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. Author manuscript; available in PMC: 2025 Aug 25.
Published in final edited form as: Child Health Care. 2023 Oct 3;54(3):311–328. doi: 10.1080/02739615.2023.2265827

Family and Child Responses to Asthma Symptoms and Associations with Sleep in Urban Children with Asthma: Does Child Weight Matter?

Katlyn Garr 1,2, Elizabeth L McQuaid 1,2,3, Sheryl J Kopel 1,2,3, Julie Boergers 1,2,3, Grace Molera 1, Daphne Koinis-Mitchell 1,2,3
PMCID: PMC12372995  NIHMSID: NIHMS1935079  PMID: 40855886

Abstract

Family asthma management is linked to children’s sleep, yet it is unclear how child and family responses to asthma symptoms affect sleep, particularly for children with overweight/obesity. We evaluated the associations between family asthma management and sleep, and whether these relationships varied by child weight status (healthy weight, overweight/obesity) among 188 children (aged 7–9; 51% Latino, 33% Black/African American, 15% non-Latino White) with persistent asthma from urban environments. Family asthma management was assessed using semi-structured interviews, sleep was assessed via actigraphy, and BMI percentiles and BMI z-scores were calculated from objective height and weight measurements. More effective family asthma management and symptom response were related to better sleep for children with a higher BMIz. Findings suggest that children with comorbid asthma and obesity from urban environments need multicomponent interventions to improve sleep.

Keywords: asthma, sleep, weight, urban children, family asthma management

Introduction

Pediatric asthma and obesity represent two of the most significant health problems in the United States, and the prevalence of comorbid asthma-obesity is steadily increasing (Di Genova et al., 2018; Wiesenthal et al., 2016). This comorbidity is understood as a complex, bidirectional etiology, influenced by a host of multilevel factors, including biological, environmental, and lifestyle factors (Dixon & Poynter, 2016). Children from urban minority backgrounds are at a higher risk for comorbid asthma and obesity (Peters et al., 2018; Stingone et al., 2011) and experience poorer outcomes (e.g., asthma-related health care utilization, activity limitations, cardiovascular problems; Sanyaolu et al., 2019; Wiesenthal et al., 2016). The sociocultural barriers (e.g., socioeconomic status, neighborhood violence, access to healthcare and medication, language barriers, acculturative stress) that families from urban backgrounds face perpetuate this disparity (Koinis-Mitchell et al., 2007; Milligan et al., 2016).

Pediatric chronic health conditions, such as asthma or obesity, can influence a variety of health outcomes, including sleep. Children with asthma who have worsening asthma-related lung function have been shown to have shorter sleep duration, poorer sleep efficiency, and more nighttime awakenings (Koinis-Mitchell et al., 2017). Similarly, research has consistently found a link between children with overweight or obesity displaying short sleep duration and late bedtimes (Miller et al., 2015; Morrissey et al., 2020). Children with comorbid asthma and obesity may be at risk for poorer sleep outcomes compared to children with only one health condition. For example, prior research shows that children with comorbid asthma and obesity display shorter sleep duration compared to youth with only obesity (Fedele et al., 2014).

Identifying modifiable factors that impact sleep can inform which health behaviors to target for children with comorbid asthma-obesity. Management of childhood asthma is complex, particularly for children from urban minority backgrounds who are at higher risk for asthma morbidity (Milligan et al., 2016), obesity (Ogden et al., 2018), and sleep problems (Coutinho et al., 2021). Families from urban environments also face stressors that can challenge family asthma management (e.g., poverty, home and neighborhood safety, lack of healthcare access and insurance; Coutinho et al., 2013; Grineski et al., 2010). For example, children living in unsafe neighborhoods may spend more time indoors, increasing exposure to indoor allergens such as secondhand smoke, mold, or pest allergens (Grant & Wood, 2022). Children living in stressful environments can also experience increased cortisol levels and airway inflammation leading to increased asthma symptoms, which can make daily asthma management more challenging (DePriest et al., 2018; Koinis-Mitchell, 2014). Therefore, asthma management is an important contributor to sleep outcomes in children with asthma from urban backgrounds regardless of their weight status.

