The Effect of CPAP on HRQOL as Measured by the Quality of Well-Being Self-Administered Questionnaire (QWB-SA)

Salma Batool-Anwar, MD, MPH¹
Olabimpe Omobomi, MD, MPH¹
Stuart F. Quan, MD^1,2

¹Division of Sleep and Circadian Disorders Medicine, Brigham and Women’s Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, MA, ²Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, AZ.

Abstract

Background: To examine the effect of continuous positive airway pressure (CPAP) on Health-related quality of life (HRQoL) as measured by the Quality of Well Being Self-Administered questionnaire (QWB-SA).

Methods: Participants from The Apnea Positive Pressure Long-term Efficacy Study (APPLES); a 6-month multicenter randomized, double-blinded intention to treat study, were included in this analysis. The participants with an apnea-hypopnea index >10 events/hour initially randomized to CPAP or Sham group were asked to complete QWB-SA at baseline, 2, 4, and 6-month visits.

Results: There were no group differences among either the CPAP or Sham groups. Mean age was 52±12 (SD] years, AHI 40±25 events/hr, BMI 32±7.1 kg/m2, and Epworth Sleepiness Score (ESS) 10±4 of 24 points. QWB-SA scores were available at baseline, and 2, 4 & 6 months after treatment in CPAP (n 558) and Sham CPAP (n 547) groups. There were no significant differences in QWB scores among mild, moderate or severe OSA participants at baseline. Modest improvement in QWB scores was noted at 2, 4 and 6- months among both Sham and CPAP groups (P <0.05).  However, no differences were observed between Sham CPAP and CPAP at any time point. Comparison of the QWB-SA data from the current study with published data in populations with chronic illnesses demonstrated that the impact of OSA is no different than the effect of AIDS and arthritis.

Conclusion: Although the QoL measured by the QWB-SA was impaired in OSA it did not have direct proportionality to OSA severity.

Introduction

Obstructive Sleep Apnea (OSA) is characterized by recurrent episodes of upper airway narrowing and oxygen desaturation with resultant frequent nighttime awakenings and daytime sleepiness (1). A strong association between OSA and obesity has been described (2), and with the global epidemic of obesity (3), the prevalence of OSA is anticipated to increase. Recent studies have reported an increase in prevalence from 22 to 37% among men, and 17 to 50% among women (4).

Health related quality of life (HRQoL) relates to a World Health Organization definition of health comprised of physical, mental, spiritual and social wellbeing (5). A variety of questionnaires are used in epidemiologic studies to assess quality of life (QoL). Studies demonstrate that QoL is worse in persons with OSA (6). Continuous positive airway pressure (CPAP) is the gold standard for treating OSA and improves daytime sleepiness among adherent patients (7). However, studies examining the effect of CPAP on quality of life have not found consistent results (8,9). These discrepancies are attributed to the fact that there are two types of questionnaires which are used to assess QoL; generic or disease specific. Utilizing data from the Apnea Positive Pressure long term Efficacy Study (APPLES), a randomized controlled trial of CPAP vs Sham CPAP, we analyzed whether CPAP improved HRQoL using the self-administered version of the Quality of Well-Being Scale (QWB-SA), a well-validated generic HRQoL instrument, that has not been validated in OSA.

Materials and Methods

 
Study Population and Protocol. APPLES was a 6-month multicenter, randomized, double-blinded, 2-arm, sham-controlled, intention-to-treat study of CPAP efficacy on three domains of neurocognitive function in OSA. A detailed description of the protocol has previously been published (10). Briefly, the participants were recruited either through local advertisement or from those attending sleep clinics for evaluation of possible OSA. Symptoms indicative of OSA were used to screen potential participants. The initial clinical evaluation included administering informed consent and screening questionnaires as well as history and physical examination and medical assessment by a study physician. Participants subsequently returned 2-4 weeks later for a baseline 24-h sleep laboratory visit, during which polysomnography (PSG) was performed to confirm the diagnosis followed by a day of neurocognitive, mood, sleepiness, and QoL testing. Inclusion criteria have been published previously and included age ≥ 18 years and a clinical diagnosis of OSA, as defined by the American Academy of Sleep Medicine (AASM) criteria. Only participants with an apnea-hypopnea index (AHI) ≥ 10 by PSG were randomized to CPAP or sham CPAP and continued in the APPLES study. Exclusion criteria included previous treatment for OSA with CPAP or surgery, oxygen saturation on the baseline PSG <75% for >10% of the recording time, history of a motor vehicle accident related to sleepiness within the past 12 months, presence of several chronic medical conditions, use of various medications known to affect sleep or neurocognitive function, and other health and social factors that may impact standardized testing procedures (e.g., shift work). After randomization, participants underwent a CPAP or sham CPAP titration and were followed for 6 months on their assigned intervention. Subsequent study visits occurred at 2, 4 and 6 months after the titration PSG. The APPLES study was approved by an institutional review board for human studies at each clinical site; informed consent was obtained from all participants at the time of enrollment as previously described.

Quality of Well-Being Scale (QWB). The QWB is a comprehensive measure of HRQoL. It has been extensively validated and can be used to calculate quality-adjusted life years (QALYs) (11). Because of its complexity, a self-administered version, the QWB-SA was developed (12). The questionnaire is sensitive to changes at the higher levels of functioning and can also produce estimates of QALY for cost-effectiveness analyses. The QWB-SA includes 5 sections. The first assesses the presence/absence of 19 chronic symptoms or problems (e.g., blindness, speech problems). These chronic symptoms are followed by 25 acute (or more transient) physical symptoms (e.g. headache, coughing, pain), and 14 mental health symptoms and behaviors (e.g., sadness, anxiety, irritation). The remaining sections of the QWB-SA include assessments of mobility (including use of transportation), physical activity (e.g., walking and bending over) and social activity including completion of role expectations (e.g., work, school, or home). Scores from each subscale are coupled with population derived weights to yield one composite score ranging from 0.09 (lowest possible health state to 1 for perfect health, with zero meaning death.

The QWB-SA was administered at the baseline study visit and at each subsequent study visit. At each visit, we collected three scores (QWB1, QWB2, and QWB3) corresponding to the day of the survey and the immediate 2 previous days. These scores included combinations of questions from the 5 sections as follows:

• Part I: Acute and chronic symptoms
• Part II: Self Care
• Part III: Mobility
• Part IV: Physical activity
• Part V: Social activity

To calculate the QWB-SA the scores for each section were computed and combined according to guidelines provided by the University of California, San Diego (UCSD) Health Services Research Center to yield the QWB score for each day. From the daily scores, the QWB Average Score was derived as the mean of QWB1+QWB2+QWB3. We used the QWB Average Score in subsequent analyses.

