scholarly journals 0977 Risk Factors For Developing Sleep Disorders In Children

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A371-A371
Author(s):  
A K Johnson ◽  
A A Santos ◽  
L G Araujo ◽  
V S Gonsalves ◽  
B L Walker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Sleep Disorders ◽  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Sleep Onset ◽  
Significant Risk ◽  
Sleep Onset Latency ◽  
Physiological Factors ◽  
Pediatric Clinic

Abstract Introduction Unidentified sleep disorders can affect emotional, cognitive and social development in children. Screening for sleep disorders within the pediatric population is not common practice during medical visits. The objective of this study is to identify specific questions related to behavioral and physiological factors having potential to screen and detect those at risk for sleep disorders in a general pediatric clinic. Methods A retrospective archive from electronic medical records was analyzed from 1,361 children patients, 0-18 years old, that visited a pediatric clinic from March-November of 2019. Children or their parents reported on the presence of eight objective behavioral and physiological factors on the Kids Sleep Screener Questionnaire (KSSQ), which were used as potential risk factors for sleep disorders. Propensity of daytime sleepiness was measured through the Epworth Sleepiness Scale for Children and Adolescents (ESS-CHAD). Scores higher than 11 were considered a positive indicator of potential sleep disorders because of excessive daytime sleepiness. Positive scores from the ESS-CHAD were used for comparison with the KSSQ factors using chi-square test of SAS software. Results Among the eight factors, snoring was the strongest risk factor and increased sleep duration was the weakest risk factor associated with a positive ESS-CHAD. Relationships among risk factors and the increased likelihood for developing sleep disorders were statistically significant (p<0.05-p<0.0001) and identified as following: snoring by 2.46 times, restless sleeper by 2.03 times, behavioral or learning difficulties by 1.43 times, nocturnal awakenings by 1.16 times, excessive sleepiness during the day by 1.10 times. Sleep onset latency and increased sleep duration were weak indicators due to a likeliness of less than one time (p<0.05) to be associated with a positive ESS-CHAD. Abnormal sleep behavior was not a statistically significant risk factor (p≥0.05) for potential sleep disorders in children. Conclusion There were associations between seven behavioral and physiological risk factors with overall sleep propensity in children. These results exhibit that the KSSQ is an important tool to identify potential sleep disorders in children and the need for follow up with a sleep specialist. The KSSQ is under validation for becoming a standard sleep screener in pediatrics. Support N/A

scholarly journals Sex and onset-age-related features of excessive daytime sleepiness and night-time sleep in patients with Parkinson’s disease

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming Liu ◽  
Ya-Jun Luo ◽  
Han-Ying Gu ◽  
Yi-Ming Wang ◽  
Man-Hua Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Risk Factor ◽  
Sleep Quality ◽  
Sleep Disorders ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Early Onset ◽  
Clinical Characteristics ◽  
Night Time

Abstract Background The clinical characteristics of Parkinson’s disease (PD) differ between men and women, and late- and early-onset patients, including motor symptoms and some nonmotor symptoms, such as cognition, anxiety, and depression. Objective To explore the features of excessive daytime sleepiness (EDS) and night-time sleep quality in PD patients of different sexes and age at onset (AAO). Methods Demographic data and clinical characteristics of 586 PD patients were collected. Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI) were used to investigate the daytime drowsiness and nocturnal sleep. Multivariate logistic regression analysis was used to explore the risk factors of EDS and poor night-time sleep quality. Results Sleep disorders were common in PD patients. EDS was more prominent in men than in women. There was no significant difference in ESS scores between late-onset PD (LOPD) and early-onset PD. LOPD patients had a higher probability of poor night-time sleep quality. Male sex, disease duration, and depression were risk factors for EDS. In all patients of both sexes and all AAO, depression was a risk factor for poor night-time sleep. Conclusion More attention should be paid to sleep disorders of PD patients, especially male LOPD patients. Depression is a common risk factor for EDS and poor sleep quality in PD patients.

scholarly journals Does the Proximity of Meals to Bedtime Influence the Sleep of Young Adults? A Cross-Sectional Survey of University Students

2020 ◽  
Vol 17 (8) ◽  
pp. 2677 ◽  
Author(s):  
Nikola Chung ◽  
Yu Sun Bin ◽  
Peter A. Cistulli ◽  
Chin Moi Chow
Keyword(s):  
Young Adults ◽  
Risk Factor ◽  
Sleep Duration ◽  
Online Survey ◽  
Sleep Onset ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Cross Sectional ◽  
Meal Timing ◽  
The Relationship

