Family asthma management in urban children and its association with sleep outcomes

Abstract

Asthma symptoms impact children’s sleep quality. However, it is unclear how families’ daily management of their child’s asthma is associated with sleep quality. We examine associations between family asthma management components and sleep duration and quality for urban children (ages 7–9 years). Additionally, we examine these associations by racial/ethnic group. Data were collected as part of a longitudinal study that examined the co-occurrence of asthma, allergic rhinitis, sleep quality, and academic functioning for urban children diagnosed with persistent asthma (N = 196). A semi-structured interview assessed family asthma management practices. Sleep quality data were collected via actigraphy. Our visual depiction of sleep outcomes show that those with higher family asthma management ratings present with longer sleep duration and better sleep quality. Among specific family asthma management components, we found a significant association between children’s adherence to asthma medications and number of nighttime awakenings. For non-Latino Black (NLB) children, we found a significant association between environmental control and sleep duration. For urban children with asthma, clinical strategies to enhance overall family asthma management have the potential to support improved sleep quality. Additionally, for NLB children, asthma management interventions that provide environmental control practices may increase sleep duration.

Keywords

Asthma sleep child urban population

Asthma is a chronic respiratory disease that impacts 8.4% of children within the United States of America (Asthma and Allergy Foundation of America, 2019). Recent studies suggest that suboptimal asthma status can negatively affect sleep outcomes in urban ethnic minority children (Koinis-Mitchell et al., 2015a, 2015b, 2017). Specifically, poorly controlled asthma and worsening lung function have been associated with lower sleep efficiency and shorter sleep duration (Kier et al., 2012; Koinis-Mitchell et al., 2017; Martin et al., 2017).

Urban children face greater challenges regarding asthma management and sleep behaviors given low medication adherence and stressors such as exposure to environmental triggers, noise, crowded housing, and concerns regarding neighborhood safety (Coutinho et al., 2013; Koinis-Mitchell et al., 2014, 2017). While the available literature focuses on the linkage between asthma outcomes and sleep quality and duration, less is known about the association of specific aspects of asthma management and sleep outcomes.

For children, asthma management happens in the context of the family, with caregivers and other family members playing a significant role in supporting their daily asthma care (McQuaid et al., 2005). Effective family asthma management involves collaboration between the caregiver, the child, and healthcare providers and includes several management components (e.g., symptom recognition and response, medication adherence, and environmental control) with the goal of achieving optimal asthma control (Klinnert et al., 1997; McQuaid et al., 2005). Given poorly controlled asthma is associated with disruptions in sleep due to nocturnal symptoms, and urban children with asthma are shown to be at greater risk for poor sleep quality (Koinis-Mitchell et al., 2017), asthma management strategies tailored to the needs of urban children and families may reduce asthma-related sleep disruption and improve asthma and sleep outcomes for this high-risk group. In this study, we examine how asthma is managed in the family context and the association of specific components of family asthma management with sleep duration and quality (efficiency and awakenings) in urban children with asthma.

Specific components of asthma management that may be relevant to children’s sleep include medication adherence, environmental control measures, family response to symptoms, and balanced integration of asthma management into daily life. Medication adherence has the potential to decrease nocturnal asthma symptoms, which can improve the consistency and duration of children’s sleep without disruptions (Garrison et al., 2011; Koinis-Mitchell et al., 2012). Implementation of environmental control measures, aimed at minimizing exposure to asthma triggers in the home, has been associated with better asthma control and lower levels of functional limitation (Everhart et al., 2011). Environmental control practices have important implications when considering children’s sleep environments and can potentially impact sleep outcomes. Optimal family response to symptoms includes accurate recognition of and effective response to the child’s respiratory symptoms, which can enhance children’s maintenance of sleep and the amount of sleep obtained. Lastly, balanced integration refers to the family’s ability to incorporate the child’s asthma management into the demands of the family’s daily routine and has been associated with better medication adherence and asthma control (Fiese et al., 2005; McQuaid et al., 2005) and decreased healthcare utilization due to asthma (Everhart et al., 2014). Balanced integration reflects family organization skills, which may support consistent sleep behaviors and better sleep quality in children with asthma (Koinis-Mitchell et al., 2015a, 2015b). However, the association between family asthma management practices and sleep duration and quality has not been examined for children with asthma.

