OBJECTIVES:

Many children with cancer have repeated and prolonged hospitalizations, and in-hospital sleep disruption may negatively affect outcomes. Our objective for this study was to characterize sleep quality and quantity in hospitalized children with cancer by using parental surveys and actigraphy, to evaluate the association between subjective and objective sleep measures, and to describe hospital-associated risk factors related to poor sleep.

METHODS:

Cross-sectional study of children aged 0 to 18 years old admitted to a pediatric oncology ward. Parents completed a baseline sleep questionnaire describing their child’s sleep at home before hospitalization, followed by daily questionnaires describing their child’s sleep for up to 3 nights while in the hospital. A subgroup of children aged 5 to 18 years wore actigraphs during the same time period. Demographic and clinical data were extracted from the electronic medical record. The primary outcome was inadequate sleep, defined by the total sleep duration adjusted for age.

RESULTS:

Among 56 participants over 135 hospital nights, 66% (n = 37) reported inadequate sleep. Actigraphy was completed on 39 nights (29%), with a median total sleep time of 477 (interquartile range 407–557) minutes. There was a strong correlation between subjective questionnaire measures and actigraphic measures (r = 0.76). No patient-specific demographic factors were related to inadequate sleep. A multivariable model indicated the following hospital-related factors were associated with inadequate sleep: noise (adjusted odds ratio [aOR] 3.0; confidence interval [CI] 1.2–7.7), alarms (aOR 3.1; CI 1.2–8.3), child’s worries (aOR 2.8; CI 1.1–7.2), and receipt of benzodiazepines (aOR 2.9; CI 1.2–7.5).

CONCLUSIONS:

A majority of children experienced inadequate sleep during hospitalization. Subjective report of sleep duration correlated well with objective measures of sleep by actigraphy. Several potentially modifiable factors were independently associated with poor sleep. Further interventional studies are required to test approaches to optimize sleep in hospitalized children with cancer.

Sleep and circadian rhythm disruption is known to be a frequent problem in adults with cancer.14  Risk factors include underlying illness severity, noise level, polypharmacy, and other aspects of the hospital environment.5  In healthy children, chronic sleep disruption has been shown to impact cognition, behavior, attention, and quality of life. Disordered sleep is a known risk factor for anxiety and depression, impaired physical and cognitive growth, and emotional dysregulation.612 

It is likely that hospitalized children with cancer are particularly vulnerable to sleep disruption. Research suggests that chemotherapeutics and corticosteroids may directly affect circadian rhythm.13,14  In addition, many pediatric oncology patients experience repeated and prolonged hospitalizations that are often characterized by nausea, pain, polypharmacy, blood transfusions, and frequent vital sign assessments.

An emerging body of research indicates that disrupted sleep may be associated with decreased immune function and increased mortality in adults with cancer.15  However, little is known about the characteristics of sleep in hospitalized children with cancer or even about the ideal methods for sleep assessment in this at-risk population.12,1618  This critical knowledge gap must first be addressed to study the ill effects of inadequate sleep in children with cancer.

In this study, we aimed to characterize sleep in children hospitalized in a pediatric oncology ward. Our primary objective was to describe sleep quality and quantity in these children and relate sleep perception (obtained from parental questionnaires) to objective sleep measures obtained by actigraphy. Our secondary objective was to identify hospital-associated risk factors associated with poor sleep. We hypothesized that a majority of children with cancer would experience inadequate sleep while hospitalized and that modifiable factors would be associated with disrupted sleep. We also hypothesized that subjective report of sleep duration would have a moderate correlation with objective measure of sleep duration by actigraphy.

In this cross-sectional observational study, we enrolled a cohort of children ≤18 years of age admitted to a pediatric oncology ward over a 3-month period. Patients were excluded if they were transferred from the PICU (because of known deleterious effects of the PICU environment and critical illness on sleep architecture)11,19  or if their parents could not read and write in English. Informed consent was obtained from parents, and assent was obtained from children ≥7 years of age. Parents were not approached for consent on the day of admission; this ensured that all patients had already been in the hospital for >24 hours at the time that hospital sleep data were acquired. We deliberately excluded the first night’s sleep in the hospital because of concern for “first night effect,” with poor sleep due to a new environment.20  The local institutional review board provided approval for this minimal-risk study.

This study involved the completion of two questionnaires by the parent.

