sleep quality, decrease abnormal sleep-related symptoms, and reduce the risk of OSA-related morbidity and mortality [15,16]. However, treatment effectiveness is limited by various factors, including low adherence due to side effects [17].
Various factors affect CPAP adherence in OSA patients, including comfort and mask fit, inappropriate pressure, nasal congestion, nasal irritation, psychological factors (such as anxiety or claustrophobia), side effects (such as dry mouth, skin irritation, or aerophagia), lifestyle factors (such as a busy lifestyle, travel, or irregular work hours), lack of support from family members or bed partners, and financial barriers [18-20]. However, the factors influencing CPAP use may vary across studies. The aim of this study was to investigate the factors affecting CPAP adherence in Thai patients with OSA and to explore factors associated with CPAP adherence.
METHODS
Study Design and Participants
A cross-sectional study was conducted at Thammasat University Hospital, Thailand between July 2021 and May 2022. OSA patients aged 18 years or older, diagnosed and confirmed by PSG, and requiring CPAP treatment at home, were included. Exclusion criteria include refusal to use CPAP, inability to communicate, and uncontrolled comorbidities such as congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, or psychiatric disorders.
Ethics approval was obtained from the Human Research Ethics Committee of Thammasat University (Medicine), Thailand (IRB No. MTU-EC-OO-6-086/64, COA No. 097/2021 on April 21, 2021), in full compliance with international guidelines including the Declaration of Helsinki, the Belmont Report, CIOMS Guidelines, and the International Conference on Harmonisation-Good Clinical Practice (ICH-GCP). All procedures were performed in accordance with these guidelines and regulations. All participants provided written informed consent. This study was registered with Thaiclinicaltrials.org with number TCTR20210630002.
Data Collection
Demographic and clinical data, including age, sex, body mass index (BMI), comorbidities, Epworth sleepiness scale (ESS) scores, in-lab PSG data, and duration of CPAP use at home, were recorded.
All patients underwent type 1 PSG at a sleep laboratory. Sleep and respiratory assessments were performed according to the American Academy of Sleep Medicine (AASM) guidelines [21]. The severity of OSA was classified based on apnea-hypopnea index (AHI) values: AHI<5 indicated no OSA; 5–14.99 indicated mild OSA; 15–29.99 indicated moderate OSA; and ≥30 indicated severe OSA.
Data collection included AHI, nadir SpO 2 levels, and severity of OSA. CPAP adherence data were collected from the CPAP machine used by the patient at home at 2 weeks, 4 weeks, 3 months, and 6 months during follow-up. Good CPAP adherence was defined as using the CPAP for ≥4 hours per night for ≥70% of nights [22].
Factors associated with CPAP usage were obtained through a questionnaire which also included patient baseline characteristics such as sex, occupation, income, education level, comorbidities, and information regarding family-related details, equipment quality, usage patterns, treating physicians’ details, healthcare personnel and services, healthcare system and hospital information, governmental policies, and collaborative machine usage (Supplementary Material in the online-only Data Supplement).
Statistical Analysis
In a previous study [23], the prevalence of good CPAP adherence at 4 weeks in OSA patients was 86.6%. We hypothesized that the prevalence in our population was the same. Our sample size was calculated to estimate a proportion with a power confidence of 80%, a type I error of 5%, and a precision margin of 5%. Therefore, the calculated sample size was 179.
Data are presented as number (%) and mean±standard deviation. The Student’s t-test was used to compare continuous variables between the groups with good and poor CPAP adherence. The chi-squared test was used to compare categorical variables between the two groups. An independent sample t-test was used to compare continuous variables with normal distributions between the two groups. The Mann-Whitney U test was used to compare two independent groups on continuous variables with non-normal distributions. To determine the set of variables associated with CPAP adherence, we used the logistic regression model with CPAP adherence as the dependent variable. Independent variables, including age, comorbidities, OSA severity, patient factors, and doctor factors, were entered into the regression model if bivariate analysis indicated statistical significance. Odds ratios (95% confidence interval) were reported for variables in the model. Two-tailed p-values of less than 0.05 were considered statistically significant. All data analyses were performed using SPSS version 26.0 software (IBM Corp.).
RESULTS
Participants
A total of 210 patients (60.5% male) were included. The mean age was 53.5±14.8 years, and the mean BMI was 30.7±6.9 kg/m2. Common comorbidities included hypertension (62.9%) and coronary heart disease (17.6%). The average ESS score was 9.4±5.3. PSG data indicated that the mean AHI was 48.9±32.0 events/hour, and the average nadir SpO 2 was 79.3%±11.4%. OSA was classified as mild (8.7%), moderate (23.6%), and severe (67.8%). CPAP adherence at 2 weeks, 4 weeks, 3 months, and 6 months was found in 59.1%, 60.0%, 57.6%, and 56.8%, respectively (Table 1).