Aim

In this study, our aim is to examine the association between overall family asthma management and specific family asthma management components and sleep duration and sleep quality (efficiency and frequency of nighttime awakenings) for urban children with asthma. We focus on four specific areas of family asthma management relevant to sleep functioning: medication adherence, implementation of environmental control measures, family response to respiratory symptoms, and balanced integration of asthma management routines (Everhart et al., 2011; Fiese et al., 2005; Garrison et al., 2011; McQuaid et al., 2012).

To achieve this aim, first, we describe children’s average sleep duration and quality at each level of family asthma management ratings and provide a visual depiction of these data. We hypothesized that higher mean sleep duration, sleep efficiency, and lower number of nighttime awakenings would be associated with higher family asthma management ratings (reflective of better management). Second, we examine ethnic group differences in family asthma management. Similar to our prior work, we hypothesized that differences in family asthma management ratings with Latino and non-Latino white (NLW) families presenting with more optimal family asthma management (Everhart et al., 2011; Tackett et al., 2020). Previous research with urban middle schoolers and their caregivers has found non-Latino Black (NLB) families’ ratings of overall asthma management and medication adherence were lower than their NLW counterparts (Tackett et al., 2020). Third, we examine the association between overall family asthma management and sleep duration and sleep quality (efficiency and nighttime awakenings). We also examine this association for specific family asthma management components. Based on our prior work showing links between poorer daily lung function and poorer sleep outcomes in this group (Koinis-Mitchell et al., 2017), we hypothesized that more effective overall family asthma management would be associated with longer sleep duration and greater sleep efficiency as well as fewer nighttime awakenings. Similarly, we hypothesized that better medication adherence and response to respiratory symptoms and greater balanced integration and implementation of environmental controls would be associated with increased sleep duration and better sleep quality. We then examined ethnic group differences in these associations. We hypothesized that the association between family asthma management and sleep quality would be more robust for NLW, given Latino, and NLB children face additional urban and cultural stressors (e.g., perceived discrimination and acculturative stress; Koinis-Mitchell et al., 2017), present with more severe asthma symptoms (Koinis-Mitchell et al., 2015a, 2015b), and poor sleep duration and quality (Guglielmo et al., 2018).

Methods

Procedures

Data were collected as part of a larger study, “Project NAPS” (Nocturnal Asthma and Performance in School), that examined the co-occurrence of asthma, allergic rhinitis, sleep quality, and academic functioning in urban school-aged children with persistent asthma and healthy controls. A full description of the study procedures is included elsewhere (Esteban et al., 2014; Koinis-Mitchell et al., 2015a, 2015b, 2017). Children and their caregivers were recruited from urban hospital-based ambulatory clinics, an asthma education program, and four urban school districts. Caregivers signed consent to contact forms providing permission for the research team to complete a phone screen accessing eligibility for study participation.

To be eligible for study inclusion, each child (1) was between seven and nine years of age, (2) had a primary caregiver who self-identified as NLW, Latino, or NLB, (3) had a legal guardian who was willing to participate, (4) resided in one of four targeted urban school districts, and (5) was diagnosed by a physician with asthma, according to caregiver report at screening. School districts were identified by specific zip codes given these urban areas have high prevalence of asthma morbidity (Rhode Island Kids Count, 2012). Children also had to meet initial persistent asthma status at screening. This was assessed by caregiver report of an asthma controller medication prescription and/or daytime asthma symptoms more than 2 days a week, nighttime asthma symptoms more than two times a month, short-acting beta-agonist use 2 days a week or more, minor limitation in normal activity, and/or two or more oral steroid bursts per year. Asthma diagnosis and persistent asthma status were then confirmed via study clinician examination during the protocol (see below).

Child exclusionary criteria included (1) moderate to severe cognitive impairment, (2) use of stimulant medication for attention deficit hyperactivity disorder, (3) intermittent asthma, (4) another pulmonary or chronic health condition, and (5) diagnosed sleep disorder.

Caregivers provided signed consent and children assent during the enrollment visit. Once enrolled, participants completed the protocol in English or Spanish according to their preference and received monetary compensation for all study visits. Except for the study clinician examination, all study visits took place at the family’s home, unless the family preferred to come to the research offices. Approval for the study was obtained from the Institutional Review Board (Project # 018108) of the local hospital. Data presented in this report were collected between 2011 and 2013, in autumn and winter during the year of each enrollee’s participation. Demographic information and the family asthma management interview were completed at the initial visit. An asthma clinic visit was completed following the first visit to evaluate asthma status. Sleep data were collected during a 4-week monitoring period following the initial visit.