Baseline Sleep Questionnaire

On the day of enrollment, the parent completed a validated questionnaire describing the child’s sleep at home before hospitalization. The Brief Infant Sleep Questionnaire (BISQ)21  was used for children aged 0 to 3 years, and the Children’s Sleep Habits Questionnaire (CSHQ) was used for children >3 years of age.22  The BISQ and CSHQ are widely used as descriptive tools by pediatric sleep researchers. The BISQ is a 13-item questionnaire completed by the parent that allows for the collection of multiple variables, including sleep duration, number and duration of night awakenings, and nocturnal sleep-onset time.21  The CSHQ is a 35-item questionnaire completed by the parent, with input from the child when age appropriate. It is used to assess variables such as “bedtime resistance, sleep onset delay, sleep duration, sleep anxiety, night awakenings, parasomnias, disordered breathing, and daytime sleepiness.”22  Each questionnaire takes <5 minutes to complete and was used to categorize the child’s sleep quality (poor sleeper or not poor sleeper) before hospitalization.

Hospital Sleep Questionnaire

For each study night, the parent completed the Sleep at Memorial Sloan Kettering Cancer Center (SAM) questionnaire (Supplemental Information). The SAM questionnaire was created as a data collection tool specifically for this study and is adapted from the Sleep in a Children’s Hospital–Parent Version (SinCH-P) survey.23  The SinCH-P survey, uses 24-hour recall to establish sleep patterns and problems in hospitalized children. Because there is no validated tool to measure sleep in hospitalized children with cancer, we adapted the SinCH-P survey with stakeholder input (experts in sleep, pediatrics, oncology, psychiatry, nursing, and family members) to reflect the unique aspects of a hospitalization for pediatric cancer. Similar to the SinCH-P survey, the SAM questionnaire, uses parent proxy to describe sleep measures (including bedtime, wake time, sleep-onset latency [SOL]) and sleep disturbances (including pain, worries, vital sign measurements, light, and noise).23  We added items specific to the study population (including use of sleep medication, blood product transfusions, overnight blood draws, and amount of screen time during the preceding day). The SAM questionnaire contains 33 items and is completed on paper by the parent, with input from the child when developmentally appropriate. It takes ∼10 to 15 minutes to complete for each study night. Parents completed the form for up to 3 consecutive nights after enrollment, or until discharge, whichever was soonest.

Patients 5 to 18 years old were invited to wear an actigraphy watch (Micro Motionlogger; Ambulatory Monitoring Inc, Ardsley, NY) on their wrists or ankles for up to 72 hours. Actigraphy, with noninvasive, long-term, continuous monitoring capability, is a useful quantitative technique for evaluating sleep and circadian rhythm disorders24  and has been validated in infants and children as a reliable method of sleep assessment. Motion sensors, or accelerometers, in the actigraphy device measure patient movement using zero-crossing mode. Time-stamped information is available on frequency and duration of activity over the course of monitoring, with a summary of total sleep time and arousals.2,2428  Studies have revealed the validity of actigraphy against the gold standard of polysomnography in adolescent patients, with overall agreement rates for sleep-wake patterns of 91% to 95%.2931 

Demographic and treatment-related variables were extracted from the electronic medical record, including age, sex, primary diagnosis, reason for hospital admission, medications administered, and hospital length of stay.

Children were categorized as poor sleepers before hospitalization on the basis of the validated BISQ and CSHQ cutoffs.21,22  The primary outcome of inadequate sleep during the hospitalization was defined as total sleep duration (as reported by the parent) less than the minimum recommended for age: 9 hours for age 0 to 3 years, 8 hours for age >3 to 6 years, and 7 hours for age >6 to 18 years. In addition, a night could be classified as inadequate sleep if there were sleep fragmentation, defined as ≥4 awakenings (regardless of age).21,22,32  This conservative definition for inadequate sleep was based on the National Sleep Foundation’s 2015 expert guidelines for minimum adequate sleep time; we chose this over the more-stringent American Academy of Sleep Medicine (AASM) 2016 recommendations so as not to overreport the prevalence of inadequate sleep in our cohort.32,33 

Total sleep duration was determined by quantifying the number of minutes between falling asleep and waking up, after subtracting the number of minutes awake during the night. Total sleep minutes as recorded by actigraphy was compared with total sleep minutes as reported by parents for the overnight period.

