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Volume 15 No. 04
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Accepted Papers





Scientific Investigations

Total Sleep Time and BMI z-score Are Associated With Physical Function Mobility, Peer Relationship, and Pain Interference in Children Undergoing Routine Polysomnography: A PROMIS Approach

Bharat Bhushan, PhD, CCSH1,2; Amanda Beneat, MD3; Charles Ward, MD3; Alex Satinsky, MS1; Michael L. Miller, MD4; Lauren C. Balmert, PhD5; John Maddalozzo, MD1,2
1Division of Otolaryngology Head and Neck Surgery, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois; 2Department of Otolaryngology Head and Neck Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois; 3Rosalind Franklin University, North Chicago, Illinois; 4Stanley Manne Children's Research Institute, Ann and Robert H. Lurie Children's Hospital, Chicago, Illinois; 5Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois

ABSTRACT

Introduction:

Sleep disturbance, especially obstructive sleep apnea (OSA) and inadequate sleep, adversely affect various health-related quality of life (HR-QoL) domains in adults. Few studies have addressed problems with HR-QoL in children with OSA or sleep-related symptoms.

Methods:

Patients between ages 5 to 17 years who were referred to the sleep laboratory from June 2017 to August 2017 for overnight polysomnography were approached to participate in the study.

Results:

A total of 86 patients were included in the final analysis; 45 patients (52.3%) were male; and the median (interquartile range) of their mean BMI z-scores was 1.7 (0.5, 2.4). The patients were categorized by OSA severity as follows: 27 (31.4%) mild OSA, 11 (12.8%) moderate OSA, 24 (27.9%) severe OSA, and 24 (27.9%) without OSA. Severity of OSA was not correlated with any PROMIS domain. In univariable analyses, BMI z-score was negatively correlated with physical function mobility score (P = .002) and positively correlated with pain interference (P = .02) and pain intensity (P = .02). Total sleep time was positively correlated with physical function mobility (P = .03) and peer relationship (P = .002). Significant correlations between several PROMIS domains were also observed.

Conclusions:

Total sleep time was associated with physical function mobility and peer relationship. Regression analysis demonstrated a relationship between BMI z-score, physical function mobility, and pain intensity in our study population.

Commentary:

A commentary on this article appears in this issue on page 541.

Citation:

Bhushan B, Beneat A, Ward C, Satinsky A, Miller ML, Balmert LC, Maddalozzo J. Total sleep time and BMI z-score are associated with physical function mobility, peer relationship, and pain interference in children undergoing routine polysomnography: a PROMIS approach. J Clin Sleep Med. 2019;15(4):641–648.


BRIEF SUMMARY

Current Knowledge/Study Rationale: The study was conducted to better understand the relationships between sleep disturbance and health-related quality of life (HR-QoL) in children undergoing routine polysomnography.

Study Impact: Associations between sleep disturbance (obstructive sleep apnea and sleep duration) and specific HR-QoL domains will identify the most appropriate and effective approach to intervention in children.

INTRODUCTION

Sleep is essential for physical and mental well-being.1,2 Sleep disturbances, especially obstructive sleep apnea (OSA) and inadequate sleep duration, affect physical, mental, and social well-being.3 OSA has been estimated to affect approximately 5% of children in the United States.4 Survey data from 1,600 United States high school students showed that approximately 25% regularly fall asleep in class and an additional 22% fall asleep while doing homework, indicating the presence of inadequate sleep. Patients may be unaware of their snoring, breathing pauses during sleep, and inadequate sleep but among children, parents usually report its consequences in the form of irritability, lack of concentration, poor grade performance, and memory impairment.5,6 As a result of these symptoms and functional impairments, children with OSA and inadequate sleep are often reported to have adversely affected health-related quality of life (HR-QoL).7 Such correlation has been poorly studied in children with OSA. In previous studies on children, both disease-specific and general assessment tools were used to estimation HR-QoL in children with OSA but results have not been consistent.810 Although studies have used various questionnaires to determine problems with specific domains, to our knowledge no study has explored potential inter-domain relationships. Therefore, we used the Patient Reported Outcomes Measurement Information System (PROMIS)-49 questionnaire, which offers an efficient method to assess individual domains permitting analysis of their inter-relationship.

