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Mental Health, Sleep, Physical Activity and Family Functioning in Adolescents with Narcolepsy by Arpita Parmar A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto © Copyright by Arpita Parmar 2018
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Mental Health, Sleep, Physical Activity and Family Functioning
in Adolescents with Narcolepsy
by
Arpita Parmar
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Institute of Medical Science
University of Toronto
© Copyright by Arpita Parmar 2018
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Abstract
Mental Health, Sleep, Physical Activity and Family Functioning in
Adolescents with Narcolepsy
Arpita Parmar
Master of Science
Institute of Medical Science
University of Toronto
2018
Background: Pediatric narcolepsy is a lifelong sleep disorder that is associated with depressive
symptoms. The factors associated with depressive symptoms are unclear. Research on family
functioning in pediatric narcolepsy is also limited. The primary objective of this study was to
evaluate sleep and physical activity (PA) as factors associated with depression scores in
adolescents with narcolepsy and controls. The secondary objective was to assess family
functioning in pediatric narcolepsy Methods: Adolescents with narcolepsy and controls were
recruited from the Hospital for Sick Children and Toronto area respectively. Participants wore an
actigraph and pedometer to measure sleep and PA respectively. Depression scores were evaluated
with the Children’s Depression Inventory-2. Family Functioning was assessed using the PedsQL
Family Impact Module Results: Sixty adolescents (30 narcolepsy; 30 controls) participated. Poor
sleep quality, excessive daytime sleepiness, and low PA levels were associated with greater
depression scores. Family functioning was impaired in pediatric narcolepsy patients.
III
Acknowledgements
First and foremost, I would like to thank my supervisor, Dr. Indra Narang for agreeing to
take me on as graduate student and providing me with the opportunity to work with the Pediatric
Sleep and Respiratory Medicine team at The Hospital for Sick Children. I am sincerely grateful
for her endless support, encouragement, guidance and strong mentorship which has been
conducive to my professional and personal growth.
I would also like to express my gratitude towards the members of my program advisory committee
members, Dr. Ann Yeh and Dr. Daphne Korczak, who have provided their expertise, guidance and
support throughout my masters.
Furthermore, I would like to thank Dr. Brian Murray for always being available to provide
his expertise and support with this project. I would also like to thank the sleep clinical team who
facilitated recruitment of the narcolepsy patients (Dr. Shelly Weiss, Dr. Alene Toulany, Dr. Suhail
Al-Saleh, Dr. Reshma Amin, Dr. Kevan Mehta, Dr. Kevin Vezina, Dr. Anya McLaren, Dr.
Saadoun Bin-Hasan, Allison Zweerink and Bridget Doan-Perera). Also a special thanks goes to
Sarah Selvadurai, who personally taught me how to accurately score and analyze actigraphy data,
and provided great support and insight as a fellow graduate student. I would like to acknowledge
members of the sleep research team (Tanvi Naik, Nishtha Pandya and Giorge Voutsas) who have
been helpful, supportive and an overall pleasure to work with. I would also like to give a big thanks
to the adolescents and families who participated in the study. Without these individuals,
conducting this study would have not been possible.
Finally, I would like to thank my family and friends for their unconditional love and support
throughout my Masters and beyond.
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Table of Contents
Abstract ........................................................................................................................................................ II
Acknowledgements ..................................................................................................................................... III
Table of Contents ........................................................................................................................................ IV
List of Tables .............................................................................................................................................. VI
List of Figures ........................................................................................................................................... VII
List of Abbreviations ............................................................................................................................... VIII
List of Appendices ...................................................................................................................................... IX
Chapter One: General Introduction ............................................................................................................... 1
1.1. Overview………………………………………………………………………………………..1
1.2. Thesis Structure……………………………………………………………………………..…..3
Chapter Two: Literature Review................................................................................................................... 4
2.1. Pediatric Narcolepsy ................................................................................................................... 4
2.1.1. Diagnosis and Symptoms .................................................................................................... 4
2.1.2. Diagnostic Tools ................................................................................................................. 8
2.1.3. Pathophysiology ................................................................................................................ 13
2.1.4. Epidemiology .................................................................................................................... 17
2.1.5. Treatment of Pediatric Narcolepsy ................................................................................... 19
2.1.5.1 Pharmaceutical Treatment…………………………………...............................................19
2.1.5.2 Non-pharmacological Treatment……………………………….......…………………....23
2.1.6 Comorbidities…………………………………….…………………………………...….24
2.2. Adolescent Mental Health ......................................................................................................... 25
2.2.1. Consequences of Adolescent Depression ......................................................................... 30
2.2.2. Biological Contributors to Depressive Symptoms in Narcolepsy..................................... 31
2.2.3. Factors Associated with Depressive Symptoms in Naroclepsy………………………….33
2.3. Sleep, Physical Activity and Mental Health…………………………………………………...34
2.4 Family Functioning in Pediatric Narcolepsy ............................................................................. 40
2.5 Summarty of Gaps in the Literature .......................................................................................... 43
Chapter Three: Rationale, Objectives and Hypotheses ............................................................................... 44
3.1. Rationale .................................................................................................................................... 44
3.2. Objectives and Hypotheses ........................................................................................................ 45
Chapter Four: Methodology ........................................................................................................................ 46
4.1. Overview………………..
4.1.1. Ethics ................................................................................................................................ 46
4.2. Study Population ....................................................................................................................... 46
4.3. Eligibility Criteria ...................................................................................................................... 47
4.3.1. Inclusion Criteria .............................................................................................................. 47
4.3.2. Exclusion Criteria ............................................................................................................. 48
4.4. Study Procedures ...................................................................................................................... 49
4.5. Study Measures ......................................................................................................................... 51
4.5.1. Outcomes .......................................................................................................................... 51
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4.5.2. Exposures .......................................................................................................................... 53
4.5.3. Covariates ......................................................................................................................... 55
4.5.4. Actigraphy Monitoring of Sleep ....................................................................................... 56
4.5.4.1 Scoring and Data Analysis………………….………………………………………….…57
4.5.4.2 Transition Probabilities …………………………………………………….……………59
4.5.5. Pedometers: An Objective Measure of Physical Activity………………………………..61
4.6. Sample Size Calculation ............................................................................................................ 61
4.7. Statistical Analyses .................................................................................................................... 62
Chapter Five: Results .................................................................................................................................. 64
5.1. Demographics ............................................................................................................................ 64
5.2. Mental Health Outcomes: Depression Scores and Anxiety Scores ........................................... 67
5.3. Sleep Patterns ............................................................................................................................ 69
5.4. Physical Activity Levels ............................................................................................................ 71
5.5. The Association between Mental Health and Sleep Patterns .................................................... 72
5.6. The Association between Mental Health, Sleep Patterns and Physical Activivity…...…….....78
5.7. Family Functioning in Pediatric Narcolepsy ............................................................................ 82
Chapter Six: Conclusions and General Discussion ..................................................................................... 86
6.1. Main Conclusions ...................................................................................................................... 86
6.2. Specific Findings ....................................................................................................................... 87
6.2.1. Mental Health in Pediatric Narcolepsy ............................................................................. 87
6.2.2. The Association between Sleep Patterns and Depressive Symptoms ............................... 89
6.2.3. Physical Activity in Pediatric Narcolepsy ........................................................................ 94
6.2.4. The Association between Physical Activity, Sleep Patterns and Depressive Symptoms . 96
6.2.5. Family Functioning in Pediatric Narcolepsy .................................................................... 98
6.3. Strengths and Limitations ....................................................................................................... 100
6.4. Future Directions for Research ................................................................................................ 102
References ................................................................................................................................................. 103
Appendices ................................................................................................................................................ 118
Supplementary Data……………………………………………………………………………….….….121
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List of Tables
Table 1: Diagnostic Tests in Narcolepsy…………………………………………………………8
Table 2: Pharmaceuticals Used in Treating Pediatric Narcolepsy………………………………22
Table 3: Rates of Depressive Symptoms and Anxiety in Pediatric Patients with Narcolepsy….29
Table 4: Questionnaires Completed at Research Visit……………………………………….…49
Table 5: Log/Diary Completed at Home…………………………………………………….….50
Table 6: Actigraph Sleep Measures and Definitions…………………………………………....58
Table 7: Participant Characteristics…………………………………………………………..…66
Table 8: Depression Scores and Anxiety Scores in Adolescents with Narcolepsy and Controls.68
Table 9: Sleep Patterns in Adolescents with Narcolepsy and Controls………………………....70
Table 10: Physical Activity Data in Adolescents with Narcolepsy and Controls…………….....71
Table 11: Associations between Sleep Patterns, Depression Scores and Anxiety Scores……....73
Table 12: Multiple Regression Analysis Results: Sleep Quality and Depression Scores……….76
Table 13: Multiple Regression Analysis Results: Excessive Daytime Sleepiness and Depression
Scores…………………………………………………………………………………………….77
Table 14: Association between Physical Activity Levels, Sleep Patterns and Depression/Anxiety
Scores in Both Groups…………………………………………………………………………...79
Table 15: Association between Physical Activity Levels, Sleep Patterns and Depression/Anxiety
Scores in the Narcolepsy and Control Group………………………………………………...….80
Table 16: Multiple Regression Analysis Results: Self-Reported Physical Activity and
Depression Scores………………………………………………………………………………..81
Table 17: PedsQL Family Impact Module Scores………………………………………………82
Table 18: Family Functioning in Pediatric Narcolepsy Compared to Other Disease Groups…..84
VII
List of Figures
Figure 1: Narcolepsy Patients Presenting with Cataplectic Facies………………………………6
Figure 2: Cataplexy across Differing Levels of Severity………………………………………...7
Figure 3: Hypnogram (A Form of PSG) Comparing a Healthy Individual to a Patient with
Narcolepsy………………………………………………………………………………………...9
Figure 4: MSLT of Patient with Narcolepsy……………………………………………………10
Figure 5A: Distribution of Hypocretin Neurons (Black Spots) in a Normal Brain Compared to a
Narcolepsy Brain……………………………………………………………………….………..15
Figure 5B: Graph Showing Decreased Hypocretin Neurons in a Narcolepsy Brain Compared to
a Normal Brain…………………………………………………………………………….……..15
Figure 6: Pathways Showing Mechanism of Cataplexy In Narcolepsy…………………………16
Figure 7: Pediatric Quality of Life Family Impact Module Total Scores Across other Pediatric
Conditions………………………………………………………………………………………..42
Figure 8: Scored Actogram of One Day for Control (17 year old female)……………………..58
Figure 9: Plot of pRA(t) and pAR(t)……………………………………………………………60
Figure 10: Conceptual Framework for Primary Objective…………………………….………..63
Figure 11: Participant Flow for Narcolepsy Group……………………………………………..64
Figure 12: Correlation Analysis between CDI-2 Total Scores and Pittsburgh Sleep Quality Index
Scores…………………………………………………………………………………………….74
Figure 13: Family Impact Scores in Pediatric Narcolepsy Compared to Other Disease Groups.85
VIII
List of Abbreviations
ADHD attention-deficit hyperactivity disorder
BMI body mass index
CBCL Child Behaviour Checklist
CDI Children’s Depression Inventory
CDI-2 Children’s Depression Inventory-2nd edition
DSM Diagnostic and Statistical Manual of Mental Disorders
EDS Excessive daytime sleepiness
EEG Electroencephalogram
Epworth Scale Adapted Epworth Sleepiness Scale
HLA Human leukocyte antigen
HRQOL Health-related quality of life
MSLT Multiple sleep latency test
MET Metabolic equivalent
NREM Non-rapid eye movement
PA Physical activity
PSG Polysomnography
PSQ Pediatric Sleep Questionnaire
REM Rapid eye movement
SCARED Screen for Child Anxiety Related Disorders
SOREMP Sleep onset rapid eye movement period
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List of Appendices
1. Phillips Actiwatch-2…………………………………………………………………….……118
2. Omron HJ-720ITCCAN pocket pedometer………………………………………………….119
3. Contributions………………………………………………………………………………...120
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Chapter One: General Introduction
1.1 Overview
Narcolepsy is a devastating lifelong sleep disorder with typical onset during adolescence
(Scammell, 2015). There has been an increase in the incidence of pediatric narcolepsy in the last
decade, particularly amongst adolescents (Markku Partinen et al., 2012). Since 2010, the
incidence of pediatric narcolepsy was 17 times higher (5.30 diagnoses per 100 000) than the
average incidence between 2002 and 2009 (0.31 diagnoses per 100 000) (Markku Partinen et al.,
2012). The hallmark feature of narcolepsy is excessive daytime sleepiness (EDS), which includes
irresistible sleep episodes known as sleep attacks (Scammell, 2015). Sleep can occur at
inappropriate times (e.g., in public places, while socializing or eating) significantly impacting daily
function (Viorritto, Kureshi, & Owens, 2012). Cataplexy is also common in narcolepsy and is
defined as the sudden loss of muscle tone in either the face, neck, limbs and trunk causing
weakness and/or complete collapse (Nevsimalova, 2014). Nocturnal hallucinations and sleep
paralysis—awakening unable to move, are further disturbing symptoms (Viorritto et al., 2012).
Patients with narcolepsy also experience disturbed nocturnal sleep (Serra, Montagna, Mignot,
Lugaresi, & Plazzi, 2008), which is characterized by frequent awakenings and sleep fragmentation
(Roth et al., 2013). Disturbed nocturnal sleep may contribute to patients with narcolepsy feeling
sleep deprived and unrested throughout the day, exacerbating the severity of daytime symptoms
(Viorritto et al., 2012).
A common co-morbidity in pediatric narcolepsy is mental illness, specifically depression
and anxiety (K. Maski et al., 2017). If left untreated, depression can have serious consequences
such as poor school performance, obesity and suicide (Glied & Pine, 2002; X. Liu, 2004). The
factors that are associated with depressive symptoms amongst adolescents with narcolepsy remain
largely unknown. Sleep patterns (duration and quality) and physical activity (PA) levels are factors
that are associated with depressive symptoms amongst otherwise healthy adolescents (Lofthouse,
Gilchrist, & Splaingard, 2009) (Smagula, Stone, Fabio, & Cauley, 2016) (Biddle & Asare, 2011),
however this association has not been examined amongst adolescents with narcolepsy. This
knowledge is important for clinical teams working to improve overall clinical management of
adolescents with narcolepsy.
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Furthermore, pediatric illnesses are known to negatively impact the entire family’s
functioning (Giallo, Roberts, Emerson, Wood, & Gavidia-Payne, 2014), however research on
family functioning in the narcolepsy population is limited. This knowledge may provide valuable
insight for clinical teams providing family centered care to patients.
The objectives of this theses are to:
1) Examine if sleep patterns (duration and quality), EDS and physical activity levels are
associated with depression scores as the primary outcome and anxiety scores as the
secondary outcome in adolescents with narcolepsy compared with healthy adolescents
2) Describe family functioning in adolescents with narcolepsy, in comparison to otherwise
healthy adolescents (not seeking medical care) and other pediatric conditions
3) Examine the association between family functioning and depression and anxiety scores
in adolescents with narcolepsy
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1.2 Thesis Structure
Chapter two is the literature review, which outlines several key integrated concepts that
produce the background of this thesis. The first section presents an overview of pediatric
narcolepsy and discusses the diagnoses and symptoms, diagnostic tools, pathophysiology,
epidemiology, treatment and comorbidities associated with narcolepsy. Chapter two also discusses
current knowledge on mental health in adolescents with narcolepsy and otherwise healthy
adolescents, and the relationship between mental health, sleep and physical activity levels. This
section also discusses current knowledge on family functioning in pediatric narcolepsy. Chapter
three presents the rationale, objectives and study hypotheses. Chapter four outlines the
methodology, including the eligibility criteria for the study population and study design. This
section also describes the study measures in detail, which includes actigraphy, a pedometer and
validated questionnaires to assess depression scores, anxiety scores and family functioning in
pediatric populations. Chapter five summarizes study results, where sleep patterns, physical
activity levels, depression and anxiety scores, and family functioning were assessed amongst
adolescents and controls. The factors associated with depressive symptoms are also described.
Family functioning amongst adolescents with narcolepsy was also compared to a community
sample (not seeking medical care) and other pediatric conditions. Chapter six is the conclusion
and the general discussion, strengths and limitations and avenues for future research related to this
thesis.
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Chapter Two: Literature Review
2.1 Pediatric Narcolepsy
2.1.1 Diagnosis and Symptoms
Narcolepsy is a sleep disorder that typically onsets between the ages of 10 and 20 years
(Scammell, 2015). The mean delay in diagnosis in pediatric narcolepsy is approximately two years
(Dias Costa et al., 2014), however can be longer than ten years in some adults (Thorpy & Krieger,
2014). The time between disease onset and diagnosis can be a challenge in narcolepsy because of
its rarity and lack of symptom recognition (Thorpy & Krieger, 2014).
The International Classification of Sleep Disorders Diagnostic and Coding Manual (3rd
edition) describes narcolepsy as a central disorder of hypersomnolence, and divides narcolepsy
into two categories: Type 1—narcolepsy with cataplexy and Type 2—narcolepsy without
cataplexy (AASM, 2014). Cataplexy is the sudden loss of muscle tone in either the face and neck
and/or limbs and trunk, which leads to weakness and the loss of voluntary muscle control
(Nevsimalova, 2014). Episodes of cataplexy are triggered by strong emotions, which are usually
positive (e.g., laugher) but can also be negative (e.g., anger, fear) (Scammell, 2015). Episodes of
cataplexy also vary in severity and range from cataplectic facies, which includes repetitive mouth
opening, tongue protrusion, and drooping eyelids (Nevsimalova, 2014; Viorritto et al., 2012) (See
Figure 1). Complete cataplexy may result in weakness and loss of tone in larger muscles of the
limbs and trunk which cause patients to collapse (See Figure 2). Patients with narcolepsy also
remain entirely conscious during episodes of cataplexy (Nevsimalova, 2014).
The hallmark feature of both type 1 and 2 narcolepsy is excessive daytime sleepiness
(EDS), which must persist daily for greater than three months (Babiker & Prasad, 2015). EDS is
characterized by the sudden onset of irresistible sleep episodes known as sleep attacks. Sleep
attacks can occur at inappropriate times (e.g., in public places, while socializing or eating)
significantly impacting daily functioning (Viorritto et al., 2012).
Other features of both types of narcolepsy include hypnagogic/hypnopompic
hallucinations, which is dreamlike imagery that is exclusive to falling asleep and/or awakening
(Peterson & Husain, 2008). Hypnagogic hallucinations occur just before falling asleep and
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hypnopompic hallucinations occur upon waking (Peterson & Husain, 2008). Hallucinations occur
in up to 50 per cent of pediatric patients with narcolepsy and are multi-sensory (e.g., auditory,
visual, kinetic). In pediatric patients with narcolepsy, hallucinations are not usually frightening
and have simple forms (e.g., colored circles, images of animals or people) that can appear to be
very realistic (Peterson & Husain, 2008). Hallucinations also frequently occur in relation to sleep
paralysis (Guilleminault & Pelayo, 2000). Sleep paralysis is the temporary inability to move
voluntary muscles during sleep-wake transitions, while being conscious (Viorritto et al., 2012).
Episodes of sleep paralysis can last in a duration of seconds or minutes and end spontaneously,
which can be frightening and confusing for young patients (Viorritto et al., 2012). Sleep paralysis
can also be interrupted by physical or verbal contact with the affected individual (Stores, 2009).
Disturbed nocturnal sleep is also a common symptom in narcolepsy and occurs in
approximately 89 per cent of pediatric narcolepsy patients (Serra et al., 2008). Disturbed nocturnal
sleep is characterized by nightly awakenings and greater periods of wakefulness after sleep onset
resulting in sleep fragmentation and overall poor sleep quality (Roth et al., 2013). Disturbed
nocturnal sleep results in reduced sleep efficiency which is the amount of time spent sleeping
divided by to the time spent lying in bed (Xu et al., 2017). Distributed nocturnal sleep may also
cause patients with narcolepsy to feel unrested in the morning and throughout the day,
exacerbating the severity of daytime symptoms (Viorritto et al., 2012).
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Figure 1: Narcolepsy Patients Presenting with Cataplectic Facies: (A) Facial weakness, onward
head and trunk flexion with active shoulder raising (B) head drop and opposing neck
hyperextension (C) Mouth opening and tongue protrusion. Figure adapted from Postiglione et al
(Postiglione et al., 2017).
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Figure 2: Cataplexy across Differing Levels of Severity. Figured adapted from Scammell et al
(Scammell, 2015)
Collapsed patient
Partial Cataplexy
Emotional Trigger (e.g., fear)
Complete Cataplexy
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2.1.2 Diagnostic Tools
Multiple tests can be used to diagnose narcolepsy including a comprehensive clinical
interview; an overnight sleep study, also called polysomnography (PSG), followed by a multiple
sleep latency test (MSLT) conducted during the day. Other tests include hypocretin levels, Human
leukocyte antigen (HLA) typing, the modified Epworth Sleepiness Scale (Epworth scale) score
and actigraphy (Nevsimalova, 2014).
Polysomnogram
PSG is conducted in sleep laboratories, and provides a comprehensive recording of
biophysiological changes during sleep including an electroencephalogram
(EEG), eye movements, muscle activity, respiratory events and heart rhythms (Douglas, Thomas,
& Jan, 1992). The EEG records sleep and wake, as well as information regarding sleep stages
(Douglas et al., 1992). Sleep is divided into two main stages, rapid-eye movement (REM) sleep
and Non-REM (NREM) sleep (Dement & Kleitman, 1957). The sleep cycle generally begins in
NREM sleep, with each stage getting progressively deeper and finally entering REM sleep
(Dement & Kleitman, 1957). The majority of total sleep time is spent in NREM sleep, which is
divided into three alternating stages: NREM-1, NREM-2, which constitute as lighter sleep and
NREM-3, which is a deep sleep (Dement & Kleitman, 1957). During the first cycle of the night,
the NREM sleep period lasts for about 80 minutes, and is followed by a 10-minute period of REM
sleep (M. A. Carskadon & Dement, 2011). During the first half of the night, the amount of
NREM-3 sleep tends to be greater, however throughout the night, REM sleep periods lengthen (M.
