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Volume 12 No. 03
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Scientific Investigations

Impact of Randomization, Clinic Visits, and Medical and Psychiatric Cormorbidities on Continuous Positive Airway Pressure Adherence in Obstructive Sleep Apnea

Rohit Budhiraja, MD1; Clete A. Kushida, MD, PhD2; Deborah A. Nichols, MS2; James K. Walsh, PhD3; Richard D. Simon, MD4; Daniel J. Gottlieb, MD, MPH1,5; Stuart F. Quan, MD1,6
1Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, MA; 2Stanford University Sleep Clinic and Center for Human Sleep Research, Redwood City, CA; 3Sleep Medicine and Research Center, St. Luke's Hospital, Chesterfield, MO; 4Providence St. Mary Medical Center, Walla Walla, Washington; 5VA Boston Healthcare System, Boston, MA; 6Arizona Respiratory Center, University of Arizona, Tucson, AZ

ABSTRACT

Study Objectives:

To evaluate factors associated with continuous positive airway pressure (CPAP) adherence in patients with obstructive sleep apnea (OSA) in the Apnea Positive Pressure Long-term Efficacy Study (APPLES) cohort.

Methods:

The data from a prospective 6-mo multicenter randomized controlled trial with 558 subjects randomized to active CPAP and 547 to sham CPAP were analyzed to assess adherence to CPAP during first 2 mo (early period) and during months 5-6 (late period).

Results:

Participants randomized to active CPAP had higher hours of nightly adherence compared to the sham CPAP group at both 2 (4.9 ± 2.0 h versus 4.07 ± 2.14 h, p < 0.001) and 6 mo (4.70 ± 2.08 h versus 3.41 ± 2.19 h, p < 0.001). Those assigned to sham CPAP were more likely to correctly identify their treatment group (70.0% versus 55.2%, p < 0.001). Irrespective of treatment group assignment, those who believed they were receiving active CPAP had higher hours of adherence than those who thought they were in the sham CPAP group at both 2 mo (4.91 ± 2.01 versus 4.17 ± 2.17, p < 0.001) and 6 mo (4.65 ± 2.10 versus 3.65 ± 2.22, p < 0.001). Among those randomized to active CPAP, older age was significantly related to CPAP use > 4 h per night. Presence of cardiovascular disorders was associated with higher hours of CPAP use, whereas presence of anxiety was associated with a trend toward lower hours of CPAP use. Presence of nasal congestion was associated with a decrease in mean daily CPAP use between the early and the late adherence period. The adherence during the week prior to a clinic visit was higher than the average adherence during the 2-mo period prior to the visit.

Conclusions:

Randomization to active therapy, belief that one is in the active treatment group, older age, and possibly presence of cardiovascular disorders are positively linked to CPAP adherence. Nasal congestion and anxiety are negatively associated with CPAP adherence. CPAP nightly usage increases as clinic visits approach.

Citation:

Budhiraja R, Kushida CA, Nichols DA, Walsh JK, Simon RD, Gottlieb DJ, Quan SF. Impact of randomization, clinic visits, and medical and psychiatric cormorbidities on continuous positive airway pressure adherence in obstructive sleep apnea. J Clin Sleep Med 2016;12(3):333–341.


INTRODUCTION

Obstructive sleep apnea (OSA) is a common disorder,1 and continuous positive airway pressure (CPAP) therapy continues to be the first-line treatment for the majority of patients with OSA. This therapy is associated with multiple salutary effects including improvement in daytime sleepiness, reduction in blood pressure2 and improvement in diabetic control.3 However, poor adherence to CPAP continues to be a major impediment to effective therapy of this disorder. Long-term adherence to CPAP of greater than 4 h per night is achieved by only 50% to 70% of patients.4,5 A number of factors have been reported to affect long-term CPAP adherence, including initial level of sleepiness, disease severity, bed-partner support, claustrophobia, and outcome expectations.5 An improved understanding of these factors and others associated with CPAP non-adherence might assist efforts to optimize use.

BRIEF SUMMARY

Current Knowledge/Study Rationale: An improved understanding of factors associated with CPAP adherence can help assist efforts aimed at optimizing CPAP use. We studied the effect of randomization, clinic visits, and preexisting medical and psychiatric conditions on CPAP adherence in Apnea Positive Pressure Long-term Efficacy Study (APPLES) study, a prospective multicenter randomized controlled trial.