In this study, we evaluated overall HR-QoL rather than symptoms directly associated with OSA to determine associations between sleep disturbance (OSA and sleep duration) and specific HR-QoL domains. Moreover, to date, the interrelationships between HR-QoL domains with each other have not been examined in children with OSA. Better understanding of these relationships will identify the most appropriate and effective approach to intervention.

The primary aims of this study were: (1) to examine the association between HR-QoL domains in children with varying severity of OSA; (2) to assess associations between HR-QoL domains and other variables that can affect the quality of life, for example, sleep duration or BMI z-score; and (3) to examine the interrelationships between specific HR-QoL among patients undergoing routine polysomnography (PSG) at our hospital. Based on clinical experience, we hypothesized that OSA, sleep related symptoms, sleep duration and BMI z-score would be associated with problems with HR-QoL and that various HR-QoL domains would also be correlated.

METHODS

Ethical Approval

This study was approved by the Institutional Review Board of Ann and Robert H. Lurie Children's Hospital of Chicago. Written informed consent was obtained from each participant before administering the questionnaire.

Study Population

Patients with symptoms reflecting possible difficulty sleeping are referred to the sleep laboratory at Ann and Robert H. Lurie Children's Hospital of Chicago. Patients seen from June 2017 to August 2017 for a routine overnight PSG were prospectively approached to participate in the study. Patients between the ages of 5 to 17 years with willingness to participate and those who provided the written informed consent were invited to participate in the research study. Responses to administered questionnaire were collected at the night of PSG and they were then classified into groups according to OSA severity based on their PSG results. Patients were excluded from analysis if they had complex medical problems (n = 13), genetic abnormalities (n = 7), craniofacial abnormalities (n = 3), or received CPAP treatment (n = 8); those with prior adenotonsillectomy (n = 8) or any other treatment of OSA (n = 6), and patients with immediate HR-QoL domain related issues (n = 9) were not included in this study. No eligible patients declined to participate in the study and their data were included in the analysis. A total of 86 patients were available for the final analysis. Data on age, sex, height, weight, and PSG findings were collected from the electronic medical record of these patients. The BMI z-score was computed using Centers for Disease Control and Prevention growth standards (www.cdc.gov/growthcharts) and online software (www.cdc.gov/epiinfo).

Health-Related Quality of Life Assessment

The National Institutes of Health's PROMIS has introduced a number of patient-reported outcome measures, which offer an efficient and cost-effective alternative to assess HR-QoL domains (http://www.healthmeasures.net). The PROMIS profile questionnaire consists of various static short-forms of the PROMIS HR-QoL domains. All patients' parents completed a validated questionnaire consisting of 49 questions distributed over six HR-QoL domains (physical function mobility, anxiety, depressive symptoms, fatigue, peer relationship, and pain interference) and a pain intensity scale (supplemental material). Each domain contained eight questions and parents of patients answered based on their observation from the past 7 days. PROMIS measures are scored on the T-score metric. High scores mean more of the concept being measured. PROMIS measures use a T-score metric in which 50 is the mean of a relevant population and 10 is the standard deviation (SD) of the population. Scores 0.5–1.0 SD worse than the mean = mild symptoms/impairment; scores 2.0 SD or worse than the mean = severe symptoms/impairment. (http://www.healthmeasures.net/score-and-interpret/interpret-scores/promis).

Polysomnography

A standard overnight PSG (Easy 3 version 3.9.34; Cadwell, Kennewick, Washington, United States) was performed. PSG consisted of continuous nocturnal monitoring of electroencephalogram; electrooculogram; chin muscle electromayogram (EMG); left and right anterior tibialis EMG; lead II electrocardiogram; instantaneous heart rate; nasal/oral airflow by thermistry, capnography, and pressure transduction; EtCO2 instantaneous wave form and trend capnometry and/ or TcPCO2 by transcutaneous monitoring; chest and abdominal wall respiratory moment by respiratory inductive plethysomnography and intercostal EMG; body position; upper respiratory tract sound by sonography; and oxygen saturation by continuous pulse oximetry. Continuous pulse volume was measured using finger plethysmography. Occurrence of apneas and hypopneas were identified and scored according to American Academy of Sleep Medicine (AASM) pediatric criteria as defined in The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications.11 PSG was interpreted by a pediatric sleep medicine specialist.