A. Carskadon & Dement, 2011).
PSG is useful in identifying deviations to normal sleep patterns and architecture (Xu et al.,
2017). The hallmark feature of narcolepsy on PSG is the presence of sleep onset REM periods
(SOREMPs) (Xu et al., 2017). SOREMPs are REM periods beginning within 15 minutes of sleep
onset at night or during daytime naps (Plazzi, Serra, & Ferri, 2008). Other PSG features associated
with narcolepsy include greater total sleep time, specifically greater time spent in lighter sleep
(NREM-1 and NREM-2) (Plazzi et al., 2008) but a reduced time in NREM-3 (Roth et al., 2013).
Narcolepsy is also associated with an increase in REM sleep (Kiran Maski & Heroux, 2016).
Patients with narcolepsy also fall into REM sleep at any time of the day, while in healthy
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individuals, REM sleep only occurs during nocturnal sleep periods (Roth et al., 2013). Patients
with narcolepsy also have a shorter sleep latency (less than 10 minutes), which is the time it takes
to fall asleep, and a shorter REM latency, which is the time it takes to enter REM sleep (Dement
& Kleitman, 1957; Xu et al., 2017). Disturbed nocturnal sleep is visible on the PSG through a
reduced sleep efficiency (less than 80%) and increased wake after sleep onset due to increased
nocturnal awakenings (Serra et al., 2008).
Data from the PSG can also rule out other sleep disorders that may be the cause of EDS,
such as obstructive sleep apnea, REM behaviour disorder and sleep related movement disorders
(AASM, 2007). PSG is considered the gold standard for assessing sleep architecture and
cardiopulmonary physiology (AASM, 2007). However, PSGs are typically undertaken in
designated sleep facilities which limits the ability to provide a realistic representation of a patient’s
natural sleeping environment (Sadeh, Lavie, Scher, Tirosh, & Epstein, 1991).
Figure 3: Hypnogram (a form of PSG) Comparing a Healthy Individual to a Patient with
Narcolepsy. The healthy individual has relatively consolidated sleep between midnight and 8 AM,
but the person with narcolepsy has fragmented sleep, a rapid entry into REM sleep (red outline),
and numerous naps during the day that include REM sleep. Figure adapted from (School, 2016).
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Multiple Sleep Latency Test
A Multiple Sleep Latency Test (MSLT) is another diagnostic test used in narcolepsy,
which is a follow-up test to the overnight PSG (Aurora et al., 2012). The MSLT includes five nap
opportunities that are twenty minutes in duration given at two hour intervals throughout the day
(Littner et al., 2005). For each nap, the sleep latency and SOREMPS are recorded (Littner et al.,
2005) (see Figure 4). On the MSLT, the generally accepted diagnostic rules regard an abnormal
mean sleep latency to be less than eight minutes (M. A. Carskadon et al., 1986). In both pediatric
and adult populations, two or more episodes of SOREMPs are also considered pathological, with
the highest number of SOREMPs at disease onset (M. A. Carskadon et al., 1986).
Figure 4: MSLT of Patient with Narcolepsy. Figured adapted from Berry et al (Berry RB, 2012)
Wake
REM
NREM-1
NREM-2
NREM-3
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Epworth Sleepiness Scale
The self-report adapted Epworth Sleepiness Scale (Epworth scale) is a measure of EDS for
pediatric populations (Johns, 2015). Epworth scale scores range from 0 to 24, with higher scores
indicating greater daytime sleepiness (Johns, 2015). Epworth scale scores greater than ten have
been reported to be clinically significant (Johns, 2015).
Hypocretin Levels
Hypocretin levels are measured by cerebrospinal fluid but not in the blood or in any other
peripheral tissue (Mignot et al., 2002). Hypocretin levels that are lower than 110 pg/ml are strongly
predictive of narcolepsy (Mignot et al., 2002).
Human leukocyte antigen typing
HLA typing is non-invasive test (done by obtaining a blood sample) used in diagnosing
narcolepsy (Nevsimalova, Mignot, Sonka, & Arrigoni, 1997). Human leukocyte antigens (HLA)
are proteins located on the surface of white blood cells and other tissues in the body (Bjorkman et
al., 1987) and certain diseases are associated with particular HLA types (Thorsby, 1997).
Narcolepsy is associated with the HLA DQB1 0602/DRB1 1501 haplotype, and over 90 percent
of narcolepsy patients with cataplexy express this marker (Nevsimalova et al., 1997). However,
HLA DQB1 0602/DRB1 1501 is also seen in the general population, and some patients with
narcolepsy do not have it, limiting it as a diagnostic tool (Nevsimalova et al., 1997).
Actigraphy
Actigraphy is an objective and non-invasive way of measuring sleep patterns (Meltzer et
al., 2016). Actigraphy is a watch-like device worn which has a built-in accelerometer that collects
data on gross motor activity and can be done in the natural sleep environment (e.g., the patient’s
bedroom at home) (Meltzer et al., 2016). Actigraphy is used in association with a sleep diary to
log sleep patterns and behaviour (Sadeh, 2011). The data is translated to periods of wake or sleep
using an algorithm specific to the device (Meltzer et al., 2016). Actigraphy is also a valid measure
of total sleep time in children (Sadeh, Raviv, & Gruber, 2000; Werner, Molinari, Guyer, & Jenni,
2008). Actigraphy has been shown to accurately discriminate between young adults with
narcolepsy and controls (Bruck, Kennedy, Cooper, & Apel, 2005). When comparing actigraphy
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to PSG data, patients with narcolepsy have a reduced total sleep time (6.39 vs 7.64 hours), and a
reduced sleep efficiency (74.38 vs 85.87 per cent) (A. Alakuijala, Sarkanen, & Partinen, 2015).
Sleep onset latency is also reported to be longer in patients with narcolepsy on actigraphy data
when compared to PSG data (16.45 vs 5.43 minutes) (A. Alakuijala et al., 2015). The fact that
actigraphy does not measure sleep stages may account for the differences seen between actigraphy
and PSG data (A. Alakuijala et al., 2015). REM sleep with minor movement activity would be
scored as sleep time on PSG data, but not on actigraphy (A. Alakuijala et al., 2015). Additionally,
patients with narcolepsy have extensive motor dysregulation during REM sleep (Nightingale et
al., 2005), which may further account for decreased total sleep time seen through actigraphy.
However, actigraphy may be more useful than PSG data to capture nocturnal sleep
fragmentation—a common feature of narcolepsy, because it measures movement activity and not
stages of sleep (A. Alakuijala et al., 2015). In summary, actigraphy is a useful objective measure
of sleep for clinical and research purposes to provide data on total sleep time and sleep efficiency
that is comparable to PSG data (Kushida et al., 2001).
A summary of diagnostic tests for narcolepsy can be found in Table 1.
Table 1: Diagnostic Tests in Narcolepsy
Test Name Features Associated with Narcolepsy
Polysomnogram Presence of SOREMPs
REM latency < 15 minutes
Sleep efficiency < 80%
Frequent nocturnal awakenings
Multiple Sleep Latency Test Mean sleep latency < 8 minutes
> 2 SOREMPs
Hypocretin levels < 110 pg/ml
Human leukocyte antigen typing Presence of DQB1:0602/DRB1 1501
haplotype
Epworth Sleepiness Scale > 10
Actigraphy Sleep efficiency <80%
Increased wake after sleep onset
Frequent nocturnal awakenings SOREMP-Sleep onset REM period
REM-Rapid Eye Movement
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2.1.3 Pathophysiology
Narcolepsy is caused by a loss of hypothalamic neurons containing hypocretin (Scammell,
2015) (Thannickal et al., 2000). An autopsy study shows that the number of hypocretin neurons
are 85 to 95 per cent lower in individuals with a history of narcolepsy compared to healthy controls
(Thannickal et al., 2000) (See Figure 5A and 5B). Hypocretin is a neuropeptide that is involved
in sleep regulation (Bourgin et al., 2000; K. Maski & Owens, 2016). Hypocretin also stimulates
target neurons in different regions of the brain that promote wakefulness (R. Y. Moore,
Abrahamson, & Van Den Pol, 2001) (Scammell, 2015). Hypocretin acts on neurons in the cortex
and basal forebrain, brain stem and hypothalamus that produce neurotransmitters norepinephrine,
serotonin, dopamine, and histamine (Gujar, Yoo, Hu, & Walker, 2011). Hypocretin deficiency
causes inconsistencies in the release of these neurotransmitters resulting in EDS, sudden lapses
into sleep and the poor regulation of REM sleep seen in narcolepsy (Gujar et al., 2011) (Lu,
Sherman, Devor, & Saper, 2006). Hypocretin deficiency reduces activity of the pathway that
suppresses REM sleep, enabling elements of REM sleep during wakefulness such as dreamlike
hallucinations (Bourgin et al., 2000; Clifford B. Saper, Fuller, Pedersen, Lu, & Scammell, 2010).
Lower hypocretin levels have also been associated with sleep fragmentation in patients in
narcolepsy (Anniina Alakuijala, Sarkanen, & Partinen, 2016), as hypocretin neurons are stabilizers
of the sleep-wake transition (Clifford B. Saper et al., 2010). Cataplexy results when strong
emotions experienced by the patient activate neuronal pathways in the medial prefrontal cortex
and the amygdala (Bassetti & Aldrich, 1996; Scammell, 2015). The medial prefrontal cortex and
amygdala activate circuits in the pons that cause muscle paralysis, resulting in partial or complete
cataplexy (Bassetti & Aldrich, 1996; Scammell, 2015) (See Figure 6).
The exact cause of hypocretin deficiency in narcolepsy is unclear (Picchioni, Hope, &
Harsh, 2007), but the leading hypothesis is that autoimmune processes destroy hypocretin neurons
(Liblau, Vassalli, Seifinejad, & Tafti, 2015). The autoimmune hypothesis suggests T cells destroy
hypocretin neurons through HLA presentation (Matsuki et al., 1992) (Mahlios, De la Herrán-Arita,
& Mignot, 2013). HLA alleles encode subtypes of Major Histocompatibility Complex classes I
and II proteins, which are responsible for presenting foreign peptides to T cells during infections
(Janeway, Travers, & Walport, 2001; Klein & Figueroa, 1986). However, in individuals with
narcolepsy, the immune system may mistaken self-peptides on hypocretin neurons as foreign
(Matsuki et al., 1992) (Mahlios et al., 2013) (Janeway et al., 2001; Klein & Figueroa, 1986). The
14
association between human autoimmune diseases and infections is also evident in narcolepsy, as
disease onset is associated with the development of infections such as streptococcus pyogenes,
influenza and H1N1 (Nohynek et al., 2012) (M. Partinen et al., 2012) (Aran et al., 2009).
Two major hypotheses that may explain the autoimmune destruction of hypocretin
producing cells include the bystander of autoreactive T cells and molecular mimicry (De la Herrán-
Arita & García-García, 2014; Mahlios et al., 2013).The bystander of autoreactive T cells
hypothesis suggests that T cells stimulated during an infection may directly stimulate surrounding
T cells by cytokines (Singal & Blajchman, 1973). The stimulated surrounding T cells (that are not
specific to a pathogen) may subsequently damage hypocretin cells (Fontana et al., 2010). The
molecular mimicry hypothesis suggests that there are structural similarities between both the
antigen of the pathogen and host (Petrie, Livak, Burtrum, & Mazel, 1995). As a result, T cell
receptor may bind to structurally related antigens on the host, activating T cells that result in cell
death (Petrie et al., 1995).
Figure 5A: Distribution of Hypocretin Neurons (Black Spots) in a Normal Brain Compared to Narcolepsy Brain
Figure adapted from Thannickal et al (Thannickal et al., 2000)
Figure 5B: Graph Showing Decreased Hypocretin Neurons in a Narcolepsy Brain Compared to Normal Brain
Figure adapted from Thannickal et al (Thannickal et al., 2000)
Less Hypocretin Neurons
Figure 6: Pathways Showing Mechanism of Cataplexy In Narcolepsy
Open white circiles are neuron-cell bodies. The green lines resemble excitatory pathways and red
lines resemble inhibitory pathways. The dotted lines indicate reduced activity of the pathway. Figure adapted from Scammell et al (Scammell, 2015)
e.g., laughter
Hypocretin loss
17
2.1.4 Epidemiology
The prevalence of narcolepsy is approximately between 25 and 50 per 100,000
(Longstreth, Koepsell, Ton, Hendrickson, & van Belle, 2007; Ohayon, 2008) however data on
pediatric narcolepsy is unavailable (Nevsimalova, 2014). The prevalence of narcolepsy has
increased significantly within the last decade (Rocca et al., 2016). Data from a study in Finland
reveals that in 2010, the average incidence of pediatric narcolepsy (patients <17 years of age) was
17 times higher (5.30 diagnoses per 100 000) than the average incidence between 2002 and 2009
(0.31 diagnoses per 100 000) (Markku Partinen et al., 2012). The highest incidence was seen in
children ages 11 to 16 years (Markku Partinen et al., 2012). In children less than 11 years of age,
there was an alarming a 177-fold increase in new cases of pediatric narcolepsy in 2010 (increase
from an average of 0.02 to 3.39 diagnoses per 100 000) (Markku Partinen et al., 2012). No increase
was seen in the incidence of adult narcolepsy (patients >20 years of age) (Markku Partinen et al.,
2012). Preliminary data from the Pediatric Working Group of the Sleep Research Network in the
United States shows a two-fold increase in number of pediatric narcolepsy cases from 2010 to
2013 (Simakajornboon et al., 2017).
It is unclear why there has been an increased incidence in pediatric narcolepsy (Rocca et
al., 2016). Some have attributed this increase to the use of the Pandemrix vaccine (used for
influenza pandemics, such as the H1N1 2009 flu) (Szakacs, Darin, & Hallbook, 2013). In Finland,
researchers retrospectively reviewed vaccination data from primary health care data bases from
January 2009 to December 2010, on all living children born between January 1991 and December
2005 (Nohynek et al., 2012). All new cases of narcolepsy in this cohort were identified from the
patient’s medical record (Nohynek et al., 2012). The incidence of narcolepsy was 9.0 in the
vaccinated as compared to 0.7 per 100,000 person years in the unvaccinated individuals (Nohynek
et al., 2012). The vaccine-attributable risk of developing narcolepsy was 1∶16,000 vaccinated 4 to
19-year-olds (95% confidence interval 1∶13,000–1∶21,000) (Nohynek et al., 2012).
18
Similar trends were seen across Europe, where Sweden also observed an increase in
pediatric narcolepsy and sixty-seven per cent of children and adolescents received a Pandemrix
vaccination (Mereckiene et al., 2012). In the Netherlands, the Pandemrix vaccination was not used
in in older children and adolescents and the incidence of narcolepsy was stated to be lower than
other nations that did (Markku Partinen et al., 2012). The relationship between the Pandemrix
vaccination and increased incidence of narcolepsy may be due to the non-specific immune-
stimulating effect of the adjuvants squalene and tocopherol in the vaccine and a more specific
triggering of the H1N1 antigen working together (Szakacs et al., 2013). However, cross-reactive
autoimmunity of hypocretin neurons is unlikely as a computer search for peptide homologies
between the H1N1 virus and hypocretin neuron-specific proteins did not reveal any potential
molecular mimicry (Fontana et al., 2010). The adjuvants in the Pandemrix vaccine may have
induced a bystander activated immune response (Carmona et al., 2010). Further research is needed
to identify the specific biological mechanisms of this relationship (Sarkanen, Alakuijala,
Dauvilliers, & Partinen, 2018).
Another possible reason for the reported increased incidence of narcolepsy could be
attributed to increased awareness (Sarkanen et al., 2018). Previous recognition of narcolepsy has
been limited especially amongst primary care health practitioners due to its rarity, however
following the H1N1 related cases there has been an increase in media attention of narcolepsy
(Sarkanen et al., 2018). Therefore, an increase in awareness of the disease may lead to an increase
in number of diagnoses, without an actual rise in incidence (Sarkanen et al., 2018).
19
2.1.5 Treatment of Pediatric Narcolepsy
2.1.5.1 Pharmaceutical Treatment
Treatment of narcolepsy is aimed at relieving disease symptoms particularly EDS,
cataplexy, hallucinations, sleep paralysis and disturbed nocturnal sleep (Bhattarai & Sumerall,
2017). A description of different pharmaceutical treatment available for narcolepsy follows:
Stimulants
EDS is the most commonly treated symptom in narcolepsy, which patients report to be the
most problematic on daily function (Wozniak & Quinnell, 2015). EDS is targeted by the use of
stimulants such as amphetamines, dextroamphetamines and methylphenidate (Billiard, 2008).
Stimulants increase monoaminergic activity by targeting the neurotransmitters dopamine and
norepinephrine to promote wakefulness and help alleviate EDS (Thorpy & Dauvilliers, 2015).
There are no studies evaluating the effect of stimulants solely in pediatric patients with narcolepsy
(Nevsimalova, 2014). One study on adults with narcolepsy (N=94), which included 13 pediatric
patients (ages 9-17 years), showed that using mazindol (non-amphetamine stimulant) for 30
months improved EDS symptoms and cataplexy (Nittur et al., 2013). This study reported Epworth
scale scores decreased from 17.7 ± 3.5 to 12.8 ± 5.1, with an average fall of -4.6 ± 4.7 (p<0.0001)
and the frequency of cataplexy fell from 4.6 ± 3.1 to 2.0 ± 2.8 episodes per week (Nittur et al.,
2013).
A more recent pharmacotherapy called Pitolisant is an alternative stimulant to promote
wakefulness in patients with narcolepsy and is currently only approved in Europe (Calik, 2017).
Pitolisant works to block histamine H3 auto receptor to increase brain histamine levels and
consequently promote wakefulness (Calik, 2017). In one open-label trial in four teenagers with
narcolepsy, the use of Pitolisant showed a decrease in EDS measured by Epworth scale scores and
a slight decrease in the severity and frequency of episodes of cataplexy (C. Inocente et al., 2012).
20
Wakefulness-promoting agents
More recently, wakefulness promoting agents—specifically, modafinil and armodafinil
have been increasingly used in treating EDS due to the side effects and addictive properties of
other stimulants (Thorpy & Dauvilliers, 2015). The mechanism of action of modafinil and
armodafinil are not well understood, but they are thought to influence the dopaminergic,
adrenergic, and histaminergic systems of the hypothalamus (Thorpy & Dauvilliers, 2015). One
pediatric study across nine centres in Europe shared their clinical experience with modafinil
(through a retrospective chart review) in treating pediatric patients with narcolepsy (combined
n=205; age range 4-18 years) (Lecendreux et al., 2012). Collectively, authors stated that 85 per
cent of patients reported improvements in symptoms of EDS (Lecendreux et al., 2012).
Central Nervous System Depressant/ Gamma Hydroxybutyrate
Another drug used in the treatment of narcolepsy is sodium oxybate (Peacock & Benca,
2010). Sodium oxybate works by improving nocturnal sleep quality, allowing patients to feel more
rested after awakening and consequently decreasing daytime symptoms (Thorpy & Dauvilliers,
2015). Sodium oxybate is also shown to be more effective than benzodiazepines at improving
nocturnal sleep) (Black, Pardi, Hornfeldt, & Inhaber, 2010). Sodium oxybate works by increasing
deep sleep—which is reduced in narcolepsy, by inhibiting the release of neurotransmitters gamma-
aminobutyric acid, glutamate and dopamine (Ahmed & Thorpy, 2010).
A recent clinical trial in pediatric narcolepsy patients shows sodium oxybate improves
nocturnal sleep quality and reduces EDS and episodes of cataplexy after one year of treatment in
drug-naïve children and adolescents (N=24, mean age: 12.20 ± 2.95 years, range: 7.10–17.03)
(Filardi et al., 2018). Sodium oxybate treatment showed a decrease in nocturnal awakenings
measured by actigraphy (32.88 ± 8.15 16.83 ± 4.08; p<0.01), a decrease in Epworth scale scores
(15.71 ± 2.56 to 10.29 ± 3.52; p<0.01) and a reduced frequency of patients reporting daily episodes
of cataplexy (83% vs 8.3%; p<0.01) (Filardi et al., 2018). A double-blind, placebo-controlled,
randomized-withdrawal, multicenter study was recently conducted to assess the efficacy of sodium
oxybate in pediatric patients with narcolepsy (ages 7-16 years) (Plazzi et al., 2017). Preliminary
results on 35 patients showed a reduction in weekly cataplexy attacks (Plazzi et al., 2017).
Although effective in treating narcolepsy, sodium oxybate can be a challenging treatment to adhere
21
to as it is typically prescribed in two nightly doses, and its euphoric effects have been linked with
abuse and dependence (Peacock & Benca, 2010).
Antidepressants
Antidepressants are also used in the treatment of narcolepsy, particularly for symptoms of
cataplexy (Peacock & Benca, 2010). Although their mechanism of action is not understood, one
study shows antidepressants inhibit norepinephrine reuptake and are effective in treating cataplexy
(Morgenthaler et al., 2007). Anti-depressants have also been used in treating sleep paralysis,
hypnagogic hallucinations (Morgenthaler et al., 2007) as they suppress REM sleep (Winokur et
al., 2001). In six clinical case reports of children and adolescents with narcolepsy (ages 7-12 years
old), patients on Venlafaxine reported reduced episodes of cataplexy and hypnagogic
hallucinations (Moller & Ostergaard, 2009).
Benzodiazepines
Benzodiazepines have been used to treat disturbed nocturnal sleep, however no studies
have assessed their efficacy in the pediatric narcolepsy population (Billiard, 2008). In adults with
narcolepsy (ages 18-60 years), a single-blind within-subject crossover study comparing placebo
and the benzodiazepine triazolam showed improvements in sleep efficiency (baseline-77.6 ± 8.2
per cent ; triazolam-87.1 ± 5.6 per cent; placebo-77.2 ± 9.6 per cent ;p<0.005) measured by PSG
(Thorpy, Snyder, Aloe, Ledereich, & Starz, 1992). A summary of pharmaceuticals used to treat
pediatric narcolepsy symptoms are provided in Table 2.