Given that sleep patterns and asthma management vary across age and development, it is important to understand how young children’s asthma self-management behaviors affect their sleep. Asthma management for school-aged children occurs within the context of the family and includes medication management, monitoring of symptoms, trigger control and effective collaboration with other systems, such as healthcare providers, schools, and other caregivers (National Heart Lung and Blood Institute [NHLBI], 2007; 2020). Family recognition and response to asthma symptoms is a component of family asthma management that may be particularly important for school-aged children’s optimal sleep and asthma control (Coutinho et al., 2021; McQuaid et al., 2005; NHLBI, 2007; 2020). While families play a critical role in children’s asthma management, during the school-age years, children are spending more time alone (e.g., school, bedtime) and becoming increasingly independent with their asthma management (McQuaid & Abramson, 2009). Further, sleep patterns are changing during the school-age years, with less napping, later bedtimes, and more independent sleep behaviors (e.g., putting self to bed, less parental seeking/support during nighttime wakings; Sadeh et al., 2000). Ensuring children are aware of how to monitor and address symptoms and seek support when their family or other adults are not present is important for optimal asthma control, healthy sleep, and their overall development of self-efficacy, wellbeing, and safety.

While our prior research demonstrated that more appropriate family asthma management was related to longer sleep duration and better sleep quality in children from urban backgrounds (Coutinho et al., 2021), other aspects of sleep were not examined, nor was the impact of children’s weight, which has particular relevance for children with asthma given the high prevalence of children with overweight/obesity living in urban environments (Peters et al., 2018). Sleep onset latency, or the amount of time it takes for children to fall asleep, is important to examine among children with asthma and obesity. Consistent sleep schedules, including falling asleep and waking up at a consistent time are important aspects of children’s sleep health and overall functioning (Koinis-Mitchell et al., 2015). Children that do not fall asleep at a consistent time or have a difficult time falling asleep may have poor sleep hygiene. For children in our sample, asthma symptoms and urban stressors, as described above, may interfere with falling asleep at a consistent time (Koinis-Mitchell et al., 2012; 2017; Martin et al., 2017). Further, how children respond to their asthma symptoms across settings is also critical, particularly during times when their parents are not present. The way a family manages and responds to asthma symptoms during the day and prior to bedtime is critical to ensure that a child falls asleep relatively easily and stays asleep.

Family asthma management may be more challenging for children with comorbid asthma and obesity due to the effects of increased weight on asthma control and management. For example, children with both conditions report more frequent and severe asthma symptoms (Lang et al., 2012), more nighttime waking due to asthma symptoms (McGarry et al., 2015), and have a poorer response to asthma controller medications (Forno et al., 2011; McGarry et al., 2015) than children with healthy weight. While the mechanisms accounting for greater asthma morbidity in children with comorbid asthma and obesity are still being studied, the increased airway inflammation that is present in obesity is considered to be an important factor (Michelson et al., 2009). Further, research shows that obesity is associated with a child’s ability to accurately perceive the onset and severity of their asthma symptoms (Kopel et al., 2010). These additional challenges may complicate the child’s and family’s ability to respond to children’s asthma symptoms efficiently and effectively. No studies have examined both the family’s and child’s response to asthma symptoms and the associations with a range of sleep outcomes in children from urban environments, and whether this association differs for children based on their weight. Results from this work can inform how we tailor asthma management and sleep interventions for children across the weight continuum.