An obstructive apnea event was defined as > 90% fall in airflow for 90% or more of the entire respiratory event compared with pre-event baseline for at least two missed breaths or the duration of two breaths as determined by baseline breathing pattern. The event was associated with continued increased respiratory effort throughout the entire period of decreased airflow. Apneas were measured from the end of the last normal breath to the beginning of the first breath that achieves the pre-event baseline inspiratory excursion. Apneas were assessed using oronasal thermal sensor unless this signal was not reliable. Cessation of airflow was then assessed using recommended alternate sensors. Hypopneas were defined as discrete respiratory events where there was 50% or greater fall in airflow as measured by pressure transduction or recommended alternative signal when the pressure signal was not reliable. Events lasted at least two missed breaths or the equivalent duration of two missed breaths as determined by baseline breathing pattern. Decrease in the nasal pressure amplitude lasted for 90% or more compared to the signal amplitude preceding the event. Additionally, hypopneas were scored when the aforementioned criteria were associated with an arousal, awakening, or greater than or equal to 3% oxygen desaturation. Severity of OSA was arbitrarily classified as mild (apnea-hypopnea index [AHI] 1 to < 5 events/h), moderate (AHI 5 to < 10 events/h), severe (AHI ≥ 10 events/h), and no OSA (AHI < 1 event/h).4,12

Statistical Analysis

Descriptive statistics summarized all variables by OSA status (mild, moderate, severe, and no OSA). Associations between variables of interest and status were assessed via Kruskal-Wallis tests for continuous variables and chi-square or Fisher exact tests for categorical variables, as appropriate. T-scores were calculated for each of the six PROMIS domains (physical function mobility, anxiety, depressive symptoms, fatigue, peer relationship, and pain interference) using the method described at HealthMeasures web page (http://www.healthmeasures.net/score-and-interpret/interpret-scores/promis). Kruskal-Wallis tests compared the six domains and the pain intensity scale by OSA status. Univariable associations between variables of interest and PROMIS outcomes were evaluated with Spearman correlation coefficients for continuous variables and Wilcoxon rank sum or Kruskal-Wallis tests for categorical variables. Associations deemed significant with a two-sided type 1 error rate of 0.15 were included in multiple regression models. Model assumptions were assessed and inclusion of higher order terms was considered, as appropriate. Spearman correlation coefficients were then utilized to assess relationships between domains. Spearman correlation coefficients greater than zero indicate a positive relationship between the two variables of interest, whereas coefficients less than zero indicate a negative relationship. Coefficients closer to 1 or −1 indicate stronger relationships. Unless otherwise specified, all analyses assumed a two-sided 5% level of significance and no corrections were made for multiple hypothesis tests.

RESULTS

A total of 86 patients were included in the final analysis. General characteristics of patients with different severity of OSA are described in Table 1. The patients were categorized by OSA severity as follows: 27 (31.4%) mild OSA, 11 (12.8%) moderate OSA, and 24 (27.9%) severe OSA. The 24 patients (27.9%) without OSA served as a control group. We found no statistically significant difference in any PROMIS score domain across OSA severity (Table 2).

Summary of cohort (n = 86).

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Table 1

Summary of cohort (n = 86).

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Comparisons of PROMIS scores by severity of obstructive sleep apnea.

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Table 2

Comparisons of PROMIS scores by severity of obstructive sleep apnea.

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Correlation of PROMIS Domains With Sleep Duration and BMI

We determined whether any PROMIS domains were associated with other parameters. In univariable analyses, age was positively correlated with fatigue (r = .29, P = .006), indicating a weak positive correlation where older patients reported higher fatigue scores. Age was negatively correlated with peer relationship (r = −.35, P = .0009) indicating a weak negative correlation where older patients reported poor peer relationship. BMI z-score was negatively associated with physical function mobility score (r = −.35, P = .002) and positively correlated with pain interference (r = .26, P = .02) and pain intensity (r = .26, P = .02); increasing BMI z-scores were associated with decreased physical mobility and increased pain interference and pain intensity. Total sleep time was positively correlated with physical function mobility (r = .22, P = .04) and peer relationship (r = .33, P = .002). AHI, the determinant of severity of OSA, was not correlated with any PROMIS domain (Table 3).

Associations between PROMIS domains and covariates of interest.

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Table 3

Associations between PROMIS domains and covariates of interest.

