psychometric aspects of obstructive sleep apnea...
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Linköping University Medical Dissertations No. 1378
Psychometric aspects of obstructive sleep apnea syndrome
Martin Ulander
Department of Clinical and Experimental Medicine, Faculty of Health Sciences, Linköping University, Linköping, Sweden
Linköping 2013
Cover image: Hypnogram (3D), by Martin Ulander Papers I and II are reproduced with permission from Wiley Paper III is reproduced with permission from Springer Printed by LiU-tryck 2013 ISBN 978-91-7519-528-5 ISSN 0345-0082
To my family
LIST OF PUBLICATIONS............................................................................................... 1
ABSTRACT..................................................................................................................... 2
ABBREVIATIONS........................................................................................................... 3
INTRODUCTION ............................................................................................................. 4
DEFINING SLEEP RELATED OBSTRUCTIVE BREATHING DISORDERS.................. 5
Definitions of specific syndroms .................................................................................................................................5
Definitions of events .....................................................................................................................................................7
DIAGNOSTIC PROCEDURE .......................................................................................... 9
EPIDEMIOLOGY........................................................................................................... 11
Prevalence in various populations ............................................................................................................................11
Risk factors for obstructive sleep apnea...................................................................................................................15 Obesity ....................................................................................................................................................................15 Age ..........................................................................................................................................................................16 Gender .....................................................................................................................................................................17
Consequences of obstructive sleep apnea.................................................................................................................17 Mortality..................................................................................................................................................................17 Hypertension ...........................................................................................................................................................21 Other cardiovascular diseases .................................................................................................................................24 Diabetes mellitus .....................................................................................................................................................28 Sleepiness................................................................................................................................................................29
Cognitive deficits ........................................................................................................................................................30 Accidents.................................................................................................................................................................31
TREATMENT ................................................................................................................ 32
Surgery........................................................................................................................................................................32
Mandibular advancement devices ............................................................................................................................32
Continuous Positive Airway Pressure ......................................................................................................................33 Adherence to CPAP treatment ................................................................................................................................34
AIMS ............................................................................................................................. 38
PAPER I: DEVELOPMENT AND INITIAL TESTING OF SECI..................................... 39
Rationale for paper I..................................................................................................................................................39
Methods.......................................................................................................................................................................39 Item generation........................................................................................................................................................39 Data collection.........................................................................................................................................................40 Statistical processing and analysis...........................................................................................................................41
Results .........................................................................................................................................................................41 Study population .....................................................................................................................................................41 Validity and reliability of the SECI.........................................................................................................................42
PAPER II: TYPE D PERSONALITY.............................................................................. 43
Rationale for paper II ................................................................................................................................................43
Methods.......................................................................................................................................................................43 Data collection.........................................................................................................................................................43 Questionnaires.........................................................................................................................................................43 Statistical processing and analysis...........................................................................................................................44
Results .........................................................................................................................................................................44 Study population .....................................................................................................................................................44 Type D personality and side effects ........................................................................................................................45 Type D personality and adherence ..........................................................................................................................45
PAPER III: DIFFERENTIAL ITEM FUNCTIONING IN THE EPWORTH SLEEPINESS SCALE .......................................................................................................................... 47
Rationale for paper III ..............................................................................................................................................47
Methods.......................................................................................................................................................................47 Data collection.........................................................................................................................................................47 Statistical analysis ...................................................................................................................................................48
Results .........................................................................................................................................................................49 Study population .....................................................................................................................................................49 General psychometric properties of the ESS...........................................................................................................49 Differential item functioning...................................................................................................................................50
PAPER IV: CPAP SIDE EFFECTS – EVOLUTION OVER TIME AND ASSOCIATION TO ADHERENCE.......................................................................................................... 51
Rationale .....................................................................................................................................................................51
Method ........................................................................................................................................................................51 Data collection.........................................................................................................................................................51 Statistical analysis ...................................................................................................................................................51
Results .........................................................................................................................................................................52 Study population .....................................................................................................................................................52 Prevalence of side effects ........................................................................................................................................53 Evolution of side effects over time..........................................................................................................................53 Association between side effects and adherence.....................................................................................................54
ETHICAL CONSIDERATIONS...................................................................................... 56
DISCUSSION ................................................................................................................ 58
Defining side effects ...................................................................................................................................................58
Measuring side effects................................................................................................................................................59
Defining adherence ....................................................................................................................................................60
Measuring adherence.................................................................................................................................................62
Side effects and adherence: Why would they be related?.......................................................................................63
Type D personality .....................................................................................................................................................64 Definition and prevalence .......................................................................................................................................64 Relationship to other personality constructs............................................................................................................65 Putative mechanisms linking type D personality to adherence ...............................................................................66
Temporal evolution of side effects ............................................................................................................................67
Defining and measuring sleepiness ...........................................................................................................................69 Epworth Sleepiness Scale........................................................................................................................................69
Future research ..........................................................................................................................................................71
CONCLUSIONS ............................................................................................................ 73
POPULÄRVETENSKAPLIG SAMMANFATTNING ...................................................... 75
ACKNOWLEDGEMENTS ............................................................................................. 77
REFERENCES .............................................................................................................. 78
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List of publications 1. Broström A, Franzén Årestedt K, Nilsen P, Strömberg A, Ulander M, Svanborg E (2010): The
side effects of CPAP treatment inventory: the development and initial validation of a new tool for
the measurement of side effects to CPAP treatment. Journal of Sleep Research 19:603-611.
2. Broström A, Strömberg A, Mårtensson J, Ulander M, Harder L, Svanborg E (2007):
Association of type D personality to perceived side effects and adherence in CPAP-treated
patients with OSAS. Journal of Sleep Research 16:439-447.
3. Ulander M, Årestedt K, Svanborg E, Broström A (2013): The fairness of the Epworth
Sleepiness Scale: two approaches to differential item functioning. Sleep and Breathing 17:157-
165.
4. Ulander M, Svensson Johansson M, Ekegren Ewaldh A, Svanborg E, Broström A (2013): Side
effects to continuous positive airway pressure treatment for sleep apnea. Changes over time and
association to adherence. Submitted to Sleep and Breathing.
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ABSTRACT Introduction Obstructive sleep apnea (OSA) is a common chronic disorder consisting of episodes with impaired
breathing due to obstruction of the upper airways. Treatment with Continuous Positive Airway Pressure (CPAP) is a
potentially effective treatment, but adherence is low. Several potential factors affecting adherence, e.g., subjective
sleepiness and personality, are only quantifiable through questionnaires. Better knowledge about psychometric
properties of such questionnaires might improve future research on CPAP adherence and thus lead to better
treatment options.
Aim Study I: To describe the devlopment and initial testing of the Side Effects of CPAP treatment Inventory (SECI)
questionnaire. Study II: To describe the prevalence of Type D personality in OSAS patients with CPAP treatment
longer than 6 months and the association with self-reported side effects and adherence. Study III: To study whether
any of the items in the Epworth Sleepiness Scale (ESS) exhibit differential item functioning and, if so, to which
degree. Study IV: To examine the evolution of CPAP side effects over time; and prospectively assess correlations
between early CPAP side effects and treatment adherence.
Patients and Methods In study I, SECI items were based on a literature review, an expert panel and interviews with
patients. It was then mailed to 329 CPAP-treated OSAS patients. Based on this, a principal component analysis was
performed, and SECI results were compared between adherent and non-adherent patients. In study II, the population
consisted of 247 OSAS patients with ongoing CPAP treatment. The DS14 was used to assess the prevalence of type
D personality, and SECI and adherence data from medical records were used to correlate Type D personality to side
effects and adherence. In study III, the population consisted of pooled data from 1167 subjects who had completed
the ESS in five other studies. Ordinal regression and Rasch analysis were used to assess the existence of differential
item functioning for age and gender. The cutoff for age was 65 years in the Rasch analysis. In study IV, SECI was
sent to 186 subjects with newly diagnosed OSAS three times during the first year on CPAP. SECI results were
followed over time within subjects, and were correlated to treatment dropout during the first year and machine usage
time after 6 months.
Results SECI provides a valid and reliable instrument to measure side effects, and non-adherent patients have higher
scores (i.e., were more bothered by side effects) than adherent patients (study I). Type D personality was prevalent in
approximately 30 % of CPAP treated OSAS patients, and was associated to poorer objective and subjective
adherence as well as more side effects (study II). Differential item functioning was present in items 3, 4 and 8 for
age in both DIF analyses, and to gender in item 8 the Rasch analysis (study III). Dry mouth and increased number of
awakenings were consistently associated to poorer adherence in CPAP treated patients. Side effects both emerged
and resolved over time (study IV).
Conclusions Differences in previous research regarding side effects and CPAP adherence might be explained by
differences in how side effects and adherence are defined. While some side effects are related to adherence, others
are not. Side effects are furthermore not stable over time, and might be related to personality. ESS scores are also
related to CPAP adherence according to previous research, but might be affected by other factors than
sleepiness, such as age and possibly gender.
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ABBREVIATIONS AASM American Academy of Sleep Medicine AHI Apnea Hypopnea Index AI Apnea Index CPAP Continuous Positive Airway Pressure CVD Cardiovascular Disease DIF Differential Item Functioning EEG Electroencephalography EMG Electromyography (surface EMG if not otherwise specified) EOG Electrooculography ESS Epworth Sleepiness Scale HR Hazard Ratio MSLT Multiple Sleep Latency Test MWT Maintenance of Wakefulness Test ODI Oxygen Desaturation Index OR Odds Ratio OSA Obstructive Sleep Apnea OSAHS Obstructive Sleep Apnea Hypopnea Syndrome OSAS Obstructive Sleep Apnea Syndrome PG Polygraphy PSG Polysomnography RDI Respiratory Disturbance Index RERA Respiratory Effort-Related Arousal RIP Respiratory Inductance Plethysmography RR Risk Ratio SD Standard Deviation SDB Sleep-Disordered Breathing SECI Side Effects to CPAP treatment Inventory UARS Upper Airway Resistance Syndrome
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INTRODUCTION Obstructive sleep apnea (OSA) is a disorder characterised by repeated events of impaired
ventilation during sleep, caused by obstruction of the upper airways (American Academy of
Sleep Medicine, 2005). These events can be total (apneas) or partial (hypopneas), and are often
associated to episodes of decreased arterial blood oxygenation level (i.e., desaturations) and brief
arousals from sleep. The term OSA syndrome (OSAS) is often used to denote symptomatic OSA,
e.g., OSA with daytime sleepiness. The diagnosis is made based on a polygraphic recording of
physiological signals related to breathing (e.g., nasal airflow, chest and abdominal movement,
heart rate and blood oxygen saturation) during sleep. Sleep can either be measured directly by
polysomnography (PSG), consisting of electroencephalography (EEG), surface
electromyography (EMG) and electrooculography (EOG), or it can be inferred indirectly from
movements and breathing patterns or from time in bed (SBU, 2007). OSA severity is often based
on the number of apneas and hypopneas per hour or sleep (the Apnea Hypopnea Index, AHI) and
the number of oxygen desaturation events per hour of sleep (the Oxygen Desaturation Index,
ODI).
OSAS has been associated to hypertension, cardiovascular disease (Parati et al., 2013), traffic
accidents (De Mello et al., 2013), obesity and hyperglycaemia/type II diabetes mellitus (Shaw et
al., 2008). The main treatment for OSAS is Continuous Positive Airway Pressure (CPAP), where
a device is used to create a positive air pressure of the upper airways. If efficient, CPAP might
alleviate symptoms and reduce the risk of negative health consequences. However, adherence
rates tend to be non-satisfactory. The reasons are not completely clear, although a number of
factors have been identified that are associated to treatment adherence.
Many patients cite side effects as a reason for non-adherence, but earlier research has been
contradictory with regard to the effects that side effects actually have on adherence. There are
also indications that symptoms and symptom reduction from the treatment might affect
adherence. The most commonly reported symptom of OSAS is daytime sleepiness, which is
often measured using the Epworth Sleepiness Scale (ESS; Johns, 1991).
There are three main approaches that are commonly used when studying CPAP adherence. One
approach focuses on technical development, such as various forms of masks and humidifiers
(Rose et al., 1993). Another approach focuses on physiological factors, such as disease severity
(e.g., as measured by AHI and ODI; Kohler et al., 2010). A third approach is to explore various
psychological and social factors related to personality, self efficacy, attitudes to treatment, beliefs
about treatment etc (e.g., Wild et al., 2004; Stepnowsky et al., 2002).
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Especially for the third approach, i.e., the social/psychological approach to CPAP adherence,
questionnaires are commonly used. This is also true for side effects, which technological
interventions often aim to reduce. For side effects, different studies have defined side effects
differently and used different approaches to assess their prevalence. For sleepiness, the ESS has
an almost hegemonic position as the main questionnaire, despite having psychometric problems
(reviewed in Chervin, 2000).
In order to approach adherence it is important to have measurement instrument with sound
methodological properties. This thesis focuses on developing ways to measure three potentially
important constructs that might affect CPAP adherence, i.e., sleepiness, side effects and
personality.
DEFINING SLEEP RELATED OBSTRUCTIVE BREATHING DISORDERS
Definitions of specific syndroms Since OSAS was first described, the definitions have changed with various terms being used,
sometimes with different meaning in different studies.
Guilleminault et al. (1976) described OSAS as a syndrome consisting of daytime
hypersomnolence and polysomnographically proven obstructive apneas. The initial description of
hypopneas is often accredited to Kurtz and Kryger (1978), but it is not certain whether they
actually described obstructive hypopneas or central hypopneas, as they could not measure
respiratory effort. Block et al. (1979) and Gould et al. (1988) described hypopneas having the
same clinical consequences as apneas, and the term Obstructive Sleep Apnea Hypopnea
Syndrome (OSAHS) was coined for patients having both apneas and hypopneas. Another group
of patients identified by Guilleminault et al. (1993) had neither apneas nor hypopneas, but still
suffered from the same symptoms as had been described earlier in OSAS patients. Despite the
absence of overt apneas/hypopneas, these patients suffered from episodes of increased
esophageal pressure, indicating increased upper airway resistance. This is referred to as Upper
Airway Resistance Syndrome (UARS). When a task force formed by the American Academy of
Sleep Medicine (AASM) and the American Thoracic Society set out to develop a standardised
terminology and outcome measures for sleep-related brething disorders, they recommended that
UARS should be included in the OSAHS category, as there was not enough evidence to suggest a
specific pathophysiology behind UARS. Instead, a new respiratory event, the Respiratory Effort
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Related Arousal (RERA) was defined as a sequence of breaths characterized by progressively
more negative esophageal pressure, terminating in either an arousal or a sudden change in
pressure to a less negative level, lasting for at least 10 s. (Quan et al., 1999).
The AASM Task force recommends the following definition of OSAHS (ibid.):
Box 1: Definition of OSAHS suggested by AASM in 1999.
Guilleminault et al. (1976) had originally used a cutoff AI (Apnea Index, as hypopneas were not
defined) of 30 apneas during seven hours of sleep, based on a study of presumably normal
sleepers. When hypopneas are added, the index naturally is higher, although it has been
questioned whether it is clinically relevant to distinguish between hypopneas and apneas (Meoli
et al., 2001). Quan et al., (1999) used an AHI cutoff criterion of 5/h or higher in adults, which is
the most commonly used cutoff value (Kripke et al., 1997). This was motivated by the fact that
earlier research had found an increased risk for traffic accidents in people with an AHI between 5
and 15/h (Young et al., 1997), a positive effect of CPAP treatment in patients with AHI between
5 and 15/h (Engleman et al. 1997), and a dose-response relationship between severity of even
mild sleep-disordered breathing and prevalence of hypertension (Young et al., 1996).
In 2005, the AASM revised the diagnostic criteria and classification of OSA again, in the
International Classification of Sleep Disorders, 2nd edition (AASM 2005). OSAS was renamed
The individual must fulfill criterion A or B, plus criterion C: A. Excessive daytime sleepiness that is not better explained by other factors; B. Two or more of the following that are not better explained by other factors: - choking or gasping during sleep. - recurrent awakenings from sleep. - unrefreshing sleep. - daytime fatigue. - impaired concentration; and/or C. Overnight monitoring demonstrates five or more obstructed breathing events per hour during sleep. These events may include any combination of obstructive apneas/hypopneas or respiratory effort related arousals.
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to ”Obstructive Sleep Apnea, Adult”, and the occurence of daytime symptoms was removed as a
mandatory diagnostic criterion:
Box 2: ICSD 2 definition of “Obstructive Sleep Apnea, Adult”
Different studies have used different scoring and diagnostic criterias, which is important to keep
in mind when going through OSA/OSAS literature.
Definitions of events An apnea has been defined, since its first description (Guilleminault et al., 1976), as a total
cessation of breathing lasting for at least ten seconds. No reason is given for the ten-second rule.
Regarding hypopneas, various definitions have been given, differing in which measurement
A, B and D or C and D satisfy the criteria: A. At least one of the following applies:
i) The patient complains of unintentional sleep episodes during wakefulness, daytime sleepiness, unrefreshing sleep, fatigue or insomnia, ii) The patient wakes with breath holding, gasping, or choking, iii) The bed partner reports loud snoring, breathing interruptions, or both during the patient’s sleep.
B. Polysomnographic recordning shows the following:
i) Five or more scoreable respiratory events (i.e., apneas, hypopneas, or RERAs) per hour of sleep. ii) Evidence of respiratory effort during all or a portion of each respiratory event (in the case of a RERA, this is best seen with the use of esophageal manometry).
OR C. Polysomnographic recording shows the following:
i) Fifteen or more scoreable respiratory events (i.e., apneas, hypopneas, or RERAs) per hour of sleep. ii) Evidence of respiratory effort during all or a portion of each respiratory event (in the case of a RERA, this is best seen with the use of esophageal manometry).
D. The disorder is not better explained by another current sleep disorder, medical or neurological disorder, medication use, or substance use disorder.
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technique is recommended (Berg et al., 1997; Thornton et al., 2012), what amount of airflow
reduction is required (Redline et al., 2007), whether an ensuing arousal is required, whether a
desaturation is required, and, in that case, how pronounced it has to be. For example, Fietze et al.
(1999) required a 50 % decrease in thoracoabdominal inductive plethysmography but did not
require any decrease in SaO2. Mooe et al., 1996 defined a hypopnea as a bout where a two per
cent decrease of SaO2 was required together with a 50 % decrease of airflow, while Shahar et al.
(2001) required a four per cent decrease in SaO2 together with a 30 % reduction of airflow.
The AASM Criteria of 1999 (Quan et al., 1999) define an obstructive hypopnea as either a ≥50 %
of airflow reduction from a previous baseline of the last two minutes preceding the event, or a
”clear amplitude reduction” together with a desaturation of >3 % or an arousal, lasting for at least
ten seconds. Thus, there are two ways in which a respiratory event can qualify as a hypopnea.