Avoiding food before bedtime is a widely accepted sleep hygiene practice, yet few studies have assessed meal timing as a risk factor for disrupted sleep. This study examined the relationship between evening meal timing and sleep quality in young adults. A total of N = 793 participants (26% male) aged between 18 and 29 years responded to an online survey, which captured sociodemographic information, lifestyle variables, and sleep characteristics. Meal timing was defined as meals more than 3 h before or within 3 h of bedtime. The outcomes were as follows: one or more nocturnal awakenings, sleep onset latency of >30 min, and sleep duration of ≤6 h. Logistic regression analyses showed that eating within 3 h of bedtime was positively associated with nocturnal awakening (OR = 1.61, 95% CI = 1.15–2.27) but not long sleep onset latency (1.24; 0.89–1.73) or short sleep duration (0.79; 0.49–1.26). The relationship remained significant after adjusting for potential confounders of ethnicity and body mass index (OR = 1.43, 95% CI = 1.00–2.04). Meal timing appears to be a modifiable risk factor for nocturnal awakenings and disrupted sleep. However, this is a preliminary cross-sectional study and highlights the need for additional research on the influence of the timing of food intake on sleep.

A Comparison of Physical Activity Levels, Sleep Disrupting Behavior, and Stress/Affective Distress as Predictors of Sleep as Indexed by Actigraphy

2021 ◽  
pp. 1-12
Author(s):  
Shireen W. Eid ◽  
Rhonda F. Brown ◽  
Carl L. Birmingham ◽  
Shane K. Maloney
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Young Children ◽  
Ambient Temperature ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Affective Distress ◽  
Sleep Impairment

Background: Several behaviors have been reported to interfere with sleep in otherwise healthy adults, including low physical activity (PA) levels. However, few studies have compared low PA with the other behavioral risk factors of objective sleep impairment, despite the behavior tending to cooccur in highly stressed and affectively distressed individuals. Thus, the authors compared objective and subjective measures of PA and other potential sleep disrupting behaviors as predictors of objective sleep (sleep onset latency, actual sleep time, total sleep duration, awake time, and sleep efficacy) at baseline (T1) and 3 months later (T2). Methods: A community-derived sample of 161 people aged 18–65 years were asked about PA, other behavior (ie, night eating, electronic device use, watching television, caffeine and alcohol use), stress, affective distress (ie, anxiety, depression), and demographics including shift work and parenting young children in an online questionnaire at T1 and T2. PA and sleep were also monitored for 24 hours each at T1 and T2 using actigraphy. Results: Multiple regression analyses indicated that sleep at T1 was associated with PA (ie, total number of steps, metabolic equivalents/time, time spent travelling) after controlling mean ambient temperature and relevant demographics. At T2, longer sleep onset latency was predicted by parenting young children and night time television viewing; shorter sleep duration was predicted by female gender; and awake time and sleep efficacy were predicted by alcohol intake after controlling T1 sleep measures, demographics, and mean ambient temperature. Conclusion: The risk factors for objective sleep impairment included parenting young children and watching television at night, whereas better sleep outcomes were associated with greater engagement with PA.

scholarly journals The Monday Effect Revisited: A Diary and Sleep Actigraphy Study

2020 ◽  
Vol 4 (2) ◽  
pp. 167-176
Author(s):  
Achim Elfering ◽  
Christin Gerhardt ◽  
Diana Pereira ◽  
Anna Schenker ◽  
Maria U. Kottwitz
Keyword(s):  
Sleep Duration ◽  
Sleep Onset ◽  
Monday Morning ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Social Stressors ◽  
Monday Effect ◽  
Work Related ◽  
Cognitive Failure ◽  
Delayed Sleep

Abstract Purpose Accidents are more likely to occur during the morning hours of Mondays (Monday effect). This might be due to a higher level of cognitive failure on Monday morning at work. Methods In a pilot actigraphy study across one working week, we explored this Monday effect and regressed daily self-reported workplace cognitive failure on weekdays (Monday versus other days), background social stressors at work, delayed sleep onset and sleep duration. Diary data were gathered from 40 full-time employees. Results Confirming our assumptions, results revealed work-related cognitive failure and sleep-onset latency on the previous night to be higher on Mondays compared to other workdays. Work-related cognitive failure correlated positively with delayed sleep-onset latency and background social stressors. In multilevel regression analysis, Monday significantly explained variations in workplace cognitive failure. The addition of background social stressors at work and sleep-onset latency to the regression model showed unique contributions to the prediction of workplace cognitive failure. No significant two-way or three-way interactions between working days, sleep-onset latency or sleep duration, and background social stressors were found. Conclusion Peak levels of cognitive failure on Monday morning and the association of cognitive failure with social stressors at work contribute to understanding the mechanisms involved in the increased prevalence of occupational accidents on Monday morning. Occupational safety interventions should address both social stressors at work and individual sleep hygiene.