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Multivariable Analysis of the Data

Multiple linear regression models included predictors deemed significant in univariable associations (α = .15) and considered inclusion of higher order terms (Table 4). The results from regression analysis indicated a significant quadratic relationship between BMI z-score and physical function mobility (P = .03 for quadratic term), after controlling for ethnicity and total sleep time. A quadratic relationship was also detected for BMI z-score as a predictor of pain intensity (P = .001 for quadratic term), after controlling for sex, ethnicity, and total sleep time. After controlling for age and total sleep time, the quadratic BMI z-score remained borderline significant (P = .047 for quadratic term) in a model with pain interference. The results from regression analysis also showed that total sleep time positively predicted physical function mobility (P = .02), after controlling for ethnicity and BMI, and was positively associated with peer relationship (P = .03) after controlling for age. A potential negative trend was also observed between total sleep time and pain interference (P = .05).

Multiple linear regression models.

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Table 4

Multiple linear regression models.

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Inter-Relationships of PROMIS Domains

We analyzed domains for possible correlations (Table 5). Physical function mobility was positively correlated with peer relationship (P = .002) and negatively correlated with depression scores (P = .006), fatigue (P < .001), pain interference (P < .001), and pain intensity scores (P < .001). Anxiety scores positively correlated with depression (P < .001), fatigue (P < .001), and pain interference (P = .009). Depression positively correlated with fatigue (P < .001) and pain interference (P < .001) and negatively correlated with peer relationship (P = .01). Fatigue positively correlated with pain interference (P < .001) and pain intensity (P = .001) and negatively correlated with peer relationship (P = .004). Peer relationship was negatively correlated with pain interference (P = .008).

Pairwise associations between domains.

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Table 5

Pairwise associations between domains.

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DISCUSSION

Children with OSA and inadequate sleep have been reported to have inattention, hyperactivity, anxiety, and depression.13,14 Limited reports are available on difficulties with other HRQoL domains such as peer relationships, pain interference, and pain intensity. The current study sought to determine the types of problems with HR-QoL encountered by children undergoing routine PSG at a tertiary care medical center.

We found total sleep time was associated with physical function mobility and peer relationship. BMI z-score was associated with physical function mobility, pain interference, and pain intensity. Regression analyses detected significant quadratic relationships between BMI and these PROMIS domains. Several HR-QoL domains included in our study were correlated with each other, showing a possibility of shared mechanism of action in our patients.

HR-QoL instruments serve as outcome measures in clinical trials, effectiveness research, and research on quality of care.15,16 Outcome measures used in previous studies on patients with OSA1720 have not been used regularly in routine clinical practice. We used HR-QoL the pediatric version of PROMIS because it provides a core set of dimensions applicable to children with a wide variety of physical and mental illnesses. To date, research examining the relationship between HR-QoL and sleep disturbances is limited by varying definitions of sleep problems in children and adolescents. Despite studies showing rates of sleep disturbances and trouble sleeping, ranging from 42% to 66% among children and adolescents with general anxiety disorder, the specific nature of the sleep problems was incompletely delineated.2023 Few pediatric studies examined HR-QoL and sleep disruption, in contrast to reports in adults of robust associations.2426

We did not find any association between HR-QoL domains and OSA severity. Palermo and Kiska found more severe sleep disturbances were associated with greater functional disability and lower HR-QoL.27 Our finding of correlation between fatigue and age was similar to that of Oginska and Pokorski, who found associations of fatigue and mood with the need of sleep and sleep index were more pronounced in younger individuals.28

Sleep disturbances are increasingly recognized as a common problem for children and adolescents with chronic pain conditions.29 We found BMI z-score correlated with pain interference and pain intensity. Although not statistically significant, we detected a trend between total sleep time and pain intensity. One previous study examined the relationship between sleep disturbances and daily functioning in adolescents with chronic pain conditions, but less is known about these associations in younger children with chronic pain conditions.27 Our findings suggest sleep exerts influence through either physical or emotional pathways, altered in some children with chronic pain, negatively affecting daily function.30 In middle-school children, daytime sleepiness has been associated with functional disability in the form of high rates of absenteeism, low school achievement, and low school enjoyment.31 In contrast, adequate sleep has beneficial effects on pain relief and recovery.32,33 Other studies have characterized associations between sleep disturbance, physical symptoms, emotional problems, and functional disability.1,27,34 Mutual causation has been proposed between pain and sleep disturbances.32 Sleep problems have been shown to correlate with pain intensity ratings among children with juvenile idiopathic arthritis and migraine, and in juvenile primary fibromyalgia syndrome.29,35

Research has produced few empirical data to reveal how specific physical disorders or symptoms related to HR-QoL may affect each other. For example, could effective treatment of a preschool child's separation anxiety during the day affect bedtime struggles, nighttime awakenings, and sleep duration? Or, how might a toddler's sleep, anxiety, and symptoms of depression respond to interventions targeting variables such as physical function mobility, or pain interference? Relationships between these factors suggest potential targets of intervention. In our study, we found that physical function mobility was negatively associated with anxiety, depression, fatigue, pain interference and pain intensity. Several other associations between different domains are reported in Table 5. To our knowledge, such associations with sleep duration have not been reported before in children.