Demographic, baseline, and daily or aggregate sleep and hospital data were described as n (%) and mean or median and spread (interquartile range, minimum, and maximum) for categorical and continuous factors, respectively. Sleep variables included bedtime (time attempted to fall asleep), SOL (minutes to fall asleep at bedtime), night waking frequency, and wake time. Total sleep time (TST) was calculated (time from bedtime to wake time; less SOL). Summary statistics were used to report the most common causes of sleep disruption. Demographic and aggregate sleep characteristics were compared between patients who had ≥1 night of inadequate sleep and patients who never had inadequate sleep during the study period by using χ2 tests and Fisher’s exact tests or independent two-sample t tests and Wilcoxon rank tests. Daily characteristics were compared between inadequate and adequate sleep nights (by using hospital day as the unit of measure) in the same manner. Univariate mixed-effects models with random intercepts for patients were constructed. A multivariable mixed-effects model included all clinically important modifiable factors that reached a P value of <.2 from univariate modeling to determine independent associations between modifiable hospital-related factors and inadequate sleep.

Act Millennium software was used to download the actigraph data, and sleep metrics were analyzed by using Sadeh’s algorithm30  in the ActionW 2.7 software (Ambulatory Monitoring, Inc). The subjective (questionnaire-generated) sleep time was compared with objective (actigraphy-generated) sleep time by using Pearson’s correlation coefficient. P values were 2-sided, with statistical significance determined at the .05 α level. Analyses were performed in R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria).

Consent was obtained from 66 patients. Ten patients did not have a completed baseline SAM questionnaire (because of the parent being too busy or forgetting to complete the survey); thus, the final data analysis included 56 patients. Fifty-four percent of enrolled subjects were male, and most patients were >5 years old (76%). The patient cohort included a wide range of oncologic diagnoses and reasons for admission, with fever and neutropenia being the most common (30%) (Table 1). Seventy-three percent of children had previous admissions to this hospital; only 15 children had never been hospitalized before. Regarding survey respondents, parents who completed the questionnaires were generally highly educated (66% with a college degree or higher) and 61% of questionnaires were completed by the patient’s mother.

TABLE 1

Selected Characteristics of the Study Sample (N = 56)

N%
Age, y, inclusive   
 0–2 
 >2–5 16 
 >5–13 26 46 
 >13–18 17 30 
Sex   
 Male 30 54 
Oncologic diagnosis   
 Brain tumor 
 Leukemia or lymphoma 16 
 Neuroblastoma 14 25 
 Sarcoma 14 25 
 Other 15 27 
Reason for admission   
 Chemotherapy 16 
 Fever and neutropenia 17 30 
 New diagnosis 
 Postoperative care 14 25 
 Stem cell transplant 
 Other 16 
N%
Age, y, inclusive   
 0–2 
 >2–5 16 
 >5–13 26 46 
 >13–18 17 30 
Sex   
 Male 30 54 
Oncologic diagnosis   
 Brain tumor 
 Leukemia or lymphoma 16 
 Neuroblastoma 14 25 
 Sarcoma 14 25 
 Other 15 27 
Reason for admission   
 Chemotherapy 16 
 Fever and neutropenia 17 30 
 New diagnosis 
 Postoperative care 14 25 
 Stem cell transplant 
 Other 16 

Seventeen patients (30%) were classified as poor sleepers before hospitalization (on the basis of the BISQ and CSHQ); this is consistent with population norms.9,21,32  Younger patients (aged <5 years) were more likely to have poor baseline sleep when compared with older children (65% vs 35%; P = .009). Children with brain tumors or neuroblastoma were most likely to be poor baseline sleepers when compared with children with other oncologic diagnoses (59% vs 41%; P = .014). Three patients routinely took medication at home to help with sleep (lorazepam in 1 child; melatonin in 2 children).

Overall, 37 patients (66%) had parent report of inadequate sleep on at least 1 hospital night. Neither demographic nor patient-specific factors predicted inadequate sleep: there was no association between inadequate sleep and age, sex, race, ethnicity, primary oncologic diagnosis, or reason for admission. Poor baseline sleepers were no more likely than good sleepers to experience inadequate sleep while in the hospital (65% vs 66%; P = .999).