Study Impact: The study determined several factors associated with increased CPAP adherence in the context of a clinical trial, including the participant belief that they had been randomized to the active CPAP group (independent of their actual assignment), presence of CVD, increasing age, absence of nasal congestion or anxiety, a decrease in ESS score with CPAP use, and an approaching clinic visit. These findings enhance our understanding of the factors that affect long-term CPAP adherence.

Adherence to CPAP therapy may plausibly be affected by the presence of comorbid medical and psychiatric conditions. Diverse medical conditions may compromise sleep continuity and reduce sleep efficiency.6,7 Prior studies suggest that medical and psychiatric conditions may influence CPAP adherence.811 Additionally, intensive follow-up has been suggested to improve adherence to CPAP.12 It is, however, unclear whether the expectation of an adherence evaluation alters patients' behavior regarding CPAP use. Specifically, it is not known whether patients increase CPAP use in the days prior to a clinic visit. Finally, in randomized controlled trials, it is undetermined whether a subjects' belief that they might be in a particular group (active or sham CPAP) influences the adherence to CPAP. If beliefs do affect adherence levels, analyses should attempt to adjust for these variables.

The Apnea Positive Pressure Long-term Efficacy Study (APPLES) was a prospective multicenter randomized controlled trial designed to assess the effect of CPAP on several neurocognitive outcomes in adults with OSA. Participants with OSA were randomized to either active CPAP or sham CPAP for 6 mo. Adherence to CPAP or sham CPAP was objectively assessed for the duration of the study. In this report, we evaluated several factors including effect of randomization, clinic visits, and preexisting medical and psychiatric conditions on CPAP adherence in this cohort. We also assessed the relationship between early (first 2 mo) and late (fifth and sixth month) adherence to CPAP therapy as well as factors associated with a decrease in adherence from the early to late period.

METHODS

Study Design

The APPLES protocol has been described in detail elsewhere.13 In brief, participants were recruited from sleep clinics and through public advertisements at five sites: Stanford University, Stanford, CA; University of Arizona, Tucson, AZ; Providence St. Mary Medical Center, Walla Walla, WA; St. Luke's Hospital, Chesterfield, MO; and Brigham and Women's Hospital, Boston, MA, Those who met no exclusion criteria during the initial interview and consented to participate in the study underwent a diagnostic polysomnogram (PSG). Those who had an apnea-hypopnea index (AHI) ≥ 10/h without severe oxygen desaturation (i.e., oxygen saturation < 75% for > 10% of the diagnostic sleep study) were randomized to either active or sham CPAP (REMStar Pro, Phillips Respironics, Murrysville, PA) for 6 mo. The participants randomized to active CPAP then underwent a titration in the sleep laboratory to determine and optimize their therapeutic pressure. A sham titration was performed for those randomized to sham CPAP. Although participants' adherence was monitored continuously during the study, for this report we calculated adherence at the 2- and 6-mo time points after initiation of CPAP. These corresponded with their follow-up visits to assess neurocognitive function.

Participants

Of the 1,516 participants screened, 1,105 were ultimately randomized with 558 randomized to active CPAP and 547 randomized to sham CPAP. There was no significant difference in the baseline characteristics of the participants randomized to active CPAP or sham CPAP (Table 1). However, the Epworth Sleepiness Scale (ESS) total score at 4 mo was significantly lower in those randomized to active CPAP (7.02 ± 4.17 versus 8.22 ± 4.26, p < 0.001).

Demographic and baseline sleep characteristics.

 

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Table 1

Demographic and baseline sleep characteristics.

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Pre-existing Medical Conditions

At their baseline visit, all participants underwent a clinical evaluation to obtain a medical history and information pertaining to symptoms of sleep disorders and other medical and psychiatric conditions such as cardiovascular disease (CVD: hypertension, coronary artery disease, congestive heart failure, angina or arrhythmias), nasal congestion, and depression (Table 2); in addition to a physical examination. A condition was considered present when the participant answered that he or she currently has it. The ESS was administered at baseline as well as at 2, 4, and 6 mo after starting active CPAP or sham CPAP.

Bivariate associations between early adherence (0–2 mo) period and comorbid medical and psychiatric disorders.

 

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Table 2

Bivariate associations between early adherence (0–2 mo) period and comorbid medical and psychiatric disorders.