22
Table 2: Pharmaceuticals Used in Treating Pediatric Narcolepsy
Drug Drug Class Target Symptoms to Treat
Methylphenidate (Ritalin) Stimulants Excessive Daytime
Sleepiness/Sleep attacks Amphetatmine Salts
(Adderall)
Pitolisant
Modafinil (Provigil) Wakefulness-promoting
agents
Excessive Daytime
Sleepiness/Sleep attacks Armodafinil (Nuvigil)
Sodium Oxybate (Xyrem) Central Nervous System
Depressants/ Gamma
Hydroxybutyrate
Excessive Daytime
Sleepiness/Sleep attacks
Disturbed nocturnal sleep
Cataplexy
Sleep paralysis
Hypnagogic hallucinations
Clomipramine (Anafranil) Antidepressants Disturbed nocturnal sleep
Cataplexy
Sleep paralysis
Hypnagogic hallucinations
Venlafaxine (Effexor)
Fluoxetine (Prozac)
Triazolam Benzodiazepines Disturbed nocturnal sleep
Unfortunately many patients with narcolepsy continue to experience symptoms and their
negative impact despite treatment (Wozniak & Quinnell, 2015). This has been shown in a
European study on adults with narcolepsy (n=67) (age range-19 to >71 years (44.8% were aged
≤40 years; 55.2% were aged >40 years)), where all patients were on standard treatment ((modafinil
(62.7%), methylphenidate (19.4%), venlafaxine (11.9%), clomipramine (11.9%), fluoxetine
(7.5%), paroxetine (4.5%), dextroamphetamine (3.0%), sodium oxybate (26.9%)) (Shneerson,
Dauvilliers, Plazzi, Myers, & Garcia-Borreguero, 2008). Despite treatment, 84 per cent of patients
reported feeling the negative impact of symptoms on a daily basis (Shneerson et al., 2008).
Specifically, 70 per cent of patients reported experiencing EDS and 31 per cent reported
experiencing daily cataplexy (Shneerson et al., 2008).
23
2.1.5.2 Non-pharmacological Treatment
There is limited evidence on the efficacy of non-pharmacological treatment to improve
symptoms in pediatric narcolepsy (Kacar Bayram et al., 2016). The most common non-
pharmacological intervention in narcolepsy is that of practicing good sleep hygiene (e.g., regular
sleep–wake schedules and planned naps) and exercise (Nevsimalova, 2014) (Kacar Bayram et al.,
2016). Patients with narcolepsy have self-reported improvement in symptoms using non-
pharmacological treatments (K. Maski et al., 2017). A cross-sectional survey of 1,699 people (8.5
per cent <17 years old) in the United States with narcolepsy (K. Maski et al., 2017). Thirty-six
percent of participants reported good sleep hygiene (e.g., naps, regular schedule) improved their
symptoms, while a small number also reported that participating in exercise (14.5%) improved
their symptoms (K. Maski et al., 2017).
In addition, scheduled naps and regular nocturnal sleep times has been assessed in a study
of 29 adults patients with narcolepsy (mean age of 43.7 ± 13.9 years; all patients were treated with
stimulants) (Rogers, Aldrich, & Lin, 2001). Patients were randomly assigned to one of three
treatment groups: 1) two 15-minute naps per day; 2) a regular schedule for nocturnal sleep; or 3)
a combination of scheduled naps and regular bedtimes (Rogers et al., 2001). Measures of symptom
severity and number of unscheduled daytime sleep periods were obtained at baseline and at the
end of the two-week treatment period, using the Narcolepsy Symptom Status Questionnaire and
24 hour ambulatory PSG monitoring (Rogers et al., 2001). Only the combination of scheduled naps
and regular bedtimes (group 3), significantly reduced both symptom severity and the amount of
unscheduled daytime sleep in patients (Rogers et al., 2001).
Patients with narcolepsy are also encouraged to participate in physical activity (PA) due
to its stimulating effect, which can promote wakefulness (Nevsimalova, 2014). However, there are
currently no studies assessing the association between PA levels and symptoms in narcolepsy in
adult or pediatric populations. Although not tested in humans, an animal study examined the
effects of PA (spontaneous wheel running activity) on wakefulness in both hypocretin knock-out
and wild type mice (Espana, McCormack, Mochizuki, & Scammell, 2007). Both groups of mice
spent a significantly greater amount of time awake (approximately 20 per cent more) during the
wheel running period (PA period) compared to when the wheel was locked (sedentary period)
(Espana et al., 2007).
24
2.1.6 Comorbidities
A co-morbidity is defined as one or more additional diseases co-occurring with a primary
condition (Valderas, Starfield, Sibbald, Salisbury, & Roland, 2009). A common co-morbidities in
pediatric narcolepsy is obesity, which is usually the result of excessive weight gain at disease onset
(K. Maski et al., 2017). Pediatric patients with narcolepsy have been reported to gain 20 to 40
pounds (9 to 18 kg) at disease onset, contributing to the prevalence of co-existing obesity, which
is reported to be as a high as 50 per cent (Plazzi et al., 2006; Poli et al., 2013; Scammell, 2015).
Excessive weight gain at disease onset in narcolepsy may be explained by decreased secretion of
leptin—a hormone involved in inhibiting hunger (Kotagal, Krahn, & Slocumb, 2004). In a case-
control study, serum levels of leptin in adults with narcolepsy have been reported to be
approximately half of healthy controls (5.9 µg/liter in narcolepsy vs. 11.4 µg/liter; p<0.05) (Kok
et al., 2002). Sleep deprivation may also be the cause of weight gain in patients with narcolepsy
(Pack & Pien, 2011). In a randomized two-period, two-condition crossover clinical study with
adults, sleep deprivation (under controlled laboratory conditions) was associated to an increase in
levels of Ghrelin—a hormone that promotes hunger (increase, 28%; p< 0.04), and a decrease in
leptin secretion (decrease, 18%; p= 0.04) (Spiegel, Tasali, Penev, & Van Cauter, 2004).
Another common co-morbidities in pediatric narcolepsy is precocious puberty (K. Maski
et al., 2017). The symptoms of narcolepsy (e.g., EDS etc.), precocious puberty and obesity tend to
occur in close temporal sequence, as they are all related to hypothalamic dysfunction (K. Maski et
al., 2017) (Poli et al., 2013). Precocious puberty, affects approximately 41 per cent of pediatric
patients with narcolepsy (Poli et al., 2013). This may be linked to the role that hypocretin plays on
Gonadotropin-releasing hormone secretion, which is important to pubertal timing determination
(Lopez, Nogueiras, Tena-Sempere, & Dieguez, 2010). Hypothalamic dysfunction in narcolepsy
may alter and accelerate the timing of puberty (Lopez et al., 2010), by increasing body fat
(Walvoord, 2010), which commonly occurs during puberty (Kotagal et al., 2004).
In a cross-sectional survey of over 1000 participants in the United States with a self-
reported diagnosis of narcolepsy, 67 per cent of respondents (973/1,450) reported co-morbidities
such as obstructive sleep apnea (9%), fibromyalgia (7.4%), and celiac disease (1.4%) (K. Maski
et al., 2017). The most common co-morbidities reported by participants were depressive symptoms
(24.8%) and anxiety (17.7%) (K. Maski et al., 2017), and are outcomes of interest in this thesis.
25
2.2 Adolescent Mental Health
Depressive Symptoms in Adolescents
Depression effects five to nine per cent of adolescents (Costello, Mustillo, Erkanli, Keeler,
& Angold, 2003; Merikangas et al., 2010; Wiens et al., 2017). Typical onset of depression occurs
during adolescence, peaking at the age of 14 years (Kessler et al., 2005). To diagnose major
depressive disorder, The Diagnostic and Statistical Manual of Mental Disorders, Fifth
Edition (DSM-V) states a child must have five of the following nine symptoms: depressed or
irritable mood, decreased interest of lack of enjoyment, decreased concentration or indecision,
insomnia or hypersomnia, change of appetite or change of weight, excessive fatigue, feelings of
worthlessness of excessive guilt, recurrent thoughts of death or suicidal ideation and/or
psychomotor agitation or retardation (APA, 2013). Symptoms must occur during the same two
week period, with at least one symptom being either depressed or irritable mood or anhedonia
(APA, 2013). Symptoms must also indicate a significant change from an individual’s baseline
presentation and cause significant functional impairments in school, social settings, and/or family
(APA, 2013).
Assessing Depressive Symptoms in Adolescents
Multiple tools may be used in the assessment of depressive symptoms (Kendall, Cantwell,
& Kazdin, 1989). During initial contact with a health care professional, screening measures, such
as questionnaires, may be used to decide whether a diagnostic assessment is necessary
(Timbremont, Braet, & Dreessen, 2004). A diagnostic assessment consists of a semi-structured
diagnostic interview by a psychiatrist to assess for presence, severity, and duration of depressive
symptoms, as well as interference with daily functioning following the DSM-V (APA, 2013). In
the research setting, self-report measures are often used to assess depressive symptoms as they are
less costly, easier to use and require less of a time commitment for the participant (Timbremont et
al., 2004).
For screening purposes, the Children’s Depression Inventory (CDI) is the most widely used
self-report measure to assess depressive symptoms in children and adolescents (Timbremont et al.,
2004). Although it cannot yield a diagnosis, the CDI provides information about the presence and
severity of depressive symptoms by asking about thoughts, feelings and behavior (Kovacs, 1985).
The CDI has been reported to successfully discriminate patients with and without depressive
26
symptoms, as CDI total scores of depressed and non-depressed youth are significantly different
(Carey, Faulstich, Gresham, Ruggiero, & Enyart, 1987; Hodges, 1990). The psychometric
characteristics of the CDI-2 in a community-based and clinical sample between 10 and 18 years
old show reliability coefficients range, for both samples, from 0.82 (test) to 0.84 (retest) in the
community sample, and 0.85 (test, clinical sample); test-retest reliability is 0.81 in the community
sample.
The CDI cutoff score of 13, which suggests possible depression in clinical settings, has
been found to have a sensitivity of 94.4% and a specificity is 67.7% (Timbremont et al., 2004). At
the cut off score of 13, the negative predictive value (the probability that a patient with a negative
screening test does not have depressive symptoms) is 97.7% and the positive predictive value (the
probability that a patient with a positive screening test has depressive symptoms) is 46.0%
(Timbremont et al., 2004). In community samples (not seeking medical care), the cut-off point
that best differentiates between depressive and community participants is 19, with a sensitivity of
94.7%, a specificity of 95.6% (Figueras Masip, Amador-Campos, Gomez-Benito, & del Barrio
Gandara, 2010). Therefore, there is support for the utility of the CDI as a screening tool for
detecting depressive symptoms in children and adolescents (Timbremont et al., 2004). The second
edition of the CDI (CDI-2) was established in 2010 and was administered to a new, nationally
representative, normative U.S. sample, aged 7–17 years (and their parents) in addition to a separate
clinically referred sample (Kovacs, 2015). The CDI-2 has also has excellent psychometric
properties (reliability coefficients is 0.91 and test-retest reliability is 0.92) (Kovacs, 2015;
Yunhee, 2012).
Anxiety Disorders in Adolescents
Anxiety disorders affect approximately ten per cent of adolescents (Costello et al., 2003;
Merikangas et al., 2010), and are also an outcome of interest for this thesis. Categories of
generalized anxiety, social anxiety, separation anxiety, obsessive-compulsive symptoms, phobias,
and panic disorders (APA, 2013). Common symptoms of anxiety disorders include: feeling
nervous, unfounded or unrealistic fears, trouble separating from parents, sleep disturbance,
obsessive thoughts and/or compulsive behaviors, trembling, sweating, shortness of
breath, stomachaches, headaches, and/or muscle tension or other physical symptoms (APA, 2013).
27
Adolescents with anxiety disorder experience significant distress and reduced level of functioning
(APA, 2013).
Assessing Anxiety Disorders in Adolescents
The Screen for Child Anxiety Related Emotional Disorders (SCARED) is a self-report
measure that was specifically developed to assess anxiety in pediatric populations, and is not a
modified version of a tool previously developed to assess anxiety in adults (Birmaher et al., 1999).
The SCARED evaluates symptoms according to DSM-IV diagnostic criteria for specific anxiety
disorders (social phobia, generalized anxiety disorder (GAD), separation anxiety disorder, panic
disorder) (Birmaher et al., 1999). The SCARED also measures school-related anxiety, which is a
prevalent problem in pediatrics (Birmaher et al., 1999).
In a community sample (n=119), using a cut off score of 22, the SCARED significantly
differentiated anxious from non-anxious children with a sensitivity of 81.8 per cent and specificity
of 52.0 per cent (compared to the gold standard of a diagnosis of anxiety disorder using psychiatric
interviews) (Desousa, Salum, Isolan, & Manfro, 2013). Another community based sample study
(n=137) used a cut off score of 25 (the current recommendation) (Birmaher et al., 1999), and
reported a sensitivity is 75.9 per cent and specificity of 68.5 per cent (compared to the gold
standard of a diagnosis of an anxiety disorder using psychiatric interviews) (Canals, Hernandez-
Martinez, Cosi, & Domenech, 2012).
Depressive Symptoms and Anxiety in Pediatric Narcolepsy
Adolescents with primary sleep disorders are almost twice as likely than healthy
adolescents to develop depressive symptoms and anxiety (Chorney, Detweiler, Morris, & Kuhn,
2008). Limited research in pediatric narcolepsy reveals similar findings: five studies to date have
quantified rates of depressive symptoms and anxiety across a wide age range of children and youth
(Dorris, Zuberi, Scott, Moffat, & McArthur, 2008; C. O. Inocente et al., 2014; Rocca et al., 2016;
Stores, Montgomery, & Wiggs, 2006; Szakacs, Hallbook, Tideman, Darin, & Wentz, 2015).
Only one study has used clinical interviews to assess depression and anxiety in children
and adolescents with narcolepsy (Szakacs et al., 2015). Szakács et al reported depression and
anxiety to occur in 20 and 10 per cent of pediatric patients with narcolepsy respectively (n=38,
ages 5.7-25 years) (Szakacs et al., 2015). Two studies in the pediatric narcolepsy population used
the CDI to assess depressive symptoms (C. O. Inocente et al., 2014) (Stores et al., 2006). However
28
both studies used a cut off score to assess for depressive symptoms for their entire cohort (C. O.
Inocente et al., 2014) (Stores et al., 2006). This is a limitation of these studies because cut off
scores on the CDI that classify an individual as having either elevated or normal scores vary
between sex and age groups (7-12 years vs 13-17 years) (Kovacs, 2015). The other two studies
used the parent version of the Child Behavioural Checklist (CBCL) to assess psychosocial
functioning (Dorris et al., 2008; Rocca et al., 2016). The CBCL may be a valid screen for children
with observable behavioral difficulties, but is limited in detecting internalizing symptoms which
include feelings of depression and anxiety (Kolko David & Kazdin Alan, 1993). Additionally, the
CBCL is a parent-report tool, and parents are relatively poor informants of children’s internalizing
symptoms (Canning & Kelleher, 1994; Kolko David & Kazdin Alan, 1993). There are also no
pediatric studies that focus solely on adolescents, who are particularly vulnerable to developing
mental health problems as they are undergoing major physical, cognitive and psychosocial changes
(D. Taddeo, M. Egedy, & J.-Y. Frappier, 2008). Detailed rates of depressive symptoms and
anxiety are found in Table 3.
29
Table 3: Rates of Depressive Symptoms and Anxiety in Pediatric Patients with Narcolepsy
CBCL: Child Behavioural Checklist
DSM IV: Diagnostic and Statistical Manual of Mental Disorders, fourth edition
CDI: Children’s Depression Inventory
NR-Not Reported
NA-Not applicable
Withdrawn/Depressed domain scores on CBCL
ᶲAnxious/Depressed domain scores on CBCL
Ω did not report depression scores as percentage, mean CDI total scores and standard deviation reported
Author & Year N Age
(years)
Tool Depressive
Symptoms (%)
Anxiety
(%)
Rocca 2016
(Rocca et al.,
2016)
29 Narcolepsy 7-16 CBCL 27.60
17.2ᶲ
7.70 0ᶲ
39 Controls
Szakács 2015
(Szakacs et al.,
2015)
38 15.3
(5.7-25)
DSM IV 20 10
Inocente 2014
(C. O. Inocente et
al., 2014)
88
11.9 ±
3.1
(<18)
CDI
(>16)
25 NA
Dorris 2008
(Dorris et al.,
2008)
12 10
(7-16)
CBCL 41.60
16.60ᶲ
Stores 2006 Ω
(Stores et al.,
2006)
42 Narcolepsy 4-18 CDI
(>19)
12.30 (8.54) NA
4.00 (4.00)
23 Controls
30
2.2.1 Consequences of Adolescent Depression
Further understanding on depressive symptoms in narcolepsy is important because of the
detrimental consequences of depression that have been reported amongst otherwise healthy
adolescents (Glied & Pine, 2002). Depression is correlated with a significant increase in number
of missed school days which may negatively impact school performance (e.g., falling behind in
classes, getting poor grades) (Glied & Pine, 2002). Poor school performance during adolescence
may limit future educational opportunities, which is a determinant of adult employment
opportunities and earning potential (Glied & Pine, 2002) (Greenberg, Stiglin, Finkelstein, &
Berndt, 1993). The majority of depressed girls and boys (65%) have reported to have participated
in at least one of the following risky behaviors: alcohol, drugs, and smoking (Glied & Pine, 2002).
Depressed adolescents are also at an increased risk for the development and persistence of obesity
during adolescence (Goodman & Whitaker, 2002; Korczak, Lipman, Morrison, & Szatmari, 2013).
In a cohort of 9374 adolescents in grades 7 through 12, having a depressed mood at baseline
predicted obesity at follow-up (odds ratio: 2.05; 95% confidence interval: 1.18-3.56) after
controlling for various factors (obesity status at baseline, age, race, gender, parental obesity,
number of parents in the home, family socioeconomic status, adolescents’ report of smoking, self-
esteem, delinquent behavior (conduct disorder), and PA levels (Goodman & Whitaker, 2002).
Importantly, depression is considered to be the most significant psychiatric risk factor for
suicide amongst adolescents (Groholt, Ekeberg, Wichstrom, & Haldorsen, 2005; Shaffer et al.,
1996) (Kessler, Borges, & Walters, 1999). Suicide rates are also higher amongst adolescents with
chronic medical conditions (Mazur-Mosiewicz et al., 2015). Additionally, a relationship between
suicide and sleep deprivation (reduced nocturnal sleep duration) has been reported amongst
healthy adolescents (X. Liu, 2004). A study in over 1000 adolescents (mean age 14.6±3.4 years)
reported that that sleeping less than 7 hours at night was significantly associated with increased
risk for suicide attempts (Odds Ratio = 2.43, 95% Confidence Interval = 1.76-3.35) after
adjustment for depressive symptoms and demographic variables such as age, sex and parental
education level) (X. Liu, 2004).
31
2.2.2 Biological Contributors to Depressive Symptoms in Narcolepsy
Decreased hypocretin levels are seen in the pathogenesis of both depression and narcolepsy
(Gujar et al., 2011; Kiran Maski & Heroux, 2016) (Tsuneki, Wada, & Sasaoka, 2017). One study
evaluated hypocretin levels in both depressed (n=15) and non-depressed (n=14) adults and
reported daily hypocretin concentrations were reduced in depressed patients compared to non-
depressed controls (Salomon et al., 2003). However, nighttime values of hypocretin were higher
in the depressed group (Salomon et al., 2003). Post-mortem evaluation in a group adults who died
from suicide (n=66) revealed the patients with major depressive disorder had significantly lower
levels of hypocretin compared patients with dysthymia or an adjustment disorder (Brundin,
Bjorkqvist, Petersen, & Traskman-Bendz, 2007). However, this is inconsistent with findings from
Schmidt et al, who reported no difference in mean hypocretin levels in 17 adults in-patients with
major depressive disorder (mean Hamilton Depression Rating Scale 13.9± 7.4) (hypocretin levels-
74.3± 17.8 pg/ml) in comparison to ten healthy controls (hypocretin levels-82.8 ± 22.1 pg/ml)
(Schmidt et al., 2011).
The mechanism explaining the interaction between hypocretin levels and depression
remains unclear due to its complexity and limited research (Tsuneki et al., 2017). Hypocretin
deficiency may affect mood states due to the cholinergic monoaminergic imbalance caused by
dysfunction in signaling pathways (Janowsky, el-Yousef, Davis, & Sekerke, 1972). Hypocretin
and dopamine (a neurotransmitter that plays a role in the pathophysiology of depression (Brown
& Gershon, 1993)) have overlapping circuits, particularly in the basal forebrain, thalamic
paraventricular nucleus, and prefrontal cortex (Deutch & Bubser, 2007). There are also
overlapping circuits for hypocretin and other monoamines, such as serotonin and norepinephrine,
which also play a role in the pathophysiology of depression (Brown & Gershon, 1993). Hypocretin
has been shown to have direct excitatory effects on serotonergic neurons (a neurotransmitter that
is decreased in patients with depression (Maes, Leonard, Myint, Kubera, & Verkerk, 2011)) so its
deficiency may contribute to low mood (R. J. Liu, van den Pol, & Aghajanian, 2002). However,
this hypothesis has only been tested in mice models, and is only speculative in humans (Scott et
al., 2011). Further research exploring how hypocretin levels are associated with depression is
needed.
32
Sleep architecture in patients with narcolepsy may also be associated with depression
(Kiran Maski & Heroux, 2016). This is relevant as the sleep architecture in patients with major
depressive disorder include SOREMPs, reduced time in NREM-3 sleep and sleep fragmentation
(Medina, Lechuga, Escandon, & Moctezuma, 2014), which are similar findings reported in patients
with narcolepsy (Xu et al., 2017) (Plazzi et al., 2008). The increase in REM sleep seen in
narcolepsy may be associated with depression, as REM sleep plays a role in affective reactivity
and emotional information processing which includes emotional memory (Kiran Maski & Heroux,
2016). It is speculated that if patients with narcolepsy spend greater time in REM sleep, they may
also spend more time consolidating negative emotional memories predisposing them to depressive
symptoms (Kiran Maski & Heroux, 2016). The amount of time spent in REM sleep and its
association with depressive symptoms has never been studied in both adult and pediatric
narcolepsy populations.