Polysomnography (PSG). The PSG montage included monitoring of the electroencephalogram (EEG, C₃-A₂ or C₄-A₁, O₂-A₁ or O₁-A₂), electrooculogram (EOG, ROC-A₁, LOC-A₂), chin and anterior tibialis electromyograms (EMG), heart rate by 2-lead electrocardiogram, snoring intensity (anterior neck microphone), nasal pressure (nasal cannula), nasal/oral thermistor, thoracic and abdominal movement (inductance plethysmography bands), and oxygen saturation (pulse oximetry). All PSG records were electronically transmitted to a centralized data coordinating and PSG reading center. Sleep and wakefulness were scored using Rechtschaffen and Kales criteria (13). Apneas and hypopneas were scored using the American Academy of Sleep Medicine Task Force diagnostic criteria (14). Briefly, an apnea was defined by a clear decrease (> 90%) from baseline in the amplitude of the nasal pressure or thermistor signal lasting ≥ 10 sec. Hypopneas were identified if there was a clear decrease (> 50% but ≤ 90%) from baseline in the amplitude of the nasal pressure or thermistor signal, or if there was a clear amplitude reduction of the nasal pressure signal ≥ 10 sec that did not reach the above criterion, but was associated with either an oxygen desaturation > 3% or an arousal. Obstructive events were scored if there was a persistence of chest or abdominal respiratory effort. Central events were noted if no displacement occurred on either the chest or abdominal channels. The AHI was computed as the number of apneas and hypopneas divided by the total sleep time. Sleep apnea was classified as mild (AHI 10.0 to 15.0 events per hour), moderate (AHI 15.1 to 30.0 events per hour), and severe (AHI more than 30 events per hour) (14).

CPAP Adherence. Nightly use of CPAP was downloaded from the device and was assessed at 2, 4, 6-month intervals. The participants were considered adherent if CPAP use was ≥ 4 hours per night for >70% of nights.

Epworth Sleepiness Scale (ESS). The ESS is a validated self-completion tool that asks subjects to rate the likelihood of falling asleep in eight common situations using four ordinal categories ranging from 0 (no chance) to 3 (high chance) (15). Scores range from 0 to 24 with a score >10 suggesting EDS (15).

Calgary Sleep Apnea Quality of Life Index (SAQLI). The SAQLI was developed as a sleep apnea specific quality of life instrument (16). It is a 35-item instrument that captures the adverse impact of sleep apnea on 4 domains: daily functioning, social interactions, emotional functioning and symptoms. Items are scored on a 7- point scale with “all of the time” and “not at all” being the most extreme responses. Item and domain scores are averaged to yield a composite total score between 1 and 7. Higher scores represent a better quality of life.

Statistical Analysis. Simple linear and multiple regression models were used to estimate the degree to which variables correlated with QWB scores.  We examined the association between the QWB-SA and the following variables: OSA severity as measured by the AHI, sleepiness as assessed by ESS, age, and baseline body mass index (BMI, kg/m²). Severity of OSA in this study was defined according to the AHI as follows: Mild (10-<15 /h), Moderate (15-<30 /h), Severe (>30 /h). Changes in QWB-SA over the duration of the study were analyzed using a mixed model repeated measures analysis of variance with participants stratified by their randomization group (CPAP or Sham CPAP). Analyses were performed using STATA (version 11, StataCop TX USA) and IBM SPSS v24 (Armonk, NY). Finally, we compared the sample means to the normative means using GraphPad Prism8.

Results

Initially, 558 participants were randomized to CPAP and 547 to Sham CPAP. As shown in Table 1, age, gender, ethnicity, body mass index (BMI, kg/m²), AHI, and ESS were similar between the CPAP and Sham CPAP groups.

Table 1. Baseline Characteristics.

SD: Standard Deviation, BMI: Body Mass Index, AHI: Apnea Hypopnea Index, ESS: Epworth Sleepiness Scale, SAQLI: Sleep Apnea Quality of Life Index, QWB: Quality of wellbeing

Men comprised of 50% of the study population and the population was generally obese (CPAP: BMI 32.4 ± 7.3; Sham: BMI 32.1 ± 6.9 kg/m²). The participants overall had at least 15 years of education, and over 50% of the participants were either married or living with someone. The sample population did not report severe excessive daytime sleepiness with the reported ESS approximately 10 in both the CPAP and SHAM groups. Similarly, there were no significant differences in SAQLI score, total sleep time or arousal index among the two treatment groups.

Scores for the QWB-SA were available at baseline and 2, 4 and 6 months after treatment in both groups. As shown in Table 2, there were no significant differences in QWB-SA at baseline between both groups.

Table 2. Mixed model analysis for the effect of time on QWB average score among CPAP and SHAM groups (N=1104).

*QWB-SA scores improved in both groups over the 6 months of follow-up, p<0.05.

In addition, scores among mild, moderate or severe OSA participants at baseline also were not different (data not shown). Modest improvement in QWB scores was noted at 2, 4 and 6- month among both Sham and CPAP groups (P<0.05). However, no differences were observed between Sham CPAP and CPAP at any time point. Furthermore, multiple regression analyses stratified by OSA severity, gender, and mean adherence to CPAP or Sham CPAP suggested significant improvement in QWB scores only among women with severe OSA in the CPAP group (data not shown, P <0.05).

Table 3 shows comparisons of the QWB-SA from the current study with published data in populations with acquired immune deficiency syndrome (AIDS), chronic obstructive lung disease (COPD), arthritis and prostate cancer (17-20).

Table 3. Comparison of sample mean to normative means.

QWB: Quality of Wellbeing, CF: Cystic Fibrosis, OSA: obstructive Sleep Apnea, AIDS: Acquired Immunodeficiency syndrome, COPD: Chronic Obstructive pulmonary Disease.

The impact of OSA is not different than the effect of AIDS and arthritis and only slightly less than with COPD and prostate cancer.

Discussion

In this study, we analyzed the effect of CPAP therapy on QoL using the QWB-SA questionnaire. We found that the cross-sectional mean QWB-SA scores were comparable to the scores found in other chronic illnesses (COPD, arthritis, cystic fibrosis, prostate cancer, and AIDS) (17-20) indicating that quality of life is adversely affected by sleep apnea similar to these chronic conditions. Although the QWB-SA modestly declined over a treatment duration of 6 months, the instrument was unable to distinguish any differences between CPAP and sham CPAP. Moreover, these findings remained after stratifying based on PAP adherence and OSA severity.