Demographic and descriptive information

Key demographic information was provided by the primary caregiver. This included child age and sex, caregiver race/ethnicity, nativity, years residing in the mainland United States of America, and experiences of acculturative stress. Family income was compared to the USA per capita poverty threshold of the year of study participation to identify enrollees living at or below the Federal Poverty Level (U.S. Department of Health and Human Services, 2005).

Asthma diagnosis and classification

Clinic study visits were performed to confirm asthma status and persistent level of severity and included review of medical history, completion of a physical exam, and spirometry (Esteban et al., 2014). Spirometry to confirm asthma diagnosis and severity was completed both before and after administration of β-agonist using the Koko incentive spirometer, version 5.0, 2014 (nSpire Health Inc., Longmont, CO). The study clinician classified asthma severity utilizing standard clinical asthma guidelines (NHLBI, 2007).

Asthma control

The Asthma Control Test (ACT; Liu et al., 2007) was administered to both children and caregivers following the completion of the monitoring period to determine asthma-related impairment. The ACT was scored into a dichotomous variable using a standard cutoff score of 19, wherein those below the score were considered to have poorly controlled asthma, while those above were considered well controlled.

Family asthma management

The Family Asthma Management System Scale (FAMSS) was used to assess family asthma management practices. The FAMSS is a semi-structured interview designed to determine how well children’s asthma is being managed by the family (Klinnert et al., 1997; McQuaid et al., 2005). In addition to an overall assessment of family asthma management (i.e., FAMSS total scale), it includes eight subscales, each assessing a specific aspect of family asthma management: Asthma Knowledge, Symptom Assessment, Family Response to Symptoms, Child Response to Symptoms, Environmental Control, Medication Adherence, Collaboration with Provider, and Balanced Integration of Asthma and Family Life (Klinnert et al., 1997; McQuaid et al., 2005). Trained research assistants administer the interview to children and their caregivers. Responses are audio-recorded for accuracy and rated on a 1 (ineffective or poor) to 9 (ideal) scale using a validated scoring system. Researchers that were trained to rate the FAMSS were evaluated on an ongoing basis regarding reliability and inter-rater consistency. This process included rating interviews conducted for other studies and conducted by other researchers. The FAMSS has been used extensively and is considered a well-validated measure of adherence (Klinnert et al., 1997; McQuaid et al., 2005).

The total FAMSS score is calculated as the mean of the subscale rating scores. In the current study, we focus on the total FAMSS score and specific aspects of family asthma management relevant to sleep: Medication Adherence, Environmental Control, Family Response to Symptoms, and Balanced Integration of Asthma and Family Life.

Sleep

Sleep outcomes included duration, efficiency, and nighttime awakenings. They were measured with a combination of the actiwatch sleep monitor (AW2; Mini Mitter Company, Bend, OR) and daily diary using standard procedures (Koinis-Mitchell et al., 2015a, 2015b, 2017). The actiwatch was worn on the nondominant hand during the 4-week monitoring period in autumn of the year of each child’s participation. Sleep and wakefulness were estimated by the Actiware-Sleep v 2.53 software.

Nights of sleep were excluded when (1) the actiwatch was off for all or part of the sleeping period, (2) the diary report was unavailable for the day in question, or (3) the child had an illness unrelated to asthma that could impact their sleep.

Sleep duration is defined as total time asleep between falling asleep at bedtime and waking up the following morning. Sleep quality was operationalized by two variables: sleep efficiency (percentage of total time asleep relative to the total time in bed each night) and number of nighttime awakenings (number of times per night participants woke up for more than 3 minutes (Koinis-Mitchell et al., 2017).

Statistical analysis plan

Utilizing univariate statistics, we first examined the distribution of sleep quality outcome and FAMSS scales, including Total Score, Family Response to Symptoms, Environmental Control, Medication Adherence, and Balanced Integration. Based on the distribution of these variables, we proceeded with parametric tests. We compared the mean and standard deviations for sleep efficiency and duration measures and continuous demographic variables by ethnic group using analyses of variance (ANOVA). Because of the more skewed distributions of the FAMSS subscales, we calculated the median and interquartile range and used the nonparametric Kruskal–Wallis test to compare the median scores by ethnicity. Other categorical demographic and socioeconomic variable counts and frequencies were compared between ethnic groups using chi-square tests. Similar to previous analyses, we considered variables as covariates in statistical models only if the variable was significantly related to both the outcome and the predictor (p < 0.05) (Koinis-Mitchell et al., 2017). Additionally, given that we were interested in racial/ethnic group differences, we did not control for them in our analyses. We report effect sizes for ANOVA as partial omega squared $(ω_{p}^{2})$ , and chi-square effect sizes are expressed as Cramer’s phi $(ϕ_{c})$ . We examined the association between family asthma management indicators and sleep quality variables using Pearson’s correlation analysis.