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CPAP Adherence

Adherence to active CPAP or sham CPAP was measured objectively using Encore Pro SmartCards (Phillips Respironics, Inc., Murrysville, PA) that were returned by the participant twice monthly. For this report, the mean hours of daily use were analyzed for the preceding 1-d, 7-d, and 2-mo periods before the 2- and 6-mo study visits. We empirically defined “early adherence” as the mean daily hours of use from CPAP initiation to the 2-mo visit and “late adherence” as the mean daily hours of use during the fifth and sixth month after CPAP initiation.

Statistical Analyses

To assess the effect of randomization on adherence, we included both those randomized to sham CPAP and those randomized to active CPAP. For the rest of the analyses, we focused only on those randomized to active CPAP therapy. For continuous variables, unadjusted comparisons between groups were made using Student unpaired t-test. Data were expressed as mean ± standard deviation. Differences in proportions were assessed using the χ2 test. An analysis of variance with repeated measures was used to compare adherence during the 2-mo period, 1-w period, and 1 night prior to each visit. The percentage of patients using active CPAP > 4 h per night was determined at 2 mo and 6 mo. The χ2 test was used to compare the proportion of participants with a particular disorder who were using active CPAP > 4 h per night during each of these periods to those who did not have that disorder. Logistic regression was used to assess odds ratios (with 95% confidence intervals) for active CPAP use > 4 h per night for different variables including baseline characteristics (age, body mass index, and sex), study site, and comorbid conditions. A linear regression model was used to examine the association between the medical and psychiatric disorders and the average hours of CPAP use at 2 mo, and at 6 mo, adjusting for confounders including age, AHI, and the decrease in ESS with CPAP therapy. The statistical significance level was set at p ≤ 0.05 (two-tailed) for all tests. Statistical analyses were conducted using SPSS version 20.0 for Windows (SSPS Inc, Chicago, IL).

RESULTS

Impact of Randomization on Adherence

Those randomized to active CPAP had higher hours of nightly adherence compared to the sham CPAP group during both the early period (4.87 ± 2.04 h versus 4.07 ± 2.14 h, p < 0.001) and the late period (4.70 ± 2.08 h versus 3.41 ± 2.19 h, p < 0.001) (Figure 1A). Among the 1,105 subjects randomized, 944 provided guesses of their treatment group at the end of the study. Of the 484 randomized to active CPAP, 55.2% guessed their allocation correctly, whereas of the 460 randomized to sham CPAP, 70.0% guessed their allocation correctly (p < 0.001). Those who guessed their group as active CPAP had higher hours of adherence than those who guessed they were in the sham CPAP group during the early period (4.91 ± 2.01 h versus 4.17 ± 2.17 h, p < 0.001) and the late period (4.65 ± 2.10 h versus 3.65 ± 2.22 h, p < 0.001) (Figure 1B). Even among participants randomized to active CPAP, those who guessed they were on sham CPAP had lower hours of adherence to CPAP during the early (4.54 ± 1.96 h versus 5.06 ± 2.17 h, p = 0.008) and the late (4.29 ± 2.15 h versus 5.05 ± 1.94 h, p < 0.001) periods.

(A) Nightly adherence by visit and randomized study arm. Sample size is 1,105 (558 CPAP, 547 sham CPAP). (B) Nightly adherence by visit and participant guessed study arm. Sample size is 944 (484 CPAP, 460 sham CPAP). *p < 0.05 indicates statistical significance. CPAP, continuous positive airway pressure.

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Figure 1

(A) Nightly adherence by visit and randomized study arm. Sample size is 1,105 (558 CPAP, 547 sham CPAP)...

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In all participants, linear regression showed that after adjustment for the study site, baseline sleepiness (ESS total score) and AHI, being randomized to active CPAP, belief that one was randomized to active CPAP, older age, and decrease in the ESS scores from baseline to 2 mo or from baseline to 6 mo, were independently associated with higher average hours per night of CPAP use during the early and late periods, respectively. Adjusted odds of CPAP use > 4 h per night during the early period were higher with increasing age (odds ratio [OR] 1.4 per 10 y increase in age [1.2–1.5], p < 0.001), decrease in the ESS scores from 0–2 mo (OR 1.05 per unit decrease in the ESS score [1.00–1.09], p = 0.049), being randomized to CPAP (OR 1.85 [1.37–2.52], p < 0.001), guessing membership in the active CPAP group (OR 1.52 [1.11–2.08], p = 0.009), after adjustment for the study site and the baseline sleepiness (ESS total score). After adjustment for these variables, as well as a decrease in the ESS score from baseline to 4 mo, the results were similar during the late period, with the effect of randomization to active CPAP being somewhat stronger than during the early period (OR 2.84 [2.06–3.94], p < 0.001).

