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. Author manuscript; available in PMC: 2023 May 31.
Published in final edited form as: J Fam Psychol. 2022 Sep 15;37(2):256–261. doi: 10.1037/fam0001032

Household Disorder and Blood Pressure in Mother-Child Dyads: A Brief Report

Samantha L Martin 1, Shameka R Phillips 2, Valene Garr Barry 1,3, Yenni E Cedillo 1, Jessica Bahorski 2,4, Makenzie Callahan 1, W Timothy Garvey 1, Paula Chandler-Laney 1
PMCID: PMC10231908  NIHMSID: NIHMS1877166  PMID: 36107692

Introduction

High blood pressure (BP) is a health problem that affects children, adolescents, and adults worldwide (Cutler et al., 2008; Hansen et al., 2007). Studies have shown that high BP accounts for approximately 50% of all cardiovascular disease (CVD) and is the leading risk factor for morbidity and mortality throughout the United States and other nations(Lawes et al., 2008). High BP is associated with genetic, environmental, and lifestyle factors for both adults and children (Taylor et al., 2009). Recent studies have reported that the familial transmission of high BP is not fully explained by genetics, thus highlighting the importance of lifestyle and environmental factors. Interest in targeting lifestyle and environment as novel ways to prevent the development of high BP in adults and children has grown significantly in recent years (Doris, 2011; Falkner & Lurbe, 2020).

It is widely accepted that demographic characteristics (i.e., age, race, and gender), as well as lifestyle factors (i.e., overweight/obesity, sodium intake, and physical activity), significantly increase the risk of high BP and hypertension in both the pediatric and adult populations (Aronow et al., 2011; Grillo et al., 2019; Hegde & Solomon, 2015; Lackland, 2014; Torrance et al., 2007; Yang et al., 2012). Environmental factors including ambient temperatures, environmental noise, organic pollutants, and metals have also been shown to have prohypertensive consequences (Giorgini et al., 2016; Munzel et al., 2014; Wang et al., 2017). Though some studies have reported on the effect of environmental factors on maternal BP and CVD, studies investigating the influence of environmental factors on BP for mothers and children remain scarce. Due to the steady rise of deaths due to CVD, clinicians and investigators alike have begun to explore potential avenues to reduce the future development of high BP and CVD by targeting both lifestyle and environmental factors.

One potential risk factor that is severely understudied is household disorder. Household disorder is characterized as environmental noise, residential crowding, and a lack of routines, structure, and limit setting within the home (Matheny, 1995). One of the most used instruments to measure household disorder for mothers and children is the Confusion Hubbub and Order Scale (CHAOS). CHAOS is a validated and reliable tool consistently documented to influence mother and child psychosocial, executive, and behavioral outcomes (Brieant et al., 2017; Cassidy, 2016; Iwinski et al., 2021). More recently, investigators have also documented that chaotic living environments (measured by the CHAOS instrument) significantly impact metabolic outcomes (LaBarre et al., 2021). To date, much of the literature has focused on noise and neighborhood confusion outside of the home and no prior study has assessed whether disorder within the home environment (i.e., household disorder) is associated with BP in a cohort of mother-child dyads. Therefore, the purpose of this study was to examine whether household disorder is associated with BP in mothers and their children after adjusting for race, age, sodium intake, and body mass index (BMI). We hypothesize that greater household disorder will be associated with higher systolic and diastolic BP in mothers and higher systolic and diastolic BP percentiles in their children.

Methods

Participants

Data for this analysis were drawn from women and children enrolled in a study investigating integrational transmission of obesity. For the parent study, women (aged 20–36) and their children were recruited when the child was 4–10 years old. Recruitment was stratified into three groups based on maternal weight and gestational diabetes status during the index pregnancy. Women were eligible for this study if they (1) had a BMI of 18.0–24.9 kg/m2 at their first prenatal care visit and did not develop GDM in pregnancy; or (2) had a BMI of ≥25.0 kg/m2 at the first prenatal care visit and did not develop GDM in pregnancy, or (3) if they were diagnosed with GDM during their index pregnancy (open BMI). Exclusion criteria for mothers included lupus, heart disease, hepatitis (B and C), HIV, extreme opioid or illicit drug use, and alcohol use during pregnancy were also excluded. Exclusion criteria for Further, women with comorbidities such as hypertension, glycosuria, preeclampsia, or type 2 diabetes in the years since the index pregnancy were also excluded. Children were excluded if they had type 1 diabetes, congenital heart disease, in-utero growth restriction, or medical conditions that impacted metabolic or cognitive health. We report how we determined the sample size, all data exclusions, all manipulations, and all measures in the study. This study’s design and its analysis were not pre-registered. The University of Alabama at Birmingham (UAB) approved all procedures, and all participant dyads provided written consent (adults) and assent (children).