33
2.2.3 Factors Associated with Depressive Symptoms in Narcolepsy
The factors associated with depressive symptoms amongst adolescents with narcolepsy
remain largely unknown. A stronger understanding of factors that are associated with depressive
symptoms is important in setting the ground work for longitudinal studies and future quasi
experimental studies which can help in developing interventions that can be tested in a rigorous
manner.
Limited research suggests associations between depressive symptoms and health-related
quality of life (HRQOL) (Rocca et al., 2016). Rocca et al found mental health (anxiety/depression
and withdrawn/depressed) scores as measured by the CBCL correlated with HRQOL, particularly
in the domains of physical (R2=-0.40, p<0.05), emotional (R2=-0.62, p<0.001) and social (R2=-
0.46, p<0.05) functioning (Rocca et al., 2016). Disease-specific symptoms (e.g., EDS, cataplexy,
sleep paralysis and hypnagogic/hypnopompic hallucinations) may also be associated with
depressive symptoms in pediatric narcolepsy (Stores et al., 2006). Stores et al suggested the
psychosocial profile of pediatric patients with narcolepsy was similar to pediatric patients with
idiopathic hypersomnolence in their study (Stores et al., 2006). Similarly, Inocente et al found
depressive symptoms measured by the CDI to be significantly correlated with EDS (Epworth Scale
scores) (R2 = 0.24, p<0.01) (C. O. Inocente et al., 2014). Inocente et al also reported patients with
elevated CDI scores had a higher prevalence of sleep paralysis (16 ± 72.2 vs 21 ± 31.8 per cent;
p<0.01) and hypnagogic/hypnopompic hallucinations (14 ± 63.4 vs 11 ± 16.7 per cent; p<0.01)
compared to patients without elevated CDI scores (C. O. Inocente et al., 2014). However, a greater
prevalence of cataplexy was not present in patients with elevated CDI scores (20 ± 90.8 vs 51 ±
77.3 per cent; p=0.16) (C. O. Inocente et al., 2014).
Other factors that may be associated with depressive symptoms in adolescents with
narcolepsy include total sleep duration, sleep quality, and physical activity (PA) levels. Sleep
patterns and PA levels are relevant factors, as they are reported to be associated with depressive
symptoms in otherwise healthy adolescents (Biddle & Asare, 2011; Roberts & Duong, 2014).
Current knowledge on the relationship between sleep patterns, PA levels and depressive symptoms
and anxiety will be discussed in the next section.
34
2.3 Sleep, Physical Activity and Mental Health
Sleep and Mental Health
Decreased sleep duration and depressive symptoms and anxiety have been reported
amongst healthy adolescents in many studies (Ojio, Nishida, Shimodera, Togo, & Sasaki, 2016)
(Baum et al., 2014) (Roberts & Duong, 2014) (Roberts & Duong, 2017). In a study of 15,637
healthy adolescents (ages 12-18 years), sleeping less than 7.5 hours at night was associated with
greater self-reported depressive symptoms and anxiety (Ojio et al., 2016). One experimental
study, randomized healthy adolescents (n=50; ages 14-17 years) into two groups, the sleep
deprived group (in bed for 6.5 hours per night for 5 nights) and a healthy sleep duration group (in
bed for 10 hours per night for 5 nights) (Baum et al., 2014). The sleep deprived group reported
poorer emotional regulation and being more anxious compared to adolescents in the healthy sleep
duration group (Baum et al., 2014). In a large study on 4,175 healthy adolescents (ages 11-17
years), sleep deprivation (≤6 hours a night) was associated with depression and anxiety, that was
diagnosed by clinical interviews (Roberts & Duong, 2014) (Roberts & Duong, 2017).
Poor sleep quality is also associated with mental health outcomes, such as depressive
symptoms and anxiety amongst healthy adolescents (Tanaka et al., 2002). One study surveying
805 junior high school students, showed better self-reported nocturnal sleep (e.g., less difficulty
in maintaining nocturnal sleep) was associated with better mental health outcomes (measured by
the General Health Questionnaire) (0.33, p<0.01) (Tanaka et al., 2002). Another study in 5399
students (in grade 8-10) showed self-reported sleep quality categorized as good (as opposed to
poor) was associated with positive mental health (assessed using the self-report Mental Health
Continuum) (Odds Ratio 1.89; 95% Confidence Interval 1.61–2.21) (Guo, Tomson, Keller, &
Söderqvist, 2018).
A potential hypothesis explaining the relationship between sleep deprivation and
depressive symptoms amongst healthy adolescents includes spending time online in bed before
sleep, which is associated with both with sleep disturbance and depressive symptoms (Lemola,
Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015). Over 95 per cent of adolescents in
the United States of America have at least one electronic media device (e.g., television, computer,
mobile phone, video game console) in their bedroom (Foundation, 2006). Due to an increase in
electronic media use before bed, adolescents report having later sleep times, worse sleep quality
35
and increased daytime sleepiness (Cain & Gradisar, 2010). Possible reasons for this include the
device’s screen light increases alertness and may be associated with melatonin suppression, a
hormone involved in regulating sleep and wake cycles (Wood, Rea, Plitnick, & Figueiro, 2013).
Disturbed nocturnal sleep associated with increased electronic media usage before bed are also
associated with depressive symptoms and anxiety (Lemola et al., 2015). This may be because of
the vast amount of time adolescents spend on social media sites (e.g., Instagram and Facebook),
which consist of psychologically arousing content (Lemola et al., 2015). The social interaction
and anxiety associated with social media may induce negative feelings interfering with the ability
to fall asleep (Lemola et al., 2015). It is also important to note that the a bidirectional relationship
between sleep deprivation (short sleep duration and/or poor sleep quality) and depression may
exist, as a high percentage of adolescents presenting to pediatric psychiatric clinics, display sleep
disturbance (Chorney et al., 2008) . Poor sleep quality as well as increased or decreased sleep
duration, are also important components of diagnostic criteria for major depression (APA, 2013;
Lofthouse et al., 2009) and are amongst the most prevalent symptoms of depression after depressed
mood itself (Chorney et al., 2008). Recent longitudinal research in a cohort of almost 5000 children
(preschool to early adolescence) reported sleep problems (e.g., difficulty falling asleep, nocturnal
awakenings, restless sleep) to be significantly associated with later internalizing difficulties (e.g.,
low mood, anxiety), but not the reverse (Quach, Nguyen, Williams, & Sciberras, 2018). Similar
findings were seen in over 4000 typically developing youth (ages 11-17 years), where sleep
deprivation (≤6 hours only on weeknights and ≤ 6 hours on both weeknights and weekend night)
was associated with depression and anxiety (diagnosed by psychiatric interviews), but the reverse
association was not seen (Roberts & Duong, 2014) (Roberts & Duong, 2017).
Furthermore, sleep deprivation in adults has been associated with increased amygdala
activation (Yoo, Gujar, Hu, Jolesz, & Walker, 2007). A neuroimaging (functional magnetic
resonance imaging) in 26 healthy adults (ages 18–30 years (mean 24.1 ± 2.3) were assigned to
either a sleep-deprivation group (accumulating approximately 35 hours without sleep in an
experimental laboratory) (n=14; 7 males) or sleep-control group (sleeping normally at home)
(n=12; 6 males) (Yoo et al., 2007). Amygdala activation was used as an outcome measure because
of the role the amygdala plays in processing emotionally significant information, particularly
aversive stimuli (Davidson, 2002). Both groups expressed significant amygdala activation when
shown increasingly negative picture stimuli, however adults in the sleep-deprivation group showed
36
a +60 per cent greater magnitude of amygdala activation, relative to adults in the control group
(Yoo et al., 2007). There was also a three-fold increase in the extent of amygdala volume that was
activated in the sleep-deprivation group (Yoo et al., 2007). The greater magnitude of amygdala
activation suggests that sleep-deprived individuals may experience negative affect states (e.g.,
depressed mood) to a greater degree than those who are not sleep deprived (Yoo et al., 2007). The
neural circuits that play a role in both emotional regulation and sleep regulation have been shown
to interact in a bidirectional fashion (C. B. Saper, Cano, & Scammell, 2005), however the exact
mechanism that explain these effects remain under investigation (Clarke & Harvey, 2012).
Physical Activity Levels and Mental Health
In typically developing adolescents, higher PA levels are associated with depressive
symptoms (Biddle & Asare, 2011). In a study of over 8000 adolescents, authors report an inverse
relationship between self-reported PA levels and self-reported depressive symptoms (depression
scores) (Kremer et al., 2014). Similar results were seen by Wiles et al with objective PA levels—
measured by an accelerometer (Wiles, Haase, Lawlor, Ness, & Lewis, 2012). Wiles et al reported
the amount of time spent participating in PA is inversely associated with depression scores in
2,951 adolescents, rather than the intensity (e.g., light, moderate or vigorous) when controlling for
age, gender, maternal education, obesity and substance abuse (Wiles et al., 2012). Wiles et al
categorized depression scores in tertiles (Mood and Feelings questionnaire scores: 0–2; 3–5; ≥6).
Total PA time was also defined in tertiles (≤270 minutes; >270–326.6 minutes; ≥326.7 minutes),
and total PA time at different intensities was based on accelerometer counts per minute at 200–
3,599 (light), 3,600–6,199 (moderate) and ≥6,200 (vigorous) (Wiles et al., 2012). A greater total
amount of time spent participating in PA was associated with a reduced odds of being depressed
(Odds Ratioadjusted total PA (tertiles): medium 0.82 (95% Confidence Interval: 0.70, 0.98); high
0.70 (95% Confidence Interval: 0.58, 0.85) (Wiles et al., 2012). However, increasing the
percentage of time spent in moderate-vigorous PA was not independently associated with a
reduction in the odds of being depressed [Odds Ratioadjusted Moderate-Vigorous PA (tertiles)
medium 1.05 (95% Confidence Interval: 0.88, 1.24), high 0.91 (95% Confidence Interval: 0.77,
1.09)] (Wiles et al., 2012). A recent systematic review and meta-analysis of 50 independent
samples involving 89 894 participants also found that a greater PA level was associated with fewer
depressive symptoms (but not a decreased diagnoses of major depressive disorder) (Korczak,
37
Madigan, & Colasanto, 2017). It is important to note that this association was stronger for cross-
sectional studies (k=36, r= –0.17; 95% Confidence Interval = –0.23 to –0.10) than for longitudinal
studies (k=14, r= –0.07; 95% Confidence Interval = –0.10 to –0.04) where the mean effect size
was weak but still significant (Korczak et al., 2017). Cross-sectional studies are limited as they
cannot examine causality and the direction of the association cannot be evaluated. It is possible
that the cross-sectional studies in this meta-analyses are actually indicative of the reverse
association of PA and depression where children with increased depressive symptoms are less
likely to participate in PA possibly due to lack of motivation and anhedonia (Korczak et al., 2017).
This systematic review and meta-analysis also found that increased frequency and intensity of PA
was more strongly associated with fewer depressive symptoms compared with increased intensity
of alone (Korczak et al., 2017).
It is speculated that participating in PA may improve depression scores because PA may
provide a distraction from negative thoughts or constant rumination in depression (Rood, Roelofs,
Bogels, Nolen-Hoeksema, & Schouten, 2009). Additionally, PA provides opportunities for social
interaction (e.g., being a part of a sports team) resulting in feelings of increased support and
connectedness with others through being a part of the activity (Wiles et al., 2012). The relationship
between PA and depressive symptoms may be bidirectional, as adolescents who are depressed may
be less inclined to participate in PA, leading to social isolation and elevated depression scores
(Kremer et al., 2014). Additionally, participation in PA may release of endorphins, which can
produce feelings of euphoria (Dishman & O'Connor, 2009; Lubans et al., 2016). Euphoric feelings
after participating in PA may be due to changes in one or more brain monoamines, with the
strongest evidence available for dopamine, noradrenaline, and serotonin.(Lin & Kuo, 2013;
Lubans et al., 2016). However, there is little empirical evidence to support this assertion in
pediatric populations (Bouix et al., 1994; Lubans et al., 2016).
The association between PA level and depression scores has not been assessed in
adolescents with narcolepsy, and will be evaluated in this master’s thesis. Currently, there is no
data on PA levels in pediatric narcolepsy. However, research on adults suggests PA levels are
lower in patients with narcolepsy compared to age and gender matched controls (Matoulek, Tuka,
Fialova, Nevsimalova, & Sonka, 2017). Adults with narcolepsy have a reduced average daily step
38
count of 6346 ± 2026 (Matoulek et al., 2017), which is lower than the recommended 10 000 steps
per day for healthy adults (Tudor-Locke & Bassett, 2004). Adults with narcolepsy also reported
more problems in performing recreational activities and playing sports compared to healthy
controls (Broughton et al., 1983) (Daniels, King, Smith, & Shneerson, 2001; Teixeira, Faccenda,
& Douglas, 2004).
Physical Activity and Sleep
Amongst healthy adolescents, participating in regular PA has been associated with better
nocturnal sleep duration and quality (Lang et al., 2016). A systematic review of 21 studies
including a total of 16 549 participants (14-24 years old) concluded that adolescents and young
adults with higher levels of PA are more likely to experience longer sleep duration and better sleep
quality (Lang et al., 2016). A positive relationship between PA levels and sleep quality, measured
subjectively using questionnaires and diaries was reported, but only two of the 21 studies used
objective measures to assess sleep quality and PA (Lang et al., 2013) (Gerber et al., 2014). In both
of these studies, sleep quality was quantified as sleep efficiency (measured using a portable EEG)
and PA was measured with an accelerometer (Lang et al., 2016). Both studies revealed increased
PA levels were associated with longer sleep duration and better sleep quality (higher sleep
efficiency) (Lang et al., 2013) (Gerber et al., 2014). Lang et al assessed 37 adolescents, and
reported participants in the high PA level group (>30 minutes of moderate-intensity physical
activity) had a significantly greater total sleep time and sleep efficiency when controlling for
gender (Lang et al., 2013). In a group of active adolescents and young adults (≥150 min/week of
moderate PA) (n= 22), Gerber et al assessed how PA intensity affected objective sleep parameters
(Gerber et al., 2014). Participants who undertook vigorous PA (≥3 × 20 min/week) vs moderate
PA alone, had a significantly greater total sleep time (414.6 ± 35.9 vs 371.2 ± 34.9 minutes;
p<0.01), but not significantly better sleep quality measured by sleep efficiency percentage (Gerber
et al., 2014).
There are two main hypotheses on the mechanism by which increased levels of PA are
associated with better sleep (Lang et al., 2013). The first is that participating in PA contributes to
circadian alignment, which is the synchronization of behavioral and physiological rhythm
(Westerterp-Plantenga, 2016). PA stabilizes the circadian clock (the suprachiasmatic nucleus of
the hypothalamus) which consequently improves nocturnal sleep quality by stabilizing the
39
circadian rhythm (Westerterp-Plantenga, 2016) (Chennaoui, Arnal, Sauvet, & Leger, 2015). The
second is that participating in PA is beneficial by decreasing depressive symptoms, anxiety and
stress and consequently improving sleep (Biddle & Asare, 2011).
40
2.4 Family Functioning in Pediatric Narcolepsy
Pediatric conditions adversely affect the well-being of the entire family, particularly the
affected patient’s parents or immediate caregivers (Kazak, 1989). Various factors of the child’s
illness such as the demanding treatment regimens, an increase in responsibilities (e.g., attending
medical appointments) a shifts in parenting roles (e.g., less time at work and more at home with
sick child) may negatively impact family functioning (Cousino & Hazen, 2013). Poor family
functioning is also associated with depressive symptoms in pediatric patients with chronic
conditions (Kemp, Langer, & Tompson, 2016) (Whittemore et al., 2002). For instance, one study
in adolescents with type-1 diabetes (n=117; mean age-14.3±2.0 years) assessed the relationship
between family functioning (measured by the Family Adaptability and Cohesion Scale) and
depression scores (measured by (Whittemore et al., 2002). This study showed that 35 per cent of
adolescents with depressive symptoms reported their families to have poorer functioning (to be
rigid and disengaged) compared with 16 per cent without depressive symptoms (Whittemore et al.,
2002). In contrast, 37 per cent of adolescents with type-1 diabetes without depressive symptoms
reported higher family functioning (to be balanced, flexible and connected) compared with 19 per
cent with depressive symptoms (Whittemore et al., 2002). Poor family functioning is also
associated with suboptimal adherence to treatment regimens (Smith, Mara, & Modi, 2018). For
example, in 48 adolescents with epilepsy (aged 13–17 years), lower family conflict (measured by
the Parental Environment Questionnaire) was associated with better treatment adherence
(b= −0.19, p=0.05) (Smith et al., 2018).
Research on family functioning in the pediatric narcolepsy population is scarce. Only one
mixed-methods study (n=58) has briefly explored the impact of pediatric narcolepsy on the family
(Kippola-Paakkonen, Harkapaa, Valkonen, Tuulio-Henriksson, & Autti-Ramo, 2016). This study
was primarily assessing the parents’ perceived support from a psychosocial intervention for
family’s with a child with narcolepsy, and identifying parent’s personal concerns regarding their
child’s diagnoses was a secondary aim (Kippola-Paakkonen et al., 2016). Parents personal
concerns about having a child with narcolepsy (e.g., child’s coping, parents coping, job stress etc.)
were assed with questionnaires (Kippola-Paakkonen et al., 2016). Questionnaires also included
open ended questions so families could provide more details on their experience with their child’s
diagnosis (Kippola-Paakkonen et al., 2016). Families shared that they were very worried about
41
the entire family’s ability to cope with the illness (e.g., parents, the affected child and the affected
child’s siblings) (Kippola-Paakkonen et al., 2016). Parents also discussed life adjustments as a
result of the diagnosis such as not having enough time with their spouse, or having to quit their
job or work part-time as their child needed more supervision at home than before (Kippola-
Paakkonen et al., 2016).
It is unclear how family functioning in pediatric narcolepsy compares to 1) families with
healthy children (not seeking ongoing medical care) and 2) families with other pediatric
conditions. This is important as it can contextualize level of impairment a family is experiencing
as a result of the illness or lack thereof (Herzer et al., 2010; McClellan & Cohen, 2007) (See Figure
7). The relationship between family functioning and depressive symptoms in pediatric narcolepsy
is also unknown. As such, investigation of family functioning is pertinent to understanding
outcomes in pediatric narcolepsy.
42
Figure 7: A comparison of family functioning across multiple pediatric chronic conditions using
published data (de Kloet et al., 2015; Jastrowski Mano, Khan, Ladwig, & Weisman, 2011; Matziou
et al., 2016; Panepinto, Hoffmann, & Pajewski, 2009)
ᶲFamily functioning was assessed using The PedsQL Family Impact Module total score, which measures the disease
impact on the family through questions about parent HRQOL, family communication, levels of worry, daily activities
and relationships (Varni, Sherman, Burwinkle, Dickinson, & Dixon, 2004). Lower scores suggest a more negative
disease impact on the family (Varni et al., 2004).
64.7
53.5
70.8
83.675.7
0
10
20
30
40
50
60
70
80
90
Chronic Pain Cancer Non-Traumatic
Brain Injury
Traumatic Brain
Injury
Sickle Cell
Anemia
Ped
sQL
Fa
mil
y I
mp
act
To
tal
Sco
reᶲ
Pediatric Condition
43
2.5 Summary of Gaps in the Literature
This literature review highlights the current knowledge on depressive symptoms in
pediatric narcolepsy. Limited research has shown adolescents with narcolepsy have co-existing
depressive symptoms, however the associated factors remain unclear. To address this gap, this
thesis will evaluate sleep (duration and quality) and PA levels in adolescents with narcolepsy, as
potential factors that are associated with depression scores. Finally, it is unclear how family
functioning in adolescents with narcolepsy compares with that of healthy adolescents (not seeking
medical care) and adolescents with other medical conditions. The association between family
functioning and depression scores and anxiety scores in adolescents with narcolepsy is also
unknown. To address this gap, this thesis will evaluate family functioning in adolescents with
narcolepsy compared otherwise healthy adolescents and adolescents with other medical
conditions, as well as assess the relationship between family functioning and depression scores
and anxiety scores. Thus, the data from this thesis will provide new and relevant knowledge to
the clinical community involved in caring for adolescents with narcolepsy.
44
Chapter Three: Rationale, Objectives and Hypotheses
3.1. Rationale
Narcolepsy is a life-long sleep disorder with no cure that commonly manifests during
adolescence (Viorritto et al., 2012). Limited research shows pediatric patients with narcolepsy
have co-existing depressive symptoms (Dorris et al., 2008; C. O. Inocente et al., 2014; Rocca et
al., 2016; Stores et al., 2006; Szakacs et al., 2015), but data in adolescents is limited. Mental illness
peaks during adolescence (Kessler et al., 2005), and has detrimental consequences such as poor
school performance, substance abuse and suicide (Carotenuto et al., 2012; Glied & Pine, 2002).
Moreover, factors associated with depressive symptoms in adolescents with narcolepsy remain
largely unknown. Sleep patterns (duration and quality) and PA levels are factors that may be
associated with depression scores amongst adolescents with narcolepsy. This is relevant as this
association has been seen amongst otherwise healthy adolescents (Smagula et al., 2016) (Biddle
& Asare, 2011).
In addition to the affected patient, pediatric conditions can affect the well-being of the
entire family (Giallo et al., 2014). Poor family functioning is also associated with depressive
symptoms in children and adolescents (Kemp et al., 2016). Family Functioning in pediatric
narcolepsy has been scarcely studied. Further research on family functioning as well as knowledge
regarding the relationship between family functioning and depression scores are important for
clinical teams providing family-centered care patients with narcolepsy.