Factors Associated with Active CPAP Adherence

During the early period (n = 435), 69% of the participants randomized to active CPAP were using CPAP > 4 h per night and the mean adherence was 4.8 ± 2.0 h. Men (n = 288) and women (n = 147) had similar mean daily adherence to CPAP (4.8 ± 2.0 h versus 4.8 ± 2.0 h, p = 0.99). Those using CPAP > 4 h per night during the early period were older (age: 54.1 ± 12.0 y versus 50.6 ± 11.6 y, p = 0.005) and had a higher baseline AHI (42.1 ± 25.5 versus 36.9 ± 21.7, p = 0.009), but had similar baseline ESS total score (10.6 ± 4.3 versus 9.9 ± 4.7, p = 0.13) compared to those using CPAP ≤ 4 h a night. However, those using CPAP > 4 h per night had lower ESS total score at 4 mo (6.7 ± 3.9 versus 7.8 ± 4.5, p = 0.03).

During the late period (n = 394), 67.3% of the participants were using CPAP > 4 h per night and the mean adherence was 4.7 ± 2.1 h. Men and women had similar average daily CPAP use (4.7 ± 2.1 h versus 4.6 ± 2.0 h, p = 0.9). In comparison to participants using CPAP ≤ 4 h per night, those using CPAP > 4 h per night were older (54.3 ± 11.8 y versus 49.1 ± 12.0 y, p < 0.001) and had lower ESS total score at 4 mo (6.5 ± 3.9 versus 7.9 ± 4.5, p = 0.002) and 6 mo (6.9 ± 4.1 versus 8.1 ± 4.4, p = 0.007) despite a similar baseline ESS total score (10.4 ± 4.5 versus 10.4 ± 4.3, p = 0.88). Among men, 68.3% were using CPAP > 4 h a night during the late period compared to 65.2% of the women (p = 0.57).

Increase in Adherence Prior to Clinic Visits

At the 2-mo time point after initiation of the active CPAP treatment, complete adherence data (2 mo, 7 d, and 1 d prior to the clinical visit) were available for 387 participants. The average nightly CPAP usage 1 d prior to the visit (5.2 ± 2.7 h) and that 1 w prior (5.1 ± 2.3 h) to the visit were significantly higher than the average nightly CPAP usage for the entire 2 mo prior to the clinic visit (4.8 ± 2.0 h, p < 0.001 for both comparisons) (Figure 2A). Similarly, these data were available for 321 participants at the 6-mo time point. The average nightly CPAP usage 1 d prior to the visit (5.5 ± 2.6 h) and that 1 w prior (5.1 ± 2.3 h) to the visit were significantly higher than the average nightly CPAP usage for the prior 2 mo (4.8 ± 2.2 h, p < 0.001 for both comparisons) (Figure 2B).

(A) Average nightly CPAP usage prior to the 2-mo clinic visit by duration of adherence measurement. During the early period (0–2 mo), the participants demonstrated a significant increase in CPAP adherence as the clinic visit approached. Sample size is 387 CPAP participants. (B) Average nightly CPAP usage prior to the 6-mo clinic visit by duration of adherence measurement. Similar to the early period of CPAP therapy, the participants demonstrated a significant increase in CPAP adherence as the clinic visit approached during the late period (5–6 mo). Sample size is 321 CPAP participants. *p < 0.05 indicates statistical significance for comparison with the 2-mo period.

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Figure 2

(A) Average nightly CPAP usage prior to the 2-mo clinic visit by duration of adherence measurement. During the early...

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Effect of Medical and Psychiatric Comorbidities

During the early period, prevalent CVD was associated with higher mean hours of active CPAP adherence (5.1 ± 2.0 h versus 4.6 ± 2.0 h, p = 0.006). The presence of anxiety was associated with a trend toward lower mean daily adherence (4.2 ± 2.0 h versus 4.8 ± 2.0 h, p = 0.07). Similarly, a higher percentage of participants with CVD (78.5%) were using CPAP > 4 h a night compared to the percentage of participants without CVD (67.6%) (Table 2). However, no other medical or psychiatric conditions were associated with CPAP adherence > 4 h nightly. A logistic regression model revealed age and the decrease in the ESS score from baseline to 2 mo to be significantly related to CPAP use > 4 h a night (Table 3). The higher odds of CPAP use > 4 h in those with CVD or lower odds with anxiety were not statistically significant. In a linear regression analysis, the presence of CVD was significantly associated with higher average hours of CPAP use and presence of anxiety was associated with a trend toward lower hours of CPAP use after adjusting for confounders (Table 4).