We evaluated the association of each family asthma management variable with standardized sleep quality variables using separate linear regression models. We then stratified the sample by the racial/ethnic group (Latino, NLB, and NLW) and constructed separate linear regression models for each family management variable and sleep outcome. Effect sizes are presented as partial eta squared (η_partial²). An alpha level of p < 0.05 was used for all statistical tests, and we conducted the statistical analysis in SAS version 9.4 (SAS Institutes, Inc.; Cary, NC).

Results

For the present study, we include only children with asthma who were not missing sleep data (N = 196). Thirty-seven children lacked any sleep data because of poor protocol adherence (n = 34) or device loss/failure (n = 3). There were no significant differences found in characteristics of sex, age, ethnicity, poverty, and asthma severity in those who did or did not have actigraphy data. A few participants wore the actiwatch for more than 4 weeks because of scheduling conflicts or due to poor protocol adherence and device malfunctions.

Of the 196 participants included in this analysis, 52% (n = 102) self-identified as Latino, 34% (n = 67) as NLB, and 14% (n = 27) as NLW. The mean child age was 8.3 years (SD = 0.9) and 54% (n = 106) were male. Most participants (70%; n = 137) lived at or below the poverty threshold. Fewer NLW participants lived at or below the poverty threshold compared to Latino and NLB participants (χ² = 13.3, φ_c = 0.26, p < 0.001; see Table 1). Furthermore, Latino and NLB participants lived in neighborhoods of greater risk compared to NLW participants (Table 1).

Table 1.

Demographic variables, asthma, family asthma management, and sleep characteristics.

	Total	Ethnicity
	Total	Latino	Black	Non-Latino white
Total sample N (%)	196 (100)	102 (52)	67 (34)	27(14)
Child age (years)—mean (SD)	8.3 (0.9)	8.3 (0.9)	8.3 (0.8)	8.3 (1.0)
Sex n(%)
Male	106 (54)	53 (52)	42 (63)	11 (41)
Female	90 (45)	49 (48)	25 (37)	16 (59)
Caregiver nativity n(%)
United States	98 (50)	23 (23)	49 (73)	26 (96)
Puerto Rico	42 (21)	42 (41)	0 (0)	0 (0)
Dominican Republic	36 (18)	36 (35)	0 (0)	0 (0)
Other	20 (10)	1 (1)	27 (18)	4 (1)
Years in United States—mean (SD)	23.5 (11.6)	17.7 (10.7)	28.6 (9.5)	32.5 (6.7)
At/below poverty threshold n(%)
Above threshold	59 (30)	21 (21)	23 (34)	15 (56)
At or below poverty threshold	137 (70)	81 (79)	44 (66)	12 (44)
Acculturative stress—mean (SD)^a	0.31 (0.32)	0.32 (0.34)	0.29 (0.27)	0.04 (0)
Asthma severity n(%)
Mild persistent	87 (44)	51 (50)	26 (39)	10 (37)
Moderate	74 (38)	37 (36)	22 (33)	15 (56)
Severe	35 (18)	14 (14)	19 (28)	2 (7)
Asthma poorly controlled (ACT % below 19 cutoff) n(%)
Poorly controlled	68 (36)	29 (30)	30 (45)	9 (33)
Well controlled	123(64)	68 (70)	37 (55)	18 (67)
FAMSS subscale—median (25th, 75th)
Family response to symptoms	5.4 (1.6)	6.0 (5.0, 7.0)	5.0 (4.0, 6.0)	6.0 (5.0, 7.0)
Environmental control	4.0 (2.6)	4.0 (2.0, 7.0)	3.0 (1.0, 7.0)	3.0 (1.0, 5.0)
Medication adherence	5.4 (1.8)	6.0 (5.0, 7.0)	5.0 (4.0, 7.0)	6.0(4.0, 7.0)
Balanced integration	4.8 (1.6)	5.0 (4.0, 6.0)	4.0 (3.0, 5.0)	4.0 (3.0, 6.0)
Total FAMSS	4.9 (1.1)	5.0 (4.5, 5.6)	4.6 (3.8, 5.3)	4.9 (3.9, 5.9)
Sleep
Sleep efficiency—mean (SD)	86.6 (3.7)	86.9 (3.7)	85.9 (3.7)	87.5 (3.6)
Sleep duration—mean (SD)	554.6 (34.7)	548.1 (32.8)	555.7 (34.5)	576.7 (33.9)
# Nighttime awakenings—mean (SD)	5.3 (2.6)	5.3 (2.7)	5.4 (2.4)	5.0 (2.5)