Assessment of quality of life (QoL) is an integral part of OSA management and various scales are being used by researchers. Studies using these instruments generally find that QoL is impaired in persons with OSA (6). However, to our knowledge, there have not been previous studies using the QWB-SA in a population with OSA. Our findings which demonstrate that the QWB-SA is low in OSA are consistent with these prior investigations.  However, in contrast to our observations, some but not all studies have noted a greater impact of OSA on QoL in those with more severe disease. For example, Baldwin et al. (21) in the Sleep Heart Health Study found that there was a higher risk of having an impact on the vitality subscale of SF-36 with greater OSA severity. In contradistinction, Fornas et al. (22) using the Nottingham Health Profile found no relationship between OSA severity and differences in QoL in a moderate size group of OSA patients. This discrepancy may relate to whether a general population as in Baldwin et al or a clinical population as in Fornas et al. was studied. Additionally, instruments used to quantify QoL may assess different domains, thus leading to different conclusions. Thus, while the QWB-SA can detect that QoL is impaired in those with OSA, it does not have the capability to distinguish subtleties related to differences in OSA severity.

At baseline, we observed that scores on the QWB-SA were comparable to those found for patients with AIDS (23) and arthritis (20) but were slightly higher than those with COPD (18), cystic fibrosis (CF) (24), and prostate cancer (19). They are notably better than chronic renal failure on hemodialysis (0.49) (25). Thus, it appears that the impact of OSA on QoL is approximately the same as several but not all other chronic conditions that are viewed by the general public as having considerably greater health consequences.

Contrary to its use in cystic fibrosis and AIDS where QWB-SA has validity as an outcome measure (18-24) we did not find that the QWB-SA was able to detect changes in QoL with the use of CPAP. This observation also is contradistinction to results from the CPAP Apnea Trial North American Program using the Functional Outcomes of Sleep Questionnaire (FOSQ) as well as analyses of the Sleep Apnea Quality of Life Inventory (SAQLI) in the APPLES (26,27) study. In contrast to QWB-SA, both the FOSQ and SAQLI are sleep specific QoL instruments. Thus, the results of our study provide additional evidence that a generic HRQoL instrument may not be sensitive to the specific QoL domains impacted by treatment of OSA using CPAP. Other studies have concluded that changes in QoL in response to CPAP therapy may vary depending on the QoL measure used and that some measures may be more sensitive to detecting changes to QoL with CPAP therapy than others (28). A randomized control trial with a total of 1256 patients comparing various QoL tools concluded that generic QoL tools may not be sufficient at detecting important changes in QoL in OSA patients as CPAP may not improve general QoL scores but rather specific QoL domains. For instance, in that analysis, the SF-36 tool demonstrated positive changes only in physical function and energy levels with CPAP (29). In contrast, a study comparing 2 sleep specific QoL instruments to the generic 36-item short form survey (SF-36), found that the FOSQ and SAQLI provided unique information about health outcomes in treated OSA patients (30) and correlated well with the SF36 survey domains. In that study, the FOSQ was found to be more sensitive to differences in CPAP adherence than the SAQLI.

To our knowledge, this is the first study examining the effect of CPAP on QoL using the QWB-SA questionnaire. A major strength of the study is that it utilized data from a large multicenter randomized controlled trial with follow up and interval documentation of CPAP adherence for up to 6 months. However, there were several limitations. First, the study population was a mixture of patients recruited from sleep clinics and the general population; this may have resulted in a differential impact on QoL. Second, overall adherence to both CPAP and sham CPAP was relatively poor although not inconsistent with the results from other studies. Finally, QoL was assessed using the average QWB-SA total scores and hence it is unclear whether there may have been improvements in specific domains over time with CPAP treatment.

In conclusion, despite the limitations, we found that QoL measured by the QWB-SA was impaired in OSA but was not found to have direct proportionality to OSA severity. Furthermore, it was not sufficiently sensitive for detecting QoL changes in OSA patients on CPAP therapy. Our data support the use of sleep apnea specific QoL questionnaires for measurement of QoL after initiation of CPAP.

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).

Abbreviations

• AASM: American Academy of Sleep Medicine
• AHI: Apnea Hypopnea Index
• AIDS: Acquired immune deficiency syndrome
• APPLES: Apnea Positive Pressure Long-term Efficacy Study
• BMI: Body mass Index
• CF: Cystic Fibrosis
• COPD: Chronic obstructive pulmonary disease
• CPAP: Continuous positive airway pressure.
• EDS: Excessive daytime sleepiness
• EEG: Electroencephalogram
• ESS: Epworth sleepiness scale
• EMG: Electromyogram
• EOG: Electrooculogram
• FOSQ: Functional Outcomes of Sleep Questionnaire
• HRQoL: health related quality of life
• OSA: Obstructive Sleep apnea
• PSG: polysomnograpgy
• QALY: Quality Adjusted life years
• QoL: Quality of Life
• QWB: Quality of well being
• QWB-SA: Quality of well being-Self administered.
• SAQLI: Sleep apnea quality of life Index
• SD: Standard deviation

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Cite as: Batool-Anwar S, Omobomi O, Quan SF. The effect of CPAP on HRQOL as Measured by the quality of Well-Being Self-Administered Questionnaire (QWB-SA). Southwest J Pulm Crit Care. 2020;20(1):29-40. doi: https://doi.org/10.13175/swjpcc070-19 PDF 

Role of Spousal Involvement in Continuous Positive Airway Pressure (CPAP) Adherence in Patients with Obstructive Sleep Apnea (OSA)

Salma Batool-Anwar, MD, MPH ²

Carol M. Baldwin, PhD, MSN ³

Shira Fass, PhD⁴

Stuart F. Quan, MD ^1,2

¹University of Arizona College of Medicine, Tucson, AZ USA

²Brigham and Women’s Hospital, Boston, MA USA

³Arizona State University College of Nursing and Health Innovation and College of Health Solutions, Phoenix, AZ USA

⁴Case Western Reserve University, Cleveland, Ohio USA

Abstract

Introduction: Little is known about the impact of spousal involvement on continuous positive airway pressure (CPAP) adherence. The aim of this study was to determine whether spouse involvement affects adherence with CPAP therapy, and how this association varies with gender.  

Methods: 194 subjects recruited from Apnea Positive Pressure Long Term Efficacy Study (APPLES) completed the Dyadic Adjustment Scale (DAS). The majority of participants were Caucasian (83%), and males (73%), with mean age of 56 years, mean BMI of 31 kg/m². & 62% had severe OSA. The DAS is a validated 32-item self-report instrument measuring dyadic consensus, satisfaction, cohesion, and affectional expression. A high score in the DAS is indicative of a person’s adjustment to the marriage. Additionally, questions related to spouse involvement with general health and CPAP use were asked. CPAP use was downloaded from the device and self-report, and compliance was defined as usage > 4 h per night.

Results: There were no significant differences in overall marital quality between the compliant and noncompliant subjects. However, level of spousal involvement was associated with increased CPAP adherence at 6 months (p=0.01). After stratifying for gender these results were significant only among males (p=0.03). Three years after completing APPLES, level of spousal involvement was not associated with CPAP compliance even after gender stratification.