Odds of using CPAP > 4 h during the early adherence (0–2 mo) period with comorbid medical and psychiatric disorders.

 

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Table 3

Odds of using CPAP > 4 h during the early adherence (0–2 mo) period with comorbid medical and psychiatric disorders.

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Linear regression analysis with early adherence as the dependent variable.

 

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Table 4

Linear regression analysis with early adherence as the dependent variable.

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During the late period, current CVD was associated with higher mean daily adherence (5.1 ± 2.0 h versus 4.5 ± 2.1 h, p = 0.009) and presence of anxiety was related to lower adherence (3.9 ± 2.0 h versus 4.8 ± 2.1 h, p = 0.03). The presence of other medical conditions was not associated with late adherence. There was a trend toward a lower proportion of subjects using CPAP > 4 h a night in those with nasal congestion and higher in those with CVD (Table 5). A logistic regression model showed that increasing age and the decrease in the ESS total score was linked to higher odds of using CPAP > 4 h a night (Table 6). In a linear regression analysis, the presence of anxiety was independently associated with lower mean hours of CPAP use (Table 7).

Association between late adherence (5–6 mo) period and comorbid medical and psychiatric disorders.

 

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Table 5

Association between late adherence (5–6 mo) period and comorbid medical and psychiatric disorders.

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Odds of using CPAP > 4 h during the late adherence (5–6 mo) period with comorbid medical and psychiatric disorders.

 

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Table 6

Odds of using CPAP > 4 h during the late adherence (5–6 mo) period with comorbid medical and psychiatric disorders.

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Linear regression analysis with late adherence as the dependent variable.

 

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Table 7

Linear regression analysis with late adherence as the dependent variable.

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Early Versus Late Adherence

There was a strong correlation between active CPAP use during the early adherence (0–2 mo) and late adherence (5–6 mo) periods (R = 0.71, p < 0.001). A majority of those with more than 4 h per night of CPAP usage during the early adherence period (84%) went on to use CPAP for 4 h or more per night during the late period, compared with only a minority of participants (26%) who had less than 4 h of CPAP use per night during the early period.

With respect to hours of CPAP use per night, late adherence decreased compared to the early adherence in 226 participants (58.4%) and stayed the same or increased in the remainder (n = 161, 41.6%). The median decrease in adherence for the whole group was 10 min/night. There were no significant differences in age (52.1 y versus 53.1 y, p = 0.27), baseline ESS total score (10.3 versus 10.6, p = 0.95) or sex proportion between these two subgroups. A higher proportion of participants with than without nasal congestion had a decrease in adherence (64.9% versus 55.0%; p = 0.06). Other medical or psychiatric conditions were not associated with change in adherence. A logistic regression model controlling for age and sex revealed that nasal congestion is associated with a greater likelihood that CPAP use during the late adherence period would be less than during the early adherence period (OR = 1.60 [1.02–2.09], p = 0.04).

DISCUSSION

One of the primary findings of this study is that participant belief that he or she had been randomized to the active CPAP group predicted higher adherence to treatment, regardless of the actual treatment assignment. Furthermore, the presence of CVD was associated with a higher adherence, whereas anxiety was associated with a trend toward lower CPAP adherence. Additionally, increasing age, absence of nasal congestion, higher early CPAP use, and a decrease in ESS score with treatment may determine long-term adherence. Finally, we observed that adherence increased significantly and progressively just prior to each study visit. These findings have important implications for our understanding of the factors that affect long-term CPAP adherence.