ACT: Asthma Control Test; FAMSS: Family Asthma Management System Scale.

^aAcculturative stress sample size: Latino: n = 97; Black: n = 58; NLW: n = 1.

Forty-four percent (n = 87) of participants had mild persistent asthma, 38 percent (n = 74) had moderate persistent asthma, and 18 percent (n = 35) had severe persistent asthma. A greater number of Latino (14%; n = 14) and NLB (28%; n = 19) participants had severe asthma than NLW (7%; n = 2) participants (χ² = 11; φ_c = 0.17, p = 0.02; see Table 1). Thirty-six percent of participants were classified as having poorly controlled asthma.

On average, NLW participants slept for a longer duration than NLB and Latino participants (mean minutes (SD) = NLW: 576.7 (33.9), NLB: 555.7 (34.5); Latino: 548.1 (32.8) (F (2193) =7.8; p < 0.001). We found no statistically significant differences in sleep efficiency or number of awakenings among Latino, NLB, and NLW participants.

Mean sleep duration and sleep quality across family asthma management ratings

We describe mean sleep duration, sleep efficiency, and number of nighttime awakenings at each family asthma management rating level for children across the sample. Figures 1–3 provide a visual depiction of mean sleep duration, sleep efficiency, and number of awakenings for each family asthma management rating level. Ratings can range from 1 (ineffective or poor) to 9 (ideal) family asthma management (McQuaid et al., 2005). In this sample, FAMSS ratings were distributed as follows: 5.7% with ratings of 2, 17.4% with ratings of 3, 32.1% with ratings of 4, 26.4% with ratings of 5, 14.3% with ratings of 6, 2.3% with ratings of 7, and 1.9% missing ratings. Participants with higher (mean 6–7) family management ratings had the longest sleep duration. Families with asthma management ratings in the midrange (3–5) had the shortest sleep duration. Families with the lowest ratings (2) had mid-high sleep duration. We found similar results for sleep efficiency. Those with higher (mean 6–7) family management ratings had the highest sleep efficiency. Families with asthma management ratings in the midrange (3–5) had the lowest sleep efficiency. Families with the lowest ratings (2) had mid-high sleep efficiency. For awakenings, those with the highest family asthma management mean ratings (5–7) had the lowest number of awakenings, those with mean family asthma management ratings of 3–4 had relatively high number of awakenings, and those with the lowest family asthma management ratings had relatively low number of awakenings.

Figure 1.

Mean sleep duration by family asthma management ratings.

Figure 2.

Mean % sleep efficiency by family asthma management ratings.

Figure 3.

Mean number of awakenings by family asthma management ratings.

Ethnic group differences in family asthma management

In this sample, we found ethnic group differences in two family asthma management components (see Table 1). Specifically, on the subscale measuring how well the family responds to asthma symptoms as they occur, Latino (median score (25th, 75th percentile) = 6.0 (5.0, 7.0)) and NLW families (6.0 (5.0, 7.0)) had higher ratings relative to NLB families (5.0 (4.0, 6.0); Kruskal–Wallis test statistic = 9.81; p-value = 0.007). Additionally, regarding the integration of the child’s asthma management into the family’s daily life, Latinos had higher scores (5.0 (4.0, 6.0)) as compared to NLW (4.0 (3.0, 6.0)) and NLB families (4.0 (3.0, 5.0); Kruskal–Wallis test statistic = 7.96; p-value = 0.019). Overall family asthma management (FAMSS total scores) did not differ across racial/ethnic groups.

Association between family asthma management and sleep outcomes

Across the entire study sample, results from linear regression found a significant association between family-reported medication adherence and the mean number of awakenings across the monitoring period (Table 2). A one standard deviation increase in medication adherence was associated with a 0.16 standard deviation decrease in mean number of awakenings (B= −0.16; 95% CI = −0.30, −0.02); F-value = 4.96; R² = 2.5%). There was no significant association between the overall family asthma management score, as well as other components of family asthma management, and specific sleep quality variables.