Conclusion: Spousal involvement is important in determining CPAP compliance in males in the 1^st 6 months after initiation of therapy but is not predictive of longer-term adherence. Involvement of the spouse should be considered an integral part of CPAP initiation procedures.

Abbreviations List

AASM: American Academy of Sleep Medicine

AHI: Apnena Hyponea Index

APPLES: Apnea Positive Pressure Long Term Efficacy Study

BMI: Body Mass Index

CPAP: Continuous positive airway pressure

DAS: Dyadic Adjustment Scale

EEG: Electroencephalogram

EMG: Electromyograms

EOG: Electroocculogram

OSA: Obstructive Sleep Apnea

PSG: polysomnography

Introduction

Obstructive Sleep apnea (OSA) is characterized by repetitive episodes of upper airway closure during sleep resulting in oxygen desaturation and frequent arousals. In addition to cardiovascular comorbidities, OSA has been linked to poor quality of life, depression and motor vehicle accidents. Recent data suggest an increase in the prevalence of OSA for both men and women (34% and 17.4% respectively) (1).

Continuous positive airway pressure (CPAP) is the treatment of choice for OSA. Poor adherence, however, remains a widely recognized problem limiting overall effectiveness of CPAP therapy. Prior studies have identified various factors and strategies to promote CPAP adherence (2). In addition to disease, educational, and technology-specific considerations that can affect CPAP adherence, social and psychological dynamics are important components of adherence as well.

Several studies have suggested that partner/spousal dyadic support can play a positive role in the patient’s overall health and health behaviors (3,4) . For example, higher CPAP adherence was reported among patients with bed partners (5), as well as persons who were married versus single (6). Little is known about the influence of spousal involvement on CPAP adherence. One study indicated that perceived spousal support predicted greater CPAP adherence among men with high disease severity; however, pressure to adhere to treatment by the wife was not of benefit and predicted poorer CPAP adherence (7). Another study indicated reduced marital conflict by OSA patients following 3 months of CPAP, suggesting that marital conflict resolution might serve as an intervention for CPAP adherence (8). Despite these hints that dyadic support may play a role in CPAP adherence, participants in both studies by Baron et al. (7.8) consisted primarily of men, and the studies focusing on CPAP adherence by Lewis et al. (5) and Gagnadoux et al. (6) included only men. Thus, the aim of the current study was to determine whether spouse involvement affects CPAP adherence and how this association differs by gender using data from a large randomized trial of CPAP versus sham CPAP to treat OSA. 

Methods

Study Population and Protocol

The Apnea Positive Pressure Long-term Efficacy Study (APPLES) was a 6-month multicenter, randomized, double-blinded, 2-arm, sham-controlled, intention-to-treat study of CPAP efficacy on three domains of neurocognitive function in OSA. Three of the 5 APPLES Clinical Centers, the University of Arizona, Stanford University and St. Luke’s Hospital (Chesterfield, MO) participated in this ancillary study. A detailed description of the protocol has previously been published (9). Briefly, participants were either recruited through local advertisement, or from attending sleep clinics for evaluation of possible OSA. Symptoms indicative of OSA were used to prescreen potential participants. The initial clinical evaluation included administering informed consent, screening questionnaires, a history and physical examination, and a medical assessment by a study physician. Participants subsequently returned 2-4 weeks later for a 24-h sleep laboratory visit, during which polysomnography (PSG) was performed to confirm the diagnosis, followed by a day of neurocognitive, mood, sleepiness, and quality of life survey testing. Inclusion and exclusion criteria have been published previously and included age ≥ 18 years and a clinical diagnosis of OSA as defined by American Academy of Sleep Medicine (AASM) criteria. Only participants with an apnea hypopnea index (AHI) ≥ 10 by PSG were randomized to continue in the APPLES study. Exclusion criteria were previous treatment for OSA with CPAP or surgery, oxygen saturation on baseline PSG <75% for >10% of the recording time, history of motor vehicle accident-related to sleepiness within the past 12 months, presence of chronic medical conditions, use of various medications known to affect sleep or neurocognitive function, and various health and social factors that may impact standardized testing procedures (e.g., shift work).

Following the PSG, participants with an AHI ≥ 10 who met other enrollment criteria were randomized to CPAP or sham CPAP for continued participation in APPLES. After randomization, participants returned to the sleep laboratory for a CPAP or sham CPAP titration PSG. Subsequent assessments were made at 2, and 6 months post-randomization at which time a test battery was re-administered. At the conclusion of their 6-month post-randomization evaluations, each participant was informed of their treatment group assignment and offered CPAP treatment going forward. Approximately 36 months after the conclusion of APPLES, participants were sent the Dyadic Adjustment Scale (DAS) questionnaire with the addition of several additional questions related to health.

Assessment of Spouse involvement

Inclusion in the current analysis required that subjects were married during the APPLES study and remained married at the time of questionnaire administration. The DAS (10), a quality of marriage questionnaire, was utilized to assess marital relationship. It is a 32-item self-report instrument that incorporates four dimensions, including a 13 item dyadic consensus, 10 item dyadic satisfaction, 5 item dyadic cohesion, and 4 item affectional expression. A high DAS score is indicative of a person’s positive adjustment to the marriage. Additionally, questions related to spouse involvement with general health and CPAP use were asked (See Appendix for full questionnaire).

Polysomnography

The PSG montage included monitoring of the electroencephalogram (EEG, C₃-A₂ or C₄-A₁, O₂-A₁ or O₁-A₂), electro-oculogram (EOG, ROC-A₁, LOC-A₂), chin and anterior tibialis electromyograms (EMG), heart rate by 2-lead electrocardiogram, snoring intensity (anterior neck microphone), nasal pressure (nasal cannula), nasal/oral thermistor, thoracic and abdominal movement (inductance plethysmography bands), and oxygen saturation (pulse oximetry). All PSG records were electronically transmitted to a centralized data coordinating and PSG reading center. Sleep and wakefulness were scored using Rechtschaffen and Kales criteria (11). Apneas and hypopneas were scored using American Academy of Sleep Medicine Task Force (1999) diagnostic criteria (12, 13). Briefly, an apnea was defined by a clear decrease (> 90%) from baseline in the amplitude of the nasal pressure or thermistor signal lasting ≥ 10 sec. Hypopneas were identified if there was a clear decrease (> 50% but ≤ 90%) from baseline in the amplitude of the nasal pressure or thermistor signal, or if there was a clear amplitude reduction of the nasal pressure signal ≥ 10 sec that did not reach the above criterion, but was associated with either an oxygen desaturation > 3% or an arousal.

Obstructive events were scored if there was persistence of chest or abdominal respiratory effort. Central events were noted if no displacement occurred on either the chest or abdominal channels. Sleep apnea was classified as mild (AHI 10.0 to 15.0 events per hour), moderate (AHI 15.1 to 30.0 events per hour), and severe (AHI more than 30 events per hour) (12).