45
3.2. Objectives and Hypotheses
Objective 1: Examine if sleep patterns (duration and quality), excessive daytime sleepiness and
physical activity levels are associated with depression scores as the primary outcome and anxiety
scores as the secondary outcome in adolescents with narcolepsy compared with controls
Hypothesis: Poor nocturnal sleep quality, excessive daytime sleepiness and lower levels of
physical activity will be associated with depression scores and anxiety scores in adolescents with
narcolepsy and controls
Objective 2: To describe family functioning in adolescents with narcolepsy, in comparison to
otherwise healthy adolescents (not seeking medical care) and other pediatric conditions
Hypothesis: Family functioning will be impaired in adolescents with narcolepsy in comparison to
healthy adolescents but similar to adolescents with other pediatric conditions
Objective 3: Examine the association between family functioning and depression and anxiety
scores in adolescents with narcolepsy
Hypothesis: Family functioning will be associated with depression and anxiety scores
46
Chapter Four: Methodology
4.1. Overview and Study Design
This was a prospective, observational, cross-sectional research study evaluating if sleep
patterns (duration and quality), EDS and PA levels are associated with depression scores as the
primary outcome and anxiety scores as the secondary outcome in adolescents with narcolepsy
compared with controls. This study also evaluates family functioning amongst adolescents with
narcolepsy in comparison to otherwise healthy adolescents (not seeking medical care) and
adolescents with chronic conditions. This study also evaluates the association between family
functioning and depression scores and anxiety scores amongst adolescents with narcolepsy.
Depression scores were assessed using the Children’s Depression Inventory-2nd edition and
anxiety scores were assessed using the Screen for Childhood Anxiety Related Emotional Disorders
(SCARED). Sleep duration and quality which were assessed objectively using actigraphy. Self-
reported sleep quality was assessed using The Pittsburgh Sleep Quality Index and EDS was
assessed using the modified Epworth sleepiness scale (Epworth scale). PA levels were measured
objectively using a pedometer. Self-report PA levels were measured using the Godin Leisure-Time
Exercise Questionnaire (Godin). Family functioning was assessed using the PedsQL Family
Impact Module. Demographic, anthropometric and clinical variables were also collected.
4.1.1. Ethics
The study protocol was approved by the research ethics board at the Hospital for Sick
Children in Toronto Ontario in March 2017 (REB #1000055883).
4.2. Study Population
The study population consisted of two groups of adolescents, the narcolepsy group and a
control group. The narcolepsy group included pediatric patients followed clinically at The Hospital
for Sick Children. The Hospital for Sick Children is the largest pediatric tertiary care center in
Canada and follows one of the largest pediatric narcolepsy population in the country. The
narcolepsy group was recruited from the dedicated pediatric narcolepsy clinic. The control group
was recruited from the greater Toronto area community via flyers in the hospital and/or through
friends and family members of the study team and participants.
47
4.3. Eligibility Criteria
Eligibility to participate in the study was determined using the following inclusion and
exclusion criteria:
4.3.1.Inclusion Criteria
Narcolepsy Group:
Participants were required to be in the age range (10-18 years). This age range was selected
as it encompasses the World Health Organization definition of an adolescence (WHO, 2014).
Participants had a confirmed diagnosis of narcolepsy made by the sleep medicine team at the
Hospital for Sick Children, which was verified by a study team member.
Diagnosis of Narcolepsy
Patients with narcolepsy were referred to the Hospital for Sick Children Sleep Medicine
team from community pediatricians or general practitioners. Symptoms were self-reported by
parents and/or patients themselves. The adapted Epworth sleepiness scale which is used in
pediatrics (Johns, 2015) was administered to all patients and their families to assess EDS at first
clinical presentation, when patients were not receiving treatment. A diagnosis of EDS was made
for any individual who scored 10 or higher on the Epworth sleepiness scale (the maximum score
was 24). All patients underwent a full clinical examination, including a neurological examination.
As per standard of clinical care, all patients with suspected narcolepsy had an overnight PSG (with
sleep EEG) to rule out other sleep disorders followed by a daytime MSLT with five 20-minute nap
periods. Human leukocyte antigen typing was also done.
For a diagnosis of narcolepsy, patients had to meet the following criteria which was adapted from
the International Classification of Sleep Disorders Diagnostic and Coding Manual (3rd edition)
(AASM, 2014):
a. History consistent with narcolepsy (EDS (Epworth Sleepiness Scale Score >10),
irresistible urge to sleep, falling asleep unexpectedly)
b. MSLT consistent with narcolepsy; defined as two or more SOREMPs and a mean sleep
latency < 8 minutes
c. Symptoms not explained by another sleep disorder, medical and/or neurological disorder.
48
Hypocretin Levels were not assessed as they are available as a clinical or research test in Canada.
Control Group:
Participants were recruited from the Toronto area community setting and were required to
be in the age range of 10-18 years. This age range was selected as it encompasses the World Health
Organization definition of an adolescence (WHO, 2014). Participants were recruited
consecutively with the goal to match for age and sex which was dependent on the availability of
healthy participants willing to participate in this study.
4.3.2. Exclusion Criteria
Narcolepsy Group:
Participants unable to speak read or write English were not eligible to participate. To avoid
potential confounding variables, patients with known global developmental delay, sleep disorder
other than narcolepsy, for example, Obstructive Sleep Apnea were excluded from the study. All
PSGs performed in children narcolepsy were re-reviewed to ensure that they did not have
Obstructive Sleep Apnea.
Control Group:
Participants unable to speak read or write English were not eligible to participate. To avoid
potential confounding variables, patients with known global developmental delay, sleep disorders
or a known chronic condition that required ongoing medical care, were excluded from the study.
There was no clinical indication for an overnight PSG in the control group so participants
were screened for sleep disordered breathing using the Pediatric Sleep Questionnaire (PSQ)
(Chervin, Hedger, Dillon, & Pituch, 2000). Additionally, the incidence of sleep disordered
breathing in otherwise healthy adolescents is less than one per cent (Petry, Pereira, Pitrez, Jones,
& Stein, 2008). The PSQ is a valid and reliable tool used in children ages 2-18 years, the PSQ has
a sensitivity of 85% and specificity 87% compared to a PSG for the diagnosis of Obstructive
Sleep Apnea (Chervin et al., 2000). Study Procedures
49
Following informed consent, participants in either group (narcolepsy or control) were
asked to complete the questionnaires independently at the research visit (see Table 4 for
questionnaires). The Pediatric Sleep Questionnaire was only completed by the control group and
was completed by the participant’s caregiver. The PedsQL Family Impact Module was only
completed by the narcolepsy group and was completed by the participant’s caregiver.
Anthropometric measures (height and weight) were obtained at the research visit for both groups
as well.
Table 4: Questionnaires Completed at Research Visit
Questionnaire Name Questionnaire Description Questionnaire
Completed by:
Children’s Depression
Inventory-2nd Edition
Evaluates the presence and severity of
depressive symptoms
Participant
The Screen for Childhood
Anxiety Related
Emotional Disorders
Evaluates the presence of anxiety Participant
Pittsburgh Sleep Quality
Index
Measure of self-reported sleep
quality/sleep disturbance
Participant
Adapted Epworth
Sleepiness Scale
Measure of daytime sleepiness Participant
Godin Leisure-Time
Exercise Questionnaire
Measure of self-reported physical activity
levels
Participant
Child Family and
Demographic
Questionnaire
Measure of family socioeconomics Participant/Participant
Caregiver
PedsQL Family Impact
Module
Measure of the disease impact on the
family by asking questions regarding the
family’s overall functioning,
communication, worry, daily activities
and relationships.
Participant Caregiver
(Narcolepsy Group
Only)
Pediatric Sleep
Questionnaire
Evaluates for probable sleep disordered
breathing
Participant Caregiver
(Control Group Only)
50
Participants were also given an actigraph to measure sleep and a pedometer to measure PA
levels at the research visit. Participants were asked to wear the actigraph for 24 hours per day for
7 continuous days (removed only during aquatic activities) and the pedometer for 7 continuous
days during hours they are awake. Participants were also asked to complete logs/diaries at home
to track sleep patterns and pedometer usage (see Table 5). Participants mailed completed
questionnaires, pedometers and actiwatches approximately one week later in the prepaid envelope
provided.
Table 5: Log/Diary Completed at Home
Log/Diary to be completed at home Log/Diary Description
Sleep Diary Tracks sleep and wake hours and awakenings during sleep
Pedometer Log Tracks when pedometer was put on and taken off
51
4.4. Study Measures
4.4.1.Outcomes
Primary Outcome
Depression Scores: The primary outcome of interest in this study were depression scores, which
were measured by the Children’s Depression Inventory-2nd edition (CDI-2) total score. The
CDI-2 is a 28-item self-report assessment that yields a total score, two scale scores (Emotional
Problems and Functional Problems), and four subscale scores (negative mood/physical symptoms,
negative self-esteem, ineffectiveness and interpersonal problems) used in children ages 7 to 17
years. The item response “I want to kill myself” was used to indicate suicidal ideation with intent.
This question was assessed immediately, and if a participant selected the option “I want to kill
myself” as the option, the principal investigator/staff physician was called immediately who
intervened as per standard clinical care (e.g., assessing patient, taken to emergency department).
Scores in the elevated range vary by gender and age group (Kovacs, 1985, 2015). The
psychometric characteristics of the CDI-2 in a community-based sample (recruited from the school
setting) and a clinical sample (recruited from pediatric mental health clinics) (ages 10 and 17 years
old) show reliability coefficients range, for both samples, from .82 (test) to .84 (retest) in the
community sample, and .85 (test) in the clinical sample (Figueras Masip et al., 2010). For this
study, the cut-off point that was used in females was 14 (ages 7-12 years) and 20 (ages 13-17
years), and the cut-off point that was used in males was 17 (ages 7-12 years) and 16 (ages 13-17
years), as recommended by the CDI-2 scoring guidelines as an elevated score (Kovacs, 1985,
2015). Different cut-off points are used to account for age and gender differences in depression
scores, particularly the increase in depressive symptoms with age and the greater prevalence of
depressive symptoms in females (Saluja et al., 2004).
Secondary Outcomes
Anxiety scores: The secondary outcome of interest in this study were anxiety scores, which were
assessed by The Screen for Childhood Anxiety Related Emotional Disorders (SCARED). The
SCARED has both child and parent versions, with 41 items and 5 factors (panic disorder or
significant somatic symptoms, generalized anxiety disorder, separation anxiety, social anxiety
disorder, significant school avoidance) that parallel the DSM-IV classification of anxiety
52
disorders. The SCARED has sound psychometric properties and is designed for children aged 8 to
18 years of age. For the total score and each of the five factors, the SCARED demonstrated good
internal consistency (alpha = 0.90), test-retest reliability (intraclass correlation coefficients = 0.70
to 0.90), discriminative validity (both between anxiety and other disorders and within anxiety
disorders). Total score ≥25 may indicate the presence of an anxiety disorder. A cut off of a total
score of 25 has a sensitivity of 71% and specificity of 67% (Birmaher et al., 1999).
Family Functioning: Another outcome of interest in this study was family functioning, which
was assessed by The PedsQL family impact module total scores. The PedsQL family impact
module is a valid and reliable 36-item parent-report questionnaire that was used to measures how
having a child with a medical condition impacts overall family functioning. Total scores
encompass scores from the following domains: parent health related quality of life (encompassing
physical, emotional, social, and cognitive functioning), communication, worry, daily activities and
relationships. A family summary score is also generated which encompasses scores from the also
domains of daily activities and overall relationships. Higher scores on the module indicate better
family functioning and less of a negative family impact from the child’s health. PedsQL family
impact module total scores were used to determine family functioning in this study as they
encompass scores from all domains (Varni et al., 2004).
53
4.4.2.Exposures
Sleep Patterns
Sleep patterns were assessed objectively for 7 days using actigraphy which are described in detail
in section 4.5.4. Sleep duration was assessed as total sleep time in a 24 hour period for an average
of 7 days. Sleep quality was assessed objectively using sleep efficiency percentage in a 24 hour
period for an average of 7 days. Table 6 provides a summary of sleep patterns of interest.
Self-Reported Sleep Quality: The Pittsburgh Sleep Quality Index provides information on sleep
quality by providing an overall sleep disturbance score (Buysse, Reynolds, Monk, Berman, &
Kupfer, 1989) (Carpenter & Andrykowski, 1998). The Pittsburgh Sleep Quality Index is a 19-item
self-rated questionnaire that assesses sleep quality and sleep disturbances in adolescents and adults.
Individuals rate the frequency of several sleep-related problems. The individual questions from
seven components ask about subjective sleep quality, sleep latency, sleep duration, habitual sleep
efficiency, sleep disturbances, use of sleeping medications and daytime dysfunction. For example,
question 5 asks about the frequency of issues (e.g., nighttime awakenings, bad dreams,
uncomfortable room/body temperature) that may have contributed to trouble sleeping in the past
month. Responses range from 0 for “not during the past month” to 3 for “three or more times a
week”. The sum of the scores for the seven components yields one global score. In adolescents
and young adults (ages 14-24 years old), the Pittsburgh Sleep Quality Index demonstrates a one-
factor model with a moderate reliability (Cronbach's alpha =0.72) (de la Vega et al., 2015). Higher
scores indicate greater sleep disturbances, where a global score greater than 5 suggests poor sleep
quality (Carpenter & Andrykowski, 1998).
Excessive Daytime Sleepiness
Daytime sleepiness was measured using adapted version of the Epworth sleepiness scale (Epworth
scale) used in pediatrics (Johns, 2015). The Epworth scale is an 8 items questionnaire that assesses
excessive daytime sleepiness at first clinical presentation. Individuals indicate how likely they are
to fall asleep in each situation (0=would never doze or sleep, 1=slight chance of dozing or sleeping,
2=moderate chance of dozing or sleeping, 3=high chance of dozing or sleeping). A diagnosis of
excessive daytime sleepiness was made for any individual who scored greater than 10 on this scale
54
(Johns, 2015). In adolescents (ages 12-18 years) the Epworth scale demonstrates a moderate
internal reliability (Cronbach's alpha= 0.73) (Janssen, Phillipson, O'Connor, & Johns, 2017).
Physical Activity Levels
PA levels were assessed objectively with a pedometer that calculates total daily step count. The
daily step count of an average of 7 days was used. Pedometer details are described in section 4.5.5.
Below are details on the questionnaire used to assess self-reported PA levels:
Self-reported physical activity (PA) levels: The Godin Leisure-Time Exercise Questionnaire
(Godin) provides a general estimation of weekend and weekday activity levels (Godin & Shephard,
1985). The Godin has been found to be reliable and valid for use in the pediatric population (Sallis,
Buono, Roby, Micale, & Nelson, 1993) and has also been used in other pediatric neurological
conditions (Kinnett-Hopkins, Grover, Yeh, & Motl, 2016). Individuals are asked to report the
frequency (number of days) of strenuous (i.e., running or jogging), moderate (i.e., fast walking),
and mild (i.e., easy, leisurely walking) PA performed for periods of 15 minutes or more during
leisure time over a usual week. The Godin health contribution score was calculated by using
metabolic equivalents (METS) using the following formula: (number of days of strenuous PA X
9 METs) + (number of days of moderate PA X 5 METs).
Health contribution scores are classified as follows: <14 indicate the individual is insufficiently
active, scores 14-23 indicate the individual is moderately active and scores >24 indicate the
individual is active (Godin & Shephard, 1985).
55
4.4.3.Covariates
Participant Demographics and Medical History:
Demographic data was collected from participants using a standardized case report form. For the
narcolepsy group this data was collected from participants/parents and medical records at the
Hospital for Sick Children at the time of the clinic visit. For the control group, this data was
collected from participants/parents at the time of their research visit. For the narcolepsy group,
patients/caregivers were asked to self-report disease symptoms (EDS, cataplexy, sleep paralysis,
hallucinations) and report their current medications (if any). The following variables were
identified as potential covariates because of the current literature reporting an association between
the respective covariate and depressive symptoms:
1. Obesity (yes or no) (Melnyk et al., 2006), which was calculated using the Body mass index
(BMI) Z-score. BMI Z score was calculated using measured height and weight according
to age and sex specific growth curves (https://zscore.research.chop.edu) Obesity was
defined as a BMI Z-score >2.0 (de Onis et al., 2007).
2. Age (years) (Saluja et al., 2004)
3. Sex (male or female) (Saluja et al., 2004)
Obesity, age and sex are associated with the exposure/independent variables of interest:
1) Sleep (Mary A. Carskadon, 2011; Chaput & Dutil, 2016; Marczyk Organek et al., 2015)
2) PA levels (Anderson et al., 2017; Cairney, Veldhuizen, Kwan, Hay, & Faught, 2014)
56
4.4.4.Actigraphy Monitoring of Sleep
PSG is the gold standard for the diagnosis of sleep-related disorders but it only measures
sleep cycles for one night and may not be a reflection of the natural sleep environment. Therefore,
actigraphy was chosen as a tool for this study to allow for longer monitoring of the sleep patterns.
Actigraphy was obtained using the Mini-Mitter Actiwatch-2 (Philips Respironics Bend, OR). The
Actiwatch-2 is a portable device worn on the wrist that detects gross motor activity through an
internal sensor. Epoch intervals are used to assess gross motor activity. Data from epoch intervals
are then stored in the device’s internal memory. The Actiwatch-2 utilizes wake sensitivity
thresholds to discriminate between sleep and wakefulness during gross motor activity. A medium
threshold defines wakefulness as an epoch with 40+ activity counts. A low threshold is defined as
an epoch with 20+ activity counts and high as 80+ activity counts (Meltzer, Walsh, Traylor, &
Westin, 2012). The raw actigraphy data is translated to sleep measures using the actigraph scoring
analysis software (Philips Actiware Version 6.0.9). Participants in this study wore the Actiwatch-
2 on their wrist for 7 days for 24 hours, and removed it only during aquatic activities (e.g.,
showering). Data collection with actigraphy for at least five nights ensures reliable estimates (≥
0.70) for sleep measures including sleep efficiency and wake after sleep onset (Acebo et al., 1999).
A sleep diary was used in conjugation with actigraphy to derive contextual information
about sleep. The sleep diary is a record of the patient’s bedtime and wake time each day, the start
and end times of any daytime naps as well as descriptive explanations of any sleep interruptions.
Sleep diaries were used to set scoring parameters for actigraphy data. Discrepancies between the
actigraph data and the sleep diary were identified and the actigraph record annotated for periods
when the device was off.
Actigraphy demonstrates feasibility and practicality in pediatric population for both
clinical and research purposes (Sadeh, 2011; Sadeh et al., 2000; Werner et al., 2008). Actigraphy
is an objective, non-invasive and cost-effective method for estimating sleep-wake patterns using
activity-based monitoring (Sadeh, 2011) (See Appendix 1). Actigraphy demonstrates good
sensitivity to detect sleep from limb activity (89-97%) (Meltzer et al., 2012) compared to PSG.
Additionally, actigraphy has been shown to be useful for can provide objective data regarding the
efficacy of the intervention (Sadeh, 2011) and has been used in pediatric narcolepsy populations
(Filardi et al., 2018).
57
However, actigraphy has limitations that should be considered. Actigraphy demonstrates
poor specificity (the ability to detect wakefulness following sleep onset) compared to
polysomnography (54% to 77%) (Meltzer et al., 2012). Data loss is another limitation with
actigraphy that can occur due to a failure to wear the actigraph and technical issues with the
actigraph (Sadeh, 2011). Using a sleep diary in conjugation with actigraphy is important to derive
contextual information about sleep and to help reduce uncertainties associated with actigraphy
(e.g., sleep and wake times) (Werner et al., 2008).
4.4.4.1. Scoring and Data Analysis
Data was collected in 30-second epochs, using a medium wake sensitivity (each epoch with
40+ activity counts was classified as being awake). The medium threshold is considered as the
most accurate in estimating sleep patterns in the pediatric population (Meltzer et al., 2012). Sleep
onset latency was scored following 10 consecutive minutes of immobility (Meltzer, Walsh, &
Peightal, 2015).
An actogram, which is graphical representation of the sleep-wake cycle that is generated
by the actigraphic scoring analysis software, is shown in Figure 8. Bedtime and wake time were
scored using event markers in conjugation with the self-report sleep diary. In the case where a
discrepancy was found between the event markers and the diary, precedence was given to the sleep
diary, as patients often forgot to press the button on the actiwatch indicating they were trying to
fall asleep. Any periods during which the watch was removed were excluded. Definitions for
actigraph sleep measures are shown in Table 6.
58
Figure 8: Scored Actogram of One Day for Control (17 year old female)
Specific sleep measures are obtained from the actogram as shown below
Table 6: Actigraph Sleep Parameter and Definitions
Sleep Parameter Scoring Definition
Total Sleep Time (over 24
hour period)*
Minutes scored as sleep between sleep onset and final
awakening
Sleep Efficiencyᶲ Percentage of time spent asleep during a defined interval
(total sleep time divided by time in bed)
Sleep Onset Latency Duration between bedtime and sleep onset
Wake After Sleep Onset Minutes scored as wake during sleep duration period
*Used as an objective measure of sleep duration
ᶲUsed as an objective measure of sleep quality
59
4.4.4.2. Transition Probabilities
An advanced algorithm for analyzing actigraphy data involves the application of a state-
transition approach to assess local temporal dynamics of rest-activity patterns (Lim et al., 2011).
This algorithm was designed by Lim and colleagues and provides a detailed means of quantifying
sleep fragmentation based on rest-activity data that is generated by actigraphy and provides an
index of fragmentation (Lim et al., 2011). The transitions probabilities algorithm has also been
used in pediatric populations (S. Selvadurai et al., 2018).
MATLAB was used to implement the algorithm. Using the algorithm, each 30-second
epoch of the actigraphic record was categorized as either rest or activity based on the number of
activity counts. An epoch with a number of counts of 0 was classified as rest, and an epoch with a
number of counts greater than 0 was classified as activity. The probability that an individual will
transition to rest after being in an active state is defined as pAR(t) (Lim et al., 2011). Likewise, the
probability that an individual will transition to an active state after being in a rest state is defined
as pRA(t) (Lim et al., 2011). Figure 9 illustrates the estimated transition probabilities pRA(t) and
pAR(t) plotted against the duration of time that is spent in rest or activity (Lim et al., 2011).