Table 2.

Linear regression models examining the association between family asthma management and sleep outcomes.

Outcomes by predictor(s)	Full sample			Latino			Black			NLW
Outcomes by predictor(s)	B	95% CI	F-value	B	95% CI	F-value	B	95% CI	F-value	B	95% CI	F-value
Sleep duration
Total FAMSS	0.06	(−0.08, 0.20)	0.71	0.10	(−0.12, 0.32)	0.81	0.05	(−0.19, 0.29)	0.20	0.09	(−0.22, 0.40)	0.34
Family response to symptoms	0.03	(−0.11, 0.17)	0.21	0.04	(−0.16, 0.24)	0.19	−0.05	(−0.29, 0.19)	0.18	0.20	(−0.15, 0.55)	1.24
Environmental control	0.09	(−0.05, 0.23)	1.61	0.05	(−0.13, 0.23)	0.29	0.29*	(0.05, 0.53)	6.06*	0.06	(−0.35, 0.47)	0.08
Medication adherence	−0.01	(−0.15, 0.13)	0.02	0.02	(−0.18, 0.22)	0.04	−0.14	(−0.38, 0.10)	1.28	0.18	(−0.15, 0.51)	1.01
Balanced integration	0.05	(−0.09, 0.19)	0.55	0.09	(−0.11, 0.29)	0.88	0.09	(−0.16, 0.34)	0.54	0.10	(−0.23, 0.43)	0.32
Sleep efficiency
Total FAMSS	0.08	(−0.06, 0.22)	1.38	0.14	(−0.08, 0.36)	1.56	0.03	(−0.21, 0.27)	0.05	0.01	(−0.30, 0.32)	0.00
Family response to symptoms	0.07	(-0.07, 0.21)	0.86	0.08	(−0.14, 0.30)	0.62	0.02	(−0.22, 0.26)	0.02	−0.05	(−0.40, 0.30)	0.07
Environmental control	0.09	(−0.05, 0.23)	1.41	0.04	(−0.16, 0.24)	0.13	0.15	(−0.09, 0.39)	1.45	0.17	(−0.24, 0.58)	0.70
Medication adherence	0.11	(−0.03, 0.25)	2.57	0.09	(−0.11, 0.29)	0.68	0.16	(−0.08, 0.40)	1.79	−0.02	(−0.35, 0.31)	0.01
Balanced integration	0.08	(−0.06, 0.22)	1.31	0.13	(−0.07, 0.33)	1.69	−0.07	(−0.32, 0.18)	0.31	0.15	(−0.16, 0.46)	0.88
Nighttime awakenings
Total FAMSS	−0.06	(−0.20, 0.08)	0.79	−0.14	(−0.38, 0.10)	1.50	0.01	(−0.21, 0.23)	0.00	−0.03	(−0.34, 0.28)	0.03
Family response to symptoms	−0.05	(−0.19, 0.09)	0.52	−0.10	(−0.32, 0.12)	0.84	0.00	(−0.24, 0.24)	0.00	0.02	(−0.35, 0.39)	0.01
Environmental control	−0.06	(−0.20, 0.08)	0.62	−0.02	(−0.22, 0.18)	0.03	−0.10	(−0.34, 0.14)	0.75	−0.15	(−0.56, 0.26)	0.52
Medication adherence	−0.16 *	(−0.30, −0.02)	4.96*	−0.16	(−0.38, 0.06)	2.14	−0.19	(−0.41, 0.03)	2.82	−0.07	(−0.42, 0.28)	0.18
Balanced integration	−0.07	(−0.21, 0.07)	0.97	−0.14	(−0.36, 0.08)	1.75	0.09	(−0.15, 0.33)	0.50	−0.16	(−0.49, 0.17)	0.97

*p < 0.05. CI: confidence interval; FAMSS: Family Asthma Management System Scales.

Race/ethnic differences in the association between family asthma management and sleep

We evaluated whether the association among specific aspects of family asthma management and sleep outcomes differed by race/ethnicity in our sample. For NLB children, we found a significant association between effective environmental control and mean sleep duration, such that a 1 standard deviation increase in environmental control strategies was associated with a 0.29 standard deviation increase in sleep duration (B = 0.29; 95% CI = 0.05, 0.53; F-value = 6.06; R² = 8.5%). This association was not significant in the Latino or NLW ethnic groups (Table 2), and no other ethnic group differences emerged in the association between overall and specific family asthma management components and sleep duration and quality outcomes.