These criteria have been referred to as the Chicago criteria (Thornton et al., 2012). The clear
amplitude reduction was specified further by the AASM in 2001 to be at least 30% with a ≥4 %
reduction in arterial oxygen saturation (Meoli et al., 2001). There were two main arguments for
this definition: the 30% reduction criterion had been used in the Sleep Heart Health Study, a
large, multicenter study aiming at relating cardiovascular disease with PSG findings (Redline et
al., 1998); and Tsai et al. (1999) had found that combining the 30 % reduction in airflow with an
oxygen desaturation criterion increased inter-rater reliability.
In 2007, the AASM revised its criteria for scoring hypopneas, and offered two possible ways to
define them (Iber et al., 2007). One is termed the ”recommended” criteria and consists of a 10 s
or longer episode of which at least 90 % of the duration of the event shows a ≥30% drop from
baseline together with a ≥4% reduction in SaO2. The other possible definition is termed the
”alternative” criteria, which require a ≥50 % reduction in airflow for at least 90 % of the duration
of an episode lasting at least 10 s, in association with a ≥3% reduction in SaO2 or an arousal. This
has led to much criticism (e.g., Ruehland et al., 2009). Ruehland et al. (2009) found that the the
median AHI in a sample consisting of 328 consecutive suspected OSA patients referred for PSG
was only 30% of the AHI according to Chicago criteria when hypopneas were defined according
to the ”recommended” criteria. Applying the ”alternative” criteria resulted in a median AHI of 60
% of the AHI according to Chicago criteria. The reason to allow two different definitions of
hypopnea was that while many clinicians favoured the ”alternative” criteria, the ”recommended”
criteria are endorsed by the Center for Medicare and Medicaid Services, and are thus important
for reimbursement purposes in the USA (Berry et al., 2012).
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DIAGNOSTIC PROCEDURE Sleep-related breathing disorders are diagnosed by recording breathing and physiological
phenomena related to breathing (e.g., arterial oxygen saturation, transcutaneous partial pressure
of carbon dioxide, heart rate) during sleep. Sleep can either be detected using EEG, EOG and
surface EMG and scored according to standard criteria (Rechtschaffen & Kales 1968, Iber el al.,
2007), or it can be deduced indirectly from movements and from the clinical interview after the
examination.
Depending on the clinical setting and the type of data recorded, clinical studies of sleep
disordered breathing are often classified into four levels (American Sleep Disorders Association,
1994):
Box 3: Diagnostic levels for SDB
There has been some discussion regarding what kind of measurement should be used to detect
apneas and hypopneas. In their 2007 scoring rules, the AASM recommended an oronasal
thermistor to detect apneas, and a nasal air pressure transducer to detect hypopneas (Iber et al.,
2007). While thermistors are relatively good at detecting apneas, they are not able to provide the
kind of quantitative information about air flow that would be used to score hypopneas according
Level I: In-lab polysomnography, including EEG, EOG, surface EMG from the chin, ECG, airflow, respiratory effort, oxygen saturation and body position, the latter either by observation or objective measurement. The PSG should be performed at a manned sleep lab with trained peronnel constantly present. Level II: Outpatient polysomnography Heart rate might substitute ECG and trained personnel is not required for all studies. Apart from this, the same data should be collected as for a level I study. Level III: Modified portable sleep apnea testing Apart from preparing the study, presence of trained personnel is not required. The device should record ventilation (at least two channels of respiratory effort, or airflow and one respiratory effort channel). ECG or heart rate and oxygen saturation should also be recorded. Level IV: Continuous single or dual bioparameter recording This is not further specified apart from that it should consist of continuous recording of at least one physiological parmeter. Personnel is not required to attend or intervene.
10
to criteria based on relative decreases in airflow (Redline et al., 2007). Berg et al. (1997) studied
three different thermistors, inductance plethysmography and nasal airflow in awake subjects
simulating hypopneas by voluntarily reducing their tidal volume, and found only weak
correlations between thermistors and plethysmography. In an experimental study using an
artificial nose, Farré et al. (1998) showed that thermistors underestimated reductions in airflow
caused by simulated hypopneas and were also sensitive to differences in the waveform of the
airflow that affected the thermistor signal but not the actual airflow. At the same time,
thermistors outperform nasal pressure sensors in detecting low levels or airflow, especially
through the mouth, thus making it possible that some events that are registered as apneas when
using a nasal pressure transducer might actually be hypopneas (Hérnandez et al., 2001).
Nasal pressure transducers and respiratory inductive plethysmopgraphy (RIP) are recommended
as alternative ways of detecting apneas. In RIP, the tidal volume is assessed by measuring the
movements of the chest wall and abdominal wall during the respiratory cycle (Cohn et al., 1982).
Most studies that have validated the use of RIP in respiratory event detection have done so
without differentiating apneas from hypopneas (Thurnheer et al., 2001; Heitman et al., 2002).
The currently recommended sensor for detecting apneas is thus a combined oronasal thermistor,
while nasal pressure and RIP are alternative methods. For hypopneas, the recommended method
is nasal air pressure, while oronasal thermistor and RIP are alternative methods (Berry et al.,
2012).
There are large differences between centres as to whether full PSG or one of the simplified PG
recording methods are used to assess sleep-related breathing disorders (Hedner et al., 2011). The
American Sleep Disorders Association (ASDA) reviewed PG and its potential role in the
diagnosis of OSA (Collop et al., 2007), and stated that it may be used in populations where there
is a high pre-test probability of OSA. They recommended against the use of PG in patient
populations with comorbid sleep disorders, mainly due to paucity of studies validating PG in
these populations. The patients that are included in the studies presented in this thesis have been
examined mainly using the portable Embletta system (ResMed). It has been validated against
PSG with good results (Dingli et al., 2003; Ng et al., 2010).
In the Nordic countries, there is an agreement that manually scored recordings including
measurement of airflow, respiratory movements and pulse oximetry during a full night recording
may be used for diagnostic purposes, since they have been found to identify pathologic AHI with
high sensitivity and specificity compared to PSG (i.e., recordings also comprising EEG) (SBU,
2007).
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EPIDEMIOLOGY
Prevalence in various populations
After the initial description, OSA was believed to be a relatively rare disorder, but it is now
known that a significant proportion of the population has sleep disordered breathing. Young et al.
(1993) used a multiple step design, where a telephone survey of state employees in Wisconsin
was followed by a questionnaire that was sent to all self-described snorers and a random sample
of the non-snorers from the telephone interview. In-hospital PSG was then performed with
thermocouples for apnea/hypopnea detection. The definition of a hypopnea was any discernible
reduction in airflow associatied with a desaturation of 4 % or more. Daytime sleepiness was
assessed using three questions about how often the subjects felt excessively sleepy during
daytime, how often they woke up unrefreshed regardless of the duration of prior sleep and how
often they had uncontrollable sleepiness that interfered with daily living. Responses were given
on a five-point scale. After invalid registrations (e.g., total sleep time less than 4 hours) had been
excluded, 602 subjects remained. They concluded that 9% of the women and 24 % of the men
had sleep-disordered breathing (defined as an AHI≥5/h), and 2% of the women and 4% of the
men had both sleep-disordered breathing and hypersomnolence.
Kripke et al. (1997) studied the prevalence of SDB in middle-aged adults (40-64 years). In a first
step, people were randomly selected for a telephone interview. Of 1,467 identified subjects, 1084
subjects were interviewed, and of those, 34 % accepted a home interview and sleep study. The
sleep study consisted of actigraphy and oximetry, but no airflow parameters were recorded. The
ODI4, i.e., the number of desaturations of at least 4% per hour of sleep, was used to assess
patients, and these data were analysed by a computer. They found an ODI≥20/h in 7.2 % of the
total population (9.3 % for men and 5.2 % for women, but the gender difference was not
significant).
Bixler et al. (2001), in a community-based study, screened 12,219 women and 4,364 men by
telephone interviews, and then selected a semi-random (patients with more risk factors or
potential symptoms of sleep-disordered breathing were more likely to be selected) subsample
who underwent a PSG. A hypopnea was defined as a 10 s or longer episode of a ≥50% drop of
airflow by at least in combination with a ≥4 % desaturation. OSA was defined as AHI≥10/h and
clinical symptomatology, e.g., daytime sleepiness, hypertension or other cardiovascular
complications. Prevalence data in the background population were extrapolated using various
weights to take the oversampling of subjects with higher pre-test probability of sleep-disordered
12
breathing into consideration. Defined as stated above, 1.2 % of women and 3.9 % of men were
found to have OSA. If an AHI≥15/h, regardless of daytime symptoms or negative health
consequences, was used to define cases, 2.2 % of women and 7.2 % of men were found to have
OSA.
Durán et al. (2001) invited 2,794 subjects between 30 and 70 years of age in Spain to a two –step
study consisting of a preliminary screening recording (i.e., in-home recording of heart rate,
snoring, oxygen saturation and body position) followed by PSG in patients that were considered
to have a high risk of OSA and a random subsample of those who were considered to have a low
risk. A hypopnea was defined as a 50% or more reduction of the airflow followed by a
desaturation of at least 4 % or an arousal. 2,148 subjects completed the first phase, and 555
underwent PSG. OSA was defined as AHI≥5/h, and OSA syndrome as OSA combined with
daytime symptoms. Daytime sleepiness was defined as sleepiness at least 3 days/week during the
last 3 months in at least one of the following situations: after awakening, during free time, at
work or driving, or during daytime in general. In both men and women, irrespective of which
AHI cutoff value was used (i.e., 5, 10, 15, 20 or 30), sleep-disordered breathing increased with
age. Based on a cutoff value of 10/h, they estimated the prevalence of sleep-disordered breathing
to be 19 % in men and 15 % in women. OSA with daytime sleepiness was found in 3.4 % of men
and 3 % in women.
In the Sleep Heart Health Study (Quan et al., 1997; Young et al., 2002), patients were recruited
from other ongoing cohort studies. They were examined using home PSG. A hypopnea was
defined as a decrease of airflow to 70% or less from baseline, lasting for at least 10 s. Both
apneas and hypopneas had to be associated to a desaturation of at least 4 % to be counted. They
found an AHI of 5-15/h in 33% of 2648 men and in 26 % of 2967 women. When using a cutpoint
of 15/h, they found a prevalence of sleep-disordered breathing of 25% of men and 11% of
women. It is important, however, to notice, that the Sleep Heart Health Study actually explicitly
excluded patients who had been treated for sleep-disordered breathing. They also oversampled
young habitual snorers (Quan et al., 1997). Both these factors might influence the prevalence
data that they report.
Hrubos-Ström et al. (2011) studied the prevalence of obstructive sleep apnea in a Norwegian
population-based sample. The Berlin Sleep Apnea Questionnaire (BSAQ; Netzer et al., 1999)
was used for an initial screening of 29,258 Norwegians between 30 and 65 years. 55.7 %
13
responded. From the responses, a stratified randomisation was performed based on age, sex,
BSAQ risk (in the BSAQ, respondents are classified as having either a high or a low risk of
OSA). The high risk strata were further stratified according to previous otitis media surgery or
diabetes. Based on this, 518 subjects were selected for in-lab PSG. Oronasal thermocouples were
used for airflow recording. A hypopnea was defined as a 30 % reduction in airflow for at least 10
s combined with a ≥4 % desaturation. The prevalence for an AHI exceeding 5/h was found to be
21 % in men and 13 % in women. Using a cutoff at 15/h the figures were 11% and 6%,
respectively.
Franklin et al. (2013) examined a randomised sample of 10,000 women in Uppsala, Sweden. In
an initial step, a questionnaire was sent by mail to the sample. From the respondents 400 subjects
were randomly selected for PSG, with an oversampling of habitual snorers as identified by the
questionnaire (i.e., patients who had answered ”often” or ”very often” on the question ”How
often do you snore loudly and disturbingly?”). Apneas and hypopneas were recorded with
oronasal thermosensor and air pressure transducer. A hypopnea was defined as a 50% reduction
in both the thermistor and air pressure transducer signal for at least 10 s together with either a
≥3% desaturation or an arousal. Obstructive sleep apnea was found in 50 % when an AHI cutoff
of 5/h was used, 20 % when an AHI cutoff of 15/h was used and 5.9 % when an AHI of 30/h was
used. There was a general increase of the prevalence with increasing age.
Most studies have had a similar design, where a general screening has been performed to identify
patients with a high risk of sleep-disordered breathing, and then objective examinations in a
selected subsample of the screened patients (Lindberg & Gislason 2000). The prevalence figures
differ somewhat between the studies. This might be due to differences in the background
population with regard to risk factors (e.g., age, smoking and BMI). Another reason might be the
differences in how sleep-disordered breathing was defined.
In some special populations, the prevalence of sleep-disordered breathing is higher. In elderly
patients, for example, Ancoli-Israel et al. (1991) randomly selected 1865 elderly subjects (i.e.,
older than 65 years), of which 1,526 agreed to be interviewed by telephone. 427 subjects
performed a sleep study. Respiration was assessed by RIP, and sleep was recorded from
actigraphy. Obstructivity was assessed from phase difference between the abdominal and
thoracic respiratory movement channels. Hypopneas were defined as a ≥50% decrease in the
respiratory signal. There is no explicitly stated duration criterion for hypopneas. They found that
14
81 % had an RDI (Respiratory Distrurbance Index; in this case defined as the number of apneas
and/or hypopneas per hour of sleep, i.e., the AHI) of 5/h or higher, 62 % had an RDI of 10/h or
higher and 44 % had an RDI of 20/h or higher. Somewhat strangely, they state that the highest
reported RDI was 349.8/h, which is indeed very high. It would mean that of an average hour, at
most 102 s would consist of normal breathing. This is not discussed.
Johansson et al. (2009) examined the prevalence of sleep-disordered breathing in a sample of
community-dwelling elderly in the municipality of Kinda, Sweden. The study was performed on
the CoroKind cohort (all inhabitants aged 65-82 years old and living in the municipality of
Kinda). Of 1130 available subjects, 876 inhabitants accepted inclusion, and of those, 346 agreed
to a home PG study. Nasal airflow for apneas and hypopneas were assessed by an airway
pressure transducer and RIP. Hypopneas were defined as a ≥10 s episode of either a 50 %
reduction of airflow or a 30 % reduction of airflow in combination with a desaturation exceeding
3%. 55 % of the subjects had sleep-disordered breathing when it was defined as an AHI≥5/h, of
which most patients (i.e., 32 % had an AHI between 5/h and 15/h).
In hypertensive subjects, sleep-disordered breathing is highly prevalent. Among patients with
drug-resistant diastolic hypertension, defined as a diastolic blood pressure exceeding 95 mmHg
despite triple antihypertensive drug therapy for at least six months, Isaksson & Svanborg (1991)
found a much higher prevalence of OSAS, defined as AI and ODI>5/h (56%) than among age
and BMI matched controls with well controlled hypertension (19%). Goncalves et al. (2007)
performed a case-control study where cases were defined as patients having a blood pressure
exceeding 140/90 mmHg in two consecutive visits and were on at least three antihypertensive
drugs including a diuretic. They were consecutively enrolled from patients at a hypertension
clinic in Porto Allegre, Brazil, between 2004 and 2006. Controls were receiving drug treatment
for hypertension but did not have a blood pressure exceeding 140/90 mmHg. Cases and controls
were matched with regard to age, gender and BMI. 63 cases and 63 controls enrolled in the
study. Breathing was analysed using a level III PG device. An air pressure transducer was used to
assess airflow. A hypopnea was defined as a 10 s or longer decrease in airflow to 50% or less of
baleline values, followed by either a desaturation of at least 3 % or an arousal, defined as an
increase of the heart rate by at least 6 beats per minute for at least two seconds following the
episode. 71 % of the patients and 38 % of the controls had an AHI of at least 10/h (p<0.001), but
both cases and controls had hypertension. The study thus indicates a high prevalence of sleep-
15
disordered breathing in drug-resistant hypertension. This has also been shown in other studies
(e.g., Logan et al., 2001).
Broström et al. (2012) examined 480 consecutive patients with hypertension at four primary care
centres in Jönköping, Sweden. Of these, 394 patients underwent a technically acceptable PG
recording. Airflow was assessed by nasal air pressure and RIP. A hypopnea was defined as an
airflow reduction of at least 30 % for at least 90 % of the duration of an event lasting at least 10 s
in combination with a desaturation of at least 4 %. OSA was defined as an AHI of 5/h or higher.
Moderate and severe OSA were defined as AHI exceeding 15/h and 30/h, respectively. 59 % had
OSA, and half of them had moderate or severe OSA.
Risk factors for obstructive sleep apnea
Several factors have been associated to an increased risk for OSA. Among the most significant
ones are obesity, age and male gender.
Obesity
Obesity or overweight is a significant risk factor for obstructive sleep-disordered berathing. 40 %
of obese men have OSAS and 70 % of OSAS patients are obese (Parati et al., 2007). In cross-
sectional studies, the odds for having OSAS increases with increased body weight (Young et al.,
1993; Franklin et al., 2013). In longitudinal population-based studies, an increase in AHI has
been associated to an increased BMI, and a decrease in BMI has been associated to a decrease in
AHI. The Wisconsin Sleep Cohort Study found that a 10 % weight gain led to an average 32 %
increase in AHI and a six-fold increase in the odds of developing at least moderate (i.e.,
AHI>15/h) OSA (Peppard et al., 2000).
Another approach has been to study weight-changing interventions in overweight or obese
patients, and their influence on the degree of sleep-disordered breathing. The published studies
have been relatively small with short follow-up times. Johansson et al. (2009b) randomized 63
obese men with moderate to severe OSA (AHI≥15/h) and CPAP to either receive weight loss
therapy (Very low calorie diet, 2.3 MJ/day) or no treatment. Both groups had similar AHI at the
beginning of the intervention, but the intervention group lost 20 kg more than the control group
over 9 weeks, and had, at the end of the intervention, a lower AHI (in average 23/h lower than
16
the control group with 17 % disease free (i.e., AHI<5/h) and 50 % with an AHI between 5 and
15/h). In the control group, in contrast, only one subject had an AHI below 15/h at the end of the
intervention. Gastric bypass surgery has been associated to a decrease in sleep respiratory
parameters as well as daytime sleepiness in 100 consecutive obesity patients undergoing weight-
reduction surgery (Rasheid et al., 2003).
While there is a clear possible pathogenetic mechanism that links obesity to OSA by narrowing
of the upper airways due to fat depositions around the upper airways (Mortimore et al., 1998),
and/or by increasing the collapsibility of the upper airways (Schwartz et al., 1991), it is also
possible to see OSA as a risk factor for obesity. Patients with OSA were more likely to gain
weight than control subjects with the same BMI but without OSA (Philips et al., 2000). The
problem with this study is, however, that weight gain data was collected retrospectively (i.e.,
patients reported their weight change during the year preceding the diagnosis of OSA). Thus, it is
hard to tell wether they increased in weight due to their OSA, or whether an unrelated increase in
weight was the cause of their OSA. Patients with OSA also have higher levels than normal
controls of the apetite-stimulating hormone ghrelin, and this is reduced by effective CPAP
treatment (Harsch et al., 2003). It is also possible, that sleepiness and other daytime symptoms
caused by sleep-disordered breathing lead to less physical acitivity.