Current Study

The primary aim of the current study was to examine the associations between family and child management of asthma (i.e., overall family management of asthma, family response to symptoms, child response to symptoms) and sleep outcomes (i.e., sleep onset latency, duration) among a sample of school-aged children from urban backgrounds. We also were interested in whether children’s weight (BMI z-score) moderated these associations. We predicted that both children and families with less optimal family management and less optimal responses to asthma symptoms would have poorer sleep outcomes. We also expected that these associations would be more robust for children with overweight/obesity

Materials and Methods

Participants

Data for this study were collected as part of a larger 5-year observational study, Project NAPS (Nocturnal Asthma and Performance in School; R01 HD057220, Koinis-Mitchell, PI), that examined asthma, physical activity, and sleep within the context of cultural factors among elementary school-aged children from urban environments. The study followed children for one school year and consisted of three 4-week monitoring periods to allow for objective asthma and sleep data collection. The current study included data from the first monitoring period.

Children were either recruited from public schools located within four large urban school districts or ambulatory pediatric clinics within a local children’s hospital. “Consent-to-Contact” forms were distributed across recruitment settings and caregivers who signed the form were contacted via phone by research assistants to determine study eligibility. Eligibility criteria consisted of the following: 1) the child was between 7 and 9 years old, 2) the child’s legal guardian was willing to participate, 3) the child had physician-diagnosed asthma or breathing difficulties in the past 12 months, 4) the child had persistent asthma (NHLBI, 2007), 5) the child attended public school in one of the four targeted school districts, and 6) the child’s caregiver self-identified as Latino (Dominican or Puerto Rician), Black or African American (AA), or Non-Latino White (NLW). Children were ineligible if they had a moderate to severe cognitive impairment, respiratory illness or other chronic health condition other than asthma, diagnosed sleep disorder (e.g., obstructive sleep apnea), or were on prescription stimulants.

Procedure

The initial study visit occurred at the families’ home and included an informed consent/assent process and completion of study measures and interviews (e.g., demographics, asthma control, family asthma management). The second study visit occurred approximately two weeks later at a hospital-based asthma and allergy clinic, where asthma and allergy diagnosis and severity were evaluated by study clinicians and asthma medication was confirmed. Following this visit, participants began a 4-week home-monitoring period, in which they tracked details related to the child’s asthma symptoms and bedtime/rise times in a daily diary and wore an Actiwatch for objective sleep monitoring. A home visit was conducted halfway through the monitoring period to download and review objective sleep data and to administer additional study measures. Study staff also took this time to encourage protocol adherence, ensure appropriate device use, and address families’ questions or concerns.

Study assessments were administered verbally in English or Spanish based on participant preference. Measures were translated into Spanish using standardized procedures (Canino & Bravo, 1994). Families were compensated for their participation at each visit. All study procedures were approved by the institution’s review board.

Measures

Demographic Information & Clinical Characteristics

Primary caregivers completed a study demographic form to provide information about the child’s age, gender, race/ethnicity, and family income. Poverty status was computed by comparing the family’s per capita income to the Federal Poverty Threshold for the year of their study enrollment (U.S. Department of Health and Human Services, 2005).

Weight and Height Assessment

Child weight (pounds) and height (inches) measurements were obtained during the clinic study visit using a calibrated digital scale and stadiometer. Measurements were then converted to metric units and children’s Body Mass Indexes (BMI; kg/m2), BMI z-scores (BMIz; standardized BMI) and age- and sex-adjusted BMI percentiles were computed using normative data from the Centers for Disease Control and Prevention (Kuczmarski et al., 2000). Children with BMI ≥ 85th percentile were classified with overweight/obesity and children with BMI < 85th percentile were classified with healthy weight. Children with a BMI < 5th percentile (underweight status) were excluded (n = 3). Weight status (healthy weight and overweight/obese) was used for descriptive analyses and BMIz was used in primary analyses.