We found that believing one was receiving active CPAP was associated with better adherence. Furthermore, higher adherence was associated with a more significant decrease in the ESS score at 4 mo compared to the baseline (an association that can be bidirectional). This suggests that a beneficial effect of CPAP in controlling sleep apnea symptoms such as sleepiness results in a better adherence. Also, patients who believe they are receiving a treatment that will help them are more likely to use it. Notably, prior studies have shown that cognitive perceptions, including outcome expectations, influence CPAP use.14 From a clinical perspective, this calls for a thorough discussion of the benefits of CPAP with patients at the initiation of therapy. It is possible that the association between perception that one is in the active CPAP group and higher adherence may be partly explained by a decrease in the total ESS scores. However, this association was highly significant even when adjusted for the decrease in the ESS score.

Our finding of progressively increasing adherence prior to each study visit emphasizes the importance of monitoring compliance at regular intervals in patients initiated on CPAP therapy. To our knowledge, progressive change in objectively measured CPAP adherence prior to a clinic visit has not been reported previously in a long-term clinical trial. Although participants in this study understood that they were expected to use CPAP regularly, and that their adherence would be continuously monitored, they still increased usage as their study visit approached. As a clinical correlate, in many cases CPAP adherence is monitored continuously by wireless technology. Nevertheless, it is possible that more frequent scheduled follow-up visits and/or face-to-face objective adherence data review, or direct feedback to the patient through applications now available for wirelessly collected adherence data, may motivate patients to increase their CPAP use. Indeed, greater adherence to CPAP therapy has been shown with intense patient education and follow-up,12 but the simple act of repeated objective adherence monitoring, and advising the patient in advance that it will be done, may be enough motivation for the patient to improve adherence. Notably, a similar increase in adherence to medications close to the time of an office visit has been previously reported.15 The frequency at which CPAP adherence needs to be monitored to optimize adherence should be assessed in future studies.

Our results suggest that CPAP adherence improves with increasing age, consistent with some previous studies.16 Although the current study was not designed to elucidate the etiology of lower adherence in younger patients, possible reasons include social embarrassment or interference with an active family, work, or social life.

A trend toward decrease in CPAP use in participants reporting nasal congestion is consistent with results of previous studies.17 Nasal congestion can increase upper airway resistance, decrease nasal cavity volume, and make it harder to breathe. CPAP can worsen the congestion and exacerbate the problem.18 Increased nasal resistance may lead to mouth opening and oral breathing,19 which are associated with a significantly higher upper airway resistance than nasal breathing.20 Oral breathing may also be associated with higher air leak, which itself has been linked to lower CPAP adherence.21 Indeed, treatment of nasal congestion has been related to better CPAP adherence.17,22

Our study confirms previous findings that long-term patterns of CPAP adherence are established early during the course of CPAP therapy.10,16,23 Patients should be closely monitored and encouraged early in the course of treatment to optimize their CPAP use. Conversely, alternative therapies could be considered early in appropriate patients with poor CPAP use in whom a clear etiology of the poor CPAP adherence cannot be identified.

We found higher CPAP adherence in persons with self-reported CVD. This may be related to an increasing provider and patient awareness of the cardiovascular outcomes in OSA, and the potential of CPAP therapy to prevent such outcomes. Frequent participant-provider contact, potentially with greater education, as happens in many patients with CVD, may be another factor whereby CVD may be associated with increased CPAP adherence. In contrast, however, recent analyses from the Sleep Apnea Cardiovascular Endpoints (SAVE) trial composed of participants with OSA and CVD, a majority of whom were Chinese, showed CPAP use ≥ 4 h/night in only approximately 50% of the participants at 6-mo follow-up.10 This could be related to the social, cultural, ethnic, economic, and educational differences between the participants in SAVE and APPLES. Also, the adherence rate in the SAVE trial was lower than that demonstrated in other larger trials. For example, Barbé et al.4 reported that 64% of their participants used CPAP for ≥ 4 h/night after a median 4-year follow-up, whereas usage > 4 h/night was 67.3% at 6 mo in our study.

Similar to some prior studies, we also did not find a correlation between presence of depression and CPAP adherence.9,10,24 Other studies,8,25 however, have shown a significant association between presence of depression or claustrophobia and diminished CPAP adherence. However, we did find a trend toward reduced CPAP adherence in the presence of anxiety. The discrepancy between our study and those showing positive associations between depression and CPAP adherence may partly be related to the fact that depression was self-reported in the current study. Furthermore, the severity of symptoms may have been lower than that encountered in patients in other “positive” trials designed primarily to look at patients with this condition. One study suggested that coping skills rather than anxiety or depressive symptoms may be related to CPAP adherence.11 In view of the conflicting results of these studies, further research is warranted to confirm or reject these associations.