Age
The OSA that is seen in children is probably a somewhat different disease with regard to causes
etc. than the OSA seen in adult subjects (Young et al., 2002b). The prevalence of sleep-
disordered breathing is high in older populations (Ancoli-Israel et al., 1991; Johansson et al.,
2009). There is also some evidence that obstructive sleep apnea might be a part of a spectrum
starting with snoring and then slowly progressing over the cause of years or decades (e.g.,
Svanborg & Larsson 1993; Young et al., 2002). There is a possibility that this might be due to a
snoring vibration-induced nerve damage to the upper airways (Friberg et al., 1998; Svanborg
2005; Hagander 2006; Sunnergren 2012). With increasing age, it has been hypothesized that
structural changes in upper airway and changes in muscle tone (e.g., Worsnop et al., 2000) and
decreased respiratory effort in response to obstructive events (Krieger et al., 1997) act together to
increase the risk for and duration of apneas and hypopneas. After 60 to 65 years of age, however,
there seems to be a plateau or even a decrease (Young et al., 2002b; Bixler et al., 1998; Bixler et
al., 2001).
17
Gender
Male gender has been associated to an increased prevalence of OSA. In women, OSA becomes
more prevalent after menopause (Bixler et al., 2001). The gender difference is seen both in
clinical and population-based samples (Lindberg & Gislason, 2000). In early epidemiological
research, it was assumed that OSA was much more common in men than in women (e.g., Block
et al., 1979). Later research has downgraded the gender difference. Still, OSA is believed to be
approximately two to three times as common in men (Young et al., 2002b). One reason might be
that both patients and doctors are less likely to suspect that a woman suffers from OSA. In sleep
clinic populations, the male predominance is higher than in population-based samples (Lindberg
& Gislason, 2000), which could support this hypothesis. Lindberg & Gislason (2000) speculate
that this might be, at least partly, due to different symptom profiles among men and women. In a
sleep clinic sample, Ambrogetti et al. (1991) found that women were more likely to report
morning headache and fatigue than men. Some studies have indicated that postmenopausal
women on hormone replacement therapy have a lower prevalence of OSA (e.g., Bixler et al.,
2001; Shahar et al., 2003), but the results are not conclusive (see Young et al., 2002b).
Consequences of obstructive sleep apnea
Mortality
The main problem with relating mortality and morbidity to obstructive sleep apnea is that there is
a strong comorbidity with obesity, which is a known risk factor for several cardiovascular
disorders.
All-cause mortality has been studied both in population-based samples and certain clinical
samples. Prospective studies using objective measurements are summarized in Table 1.
18
Table 1: Prospective studies of mortality in obstructive sleep apnea.
Reference Study population Sleep study Results Comment Bliwise et al., (1988)
198 subjects who had undergone PSG (35 % men), dichotomized for age (cutoff age 65 years) and BMI (median, i.e., cutoff BMI 35 kg/m2)
PSG. OSA was defined as RDI≥10/h.
Mortality was 222.2/1000 person years among high‐RDI subjects vs 83.3/1000 person years among low‐RDI subjects.
Age was a major confounder, and there was no significant association between RDI and death after adjusting for it.
Campos‐Rodriguez et al., (2012)
1116 patients (only women) referred to sleep study for suspected OSA were followed for a median of 6 years.
PSG or PG. AHI<10 (controls), 10‐29 or ≥30. OSA patients were subdivided into treated or untreated groups.
After adjustment for age, BMI, hypertension, diabetes and previous cardiovascular events, HR for cardiovascular death was 3.50 (1.23‐9.98) among untreated patients with AHI>30 vs controls.
No risk increase in CPAP treated patients or in milder disease.
He et al., (1988) 385 patients (only men) referred for suspected sleep apnea
PSG. AI>20 Probability of cumulative 8‐year survival was 96% among patients with AI<20/h and 63% among patients with AI>20/h (p<0.05).
Only 22 deaths.
19
Table 1: Prospective studies of mortality in obstructive sleep apnea (continued) Reference Study population Sleep study Results Comment Korostovtseva et al., (2011)
147 patients (61 % men) with hypertension and high risk for OSAHS were followed for a median of 3.9 years.
PG. AHI <5, 5‐15, 15‐30 and >30.
After adjustment for sex, age, BMI, duration of hypertension, alcohol, smoking, physical acitivity, heredity, coronary artery disease, glucose metabolism, OR for cardiovascular fatal events was 9.2 (1.2‐72) in AHI>30 vs <5.
Patients with AHI 5‐30 had no significantly increased cardiovascular mortality.
Lavie et al., (2005)
14589 patients (only men) referred to a sleep clinic for suspected or definite OSAS. Median follow‐up was 4.6 years.
PSG. OSA was defined as RDI≥10/h.
All‐cause mortality increased with RDI and BMI.
RDI was related to mortality only in younger (<50 years) subjects.
Mant et al., (1995)
163 retirement village residents over 70 years (21 % men) were followed for 4 years
PG. RDI>15 No association between RDI and mortality.
Few cases with high RDI.
Marin et al., (2005)
1387 patients (only men) vs 264 controls matched for age and BMI followed for 10 years.
PSG. AHI>30/h Untreated severe OSA adjusted OR 2.87 (1.17‐7.51) for fatal cardiovascular events
Marshall et al., (2008)
Busselton Health Study. 380 subjects from Busstelton, WA, Australia (73 % men) underwent a home sleep study. Mean follow‐up was 13.4 years.
PG. RDI<5, RDI 5‐15 and RDI>15 groups were compared.
HR 6.24 (2.01‐19.39) for death after adjustment for age, gender, BMI, smoking status, total cholesterol, HDL, diabetes, angina and mean arterial pressure.
Mean arterial pressure was defined as 2/3*systolic blood pressure + 1/3*diastolic blood pressure, which is not the standard definition (i.e., 1/3*systolic blood pressure + 2/3*diastolic blood pressure; Sesso et al., 2000).
20
Table 1: Prospective studies of mortality in obstructive sleep apnea (continued) Reference Study population Sleep study Results Comment Martinez‐Garcia et al., (2012b)
939 elderly (>64 years; 64 % men) patients with a suspicion of OSA. Mean follow‐up was 5.8 years.
PSG or PG. AHI<15 (controls), AHI 15‐30, AHI>30 without treatment, AHI>30 with treatment.
Adjusted HR(for age, BMI, sex, sleepiness, smoking, co‐morbidities) for all‐cause mortality was 1.99 (1.42‐2.81), fatal stroke HR was 4.63 (1.03‐20.8), fatal heart failure HR was 3.93 (1.13‐13.65) when comparing untreated AHI>30 to controls.
No increased risk for fatal ischaemic heart disease. No increased risk in the AHI 15‐30 group. CPAP treated patients had no increased HR for death.
Punjabi et al., (2009)
Sleep Heart Health study, 6,441 subjects (47 % men). Average follow‐up was 8.2 years.
PSG. AHI<5/h vs AHI>30/h
AHI>30 in 40‐70‐year old men HR for death was 2.09 (1.31‐3.33) after adjusting for age, race, BMI, smoking, blood pressure, hypertension, diabetes and CVD.
For older men, and for women irrespective of age, there was no significant association between AHI and mortality.
Tang et al., (2010)
93 peritoneal dialysis patients (52 % men). Median follow‐up was 41 months.
PSG. AHI<15 vs ≥15
Adjusted HR for all‐cause mortality in the AHI>15 group was 1.72 (1.03‐2.88) after adjusting for age, gender, diabetes, minimum SaO2 and kidney function.
Yaggi et al., (2005)
1,022 patients (71 % men) referred to a sleep clinic, aged at least 50 years and no previous stroke or TIA. Median follow‐up was 3.4 years.
PSG. AHI>5/h After adjusting for age, sex, race, smoking status, alcohol, BMI, diabetes, hyperlipidemia, atrial fibrillation, and hypertension the HR for stroke or death was 1.97 (1.12‐3.48).
Combined end‐point
Young et al., (2008)
1522 subjects (55 % men) from the Wisconsin Sleep Cohort were followed for 18 years.
PSG. AHI <5, 5‐15, 15‐30 and >30.
Adjusted HR for all‐cause mortality (AHI>30 vs <5) was 3.0 (1.4‐6.3) after adjusting for age, sex and BMI.
No significant risk increase among those with AHI<30, but significant p for trend (p=0.008).
21
OSA has also been associated to increased risk of death in patients with stroke (Sahlin et al.,
2008; Martinez-Garcia et al., 2009) and coronary artery disease (Peker et al., 2000). In a study on
3,100 Swedish 30 to 69-year-old men, Lindberg et al. (1998) found the adjusted HR for death
within 10 years to be 2.2 (95% CI 1.3-3.8), but only in subjects younger than 60 years. That
study, however, used questionnaires to assess sleep-disordered breathing.
Hypertension
Lugaresi et al. (1980) described an association between systemic arterial hypertension and
snoring. Especially so called ’non-dipping’ hypertension, i.e., hypertension that does not
decrease during the night, is associated to obstructive sleep apnea (Davies et al., 2000). Brooks et
al. (1997) induced increased night-time blood pressure in dogs by inducing intermittent
obstruction of the upper airways, and Troncoso Brindeiro et al. (2007) exposed rats to a chronic
intermittent hypoxia protocol, which increased their blood pressure. Hardy et al. (1994) showed
that experimentally induced hypoxia causes increased blood pressure in humans. Not only
desaturations, but also disturbed sleep, may affect blood pressure.
Both age and BMI are risk factors for obstructive sleep apnea, but they also increase the risk of
hypertension. There was therefore initial concerns whether hypertension was actually an effect of
OSA, or whether obesity and/or age were confounders, creating a spurious relationship. The
same is true for alcohol consumption and tobacco use. Caffein has been proposed as a potential
confounder as it increases norepinephrine excretion, but Bardwellet al. (2000) did not find it to
be a likely major confounder.
Several cross-sectional studies have described either a higher prevalence than expected of sleep-
disordered breathing among hypertensive patients (e.g., Hedner et al., 2006; Broström et al.,
2012) or a higher than expected degree of hypertension among patients with obstructive sleep
apnea (e.g., Davies et al., 2000; Carlson et al., 1994; Levinson et al., 1993). In a population-
based cross-sectional study of a subsample of the Sleep Heart Health Study, Javier Nieto et al.
(2000) found a successive increase in blood pressure in parallel with higher SDB indices, which
was partially, but not totally, explained by BMI. These studies generally are not able to discern
causative relationships.
22
Table 2: Prospective studies of associations between OSA and hypertension using objective
measurements.
Reference Subjects Sleep study Results Marin et al., (2013)
1889 normotensive patients (76% men) referred for sleep study were followed for a median time of 12.2 years.
PSG. AHI 5‐14.9/h was mild, AHI 15‐29.9 was moderate and higher AHI was severe OSA.
Adjusted HR (AHI, age, sex, SBP, DBP, BMI, alcohol, smoking, medication, glucose, lipids, menopausal status, BMI change) for patients who declined CPAP was 1.96 (1.44‐2.66), and for CPAP treated patients 0.71(0.53‐0.94).
O’Connor et al., (2009)
Sleep Heart Health Study. Prospective study of 2470 non‐hypertensive subjects (44.7% men), mean age 59.6 years. 5 years follow‐up.
PSG No significant association between baseline AHI and hypertension after adjustment for BMI.
Peker et al., (2002)
182 30‐69‐year‐old men investigated for sleep apnea followed for 7 years.
PG. >30 oxygen desaturations/night
Adjusted OR (adjusted for BMI, blood pressure and age) for cardiovascular disease (hypertension, angina, myocardial infarction, stroke, cardiovascular death, heart failure) was 4.9 (1.8‐13.6) and for hypertension 3.7 (1.1‐13.1).
23
Table 2: Prospective studies of associations between OSA and hypertension using objective
measurements (continued).
Reference Subjects Sleep study Results Peppard et al., (2000b)
Prospective study of the Wisconsin Sleep Cohort. 709 subjects (55% men) followed for 4 years.
PSG. No OSA (AHI 0), AHI 0.1‐4.9, AHI 5‐14.9 or AHI 15 and higher
Adjusted OR (baseline hypertension, age, sex, BMI, waist and neck circumferences, menopause, exercise, alcohol and smoking) for hypertension: AHI 0: 1.0, AHI 0.1‐4.9 1.42 (1.13‐1.78), AHI 5‐14.9 2.03 (1.29‐3.17), AHI≥15 2.89 (1.46‐5.64). P for trend 0.002.
Two of the larger studies, by Peppard et al., (2000b) and by O’Connor et al., (2009) are
contradictory. There are several differences between the Peppard and O’Connor studies, that
might explain the differences (Peppard, 2009). The Sleep Heart Health cohort was older, the
control population was defined differently (i.e., the Wisconsin cohort defined healthy subjects as
subjects with no respiratory events while the Sleep Heart Health cohort definition was AHI≤5/h).
Also, the baseline prevalence of hypertension and the gender distribution differed between the
two studies.
Several intervention studies have examined the effects of CPAP on patients with OSA. The
findings are inconclusive in most (early) trials, which were mostly small and of poor quality.
Some found a decrease in ambulatory blood pressure (e.g., Engleman et al., 1996 in non-dipping
hypertensives; Guilleminault et al., 1996), while other studies were unable to find any significant
effect of the treatment (e.g., Ali et al., 1992). This is also shown in a placebo-controlled trial,
where daytime blood pressure was lowered in both patients on active and sham CPAP treatment
(Dimsdale et al., 2000), and incident hypertension was not lower in the treatment group among
non-sleepy OSA patients that were randomized to CPAP or placebo (Barbé et al., 2013), and a
meta-analysis of studies with oral appliances in OSA patients found modest effects on some, but
not all, measures of hypertension (Iftikhar et al., 2013).
24
Other cardiovascular diseases
Several studies have indicated an association between obstructive sleep apnea and ischemic heart
disease. In a cross-sectional examination of the Sleep Heart Health study cohort (n=6,424, of
which 5,250 were used in the full model mainly due to missing data), subjects belonging to the
highest quartile regarding AHI had a higher relative odds for having had a heart failure or stroke,
but the relative odds was not significantly increased for coronary artery disease (Shahar et al.,
2001). Similar findings, i.e., an increased prevalence of prior stroke (OR 2.57, 95 % CI 1.03-
6.42) but not coronary artery disease was found by Rice et al., (2012) among diabetic men in a
cross-sectional study. Mooe et al., (1996) and Peker et al., (1999) both found that sleep-
disordered breathing was significantly associated to ischemic heart disease in case-control
studies. Koskenvuo et al., (1987) found self-reported frequent or habitual snoring to increase the
relative risk of a combined end-point of stroke and ischemic heart disease.
25
Table 3: Prospective studies of other cardiovascular outcomes with objective recordings
Reference Subjects Sleep study Results Comment Arzt et al., (2005)
Wisconsin sleep cohort. 1189 subjects (55% men) followed for 4 years.
PSG. AHI>20 vs AHI<5 After adjusting for age, sex, BMI, smoking and hypertension, OR for stroke was 3.83 (1.17‐12.6) in a cross‐sectional analysis and 3.08 (0.74‐12.8) in a prospective analysis.
Few strokes, underpowered
Marin et al., (2005)
1387 patients (only men) referred for sleep apnea and 264 controls matched for age and BMI.
PSG. Untreated severe OSA (i.e., AHI>30)
After adjusting for BMI, sex, diabetes, smoking, alcohol, cholesterol, triglycerides, hypertension, cardiovascular disease, lipid lowering and antihypertensive drugs, the OR for non‐fatal cardiovascular events for patients were 3.17 (1.12‐7.51).
Marshall et al., (2012)
Busselton Health Study. 380 subjects from Busstelton, WA, Australia (73% men) underwent a home sleep study.
PG. Snoring sounds were recorded.
No association between snoring and stroke or cardiovascular events.
Hypothesis: snoring injures carotid arteries by vibration (Amatoury et al., 2006).
Mooe et al., (2001)
407 patients with coronary artery disease (68% men) were followed for a median of 5.1 years.
PG. AHI>10 or ODI>5 Adjusted hazard ratio (adjusted for diabetes, left ventricular ejection fraction, coronary intervention, age, sex, BMI and hypertension) for stroke was 2.98 (1.43‐6.20) for AHI>10.
26
Table 3: Prospective studies of other cardiovascular outcomes with objective recordings (continued) Reference Subjects Sleep study Results Munoz et al., (2006)
394 stroke‐free subjects from the general population aged 70 to 100 years (57% men) were followed for 6 years.
PSG. AHI>30/h vs <30/h
After adjustment for sex, the HR for stroke was 2.52 (1.04‐6.01) in the AHI>30 group.
Peker et al., (2002)
182 30‐69‐year‐old men investigated for sleep apnea followed for 7 years
PG. >30 oxygen desaturations/night
Adjusted OR (adjusted for BMI, blood pressure and age) for cardiovascular disease (hypertension, angina, myocardial infarction, stroke, cardiovascular death, heart failure) was 4.9 (1.8‐13.6)
Redline et al., (2010).
5422 subjects (45% men) aged >40 years in the Sleep Heart Health Study (all were stroke‐free at inclusion) were followed for an average of 8.7 years.
PSG. Patients were grouped in AHI quartiles.
Among men in the highest obstructive AHI quartile (>19.13/h) the adjusted HR for stroke was 2.86 (1.10‐7.39) after adjusting for age, BMI, race, smoking, systolic blood pressure, antihypertensive medication, and diabetes). No significant risk increase in the highest quartile was found for women.
Tang et al., (2010)
93 peritoneal dialysis patients (52% men). Median follow‐up was 41 months.
PSG. AHI was used as a continuous variable.
HR for a +1/h increase in AHI for cardiovascular events was 1.02 (1.01‐1.03) after adjusting for age, gender, diabetes and creatinine clearance.
27
Table 3: Prospective studies of other cardiovascular outcomes with objective recordings (continued) Reference Subjects Sleep study Results Comment Yaggi et al., (2005)
1022 patients referred to a sleep clinic, aged at least 50 years and no previous stroke or TIA. Median follow‐up time was 3.4 years.
PSG. AHI>5/h After adjusting for age, sex, race, smoking status, alcohol, BMI, diabetes, hyperlipidemia, atrial fibrillation, and hypertension the HR for stroke or death was 1.97 (1.12‐3.48)
Combined end‐point
It has also been shown that stroke patients with OSA who do not tolerate CPAP have a higher
degree of non-fatal cerebrovascular events than those who do (Martinez-Garcìa et al., 2012) and
that they require a longer post-stroke hospitalization (Kaneko et al., 2003). Functional outcomes
are better in some studies in CPAP-treated patients compared to untreated patients (e.g., Ryan et
al., 2011), but some studies have not found an effect on outcome (reviewed in Tomfohr et al.,
2012).