A completed SAM questionnaire was available on a total of 135 hospital nights (range 1–3 nights per patient). Children were in private rooms for 31% of nights. On 64% of nights, parents reported that the child’s sleep was disrupted by a nursing intervention (including vital signs measurement, medication administration, or blood draw). It is noteworthy that on 54% of nights (n = 73), parents reported that thoughts and worries kept their child awake. On 41% of nights, parents reported that pain interfered with the child’s sleep. Parents reported that the child’s sleep was disrupted by noise in the hallway (10% of nights), noise in the room (33% of nights), light in the hallway (13% of nights), and light in the room (21% of nights) (Fig 1).

FIGURE 1

Factors disrupting patient’s sleep (N = 135 hospital nights). This graphic reveals the factors (on a per-night basis) that woke a sleeping child between the hours of 7 pm and 7 am, as described by parent proxy.

FIGURE 1

Factors disrupting patient’s sleep (N = 135 hospital nights). This graphic reveals the factors (on a per-night basis) that woke a sleeping child between the hours of 7 pm and 7 am, as described by parent proxy.

Close modal

Parents reported a median number of 2 (interquartile range [IQR] 1.5–4) awakenings per night. Regarding morning awakening, parents reported that the child woke on his or her own only 54% of the time (n = 75); for teenagers, only 46% awoke spontaneously. On 33% of mornings, the child awoke when approached by medical staff, and on 7% of mornings, the child awoke to environmental noise. Parents were asked about the child’s daytime behaviors that may have contributed to difficulty sleeping and reported that 59% of children had napped during the previous day (77% for children aged ≤5 years; 51% for the older children). More than one-quarter (27%) of children had engaged in >4 hours of screen time.

Data extracted from the electronic medical record indicated that on 89% of nights, children received medication(s) between the hours of 7 pm and 7 am, and on 29% of nights, a blood transfusion occurred. Seventy-nine percent of nights included a blood draw between the hours of 7 pm and 7 am, and 95% of nights had vital signs measurements ≥3 times during the overnight period. Children received opiates on 55% of hospital days and benzodiazepines on 33% of hospital days. Over the course of the study, 12.5% of children were prescribed medication specifically to facilitate sleep.

Overall, sleep was classified as inadequate by parental report on 41% (n = 56) of the 135 hospital nights. In univariate analyses, the following factors were associated with inadequate nighttime sleep: thoughts and worries (odds ratio [OR] 3.1; confidence interval [CI] 1.3–7.7) and noise (including noise from people talking [OR 5.6; CI 1.9–16.7] and alarms beeping [OR 4.5; CI 4.4–4.5]). Having a roommate (OR 2.2 [CI 0.9–5.5]; P = .08), experiencing pain (OR 2.1 [CI 0.9–4.9]; P = .09), ≥4 hours of screen time on the preceding day (OR 2.0 [CI 0.8–5.5]; P = .163), and receipt of benzodiazepines (OR 1.98 [CI 0.8–5.0]; P = .148) all revealed a trend toward association with inadequate sleep, but they did not reach statistical significance. Multivariable regression analysis, with adjustment for all modifiable factors that reached a P value of <.2 from univariate modeling, revealed that inadequate sleep was independently associated with noise from people talking, alarms beeping, a child’s thoughts and worries, and receipt of benzodiazepines (Table 2).

TABLE 2

Multivariable Logistic Regression Analysis Associations Between Hospital Factors and Inadequate Sleep (N = 135 Nights)

OR (95% CI)P
Alarms beeping 3.10 (1.16–8.32) .02a 
Child’s worries 2.78 (1.07–7.17) .04a 
Pain 1.52 (0.63–3.65) .35 
People talking 3.04 (1.21–7.65) .02a 
Receipt of benzodiazepines 2.94 (1.15–7.53) .02a 
Roommate 1.81 (0.78–4.25) .17 
Screen time 2.21 (0.86–5.66) .10 
OR (95% CI)P
Alarms beeping 3.10 (1.16–8.32) .02a 
Child’s worries 2.78 (1.07–7.17) .04a 
Pain 1.52 (0.63–3.65) .35 
People talking 3.04 (1.21–7.65) .02a 
Receipt of benzodiazepines 2.94 (1.15–7.53) .02a 
Roommate 1.81 (0.78–4.25) .17 
Screen time 2.21 (0.86–5.66) .10 
a

Statistical significance defined as P<.05

Any nighttime nurse interruption was associated with disrupted sleep. In aggregate, vital signs measurement, administration of medication, and/or blood draws more than tripled the risk for inadequate sleep (OR 3.4; CI 1.2–9.6).