scholarly journals 193 Sleep Variability and Affect Dynamics Among College Students during COVID-19 Pandemic

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A78-A78
Author(s):  
Zahra Mousavi ◽  
Jocelyn Lai ◽  
Asal Yunusova ◽  
Alexander Rivera ◽  
Sirui Hu ◽  
...  
Keyword(s):  
Emerging Adults ◽  
Sleep Duration ◽  
Sleep Onset ◽  
Positive And Negative Affect ◽  
Sleep Onset Latency ◽  
Deep Sleep ◽  
Onset Latency ◽  
Sleep Patterns ◽  
Light Sleep ◽  
Affect Dynamics

Abstract Introduction Sleep disturbance is a transdiagnostic risk factor that is so prevalent among emerging adults it is considered to be a public health epidemic. For emerging adults, who are already at greater risk for psychopathology, the COVID-19 pandemic has disrupted daily routines, potentially changing sleep patterns and heightening risk factors for the emergence of affective dysregulation, and consequently mood-related disturbances. This study aimed to determine whether variability in sleep patterns across a 3-month period was associated with next-day positive and negative affect, and affective dynamics, proximal affective predictors of depressive symptoms among young adults during the pandemic. Methods College student participants (N=20, 65% female, Mage=19.80, SDage=1.0) wore non-invasive wearable devices (the Oura ring https://ouraring.com/) continuously for a period of 3-months, measuring sleep onset latency, sleep efficiency, total sleep, and time spent in different stages of sleep (light, deep and rapid eye movement). Participants reported daily PA and NA using the Positive and Negative Affect Schedule on a 0-100 scale to report on their affective state. Results Multilevel models specifying a within-subject process of the relation between sleep and affect revealed that participants with higher sleep onset latency (b= -2.98, p<.01) and sleep duration on the prior day (b= -.35, p=.01) had lower PA the next day. Participants with longer light sleep duration had lower PA (b= -.28, p=.02), whereas participants with longer deep sleep duration had higher PA (b= .36, p=.02) the next day. On days with higher total sleep, participants experienced lower NA compared to their own average (b= -.01, p=.04). Follow-up exploratory bivariate correlations revealed significant associations between light sleep duration instability and higher instability in both PA and NA, whereas higher deep sleep duration was linked with lower instability in both PA and NA (all ps< .05). In the full-length paper these analyses will be probed using linear regressions controlling for relevant covariates (main effects of sleep, sex/age/ethnicity). Conclusion Sleep, an important transdiagnostic health outcome, may contribute to next-day PA and NA. Sleep patterns predict affect dynamics, which may be proximal predictors of mood disturbances. Affect dynamics may be one potential pathway through which sleep has implications for health disparities. Support (if any):

Mindfulness and Behaviour Therapy for Insomnia: An Assessment of Treatment Effect in a Sleep Disorders Clinic Population with Insomnia

2020 ◽  
pp. 1-15
Author(s):  
Allie Peters ◽  
John Reece ◽  
Hailey Meaklim ◽  
Moira Junge ◽  
David Cunnington ◽  
...  
Keyword(s):  
Sleep Disorders ◽  
Treatment Effect ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Health Concern ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Subjective Sleep ◽  
Sleep Parameters

Abstract Insomnia is a common major health concern, which causes significant distress and disruption in a person's life. The objective of this paper was to evaluate a 6-week version of Mindfulness-Based Therapy for Insomnia (MBTI) in a sample of people attending a sleep disorders clinic with insomnia, including those with comorbidities. Thirty participants who met the DSM-IV-TR diagnosis of insomnia participated in a 6-week group intervention. Outcome measures were a daily sleep diary and actigraphy during pre-treatment and follow-up, along with subjective sleep outcomes collected at baseline, end-of-treatment, and 3-month follow-up. Trend analyses showed that MBTI was associated with a large decrease in insomnia severity (p < .001), with indications of maintenance of treatment effect. There were significant improvements in objective sleep parameters, including sleep onset latency (p = .005), sleep efficiency (p = .033), and wake after sleep onset (p = .018). Significant improvements in subjective sleep parameters were also observed for sleep efficiency (p = .005) and wake after sleep onset (p < .001). Overall, this study indicated that MBTI can be successfully delivered in a sleep disorders clinic environment, with evidence of treatment effect for both objective and subjective measures of sleep.