Almost every type of cardiac arrhythmia has been described in OSA, especially in more severe
cases. This also includes severe forms of cardiac arrhytmias (Guilleminault et al., 1983). In the
Sleep Heart Health study, 228 subjects with severe OSA (AHI≥30/h) were compared to healthy
controls. After adjusting for age, sex, body mass index, and prevalent coronary heart disease,
there was a higher prevalence of atrial fibrillation, nonsustained ventricular tachycardia, and
complex ventricular ectopy in patients with OSA (Mehra et al., 2006). Atrial fibrillation has also
been associated to obstructive sleep apnea in other studies (reviewed in Goyal & Sharma 2013).
There seems to be an increased risk of relapse of atrial fibrillation after AF catheter ablation in
OSA patients (Ng et al., 2011). There are also some studies that indicate that CPAP treatment
might alleviate cardiac arrhythmias (Harbison et al., 2000; Abe et al., 2010). Many studies (e.g.,
Harbison et al., 2000) have only found an increased prevalence of nocturnal arrhythmias, but
Namtvedt et al., (2011) found an increased prevalence of daytime arrhythmias as well.
28
Diabetes mellitus
Type 2 diabetes mellitus and impaired glucose tolerance have been associated to OSA in cross-
sectional studies. Insulin resistance as measured by oral glucose tolerance tests has been
associated to oxygen desaturations (Tiihonen et al., 1993). Lindberg et al., (2007) found in a
population-based sample of 6,779 women that self-reported snoring and daytime sleepiness were
associated to diabetes, at least in older women (i.e., ≥50 years old). In the Nurses' Health Study
(Al-Delaimy et al., 2001), self-reported snoring was associated with an increased risk of diabetes
during a ten-year follow-up period. OSA is independently associated with dyslipidemia and
higher fasting insulin (Coughlin et al., 2004). A higher prevalence of insulin resistance was also
found in a clinical sample of patients referred to a sleep clinic for suspected OSA. Patients with
OSA (defined as AHI≥5/h) were more insulin resistant than non-OSA subjects (Ip et al., 2002).
A high prevalence of OSA has been shown in clinical samples of patients with type 2 diabetes
mellitus. In the SleepAHEAD study, Foster et al., (2009) found that 86 % of type 2 diabetic
subjects suffered from OSA, and 22.6 % had severe OSA, defined as an AHI≥10/h, with obesity
explaining the link.
Prospective studies have indicated that OSA might be an independent risk factor of diabetes. In
an observational cohort study, 544 diabetes-free consecutive patients referred to a sleep clinic for
suspected OSA between 2000 and 2005 were included. Mean follow-up time was 2.7 years.
There was a higher incidence of diabetes in patients with OSA, with a dose-response relationship
when patients were divided into quartiles depending on their AHI after adjusting for age, gender,
race, baseline fasting blood glucose, BMI, and change in BMI (Botros et al., 2009). Diabetes
patients with OSA have worse glycemic control than those without OSA with a dose-response
relationship with regard to OSA severity (Aronsohn et al., 2010).
Studies of effects of CPAP treatment on insulin resistance and type 2 diabetes have yielded
inconsistent results. West et al., (2007) randomized 42 diabetic newly diagnosed OSA patients
(ODI≥10/h) to receive either therapeutic or sham CPAP with follow-up after three months.
Although patients in the therapeutic group improved significantly with regard to daytime
sleepiness, they did not improve with regard to HbA1C or insulin resistance. Dawson et al.,
(2008) compared nocturnal glucose during the diagnostic PSG and a night on CPAP in type 2
diabetic patients with newly diagnosed OSA after on average 41 days and found an improved
glucose control on CPAP than during the diagnostic study. The patients in that study had a more
well-regulated diabetes than those in the study by West et al., (2007).
29
Sleepiness
When a differentiation is made between OSA and OSAS, it is usually based on the prevalence of
daytime sleepiness. To measure subjective acute sleepiness, single-item instruments are usually
used, such as KSS (Åkerstedt & Gillberg, 1990) or SSS (Hoddes et al., 1973). Most often,
sleepiness over a longer time period is assessed using the Epworth Sleepiness Scale (ESS). Both
ESS and KSS and SSS have been validated against physiological measures of sleepiness. The
ESS was developed by Johns (1991), and consists of eight items, describing situations where the
respondent is asked to rate the probability of falling asleep on a four-level Likert-type scale,
ranging from 0 to 3, where higher scores indicate a higher probability of falling asleep. There has
been criticism regarding the items in the ESS, based on the fact that the item generation and
selection process is not described in detail. Two questions (item 3, about the probability of falling
asleep in a public meeting, and item 4, about the probability of falling asleep as a passenger in a
car for an hour), have been published as a poster (Miletin & Hanly, 2003), but there is no
information about the generation and selection process for the other items. Item 8 asks for the
probability of falling asleep “in a car, that has stopped for a few minutes in traffic”. It does not
state whether the respondent drives the car or is a passenger, despite the fact that this is likely to
affect the soporificity (i.e., sleep-inducing potential) of the situation. In the pictorial ESS (Ghiassi
et al., 2011), which is an ESS version where all items and response alternatives are described
using cartoon-like pictures, item 8 is shown with a passenger in the backseat falling asleep. As a
part of the development process of the pictorial ESS, a version with the driver falling asleep was
also shown, and this was found to decrease the respondents’ scores on the item (Ghiassi, personal
communication). The passenger version was chosen to enable more people to answer the
questionnaire.
Physiological measurements of sleepiness are usually based on EEG-derived indices, usually the
sleep latency during various conditions and after having received various instructions. The
Multple Sleep Latency Test (MSLT; Richardson et al., 1978) and the Maintenance of
Wakefulness Test (MWT; Mitler et al., 1982) are both based on this principle, but differ in which
instructions are given and the specific details (e.g., the duration of each specific trial). In the
MSLT, patients are asked to lie down with closed eyes in a dark room and try to go to sleep
(Carskadon et al., 1986), while the MWT instructs patients to try to remain awake for 40 minutes
in a semi-supine position. Other physiological measurements of sleepiness include EEG alpha
30
power, which increases in sleepy drivers (Kecklund & Åkerstedt, 1993), and blink duration,
which also increases with increasing sleepiness (Åkerstedt et al., 2005).
The Epworth Sleepiness Scale has been validated against MSLT and MWT, with varying results.
Fong et al., (2005) compared ESS and MSLT in patients with OSAS and found that while MSLT
sleep latency was significantly shorter in severe OSAS than in mild or moderate OSAS, no
significant differences were found for ESS scores between different severity groups. ESS scores
were, however, significantly correlated to MSLT, albeit weakly. Chervin et al. (1997) found a
negative correlation (rho=-0.37) between ESS and MSLT in one study, but in another study they
did not find any correlation at all (Chervin & Aldrich 1999). Olson et al., (1998) found no
correlation between the ESS and AHI, or between MSLT and AHI. However, their study sample
was a mixed sample with SDB, narcolepsy, chronic fatigue syndrome, circadian rhythm disorders
and hypersomnia of other causes. When only patients with OSA were included, the findings were
similar (i.e., no significant correlation). Johns (1993) found a correlation between ESS and AHI
in patients with SDB. Findings are, in other words, contradictory.
Treatment studies have indicated that CPAP treatment might alleviate sleepiness in patients with
OSAS. Hardinge et al., (1995) found that CPAP treatment improved ESS scores both after two
months and one year on treatment in patients with OSA. There was, however, no control group.
Kribbs et al., (1993b) compared subjective sleepiness (SSS), objective sleepiness (MSLT) and
psychomotor consequences of sleepiness (psychomotor vigilance task, PVT) prior to, during and
after one night without CPAP after treatment initiation. They found that MSLT improved on
CPAP treatment, and worsened after a night without it. The PVT and SSS showed similar trends,
but they were not fully significant. However, only 15 patients were studied. In another study,
Engleman et al. (1998), found a significant decrease in sleepiness, measured by both ESS and
MSLT in OSAS patients with an AHI≥15/h. That study consisted of 23 subjects with an AHI of
at least 15/h. Engleman et al. (1994b) also found improvements in MSLT and ESS when
compared CPAP to placebo.
Cognitive deficits In the Sleep Heart Health Study, word fluency, digit-symbol substitution and delayed word recall
were studied. There was no consistent pattern between OSA severity indices and
neuropsychological test results after adjusting for gender, age, education, occupation, field
centre, diabetes, hypertension, BMI, use of CNS medications, and alcohol drinking status
(Boland et al., 2002).
31
Several studies have examined neuropsychological sequelae of OSAS, but many of the studies
contain methodological weaknesses (Aloia et al., 2004). These include failure to control for
learning effects from repeated uses of psychometric tests, failure to account and control for
adherence, inconsistent methods for controlling for demographic factors and making the
diagnosis. Besides, different studies use different tests and measure different constructs. In a
meta-analysis, Beebe et al., (2003) reported that vigilance and executive functions were most
clearly affected by OSA, in contrast to general intelligence and verbal ability. Antic et al. (2011)
found a dose-response relationship between hours of nightly CPAP use and daytime sleepiness as
measured by the ESS, and a significant effect on executive functioning and verbal memory, but
no significant dose-response effect for the latter two.
Accidents
Young et al. (1997) studied traffic accidents in a sample of subjects from the Wisconsin Sleep
Cohort study. Men who had an AHI≥5/h had three to four times as high odds of having been
involved in a traffic accident during the last five years, and men and women combined, with an
AHI of at least 15/h, had an adjusted odds ratio of 7.2 compared to normal sleepers for having
had multiple accidents. The models were adjusted for gender, age and miles driven per year.
Interestingly, adding sleepiness to the models did not improve them. Sleepiness has, however,
been associated to driving performance in several other studies (e.g., Åkerstedt et al., 2001;
Åkerstedt et al., 2013).
In a case-control study, Teràn-Santos et al. (1999) included 102 patients who sought emergency
treatment to 152 controls that were matched for age and gender but with no history of traffic
accidents for the two years prior to enrolment. Data were adjusted for use or nonuse of alcohol,
visual-refraction disorders, BMI, years of driving, medications causing drowsiness, work and
sleep schedule (work during the day and sleep at night or some other pattern), kilometers driven
per year, and presence or absence of arterial hypertension. The adjusted odds ratio for having had
a traffic accident during the last two years was 7.2 for cases having an AHI of at least 10/h
compared to controls.
32
TREATMENT Treatment of OSA focuses on alleviating the symptoms of sleep-disordered breathing and on
counteracting the potential health hazards that might follow from it. The main treatment
approach, at least in more severe acases of OSA, is CPAP, in which a device is used to produce a
positive air pressure of the upper airways to keep them unobstructed during sleep. Another
approach is to advance the mandible using an oral appliance, thereby preventing the soft tissues
to fall back and obstruct the airways (Soll & George, 1985). Surgery where soft tissue of the
upper airways is removed has also been used.
Surgery Surgery was popular in the beginning of the 1990’s, but lost momentum as the relapse rate, at
least in overweight subjects and in those with severe disease, was very high (Larsson et al., 1991)
and due to lack of evidence of efficacy (SBU, 2007). However, in patients that did not relapse in
four years after surgery, the treatment is effective even after long time (Browaldh et al., 2011),
and it has recently been shown that UPPP is efficacious in reducing the AHI from severe to
moderate levels in selected patients as compared to untreated controls (Browaldh et al., 2013).
The main surgical approach to OSA in adults is removal of soft tissues surrounding the upper
airways (Fujita, 1984). Due to the relationship between obesity and OSA, surgery aiming
primarily at weight reduction has been studied more extensively due to its potential effects on
OSA. Several studies (reviewed in Sarkhosh et al., 2013) have examined the effects of OSA
disease severity measures. In summary, weight reduction is a potentially effective treatment, with
malabsorptive surigical approaches probably more efficient than purely restrictive approaches.
Interestingly, sleep-disordered breathing might improve relatively early postoperatively (Varela
et al., 2007).
Mandibular advancement devices Regarding mandibular advancement devices, Gotsoupolos et al., (2002) compared the effect of
devices to an untreated control condition in a randomized, cross-over design. They found that in
73 consecutive patients with OSA, both respiratory indices and sleepiness (measured both by the
ESS and MSLT) improved on active treatment, as compared to placebo. In another cross-over
trial, Barnes et al., (2004) compared 114 OSA patients with mild to moderate disease (AHI 5-
30/h) who were assigned to CPAP, a mandibular advancement device or placebo (in the form of
a pill). Evaluation was performed by the Maintenance of Wakefulness Test (MWT) and ESS on
33
the last day of each treatment (patients used each treatment for three months), neurocognitive
tests, quality of life and daytime symptoms and blood pressure. Daytime sleepiness was more
common when measured subjectively (i.e., ESS) than objectively (i.e., MWT). Subjective
sleepiness decreased with treatment, compared to both placebo and baseline conditions. There
was no similar effect on objective sleepiness. Regarding neurocognitive function, both CPAP and
the oral device improved vigilance and paced serial addition, but not other neurocognitive tests
used. Quality of life improved with both active treatments, and nocturnal diastolic blood pressure
improved slightly by the oral device, but no other effects on blood pressure were found. There
were no outcome differences between sleepy and nonsleepy subjects. An effect of oral devices on
diastolic blood pressure was also found by Gotsopoulos et al. (2004), in a randomized cross-over
trial were patients were using an active oral device and a control oral device.
Continuous Positive Airway Pressure Sullivan et al., (1981) first described CPAP treatment in five patients with OSA. CPAP works
like a splint, preventing the collapse of the upper airways. A mask is fitted to cover either the
nose or the nose and the mouth. The air pressure is titrated, either manually or automatically, to a
level where breathing is unobstructed. Ayas et al., (2004) found no significant differences
between auto-CPAP and traditional CPAP with a constant pressure that had been titrated
manually with regard to treatment adherence or effects on daytime sleepiness or breathing-related
parameters.
Several studies have examined the effect of CPAP on sleep parameters, such as AHI. CPAP
treatment has been found effective in improving these (e.g., Jenkinson et al., 1999; Barnes et al.,
2004). Daytime sleepiness also improves by CPAP treatment, both when assessed subjectively
(e.g., Jenkinson et al., 1999; Barnes et al., 2004; Siccoli et al., 2008) and objectively (e.g.,
Engleman et al., 1998; Siccoli et al., 2008). However, Barnes et al. (2004) did not find any
improvement in objective daytime sleepiness as assessed by MWT. In non-sleepy patients, the
benefits from treatment might be smaller (Barbé et al., 2001).
Patients with OSA are more likely to be sleepy drivers. CPAP might improve driving
performance, at least partly, in driving simulator situations (Vakulin et al., 2011). This has also
been shown with mandibular advancement devices (Hoekema et al., 2007) and with surgery
(Haraldsson et al., 1991).
34
Adherence to CPAP treatment
Adherence is defined by the World Health Organization as “The extent to which a person's
behavior (in terms of taking medications, following diets, or executing lifestyle changes)
coincides with medical or health advice”. Early studies indicated good adherence, at least
subjectively (Sanders et al., 1986). In the beginning of the 1990s, studies indicated that
adherence to CPAP treatment was poor. For example, several studies with objective
measurement of machine time (e.g., Reeves-Hoche et al., 1994; Engleman et al., 1994) found a
mean CPAP usage time of 4.7 h/night, with no correlation to OSA severity indices. This led to a
definition of CPAP adherence as using the machine for at least four hours/night at least 70 % of
the nights (Sawyer et al., 2011). Patients tend to over-report their adherence (Kribbs et al., 1993).
Several studies have indicated that there is a dose-response relationship between adherence
(measured as machine usage) and treatment effect on outcomes such as subjective daytime
sleepiness (Antic et al., 2011; Weaver et al., 2007), but with more contradictory findings
regarding objective daytime sleepiness. Antic et al. (2011) did not find a dose-response effect of
MWT, but Weaver et al. (2007) found it with regard to MSLT. Zimmerman et al., (2006) found a
dose-response relationship between CPAP use and memory impairment when patients were
divided into three groups (i.e., <2 hours/night, 2-6 hours/night and >6 hours/night), and in a
retrospective study, Campos-Rodriguez et al. (2005) found a dose-response relationship between
CPAP adherence and survival, with highest mortality among those using CPAP for less than one
hour per night compared to those using it 1-6 h/night and >6 h/night, respectively.
Mask leakage was early on identified as a potential cause of problems with CPAP (Richards et
al., 1996). Interventions were mainly aimed as technical and device-related factors, such as
heated humidification (Massie et al., 1999) and various mask types (Massie & Hart 2003). A
more recent intervention along similar (i.e., device-related) lines is the development of auto-
titration, where the CPAP device continuously adjusts the delivered pressure to what is needed to
counteract apneas. No significantly superior effect has been conclusively shown from auto-CPAP
compared to fixed-pressure CPAP (Ayas et al., 2004). Bi-PAP, where a different pressure is
exerted during exspiration than during inspiration (Hörmann et al., 1994), has been tested, but
without any certain effect on adherence (Smith et al., 2009).
Regarding side effects, findings are somewhat contradictory. It is quite common that patients
report side effects as a reason for non-adherence or treatment dropout (Nino-Murcia et al., 1989),
but other studies have given less clear results. For example, Pépin et al., (1995) found no
35
correlation between side effects and adherence in a cross-sectional study, but Janson et al.,
(2000) found an increased risk of dropout in patients with nasal side effects.
Patient characteristics
Socioeconomic status is related to adherence, at least in healthcare providing systems where
patients pay a significant amount of the treatment by themselves (Brin et al., 2005; Simon-Tuval
et al., 2009; Billings et al., 2011). Patients with a lower income and/or socioeconomic status are
less likely to obtain a CPAP device. Adherence might also be associated to spousal support
(Broström et al., 2010), especially collaborative support (Glazer Baron et al., 2012).
Regarding age and gender, findings have been somewhat inconsistent. Woehrle et al. (2011)
found that, among experienced users, high age were associated to a higher adherence, measured
both as nights per week and hours per night. Males were slightly more adherent than females
when adherence was measured by average nightly use, but the gender difference was small and
of uncertain significance. Higher age was also found to be associated to better adherence by
Simon-Tuval et al., (2009). Among new patients, when adherence was assessed during the first
week of treatment, Ye at al., (2012), found that neither age, gender nor socioeconomic status
(measured as education level and employment status) were associated to CPAP adherence,
although married subjects used the CPAP more and black subjects used the CPAP less.
Tzischinsky et al., (2011) found no correlation between age and gender and the decision to get a
CPAP or not. Sin et al., (2002) found women to be more adherent than men, and older patients to
be more adherent than younger patients.
CPAP use tends to be stable in long-term users with a slight increase over time (Sucena et al.,
2006).
Early research on factors related to treatment focused on disease characteristics, such as AHI and
oxygen saturation levels prior to treatment, as well as improvement when on CPAP (Engleman et
al., 1996b; Sin et al., 2002). Sin et al. did, however, not find any relationship between total sleep
time, AHI, BMI, oxygen saturation and PLM index to CPAP mean use time.