A locally weighted scatterplot smoothing regression was performed with the pRA(t) and
pAR(t) plots. The plot is divided into 3 regions: a falling region, a constant non-zero probability
region and a rising region (Lim et al., 2011). A weighted average of data within the constant non-
zero probability region on each plot to calculate an index of fragmentation (Lim et al., 2011). Data
from the pRA(t) plot produces a kRA value which indicates the degree of fragmentation during
rest. Therefore higher kRA values indicate greater fragmentation during sleep (Lim et al., 2011).
Data from the pAR(t) plot produces a kAR value which indicates the degree of fragmentation during
activity. Higher kAR values indicate greater fragmentation during activity (wake hours) (Lim et al.,
2011).
60
Figure 9: Plot of pRA(t) and pAR(t)
(A) and (B) show the plot for pRA(t) and pAR(t), respectively. Observed values are indicated as
blue dots. The solid line represents the fitted LOWESS curve, and the dashed line represents
estimates for kRA and kAR. Figure Adapted from Selvaduari 2017 (Sarah. Selvadurai, 2017).
0
0.1
0.2
0.3
0.4
0.5
0.6
0 10 20 30 40 50 60
t = Time in Minutes
RA
Tra
nsi
tio
n
Pro
ba
bil
ity Fa
llin
g
Re
gio
n
Co
nst
an
t R
egi
on
Ris
ing
Reg
ion
kRA
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0 20 40 60 80 100 120 140
t = Time in Minutes
A R
Tra
nsi
tio
n
Pro
ba
bil
ity Fa
llin
g R
egi
on
Co
nst
an
t R
egi
on
Ris
ing
Reg
ion
kAR
A.
B.
61
4.4.5.Pedometers: An Objective Measure of Physical Activity
Pedometers are widely used instruments to obtain an objective measure of free living PA
by calculating the number of steps taken per day (Colley, Janssen, & Tremblay, 2012). Pedometers
use different mechanic or electronic motion sensors to quantify the amount of PA by accumulating
steps (Butte, Ekelund, & Westerterp, 2012). Pedometers are cost-effective, easy to use and their
data is easy to analyze, making them one of the most widely used objective PA measures across
all age groups (Clemes & Biddle, 2013). Some disadvantages of using pedometers to measure PA
is that they cannot measure water-based activities (e.g., swimming) or cycling (Clemes & Biddle,
2013). It is also not possible to measure the PA intensity, so a distinction between light PA and
moderate PA cannot be made (Clemes & Biddle, 2013).
In this study, all participants were asked to wear the Omron HJ-720ITCCAN pocket
pedometer (See Appendix 2) daily for seven days during wake hours, but not during sleep and
aquatic activities (e.g., swimming). An average daily step count was obtained based on seven days
of data. The Omron HJ-720ITCCAN pedometer provides a reliable and valid estimate of steps
taken, when compared to manually counted steps on a treadmill (±1.1% of manually counted steps)
(Lee, Williams, Brown, & Laurson, 2015). In a pediatric sample (N=41; mean age 5.43±0.63
years) the Omron HJ-720ITCCAN pocket pedometer step counts had a moderately high
correlation to accelerometer activity counts (r = 0.64, p < 0.001) (De Craemer et al., 2015).
4.5. Sample Size Calculation
The primary outcome of interest in this study were depression scores, which were measured
by the Children’s Depression Inventory-2nd edition (CDI-2) total score. The comparison of
Children’s Depression Inventory-2nd edition (CDI-2) scores between narcolepsy and control group
was conducted using two sample t-test assuming unequal variances (i.e. Satterthwaite t test).
Therefore, given a power of 0.8 and a type-1 error (alpha) of 0.05, assuming the mean ± standard
deviation of CDI-2 depression scores for narcolepsy and control group are 13±9 and 7±7
respectively (Stores et al., 2006), a total of 60 participants (i.e. 30 for each group) were needed to
achieve a power of 80 per cent.
62
4.6. Statistical Analyses
Descriptive statistics (means, standard deviations, and frequencies) were applied to
evaluate demographics, depression scores, anxiety scores, sleep patterns and PA levels in both
adolescents with narcolepsy and controls. The Shapiro-Wilk Test was used to examine normalcy
of distribution. Between-group comparisons were performed with independent sample t-tests and
chi-square tests as appropriate. To help decide what exposure variables should be entered in to the
regression models, pearson or spearman correlation analyses were done as appropriate, to evaluate
the association between sleep patterns, PA levels and depression scores. Exposure variables that
were associated with CDI-2 total scores at the <0.05 significance level were considered potential
factors associated with depression scores and were entered into the multiple linear regression
models.
Hierarchical multiple linear regression models were used to examine the relationship
between the primary outcome of depression scores (CDI-2 total scores) and self-reported sleep
quality, EDS and PA levels while controlling for various covariates (e.g., co-existing obesity).
For each analyses, group status (narcolepsy or control) was forced into the first block on step 1.
Co-existing obesity (yes or no) was entered on step 2 and age and sex were entered on step 3 and
self-reported sleep quality (Pittsburgh sleep quality index scores >5 yes or no) was entered on step
4. A stepwise entry method was used from step 2 to 4. Two additional analyses were run with the
exception of self-reported PA levels (Godin health contribution score) and EDS (Epworth scale
scores) on step 4. Self-reported sleep quality, EDS and PA levels could not be run in the same
model as they were significantly correlated.
Secondary data analyses was used to compare PedsQL Family Impact Module total scores
in the narcolepsy group to published data of a community sample (not seeking medical care)
(Medrano, Berlin, & Hobart Davies, 2013) and other pediatric disease groups (de Kloet et al.,
2015) (Jastrowski Mano et al., 2011) (Matziou et al., 2016). These studies were selected because
they: 1) Examined family functioning in youth or adolescents using the PedsQL Family Impact
Module and 2) Reported the mean and standard deviation (SD) of PedsQL Family Impact Module
total and domain scores. One-way analysis of variance and Tukey Honestly Significant Difference
Post Hoc Tests which accounted for multiple comparisons, using an interactive statistics webpage
(http://statpages.info/anova1sm.html) was performed. The association between depressive
63
symptoms and family functioning was evaluated using correlation analysis, however multiple
regression analysis (stepwise entry method) could not be performed because no significant
correlations (p<0.05) were identified. All other analyses were performed using SPSS version 23.0.
Figure 10: Conceptual Framework for Primary Objective
EXPOSURES PRIMARY OUTCOME
Sleep quality and duration
Excessive Daytime Sleepiness Depression Scores*
Physical Activity Levels
COVARIATES
Obesity, age, sex,
*A bidirectional relationship between exposures and primary outcome has been reported in the literature (Chorney et
al., 2008) (Korczak et al., 2017) but directionality will not be assessed by this master’s thesis
64
Chapter Five: Results
5.1. Demographics
From March 2017 to March 2018, a total of 35 adolescents with narcolepsy were eligible
for this study based on the inclusion/exclusion criteria. See Figure 11.
Figure 11: Participant Flow for the Narcolepsy Group
35 Patients Eligible
31 Patients Consented
30 Patients Completed Questionnaires
Complete Data:
Questionnaires (N=30)
Actiwatch (N=29)
Pedometer (N=22)
Malfunctioning pedometer (N=7)
Did not wear actiwatch/pedometer (N=1)
Patient Withdrawn (N=1)
(due to study time commitment)
Patients could not be contacted (N=3)
Patients not interested in research (N=1)
65
The control group consisted of 30 participants. Pedometer data was lost on three
participants due to a malfunctioning pedometer that did not collect data. Participants refused to
wear the pedometer again.
Detailed participant demographics can be found in table 7. The narcolepsy group was
significantly younger than the control group (mean age=13.8 ± 2.2 vs 14.9 ± 1.5 years; p=0.03).
The narcolepsy group also had a significantly higher BMI Z-score, a greater percentage of
individuals with co-existing obesity, and differences in distribution of race compared with the
control group.
66
Table 7: Participant Characteristics
Narcolepsy
N=30
Control
N=30
p value
Age (years) 13.8 ± 2.2 14.9 ± 1.5 0.03
Sex (% Male) 76.7 56.7 NS
Race (%)
Caucasian 46.7 70.0
0.01ᶲ
Black 13.3 0.0
Mixed 33.3 10.0
Other♦ 6.7 20.0
BMI Z Score 1.4 ± 0.8 0.3 ± 0.9 <0.01
Co-existing Obesity (%Yes) 30 3.3 <0.01
Narcolepsy Group Only
Narcolepsy Symptoms (%)*
EDS 100
Not Applicable
Cataplexy 75.9
Sleep paralysis 17.2
Hypnagogic Hallucinations 10.3
Medication (% Using)
Modafinil 33.3
Ritalin 26.7
Dexedrine 6.7
Strattera 3.3
Clomipramine 3.3
Not taking any medication 26.7
Obstructive Apnea Hypopnea Index 0.7 ± 0.7 Values reported as mean and ± standard deviation unless otherwise specified NS-Not Significant
EDS-Excessive Daytime Sleepiness ♦Other races include Arab/West Asian, Chinese, Korean, South Asian *symptoms reported by patients and caregivers
ᶲassesses significant difference between distributions of races between groups
67
5.2. Mental Health Outcomes: Depression Scores and Anxiety Scores
Depression scores (CDI-2 total scores) were significantly higher in the narcolepsy group
than the control group (p=0.01). A significantly greater proportion of patients in the narcolepsy
group also had depression scores in the elevated range (p=0.02). More patients in the narcolepsy
group (10%) reported suicidal ideation than the control group (3.3%), however this difference was
not statistically significant. Anxiety scores (SCARED total scores) were not significantly different
between groups. SCARED separation anxiety disorder (p=0.03) and social anxiety disorder
(p<0.01) subscale scores were significantly higher in the narcolepsy group compared to the control
group.
In this study, there were no significant associations between depression scores, sex, the
presence of obesity and age (data not shown). Details of all scores can be found in Table 8.
68
Table 8: Depression Scores and Anxiety Scores in Adolescents with Narcolepsy and Controls
Narcolepsy
N=30
Control
N=30
P value
Depression Scores
CDI-2 Total Score 10.9 ± 6.8 6.7 ± 5.5 0.01
CDI-2 Total Elevated (%Yes)* 23.3 3.3 0.02
CDI-2 Suicidal ideation (%Yes) “I think about killing myself but would not do it” 10.0 3.3 NS
Anxiety Scores
SCARED Total Score 20.5 ± 11.8 16.1 ± 10.9 NS
Panic Disorder or Significant Somatic
Symptoms 3.1 ± 2.8 3.6 ± 3.4 NS
Generalized Anxiety Disorder 6.7 ± 5.2 6.1 ± 4.5 NS
Separation Anxiety Disorder 3.2 ± 2.7 1.8 ± 1.7 0.03
Social Anxiety Disorder 6.2 ± 4.3 3.5 ± 2.8 <0.01
Significant School Avoidance 1.3 ± 1.4 0.9 ± 1.1 NS Values reported as mean and ± standard deviation unless otherwise specified
CDI-2-Children’s Depression Inventory 2nd edition
SCARED-The Screen for Childhood Anxiety Related Emotional Disorders
NS-Not significant
*CDI-2 total elevated cut-off points: [Females: ages 7-12 years-(14)/ages 13-17 years-(20)] [males: ages 7-12 years-(17)/ages 13-
17 years-(16)]
69
5.3. Sleep Patterns
Actigraphy data revealed the narcolepsy group had a significantly shorter total sleep time
(p=0.03) and lower sleep efficiency (p<0.01). The narcolepsy group also had a significantly greater
wake after sleep onset and number of awakenings (p<0.01). Sleep diary data revealed that the
narcolepsy group also had significantly greater self-reported nocturnal awakenings per night
(p<0.01). Pittsburgh Sleep Quality Index scores were also significantly higher in the narcolepsy
group, with a mean score of 5.7 ± 4.1 which is in the poor sleep quality range (>5) (p<0.01). More
patients were in the poor sleep-quality range in the narcolepsy group compared to the control group
and a trend towards significance was seen (50 vs 26.6 per cent; p=0.06). The narcolepsy group
also had a significantly higher Epworth scale scores (p<0.01). There were no significant
differences in transition probabilities between the narcolepsy and control groups. Details on sleep
patterns can be found in Table 9.
70
Table 9: Sleep Patterns in Adolescents with Narcolepsy and Controls
Narcolepsy
N=30*
Control
N=30
P
value
Actigraphy Data
Total Sleep Time (hours) 6.5 ± 1.5 7.2 ± 0.8 0.03
Sleep Efficiency (%) 63.7 ± 13.2 80.5 ± 11.8 <0.01
Wake After Sleep Onset (min) 88.1 ± 36.4 37.9 ± 12.6 <0.01
Awakenings (number) 52.1 ± 14.9 34.4 ± 7.7 <0.01
kAR (Wake Fragmentation) 0.03 ± 0.01 0.03 ± 0.02 NS
kRA (Sleep Fragmentation) 0.03 ± 0.01 0.03 ± 0.01 NS
Self-Reported Nocturnal Awakenings
(number per night) 2.6 ± 2.2 0.6 ± 0.9 <0.01
Pittsburgh Sleep Quality Index Score 5.7 ± 4.0 3.6 ± 2.3 0.02
Patients in Poor Sleep Quality Range
(% with Pittsburgh Sleep Quality Index >5) 50 26.6 0.06
Epworth Sleepiness Scale Score 13.1 ± 4.5 4.8 ± 3.5 <0.01 NS-Not significant
kAR-probability of transition from active state to rest (Fragmentation during wake)
kRA- probability of transition from rest to active state (Nocturnal Sleep Fragmentation)
*N=29 for actigraphy data in narcolepsy group
71
5.4. Physical Activity Levels
Objective PA levels (pedometer step counts) were less than 10 000 steps daily in both
groups. However, self-reported PA levels in both groups were in the active range (Godin health
contribution score >24). The control group had a significantly greater percentage of patients with
Godin health contribution scores in the active range (93.3 vs 73.3 per cent; p=0.04). The Godin
health contribution score and pedometer step count (daily average) were not significantly
correlated (r=0.04; p=0.807). Detailed PA data is found in Table 10.
Table 10: Physical Activity Data in Adolescents with Narcolepsy and Controls Narcolepsy
N=30*
Control
N=30*
P value
Pedometer step count (daily average) 7808.7 ± 3089.5 6939.3 ± 2205.6 NS
Godin Health Contribution Score 47.3 ± 31.7 59.3 ± 25.8 NS
Godin Health Contribution Score in
Active Range (% ≥24) 73.3 93.3 0.04
Strenuous Activity (days per week) 3.2 ± 2.5 4.3 ± 2.5 NS
Moderate Activity (days per week) 3.7 ± 3.7 4.5 ± 2.3 NS
Mild (days per week) 2.5 ± 3.4 3.4 ± 3.4 NS NS-Not Significant
*N=22 and 27 for pedometer data in narcolepsy and control group respectively
72
5.5. The Association between Mental Health and Sleep Patterns
Spearman's Rank-Order Correlation Analysis
In analyses using both groups, greater depression scores (CDI-2 total scores) were
associated with worse self-reported sleep quality (Pittsburgh sleep quality index scores) (r=0.586;
p<0.01) (See Figure 11) and greater EDS (Epworth scale scores) (r=0.463; p<0.01). Greater
anxiety scores (SCARED total scores) were also associated with poorer self-reported sleep quality
(r=0.632; p<0.01) and greater EDS (r=0.320; p=0.01).
In the narcolepsy group, greater depression scores (r=0.427; p=0.02) and anxiety scores
(r=0.603; p<0.01) were associated with worse self-reported sleep quality. In the control group,
greater depression scores were associated with worse self-reported sleep quality (r=0.521; p<0.01)
and greater EDS (r=0.420; p=0.02). In the control group, greater anxiety scores were also
associated with worse self-reported sleep quality (r=0.580; p<0.01) and greater EDS (r=0.511;
p<0.01). See Table 11 for details on associations.
73
Table 11: Associations between Sleep Patterns, Depression Scores and Anxiety Scores
CDI-2 Total Score
(Depression Scores)
SCARED Total Score
(Anxiety Scores)
Narcolepsy Group (N=30)
Excessive Daytime Sleepiness
(Epworth Sleepiness Scale)
r 0.360 0.164
p 0.051 0.350
Self-Reported Sleep Quality
(Pittsburgh Sleep Quality Index)
r 0.427* 0.603**
p 0.02 <0.01
Total Sleep Time (hours)
(Actigraphy)
r 0.045 0.135
p 0.817 0.486
Sleep Efficiency (%)
(Actigraphy)
r 0.166 0.037
p 0.391 0.849
Control Group (N=30)
Excessive Daytime Sleepiness
(Epworth Sleepiness Scale)
r 0.420* 0.511**
p 0.02 <0.01
Self-Reported Sleep Quality
(Pittsburgh Sleep Quality Index)
r 0.521** 0.580**
p <0.01 <0.01
Total Sleep Time (hours)
(Actigraphy)
r -0.293 -0.205
p 0.117 0.277
Sleep Efficiency (%)
(Actigraphy)
r 0.193 0.126
p 0.306 0.507
Both Groups (N=60)
Excessive Daytime Sleepiness
(Epworth Sleepiness Scale)
r 0.463** 0.320*
p <0.01 0.01
Self-Reported Sleep Quality
(Pittsburgh Sleep Quality Index)
r 0.586** 0.632**
p <0.01 <0.01
Total Sleep Time (hours)
(Actigraphy)
r -0.183 -0.017
p 1.65 0.896
Sleep Efficiency (%)
(Actigraphy)
r -0.142 -0.030
p 0.285 0.823
CDI-2-Children’s Depression Inventory 2nd edition
SCARED-The Screen for Childhood Anxiety Related Emotional Disorders
*p<0.05
**p<0.01
r-Correlation Coefficient
N=29 for actigraphy data in narcolepsy group
74
Figure 12: Correlation Analysis between CDI-2 Total Scores and Pittsburgh Sleep Quality Index
Scores (Total N=60; N=30 for both adolescents with narcolepsy and controls)
This graph shows a positive association between depression scores (CDI-2 Total Scores) and Pittsburgh
Sleep Quality Index Scores in both adolescents with narcolepsy and controls
r=0.521; p<0.01
r=0.586; p<0.01
r=0.427; p=0.02
75
Multiple Linear Regression Models
Data from the hierarchical multiple linear regression models with depression scores (CDI-
2 total scores) as the dependent variable are shown in Tables 12, 13 and 18. Because of the limited
sample size, only variables that were significantly associated with depression scores in the
correlation analyses as well as covariates: age, sex and presence of obesity were include in the
regression model using a stepwise entry method. Because depression scores differed significantly
between groups, group was forced into the model at step 1 explaining 10% of the variance in
depression scores. Age was included in the model at step 2, which explained an additional 7% of
the variance. The following covariates: presence of obesity (yes or no) and sex (male or female)
were not selected to be included in the final models of the stepwise regression.
The addition of the variables self-reported sleep quality (Pittsburgh Sleep Quality Index
scores >5 (yes or no)) at step 3 explained 18% of the variance. The unstandardized β coefficients
indicate that participants with a Pittsburgh Sleep Quality Index scores greater than 5 (indicating
poor self-reported sleep quality) have depression scores that are 6.1 units higher than participants
with Pittsburgh Sleep Quality Index scores less than 5 (See Table 14).
Epworth scale scores were included in a separate regression model because of the
significant correlation between Pittsburgh Sleep Quality Index and Epworth scale scores (r=0.337;
p<0.01). The inclusion of Epworth scale scores at step 3 explained 15% of the variance in
depression scores. The unstandardized β coefficients indicate that for every unit increase in
Epworth scale scores (indicating greater EDS), there is a 0.7 unit increase in depression scores.
Age was also significantly associated with CDI-2 total scores in this model (β=1.10; p=0.01) (See
Table 15).
In summary, self-reported sleep quality, EDS and age predict depression scores amongst
adolescents with narcolepsy and controls.
76
Table 12: Multiple Regression Analysis Results: Sleep Quality and Depression Scores
Model Unstandardized
Coefficients
p 95% Confidence
Interval for β
R2 R2
change
Overall
p value
β SE Lower
Bound
Upper
Bound
1 (Constant) 14.9 2.6 <0.01 9.7 20.3 0.10
--
0.02
Group -4.2 1.7 0.02 -7.6 -0.9
2 (Constant) 3.1 6.1 0.61 -9.1 15.3 0.18 0.07 0.01
Group -5.2 1.7 <0.01 -8.6 -1.8
Age 0.9 0.4 0.04 0.1 1.8
3 (Constant) 3.5 5.4 0.52 -7.4 14.4 0.36 0.18 <0.01
Group -3.5 1.6 0.03 -6.6 -0.3
Age 0.6 0.4 0.16 -0.2 1.3
Pittsburgh Sleep
Quality Index
Score >5
6.1 1.6 <0.01 2.9 9.3
Dependent Variable: CDI-2 Total Scores
Pittsburgh Sleep Quality Index Score >5 (Yes or No)-Yes indicates poor self-reported sleep quality
The unstandardized regression coefficient (β), standard error (SE) of the coefficient, p value of the coefficient, R2 , R2 change and
overall p value for the model are shown for the primary outcome (CDI-2 total scores)
The unstandardized regression coefficient (β) reflects the change in the outcome per unit change in the predictor variable.
77
Table 13: Multiple Regression Analysis Results: Excessive Daytime Sleepiness and Depression
Scores
Model Unstandardized
Coefficients
p 95 %
Confidence
Interval for β
R2 R2
change
Overall
p value
β SE Lower
Bound
Upper
Bound
1 (Constant) 14.9 2.6 <0.01 9.7 20.3 0.10
--
0.02
Group -4.2 1.7 0.02 -7.6 -0.9
2 (Constant) 3.1 6.1 0.61 -9.1 15.3 0.18 0.07 0.01
Group -5.2 1.7 <0.01 -8.6 -1.8
Age 0.9 0.4 0.04 0.1 1.8
3 (Constant) -13.0 7.3 0.08 -27.7 1.7 0.32
0.15
<0.01
Group 0.1 2.2 0.97 -4.4 4.5
Age 1.1 0.4 0.01 0.3 1.9
Epworth
Sleepiness
Scale
0.7 0.2 <0.01 0.3 1.0
Dependent Variable: CDI-2 Total Scores
Epworth Sleepiness Scale is a measure of excessive daytime sleepiness
The unstandardized regression coefficient (β), standard error (SE) of the coefficient, p value of the coefficient, R2 , R2 change and
overall p value for the model are shown for the primary outcome (CDI-2 total scores)
The unstandardized regression coefficient (β) reflects the change in the outcome per unit change in the predictor variable.