CPAP adherence

The primary dependent variable of interest was CPAP adherence and was assessed by nightly use of CPAP at the 6-months follow up visit. CPAP use was downloaded from the device and the participants were considered to be adherent if the mean CPAP use was > 4 hours per night at 6-months. Long-term CPAP adherence was measured as self-reported adherence (hours per night) at the time of the DAS administration.

Statistical Analysis 

Statistical analyses were performed using STATA (Version 11, StataCorp TX USA). Univariate and multivariate logistic regression models were used to estimate the degree to which variables correlated with CPAP adherence. We examined the association between CPAP adherence and following variables: OSA severity as measured by the AHI, age, baseline body mass index (BMI, kg/m²), spousal involvement and the DAS. For these models, dichotomous variables were created for OSA severity (AHI < 15 vs. ≥ 15), obesity (BMI <30 kg/m² vs. ≥30 kg/m²) and CPAP adherence (< 4 hours/night vs. ≥4 hours/night). Spousal involvement was ascertained using a 5 point Lickert scale and analyzed as a continuous variable.

To assess predictors of CPAP adherence we used multiple regression models. Unpaired t-tests were used to assess the effect of gender, age, OSA severity, BMI, and CPAP adherence in both the CPAP and Sham CPAP groups. Data for continuous and interval variables were expressed as mean ± SD, and as a percentage for categorical variables. Statistical significance was set at a P value <0.05, two-tailed. The variables that produced P value of < 0.05 were included in the final model.

Results

Baseline demographic data on participants (N=194) who completed the DAS are outlined in Table 1.

Table1. Baseline Characteristics of APPLES Participants Who Completed Dyadic Data.

The majority of the participants were Caucasian (83%) and males (73%), with mean age of 56 years and a mean BMI of 31 kg/m². Over half of the participants had severe OSA (62%). Table 2a demonstrates CPAP adherence at 6 months using multivariate analysis.

Table 2A. Multivariate Analysis of Adherence to CPAP or Sham CPAP at 6 Months.

The CPAP adherence was independently associated with advanced age (p < 0.01) and increasing spousal involvement (p < 0.01). After stratifying by treatment group, the association between CPAP adherence and spousal involvement was seen only amongst the CPAP group (Table 2b).

Table 2B. Multivariate Analysis of Adherence to CPAP at 6 Months.

Adjustment to marriage as reflected by items on the DAS questionnaire, however, was not associated with CPAP adherence.

Notably, after gender stratification, significant association between spousal involvement and CPAP adherence was limited to men alone (p=0.03). Three years after completing APPLES, 82 participants were still adherent by self-report (Table 3).

Table 3. Multivariate Analysis CPAP Adherence 3 years After Completing APPLES Study (based on subjective adherence).

At this time point, spousal involvement was not associated with CPAP adherence even after gender stratification.

Discussion

This multicenter double blind study demonstrates that spousal involvement is important in determining CPAP adherence during the initial treatment period, but has no effect on long-term adherence. Notably, the positive results for adherence were seen only among husbands using CPAP, but there was no effect on wives using CPAP. In line with previous research, we also found that increase in age was associated with greater CPAP adherence among both men and women.

Prior studies have indicated that married versus single, CPAP patients with bed partners, perceived spousal support, and quality of marital relationship all play a role in promoting CPAP adherence (5-8). Although these studies support the idea of social support as a conduit to CPAP adherence, the role of spousal involvement was not clear, sample sizes in the spousal role studies were small, and CPAP users were men, which reduces generalizability.

Baron et al. (3) used a spousal involvement measure, including positive and negative collaboration and one-sided items one week after beginning CPAP treatment (N=23 married men on CPAP), in addition to an interpersonal measure of supportive behaviors at 3 months to evaluate interpersonal qualities (n=16/23 responded). These investigators found that perceived collaborative involvement was related to greater CPAP adherence at 3 months (p=0.002). These findings are similar to our study in that spousal support, at least for husbands on CPAP, fostered greater adherence during the initial period of treatment.

Our observations and those of Baron et al. (14) fit well with the theories of motivation. The fundamental fact of motivation and adherence in healthcare is that individuals cannot be forced to change their behaviors. The behavior change, in this case the CPAP adherence, may be initiated by extrinsic motivation. External motivation may be rewards, punishments, or pressure from other people, such as family members or healthcare providers. However, extrinsic motivation, such as spousal pressure, is less effective in the long-term. In order to sustain long term behavioral change for CPAP adherence one needs to rely on intrinsic motivation which can be strengthened by examining the decisional balance of the ratio between a patient’s perceived pros and cons for engaging in a health behavior. The decisional balance has been found to be predictive of adherence to treatment in a variety of healthcare settings.

Our study also found increased age as an independent predictor of CPAP adherence at 6-months, yet the results were not significant for long-term adherence. Previous studies have also demonstrated conflicting results on the association between age and CPAP adherence. Sin et al. (15) found that a 10 year increment in age resulted in 0.24 ± 0.11-h increase in CPAP use. Alternatively, McArdle and colleagues (16) found that older patients were less likely to use their CPAP machines. Similarly, Janson et al. (17) found older age to be an independent risk factor for discontinuing CPAP treatment, and this finding was thought to be secondary to nasal, or pharyngeal problems. In another study, Russo-Magno et al. (18) found that adherent patients were younger in age compared to non-adherents, and increasing age made CPAP adherence difficult. Cognitive and physical impairments were thought to be contributing to difficulty with CPAP adherence. Mean age in this cohort was 73 years, which was higher than the mean age in our study. It is possible that these inconsistent associations of age on CPAP adherence may be related to the length of follow-up as well. With longer durations, the effect of time on comorbidities in the elderly may make adherence more difficult.

To our knowledge, this is the first study to demonstrate a gender bias in support for CPAP adherence. While men on CPAP were significantly more likely to adhere with support from their wives, there was no such effect for married women on CPAP, suggesting little to no support from their husbands. Although the effect of gender on CPAP adherence and spousal involvement has not been studied, previous research suggests that women are more likely to be the health caregivers in families, and thus exercise more social control (19). It is the social norm and expectation that women are often involved in their husbands’ health. As indicated in the literature regarding type 2 diabetes (20), male patients and their wives shared an expectation that the wives will be involved in their care while female patients and their husbands did not have similar expectations. We can support this finding in relationship to CPAP adherence.

Not surprisingly, spousal support for adherence did not apply to sham CPAP. This suggests that if an intervention is not having any perceived benefit, spousal involvement will have little impact on adherence. 