McArdle et al. (1999) found, however, that patients with an AHI exceeding 15/h were more
likely to be adherent than patients with an AHI below 15/h. Snoring and subjective sleepiness
were also associated to a higher degree of CPAP adherence. In univariate analyses, gender, age,
BMI, arousal index, CPAP pressure and driving problems were associated to adherence, but
these findings disappeared in the multivariate analysis. Regarding driving problems (patients
experiencing sleepiness during driving were more likely to be adherent), there is always a
36
possibility that non-adherent patients under-report driving problems as to justify continued
driving despite poor adherence.
Psychological factors
Various psychological constructs have been associated to CPAP adherence, some of which are
known from other medical fields, such as the Health Belief Model (Rosenstock et al., 1988) and
the Social Cognitive Theory (Bandura, 1989). The main components of the Health Belief Model
model are a concern for health issues; a perceived threat or a belief that one is particularly
vulnerable to a certain health outcome; and a belief that one can affect this outcome by following
a specific treatment regime. The ultimate behavior is seen as the result of weighing pros and cons
of the treatment with the perceived risk and effectiveness of the treatment. The Health Belief
Model has been applied to CPAP treatment in OSA patients by Olsen et al., (2008). In a
prospective study, 77 consecutive OSA patients where a decision to initiate CPAP treatment had
been made on clinical grounds were included. Prior to initiating treatment, they were asked to
complete various questionnaires to assess constructs of the Health Belief Model (symptoms by
the ESS and the Functional Outcomes of Sleepiness Scale; self-efficacy, risk perception and
outcome expectancy by the Self Efficacy Measure for Sleep Apnea). Outcome was adherence
defined as meter reading after approximately four months on CPAP. The Health Belief Model
constructs together with biomedical indices (i.e., RDI, AI, BMI, ESS score, nadir of oxygen
saturation and percentage of TST at <80% oxygen saturation) prior to treatment could explain
31.8 % of adherence after four months.
The Social Cognitive Theory has also been used to explain CPAP adherence by Stepnowsky et
al., (2002). According to SCT, the decision to engage in treatment is a result of a health concern,
expectations about the outcome of the treatment as well as the potential consequences of
abstaining and social support and self-efficacy. Stepnowsky et al., (2002) examined constructs
related to social cognitive theory in 51 patients with newly diagnosed OSA, who completed ESS
and SCT (Self-efficacy, Outcome expectations, social support and knowledge; a questionnaire
that was developed specifically for that study). The questionnaires were completed at treatment
initiation and after one week and one month on CPAP, respectively. Together with ESS and
CPAP pressure, self-efficacy, social support and knowledge could explain 31 % of the variance
in cross-sectional CPAP adherence after one week and 40 % after one month.
A person’s personality might affect his or her behavior, but this has not been extensively studied
in obstructive sleep apnea. Type D personality (the ‘D’ stands for ‘distressed’; Denollet, 2000) is
used to describe a personality type consisting of a combination of social inhibition and negative
37
affectivity. Social inhibition is defined as a tendency to inhibit expressions and behaviours in
social situations. Social inhibition is related to, but not identical to, introversion. It correlates
with extraversion in the Eysenck Personality Questionnaire (Denollet, 1998) in congestive heart
failure patients. The other component is negative affectivity, which is a tendency to experience
negative emotions more often and more strongly than positive ones. Type D personality has been
associated to worse outcome in several diseases, mainly cardiovascular disorders (for a review,
see Pedersen & Denollet, 2003). Most mechanistic studies have focused on physiological
phenomena relating stress to inflammatory responses etc., but it is also conceivable that a
negative affectivity might increase the likelihood of experiencing side effects to a treatment, and
in combination with a lower likelihood of seeking help due to social inhibition, this might affect
the long-term adherence to prescribed treatments. This might also be true for CPAP users.
Denollet has developed an instrument, DS14 (Denollet, 2005) to classify people as having a type
D personality or not.
In conclusion, OSA is a common disorder, with several potentially severe adverse consequences.
There is effective treatment, but problems with CPAP adherence have been known to sleep
medicine for the last 20 years. While progress has been made, there are still significant open
questions regarding the causes of poor adherence. Answering those might help in developing
clinical routines to improve patient care. Psychosocial factors are getting more attention.
However, regarding side effects and sleepiness, previous research has given conflicting results.
Regarding personality and its impact on CPAP adherence, it has largely been overlooked in
previous research. These factors are often subjective and must therefore be analyzed using
questionnaire data. In order to obtain reliable and valid data, there is a need for reliable and valid
questionnaires. The overall aim of the thesis is thus to develop and/or validate questionnaires to
examine various factors that are related to CPAP adherence.
38
AIMS The overall aim of the thesis was to develop and/or validate questionnaires to examine various
factors that may be related to CPAP adherence. Specific aims were as follows:
1. To describe the development and initial testing of a new instrument, the Side Effects of
CPAP Inventory (SECI);
2. To measure the frequency and magnitude of CPAP side effects, as well as their impact on
CPAP use;
3. To describe the prevalence of Type D personality in OSAS patients with CPAP treatment
longer than 6 months and the association to self-reported side effects and adherence;
4. To examine whether any of the items in the ESS exhibit differential item functioning with
regard to age or gender, and if so, to which degree;
5. To examine the evolution of CPAP side effects over time; and
6. To prospectively assess correlations between early CPAP side effects and treatment
adherence.
39
Paper I: Development and initial testing of SECI
Rationale for paper I SECI stands for Side Effects of CPAP Inventory. The rationale behind developing the
questionnaire was that side effects are common (Weaver & Grunstein, 2008), and are often
reported by patients to be significant causes of treatment non-adherence (e.g., Broström et al.,
2010). Prior to the development of the SECI, however, there was no validated instrument to
assess or quantify CPAP side effects in a systematic way.
Methods
Item generation Items (i.e., potential side effects) were generated by a literature review and in-depth interviews
with 23 patients using CPAP for OSAS. Based on these, a primary list of twenty side effects was
generated. This list was reviewed by a multi-professional panel consisting of physicians, nurses
and nurse researchers with experience from CPAP and sleep apnea research. Five of the side
effects were removed from the initial list (e.g., claustrophobia, which was thought to be covered
by “anxiety during treatment”). The resulting list thus contained the following fifteen side
effects:
Box 4: Side effects included in the SECI
1. Blocked up nose. 2. Runny nose. 3. Nose bleed. 4. Dry throat. 5. Irritated eyes. 6. Irritated bowl. 7. Transient deafness. 8. Feeling uncomfortable because of wearing the CPAP in front of others. 9. Increased awakenings. 10. Uncomfortable pressure of the mask. 11. Mask leaks. 12. Cold air. 13. Disturbing noise. 14. Problems exhaling. 15. Anxiety during treatment.
40
As it was not clear which aspects of a side effects would affect the patients (e.g., whether a rare
but severe side effect would affect adherence differently than a frequent but less severe side
effect), three questions were used for each side effect. They cover frequency (“How frequently
does this side effect occur?”), magnitude (“How great a problem does this side effect cause?”)
and perceived impact on adherence (“How does this side effects decrease your use of CPAP?”).
Each question is answered on a five-point Likert-type scale, ranging from one (least concern) to
five (most concern). An example item with all three questions and response alternatives is
presented below:
Box 5: Item 1 in SECI, as it is presented to patients.
Blocked up nose 1 2 3 4 5
How frequently does this side
effect appear?
Never Seldom Sometimes Often Very
often
How great a problem does this
side effect
cause you?
No
problems
Small
problems
Some
problems
Great
problems
Very
great
problems
How does this side-effect
decrease your
adherence?
Not at all A little Moderately Much Very
much
Three scores were initially calculated for the SECI. They consisted of the sum scores of all
frequency, magnitude and impact questions, respectively. The total score for each of the scales
(i.e., frequency, magnitude and impact) thus ranged from 15 to 75, with lower scores indicating
less problems. A pilot test was performed on 20 CPAP treated OSAS patients to assess
readability, clarity and layout.
Data collection To assess reliability and validity of SECI, consecutive OSAS patients from three sleep centres
were included in a cross-sectional study. The centres were located in Linköping, Jönköping and
Stockholm. The Linköping centre is located at a university hospital, the Jönköping centre at a
county hospital and the Stockholm centre is a private care clinic where the care provided is
reimbursed by the County Council of Stockholm. Inclusion criteria were age>18 years and a
diagnosis of OSAS (clinical symptoms and AHI≥10/h) and CPAP-treatment for at least 2 weeks.
Patients were diagnosed using in-home PG comprising airflow, respiration movements, pulse
41
oximetry and body position. Exclusion criteria were life-threatening disease with short expected
survival, severe psychiatric disease, dementia, communication problems or inability to read or
speak Swedish. 350 patients fulfilled the eligibilty criteria, and were mailed questionnaires as
well as general background questions pertaining patient demographics. Clinical background
variables, i.e., co-morbidities, blood pressure, BMI, ESS and OSAS severity variables (AHI and
ODI) as well as objective data on adherence (machine usage time) were taken from medical
records.
Statistical processing and analysis Statistical analyses were performed in SPSS, version 15.0.1.1 (SPSS Inc, Chicago, IL, USA).
Reliability was assessed by item-total correlations adjusted for overlap and Cronbach’s alpha if
item deleted (Nunnally & Bernstein, 1994). Relationship among items as well as scales were
analysed by Spearman’s correlation coefficient.
To test construct validity, principal component analysis (PCA) was performed. Prior to this, the
data had been examined by Bartlett’s test of sphericity and measure of sample adequacy, both in
each variable and overall (Kayser-Meyer-Olkin measure). Principal components with
eigenvalues of 1.0 or greater were extracted in a first analysis (five components), and in an
anlysis based on the scree plot, two components were extracted. Varimax rotations were applied
on all analyses. Factor loadings ≥ 0.4 were considered significant. Cross-validation was
performed on a random split of the sample into two groups. Known group validity was examined
using a dichotomization of the sample into two groups, with a cutoff adherence of 4 hours/night
of CPAP use. Non-parametric statistics was used to compare the two groups.
Results
Study population After one reminder, 329 out of the 350 patients responded, resulting in a response rate of 94 %.
The non-responders did not differ significantly from the responders with regard to age,
education, marital status, co-morbidities, disease severity measures or duration of CPAP
treatment. The duration of treatment ranged from 2 weeks (i.e., the minimum time on CPAP to
be eligible for inclusion in the study) to 182 months, with an average of 39 months. All patients
used fixed-pressure devices and 21 % had humidifiers.
42
Validity and reliability of the SECI Internal consistency and item-total correlation were both satisfactory. Nose bleed, however, had
somewhat weaker item-total correlation, and ”disturbing noise” and ”feeling uncomfortable
wearing the CPAP in front of others” had lower item-total correlations in the frequency scale.
Generally, the frequency scale showed the least item-total correlation, and had higher scores than
the other scales.
The scree plot criterion was used for the final principal component analysis, after a component
extraction based on the Kaiser criterion (i.e., eigenvalues ≥ 1.0). The latter led to a five-
component solution for the frequency scale and four-component solutions for the magnitude and
impact scales, and several items loading on multiple questions. The scree plot, however, gave
two components, and when extraction was fixed at two components in a new analysis, the two
principal components extracted could be said to represent device-related and symptom-related
side effects, respectively. The cross-validation validated the components for the impact and
magnitude scales, but not for the frequency scale.
The non-adherent patients, i..e, the patients who used the CPAP less than four hours per night,
scored significantly higher on all scales (i.e., frequency, magnitude and impact) and four of six
subscales (i.e., all except for the frequency/device subscale and the magnitude/symptom
subscale).
43
Paper II: Type D personality
Rationale for paper II Type D personality has been described as a risk factor for cardiovascular disease, and it was
speculated that poor adherence to treatment could be a part of the link (Denollet et al, 1996). One
of its main components, i.e., negative affectivity, has been related to health complaints (Watson
& Pennebaker, 1989). It is reasonable to expect that there could be a correlation between
perceived side effects and type D personality, but it had not been studied. Given the fairly high
prevalence of type D personality in other populations (e.g., Denollet, 2005), an association
between type D personality and adherence would need to be taken into consideration when
designing interventions to improve CPAP adherence.
Methods
Data collection All CPAP treated OSAS patients at the department of Clinical Neurophysiology, University
hospital of Linköping, with OSAS defined as an AHI≥10/h and clinical symptoms, age≥18 years
and CPAP duration of at least six months were invited. Patients were excluded if they suffered
from other life-threatening diseases with short survival time, severe psychiatric disease,
dementia, communication problems or inability to read and speak Swedish.
350 patients fulfilled the eligibility criteria. Questionnaires were sent to them by mail about
demographics, side effects (i.e., SECI) and a Swedish translation of the DS14 (Denollet, 2005).
Data about objective adherence (i.e., machine usage time) were collected from their medical
records.
Questionnaires DS14 conists of 14 items, and are answered using a five-point Likert-type scale, with scores
ranging from 0 to 4 for each item. The test is divided into two subscales, termed negative
affectivity (NA) and social inhibition (SI). Each scale consists of seven items. The result of DS14
is a sum score for each scale, after reversal of two reversely worded items in the SI scale. To be
defined as having a type D personality, a person must score at least ten points on each of the two
subscales. For each subscale, it is also possible to categorize respondents into seven groups,
depending on their score (very low, low, below average, average, above average, high and very
high). A Swedish translation of the DS14 was made for the study. DS14 was originally
44
developed in Dutch, and translated to English by Denollet (2005). The English translation was
used as a basis for the Swedish version. The Swedish translation was back-translated into English
and approved by Denollet.
SECI was used to assess side effects to CPAP treatment. Daytime sleepiness was measured using
ESS (Johns, 1991).
Statistical processing and analysis All statistical calculations were performed in SPSS (SPSS Inc, Chicago, IL, USA). Patients were
classified, based on the criteria from Denollet (2005) (i.e., scores of at least 10 on both subscales
of the DS14), as having or not having type D personality. Both parametric and non-parametric
statistics were used, as appropriate. For continuous variables, Student’s t test or Mann-Whitney
U test were used. Categorical variables were analysed using chi-sqauare test or Fisher’s exact test
as appropriate, and to test differences between people in the seven categories, Kruskal-Wallis test
was used.
In the statistical analysis of SECI responses, for each question, answers 1 and 2 (i.e., ”never” and
”occasionally” for frequency questions, ”no problems” and ”small problems” for magnitude
questions and ”not at all” and ”a little” for impact questions, respectively) were amalgamated
into one response category. In the same way, responses 4 and 5 for each question (i.e., ”often”
and ”very often” for frequency questions, ”great problems” and ”very great problems” for
magnitude questions and ”much” and ”very much” for impact questions, respectively), were
amalgamated into one response category. Thus, the five-point scale originally used in SECI was
reduced to a three-point scale.
Adherence was defined both as machine usage time above or below 4 fours per night, and
machine usage above 85% of the self-rated mean total sleep time. In addition, average machine
usage time was used as a continuous variable.
Results
Study population After one reminder, 247 subjects answered the questionnaires. The response rate was thus 70.6%.
The non-responders did not differ from the responders with regard to age or duration on CPAP.
All patients had fixed pressure devices, and 11% had humidifiers. The time on CPAP treatment
did not differ significantly between type D and non-type D patients (mean 57 month, range 6-144
45
months among type D patients as compared to a mean duration of 39 months ranging from 6 to
182 months among non-type D patients). Excessive daytime sleepiness, as measured by the ESS,
did not differ between groups prior to treatment initiation. Co-morbidities were similar in both
groups, with hypertension and diabetes being the most common.
Type D personality and side effects Type D personality was present in approximately one third of the total sample, with no
significant difference between genders. 28 % of the men and 39 % of the women fulfilled the
criteria. The mean (SD) total score for DS14 was 19.5(8.2), and for the individual subscales, the
scores were 8.2(5.7) and 11.2(3.4), for the NA and SI scales, respectively.
Regarding side effects, the most common side effects (i.e., the side effects that a highest
percentage of both type D and non-type D patients scored as occuring often or very often) were
dry throat, uncomfortable pressure from the mask, mask leaks, feeling uncomfortable because of
wearing the CPAP in front of others and blocked up nose).
The least frequent side effects, defined as the side effects that had the largest percentage of
”never” or ”seldom” responses, were anxiety during treatment, nose bleed, problems exhaling,
cold air and transient deafness. Frequency and magnitude for side effects were correlated among
both type D and non-type D patients. For all side effects, except for cold air, problems exhaling
and anxiety during treatment, patients with type D personality scored significantly higher than
those without.
Type D personality and adherence Adherence was measured in several ways. Data about subjective adherence were taken from the
SECI questionnaires. For nine of the side effects, type D patients indicated significantly higher
degrees of negative impact on adherence than non-type D patients. This was the case for all side
effects except transient deafness, feeling uncomfortable because of wearing the CPAP in front of
others, cold air, disturbing noise, problems exhaling and anxiety during treatment.
Regarding objective use, type D patients were less adherent according to all three employed ways
of defining adherence (i.e., machine use as a continuous variable, machine use below or above
four hours per night and machine use below or above 85% of self-reported total sleep time). The
mean (SD) usage time among non-type D patients was 378.2 (116.6) minutes/night, as compared
to 292.1 (38.4) minutes/night among non-type D patients (p<0.001). 62% of the type D patients,
compared to 30% of the non-type D patients, used the CPAP less than 85% of their estimated
46
total sleep time. Using a four- hour cutoff, 51% of type D patients vs. 16% of non-type D patients
were adherent.
47
Paper III: Differential item functioning in the Epworth Sleepiness Scale
Rationale for paper III ESS is very frequently used in sleep medicine and sleep research, and the original description of
it has been cited over 5000 times as of 2013. It is used in CPAP research to measure daytime
symptoms prior to treatment and improvement from treatment. Since its initial description, it has
been the subject of a lot of criticism, mainly related to its psychometric properties. As pre-CPAP
sleepiness and improvement on treatment might be related to adherence, it is important to
examine the methods used to assess sleepiness in OSAS patients. There is a lack of knowledge as
to whether ESS has similar psychometric properties in different populations, although it is
reasonable to believe that some of the items might behave differently depending on what kinds of
situations the respondents regularly experience.
Methods
Data collection
No data were collected specifically for this study. The study is based on analyses performed on
1168 subjects that had completed the ESS in five other studies that have been published
elsewhere (Broström et al., 2004; Broström et al., 2007; Johansson et al., 2009; Broström et al.,
2010; Broström et al., 2012). Broström et al., (2007) and Broström et al., (2010b) contributed
with 240 and 171 respondents, respectively. They are included in this thesis as papers I and II.
Johansson et al., (2009) contributed with 331 respondents. It is a sample consisting of home-
dwelling elderly (aged 65-82 years) subjects living in the municipality of Kinda outside
Linköping, Sweden (the CoroKind study). The CoroKind study was actually larger, but only data
for the subsample undergoing night-time polygraphy (Johansson et al., 2009) was used. All
home-dwelling elderly in the age group living in Kinda were invited to take part in the CoroKind
study, which was a general population-based cohort study. 876 of 1,130 subjects initially agreed
to participate in the cohort. Of those, 346 agreed to take part in a home polygraphy, and in
conjunction with that they also completed the ESS. 15 patients were subsequently excluded due
to poor technical quality of their polygraphic recordings, and ESS data for those were not
available for the differential item functioning study (i.e., the present study).