78
5.6. The Association between Mental Health, Sleep Patterns and
Physical Activity
Spearman's Rank-Order Correlation Analysis
In analyses using both groups, greater depression scores were associated with lower self-
reported PA levels (Godin Health Contribution score) (r=-0.434; p<0.01). Greater anxiety total
scores were also associated lower self-reported PA levels (r=-0.274; p=0.03). Greater self-reported
PA levels were also associated with better self-reported sleep quality (r=-0.288; p=0.03). See Table
14 for details on associations.
When evaluating the narcolepsy group, greater depression scores were associated with
lower self-reported PA levels (r=-0.512; p<0.01). Greater self-reported PA levels were associated
with better self-reported sleep quality (Pittsburgh sleep quality index) (r=-0.427; p=0.02). No
significant associations were seen in the control group. See Table 15 for details on associations.
79
Table 14: Association between Physical Activity Levels, Sleep Patterns and Depression/Anxiety
Scores in Both Groups (N=60)
Godin Health
Contribution
Score
Pedometer
Average # of Daily
Steps
CDI-2 Total Score (Depression Scores) r -0.434** -0.004
p <0.01 0.979
SCARED Total Score (Anxiety Scores) r -0.274* -0.113
p 0.03 0.441
Epworth Scale r -0.117 0.086
p 0.375 0.556
Pittsburgh Sleep Quality Index r -0.288* 0.093
p 0.03 0.527
Total Sleep Time (Actigraphy) r 0.230 -0.150
p 0.08 0.304
Sleep Efficiency % (Actigraphy) r 0.152 -0.268
p 0.249 0.060
Pedometer Average Daily Steps N=49
CDI-2-Children’s Depression Inventory 2nd edition
SCARED-The Screen for Childhood Anxiety Related Emotional Disorders
*p<0.05
**p<0.01
80
Table 15: Association between Physical Activity Levels, Sleep Patterns and Depression/Anxiety
Scores in the Narcolepsy and Control Group
Narcolepsy Group
(N=30)
Control Group
(N=30)
Godin
Health
Contribution
Score
Pedometer
Average
Daily
Steps
Godin
Health
Contribution
Score
Pedometer
Average
Daily
Steps
CDI-2 Total Score
(Depression Scores)
r -0.512** -0.302 -0.262 0.202
p <0.01 0.173 0.163 0.311
SCARED Total Score
(Anxiety Scores)
r -0.271 -0.170 -0.277 -0.027
p 0.148 0.449 0.139 0.893
Epworth Scale r 0.024 -0.063 0.041 0.063
p 0.901 0.782 0.828 0.753
Pittsburgh Sleep Quality Index r -0.427* 0.112 -0.065 -0.096
p 0.02 0.618 0.733 0.635
Total Sleep Time (Actigraphy) r 0.158 -0.004 0.196 -0.290
p 0.412 0.986 0.300 0.142
Sleep Efficiency %
(Actigraphy)
r 0.085 -0.062 -0.029 -0.347
p 0.661 0.786 0.878 0.076
Pedometer Average Daily Steps N=22 for Narcolepsy group and N=27 for Control Group
CDI-2-Children’s Depression Inventory 2nd edition
SCARED-The Screen for Childhood Anxiety Related Emotional Disorders
*p<0.05
**p<0.01
81
Multiple Linear Regression Models
The first two steps of the regression analyses are identical to table 12 and 13. The inclusion
of Godin Health Contribution scores at step 3 explained 9% of the variance. The unstandardized
β coefficients indicate that for every unit increase in Godin Health Contribution scores (indicating
greater self-reported PA), the CDI-2 total scores decrease by 0.10 (See Table 16).
In summary, self-reported physical activity levels depression scores amongst adolescents
with narcolepsy and controls
Table 16: Multiple Regression Analysis Results: Self-Reported Physical Activity and
Depression Scores
Model Unstandardized
Coefficients
p 95% Confidence
Interval for β
R2 R2
change
Overall
p value
β SE Lower
Bound
Upper
Bound
1 (Constant) 14.9 2.6 <0.01 9.7 20.3 0.10
-- 0.02
Group -4.2 1.7 0.02 -7.6 -0.9
2 (Constant) 3.1 6.1 0.61 -9.1 15.3 0.18 0.07
0.01
Group -5.2 1.7 <0.01 -8.6 -1.8
Age 0.9 0.4 0.04 0.1 1.8
3 (Constant) 13.4 7.2 0.07 -0.9 27.8 0.26 0.09 <0.01
Group -3.6 1.7 0.04 -7.1 -0.1
Age 0.3 0.5 0.48 -0.6 1.3
Godin Health
Contribution
Score
-0.1 0.0 0.02 -0.1 -0.01
Dependent Variable: CDI-2 Total Scores
Godin Health Contribution Score-measure of self-reported PA level
The unstandardized regression coefficient (β), standard error (SE) of the coefficient, p value of the coefficient, R2 , R2 change and
overall p value for the model are shown for the primary outcome
The unstandardized regression coefficient (β) reflects the change in the outcome per unit change in the predictor variable.
82
5.7. Family Functioning in Pediatric Narcolepsy
Family functioning was assessed in the narcolepsy group using the PedsQL Family Impact
Module. PedsQL Family Impact Module total, communication and worry scores were significantly
lower than published scores of a community sample that were not seeking medical care (Medrano
et al., 2013). See Table 17 for details on scores.
Pearson Correlation Analysis
No significant associations were seen between family functioning scores and patient
depression scores or anxiety scores. See Supplementary table 1 for details on associations.
Table 17: PedsQL Family Impact Module Scores
Narcolepsy
n=30
Community
Sample
n=726-901a
(Medrano et al., 2013)
P value
Family Impact Total Score 64.0 ± 19.8 70.8 ± 14.5 0.01
Parent HRQOL Total Score 69.4 ± 19.8 69.4 ± 15.5 NS
o Parent Physical Functioning Score 68.1 ± 22.8 64.9 ± 17.4 NS
o Parent Emotional Functioning Score 61.7 ± 21.2 67.6 ± 17.9 NS
o Parent Social Functioning Score 77.1 ± 24.3 74.4 ± 19.1 NS
o Parent Cognitive Functioning Score 74.5 ± 23.2 73.5 ± 18.6 NS
Communication 70.4 ± 24.2 81.9 ± 17.7 <0.01
Worry 42.3 ± 21.8 78.1 ± 20.1 <0.01
Family Summary Score 64.4 ± 26.0 65.5 ± 18.5 NS
o Daily Activities 59.4 ± 32.6 63.2 ± 22.5 NS
o Family Relationships 66.7 ± 24.8 67.0 ± 19.4 NS NS=Not Significant
a sample size is a range because scores were only calculated on completed scales.
83
PedsQL Family Impact Module total, parent HRQOL, family summary worry and
communication scores in the narcolepsy group were significantly lower than the traumatic brain
injury group (e.g., skull/brain trauma, concussions). All scores in the narcolepsy group were
similar to adolescents with chronic pain.
Lowest Scores on the PedsQL Family Impact Module were seen in the worry domain,
which asks about worries related to medical treatments efficacy, treatment side-effects, others
reaction to their child’s illness, illness affecting other family members and the child’s overall future
(Varni et al., 2004). Scores in the worry domain were similar to adolescents with chronic pain and
significantly lower than pediatric patients with non-traumatic (e.g., stroke) and traumatic brain
injuries. See Table 18 for details on all scores and Figure 12 for graphical comparison of PedsQL
Family Impact Module total and worry scores. See Supplementary table 2 on details on the
characteristics of the studies that were used in the secondary data analysis.
84
Table 18: Family Functioning in Pediatric Narcolepsy Compared to Other Disease Groups
Narcolepsy
(A)
Chronic
Pain (Jastrowski
Mano et al., 2011) (B)
Cancer (Matziou et al.,
2016) (C)
Non-
Traumatic
Brain
Injury (de
Kloet et al.,
2015) (D)
Traumatic
Brain
Injury (de
Kloet et al.,
2015) (E)
Relevant
Group
Differences
Sample Size 30 458 92 27 81 --
Age (years) 13.8 ± 2.2 13.7 ± 2.7 9.1 ± 4.1 13.0 (range: 5.0-22.0) A>C*
Family Impact
Module Total
Score
64.0 ± 19.8 64.7 ± 19.5
53.5 ± 17.4 70.8 ±19.6 83.6 ±16.1 A<E*
Parent HRQOL
Score
69.4 ± 19.8 67.4 ± 20.7
54.6 ± 19.9
72.6 ±19.7 85.1±17.0
A>C*
A<E*
Family
Summary
Scoreᶲ
64.4 ± 26.0
66.1 ± 23.4
58.1 ± 21.8
72.3 ± 21.4 80.8 ± 18.3
A<E*
Worry 42.3 ± 21.8 46.8 ± 22.6
NR 60.7 ± 28.0 83.2 ± 21.6
A<D*
A<E*
Communication 70.4 ± 24.2 74.3 ± 23.9
NR 69.4 ± 24.7
90 ± 17.1
A<E*
Only significant differences with the narcolepsy group reported
All values reported as mean and standard deviation unless otherwise specified
*p<0.01 ᶲFamily Summary Scores comprised of daily activities and family relationships scale scores
NR=Not reported
HRQOL-Health Related Quality of Life
85
64.4 64.7
53.5
70.8
83.6
70.842.3 46.8
60.783.2
78.1
0
10
20
30
40
50
60
70
80
90
Narcolepsy Chronic Pain Cancer Non-TraumaticBrain Injury
TraumaticBrain Injury
CommunitySample
Pe
dsQ
L Fa
mily
Imp
act
Tota
l Sco
re
Pediatric Condition
Figure 13: Family Functioning Scores in Pediatric Narcolepsy Compared to a Community
Sample and other Pediatric Conditions
Family Impact Total Scores Worry Subscale Scores
86
Chapter Six: Conclusions and General Discussion
6.1 Main Conclusions
This thesis examined the relationship between sleep patterns, PA levels and mental health
outcomes (depression scores and anxiety scores) amongst adolescents with narcolepsy. The
primary objective of this thesis was to examine if sleep patterns (duration and quality), EDS and
PA levels are associated with depression scores as the primary outcome and anxiety scores as the
secondary outcome in adolescents with narcolepsy compared with healthy adolescents. The
hypothesis that poor nocturnal sleep quality, EDS and lower levels of PA would be associated with
greater depression scores amongst adolescents with narcolepsy and controls was supported.
The second objective of this thesis was to describe family functioning in adolescents with
narcolepsy, in comparison to otherwise healthy adolescents (not seeking medical care) and
adolescents with other pediatric conditions. The hypothesis that family functioning would be
impaired in adolescents with narcolepsy in comparison to healthy adolescents but similar to
adolescents with other pediatric conditions was supported. The third objective of this thesis was to
examine the association between family functioning and depression scores and anxiety scores in
adolescents with narcolepsy. The hypothesis that family functioning would be associated with
depression scores and anxiety scores was not supported.
87
6.2 Specific Findings
6.2.1 Mental Health in Pediatric Narcolepsy
Depression Scores
The findings from this cohort of adolescents with narcolepsy are that a greater percentage
of adolescents with narcolepsy report elevated depression scores compared to controls (23.3 vs 3.3
per cent; p=0.02). Similarly, Inocente et al reported depression scores to be elevated in 25 per cent
of children and youth with narcolepsy that were less than 18 years of age (n=88) (C. O. Inocente
et al., 2014). Similar findings were also reported by Rocca et al (n=29), who used the CBCL and
reported 27.6 per cent of patients to have elevated scores in the withdrawn and depressive domain,
compared to only 7.7 per cent of controls (n=39) (Rocca et al., 2016). In a cohort of pediatric
patients with narcolepsy aged 4 to 18 years (n=42), Stores et al reported mean CDI-2 total scores
to be 12.30 ± 8.54 (Stores et al., 2006), which are comparable to this cohort where mean CDI-2
total scores were 10.9 ± 6.8.
Suicidal Ideation
This was the first study to report on suicidal ideation in the pediatric narcolepsy population.
Ten per cent of patients in the narcolepsy cohort selected the option “I think about killing myself
but would not do it” compared to 3.3 per cent of controls. This difference was not statistically
significant, which may be attributed to the small sample size. The percentage of patients reporting
suicidal ideation in this cohort of adolescents with narcolepsy are similar to other pediatric
neurological conditions such as traumatic brain injury and hydrocephalus and stroke where rates
were 11.1, 12.5 and 17.7 per cent respectively (Mazur-Mosiewicz et al., 2015). No patients in the
narcolepsy group reported suicidal ideation with intent (“I want to kill myself”), in contrast to the
3 and 1.9 per cent of pediatric patients with epilepsy and traumatic brain injury respectively
(Mazur-Mosiewicz et al., 2015).
88
Anxiety Scores
SCARED social anxiety disorder scores were significantly higher in the narcolepsy group
than the control group (5.9 ± 4.1 vs 3.5 ± 2.8 p<0.01). Social anxiety disorders, such as panic
attacks and social phobias, are reported to occur in 53 per cent of adults with narcolepsy (Fortuyn
et al., 2010), which typically onset after the diagnoses of narcolepsy (Ohayon et al., 2014). It has
been suggested that social anxiety in patients with narcolepsy may be associated with experiencing
disease symptoms (e.g., EDS, cataplexy) unexpectedly, causing embarrassment in social settings
(Morse & Sanjeev, 2018; Stores et al., 2006). This may be especially challenging for adolescents,
who like to conform to their peers in social settings (D. Taddeo, M. Egedy, & J. Y. Frappier,
2008).
SCARED separation anxiety disorder scores were also significantly higher in the
narcolepsy group than the controls (3.2 ± 2.7 vs 1.8 ± 1.7; p=0.02). Separation anxiety is the
excessive fear about being separated from home or an attachment figure (e.g., parent) (APA, 2013).
Separation anxiety is more common among adolescents with chronic conditions than otherwise
healthy adolescents (Chavira, Garland, Daley, & Hough, 2008). This may be because there is
anxiety about needing medical attention while being away from caregivers who know the child
and the condition well (e.g., dealing with a cataplexy episode, falling asleep in a public place)
(Chavira et al., 2008).
In summary, findings from this thesis underscore the prevalence of co-morbid depressive
symptoms and anxiety in adolescents with narcolepsy. Alarmingly, almost one fourth of the
adolescents with narcolepsy had elevated depression scores in this cohort and ten per cent reported
suicidal ideation. This suggests health care teams should prioritize screening for co-existing
depressive symptoms at routine clinical visits. Early identification of depressive symptoms in
patients can allow for early intervention. Interventions include referrals to an adolescent medicine
specialist and/or child psychiatrist who can further assess the patient and provide them with
appropriate treatment (e.g., antidepressant medication, cognitive behavioral therapy) to improve
patient mental health.
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6.2.2 The Association between Sleep Patterns and Depression Scores
This thesis evaluated factors associated with depression scores amongst adolescents with
narcolepsy and controls. In the narcolepsy group, poor self-reported sleep quality was associated
with greater depression scores. In the control group, poor self-reported sleep quality and greater
EDS were associated with greater depression scores. Associations may have been present in the
control group because sleep-related problems such as poor sleep quality (Gradisar, Gardner, &
Dohnt, 2011), EDS (J. A. Owens, Dearth-Wesley, Lewin, Gioia, & Whitaker, 2016) and EDS as a
result of chronic sleep loss (Ojio et al., 2016) are commonly experienced by otherwise healthy
adolescents. Sleep-related problems in healthy adolescents may occur for reasons such as
excessive use of electronic media before bed, early school start times, as well as increased extra-
curricular and academic demands leading to less opportunities for sleep (M. Moore & Meltzer,
2008; J. Owens, 2014; J. A. Owens et al., 2016). A discussion of the association between
depression scores and sleep patterns in both the narcolepsy and control group will follow:
Sleep Quality
The association between sleep quality and depression scores has not been previously
assessed amongst adolescents with narcolepsy however has been reported in adults with
narcolepsy. Dauvilliers et al reported adult narcolepsy patients (mean age 42.8±16.7 years) with
moderate to severe depressive symptoms had significantly worse self-reported sleep quality
(measured by Pittsburgh sleep quality index score) than patients with mild or no depressive
symptoms (8.3 ± 3.7 vs 5.9 ± 3.0; p<0.01) (Dauvilliers et al., 2009). The association seen in the
control group has been reported in a study of 889 adolescents (mean age 15.71±1.57 years) where
depression scores (evaluated with the Center for Epidemiologic Studies – Depression scale) were
positively associated with Pittsburgh Sleep Quality Index scores (r=0.58; p<0.01) (Raniti,
Waloszek, Schwartz, Allen, & Trinder, 2018).
Although there were significant associations in both the narcolepsy and control group, it is
important to note that self-reported nocturnal sleep quality was significantly worse in the
narcolepsy group compared to the controls, with a mean score of 5.7 ± 4.1, which is in the poor
sleep quality range (>5). Additionally, 50 per cent adolescents in the narcolepsy cohort were in
the poor sleep quality range as opposed to 26.6 per cent of controls, and this difference trended
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towards statistical significance (p=0.06). Poor nocturnal sleep quality is a common feature in
narcolepsy, and has also been reported in adults with narcolepsy (Rovere, Rossini, & Reimao,
2008). In adults with narcolepsy, 45 per cent report being unsatisfied with their nocturnal sleep
quality compared to only 10 per cent of healthy controls (p<0.01) (Rovere et al., 2008). Disturbed
nocturnal sleep may be due to the use of stimulant medication—the standard treatment in
narcolepsy (Corkum, Panton, Ironside, Macpherson, & Williams, 2008). Over 75 per cent of the
sample was taking stimulant medication, which has been associated with sleep problems in the
pediatric attention deficit hyperactivity disorder (ADHD) population (Storebo et al., 2015).
Children with ADHD on Ritalin (methylphenidate) have a 60 per cent greater risk of having sleep
problems (Relative Risk 1.60, 95% Confidence Interval 1.15-2.23; 13 trials, 2416 participants)
(Storebo et al., 2015). However, no studies have directly compared the presence of disturbed
nocturnal sleep in patients with narcolepsy (adult or pediatric) who are on and off stimulants (Roth
et al., 2013).
Excessive Daytime Sleepiness
The relationship between EDS and depression scores seen in this study, has been
previously reported in both adults and adolescents with narcolepsy (Dauvilliers et al., 2009).
Dauvilliers et al also reported patients with moderate to severe depressive symptoms had
significantly higher Epworth scores than patients with mild or no depressive symptoms (14.9 ± 5.1
vs 12.5 ± 4.9; p<0.01) (Dauvilliers et al., 2009). Inocente et al also reported Epworth scale scores
to be significantly higher in adolescents with narcolepsy who had elevated CDI total scores (18
(6–23) vs 15.5 (4–22); p=0.05) (C. O. Inocente et al., 2014). In a longitudinal study on healthy
adolescents (n=2787), EDS (Epworth scale scores >10) was reported to be associated with
depressive symptoms measured by the Beck Depression Inventory (Luo, Zhang, Chen, Lu, & Pan,
2018). The presence of EDS at baseline also predicted depressive symptoms at one year follow-
up amongst otherwise healthy adolescents (Adjusted Odds Ratio 2.26 (95% Confidence Interval
1.47–3.49)) (Luo et al., 2018).
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Sleep Duration
The relationship between short sleep duration and depression scores has been reported in
many studies (Ojio et al., 2016) (Baum et al., 2014) (Roberts & Duong, 2014) (Roberts & Duong,
2017). It is unclear why this association was not seen this study, but it may be due to the small
sample size.
Adolescents in the narcolepsy group had a significantly shorter total sleep time than
controls (measured by actigraphy). Disturbed nocturnal sleep may explain why adolescents with
narcolepsy have a shorter total sleep time than controls. Disturbed nocturnal sleep is a common
feature of narcolepsy and is characterized by frequent nightly awakenings resulting in sleep
fragmentation (Roth et al., 2013). In the adult narcolepsy population, patients with disturbed
nocturnal sleep report a significantly shorter total sleep time than patients without disturbed
nocturnal sleep (6 hours vs 7 hours; p<0.01) (Rosenthal et al., 1990). Other features of disturbed
nocturnal sleep seen in the narcolepsy group include greater self-reported nocturnal awakenings
(2.6 ± 2.2 vs 0.6 ± 0.9 awakenings per night; p<0.01) and lower sleep efficiency (63.7 ± 13.02 vs
80.5 ± 11.8 per cent; p<0.01), which may contribute to shorter total sleep duration. The poor sleep
efficiency seen in this cohort at 63.7 ± 13.02 per cent confirms findings by Alakuijala et al who
reported a low sleep efficiency percentage in adolescents with narcolepsy at 74.38 ± 9.39 per cent
(A. Alakuijala et al., 2015). A healthy sleep efficiency should be greater than or equal to 80 per
cent, and anything lower suggests poor sleep quality (Anniina Alakuijala et al., 2016).
It is important to note that actigraphy is sensitive to movement activity and may
underestimate total sleep time and sleep efficiency and overestimate the number of awakenings
an individual experiences (A. Alakuijala et al., 2015; Rupp & Balkin, 2011). Sleep time with minor
movement that would be scored as wake using actigraphy, would be sleep when recorded by PSG
(A. Alakuijala et al., 2015; Rupp & Balkin, 2011). Actigraphy data from this study is similar to
another study who assessed sleep patterns using actigraphy amongst adolescents with narcolepsy
(mean age 15.5 ± 6.5 years) (A. Alakuijala et al., 2015). Alakuijala et al reported an average total
sleep time of 6.4 ±1.1 hours in their cohort (n=56) (A. Alakuijala et al., 2015), which is similar to
the 6.5 ± 1.5 hours seen in the narcolepsy group in this study. Actigraphy may also underestimate
sleep efficiency percentage due to the high sensitivity to minor movements (A. Alakuijala et al.,
2015). However, motor disturbances during sleep are common in patients with narcolepsy and can
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contribute to disturbed nocturnal sleep (A. Alakuijala et al., 2015). PSG data on adults with
narcolepsy shows the mean motor activity index (defined as movement episodes per hour of sleep
excluding electromyographic-related muscle activity in the chin or tibialis anterior channels) is
significantly higher than controls (59.9 vs 15.4 per hour of sleep; p<0.01) (Frauscher et al., 2011).