Discussion

In this study, we assessed the association between overall family asthma management and aspects of family asthma management relevant to sleep outcomes (medication adherence, environmental control measures, family response to respiratory symptoms, and balanced integration) in a sample of urban children with persistent asthma. We examined ethnic group differences in these associations. This study provides a novel perspective by examining the association of family asthma management behaviors and children’s sleep quality. The visual depiction provides an in-depth understanding of sleep quality and duration at different levels of family asthma management that can inform intervention development. Furthermore, this study adds to the literature by combining self-report and objective measurement of sleep duration and quality in urban, ethnic minority children with persistent asthma.

Sleep outcomes as a function of asthma management ratings

The visual representations of sleep outcomes at each level of family asthma management shed light on which group may need tailored family asthma management and sleep interventions. Those with family asthma management ratings of 6–7 appeared to have the most optimal sleep outcomes, which is what we would expect. Those with 3–4 ratings may be most at risk with respect to poorer sleep because of poorer family asthma management across several areas that may not be adequately captured and addressed within their health care. This needs to be further investigated to better support sleep and asthma outcomes for this population.

Interestingly, families who presented with the poorest mean family asthma management (mean rating = 2) for their children also seem to have relatively more optimal sleep outcomes. When evaluating family asthma management, the family’s overreliance on the child to identify symptoms, understand, and adhere to the medication management plan contributes to lower FAMSS ratings. It could be that for families who are more reliant on the child to manage their asthma, this reliance may support better sleep quality. Caregivers may be more responsive to the child’s cues for sleep and in this manner support more effective sleep hygiene habits. Alternatively, lower family asthma management ratings may be reflective of chronic stress. For children, whose families are experiencing many stressors, longer sleep duration may be a function of coping with stress. Future research focusing on better understanding family asthma management practices as well as sleep hygiene practices for this population would inform clinical interventions to enhance asthma management and sleep outcomes.

Ethnic group differences in family asthma management

Our results confirm previous findings that suggest racial/ethnic group differences in components of family asthma management (Everhart et al., 2014; Tackett et al., 2020), specifically family response to respiratory symptoms and balanced integration of asthma management into family routines. For NLB families, interviewer ratings of family response to asthma symptoms were lower, reflecting a less optimal response. This subscale assesses the degree of appropriateness of the family’s actions to address respiratory symptoms, their use of an asthma action plan, and monitoring of the child’s symptoms (McQuaid et al., 2005). Racial disparities exist in healthcare quality for NLB children with asthma (Harper et al., 2015). For example, NLB children are less likely to receive an asthma action plan than NLW children (Akinbami et al., 2016). While in this sample we did not find racial group differences in whether the family had an asthma action plan, ratings of patient-provider collaboration were lower for NLB families when compared with NLW and Latino families (data not shown). This indicates a less positive relationship with the provider, which may be reflected in decreased likelihood to follow the asthma action plan. Additionally, limited asthma knowledge and health literacy may also be a factor in NLB families’ response to symptoms (DeWalt et al., 2007). These findings suggest the need for clinical interventions that support families in recognizing and appropriately responding to early signs of an asthma exacerbation.

Balanced integration assesses the extent to which the family can attend to daily asthma management while integrating those needs with the day-to-day demands of family life, particularly school attendance and social activities (McQuaid et al., 2005). We found that Latino participants had higher ratings in this aspect of family asthma management. Aspects of Latino culture, including familismo, may support a more integrated approach to family asthma management and consideration of demands of daily asthma management with other family obligations. Previous findings suggest differences in balanced integration across Latino subgroups (Everhart et al., 2014). Latinos in Puerto Rico had lower ratings than Latinos in the mainland United States (Everhart et al., 2014). Identifying specific aspects of the experiences of Latino families that enhance the integration of the child’s asthma management with the other demands of daily family life is an important step to improve family asthma management. For example, understanding how families can develop consistent asthma management routines that fit with other demands of family life can have significant implications for the child’s asthma management and control. In understanding these family asthma management rating differences, it is important to note that while raters were not blinded to the racial/ethnic background of research participants, ratings were not always made by the same research assistant that worked directly with the family.