The study has several limitations that may constrain potential interpretations. First, there was a meticulous follow-up, and continuous efforts to encourage and objectively assess adherence. These measures may not be as frequent or rigorous in actual clinical practice. Hence, adherence in clinical settings may be lower than what the APPLES data reveal. Second, the medical and psychiatric conditions were based on self-report rather than physician evaluation or standardized diagnostic measures. Thus, there may be some misclassification of the presence of such conditions. Moreover, the severity of these disorders was not assessed. Additionally, there may be other conditions such as chronic obstructive pulmonary disease that may limit adherence, but were not assessed in this study. Third, in the absence of a standard definition of optimal adherence, we chose the frequently employed criterion of CPAP usage > 4 h a night to differentiate good adherence from poor adherence. However, the results were generally similar when we analyzed hours of CPAP use as a continuous variable. Fourth, several factors including patient education and socioeconomic factors may also be important in ensuring optimum adherence to CPAP therapy, but were not evaluated in the current study. Finally, although we found that the belief of membership in the active CPAP group was associated with higher adherence, we do not know at what point in time the subjects came to the conclusion that they were on CPAP or sham. Hence, it is not possible to know if the belief was the cause of increased adherence.

In conclusion, this study shows that CPAP adherence is higher in those randomized to or who believe they are receiving active CPAP therapy compared to those initiated on or believe they are on placebo (sham CPAP); CPAP adherence increases progressively and repeatedly as a clinic visit approaches. Furthermore, higher CPAP use is observed in persons who are older and have cardiovascular disorders, and lower CPAP use is noted in those with nasal congestion and anxiety or claustrophobia. Better education including information about the CPAP's potential benefit on cardiovascular outcomes, more frequent objective adherence assessments, and treatment of nasal congestion may potentially enhance adherence to CPAP therapy in patients with OSA. Specific strategies targeting younger patients may also improve adherence to this therapeutic modality.

DISCLOSURE STATEMENT

This was not an industry supported study. Dr. Kushida has received research support from Cephalon, Pacific Medico, ResMed, Apnex, Impax Laboratories, and Aerial Biopharma; received royalties from Philips Respironics; and has consulted for Seven Dreamers Laboratories, Zephyr, and Jawbone. Dr. Walsh has received research support from Apnex, Merck, Respironics, Novo Nordisk, and Vanda and has consulted for Aptalis, Balanced Therapeutics, Philips Respironics, Sun, Cereve, Intec, and Esai. Dr. Quan is a consultant to the Global Corporate Challenge. The other authors have indicated no financial conflicts of interest.

ABBREVIATIONS

AHI

apnea-hypopnea index

APPLES

Apnea Positive Pressure Long-term Efficacy Study

CPAP

continuous positive airway pressure

CVD

cardiovascular disease

ESS

Epworth Sleepiness Scale

OSA

obstructive sleep apnea

PSG

polysomnogram

SAVE

Sleep Apnea Cardiovascular Endpoints trial

ACKNOWLEDGMENTS

The Apnea Positive Pressure Long-term Efficacy Study (APPLES) study was funded by contract 5UO1-HL-068060 from the National Heart, Lung and Blood Institute. The APPLES pilot studies were supported by grants from the American Academy of Sleep Medicine and the Sleep Medicine Education and Research Foundation to Stanford University and by the National Institute of Neurological Disorders and Stroke (N44-NS-002394) to SAM Technology. In addition, APPLES investigators gratefully recognize the vital input and support of Dr. Sylvan Green, who died before the results of this trial were analyzed, but was instrumental in its design and conduct.

Administrative Core: Clete A. Kushida, MD, PhD; Deborah A. Nichols, MS; Eileen B. Leary, BA, RPSGT; Pamela R. Hyde, MA; Tyson H. Holmes, PhD; Daniel A. Bloch, PhD; William C. Dement, MD, PhD

Data Coordinating Center: Daniel A. Bloch, PhD; Tyson H. Holmes, PhD; Deborah A. Nichols, MS; Rik Jadrnicek, Microflow, Ric Miller, Microflow Usman Aijaz, MS; Aamir Farooq, PhD; Darryl Thomander, PhD; Chia-Yu Cardell, RPSGT; Emily Kees, Michael E. Sorel, MPH; Oscar Carrillo, RPSGT; Tami Crabtree, MS; Booil Jo, PhD; Ray Balise, PhD; Tracy Kuo, PhD