Sleep Outcomes

Sleep data were collected using an Actiwatch sleep monitor (MiniMitter Company, Bend, OR, USA) that children wore on their non-dominant wrist during the monitoring period. Actiware-Sleep V 2.53 software was used to estimate one-minute epochs of sleep or wakefulness. Participants indicated the start and end of sleep periods by pressing a “lights off” or “lights on” button on the device, which bracketed the recorded sleep period. Caregivers also completed daily diaries with input from their children when helpful to obtain additional sleep information (e.g., morning wake times, evening bedtimes, non-asthma related sleep disruptions, times when device was not worn). Standard scoring procedures were applied to each sleep episode using Actiwatch data and sleep diary information (Acebo et al., 2005; Koinis-Mitchell et al., 2015). Data were excluded if there were insufficient data to score (e.g., device not worn, device errors). Consensus meetings were held by researchers with expertise in Actiwatch use to guide scoring as needed.

Sleep variables used in the current study included 1) sleep onset latency (the average time between going to bed and actually falling asleep) and 2) sleep duration (amount of time between evening sleep onset and morning waking excluding night waking time).

Family Asthma Management

The Family Asthma Management System Scale (FAMSS) is a semi-structured interview developed to assess how well families manage children’s asthma (Klinnert et al., 1997; McQuaid et al., 2005). The FAMSS consists of a total scale score to capture overall family asthma management and eight subscales to assess specific aspects of family asthma management (Asthma Knowledge, Symptom Assessment, Response to Symptoms – Family and Child, Environmental Control, Medication Adherence, Collaboration with Provider, and Balanced Integration of Asthma and Family Life). The total FAMSS score is calculated as the mean of the subscale rating scores. Trained research assistants administered the interview to children and their caregivers. Responses were audio-recorded for accuracy and rated on a scale of 1 (ineffective/poor) to 9 (ideal) using a validated scoring system. The FAMSS has been used extensively and is considered a well-validated measure of asthma management (Quittner et al., 2008), including with minority samples (Celano et al., 2011; Naar-King et al., 2013; McQuaid et al., 2005). Internal consistency for the current study was α = .77.

For the current study, the Total Family Asthma Management, Family Response to Symptoms, and Child Response to Symptoms scales were used as the primary predictors in study analyses. To assess the family’s response to symptoms, the caregiver and child are asked to describe what happens when the child starts to have asthma symptoms when they are with the family. The caregiver and child are prompted with follow-up questions if necessary (e.g., What next? What if that doesn’t work?). To assess the child’s response to asthma symptoms, the child describes what happens when they start to have asthma symptoms and they are by themselves or around people who do not know about asthma. The child is encouraged to answer without the help of their caregiver and the same prompts are given, if necessary.

Asthma Control

Asthma control and its associated impairment were assessed using the Asthma Control Test (ACT; Liu et al., 2007), a 5-item self-report measure that assesses general asthma symptoms (e.g., shortness of breath, use of rescue medications) experienced over the past 4 weeks. Trained research assistants administered the ACT verbally to caregivers and children with corresponding pictorials and response options. For young children, the first few items are directed to the child and the rest are directed to the caregiver (Liu et al., 2007). Scores range from 5 (poor control) to 25 (well controlled), with higher scores indicating better asthma control. Children who score below 19 are classified as having poorly controlled asthma and children who score above 19 are classified as having well controlled asthma.

Analytic Plan

Descriptive statistics for demographic variables, clinical characteristics, and key study variables were computed for the entire sample and by weight status (healthy weight, overweight/obese) and racial/ethnic background (Latino, Black/African American, Non-Latino White). Pearson’s correlations were used to evaluate if poverty and asthma severity were potential covariates in associations with FAMSS predictors and sleep outcomes. Poverty was associated with sleep duration and FAMSS predictors (i.e., overall family asthma management, family response to asthma symptoms, child response to asthma symptoms) at a p-level < .05, and therefore, controlled for in relevant analyses. Before analyses, assumptions of normality were evaluated and confirmed. One outlier was found for sleep onset latency and removed.