Two other studies contributed with 216 subjects each. One was the Hypersleep study, which is an
ongoing prospective, longitudinal study of hypertensive patients in Jönköping, Sweden. All
patients between the ages of 18 and 65 years with a diagnosis of hypertension at four primary
48
care centres in Jönköping, Sweden were eligible. The Hypersleep sample is further described in
Broström et al. (2012).
The final study consisted of heart failure patients (Broström et al., 2004). Subjects were ≥18
years and NYHA class II-IV. Exclusion criteria were unstable heart failure, severe psychiatric
disease, suffering from another life-threatening disease, severe chronic pulmonary disease,
communication problems or inability to read and speak Swedish. Patients were recruited from a
medical ward at a university hospital, two heart failure clinics (one university hospital and one
county hospital) and five primary health care centres in southern Sweden.
Statistical analysis
Statistics were performed in Stata, version 10 (StataCorp 2007, College Station, Texas, USA)
and RUMM2020 (RUMM Labs Pty Ltd, Australia). Baseline statistics were performed using chi-
square test and Student’s t test. Two approaches to differential item functioning were used. An
ordinal regression, based on the method described by Zumbo (1999) was used, as well as a
polytomous Rasch model.
For the ordinal regression, a hierarchical ordinal regression model was developed for each item.
This was done in a multi-step process, where item score was predicted from total ESS score in
the first model. Then, age was added to the model. DIF was considered to be present when the
grouping variable (i.e., age or gender) was significantly associated to item score after adjusting
for the total ESS score. The change of McFadden pseudo-R2 change was used to assess the
magnitude of DIF, when present. Interaction terms between ESS total score and age and gender,
respectively, were used to assess non-uniform DIF.
The Rasch model parameters were estimated using Joint Maximum Likelihood estimation
(Hambleton et al., 1991), an iterative process where person and item parameters are estimated
jointly. The sample was grouped into eight groups depending on their subjective daytime
sleepiness. Global fit and individual item fit were assessed by comparing fit residuals for person
and item parameters to expected values, and by item-trait interaction. Visual inspection of item
characteristic curves was also used to examine potential misfit.
Person and item parameters were compared to assess targeting. Differential item functioning
were examined by dividing each of the eight groups that had been formed by dividing the sample
according to their abilities (i.e., daytime sleepiness) by age (i.e., below or above 65 years) or
gender, and then perform ANOVAs. Age and gender were tested as main affects for uniform
DIF, and interaction terms between daytime sleepiness group and age and gender respectively
were used to examine non-uniform DIF. The resulting p values were adjusted due to the number
49
of ANOVAs performed (i.e., one for each item, two main effects and one interaction effect),
meaning that the level of significance was set at p<0.0021.
Results
Study population
The initial sample consisted of 1175 subjects. Eight subjects were excluded due to missing data
on individual ESS items, leaving 1168 subjects available for further analysis. 25 subjects were
excluded as they scored 0 on the ESS, and thus did not contribute any information to the item
parameter estimation process. For a further 45 subjects, there were no data on age, and they were
thus excluded from the DIF analyses for age. These subjects were, however, included in the DIF
analyses for gender. The total sample, after exclusion of subjects on the grounds listed above,
consisted of 715 men and 453 women, with a mean (SD) age of 67.8 years (12.2 years) and a
mean (SD) ESS score of 7.7 (4.5). Females were significantly older than males (70.4 vs 66.1;
p<0.05). There were also significantly more females in the old (i.e., 65 years or older) age group
than in the young (i.e., below 65 years) (p<0.001).
General psychometric properties of the ESS
Cronbach’s alpha was 0.80, and persons and items were distributed along the same portion of the
difficulty axis, i.e., the items measured the same degree of sleepiness as the respondents reported.
When fixing the mean trait location at 0 on the logit scale, the mean (SD) trait location was
-1.093 (1.186). The mean (SD) item fit residual was -0.6733 (2.716) and the mean (SD) person
fit residual was -0.309 (0.785). There was a global misfit in item-trait interaction, which, by
inspecting individual item characteristic curves, was deemed to be largest in item 5 (“Lying
down to rest in the afternoon, when circumstances permit”; the ICC is presented in the paper).
There were no reversed response alternatives. Item difficulties ranged from -2.087 to 1.98. The
lowest difficulty was found for item 5, and the highest difficulty was found for item 8 (“In a car,
that has stopped for a few moments in traffic”).
50
Figure 1: Logit-scale thresholds for ESS
Differential item functioning
The Rasch model demonstrated DIF för age in items 3 (“Sitting inactive in a public place, e.g., a
theater or a meeting”), 4 (“As a passenger in a car for an hour without a break”) and 8 (“In a car,
that has stopped for a few minutes in traffic”) and for gender in item 3. The findings regarding
age were reproduced in the ordinal regression model, however, there was no demontsrated
gender-related DIF in the ordinal regression model. Generally, the magnitudes of DIF were
small.
51
Paper IV: CPAP side effects – evolution over time and association to adherence
Rationale A significant proportion of patients report various side effects of CPAP treatment. While
patients often refer to side effects as a reason for poor adherence, studies where reported side
effects are related to adherence have yielded conflicting results. As many side effects are
potentially treatable, it is important to examine associations between side effects and adherence
in more detail.
Method
Data collection
Data were collected from patients with OSAS, defined as AHI≥10/h and ESS≥10, where a
decision to initiate CPAP treatment had been made on clinical grounds. The centres were the
ENT department at the County hospital of Jönköping, the department of clinical neurophysiology
at the University hospital of Linköping, Sweden and the private clinic Aleris FysiologLab in
Stockholm, Sweden. 186 patients were consecutively recruited to the study. Exclusion criteria
were acute CPAP initiation, severe co-morbidity with expected short survival, malignancy,
psychiatric disorders and problems communicating in Swedish. The diagnosis of OSAS was
based on PG recordings (Embletta, ResMed), and all recordings were scored manually.
Humidifiers were given to all patients at Aleris FysiologLab when treatment was initiated. At the
other centres, it was given to patients who complained of dry mouth or blocked up nose.
Patients had scheduled routine follow-up visits after 1-2 weeks, 3-6 months and 9-12 months,
respectively. Clinical data, as well as CPAP objective machine usage data were collected by
CPAP nurses at these visits. In, addition, demographic data (i.e., education, marital status, co-
morbidities) were collected at the first visit. In conjunction with the visits, patients were asked to
complete SECI and ESS. Patients were followed for one year.
Statistical analysis
Statistics were performed in R version 2.14 (The R Foundation for Statistical Computing,
Vienna, Austria). The SECI responses were recorded as follows: for each side effect, if the
respondent had answered 4 or 5 to any of the three subscale questions (i.e., regarding frequency,
magnitude and impact on adherence), the side effect in question was considered to be
52
”significant”. Treatment adherence was measured as treatment dropout (i.e., patients returning
their machine) as well as objective machine time as a continuous variable. Adherence data were
taken from medical records. To test for differences in background variables between patients
treated at the different centres, chi-square test or Fisher’s exact test were used for categorical
variables and ANOVA was used for continuous variables. Comparisons between patients who
dropped out during the first year and those who did not were performed using Chi-square or
Fisher’s exact test for categorical data and Kolmogorov-Smirnov’s test for continuous data.
To examine how side effects varied over time, McNemar’s test was used to examine whether the
existence of a side effect at one point in time was significantly related to the existence of that
side effect at another point in time. Cohen’s κ was calculated to assess the correlation between a
side effect at two points in time. Side effects were measured at three points (i.e., after 1-2 weeks,
3-6 months and 9-12 months), and comparisons were made between all pairs of points (i.e., side
effects after 1-2 weeks vs. 3-6 months, side effects after 1-2 weeks vs 9-12 months and side
effects after 3-6 months vs 9-12 months). Thus, three comparisons were made.
Associations between side effects and adherence were examined for the side effects that were
reported at the first point in time, i.e., after 1-2 weeks. These side effects were compared to
dropout using both relative risks and Mantel-Haenszel ORs with adjustment for treatment centre.
Linear regression was used to assess associations between early side effects and machine usage
time after six months. Treatment centre, mask type and pre-treatment AHI were added to account
for potential differences in initiation routines and disease severity.
Results
Study population
186 subjects were included. Of these, 78 % were men, and their age ranged fron 20 to 76 years.
Patients at Aleris FysiologLab had lower BMI, and patients from the county hospital of
Jönköping had larger neck circumference than those at the other centres. Drop-outs occured
continuously throughout the first year. After 1-2 weeks, 17 patients had dropped out. After six
months, 39 additional patients had dropped out, and after one year, 30 additional patients
haddropped out. At the end of the first year, 100 patients remained on CPAP. There were no
difference with regard to any of the background variables between those who dropped out and
those who did not. There was no difference in dropout rate between treatment centres.
53
Prevalence of side effects
After 1-2 weeks, the five most frequent side effects were dry mouth (38%), blocked up nose
(32%), mask pressure (26%), increased number of awakenings (22%), and mask leakage (21%).
The side effects that most patients thought were of significant magnitude were mask pressure
(19%), blocked up nose (16%), mask leakage (14%), dry mouth (13%) and increased number of
awakenings (13%). The side effects that were considered to have a significant negative impact on
adherence by the largest proportions of patients were mask pressure (17%), dry mouth (15%),
increased number of awakenings (15%), and mask leakage, blocked up nose and problems
exhaling at 13% each.
After 3-6 months, the most frequent side effects were dry mouth (33%), increased number of
awakenings (20%), mask pressure (19%), blocked up nose (14%), and mask leakage and noise at
13% each. The side effects that most patients thought were of significant magnitude were
increased number of awakenings (14%), dry mouth (11%), mask pressure (10%), noise (9%) and
blocked up nose (9%). The side effects that were considered to have a significant negative impact
on adherence by the largest proportions of patients were blocked up nose (9%), mask leakage
(7%), noise (6%), increased number of awakenings (6%), and dry mouth (5%).
After 9-12 months, the most frequent side effects were dry mouth (39%), blocked up nose and
mask leakage, at 26% each, mask pressure (23%), and irritated eyes (17%). The side effects that
most patients thought were of significant magnitude were dry mouth (19%), blocked up nose and
mask leakage at 14% each, mask pressure (10%) and noise (9%). The side effects that were
considered to have a significant negative impact on adherence by the largest proportions of
patients were mask pressure (15%), blocked up nose (13%), runny nose (9%), and cold air and
noise at 8% each.
Evolution of side effects over time
Evolution of side effects were studied within subjects. Side effects could both resolve and
emerge within subjects. Among the patients who did not suffer from dry mouth, mask leakage
and blocked up nose at the first assessment after 1-2 weeks, 32, 28 and 27%, respectively,
experienced these side effects as significant problems after one year. At the same time, cold air,
problems exhaling and feeling uncomfortable using the CPAP in front of others were resolved in
a large proportion of patients. Dry mouth, blocked up nose, mask pressure and mask leaks were
among the most common and significant side effects at all points of time. Increased number of
awakenings was a common side effect except for at the last assessment after 9-12 months.
54
Association between side effects and adherence
When dropout during the first year on CPAP was used as the definition of adherence, there was
an increased risk of non-adherence for some, but not all, of the side effects reported after 1-2
weeks. These side effects were dry mouth (RR 1.65, 95% CI 1.11-2.45), transient deafness (RR
2.14, 95% CI 1.32-3.47), increased number of awakenings (RR 1.90, 95% CI 1.31-2.76),
problems exhaling (RR 2.00, 95% CI 1.38-2.90) and anxiety during treatment (RR 1.77, 1.13-
2.78). As patients were treated at three different centres, and as there might be subtle differences
in treatment initiation procedures, as well as some differences regarding background variables
(i.e., BMI being somewhat lower in one centre and neck circumference being somewhat greater
in one), the sample was stratified for treatment centre and re-analyzed using Mantel-Haenszel
OR. In this analysis dry mouth (MH-OR 95% CI 1.14-4.40), feeling uncomfortable about using
the CPAP in front of others (MH-OR 95% CI 1.48-6.94), increased number of awakenings (1.42-
6.27) and problems exhaling (MH-OR 95% CI 1.68-12.61) after 1-2 weeks were significantly
related to an increased risk of dropping out during the first year.
When adherence was defined as machine usage time after six months (a continuous variable),
there was a significantly worse adherence in patients experiencing significant problems after 1-2
weeks with blocked up nose, dry mouth and increased number of awakenings. Somewhat
paradoxically, there was an inverse relationship between experiencing cold air as a significant
side effect after 1-2 weeks and machine usage after 6 months (i.e., patients who complained
about cold air after 1-2 weeks used the CPAP more after 6 months than those who did not). The
analysis is presented in Table 4. AHI, Centre, BMI and neck circumference were adjusted for in
the presented table (BMI and neck circumference have been added, and are not included in the
model presented in mansucript IV).
55
Table 4: Side effects after two weeks and adherence after six months. Coefficients are beta coefficients for machine usage time in hours. Negative coefficients indicate that patients experiencing a side effect after 1-2 weeks use the CPAP less after 6 months than those who did not experience the side effect in question after 1-2 weeks. Side effects that are significantly related to adherence are boldfaced. Side effect Coefficient Standard Error Adjusted R2 P value
Blocked up nose
-0.89 0.37 0.06 0.0196
Runny nose -0.94 0.55 0.04 0.0911
Nose bleed 0.73 0.74 0.02 0.3281
Dry throat -0.84 0.38 0.05 0.0276
Irritated eyes 0.08 0.63 0.01 0.9005
Irritated bowl -1.24 0.75 0.03 0.0992
Transient deafness
-2.25 1.16 0.04 0.0552
Feeling uncomfortable about using the CPAP in front of others
-0.85 0.79 0.03 0.2800
Increased number of awakenings
-1.17 0.42 0.08 0.0062
Mask pressure -0.45 0.41 0.02 0.2801
Mask leaks -0.57 0.45 0.03 0.2113
Cold air 1.67 0.72 0.06 0.0215
Disturbing noise 0.02 0.53 0.01 0.9663
Problems exhaling
-1.11 0.58 0.04 0.0601
Anxiety during treatment
-0.34 0.73 0.01 0.6431
56
ETHICAL CONSIDERATIONS Several aspects of the studies can be discussed from an ethical perspective. For the development
of SECI, interviews were performed in order to find side effects to include. Study III used pooled
data from other studies, and did not contain a data collection phase. Data regarding CPAP usage
time was taken from the CPAP devices, as they record adherence data on a night-by night basis.
All studies were approved by the Ethical Review Board in Linköping apart from paper III, for
which no ethical approval was needed (contact was taken with the Ethical Review Board prior to
the study) as no data could be associated to specific individuals. All studies from which data
were taken were approved by the regional Ethics committee.
Four general ethical principles are often referred to in healthcare and medical research (SBU,
2013; Beauchamp & Childress, 2009). These are the principles of autonomy, justice,
nonmaleficience and beneficience.
Regarding justice, nonmaleficience and beneficience, all studies were observational, and thus did
not include any situations where patients were not given care they would have been given if not
participants in the studies, nor were they exposed to any treatment-related risks other than those
which arise from standard care. Regarding autonomy, especially informed consent, this was
collected when informing patients about the study. No data from medical records etc. were
available to researchers prior to obtaining written consent.
Regarding interviews, they were not performed by the healthcare personnel involved in treating
the patients, as to minimize the risk of patients not being able to share potential barriers related to
CPAP treatment due to a desire to maintain a positive relationship to the care provider.
Regarding autonomy in study III, about DIF in the ESS, data were taken from different previous
studies, all of which had obtained informed consent for their respective purpose. However, no
new informed consent was obtained for the present study, as no identifiable data that could be
traced to a specific individual was analyzed. The data file used for the analysis only contains age,
gender, ESS responses and what original study the subject participated in, but as the study has
over 1,000 participants, it is impossible to identify individual subjects from this information. This
is why no results with regard to any other background variables other than age, gender and ESS
are given in that study. Obtaining informed consent would not be feasible, especially as not all
subjects were alive, some data was collected several years prior to the analysis of Differential
Item Functioning was performed. This was discussed with the Regional Ethics Board, who
approved of this approach.
57
Data regarding CPAP adherence was taken from the CPAP machines, as they record data of
machine usage and delivered pressure on a night-to-night basis. It could be seen as potentially
intruding the patients’ autonomy. This, however, was not done for the study but is done regularly
in CPAP care and is the basis for feedback to the patients.
58
DISCUSSION The aims of this thesis are all related to development and vaildation of questionnaires in OSA.
Three of the papers have used SECI, which is the first validated questionnaire for CPAP related
side effects. We have shown that CPAP side effects can be measured and that they are related to
treatment adherence as well as personality constructs (i.e., type D personality). We have also
shown that ESS exhibits DIF, at least with regard to age.
Defining side effects Previous research regarding side effects and adherence have provided various, and sometimes
conflicting, results. It is common among patients to cite side effects as a reason for poor
adherence (Nino-Murcia et al., 1989; Engleman et al., 1994; Broström et al., 2010), but studies
have yielded less clear results (Engleman et al., 1996b; Jansson et al., 1999; Olsen et al., 2008).
One reason might be that there is no generally agreed-upon definition of what CPAP side effects
are, or how they should be measured. Several studies which have examined side effects have
done so without explicitly stating how they developed the instruments they used. Previous
research has also defined side effects in different ways, and asked about them in different ways.
We found, for example, an increased number of awakenings to be associated to poorer
adherence. Engleman et al. (1996b), did ask about frequent awakenings, but did not define this as
a side effect from CPAP treatment. Rather, they defined this as one of several ”nuisance
problems”, and while they did not found a relationship between side effects and adherence, they
did find it between nuisance problems and adherence. Pépin et al. (1995) did list a number of
potential side effects that they asked patients about (i.e., adverse effects of the nasal mask, such
as allergy over the face, discomfort to the ridge of the nose, pain in the teeth or gums, and air
leaks near the eyes or over the face, dryness of nose or mouth in the mornings, sinusitis,
sneezing, nasal drip, nasal congestion, nose bleeding, and air swallowing). However, they did not
report how answers were given. Meurice et al. (1994) reported side effects among compliant and
non-compliant users, but they did not report how these data were obtained. In summary, previous
studies have had different approaches to side effects as well as to adherence.
59
Measuring side effects In the three papers using SECI presented here, SECI scores are calculated in different ways.
Responses in all studies have been given on five-point Likert type scales regarding frequency,
magnitude and perceived impact on adherence. However, in two of the studies (i.e., papers II and
IV), different forms of rescoring criteria were employed. In paper II, responses were reduced to
three response alternatives by amalgamating scores 1 and 2 and 4 and 5, respectively, on each
item. This was done to obtain more responses in each category, thereby increasing group sizes.