There are several limitations to this study. A major limitation is self-reported long term CPAP adherence. Additionally, our study was limited to husbands and wives on CPAP completing the DAS; their respective spouses were not asked about their degree of involvement. Moreover, it is unclear which components of spouse involvement played a role in CPAP adherence. We cannot assume that patients welcome all types of spouse involvement. Spouse involvement may be perceived by patients as control and nagging and may not be advantageous for all patients (21). In the context of chronic illness significant differences are demonstrated across couples in expectations for spouse involvement (20).

Despite these limitations, to our knowledge this is the first study of its type that examined spousal support for both men and women on CPAP supporting generalizability of our findings. Other strengths of this study include a large number of participants across multiple sites, randomized CPAP and Sham CPAP control groups, and objective documentation of CPAP adherence at 6 months.

Dyadic coping has been utilized in other health related interventions and can also be used to improve CPAP adherence. Ye et al. (4) has provided a comprehensive review of dyadic support in CPAP adherence, including methodological considerations, recommendations for future research, and implications for interventions. In tandem with the Ye et al. (4) review, our findings, particularly with respect to the need for spousal support of wives on CPAP, can provide a springboard for future clinical/intervention studies to promote CPAP adherence for men and women, to develop gender-relevant training needs to support their spouse on CPAP, and to determine spousal support activities that are the most efficient at promoting CPAP adherence.

Acknowledgments

APPLES 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 yrs), Thomas H. Murray, PhD (1 yr)

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Cite as: Batool-Anwar S, Baldwin CM, Fass S, Quan SP. Role of spousal involvement in continuous positive airway pressure (CPAP) adherence in patients with obstructive sleep apnea (OSA). Southwest J Pulm Crit Care. 2017;14(5):213-27. doi: https://doi.org/10.13175/swjpcc034-17 PDF

Lack of Impact of Mild Obstructive Sleep Apnea on Sleepiness, Mood and Quality of Life

Stuart F. Quan, M.D.^1,2,6

Rohit Budhiraja, M.D.³

Salma Batool-Anwar, M.D., M.P.H.²

Daniel J. Gottlieb, M.D., M.P.H.^1,2,4

Phillip Eichling, M.D., M.P.H.^7,8

Sanjay Patel, M.D., M.S.^1,2

Wei Shen, M.D.^6,9

James K. Walsh, Ph.D.⁵

Clete A. Kushida, M.D., Ph.D.¹⁰

¹Division of Sleep Medicine, Harvard Medical School, Boston, MA

²Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA

³Department of Medicine, Tufts University School of Medicine, Boston, MA

⁴VA Boston Healthcare System, Boston, MA

^S5leep Medicine and Research Center, St. Luke's Hospital, Chesterfield, MO

⁶Arizona Respiratory Center, University of Arizona, Tucson, AZ

⁷College of Medicine, University of Arizona, Tucson, AZ

⁸Comprehensive Sleep Solutions, Tucson, AZ

⁹Southern Arizona VA Health Care System, Tucson, AZ

¹⁰Stanford University Sleep Clinic and Center for Human Sleep Research, Redwood City, CA

Abstract

Background and Objectives: Obstructive sleep apnea (OSA) is associated with sleepiness, depression and reduced quality of life. However, it is unclear whether mild OSA has these negative impacts. Using data from the Apnea Positive Pressure Long-term Efficacy Study (APPLES), this study determined whether participants with mild OSA had greater sleepiness, more depressive symptoms and poorer quality of life in comparison to those without OSA.

Methods: 239 persons evaluated for participation in APPLES with a baseline apnea hypopnea index (AHI) < 15 /hour were assigned to 1 of 2 groups: No OSA (N=40, AHI < 5 /hour) or Mild OSA (N=199, 5 to <15 /hour) based on their screening polysomnogram. Scores on their Epworth Sleepiness Scale (ESS), Stanford Sleepiness Scale (SSS), Hamilton Rating Scale for Depression (HAM-D), Profile of Mood States (POMS) and Sleep Apnea Quality of Life Index (SAQLI) were compared between groups.

Results: There were no significant differences between the No OSA and Mild OSA groups on any of the 5 measures: ESS (No OSA, 9.8 + 3.5 vs Mild OSA, 10.6 + 4.3, p=0.26), SSS,(2.8 + 0.9 vs. 2.9 + 1.0, p=0.52), HAM-D (4.6 + 3.0 vs. 4.9 + 4.7, p=0.27), POMS (33.5 + 22.3 vs. 28.7 + 22.0, p=0.70), SAQLI (4.5 + 0.8 vs. 4.7 + 0.7, p=0.39).

Conclusion: Individuals with mild OSA in this cohort do not have worse sleepiness, mood or quality of life in comparison to those without OSA.

For accompanying editorial click here.

Abbreviations

AHI                Apnea Hypopnea Index

APPLES           Apnea Long-term Efficacy Study

BMI                Body Mass Index

HAM-D           Hamilton Rating Scale for Depression

IRB                Institutional Review Board

ESS                Epworth Sleepiness Scale

OSA               Obstructive Sleep Apnea

PSG                Polysomnogram

POMS              Profile of Mood States

RDI                 Respiratory Disturbance Index

SAQLI             Sleep Apnea Quality of Life Index

SSS                Stanford Sleepiness Scale

WAIS              Wechsler Adult Intelligence Scale

Introduction

Obstructive sleep apnea (OSA) is an important sleep related breathing disorder with prevalence rates between 3-17% in men and 3-9% in women (1,2). With the rising trend of obesity, it is becoming increasingly more common (2,3). In a number of longitudinal cohort studies, severe OSA is associated with an increased incidence of hypertension, cardiovascular disease and death (4-9). It also is adversely associated with a number of neurocognitive and behavioral outcomes including depression (10), sleepiness (11), and poor quality of life (12).

The most commonly used metric to classify severity of OSA is the apnea-hypopnea index (AHI) which is the number of apnea or hypopnea events per hour of sleep. Persons with an AHI < 5 are not considered to have OSA (13). In contrast, an AHI > 5 and < 15, AHI > 15 and <30, and an AHI > 30 are classified as mild, moderate, and severe respectively (14). It is generally accepted that OSA can negatively impact mood, wakefulness and quality of life. However, it is unclear whether mild OSA can have such effects (10, 11, 15). Epidemiological studies have generally shown that individuals with OSA are sleepier than those without OSA (16). Existing data in persons with mild OSA referred to sleep clinics are either limited primarily to assessments of sleepiness or have conflicting results (12, 17, 18).

The Apnea Positive Pressure Long-term Efficacy Study (APPLES) is a randomized, double-blinded, sham-controlled, multi-center trial of continuous positive airway pressure (CPAP) therapy designed to determine whether CPAP improves neu­rocognitive function over a 6-month test period (19). The present study is an analysis of the relationship between assessments of mood, sleepiness and quality of life in those without OSA versus mild OSA at the baseline visit (pre-randomization) in those screened for participation in APPLES. Our intent was to determine whether there was any association between mild OSA and these domains.