610 Self-Reported Sleep and Circadian Characteristics Predict Future Substance Use: A Longitudinal Analysis from the NCANDA Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A240-A240
Author(s):  
Brant Hasler ◽  
Jessica Graves ◽  
Meredith Wallace ◽  
Stephanie Claudatos ◽  
Fiona Baker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Substance Use ◽  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Parental Education ◽  
Negative Binomial ◽  
Longitudinal Design ◽  
Random Effect ◽  
Cannabis Use ◽  
Increased Risk

Abstract Introduction Growing evidence indicates that sleep characteristics predict later substance use and related problems during adolescence and young adulthood. However, most prior studies have assessed a limited range of sleep characteristics, studied only a narrow age span, and included relatively few follow-up assessments. Here, we used multiple years of data from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study, which spans the adolescent period with an accelerated longitudinal design, to examine whether multiple sleep characteristics in any year predict substance use the following year. Methods The sample included 831 participants (423 females; age 12–21 years at baseline) from NCANDA. Sleep variables included the previous year’s circadian preference, sleep quality, daytime sleepiness, timing of midsleep (weekday and weekend), and sleep duration (weekday and weekend). Each sleep variable’s association with the subsequent year’s substance use (cannabis use or alcohol binge severity) across years 1–5 was tested separately using generalized linear mixed models (zero-inflated Negative Binomial for cannabis; ordinal for binge severity) with age, sex, race, visit, parental education, previous year’s substance use (yes/no) as covariates and subject as a random effect. Results With regard to cannabis use, greater eveningness and shorter weekday sleep duration predicted an increased risk for additional days of cannabis use the following year, while greater eveningness and later weekend midsleep predicted a greater likelihood of any cannabis use the following year. With regard to alcohol binge severity, greater eveningness, greater daytime sleepiness, and shorter sleep duration (weekday and weekend) all predicted an increased risk for more severe alcohol bingeing the following year. Post-hoc stratified analyses indicated that some of these associations may differ between high school-age and college-age participants. Conclusion Our findings extend prior work, indicating that eveningness and later sleep timing, as well as shorter sleep duration, especially on weekdays, are risk factors for future cannabis use and alcohol misuse. These results underscore a need for greater attention to sleep characteristics as potential risk factors for substance use in adolescents and young adults and may inform future areas of intervention. Support (if any) Grants from NIH: R01AA025626 (Hasler) and U01AA021690 (Clark) and UO1 AA021696 (Baker & Colrain)

Comorbidity and severity-of-illness risk adjustment for hospital-onset Clostridioides difficile infection using data from the electronic medical record

2020 ◽  
pp. 1-7
Author(s):  
Stephanie M. Cabral ◽  
Katherine E. Goodman ◽  
Natalia Blanco ◽  
Surbhi Leekha ◽  
Larry S. Magder ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Risk Adjustment ◽  
Community Hospital ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Bivariate Analysis ◽  
Patient Admissions ◽  
Clostridioides Difficile ◽  
Risk Adjustment Model

Abstract Objective: To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment. Patients: All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018. Methods: Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities. Results: At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change. Conclusions: Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

Sleep habits and their association with daytime sleepiness among medical students of Tanta University, Egypt

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Salwa A. Atlam ◽  
Hala M. Elsabagh
Keyword(s):  
Medical Students ◽  
Sleep Disorders ◽  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Sleep Habits ◽  
Restless Legs ◽  
Sleeping Pills ◽  
Life Pattern ◽  
Waking Up ◽  
Malaysian Students