Primary analyses were conducted using a series of simple and hierarchical multiple linear regressions to evaluate whether FAMSS predictors (overall family asthma management, family response to asthma symptoms, child response to asthma symptoms) were related to sleep outcomes (sleep onset latency, sleep duration). To determine if weight (BMIz) moderated these associations, we used the SPSS PROCESS Macro version 4 (Hayes, 2013). Mean-centered FAMSS predictors and sleep outcomes were entered as independent and dependent variables, respectively. Interaction terms were created by multiplying mean-centered FAMSS predictors and BMIz and then entered as moderators. Covariates were included in the models as necessary. Significant interactions were probed and graphed via the PROCESS Macro. All statistical analyses were performed in SPSS version 28 (SPSS Inc., Chicago, IL) and an alpha level of 0.05 indicated statistical significance.

Results

Preliminary Analyses

Sociodemographic and clinical characteristics of 188 participants with persistent asthma appear in Table 1. Child age ranged from 7–9 years old (M = 8.3, SD = .87) and 52.3% were male. Approximately half of children identified as Latino (51.6%), followed by 33% Black/African American, and 15.4% as non-Latino White. Most participants (63.8%) lived at or below the poverty threshold. Most children had either mild (37.8%) or moderate persistent (35.1%) asthma, and approximately half (51.1%) had well-controlled asthma. There were approximately 42% of children with overweight or obesity. There were no differences in weight status (healthy weight, overweight/obesity) by racial/ethnic group. Of 188 participants, 84% of children (n = 158) had valid objective sleep data. Reasons for missing sleep data include children not wearing the Actiwatch for all or part of the night or insufficient data to determine sleep onset latency or sleep duration.

Table 1.

Sociodemographic, asthma, family asthma management, weight, and sleep characteristics for the overall sample.

Racial/Ethnic Group
 Weight Status
Total Latino Black Non-Latino White Ethnic
Group
Differences		 Healthy
Weight		 Overweight/ Obesity Weight
Status
Differences
Sample – N(%) 188 97(51.6) 62(33) 29(15.4) 87(46.3) 78(41.5)
Child age – M(SD) 8.39(.87) 8.41(.88) 8.41 (.86) 8.32(.90) F(2,185) = .14 8.37(.88) 8.39(.88) t(163) = −.16
Sex – n(%) χ2 = 4.0 χ2 = .95
 Male 99(52.7) 47(48.5) 39(62.9) 13(44.8) 49(56.3) 38(48.7)
 Female 89(47.3) 50(51.5) 23(37.1) 16(55.2) 38(43.7) 40(51.3)
At/below poverty – n(%) 120(63.8) 78(80.4) 34(54.8) 8(27.6) χ2 = 30.1*** 50(57.5) 54(69.2) χ2 = 2.1
Asthma severity – n(%) χ2 = 7.3 χ2 = 1.9
 Mild persistent 71(37.8) 38(39.2) 20(32.3) 13(44.8) 37(42.5) 34(43.6)
 Moderate persistent 66(35.1) 33(34.0) 22(35.5) 11(37.9) 32(36.8) 34(43.6)
 Severe persistent 28(14.9) 12(12.4) 15(24.2) 1(3.4) 18(20.7) 10(12.8)
Asthma control – n(%) χ2 = 5.7 χ2 < .01
 Poorly controlled 69(36.7) 29(29.9) 31(50.0) 9(31.0) 36(41.4) 32(41.0)
 Well controlled 96(51.1) 54(55.7) 26(41.9) 16(55.2) 51(58.6) 45(57.7)
FAMSSa – M(SD)
 Family response 5.28(1.59) 5.53(1.47)* 4.90(1.63) 5.29(1.78) F(2, 183) = 3.0* 5.27(1.63) 5.31(1.59) t(162) = −.16
 Child response 3.62(1.87) 3.31(1.91) 3.93(1.77) 4.00(1.83) F(2.182) = 2.8 3.78(1.83) 3.54(1.91) t(161) = .81
 Total FAMSS 4.79(1.12) 4.92(1.02) 4.67(1.19) 4.63(1.31) F(2,183) = 1.3 4.81(1.12) 4.81(1.16) t(162) = −.02
Sleep outcomes – M(SD)
 Sleep onset latency 18.67(9.11) 17.21(8.60) 19.48(9.60) 21.81(9.10) F(2,155) = 2.8 19.91(9.31) 17.38(8.83) t(149) = 1.7
 Sleep duration 555.37(35.17) 549.74(34.18) 553.84(33.67) 576.93(34.49) F(2,155) = 6.2** 562.93(32.09) 547.23(37.89) t(149) = 2.8**
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a