Baseline characteristics of OSA patients
Factors Associated with CPAP Usage
Table 2 illustrates the factors, determined through bivariate analyses, which influenced CPAP adherence. Significant factors at 2 and 4 weeks included OSA severity, mask removal at night, timing of CPAP use relative to sleep, irregular sleep patterns, time constraints, illness during CPAP use, experiencing dry mouth after CPAP use, and follow-up schedules longer than 6 months. Additionally, age and cerebrovascular disease emerged as significant factors at 4 weeks. However, hypertension and coronary heart disease were not associated with CPAP adherence at any time points (2 weeks, 4 weeks, 3 months, and 6 months). Additionally, socioeconomic status (including income, education level, and occupation) and psychological factors (such as anxiety and claustrophobia) were not related to CPAP adherence at any time point. Device type (auto-CPAP or CPAP) was also not linked to adherence to CPAP use.
Comparison of good and poor CPAP adherence in OSA patients
After 3 months of use, significant factors affecting adherence were similar to those at 2 weeks. Additionally, age and doctor recommendations to use CPAP were associated with adherence. Notably, maintaining good adherence at 2 and 4 weeks positively influenced adherence at 3 months (Table 2).
For the 6 months, significant factors affecting usage were similar to those observed in the first 3 months, with the exceptions of sleeping before using CPAP and being sick during CPAP use. Importantly, maintaining good adherence at 2 weeks, 4 weeks, and 3 months had a positive impact on sustaining adherence at 6 months as well (Table 2).
Logistic regression analysis revealed significant factors associated with CPAP adherence (Table 3). Factors influencing CPAP adherence at the 2-week mark included mask removal at night, lack of time to use CPAP, illness during CPAP use, and follow-up schedules exceeding 6 months. At 4 weeks, adherence was influenced by cerebrovascular disease, irregular sleep patterns, and good adherence at 2 weeks. For the 3 months, factors affecting adherence included mask removal at night, irregular sleep patterns, illness during CPAP use, dry mouth after CPAP use, follow-up schedules exceeding 6 months, and doctor recommendations to use CPAP. Factors associated with adherence at 6 months included mild OSA, irregular sleep patterns, dry mouth after CPAP use, and follow-up schedules exceeding 6 months (Table 3).
Logistic regression of factors associated with CPAP adherence in OSA patients
DISCUSSION
This study examines factors associated with CPAP adherence among OSA patients. CPAP adherence decreased over time, with percentages of adherence at 2 weeks, 4 weeks, 3 months, and 6 months being around 60% or slightly lower. Our results demonstrated that there were several factors affecting CPAP usage in both the short-term and long-term. Notably, good adherence at earlier time points correlated with continued adherence at later stages. This suggests that early adherence may translate into sustained benefits over several years, influencing long-term cardiovascular health and quality of life. Therefore, physicians should emphasize and encourage patients to use CPAP during initial visits and early follow-up appointments. CPAP adherence was associated with a reduction in major adverse cardiac and cerebrovascular events in OSA patients, as reviewed in a meta-analysis by Sánchez-de-la-Torre et al. [24]. Additionally, CPAP adherence was linked to improvements in long-term quality of life for OSA patients, reviewed by Weaver and Grunstein [25].
Good adherence to CPAP positively impacts effective treatment of OSA. However, some patients have poor CPAP adherence. Our results demonstrated that patients with cerebrovascular disease displayed poor CPAP adherence at 4 weeks. Stroke patients may face issues such as muscle weakness and cognitive impairment, which could hinder CPAP usage. Interestingly, our older patients exhibited better CPAP adherence at both 4 weeks and 3 months compared to younger patients. A study by Aalaei et al. [26] similarly found that higher age was associated with CPAP adherence. Furthermore, a study by Nsair et al. [27] found that OSA patients younger than 60 years were associated with low CPAP compliance. These findings could be attributed to greater disease awareness and the presence of comorbidities in older patients, leading to improved CPAP compliance. A previous study by Lykouras et al. [28] showed that comorbidities, including diabetes, hypertension, and heart failure, did not affect CPAP adherence at one year in OSA patients. In accordance with our study, hypertension and coronary heart disease were not associated with CPAP adherence at various time points (2 weeks, 4 weeks, 3 months, and 6 months). It is possible that patients may be unaware of the association between the benefits of CPAP usage and its cardiovascular protective effects.
Various components of CPAP devices affect adherence, including device type (auto-CPAP or fixed pressure CPAP), mask type, optimal pressure, positional status (positional vs. non-positional OSA), and rapid eye movement (REM) predominance [25]. Our study showed that auto-CPAP did not influence CPAP adherence at any time point. This finding indicates that OSA patients can use any CPAP type. However, other CPAP factors that might potentially influence adherence were not collected. Moreover, our results showed that removing the mask at night and experiencing dry mouth did not influence adherence at 4 weeks, but they did affect CPAP adherence at other time points, possibly because patients had begun to adapt to and become familiar with using the CPAP machine.
According to previous studies, several factors influence CPAP adherence. A review by Shapiro and Shapiro [18] categorized factors affecting adherence to CPAP usage into 7 groups: treatment method, patient, family, physician, healthcare professionals, healthcare facility, and governmental policies. They highlighted that the treatment method could be influenced by various causes, such as lifestyle changes and adverse side effects. This aligns with our study, which identified side effects after CPAP use as affecting CPAP adherence.