AbstractObjectivesThis study aimed to assess the sleep quality (habits and disorders) and the daytime sleepiness among medical students.MethodsA cross-sectional questionnaire-based study was conducted during September 2018, through November 2018 at the Faculty of Medicine, Tanta University, Egypt. The study recruited undergraduate Egyptian and Malaysian students and applied a modified form of two questionnaires, namely the Sleep Habits and Life Style and the Epworth Sleepiness Scale (ESS)”. Statistical analysis was done using SPSS. The results were expressed as frequency, percentage, and mean ± standard deviation (SD). Chi-square test was used to explore associations between categorical variables. An independent sample t-test was used to detect the mean differences between groups. Ordinal regression analyses were done on the ESS findings in relation to demographics and sleep habits. p-values<0.05 were accepted as statistically significant.ResultsThe study included 899 medical students. Most of the participants were Egyptians (67%), rural residents (57.4%), and in the preclinical stage (79.5%). Males represented 66.0% of the study participants and participants average age (SD) was 21.98 (1.13) years. The average durations (SD) of night sleep were 7.3 (1.6) hours in work days and 8.7 (2.1) hours during the weekends. Both were significantly longer among young (<21 years-old) and preclinical students (p<0.05). Students had on average (SD) 1.33 (0.29) hours duration of napping, but 60% of the participants never or rarely scheduled for napping. Larger proportion of male and Malaysian students sometimes scheduled for napping more significantly than their peers (p<0.05). Only 16.24% of students reported that the cause of daytime napping was no enough sleep at night. The students reported sleep disorders of insomnia in the form of waking up too early, trouble falling asleep, or waking up at night with failure to re-sleep (31, 30, and 26%, respectively). Snoring (22.2%) and restless legs (22.0%) were also reported by the students. High chances of dozing off was reported by 22.02% of the participants, of which 10% used sleeping pills, 41.4% suffered psychological affection, and 34.8% reported life pattern affection. We found an increased chance of daytime sleepiness among males (0.430 times) and Egyptian (2.018 times) students. There was a decreased chance of daytime sleepiness in students from rural areas and those below 21-years-old (0.262 and 0.343 times, respectively). Absence of chronic diseases suffering was significantly associated with 5.573 more chance of daytime sleepiness or dozing off. In addition, enough and average sleep at night significantly decreased the chance of daytime sleepiness by 6.292 and 6.578, respectively, whereas daytime consumption of caffeinated beverages significantly decreased the chance of daytime sleepiness by 0.341.ConclusionThere was unbalanced sleep duration in work days and weekends as well as lack of scheduling for napping among the students. Sleep disorders as insomnia, snoring, and restless legs were associated with excessive daytime sleepiness. Some students who suffered daytime sleepiness also underwent psychological and life pattern affection including taking sleeping pills. Enough and average sleep duration at night as well as daytime consumption of caffeinated beverages decreased the chance of daytime sleepiness.

scholarly journals 1253 Multiple sleep onset REM episodes in middle age woman with excessive daytime sleepiness – Is this automatically assumed narcolepsy?

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A477-A477
Author(s):  
Kamal Patel ◽  
Bianca J Lang
Keyword(s):  
Daytime Sleepiness ◽  
Sleep Latency ◽  
Sleep Onset ◽  
Sleep Time ◽  
Multiple Sleep Latency Test ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Obstructive Sleep ◽  
Falling Asleep ◽  
Multiple Sleep Latency

Abstract Introduction Presence of sleep onset REM episodes often raises concerns of narcolepsy. However other conditions have shown to have presence of sleep on REM episodes which include but not limited to obstructive sleep apnea, sleep wake schedule disturbance, alcoholism, neurodegenerative disorders, depression and anxiety Report of Case Here we present a case of 30 year old female with history of asthma, patent foraman ovale, migraine headache, and anxiety who presented with daytime sleepiness, falling asleep while at work, occasional scheduled naps, non-restorative sleep, sleep paralysis, and hypnopompic hallucination. Pertinent physical exam included; mallampati score of 4/4, retrognathia, high arched hard palate, crowded posterior oropharynx. She had a score of 16 on Epworth sleepiness scale. Patient previously had multiple sleep latency test at outside facility which revealed 4/5 SOREM, with mean sleep onset latency of 11.5 minutes. She however was diagnosed with narcolepsy and tried on modafinil which she failed to tolerate. She was tried on sertraline as well which was discontinued due to lack of benefit. She had repeat multiple sleep latency test work up which revealed 2/5 SOREM, with mean sleep onset latency was 13.1 minutes. Her overnight polysomnogram prior to repeat MSLT showed SOREM with sleep onset latency of 10 minutes. Actigraphy showed consistent sleep pattern overall with sufficient sleep time but was taking hydroxyzine and herbal medication. Patient did not meet criteria for hypersomnolence disorder and sleep disordered breathing. Conclusion There is possibility her medication may have played pivotal role with her daytime symptoms. We also emphasize SOREMs can be present in other disorders such as anxiety in this case and not solely in narcolepsy

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