Possible Mechanisms Explaining the Association between Sleep and Depression Scores
Sleep deprivation, which includes short total sleep duration and/or poor sleep quality
(Paruthi et al., 2016) was seen in both the narcolepsy and control group. The association between
sleep deprivation and depression and anxiety scores was seen in both groups. The mechanisms that
explain the relationship between sleep deprivation and depression and anxiety scores is largely
unknown (Palmer, Oosterhoff, Bower, Kaplow, & Alfano, 2018). Some evidence suggests that
sleep deprivation increases vulnerability for depressive symptoms via emotional processes
(Gregory & Sadeh, 2016). Specifically, sleep deprivation leads to disrupted emotional regulation
(the ability to control and modulate emotions) (Gross, 1998; Palmer & Alfano, 2017). Limited
research from correlational and experimental studies amongst adolescents suggests sleep
deprivation leads to poorer emotional regulation and increased reactivity to negative emotional
stimuli (Baum et al., 2014; McMakin et al., 2016; Vriend et al., 2013). EDS, as a result of sleep
deprivation is also associated with poor-emotional self-regulation in typically developing
adolescents (J. A. Owens et al., 2016).
The relationship between sleep deprivation and disrupted emotional regulation has also
been reported in adults (Palmer & Alfano, 2017). Sleep deprived adults report experiencing
heightened reactivity to negative emotional experiences (Palmer & Alfano, 2017). A study using
functional magnetic resonance imaging in sleep deprived adults showed, greater activation of the
amygdala in response to negative stimuli compared to adults who were not sleep deprived (Yoo et
al., 2007). This study also showed decreased connectivity between the medial prefrontal cortex
and the ventral anterior cingulate cortex in sleep deprived adults, which can weaken emotional
control (Yoo et al., 2007).
Interestingly, preliminary data suggests that adolescents may be even more susceptible to
the emotional effects of sleep deprivation than adults (McGlinchey et al., 2011) because they are
93
still developing emotional regulation skills (J. A. Owens et al., 2016). Regions of the brain that
govern emotional regulation (the prefrontal cortex, amygdala and ventral anterior cingulate cortex)
are still undergoing developmental changes during adolescence (Casey, 2015), and sleep
deprivation may adversely affect their normal function (Holm et al., 2009; Telzer, Fuligni,
Lieberman, & Galvan, 2013).
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6.2.3 Physical Activity in Pediatric Narcolepsy
This thesis was the first to evaluate PA levels in the pediatric narcolepsy population. Daily
step count between the narcolepsy and control group were not significantly different. However,
both groups had lower PA levels than the recommended daily 10 000 to 15 000 steps for
adolescents (Tudor-Locke & Bassett, 2004). This is consistent with results from the 2012 to 2013
Canadian Health Measures Survey showing that over 90 per cent of children and adolescents (ages
5–17 years) do not meet Canada’s recommended guideline of 60 minutes of moderate-to-vigorous
PA daily (Canada, 2015). The lack of PA seen in both groups of adolescents may be due to
competing demands (e.g., school work, extracurricular commitments) taking time away from PA
(Bauer, Yang, & Austin, 2004).
Objective PA levels in this group of adolescents with narcolepsy are similar to findings
from the adult narcolepsy population (Matoulek et al., 2017). One study on adults with narcolepsy
(n=42, mean age 34.9 ± 10.0) reported patients had a low average daily step count of 6346 ± 2026
(Matoulek et al., 2017) which is similar to this cohort of adolescents with narcolepsy. Lower PA
levels in narcolepsy may also be associated with low cardiopulmonary fitness, which has been
reported in the adult narcolepsy population (Matoulek et al., 2017). PA levels may also be lower
in adolescents with narcolepsy because of decreased spontaneous activity levels (Bruck et al.,
2005). Decreased spontaneous activity has been reported in adults with narcolepsy, even when
treated with stimulants (Bruck et al., 2005).
Even though adolescents in both groups had low objective PA levels, the vast majority of
participants self-reported PA levels to be in the ‘active’ range (Godin health contribution score
>24). This discrepancy between objective and self-report data may be due to bias seen with self-
report measures (e.g., over estimating PA levels) (Prince et al., 2008). Additionally, some
adolescents may have taken off the pedometer during certain types of aquatic PA (e.g., swimming),
or simply did not wear the device whilst partaking in PA. Although both groups of adolescents
self-reported high PA levels, a significantly lower percentage of adolescents in the narcolepsy
group were in the active range compared to the control group (73.3 vs 93.3 per cent; p=0.04). It
can be speculated that adolescents in the narcolepsy group self-reported lower PA levels, because
they feel that the symptoms of their condition (e.g., EDS, cataplexy) make it more difficult for
95
them to participate in PA. Godin health contribution scores in adolescents with narcolepsy (47.3 ±
31.7) are similar to other adolescents with neurological disorders such as monophasic acquired
demyelinating syndrome (54.0 ± 49.0) and multiple sclerosis (40.0 ± 46.0), who report symptoms
of fatigue from lack of sleep, as well as depression (Grover et al., 2015)
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6.2.4 The Association between Physical Activity, Sleep Patterns and
Depressive Symptoms
This thesis also evaluated the association between depression scores and PA levels in
adolescents with narcolepsy and controls. In adolescents with narcolepsy, self-reported and
objective PA levels were associated with depression scores. However, this association was not
seen in the control group, which is in contrast to larger studies in healthy adolescents where an
inverse association between PA levels and depression scores have been reported (Biddle & Asare,
2011) (Kremer et al., 2014; Wiles et al., 2012). This association may have not been seen in control
group of this study due to the small sample size (n=30).
The association between increased PA levels and lower depression scores may be
explained through a biological mechanism, specifically how participating in PA releases
endorphins, which are associated with feelings of euphoria (Dishman & O'Connor, 2009; Lubans
et al., 2016). Euphoric feelings after participating in PA may be due to changes in one or more
brain monoamines, with the strongest evidence available for dopamine, noradrenaline, and
serotonin (Lin & Kuo, 2013; Lubans et al., 2016). However, further research is needed to support
this assertion in pediatric populations (Bouix et al., 1994; Lubans et al., 2016).
The association between increased PA levels and lower depression scores may also be
explained through psychosocial pathways. Participating in PA in group settings may provide
adolescents with opportunities for social interaction and peer-conformity, and consequently
improve mood (Wiles et al., 2012). The relationship between PA and depression scores may also
be bidirectional (Korczak et al., 2017). It can be speculated that adolescents with narcolepsy may
not be interested in participating in PA because of co-existing depressive symptoms, which
encompass low mood and self-esteem. It can also be speculated that adolescents with narcolepsy
may avoid participating in PA such as team sports, because of the symptoms of their condition
(e.g., episodes of cataplexy), which occur unexpectedly and may be embarrassing (Matoulek et al.,
2017).
In the narcolepsy group, greater self-reported PA levels were also associated with and
better self-reported sleep quality. The association between PA and improved sleep quality has been
97
reported amongst adolescents with obesity (n=20; mean age: 14.5 ± 1.5 years; body mass index:
34.0 ± 4.7 kg/m2) who participated in a 12-week exercise-training program (3 hours of
exercise/week) (Mendelson et al., 2016). In this study, PSG data showed an increase in sleep
efficiency from baseline (+7.6 per cent; effect size: 0.76; p = 0.028) (Mendelson et al., 2016).
The relationship between PA and sleep quality may be attributed to the increase in deep
sleep (NREM-3) that has been associated with participating in regular PA (Driver & Taylor, 2000).
One study reports that healthy adolescents who participate in PA for 3.5 hours per week spend a
greater percentage of total sleep time in NREM-3 sleep compared to adolescents who engage in
PA for less than 3.5 hours per week (37.4 ± 6.4 vs 29.3 ± 4.3 per cent of total sleep time; p<0.001)
and have a greater sleep efficiency (95.19 ± 3.68 vs 94.72 ± 3.91 per cent; p<0.05) (Brand et al.,
2010). Additionally, an experimental study in healthy adult males (mean age 27.1 ±1.3 years) has
shown that sleep deprivation reduces free-living PA (Schmid et al., 2009), so it can be speculated
that feeling more rested (from having better sleep quality) may allow adolescents to have more
energy to participate in PA.
Surprisingly, there was no association between EDS and PA levels (both objective and
self-reported) in adolescents with narcolepsy, for reasons that are unclear. Although of PA has
been recommended for patients with narcolepsy to improve wakefulness due to its stimulating
effect (K. Maski & Owens, 2016), evidence for this statement is lacking. There were also no
associations seen between total sleep duration (measured by actigraphy) and PA levels. This may
be because of pedometers were used as an objective measure of PA, which are unable to assess
PA intensity. In the two studies assessing objective PA and sleep amongst healthy adolescents, an
accelerometer was used to assess PA (Lang et al., 2013) (Gerber et al., 2014). Both studies reported
that greater PA intensity is associated with longer sleep duration (Lang et al., 2013) (Gerber et al.,
2014).
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6.2.5 Family Functioning in Pediatric with Narcolepsy
PedsQL family impact module total, communication and worry subscale scores in this
cohort of adolescents with narcolepsy were significantly lower than a community sample not
seeking ongoing care for a medical condition (Medrano et al., 2013). PedsQL family impact
module total scores may be significantly lower in the narcolepsy group compared to a community
sample because of the greater burden associated with caring for a child with a medical condition,
leaving less time for enjoyable activities (McClellan & Cohen, 2007).
PedsQL family impact module total, parent HRQOL, family summary worry and
communication scores in the narcolepsy group were significantly lower than the traumatic brain
injury group (e.g., skull/brain trauma, concussions) (de Kloet et al., 2015). PedsQL family impact
module total scores may be significantly lower in the narcolepsy group compared to children and
adolescents with traumatic brain injuries for various reasons. Firstly, pediatric narcolepsy is more
rare than pediatric traumatic brain injury, and rare diseases receive less public awareness and
support from health care teams as information on disease progression is limited (Dharssi, Wong-
Rieger, Harold, & Terry, 2017). Lack of support and disease awareness may cause patients their
families to feel misunderstood and isolated, which negatively impacts overall family functioning
(Kippola-Paakkonen et al., 2016). Parents of pediatric patients with narcolepsy have reported
relying on support from close family and friends, and that support from health care professionals
is less common (Kippola-Paakkonen et al., 2016). Secondly, the disease course of narcolepsy
varies greatly from traumatic brain injury. Narcolepsy is a chronic condition with no cure,
unpredictable symptoms and limited treatment options (Kiran Maski et al., 2017). In contrast, a
traumatic brain injury is the result of an acute adverse event, where the patient is recovering to a
point where they may eventually lead the life of a healthy child (de Kloet et al., 2015). PedsQL
Family Impact Module total scores in the narcolepsy group are similar to a pediatric chronic pain
population, who experience a similar disease course of no cure, unpredictable symptoms and
limited treatment options (Jastrowski Mano et al., 2011).
Scores in the worry domain of the PedsQL Family Impact Module were the lowest of all
domains, and significantly lower than families with a traumatic and non-traumatic brain injury (de
Kloet et al., 2015). The worry domain asks caregivers questions regarding anxiety about medical
treatments efficacy, treatment side-effects, others reacting to their child’s illness, illness affecting
99
other family members and their child’s overall future (Varni et al., 2004). The low scores in the
worry domain may be because caregivers of children with non-curative chronic conditions often
experience a profound uncertainty with respect to their child’s future (Bally et al., 2018).
Caregivers of children with chronic conditions have described feeling fearful, alone and powerless
about their child’s diagnosis and ambiguity about their future health or overall future (Bally et al.,
2018). It can be speculated that families with narcolepsy may worry about daily symptoms (e.g.,
EDS and cataplexy) interfering with productivity in the school setting, which would limit future
educational opportunities (e.g., going to university) and further employment prospects. Parents of
pediatric patients with narcolepsy have also reported worrying about the lack of resources available
to help their child and their family in coping with the illness (Kippola-Paakkonen et al., 2016).
Family summary scores, which assess how daily activities and family relationships are
affected by an illness (Varni et al., 2004), were also significantly lower than families with a
traumatic brain injury (de Kloet et al., 2015). Caring for a child with a chronic condition may also
put greater demands on a caregiver like taking time off work to take their child to routine clinic
appointments, administering medication to their child which may impede daily routines (Herzer et
al., 2010). Added stress and workload may also cause tension between family members, as one
caregiver may feel a greater burden if responsibilities are not evenly distributed between parents
(Herzer et al., 2010). Daily activities may require more effort and flexibility in families caring for
a child with a chronic condition (Herzer et al., 2010). For example, in narcolepsy, caregivers may
find it more challenging to get their child up and ready for school, due to sleep fragmentation and
subsequent early morning fatigue.
These findings highlights how important it is for health care professionals to provide
family-centered care to patients with narcolepsy. This can be also done in collaboration with
services that offer support to the family (e.g., social work, child life specialists) who need to
understand specific needs of the family and connect them to the appropriate resources (e.g.,
community centers with activities for the entire family).
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6.3 Strengths and Limitations
This is the first study to evaluate and describe the relationship between sleep patterns, PA
levels and depression and anxiety scores amongst adolescents with narcolepsy. This study also
provides preliminary findings regarding the predictors of depression scores amongst adolescents
with narcolepsy. Furthermore, this study uses both objective and self-report measures to assess
sleep patterns and PA levels. Lastly, a control group was a part of the study to assess how having
a diagnosis of narcolepsy affects sleep patterns, PA levels and depression scores.
However, this study has several limitations that require consideration. The sample size of
the study population was small, and further large scale studies are needed to confirm these results.
Also this study was powered to evaluate and compare the differences in depression scores (CDI-2
total scores) between adolescents with narcolepsy and controls, and not powered to evaluate if
sleep patterns, EDS and PA levels are associated with depression scores.
The participants in this study were predominately male, so gender-specific effects of sleep
patterns, PA levels and depression and anxiety scores could not be evaluated. Additionally,
females typically report higher depression scores than males, so it is possible that depressive
symptoms were under-represented in this cohort. We also did not match the control group for age
and sex due to limited availability of participants. There was also a statistically significant
difference between ages between the control group and the narcolepsy group where the control
group was approximately one year older (14.9±1.5 vs 13.8±2.2 years; p=0.03) and being of older
age has been reported to be associated with higher depression scores (Saluja et al., 2004). However
the mean age of both groups were within a year of each other and limited to the early teen years.
Also, the observational nature of this study may introduce a potential for bias, as variables such as
sex, age and the presence of co-existing obesity have been reported to be associated with the
exposure variables (sleep patterns and PA levels) as well as the primary outcome (depression
scores). Additionally, it is likely that there are bidirectional relationships between the exposure
and outcome variables in this thesis, which could not be assessed because data was only collected
at one time point. Furthermore, it is unclear if patients had depressive symptoms/anxiety prior to
their narcolepsy diagnosis as this data was not available. Additionally, depressive symptoms and
anxiety were assessed by patient-report measures in this study and not through clinical interviews
by a child and adolescent psychiatrist. Clinical interviews are necessary to identify external factors
101
(e.g., bullying, parents divorcing) that may contribute to depressive symptoms. The patient-report
measures have been validated in pediatric populations, but not in patients with narcolepsy.
Moreover, actigraphy estimates sleep-wake cycles based on gross motor activity, but
cannot differentiate sleep stages which can only be done using PSG. However, PSG can be
burdensome for adolescents and their families, which could potentially influence recruitment bias.
Also, PSG only provides information about sleep patterns on one night and are not performed in
an adolescent’s natural sleeping environment. Another limitation was the use of pedometers for
assessment of PA levels. Pedometers only provide a step count and do not describe the level of
intensity PA was performed at. Future research on PA levels amongst adolescents with narcolepsy
should consider the use of an accelerometer, to assess PA intensity. Also there may have been
reporting bias in the pedometer tracking log, where the participant stated they wore the device
throughout the day when they actually did not. Moreover, the use of self-report measures for PA
may introduce recall and response bias, as patient may over/under estimate how much PA they
participate in for reasons such as inaccurate memory and social desirability (Prince et al., 2008).
Finally, using secondary data analyses for the comparison between family functioning scores is
also a limitation, as group differences could only be evaluated in the studies that reported mean
and standard deviation values of scores.
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6.4 Future Directions for Research
Future research is needed to confirm and build on the findings of this study. Longitudinal
studies assessing depressive symptoms from the time of disease onset throughout adolescence and
adulthood is important to understand when depressive symptoms and anxiety begin and how they
change over time. Longitudinal studies may also be useful in assessing the direction of the
association (e.g., bidirectional) between sleep patterns, PA levels and depressive symptoms. It is
also important to assess other factors that may be associated with depressive symptoms, such as
pathophysiological features of narcolepsy (e.g., amount of time spent in REM sleep, hypocretin
levels). Future studies should also include a qualitative component with structured interviews
where adolescents with narcolepsy discuss how the condition impacts their mental health and
psychosocial functioning, and the type of support they would like to receive from their caregivers
and health care teams. Qualitative research can also be useful in understanding barriers adolescents
with narcolepsy face to participating in PA. Finally, this thesis identified many patients with
elevated depression and anxiety scores, so patients were refereed on to services from adolescent
medicine and child psychiatry for additional support. It would be interesting to assess what affect
these services had on depressive symptoms and anxiety amongst adolescents with narcolepsy, and
what strategies (e.g., pharmaceuticals, behavioural intervention) were most beneficial to patients.
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The Actiwatch-2 is a portable
device, similar to a watch and is
worn on the non-dominant wrist by
a subject to measure gross motor
activity over extended periods of
time. With an internal sensor, the
Actiwatch-2 can detect movement
and translate this information to
internal memory. Also with the
light sensor and event marker, it
has the ability to record events of
significance (i.e. lights-out time).
With these features, the Actiwatch-
2 can provide objective measures
of wake-sleep cycles in the natural
home environment including sleep
duration and sleep efficiency,
providing an overall measure of
potential sleep disturbance.
Appendices
1. Phillips Actiwatch-2
119
2. Omron HJ-720ITCCAN pocket pedometer
120
Contributions
A number of investigators contributed to the research work presented in this thesis. Dr.
Shelly Weiss (pediatric neurologist), Allison Zweerink (sleep medicine nurse practitioner) were
staff with expertise in pediatric sleep medicine who assisted in screening and recruiting eligible
patients. Dr. Alene Toulany a staff pediatrician specializing in adolescent medicine who provides
mental health support and resources to narcolepsy patients. Dr. Brian Murray was the adult
neurologist who specializes in narcolepsy. Zihang Lu was the biostatistician that provided
assistance with the statistical analysis for this thesis. Funding for this study was provided by the
SickKids Research and Training Centre through the Restracomp Graduate Scholarship and Wake
up Narcolepsy. Additional funding sources for Arpita Parmar included the travel grant from the
SickKids Research and Training Centre.
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Supplementary Data
Supplementary Table 1: Associations between Family Functioning and Mental Health in
Adolescents with Narcolepsy
PedsQL Family Impact Module Domains
Total
Score
Family
Summary
Score
Parent Health-
related Quality of
Life Score Worry Communication
CDI-2 Total
(Depressive
Symptoms)
r -0.140 -0.221 -0.064 -0.134 -0.069
p 0.461 0.242 0.36 0.479 0.718
SCARED Total Score
(Anxiety)
r -0.265 -0.270 -0.258 -0.212 -0.223
p 0.157 0.149 0.168 0.260 0.237 CDI-2-Children’s Depression Inventory 2nd edition
SCARED-The Screen for Childhood Anxiety Related Emotional Disorders
*p<0.05
**p<0.01
Supplementary Table 2: Characteristics of Studies used in Secondary Data Analysis
Author, Year of
study and
country
Population and Sample Size Age (y) Race (%White)
>Post-
Secondary
Education
(%Yes) Sex
(%Male) Medrano 2013
USA
(Medrano et al.,
2013)
Community
N=726-901
-Patients not seeking medical care
recruited from community)
*Sample size is a range because scores
were only calculated on completed scales
8.8±3.9
37.4*
82.9 60
47.7
de Kloet 2015
Netherlands
(de Kloet et al.,
2015)
Traumatic
Brain Injuryᶲ
N=81
-Skull/brain trauma
-Concussion
-Contusion cerebri
-Neurological trauma
13
(range
5–22)
NR 47
60
Non- Traumatic
Brain Injuryᶲ
N=27
-Brain tumor
-Meningitis/encephalitis
-Stroke
-Acute disseminated encephalomyelitis
-Multiple sclerosis
-Central nervous system demyelinating
disease
-Hypoxia-ischemia
123
Jastrowski Mano
2011
USA
(Jastrowski
Mano et al.,
2011)
Chronic Pain
N=458
-Headache (34%)
-Musculoskeletal pain (26%)
-Abdominal pain (23%)
-Complex Regional Pain Syndrome (3%)
-Other (14%)
(generalized pain, pain secondary to
trauma, unclear pain diagnosis)
-Patients were experiencing pain for at
least 3 months
13.7±2.7 78 NR
32
Vasiliki Matziou
2016
Greece
(Matziou et al.,
2016)
Cancer
N=92
-Leukemia–lymphoma (18%)
-Solid tumors (7.3%)
-Others (8.2%)
-On treatment (80.2%)
-Relapse (8.1%)
-Remission (11.6%)
9.1± 4.1
15.2*
NR 29
All values reported as mean and standard deviation unless otherwise specified
NR-Not Reported
*% of patients in adolescent age range (10-18 years)
ᶲPatients recruited from hospitals not rehabilitation/treatment center