Methods

Participants and Study Design

The study design, recruitment procedures, and inclusion and exclusion criteria for APPLES have been described extensively (19). The institutional review board (IRB) at each site approved the study protocol. Briefly, APPLES was a multisite study conducted at 5 clinical centers: 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. Participants were recruited into the study primarily from patients scheduled into a regular sleep clinic for evaluation of possible OSA, and from local adver­tising. Recruitment began in November 2003 and was completed in August 2008. Initial enrollment required age > 18 years and clinical symptoms of OSA, as defined by American Academy of Sleep Medicine (AASM) criteria (14). At enrollment, participants underwent a screening diagnostic polysomnogram (PSG) and baseline neurocognitive testing including the standardized assessments described below. Only participants with an apnea hypopnea index (AHI) > 10 events per hour continued to the clinical trial and were randomized subsequently to sham or active CPAP for 6 months as previously reported (19). Excluded were individuals who had 1) prior OSA treatment with CPAP or surgery, 2) household members with current/past CPAP use, 3) a sleepiness-related automobile accident within the year prior to potential enrollment, (4) oxygen saturations < 75% for > 10% of the diagnostic polysomnogram (PSG) total sleep time; or (5) conditions or use of medications that could potentially affect neurocognitive function and/or alertness. For the pres­ent analysis, data from both randomized and non randomized participants at the time of the screening polysomnography visit were utilized. In addition to new information, some of the material related to sleepiness reported herein represent reanalysis of data in a different format from what has been published in a previous paper (20).

Polysomnography

Polysomnography was conducted as previously described using signals from a nasal pressure cannula, nasal/oral thermistor, thoracic and abdominal piezo bands, and a pulse oximeter to classify apnea and hypopnea events. An apnea was identified by a > 90% amplitude decrease from baseline of the nasal pressure signal lasting > 10 sec. Hypopneas were scored if there was a > 50%, but < 90% decrease from baseline of the nasal pressure signal, or if there was a clear amplitude reduction of the nasal pressure signal that did not reach the above criterion but it was associ­ated with either an oxygen desaturation > 3% or an arousal, and the event duration was ≥ 10 seconds. Obstructive apneas were identified by persistence of chest or abdominal respiratory effort during flow cessation. Central apneas were noted if no displacement occurred on either the thoracic or abdominal chan­nels. All studies were scored at the central reading center located at Stanford University.

Assessments of Sleepiness

Epworth Sleepiness Scale (ESS): The ESS is a validated self-administered questionnaire that asks an individual to rate his or her probability of falling asleep on a scale of increasing probability from 0 to 3 in 8 different situations (21). The scores for the 8 questions are summed to obtain a single score from 0 to 24 that is indicative of self-reported sleep propensity. The ESS prior to randomization was administered at the time of the clinical evaluation and on the night of the diagnostic PSG. The value at the time of the diagnostic PSG was used, but if not available, then the value at the time of the clinical evaluation was substituted.

Stanford Sleepiness Scale (SSS): The SSS asks a person to rate current moment sleepiness on a scale of one to seven (22). Each numerical rating has an associated descriptor, for exam­ple a rating of 1 is described as “feeling active, vital, alert, or wide awake,” while a rating of 7 is described as “no longer fighting sleep, sleep onset soon; having dream-like thoughts.” For APPLES the SSS was administered at 10:00, 12:00, 14:00, and 16:00 on the day following the diagnostic PSG; the variable analyzed was the mean score from these 4 trials. 

Assessments of Mood

Profile of Mood States (POMS): The POMS assesses mood by asking respondents how they feel at that moment according to a series of 65 descriptors such as “unhappy, tense or cheerful” (23). Possible responses are not at all, a little, moderately; quite a lot, extremely. Six mood states are used in the POMS: tension, depression, anger, vigor, fatigue, and confusion, which can be combined to form the total POMS mood disturbance score. Higher scores represent more negative mood states. For this analysis, total mood disturbance score was used.

Hamilton Rating Scale for Depression (HAM-D): The HAM-D is a validated 21-item clinician-administered assess­ment of the severity of depression (24). APPLES used a modified version of this test, the GRID Hamilton Rating Scale for De­pression that was developed through a broad-based inter­national consensus process to both simplify and standardize administration and scoring in clinical practice and research (25). In this scale, 17 items (e.g., depressed mood, suicide, work and anhedonia, retardation, agitation, gastrointestinal or general somatic symptoms, hypochondriasis, loss of insight or weight) are scored using either a 3- or 5-point scale based on intensity and frequency, and are summed to provide a single score. Higher scores reflect more depressive symptoms.

Quality of Life Assessment

Calgary Sleep Apnea Quality of Life Index (SAQLI): The SAQLI was developed as a sleep apnea specific quality of life instrument (26). It is a 35 item instrument that captures the adverse impact of sleep apnea on 4 domains: daily functioning, social interactions, emotional functioning and symptoms. Items are scored on a 7- point scale with “all of the time” and “not at all” being the most extreme responses. Item and domain scores are averaged to yield a composite total score between 1 and 7. Higher scores represent better quality of life.

Statistical Analyses

For this analysis, participants who had an AHI < 5 were assigned to the No OSA group, and those who had an AHI > 5, but < 15 were assigned to the Mild OSA group. Body mass index (BMI) was computed as weight (kg)/height (m)². Participants’ race/ethnicity were classified as self-reported white or non-white. Marital status was categorized as married or not married. For continuous variables, unadjusted comparisons between the No OSA and Mild OSA groups were made using Student’s t-test. Differences in proportions were assessed using the χ² test. Analysis of covariance was performed to adjust for differences in study site, age and BMI. Data are expressed as mean + standard deviation (SD) or percentages. P < 0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics Version 20 (Chicago, IL).

Results

In Table 1 are shown the demographic data for the No OSA and Mild OSA groups.

Table 1: Demographic Information

 

The groups were comparable with respect to gender, race, educational achievement, marital status and intelligence. By definition, the AHI for the Mild OSA group was significantly higher than for the No OSA group (10.9 + 2.5 vs. 3.1 + 1.4, p<0.01). However, participants in the No OSA were slightly younger than those in the Mild OSA group (42.1 + 15.1 vs. 47.1 + 13.1 years, p=0.03). There also was a slight trend for those in the Mild OSA group to have a higher BMI (27.3 + 4.5 vs. 29.0 + 5.9 kg/m², p=0.11). Some differences related to study site were noted as well. For the HAM-D, there was a trend for the mean score of both groups combined to be higher at the Brigham and Women’s Hospital site [N=51] in comparison to the University of Arizona site [N=59] (6.1 + 5.3 vs. 3.6 + 3.6, p=0.046). Similarly, there was a trend for the ESS to be lower at the University of Arizona site [N=61] comparison to the St. Luke’s Hospital Site [N=29] (9.2 + 33 vs. 11.1 + 3.3, p=0.051).