Association between family asthma management and sleep outcomes

We found that medication adherence, a specific aspect of family asthma management closely tied to symptom control, was related to sleep quality. Specifically, better medication adherence was associated with fewer nighttime awakenings. Given the negative impact of asthma symptoms on sleep quality (Kier et al., 2012; Koinis-Mitchell et al., 2017; Martin et al., 2017), our findings indicate that consistent medication adherence in ethnic minority and urban children is an important step toward improving sleep quality. Supporting medication adherence and improved sleep are important priorities to enhance overall health for children with asthma (Koinis-Mitchell et al., 2015a, 2015b, 2017). Consequently, children and families will benefit from their healthcare provider’s focus on barriers to adequate medication adherence and guidance to overcome them (McQuaid et al., 2012). Healthcare providers should be aware of barriers that may exist to obtaining and refilling medications, particularly for low-income families. Families may benefit from guidance regarding the best times to administer the medication (e.g., in the morning and evening right before brushing teeth), strategies to have the medication readily available, and from simplified medication regimes to improve medication adherence.

Ethnic group differences in the association between family asthma management and sleep outcomes

In our sample, more optimal environmental control of asthma triggers was associated with longer sleep duration for NLB children. NLB children have been found to have higher levels of exposure to environmental asthma triggers, such as second-hand smoke in the home (Everhart et al., 2011; Fedele et al., 2016). Therefore, our findings confirm that environmental control measures are essential to managing asthma symptoms and supporting better sleep in this population. Providing urban families with access to social and physical conditions within their communities that support healthy home environments is an important first step. In urban and low-income communities, the development of housing policies and regulations that promote healthy and safe communities (e.g., green spaces) and home environments is crucial to improve asthma, sleep, and overall health outcomes for children. Providing additional support to families and homeowners (including financial resources) to minimize pest infestation and exposure to second-hand smoke may be an important factor in addressing health disparities.

Several limitations of the current study are important to note. While we used objective and subjective measures to assess sleep and asthma, the cross-sectional study design does not allow for causal inferences. Daily measures of asthma and sleep were collected over the study monitoring period but were aggregated into mean summary variables. We did not examine weekday and weekend sleep outcomes separately. We did not collect data on specific sleep hygiene practices and families’ understanding of the impact of those practices on sleep quality or overall health.

Implications for practice

Overall, the mean ratings for family asthma management in this sample were in the midpoint range. Consequently, developing strategies and interventions that further enhance effective family asthma management for children from urban and ethnic minority communities is important. Interventions aimed at improving access to medications and enhancing asthma medication adherence are an important target of further research. Additionally, future research should include an assessment of the impact of medication adherence on other aspects of child functioning, including sleep and academic performance (e.g., school absences).

Developing programs that support appropriate environmental trigger management in the home will enhance asthma and sleep outcomes in urban communities. For urban communities, public campaigns and programs that promote and support integrated pest management practices as well as improve housing conditions are critical for enhancing overall asthma and sleep health. Interventions that focus on the child’s sleep setting to target trigger management and sleep disrupters as well as sleep hygiene practices may be key to enhancing sleep outcomes in urban children with asthma. Future research is needed to better understand family practices that support optimal asthma management and sleep practices. Urban and ethnic minority children and their caregivers may benefit from culturally tailored interventions that increase asthma knowledge, medication adherence, and collaboration with the healthcare provider and dispel concerns about medications. Future research is needed to better understand sleep hygiene practices as well as caregiver attitudes regarding their children’s sleep. This will support the development of culturally targeted interventions aimed at educating families about healthy sleep practices, including the benefits of sleep, and the effect of electronic devices on sleep health. Multicomponent interventions that address asthma control and sleep are important for improved health outcomes for urban children with asthma.

Conclusion

In summary, our findings support the importance of considering the association between family asthma management behaviors and sleep outcomes for urban and ethnic minority children with asthma. Further research is needed to better understand how to support improved family asthma management and sleep hygiene practices in this population. Additionally, culturally tailored interventions that address both asthma control and sleep hygiene have the potential to enhance overall health for urban and ethnic minority children with asthma.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grants 3R01HD057220 and 3R01HD057220-03S1 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and 3R01 MD01222502-S1 from the National Institute for Minority Health and Health Disparities of the National Institutes of Health (NIH).

Ethical approval

Approval for the study was obtained from the Rhode Island Hospital Institutional Review Board (Project # 018108).

ORCID iD

Maria T Coutinho

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