Family Asthma Management System Scale. Domains reported: family response to asthma symptoms, child response to asthma symptoms, and overall family asthma management. Scores range from 1 to 9, with higher scores indicating better management.

*

p < .05

**

p < .01

***

p < .001

Racial/Ethnic Group Differences in Family Asthma Management and Sleep

One-way ANOVAs were conducted to examine racial/ethnic group differences in overall family asthma management, family response to asthma symptoms, and child response to asthma symptoms (see Table 1 for specific results). Notably, family response to asthma symptoms differed by racial/ethnic group, F(2, 183) = 3.00, p = .05, such that Latino families had more optimal family response to symptoms relative to Black families.

Racial/ethnic group differences were found in sleep outcomes, such that NLW children were found to have longer sleep duration when compared to Black children and Latino children, F(2, 155) = 6.18, p = .003.

Weight Status Differences in Family Asthma Management and Sleep

Independent samples t-tests showed no differences in weight status (healthy weight, overweight/obese) and overall family asthma management, family response to asthma symptoms, and child response to asthma symptoms (Table 1).

Weight status differences were found in sleep outcomes, such that children with healthy weight demonstrated longer sleep duration than children with overweight/obesity, t(149) = 2.76, p = .007.

Associations of Family Asthma Management and Sleep

In the overall sample, results from individual regression models showed that overall family asthma management, family response to asthma symptoms, and child response to asthma symptoms were not related to sleep onset latency or sleep duration.

Moderator Effects of Child BMIz on the Association Between Family Asthma Management and Sleep Onset Latency

Three successive moderation regression models were conducted to examine whether associations between family asthma management indicators (total score, family response to asthma symptoms, child response to asthma symptoms) and sleep onset latency were moderated by BMIz. See Table 2 for a summary of regression analyses. BMIz moderated the association between overall family asthma management and sleep onset latency, b = −1.20, p = .05, such that for children with a higher BMIz, more optimal overall family asthma management was related to shorter sleep onset latency, b = −2.11, p = .02 (Figure 1). Similarly, BMIz moderated the relation between child response to symptoms and sleep onset latency, b = −.79, p = .05, such that for children with a higher BMIz, more optimal child response to symptoms was related to shorter sleep onset latency, b = −1.05, p = .05 (Figure 2). BMIz did not moderate the association between family response to symptoms and sleep onset latency.

Table 2.

Moderator effects of child BMIz in the associations between family asthma management and child sleep outcomes.

Dependent Variable B(SE) 95% CI t p
Sleep Onset Latency
 Model 1
  Child Response to Symptoms .52(.58) −.63, 1.67 .89 .37
  BMIz −1.33(.75) −2.81, .15 −1.77 .08
  Child Response X BMIz −.79(.40) −1.58, .00 −1.98 .05
 Model 2
  Family Response to Symptoms .61(.63) −.63, 1.84 .97 .33
  BMIz −1.27(.76) −2.78, .24 −1.66 .10
  Family Response X BMIz −.63(.44) −1.49, .24 −1.43 .16
 Model 3
  Total FAMSS .27(.88) −1.47, 2.02 .31 .76
  BMIz −1.26(.75) −2.74, .23 −1.67 .10
  Total FAMSS X BMIz −1.20(.61) −2.41, .00 −1.97 .05
Sleep Duration a
 Model 4
  Child Response to Symptoms −3.78(2.26) −8.25, .69 −1.67 .10
  BMIz −5.85(2.95) −11.68, −.03 −1.99 .05
  Child Response X BMIz 3.76(1.55) .70, 6.83 2.43 .02
 Model 5
  Family Response to Symptoms −3.44(2.49) −8.37, 1.49 −1.38 .17
  BMIz −5.77(2.98) −11.65, .12 −1.94 .05
  Family Response X BMIz 2.81(1.69) −.53, 6.15 1.66 .10
 Model 6
  Total FAMSS −3.43(3.76) −10.86, 4.00 −.91 .36
  BMIz −5.58(2.98) −11.48, .31 −1.87 .06
  Total FAMSS X BMIz 3.29(2.42) −1.50, 8.08 1.36 .18
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a