Table 2 shows the comparisons between the No OSA and Mild OSA groups for the sleepiness, mood and quality of life metrics.

Table 2: Sleepiness, Mood and Quality of Life in No OSA and OSA Groups

There were no statistically significant differences observed for any of these variables. The table also shows the power in this study to detect clinically significant differences in these metrics. As shown, there is 90% power to demonstrate a 1.92, 0.52, 12.72, 1.90 and 0.45 difference between groups in the ESS, SSS, POMS, HAM-D and SAQLI respectively. Furthermore, although the No OSA group was slightly younger, there were no significant correlations between age and the ESS, SSS, POMS, HAM-D, and the SAQLI (r values between 0.04 and 0.11).

Discussion

In this analysis, we show that using a commonly accepted definition of mild OSA, sleepiness and mood are not different in comparison to persons without significant OSA. Furthermore, there was no evidence that mild OSA negatively impacts quality of life. These data suggest that mild OSA as currently defined has little adverse impact on sleepiness, mood and quality of life.

We observed that there were no differences in the ESS between participants with No OSA in comparison to those with Mild OSA. Results from other large cohorts are conflicting. Our results are consistent with those of Lopes et al (12) who also did not find that the ESS was elevated in those with Mild OSA in a large population of patients undergoing PSG for suspected OSA. In contrast, a cohort of Chinese patients with mild OSA had a greater prevalence of subjective daytime sleepiness in comparison to those with primary snoring (18). However, the ESS was not higher in contrast to the Sleep Heart Health Study in which the ESS appeared to be greater in those with Mild OSA (16). Similarly, excessive daytime sleepiness was more commonly reported among a cohort of Japanese women participating in a cardiovascular risk study (17). In this latter study, OSA status was determined using pulse oximetry and not PSG. A number of other studies also have reported sleepiness data in subjects with mild OSA. However, small sample sizes, populations with specialized characteristics, and lack of specific comparisons between persons with mild OSA and no OSA limit their interpretability (27-32).

In this study, mood as assessed by the POMS and the HAM-D was not worse in the Mild OSA group. Although depressive symptoms and use of anti-depressants are commonly noted among patients with OSA (33-35), studies of whether mood is affected by mild OSA are few. In 2 studies performed in patients seen in an otolaryngology clinic (27, 31), the Beck's Depression Inventory (BDI) was not different in comparison to either a control group or primary snorers. Similarly, in a group of elderly Koreans referred to a sleep clinic, the BDI was not elevated in comparison to an age-matched control group (36). Our findings extend these previous reports by showing that using two different assessments of mood, there was no adverse impact of mild OSA.

Quality of life in this study was not affected by mild OSA. In contrast, in a number of studies, quality of life assessed with various instruments is impaired in persons with OSA (37-40). However, there are few studies in which the potential impact of mild OSA has been examined. In a relatively small study performed in patients from an otolaryngology clinic, scores on the SAQLI in patients with mild OSA were the same as a group of primary snorers (31). Similarly, in an analysis of 461 elderly women who underwent PSG in the Study of Osteoporotic Fractures cohort, scores on the Functional Outcomes of Sleep Questionnaire were the same across tertiles of OSA severity (41). Thus, our findings demonstrating a lack of association between mild OSA and quality of life are consistent with these previous studies.

Our failure to demonstrate an association between mild OSA and sleepiness, mood and quality of life provides additional data challenging the commonly used threshold for “defining disease” in the assessment of OSA. The traditional cutpoint of 5 originated more than 30 years ago when only apneic events were scored (42, 43). In the intervening years, it has been accepted that hypopneas have pathophysiologic significance and are now incorporated into the AHI (44). Additionally, some clinicians advocate including the more subtle respiratory effort related arousals into a broader respiratory disturbance index (RDI) (45). The data in this study suggest that at least for some domains of OSA symptomatology, mild OSA based on the application of current scoring criteria to older thresholds may in fact be part of a normal population.

Despite our findings, clinicians, insurers and policy makers should be cautioned about using the AHI as the sole metric in determining whether or not to treat an individual patient. The impact of OSA insofar as behavioral and neurocognitive domains are concerned appears to be quite heterogeneous. For example, 54% of individuals in the Sleep Heart Health Study with moderate to severe OSA were not sleepy on any one of 3 measures of sleepiness. Conversely, some individuals with less severe OSA may be sleepy (16). In our study, the mean ESS in both the No OSA and Mild OSA groups was above what would be expected for an unselected general population suggesting that other causes of sleepiness were present in the cohort (16). Thus, before deciding to initiate OSA specific treatment for Mild OSA, clinicians should consider whether there are other explanations for the patient’s symptoms, and not just treat the AHI.

This study does have three major limitations. First, it might be argued that our study was underpowered to detect small differences between the No OSA and Mild OSA groups. However, sufficient statistical power was present to detect clinically important differences (Table 2). For example, it has been proposed that the minimally important difference on repeated administrations of the SAQLI is approximately 1 (46). Our results demonstrated that we had 90% power to detect a change of 0.5. Moreover, our findings are consistent with the limited number of studies previously performed. Second, our participants were a mixture of individuals recruited from sleep clinics and those responding to advertisements. Thus, they may not be representative of the general populace. Third, it is possible that the No OSA group included some individuals who actually had mild OSA. Inasmuch as all participants were considered by clinicians to have symptoms consistent with OSA, some individuals in the No OSA group may have had falsely “negative” PSGs. Such misclassification would bias towards a null effect. The extent to which this occurred is not known, but night to night variability of the AHI is relatively low (47). Thus, we suspect this potential bias is small. Despite these limitations, however, the APPLES cohort was geographically and ethnically diverse, and had a representative gender distribution.

In conclusion, evidence from this analysis does not indicate that mild OSA has any impact on sleepiness, mood or quality of life. This raises concerns whether the current AHI criteria for distinguishing mild OSA from no clinically significant OSA needs to be reassessed. Nevertheless, additional comparisons between individuals who are truly without OSA symptoms and those with mild OSA as currently defined need to be performed before a final conclusion can be determined.  

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Acknowledgements

APPLES 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 HealthCenterscfor 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 yrs), Thomas H. Murray, PhD (1 yr).

None of the authors claim any conflicts of interest relevant to the article.

Reference as: Quan SF, Budhiraja R, Batool-Anwar S, Gottlieb DJ, Eichling P, Patel S, Shen W, Walsh JK, Kushida CA. Lack of impact of mild obstructive sleep apnea on sleepiness, mood and quality of life. Southwest J Pulm Crit Care. 2014;9(1):44-56. doi: http://dx.doi.org/10.13175/swjpcc082-14 PDF