The three subscales, i.e., regarding frequency, magnitude and impact on adherence, were,
however, analyzed separately. We could still see meaningful correlations between reported side
effects and personality. In the fourth manuscript, a further simplification was done, by combining
the scores from all three subscales into one score, and by reducing the number of groups by
combining those who scored 1 or 2 with those who scored 3. The idea is to simplify SECI, by
reducing the number of items and thus make it easier to complete and score. This is especially
important in studies comprising several questionnaires, as there is a risk that respondents become
overwhelmed by the number of items. It might also make it easier to score in clinical situations.
If item scores are summed, as was done in the original study, reporting ten infrequent side effects
is equal to reporting one severe side effect, although it might be supposed that they affect side
effects differently. At present, it is too early to decide which approach to scoring is preferrable in
SECI. Reducing the number of response alternatives might reduce the information obtained from
the responses, as small changes with regard to frequency, severity or impact from a given side
effect on adherence might be less easy to spot. Future research should examine these issues
further, for example by comparing the amount of variance in CPAP adherence explained by
SECI when different scoring criteria are used. This could possibly have been done in a study with
a design similar to paper IV, but given the relatively large dropout in this study, a future study
will be examining this instead.
It is also interesting to note, that while some side effects were related to CPAP adherence, others
were not, and it might thus make sense to treat side effects as separate entities rather than to sum
them. Kwiatkowska et al., (2008) asked CPAP patients about severity and frequency of 11 side
effects (nasal congestion, rhinorrhea, rhinosinusitis, epistaxis (nose bleeding), skin abrasion from
mask, conjunctivitis from air leaks, aerophagy (air swallowing), sinus discomfort, sleep
fragmentation, anxiety and claustrophobia) that were rated regarding frequency and severity on
Likert-type scales ranging from 0 to 4. These two responses were then multiplied, and the
products were added. While this approach would be possible to perform in SECI as well, as each
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side effect is rated on several subscales, and has some interesting features, e.g., by allowing for a
change in the frequency of a side effect to be more or less important depending on the perceived
severity of the side effects, it also has problems.
If an answer to a question in a questionnaire is conceptualized as depending on a true score and
an error component, i.e.,
So = St + Se
Where So is the observed score, St is the true score and Se is the error score, then the product of
two observed scores S1 and S2, each consisting of a true score and an error, will be
So = (S1t + S1e) (S2t + S2e) = S1t S2t + S1t S2e + S1e S2t + S1e S2e
Of special concern are the two terms S1t S2e and S1e S2t, as they represent a heteroscedastic error,
i.e., there is a relationship between the obtained score and the size of the error. This might affect
the validity of statistical analyses, especially those based on a homoscedasticity assumption e.g.,
regression and ANOVA (Bobko 2001). This is why SECI scores are not multiplied. Besides, we
found that the frequency scale in SECI had lower reliability than the other scales, and it might be
removed in further versions of the SECI. Another possible approach that would not suffer from
the potential homoscedasticity problem described above would be to sum the scores for each side
effect rather than summing the scores for all frequency questions, magnitude questions and
impact questions separately.
Defining adherence CPAP adherence has been defined in different ways in different studies. One problem is that it is
not known how much CPAP is needed in order to have effect, but there seems to be a dose-
response relationship, with a threshold effect, i .e., a level (threshold) of CPAP use above which
further increases in use are not likely to improve outcome (Weaver et al., 2007). It is, however,
not certain that the dose-response relationship is similar for all treatment effects. For example,
Weaver et al. (2007) found, that there were different dose-response relationships between CPAP
use and ESS, MSLT and FOSQ (Functional Outcomes of Sleepiness Questionnaire; Weaver et
al., 1997). Antic et al. (2011) found a dose-response relationship for ESS, the vitality subscale of
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SF-36 (Ware, 1993) and FOSQ, but not for MWT. Nor did they find any dose-response
relationships for verbal memory and executive function, alhtough they both improved in CPAP
treated patients. Interestingly, although they found an improvement in ESS, approximately 20 %
of those who used the CPAP 7 hours per night or more still had pathologic ESS scores after three
months on treatment. Zimmerman et al. (2006), however, found that normalization of visual
memory performance in CPAP users were eight times more likely in those who used CPAP more
than six hours per night than in those who used it less than two hours per night, among memory-
impaired new CPAP users after three months on CPAP as compared to prior to CPAP initiation.
A problem with studying CPAP dose-response effects experimentally is that there seems to be a
”carry over” effect after CPAP use, at least according to some studies. In chronic sleep
restriction, the correlation between subjective sleepiness and sleep restriction, as well as
cognitive performance, may be less clear than in acute sleep restriction, but a modest sleep
restriction occuring chronically can have a cumulative effect (Van Dongen et al., 2003). It is also
known from studies of sleep deprivation and sleep need that people differ in their sensitivity to
sleep loss and the effects from sleep loss (Van Dongen et al., 2004). It might therefore be
questioned whether it is appropriate with an adherence definition basically stating that a patient is
adherent if he or she uses the CPAP more than a fixed number of hours per night.
Regarding the consequences of OSAS, there are at least two general pathophysiological
mechanisms that can be responsible. One mechanism might be that OSAS leads to chronic sleep
deprivation through sleep fragmentation. Sleep fragmentation might cause similar effects as sleep
restriction (reviewed in Bonnet & Arand, 2003). Another mechanism might be oxygen deficits,
which might lead to generation of free oxygen radicals (Christou et al., 2003), sympathetic
activation (Hedner et al., 1988) and inflammation (Shamsuzzaman et al., 2002). UARS might be
another clue to the various pathogenetic mechanisms linking sleep-disordered breathing to
symptoms and long-term adverse health consequences. In UARS, patients have few if any
desaturations, yet they might still have arousals, and they still become sleepy (Black et al., 2002).
Inflammatory factors might also contribute to sleepiness, as there is an increase in sleep-
promoting inflammatory cytokines in obstructive sleep apnea, as compared to normal controls
(Vgontzas et al., 1997). Similarly, sleep fragmentation, without oxygen desaturations, might
contribute to the cardiovascular aspects of OSAS (Lofaso et al., 1996). Our understanding of
these pathogenetic processes influence how we should look at CPAP adherence. If the main
problem with OSAS is sleep fragmentation and the resulting sleep deprivation, then it would be
reasonable to use either machine usage time or a cutoff time to define which patients are adherent
or not. The existence of thresholds for CPAP effects (Weaver et al., 2007) might indicate that a
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cutoff time might be reasonable; however, it would be different for different outcome measures
and probably also for different subjects (Van Dongen et al., 2004). If the main problem with
OSAS is desaturations, then the main objective with CPAP treatment is to reduce sleep without
CPAP. In that case, simply measuring the time a subject uses his or her CPAP is inadequate, as
we would also want to know the exposure to desaturations, which is related to sleep time without
CPAP. In that case, a percentage of the total sleep time spent with CPAP would probably be a
more accurate measure of adherence. Most likely, however, both mechanisms (i.e., desaturations
and sleep fragmentation) combine to cause adverse short and long-term effects, and thus both
need to be reflected in a definition of CPAP adherence. In one of the studies (i.e., study II), one
of the definitions of adherence was using the CPAP at least 85 % of self-rated sleep time. While
85 % is a arbitrary cut point, the definition has two advantages: it takes interindividual
differences in sleep need into asccount, and it limits the amount of sleep without CPAP that is
allowed, thereby limiting the adverse affects of oxygen desaturations. It was, however, not
possible to use this definition in paper IV, as data about subjective sleep time was not available.
The effects of type D personality on adherence were similar regardless of the definition of
adherence. Dropout, as was used in paper IV, represents the ultimate failure of treatment; as long
as the patient retains the machine, there is a hope that future interventions might have an effect.
Measuring adherence Traditionally, CPAP adherence has been defined either as treatment dropout, as subjectively
reported CPAP use or as objectively measured machine usage time. CPAP devices automatically
measure the amount of time they are used with an effective pressure (Kribbs et al., 1993), and
can report this as a night-by-night measure. Regarding treatment dropout, the main problem is
the risk that dropouts are unrecognized, as patients may keep the machine without ever using it.
If follow-up only occurs in the beginning of treatment, and patients are thereafter responsible for
contacting the CPAP provider whenever they need, but without scheduled follow-up, this might
be an issue. It can be assumed that the probability of patients retaining the device without ever
intending to use it is decreased if the health care provider requires a monthly fee for the device as
is done in some, but not all, parts of Sweden (Swedvox, 2011). This is less of a problem in
studies, however, where follow-up is more controlled. Approximately 8 to 15 % of patients
refuse CPAP initially, i.e., they never start treatment or return the device after the first night
(Smith et al., 2009). Rates of refusal or poor long-term use range from 20 to 83 % in the
literature (Smith et al., 2009), partly depending on different definitions of adherence.
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Subjective data regarding CPAP use tend to produce higher results than objective data. For
example, Engleman et al., (1996b) found that patients systematically over-estimated their CPAP
use as compared to internal meter readings. The objectively measured mean use was 5.1±2.5
h/night, but subjective mean use per night according to self-report data was 6.0±1.9 h/night
(p=0.0003). Their subjective and objective adherence were, however, correlated (r=0.68). It is
also known that patients might exhibit non-adherence by not using the CPAP at all certain nights.
Regarding objective machine usage time, it is sometimes used as a continuous variable. This is
becoming more common, as CPAP devices record it and as it allows for statistical methods that
are used on interval data rather than categorical data.
We employed several approaches to adherence, and obtained similar results with them.
Side effects and adherence: Why would they be related? The side effects that have been reported are based on subjective experiences. For some side
effects, e.g., anxiety during treatment, this is the only possible approach, while other side effects
could, at least in theory, be assessed objectively (e.g., increased number of awakenings). The
idea behind examining side effects as a potential reason for non-adherence is that side effects
might increase the perceived cost of treatment. Some sort of ”cost of treatment” is involved in
several theoretical models of patient adherence. The Health Belief Model, for example, lists four
variables that will affect the likelihood of a subject engaging in health-related behaviours
(Carpenter, 2010):
1) Perceived susceptibility of a negative outcome (e.g., experienced sleepiness, perceived
susceptibility of cardiovascular events etc.).
2) Perceived severity of the negative outcome (e.g., death in the case of cardiovascular
events; anything from impaired daily functioning to car crashes in the case of daytime
sleepiness).
3) Belief in the efficacy of the health-related behaviour in question (in this case, using the
CPAP).
4) Perceived barriers related to the health-related behaviour. This is where side effects come
in.
The Trans-theoretical model, which has been used to explain CPAP adherence by Stepnowsky et
al., (e.g., Stepnowsky et al., 2006), is based on motivational readiness to adopt or cease a certain
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behavour. This motivational readiness is perceived as a continuous scale, and the patient’s
position along this scale might change over time depending on a cognitive ’balance sheet’ where
advantages and disadvantages are weighted against each other (Stepnowsky et al., 2002). Side
effects would then count as a potential disadvantage, thereby making patients less likely to be
adherent CPAP users.
Side effects can also be understood from the MET (Motivation to Engage in Treatment) model of
Drieschner (Drieschner, 2004). Based upon Social cognitive theory as well as the transtheoretical
model, he lists six determinants of treatment motivation is listed:
1) Level of suffering, e.g., symptoms from OSAS.
2) Outcome expectancy, e.g., a belief that daytime sleepiness will disappear.
3) External pressure, e.g., from a partner.
4) Perceived suitability of treatment, e.g., attitudes to CPAP (Broström et al., 2011).
5) Problem recognition, e.g., realising that one has SDB (Broström et al., 2010).
6) Perceived cost of treatment, to which side effects can be thought to belong.
No matter what model one chooses, side effects do not occur in isolation. They might be affected
by social support, e.g., from a spouse (Broström et al., 2010), by technical factors related to
treatment (e.g., humidification and heating of air); and they might also be affected by personality
factors, such as type D personality.
Type D personality
Definition and prevalence Type D personality was initially studied in cardiovascular research, where it was associated to
all-cause mortality in patients with coronary artery disease (Dennolet et al., 1996). In this paper,
Denollet et al. hypothesized that poor adherence might be one possible cause for this association,
but a lot of the later work trying to discern the relationship between type D personality and
mortality tended to focus on various biomedical stress-related risk factors and how these differed
in people exhibiting a type D personality (e.g., Habra et al., 2003). Our study regarding type D
personality as a factor affecting treatment adherence has later been confirmed in studies
concerning adherence to medication in myocardial infarction patients (Williams et al., 2011).
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We found a fairly high prevalence of type D personality in our study. 30 % of the total sample
fulfilled the criteria when using the cutoff values provided by Denollet (2005). These cutoff
values were chosen based on a median split of his validation sample. He motivated this by a
cluster analysis performed in patients with coronary heart disease, as well as median splits having
been used to delineate personality types in previous research (as discussed in Denollet et al.,
1996). The reported prevalence of type D personality in the original validation paper for DS14
ranged from approximately 20 % in normal controls to approximately 50% in hypertensive
subjects, implying that our prevalence figures are in line with what has been reported previously.
Besides, the use of median splits by Denollet when deciding on the cutoff levels for defining type
D personality means that, by definition, 50% of his subjects would have been high-NA (negative
affectivity) and 50% would have been high-SI (social inhibition) subjects. If the constructs are
independent, a prevalence of 25% would be expected.
Relationship to other personality constructs Several other personality constructs might be related to type D personality. The BIS/BAS model
of personality, for example, states that there are two basic motivational systems underlying
personality: the goal of the behavioural inhibition system (BIS) is to avoid negative
consequences (i.e., punishment), while the goal of the behavioural activating system (BAS) is to
seek reward (Carver & White, 1994). BIS is related to negative affectivity and anxiety, and high
levels of BIS is related to worse CPAP adherence (Moran et al., 2011). Both negative affectivity
and social inhibition are correlated to neuroticism (De Fruyt & Denollet, 2002), but they are not
identical constructs (Denollet 2000). While Moran et al., (2011) found a univariate correlation
between neuroticism and adherence, it did not retain statistical significance in their final model.
The DS14 has been examined using various psychometrical approaches. The original
development was based on classical test theory, but this has been combined with item response
theory in a more recent study (Emons et al., 2007), indicating that there is a high reliability in the
range around the cutoff. They did find some evidence of differential item functioning for
hypertension, but none that was considered practically significant.
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Putative mechanisms linking type D personality to adherence There are several potential mechanisms that might link type D personality to poor adherence. It
has been reported elsewhere that negative affectivity is associated to a higher degree of symptom
awareness. For example, Watson and Pennebaker (1989) found clear and consistent associations
between negative affectivity and health-related complaints by using a battery of different
symptom reporting scales. The tendency to experience more symptoms, including CPAP side
effect, could be understood in terms of an increased perceived cost of treatment in light of
Drieschner’s model (presented above). Besides, social inhibition might decrease the likelihood of
patients discussing their experienced side effects with the CPAP provider. Pelle et al., (2009)
found that cardiac failure patients with type D personality were almost two times as likely as
non-type D patients (OR 1.80 after adjusting for sociodemographic factors, etiology of heart
failure and disease severity) to fail to contact their care provider in case of worsened heart failure
symptoms. As there are potentially efficient countermeasures that can be applied for most CPAP
side effects, the combination of a higher tendency to experience side effects with failure to report
side effects might act together to worsen adherence.
It would be tempting to use SECI to screen for side effects, in order to counteract the potentially
lower tendency to report troublesome side effects in type D patients. After all, they did report
more side effects in the questionnaire. However, in the present study, SECI as well as the DS14
were mailed to the participants, and the authors were not directly involved in providing the care.
If patients are supposed to hand in the questionnaire to the nurse responsible for providing
CPAP treatment, there is a greater risk that type D patients will underreport side effects as
compared to when they get to answer SECI as part of a study.
Our study regarding the association between type D personality and adherence was cross-
sectional. One can expect that subjects suffering from daytime symptoms of a sleep-related
breathing disorder might be more prone to endorse the kind of questions that are asked in DS14,
due to their daytime sleepiness and fatigue (e.g., ”I am often irritated”, ”I am often in a bad
mood” and ”I am often down in the dumps”). Symptoms of sleep-disordered breathing might be
more prevalent among non-adherent users, and it might thus be possible that at least parts of the
expressed negative affectivity might be related to poor adherence rather than causing poor
adherence. It would be difficult to design a study that can answer this question definitely, but one
step toward further understanding of the relationship between the constructs of type D
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personality and CPAP adherence would be a prospective study, where DS14 would be used at
several points of time, both prior to CPAP initiation and at various stages of treatment. A study
like that would also be able to include patients that are new to CPAP. In the present study on type
D personality patients had had CPAP for at least six months. As we have shown that side effects
vary over time, it would be interesting to examine whether the association between type D
personality and side effects vary over time as well, and if it is different for different side effects.
Denollet showed, however, that mood did not affect DS14 scores (Denollet 2005).
Temporal evolution of side effects Regarding side effects, as has been mentioned, one important factor that has often been
overlooked in previous research has been whether side effects are stable over time or not. Based
on theoretical considerations, it can be argued that they are most likely not, especially in a
clinical sample. If a subject experiences a side effect that bothers him or her, s/he will likely
either ask for help or become less adherent. Many side effects can be dealt with effectively, e.g.,
by changing mask, adding a humidifier etc. Conversely, if patients stop using their CPAP, they
will stop experiencing side effects. In the light of existing countermeasures for many side effects,
studying their natural course without intervening would not be ethical. The study of side effects
over time allowed for patients to contact the provider to get help.
There are thus at least two possible ways that side effects might diminish over time in a clinical
sample. Patients might learn to cope with side effects (e.g., by interventions from the provider or
by applying other coping strategies), or patients experiencing a side effect might be more likely
to stop using the device. In that case, the absolute frequency of a given side effect would
decrease over time.
We found that the evolution of side effects over time is somewhat complicated. First of all, side
effects are not stable over time. They do resolve in some patients. Whether this is due to
interventions or not cannot be determined from the present study, as it was not designed to
examine causes for variations in side effects over time. It is likely that mask changes etc. might
affect side effect profiles. In some subjects, side effects also evolved over time. The evolution of
side effects over time was studied within subjects. In other words, in order to be included in the
analyses, subjects had to have been using the CPAP long enough to complete SECI at at the
points in time that were compared. Two statistical approaches were used to analyse side effects,
Cohens κ and McNemar’s test. Cohen’s κ was used to assess to which degree the existence of a
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side effect at one point in time correlated with the existence of the same side effect at a different
point in time, as compared to what would have been expected if side effects occurred randomly.
A κ of 0 would indicate that the variation in side effects over time within a subject is no more or
less than what could be expected from chance. In other words, the existence of a side effect at
one point of time has nothing to do with the existence of the same side effect, within the same
individual, at a later point of time. A κ of 1 would indicate that the side effect in question,
measured at different points of time, is perfectly correlated and thus stationary, i.e., it does not
emerge in those who do not suffer from it and it does not resolve in those who do. A κ of -1
would indicate that the side effect is perfectly inversely correlated to itself at a differnt point in
time, i.e., it resolves over time in all patients who suffered from the side effect at the beginning
and emerges in all patients who did not. This, of course, is highly unlikely, and the observed κ
values range from -0.07 to 0.42, indicating that most rleationships are positive but not very
strong. One reason for this could be that side effects resolve with time, e.g., due to the reasons
given above. McNemar’s test was used to test wether side effects differed in their frequency over
time. A significant p-value indicates that there is a difference in the prevalence of a side effect
between the two different measurements that are compared.