Poverty was a covariate for sleep duration models.

Figure 1.

Figure 1.

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Moderating Effect of Child BMIz on the Association Between Family Asthma Management and Sleep Onset Latency

Figure 2.

Figure 2.

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Moderating Effect of Child BMIz on the Association Between Child Response to Asthma Symptoms and Sleep Onset Latency

Moderator Effects of Child BMIz on the Association Between Family Asthma Management and Sleep Duration

Three additional moderation analyses were conducted to examine whether associations between family asthma management indicators (total score, family response to asthma symptoms, child response to asthma symptoms) and sleep duration were moderated by BMIz (see Table 2). Child BMIz did not moderate the association between overall family asthma management or family response to asthma symptoms and sleep duration. Child BMIz moderated the relation between child response to asthma symptoms and sleep duration, b = 3.76, p = .02, such that for children with a lower BMIz, more optimal child response to symptoms was related to shorter sleep duration, b = −3.80, p = .10, and for children with a higher BMIz, more optimal child response to symptoms was related to longer sleep duration, b = 3.74, p = .08 (Figure 3).

Figure 3.

Figure 3.

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Moderating Effect of Child BMIz on the Association Between Child Response to Asthma Symptoms and Sleep Duration

Discussion

The present study focused on associations between family asthma management (overall family asthma management and family and child response to asthma symptoms) and sleep outcomes among a sample of school-aged children with persistent asthma from urban backgrounds. Given the high rates of comorbid asthma and obesity (Di Genova et al., 2018), we explored whether children’s weight (BMIz) played a role in these associations. This study addressed prior gaps in the literature in that it examined both the family’s and child’s response to asthma symptoms, utilized objective weight and sleep data, and highlights the unique role that children’s weight may have in the asthma management process. Further, this study examined multiple sleep outcomes, including sleep onset latency, which is a sleep indicator that has been underexamined in this area of research. Results from this study can inform how we tailor future asthma management and sleep interventions for early school-aged children from urban environments.

Almost half of the current sample had overweight or obesity, supporting the high prevalence of comorbid asthma and obesity among children from urban backgrounds (Di Genova et al., 2018; Wiesenthal et al., 2016). Findings showed that the relation between asthma management and children’s sleep differed by children’s weight. Specifically, for children with a higher BMIz, more effective overall family management of asthma and child response to asthma symptoms were related to more optimal child sleep outcomes. It may be that more appropriate asthma management and response to asthma symptoms lead to better asthma control throughout the day and night, making it easier for children to fall and stay asleep (Coutinho et al. 2021; Koinis-Mitchell et al., 2015). Of note, children with healthy weight slept longer and had slightly more well controlled asthma compared to children with overweight/obesity. This potentially suggests that optimal asthma management by the family and children’s appropriate symptom response may serve as a protective factor for sleep health among children with comorbid asthma and obesity. It is possible that children in this sample with comorbid asthma and obesity may require additional supports and increased clinical attention, allowing for a more appropriate assessment and response to symptoms. More research is needed to better understand what may be helpful and challenging to children with comorbid asthma and obesity, to support their asthma management and sleep needs more effectively.