Clinical Coordinating Center: Clete A. Kushida, MD, PhD, William C. Dement, MD, PhD, Pamela R. Hyde, MA, Rhonda M. Wong, BA, Pete Silva, Max Hirshkowitz, PhD, Alan Gevins, DSc, Gary Kay, PhD, Linda K. McEvoy, PhD, Cynthia S. Chan, BS, Sylvan Green, MD

Clinical Centers

Stanford University: Christian Guilleminault, MD; Eileen B. Leary, BA, RPSGT; David Claman, MD; Stephen Brooks, MD; Julianne Blythe, PA-C, RPSGT; Jennifer Blair, BA; Pam Simi, Ronelle Broussard, BA; Emily Greenberg, MPH; Bethany Franklin, MS; Amirah Khouzam, MA; Sanjana Behari Black, BS, RPSGT; Viola Arias, RPSGT; Romelyn Delos Santos, BS; Tara Tanaka, PhD

University of Arizona: Stuart F. Quan, MD; James L. Goodwin, PhD; Wei Shen, MD; Phillip Eichling, MD; Rohit Budhiraja, MD; Charles Wynstra, MBA; Cathy Ward, Colleen Dunn, BS; Terry Smith, BS; Dane Holderman, Michael Robinson, BS; Osmara Molina, BS; Aaron Ostrovsky, Jesus Wences, Sean Priefert, Julia Rogers, BS; Megan Ruiter, BS; Leslie Crosby, BS, RN

St. Mary Medical Center: Richard D. Simon Jr., MD; Kevin Hurlburt, RPSGT; Michael Bernstein, MD; Timothy Davidson, MD; Jeannine Orock-Takele, RPSGT; Shelly Rubin, MA; Phillip Smith, RPSGT; Erica Roth, RPSGT; Julie Flaa, RPSGT; Jennifer Blair, BA; Jennifer Schwartz, BA; Anna Simon, BA; Amber Randall, BA

St. Luke's Hospital: James K. Walsh, PhD, Paula K. Schweitzer, PhD, Anup Katyal, MD, Rhody Eisenstein, MD, Stephen Feren, MD, Nancy Cline, Dena Robertson, RN, Sheri Compton, RN, Susan Greene, Kara Griffin, MS, Janine Hall, PhD

Brigham and Women's Hospital: Daniel J. Gottlieb, MD, MPH, David P. White, MD, Denise Clarke, BSc, RPSGT, Kevin Moore, BA, Grace Brown, BA, Paige Hardy, MS, Kerry Eudy, PhD, Lawrence Epstein, MD, Sanjay Patel, MD

Sleep HealthCenters for the use of their clinical facilities to conduct this research

Consultant Teams

Methodology Team: Daniel A. Bloch, PhD, Sylvan Green, MD, Tyson H. Holmes, PhD, Maurice M. Ohayon, MD, DSc, David White, MD, Terry Young, PhD

Sleep-Disordered Breathing Protocol Team: Christian Guilleminault, MD, Stuart Quan, MD, David White, MD

EEG/Neurocognitive Function Team: Jed Black, MD, Alan Gevins, DSc, Max Hirshkowitz, PhD, Gary Kay, PhD, Tracy Kuo, PhD

Mood and Sleepiness Assessment Team: Ruth Benca, MD, PhD, William C. Dement, MD, PhD, Karl Doghramji, MD, Tracy Kuo, PhD, James K. Walsh, PhD

Quality of Life Assessment Team: W. Ward Flemons, MD, Robert M. Kaplan, PhD

APPLES Secondary Analysis-Neurocognitive (ASA-NC) Team: Dean Beebe, PhD, Robert Heaton, PhD, Joel Kramer, PsyD, Ronald Lazar, PhD, David Loewenstein, PhD, Frederick Schmitt, PhD

National Heart, Lung, and Blood Institute (NHLBI)

Michael J. Twery, PhD, Gail G. Weinmann, MD, Colin O. Wu, PhD

Data and Safety Monitoring Board (DSMB)

Seven-year term: Richard J. Martin, MD (Chair), David F. Dinges, PhD, Charles F. Emery, PhD, Susan M. Harding MD, John M. Lachin, ScD, Phyllis C. Zee, MD, PhD

Other term: Xihong Lin, PhD (2 y), Thomas H. Murray, PhD (1 y).

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