It is likely that the prevalence of sleep disruption described above is an underestimation of the true burden of inadequate sleep in our cohort. By using the more-stringent guidelines for minimum sleep, as delineated by the AASM (<12 hours in children <1 year old, <11 hours in children <3 years old, <10 hours in children <6 years old, <9 hours in children <13 years old, and <8 hours in children <18 years old), sleep was classified as inadequate on 82 nights (61%). Forty-four patients (79%) met AASM criteria for inadequate sleep on at least one hospital night.

Actigraphy data were obtained from 20 patients over 39 days. Representative actigraphy tracings for children with both inadequate and adequate sleep are shown in Figure 2. TST captured by actigraphy ranged from 130 to 630 minutes, with a median TST over 39 nights of 477 (IQR 407–557) minutes, compared to 550 (IQR 492–630) minutes by parental report (P < .001). Parents frequently underestimated the number of nighttime awakenings: parents reported a median of 2 (IQR 1–3.75) awakenings per night, compared with a median of 7 (IQR 4.75–9) awakenings per night as measured by actigraphy (P < .001). Therefore, parents overestimated the child’s TST. Despite this, a strong and consistent correlation was noted between actual sleep time (as measured by actigraphy) and parent-reported sleep time (r = 0.76) (Fig 3).

FIGURE 2

Representative actigrams for pediatric oncology patients with (A) adequate sleep and (B) inadequate sleep. Each row represents a 24-hour period, and the vertical axis indicates activity count. The light blue shaded area represents the parent’s survey report of the child’s bedtime and wake time, and the dark blue box outlines the nighttime period between 10 pm and 8 am. When compared with daytime hours, Figure 2A shows less activity and more consolidation of rest (scored as sleep in red) during the night, whereas Figure 2B shows high levels of activity with many short and fragmented periods of rest.

FIGURE 2

Representative actigrams for pediatric oncology patients with (A) adequate sleep and (B) inadequate sleep. Each row represents a 24-hour period, and the vertical axis indicates activity count. The light blue shaded area represents the parent’s survey report of the child’s bedtime and wake time, and the dark blue box outlines the nighttime period between 10 pm and 8 am. When compared with daytime hours, Figure 2A shows less activity and more consolidation of rest (scored as sleep in red) during the night, whereas Figure 2B shows high levels of activity with many short and fragmented periods of rest.

Close modal
FIGURE 3

Correlation between objective and subjective measures of sleep. There was a strong and consistent correlation between actual sleep time (as measured by actigraphy) and parent-reported sleep time on the SAM questionnaire (r = 0.76).

FIGURE 3

Correlation between objective and subjective measures of sleep. There was a strong and consistent correlation between actual sleep time (as measured by actigraphy) and parent-reported sleep time on the SAM questionnaire (r = 0.76).

Close modal

The authors of a recent systematic review of sleep in hospitalized children with cancer found only 7 articles and concluded that given the small body of literature and few subjects involved, “hospitalized pediatric cancer patients seem to experience more sleep disruptions in comparison to age-related norms.”16  The hospital environment was implicated as playing a considerable role in sleep disruption.16  The authors of the review called for future research involving prehospitalization sleep assessment (to identify preexisting disordered sleep that may increase risk for disordered in-hospital sleep), longitudinal studies (involving ≥2 consecutive nights), and incorporation of objective sleep assessments (such as actigraphy) in addition to questionnaires.16  We have completed just such a study, which adds valuable information to the limited literature surrounding this important topic and provides pilot data for future interventional studies.

Sixty-six percent of our cohort experienced inadequate sleep. Similar to the literature on adults with cancer, our study revealed that disrupted sleep in children is associated with noise, medications, and other aspects of the hospital environment.34  Importantly, we found that inadequate sleep in children was associated with nighttime thoughts and worries. Surprisingly, although disordered sleep before hospitalization was described as an important risk factor for sleep disruption in adults, this was not observed in our pediatric cohort.

One might have expected that poor in-hospital sleep would be related to patient-specific risk factors (eg, age, diagnosis, or history of poor sleep at home). However, this was not the case in our study. Factors associated with poor in-hospital sleep were all related to the hospital care environment. As an example, a major disruptor of sleep was blood draws scheduled for the early morning hours. The timing of these blood draws was likely dictated by perception of workflow demands, with physicians’ desires for results available before morning rounds, rather than any time-sensitive patient-specific need. This presents an opportunity for culture change. Similarly, it may be wise for oncology teams to reexamine their approach to frequency of overnight vital signs measurement, timing of medication administration, and minimization of noise.