While families in the sample appeared to respond more appropriately to asthma symptoms than children, our findings demonstrate that the way in which children respond to asthma symptoms may contribute more so to their sleep. Of note, caregivers and children are together when describing their step-by-step responses to the child’s asthma symptoms during the FAMSS interview. The family is first asked to describe their response to their child’s asthma symptoms, and then the child is asked how they respond to their asthma symptoms when they are alone and no adult is present. The age range (7–9 years) of the children in the current sample warrants consideration when interpreting these findings. School-aged children of this age range are learning how to balance developing more independence with their asthma management while also knowing when and how to seek support. Interventions with school-aged children should focus both on providing guidelines-based strategies on how to respond to their asthma symptoms when they are alone, such as when they are in their sleep space, and on how and when to seek support from adults and peers. Future research should explore how children’s responses to asthma symptoms differ across settings (e.g., bedroom, classroom) and time of day (e.g., day, night) to better inform tailored asthma management interventions.

Strengths & Limitations

Strengths of our study include the use of objective sleep data measured by actigraphy, resulting in increased reliability compared to self-report data; a racial/ethnic and socioeconomic diverse sample; and assessment of family asthma management from multiple informants. Despite these strengths, there are important limitations to consider when interpreting study results. The current study consisted of a cross-sectional design, and a longitudinal design would allow for testing causal mechanisms between family asthma management and sleep. Children participating in the current study were between the ages of 7 and 9 years old, and our findings may not be generalizable to older children, who may have a greater knowledge and understanding of their asthma symptoms and how to respond to the symptoms. Future research should explore how the relation between asthma management and sleep differs across developmental stages. While children are directed to independently describe how they respond to asthma symptoms when they are alone during the FAMSS interview (McQuaid et al., 2005), it is possible that their responses were influenced by their caregiver’s response. In addition, asthma control was measured through self-report (Asthma Control Test), and objective measurements of asthma control should be used in future studies. Using BMIz as a moderator allowed for direct comparison of BMI across age and gender; however, BMIz produces a restricted range, particularly for those with extremely high BMIs, and this should be considered when interpreting our results. Although outside of the scope of the current study, other factors, in addition to weight, may affect the association between family asthma management and sleep, such as nighttime routines, environmental triggers, or caregiver involvement, and should be explored in future studies.

Implications for Practice

Findings from the current study underscore the importance of considering children’s weight in asthma management interventions, and more research is necessary to inform how to best structure asthma management interventions based on children’s weight. Children’s more appropriate response to asthma symptoms was related to better sleep for children with a higher BMIz. For all children with asthma, adherence is important, including ensuring medications are accessible and children know how to assess and respond to asthma symptoms when they wake up during the night (NHLBI, 2007; 2020). This may be particularly important for children with a higher BMIz, since they are more vulnerable to altered metabolism and energy regulation and severe airway disease (Dixon & Poynter, 2016), as well as lower levels of physical activity (Koinis-Mitchell et al., 2021), which may influence sleep health. In addition, it may be beneficial for interventions to support children with comorbid asthma and obesity in appropriately assessing and responding to their asthma symptoms when they are alone or in their bedroom, such as knowing when to seek help from an adult or keeping asthma medications by their bed. Considerations of children’s age range is critical when ensuring strategies to improve asthma and sleep are developmentally appropriate. For example, given the age range (7–9 years) of the current sample, concrete strategies that clearly delineate what children should do when experiencing asthma symptoms prior to and during bedtime and reviewing these strategies with the child and caregiver may be helpful in optimizing children’s sleep and asthma management. It is essential that interventions designed to target children with comorbid asthma and obesity be multidimensional to address the specific needs for asthma, weight, and sleep among children from urban backgrounds.

Funding

This work was supported by The National Institutes of Health, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant # R01 HD057220 to D.K.M.).

Footnotes

Disclosure Statement

The authors report there are no competing interests to declare.

Data Availability Statement

The dataset analyzed in the current study is available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The dataset analyzed in the current study is available from the corresponding author upon reasonable request.

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