In this study, we confirm the previous findings that children with inadequate sleep during PSG have direct relationship with physical function mobility. Inadequate sleep duration predisposes children to increased risk of reduction in physical function mobility. The lower physical mobility, as found in our study, is likely to adversely influence children's development and long-term health outcomes. The physical activity of healthy children in the United States is characterized by 30 minutes to 1 hour per day of physical education at school and participation in organized sports activity in the after-school hours. Like a floor and ceiling pattern, children were reported to have either a more active or a more sedentary pattern of physical activity. Educating children about the side effects of inadequate sleep to modify unhealthy behaviors such as watching TV until late night and to increase physical activity may ultimately lead to more favorable outcomes.36

In our study, we also found an association between total sleep time and peer relationship, which remained after controlling for differences in age. To our knowledge, this has not been reported earlier in patients with especially inadequate sleep but other sleep disease conditions. For example, children with a diagnosis of cancer are reported to experience severe adverse effects due to treatment and consequently often have difficulties keeping up with their peers and maintaining normal activities.37 Young patients with cancer experience teasing and withdrawal from their peers at school.38 Although children with inadequate sleep, because they are lethargic, may also experience physical limitations and teasing from peers, they are often not exposed to the intense medical interventions that are common in pediatric cancer. Thus, the similar HR-QoL outcomes in our study patients were an unexpected but important finding.

Possible limitations to our study include that race data were not reported, because 49% of patients seen in the sleep center chose not to report their race. Other limitations are that responses to questionnaires rely on parents' recall rather than prospective quantitative measurements. Children differ from adults in their understanding of health, the causes of illness, and their beliefs about how medications work. For all these reasons, we cannot expect significant correlations between child and parent ratings. It is more important to recognize the contexts in which parents are normally able to make reasonably accurate judgments for their children. These may include the effect on the family, sibling relationships, and to a lesser extent school progress. Parents are less able to make judgments regarding symptom experience, peer relationships, or future worries. The only solution is to regard each assessment as valid and contributing to the total picture regarding the child's quality of life. The lack of association between OSA and HR-QoL could be due to the small number of study participants in our study. Future longitudinal work may examine HR-QoL and home sleep study data and also may allow for examining the effect of interventions to target particular components of the model on functional disability. In this manner, the most appropriate and powerful target(s) for intervention efforts may be identified. Finally, no formal correction was made for multiple testing; thus, statistical significance at the traditional alpha level should be interpreted cautiously. Future studies are warranted to further asses these relationships.

CONCLUSIONS

Although we did not find an association between OSA and any HR-QoL domain included in our study, we did find that the total sleep time was associated with physical function mobility and peer relationship. A potential trend with pain interference was also observed. Additionally, we found significant quadratic relationships between BMI z-score and physical function mobility and pain intensity. Reciprocal interactions between HR-QoL domains in sleep-disturbed children and their parent's reported outcomes clearly warrant more research and clinical attention.

DISCLOSURE STATEMENT

All authors have seen and approved the manuscript. These results were presented at the annual meeting of the American Society of Pediatric Otolaryngology (ASPO), April 22, 2018, Gaylord National Resort and Convention Center, National Harbor, MD. The authors report no conflicts of interest.

ABBREVIATIONS

AASM

American Academy of Sleep Medicine

AHI

apnea-hypopnea index

HR-QoL

health-related quality of life

OSA

obstructive sleep apnea

PROMIS

Patient Reported Outcomes Measurement Information System

PSG

polysomnography

ACKNOWLEDGMENTS

Author contributions: BB--generated the project, study design, intellectual input, IRB approval, data collection, wrote manuscript, critical revision, managed project, corresponding author; AB--data collection; CW--data collection; AS--data collection; MM--study design, intellectual inputs, critical revision; LB--data analysis; JM--intellectual input and critical revision.

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