Patient-related factors, such as age and ethnicity, have been found to influence CPAP adherence. OSA patients with a high BMI and severe OSA may exhibit better CPAP adherence than those with less severe levels [18,29]. These results may indicate improved insulin sensitivity, reduced systemic inflammation, and decreased cardiovascular disease risk [30]. Some studies have found a positive correlation between family and good CPAP adherence [18-20]. However, our study did not find significant family-related factors. A study in Turkey by Ercelik et al. [31] revealed that 67% of OSA patients had CPAP compliance, and factors affecting CPAP usage included OSA severity, anxiety/depression scores, type of mask used, difficulty tolerating CPAP, difficulty falling asleep, abdominal distension, facial sores, air leakage, perceived benefit from the device, daytime sleepiness, and belief in receiving appropriate therapy. Moreover, CPAP level may affect CPAP compliance in OSA patients. A study by Saiphoklang et al. [32] found that BMI, neck circumference, respiratory disturbance index, and nadir SpO2 were associated with CPAP level. Interestingly, lower socioeconomic status, including educational level and income, was associated with CPAP adherence in OSA patients, as shown in studies by Palm et al. [33] and Wickwire et al. [34]. Moreover, psychological factors, particularly claustrophobia, significantly affected CPAP adherence [35,36]. Mindfulness, which includes anxiety and depression, was also associated with CPAP adherence, as demonstrated by Li et al. [37]. However, these findings were not observed in our study. In contrast to some studies by Wells et al. [38] and Stepnowsky et al. [39], mood factors—including anxiety, depression, and stress—did not influence CPAP adherence.
Factors related to physicians, healthcare professionals, and healthcare facilities play a crucial role in providing services, follow-ups, and CPAP education to encourage accurate and appropriate usage [18]. This aligns with our study, where patients with a follow-up schedule longer than 6 months demonstrated poorer CPAP adherence. This prolonged follow-up may lead to a lack of continuous stimulation for CPAP usage. Moreover, our findings revealed that suggestions from physicians also affected better CPAP adherence.
Our study is the largest report in Thailand to reveal factors influencing CPAP adherence, primarily related to the patients themselves. Overall, our study indicates the importance of considering various factors—such as disease severity, patient habits, and medical advice—when addressing CPAP adherence among OSA patients. Identifying and addressing these factors early on may improve long-term adherence and treatment outcomes. Therefore, it is essential to monitor CPAP adherence from the initial stages to ensure effective long-term usage. Similarly, a study by Van Ryswyk et al. [40] found that early CPAP adherence had the greatest predictive value for identifying those at highest risk of non-adherence to long-term CPAP therapy.
This study has a few limitations. Firstly, it was conducted in the COVID-19 pandemic era, potentially influencing CPAP compliance. Secondly, the study was conducted in a single research center in Thailand; therefore, the results might not be generalizable to other ethnicities or countries. Lastly, some CPAP factors—such as mask type, optimal pressure, positional status in OSA, and REM predominance—that might potentially influence adherence were not collected, meaning these factors might be missed as significant determinants for CPAP adherence. A longer multicenter study is needed to further explore the associated factors and address correctable factors contributing to poor CPAP adherence in OSA patients.
In conclusion, only about half of OSA patients exhibited good adherence to CPAP. Several factors influenced CPAP adherence. Patients who demonstrated good compliance in the short term were likely to maintain good compliance over the long term.
Supplementary Materials
The online-only Data Supplement is available with this article at https://doi.org/10.17241/smr.2024.02551.
Notes
Availability of Data and Material
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Author Contributions
Conceptualization: Apiwat Pugongchai, Kanyada Leelasittikul, Shayada Suksupakit, Nithita Sattaratpaijit, Narongkorn Saiphoklang. Data curation: Apiwat Pugongchai, Kanyada Leelasittikul, Eakaphop Boonyasai, Shayada Suksupakit, Nithita Sattaratpaijit. Formal analysis: Apiwat Pugongchai, Narongkorn Saiphoklang. Methodology: Apiwat Pugongchai, Kanyada Leelasittikul, Shayada Suksupakit, Narongkorn Saiphoklang. Project administration: Apiwat Pugongchai, Kanyada Leelasittikul, Shayada Suksupakit. Funding acquisition: Eakaphop Boonyasai, Shayada Suksupakit. Supervision: Nithita Sattaratpaijit. Validation: Narongkorn Saiphoklang. Visualization: Nithita Sattaratpaijit, Narongkorn Saiphoklang. Writing—original draft: Apiwat Pugongchai, Kanyada Leelasittikul. Writing— review & editing: Nithita Sattaratpaijit, Narongkorn Saiphoklang.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Funding Statement
Financial support was provided by Thammasat University Hospital, Thailand (Grant number: 2/2564).
Acknowledgements
The authors thank Michael Jan Everts, Faculty of Medicine, Thammasat University, for proofreading this manuscript. This work was supported by Thammasat University Research Unit in Allergy and Respiratory Medicine, as well as Sleep Center of Thammasat, Medical Diagnostics Unit at Thammasat University Hospital, Thailand.