It is notable that on 54% of nights, parents reported that worries kept the child awake. On the basis of study design (with questionnaires completed by parent proxy), it is impossible to determine if this was truly the child’s worries or perhaps the parent’s projection or anxiety. Further research is needed to explore this relationship. It is also noteworthy that parents consistently overestimated the child’s sleep time. We hypothesize that this occurred because if a child had a brief nighttime awakening (long enough to disrupt the child’s sleep but not severe enough to disrupt the parent’s sleep), it went unnoticed by the parent. Therefore, the inadequate sleep rate we describe in this article is likely an underestimation of the true magnitude of poor sleep in hospitalized children with cancer.

The pharmacologic approach to sleep in this cohort deserves attention. At home, 5% of children regularly took medication to facilitate sleep. In the hospital, 12.5% of children were prescribed medication for this purpose. However, many commonly used medications, particularly anticholinergics and benzodiazepines, have been shown to have deleterious effects on sleep quality.11,35  It is, therefore, not surprising that the children who received benzodiazepines had nearly triple the odds of inadequate sleep (adjusted OR 2.94; P = .02). Rather than seeking a quick fix, children would likely benefit from a nonpharmacologic approach to sleep optimization.9,36,37 

Importantly, this study established a strong correlation between objective actigraphic measures of sleep and parental perception of sleep as reported in the SAM questionnaire. This finding suggests that tracking changes in parental report of sleep quality may be used as a reliable surrogate in longitudinal assessments testing response to interventions to promote sleep.

This study has several limitations. Requirement for informed consent may have introduced selection bias: it is possible that parents of the sickest children and/or parents who were most fatigued or stressed may have opted out of participating in this study. It is possible that parents did not accurately report the child’s baseline, prehospitalization sleep quality, because at home, children are likely to sleep in a different room from their parents. Importantly, although the SAM questionnaire was developed by experts in sleep, pediatrics, development, and psychometrics, it was never formally validated. Additionally, because this is a single-center study, results may not be generalizable to other pediatric oncology units. Future studies could be used to compare hospitalized children with and without cancer to better understand the findings that are due to hospitalization in general versus those unique to cancer. Finally, we did not assess the effect of inadequate in-hospital sleep on postdischarge outcomes, including disordered sleep after discharge, anxiety, depression, posttraumatic stress, and possible impact on cancer treatment. This will be an important area for future study.

Despite these limitations, this study has important strengths, including acquiring a baseline (preadmission) sleep assessment to assess the effect of underlying sleep disorders in addition to sleep disruptors that are unique to the hospital setting. We also incorporated actigraphy to provide objective measures of sleep and used a longitudinal design in which multiple nights of sleep could be captured.

As shown by both subjective and objective measures of assessment, children with cancer frequently experience inadequate sleep while hospitalized. Opportunities for improvement include eliminating unnecessary overnight interventions, rethinking how best to keep patient rooms quiet, addressing patient and parent anxiety, and increasing awareness among hospital staff about the importance of nonpharmacologic approaches to sleep promotion. With this study, we lay the groundwork for future interventional research designed to test the feasibility and efficacy of a sleep hygiene protocol on optimizing sleep in hospitalized children with cancer.

We thank Drs R. Scott Dingeman, Julia Kearney, and James Killinger for their help with study design and Dr Lisa J. Meltzer for providing her expert review of the SAM questionnaire used in this study.

Dr Traube designed the study, coordinated and supervised data collection, participated in data analysis, and drafted the manuscript; Dr Rosenberg participated in study design, prepared the data collection instruments, coordinated and participated in data collection, and drafted the manuscript; Ms Thau, Mr Seghini, and Drs Gulati and Taylor participated in study design, conducted data collection, and reviewed and revised the manuscript; Dr Gerber and Ms Mauer conducted the statistical analyses and reviewed and revised the manuscript; Dr Silver participated in study design, helped design the data collection instrument, and reviewed and revised the manuscript; Dr Kudchadkar designed the study, participated in data analysis, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Supported by the Department of Pediatrics at Weill Cornell Medical Center.

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data