However, side effects do not only resolve. They also emerge. This is harder to explain, but there
might be reasons for this phenomenon as well. As sleep improves due to unobstructed breathing
during sleep, it is possible that side effects that were less likely to be perceived as disruptive to
sleep are noticed to a higher extent and thus reported to a higher extent. It might also be that
people lose weight or change their drinking habits as a result of having been diagnosed with a
disorder that is affected by BMI and alcohol consumption, and that their disease severity and the
pressure needed to alleviate the most severe daytime symptoms is reduced, thereby causing a
higher-than-needed pressure to be delivered. As some side effects are likely to be related to
CPAP pressure, this might lead to emergence of new side effects.
One of the main daytime symptoms that have been studied is sleepiness. If side effects can be
perceived as costs of treatment, daytime sleepiness might be perceived as contributing to the
level of suffering caused by the disorder. Measurement of sleepiness has attracted a lot of
attention, as it is a common concern with potentially great impact on traffic accidents (reviewed
in De Mello et al., 2013).
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Defining and measuring sleepiness A significant problem with measuring sleepiness is how to define it (Shen et al., 2006). It is
sometimes defined as the tendency of falling asleep, sometimes termed sleep propensity. This is
what the MSLT aims to measure (Carskadon et al., 1986). MSLT infers the propensity to fall
asleep from the time it takes for this to happen during specified circumstances. This is not the
same as sleep need. In insomnia, sleepiness as measured by the MSLT is not increased despite
poor nocturnal sleep, which is different from what is seen in experimentally sleep-deprived good
sleepers (Bonnet & Arand. 1997). It is also interesting to note, that while MSLT and MWT are
correlated, the correlation is only moderate. For example, Sangal et al. (1992), found a
correlation coefficient of 0.41 in 258 patients evaluated for sleep apnea or daytime sleepiness,
with a factor analysis indicating that two factors could explain 91% of the variance. It might be
argued that readiness to fall asleep is not the same as inability to stay awake. In light of the ESS,
this might raise concerns as to whether item 5 (”Lying down to rest in the afternoon when
circumstances permit”) measures the same construct as the others, as a subject lying down to rest
in the afternoon is not necessarily trying to stay awake, which is implied in most of the other
items. However, factor analysis of the ESS has not indicated that this item loads on a different
factor than others measuring sleep in more active situations (Johns, 1992).
It might furthermore be reasonable to differentiate between sleep propensity and drowsiness, as
there are patients who report drowsiness without being obviously sleepy when sleepiness is
measured objectively. Åkerstedt (1998) describes sleepiness as an active attempt by the nervous
system to go to sleep, from which it follows that a person who is not fighting sleep would not
experience sleepiness (Åkerstedt, 1998; Cluydts et al., 2002). Accident risk is related to
subjective sleepiness, but with large interindividual differences (Ingre et al., 2006). While there is
a relationship between subjective sleepiness and performance, it might thus be suspected that
they are not simply the same construct.
Epworth Sleepiness Scale ESS is based on the concept of soporificity, which is ”the extent to which any particular activity
or situation facilitates dozing in most subjects” (Johns, 1994) and a four-process model of
sleepiness consisting of antagonistic sleep- and wake-promoting processes (Johns, 1998). The
items were chosen to represent different degrees of soporificity, based, mostly it seems, on
theoretical considerations (the item generation process is not described in detail). The assumption
that the described situations are indeed different regarding their soporificity is corrobated by the
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Rasch analysis by us as well as by others (Hagell & Broman, 2007; Violani et al., 2003).
Comparing these three studies show similar results.
Table 5: Item hierarcy in different studies of the Epworth Sleepiness Scale
Ulander et al (2013) Hagell & Broman (2007) Violani et al., (2003)
Lying down in afternoon Lying down in afternoon Lying down in afternoon
Watching TV Watching TV Watching TV
Sitting and reading Sitting and reading Sitting and reading
Passenger in a car 1h Passenger in a car 1h Sitting quietly after lunch
Sitting quietly after lunch Sitting quietly after lunch Passenger in a car 1h
Inactive in public place Inactive in public place Inactive in public place
Talking to someone In a car that has stopped In a car that has stopped
In a car that has stopped Talking to someone Talking to someone
Most items are arranged in similar order, except for items 4 and 7 that are reversed in the study
by Violani et al. (2003), and items 6 and 8 that are reversed in our study. Those are boldfaced in
the table. Another finding that has been reproduced is the gap in item difficulty between items 6
and 8 and the other items. This might explain the results from factor analyses of ESS, as a
confirmatory factor analysis indicated that a two-factor solution with items 6 and 8 loading on a
second factor provided a better fit (Smith et al., 2008). A consequence of this could be that
relationships between ESS and other measurements of daytime sleepiness are not linear, as an
increase in one point on ESS has different meanings depending on which item the increase
affects. Interestingly, Sangal et al., (1999) found that a cubic model is best at explaining the
association between ESS and MWT (ESS was the dependent variable). While a cubic model has
more freedom to ”wiggle” and thus might provide better fit for purely mathematical reasons, it is
noteworthy that they found a downward slope at very low MWT values (to approximately 2.5
mins) followed by a plateau at ESS=18 (i.e., the score that one would get by indicating a high
chance of dozing in all situations except for two), and then again a downward slope as MWT
reached 12.5 minutes.
We used two ways to assess DIF in the ESS: ordinal regression and Rasch analysis. They both
gave similar results. The findings were most clear for age, which was a cause of DIF in items 3, 4
and 8. Older people tended to score lower on items 3 and 4, and higher on item 8. In this study
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’old’ was defined as being older than 65 years. This cutoff was chosen as it would most likely
represent retirement age. It was assumed that lifestyle changes occuring due to retirement would
cause differences in how the situations described in the ESS are handled. Items 3 and 4 (”Sitting
inactive in a public place, e.g., a theater or a meeting” and ”As a passenger in a car for an hour
without a break”) might represent situations that sleepy elderly people stop exposing themselves
to as a result of being sleepy. Regarding item 8, it is noteworthy that it does not specify whether
the respondent is a passenger or a driver. It is possible, although it has not been studied
explicitly, that people who regularly drive answer the question from that perspective, while
elderly people, as a result of driving less, answer the question from the perspective of a
passenger. No doubt, being a passenger is more soporific than being a driver, and this might
affect the psychometric properties of the item. This also raises the question whether having a
drivers’ license might be a cause of DIF in item 8. This has not been studied.
Regarding the gender DIF, it was only found in the Rasch analysis, and the women were
significantly older than the men. This raises some doubts as to whether this represents true DIF,
or whether it is a spurious finding.
Both models had some issues with misfit. This might be due to sample size. Both models,
however, produced similar results, i.e., DIF in the same items with regard to age. There are
reasonable reasons as to whether these items would show DIF. Another study that performed a
Rasch analysis showed DIF for age in items 1, 2, 4 and 8 when a cutoff at 40 years was used
(Martinez et al., 2011). A visual inspection of the item characteristic curves indicate that model
fit is reasonable.
The DIF presented is fairly small, meaning that it is probably not affecting the judgement of
individual ESS scores or smaller studies. It might be a reason for concern in larger studies,
however.
Future research The studies presented in this thesis only elucidate a small part of the vast field that is CPAP
adherence in OSAS. Further reserach is warranted, and ongoing.
Regarding CPAP side effects, further research will continue to develop and validate SECI with
regard to scoring procedure and the development of side effects over time. Most models of
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adherence in some way assume that whether a patient engages in treatment or not is a
consequence of a rational choice, where the advantages and disadvantages of different
behaviours are weighed against each other. While there might be an ongoing rational process in
patients deciding to adopt a behaviour or not, it is likely not the whole truth. A lot of behaviours,
when repeated over time in a specific context, develop into habits. Central to the concept of
habits is automaticity, i.e., that the behaviour is performed more or less without thinking (Aarts et
al., 1998). Automaticity is not included in any of the models reviewed here, but might be
important to understand health-related behaviours (Nilsen et al., 2013). It is possible that
formation of habits might affect the association between side effects and adherence. It will also
be important to look further into how various interventions affect the perception of side effects in
CPAP patients.
Regarding personality and adherence, prospective longitudinal studies are important to further
elucidate what effects personality might have on adherence and how personality factors can be
taken into account in clinical CPAP care. It is also possible that habit-forming might be related to
personality traits, and thus that personality might affect the ability to make CPAP use into a
habit.
Regarding the ESS, several questions remain unanswered. One is related to the association
between ESS and other measurements of sleepiness. While there is a correlation, it is modest at
best. One reason might be that various tests actually measure different aspects or dimensions of
sleepiness. Another might be that the items of the ESS are not evenly distributed with regard to
difficulty, and thus that changes in ESS socres are dependent on where they occur. It is possible
that this could be used to improve the correlation between ESS and objective measurements of
daytime sleepiness, but it has not been studied. Knowledge about item difficulty distribution and
hierarchy might also be employed in studying what ESS score changes are clinically significant.
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CONCLUSIONS Aim 1: To describe the development and initial testing of a new instrument, the side effects to
CPAP inventory (SECI).
Conclusion: SECI is a reliable and valid instrument to assess side effects of CPAP treatment in
patients with OSA. SECI scores are, as would be expected, higher among non-adherent users
than among adherent users.
Aim 2: To measure the frequency and magnitude of CPAP side effects, as well as their impact on
CPAP use.
Conclusion: Both study I and IV show that side effects might have an impact on CPAP use. The
most frequently occuring side effects in study I were dry throat and mask-related problems. In
study IV, the most severe and frequently occuring side effects were dry mouth, blocked up nose,
increased number of awakenings and mask-related problems at all points of time.
Aim 3: To describe the prevalence of type D personality in OSAS patients with CPAP treatment
longer than 6 months and the association with self-reported side effects and adherence.
Conclusion: Approximately 30% of the patients fulfilled the criteria for type D personality.
These patients experienced more side effects and had lower adherence to treatment.
Aim 4: To examine whether any of the items in the ESS exhibits DIF with regard to age or
gender, and if so, to which degree.
Conclusion: Items 3, 4 and 8 exhibited DIF for age both in a Rasch analysis and when ordinal
regression was used. In the Rasch analysis, but not the ordinal regression, item 3 also exhibited
DIF for gender. The DIF for gender might, however, be a spurious finding related to an age
difference between men and women in the sample. Generally, the DIF were small, and are
probably not clinically relevant in individual patients, although they might be relevamt in large
studies.
Aim 5: To examine the evolution of CPAP side effects over time.
Conclusion: Side effects can both resolve and emerge over time. While patients reporting
anxiety during treatment after 1-2 weeks on CPAP either dropped out or stopped experiencing it,
other side effects, such as upset bowel, became more common after a 9-12 months than after 1-2
weeks. More research is needed to explore these changes over time.
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Aim 6: To prospectively assess correlations between early CPAP side effects and treatment
adherence.
Conclusion: Patients reporting dry mouth or increased number of awakenings after 1-2 weeks
had significantly lower adherence. Transient deafness, difficulties exhaling and anxiety during
treatment were related to poor adherence measured as drop-out rate during the first year, as were
dry mouth, feeling uncomfortable about using the CPAP in front of others, increased number of
awakenings and problems exhaling as significant problems after 1-2 weeks when stratifying for
treatment centre. No associations were found for other side effects. The conflicting results that
have been reported previously are probably related to different definitions of side effects and
adherence.
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POPULÄRVETENSKAPLIG SAMMANFATTNING Obstruktivt sömnapnésyndrom (OSAS) är en kronisk sjukdom som kännetecknas av att de övre
luftvägarna faller samman och hindrar andning under sömn. Detta ger upphov till
andningsuppehåll, som ger dagsymptom (sömnighet, trötthet med mera) och öka risken för att på
sikt drabbas av bland annat högt blodtryck och hjärtsjukdomar. OSAS kan ofta behandlas
framgångsrikt, till exempel med CPAP (Continuous Positive Airway Pressure). Detta är en
maskin som via en mask skapar ett övertryck i de övre luftvägarna, som hålls öppna och tillåter
fri andning. Behandlingen kan eliminera såväl andningsuppehållen som deras negativa
konsekvenser. Trots detta använder många patienter inte CPAP-maskinen som tänkt. Patienter
anger bland annat sidoeffekter (exempelvis tryck av masken, muntorrhet eller dålig sömn) som
orsaker, men tidigare forskning har visat motsägelsefulla resultat om detta verkligen kan förklara
bristande följsamhet. Den här avhandlingen består av fyra studier i vilka vi sökt ökad kunskap
om vilka faktorer som påverkar följsamhet till CPAP-behandling hos patienter med OSAS. Fokus
har legat på sådana faktorer som kan mätas med frågeformulär. I avhandlingen har vi utarbetat ett
eget frågeformulär för att mäta sidoeffekter till CPAP-behandling. Det har inte funnits förut, och
en anledning till att tidigare studier gett motstridiga resultat kan vara att de definierat och mätt
sidoeffekter och följsamhet på olika sätt. SECI, Side Effects to CPAP treatment Inventory,
innehåller en lista med 15 vanliga sidoeffekter, som patienten ombeds skatta på en skala på en
femgradig skala avseende hur ofta biverkningen förekommer, hur allvarlig den är och i vilken
utsträckning den påverkar deras följsamhet. Vi har i två studier i avhandlingen visat att SECI
både kan skilja mellan följsamma och icke följsamma patienter, och att man utifrån SECI delvis
(men inte helt) kan förutsäga vilka patienter som kommer att bli följsamma utifrån tidig
biverkningsprofil. Vi anser oss kunna svara på frågan om kopplingen mellan biverkningar och
följsamhet: vissa biverkningar, som ökat antal uppvaknanden och muntorrhet, är betydelsefulla
för följsamheten, medan andra inte är det. En del biverkningar, som oro under behandlingen eller
störande ljud, kan påverka följsamheten beroende på hur man undersöker följsamhet. Ytterligare
andra biverkningar tycks inte påverka följsamheten. Vidare såg vi att sidoeffekter delvis varierar
över tid, dvs det kan både tillkomma och försvinna sidoeffekter. Detta har inte studerats tidigare.
I en delstudie har vi studerat förekomsten av typ D-personlighet (D står för ’distressed’), vilket
kännetecknas av en ökad benägenhet att uppleva negativa känslor och att man är hämmad eller
tillbakadragen i sociala sammanhang. Var och en för sig utgör dessa personlighetsdrag inte typ
D-personlighet, utan endast när de förekommer tillsammans i tillräcklig grad. Ungefär 30% av
deltagarna i den studien, som också var CPAP-behandlade patienter med OSAS, uppfyllde
76
kriterierna för typ D-personlighet (det avgörs via ett frågeformulär). Dessa personer upplevde fler
biverkningar av sin CPAP-behandling och använde CPAP-maskinerna i mindre utsträckning än
övriga.
I ett av delarbetena fokuserade vi på ett frågeformulär, Epworth Sleepiness Scale (ESS), som ofta
används inom sjukvård och forskning för att mäta dagsömnighet. Patienten får ange risken att
han/hon skulle somna i åtta olika situationer på en fyragradig skala från 0 till 3, där höga siffror
motsvarar en stor upplevd sannolikhet att somna. Dessa siffror summeras sedan och ger en poäng
mellan 0 och 24. ESS har fått väldigt stor spridning sedan det utvecklades i början av 1990-talet,
eftersom det är enkelt och billigt. Det har dock kritiserats på flera grunder, bland annat för
urvalet och formuleringen av de situationer i vilka man ska skatta risken att somna. En del av
dessa situationer kan man tänka sig påverkar äldre och yngre, eller män och kvinnor, olika. Med
olika statistiska metoder har vi undersökt om så är fallet. Data bestod av 1,175 personer som fått
fylla i frågeformuläret i tidigare studier. Vi fann att personer över 65 svarade annorlunda på tre
av frågorna än personer under 65 år. Det är inte det samma som att personer över 65 är mer
sömniga, utan även om man tar hänsyn till individuella skillnader i sömnighet kvarstår en
tendens att en person över 65 år svarar annorlunda än en lika sömnig person under 65 år. En
möjlighet är att pensionärer hanterar dagsömnighet annorlunda än yngre, kanske i högre
utsträckning genom att undvika vissa situationer. Skillnaderna i hur man svarar på
frågeformuläret är för små för att ha betydelse på individnivå, men kan ha betydelse för hur man
tolkar resultat av stora studier där många personer ingår.
77
ACKNOWLEDGEMENTS No thesis is the result of the work of a single person. Too many people have supported me throughout these years for it to be possible to mention every single one of them. There are, however, a few that I would like to take this opportunity to thank especially. First of all professor Eva Svanborg, my main supervisor, clinical tutor and head of clinic, for waking up my interest in sleep medicine and sleep research, for providing me with generous research opportunities that few clinically active physicians have. Thank you for all your support throughout these years, for your hospitality and kindness, and for sharing your immense knowledge in sleep and related areas. I would also like to thank professor Anders Broström, my co-supervisor and research colleague for all your enthusiasm, creativity and deep and broad methodlogical knowledge, spanning from phenomenography to structural equation modeling, but also for your encouragement and support with everything from discussing research ideas to taking me to concerts and movies that I would certainly not have seen otherwise. David Lorr, for being a constant source of inspiration and new ideas, as well as for your hospitality. Kristofer Årestedt, my research colleague and statistical discussion partner, for helpful comments and fruitful cooperations in more papers than are included in the thesis, and more to come. Furthermore, I would like to thank Peter Johansson at the dept of Cardiology and Per Nilsen at the department of Social medicine for introducing me to new fields of research and new concepts. I look forward to a future fruitful collaboration. Anna Strömberg, Jan Mårtensson, Malin Svensson Johansson and Amanda Ekegren Ewaldh for fruitful research collaboration, and Lena Harder for fruitful research collaboration and for introducing me to polygraphy. All patients who have been involved in the research. It would not be possible without you. My future and previous research colleagues in Jönköping, including Ola Sunnergren at the ENT department, and colleagues at the ENT and Pulmonology departments. My colleagues and friends at the Department of Clinical Neurophysiology at the University Hospital in Linköping including the CPAP unit. My international colleagues and hopefully future research partners, especially Harald Hrubos Strøm, Oslo, for valuable and stimulating discussions and hospitality. My friends, especially Emma Colnerud Nilsson, Per Hellman and Hans Petersson, who have given me well needed opportunities to relax from research and clinical work. And last, but definitely not least, my dear and beloved family, my parents Bengt and Ulrike, my sisters Nina, Veronica and Jenny and your respective families; Peter, Rich, Fredrik, Simon, Joel, Alexander, Harry and Märta. Words are not enough to express my love for you.
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