Procedure

During an in-person visit at the UAB Comprehensive Women’s Reproductive Health Facility, height, weight, and BP were measured mother and child. Surveys were administered to assess maternal educational attainment, household socioeconomic status (SES), and household disorder. Dietary intake was assessed with three 24-hour diet recalls. Demographics (marital status, education, race, and ethnicity), household size and income, breastfeeding history were self-reported.

Anthropometrics

Weight and height were measured twice using a digital scale with a stadiometer (Solo Detecto Eye-Level Physicians Scale, Webb City. MO). Height was measured to the nearest 1 cm, and weight was measured to the nearest 0.1 kg. A third measurement was collected if the difference between the first two measurements was greater than 0.1 kg and 0.1 cm for weight and height, respectively. Maternal BMI was calculated as weight (kg)/ height2 (m2). Child BMI z-scores were derived using reference data from the Center for Disease Control and Prevention (Centers for Disease Control and Prevention, 2000).

Blood Pressure

BP measurements were collected using a digital sphygmomanometer (Spot Vital Signs LXi Device; Welch Allyn; Skaneateles Falls, NY) with appropriate use of adult or pediatric cuffs, as needed. Child BP pressure measurements were converted to percentiles using the American Academy of Pediatrics reference data (Flynn et al., 2017). If BP measurements were >180 mmHg (systolic) or >110 mmHg (diastolic) for mothers and above the 99th percentile for children, they were given an 8 oz bottle of water and required to rest for 5 minutes before the measure was repeated.

Confusion Hubbub and Order Scale

Maternal perception of household disorder in the home environment was assessed using the Confusion, Hubbub, and Order Scale (CHAOS)(Matheny, 1995). The CHAOS questionnaire is a 15-item survey to assess the routines, noise, and environmental chaos. Statements such as “It’s a real zoo in our home”, “the atmosphere in our home is calm”, “we always seem to be running late”, and “we are usually able to stay on top of things” are used to assess the home environment. In this study, statements were verbally provided by the research staff and mothers were asked to assign a number between 1 and 4 to indicate how each statement describes their home environment. The 4-point Likert scale included “very much like your home environment (4)”, “a little bit like your home environment (3)”, “somewhat like your home environment (2)”, and “not at all like your home environment (1)”. Responses were then summed to derive an overall CHAOS score with higher scores reflecting a more disorganized home environment with a higher level of noise, residential crowding, and a lack of predictability and organization.

Dietary Recall

Total energy (kcals/day) and sodium (g/day) intake were derived from three 24-hour dietary recalls. Diet recalls were verbally administered in-person to participants at each of the two study visits and one via phone in between each of the two visits. Recall data were analyzed using the Automated Self-Administered Recall System (ASA24, National Cancer Institute: Bethesda, Maryland; (Falkner & Lurbe, 2020)). Totals for each 24-hour period were averaged together.

Statistical Analysis

Descriptive statistics were calculated for mother-child dyads. Pearson’s correlation and partial correlation coefficients were used to investigate whether household disorder was associated with BP. Covariates were examined in three separate models. Model 1 adjusted for demographic characteristics (i.e., age, race, and group membership). Model 2 adjusted for model 1 factors + sodium intake. Model 3 adjusted for model 2 factors + maternal BMI or child BMI z-score, as appropriate. Statistical significance was set at p< 0.05, and all analysis were performed using SAS (version 9.4, SAS Institute Inc. Cary, NC).

Results

Two hundred and twenty-five dyads were enrolled in this study. Nine dyads did not complete the household disorder scale and were excluded from the final analysis. The characteristics of the 216 dyads included in this analysis are provided in Table 1. Overall, mothers within this cohort were employed (66.98%), unmarried (67.91%), and 87% were African American. Household income ranged from less than $25,000 to $150,000 or more, with 47.91% of the mothers having an income less than $25,000.

Table 1.

Characteristics of the mothers and children in the sample (data are mean ± SD unless noted).

Mothers
N 216
Age 33.70 ± 5.22
Maternal Ethnicity (%)
      Hispanic or Latino 1.85%
      Not Hispanic or Latino 97.69%
     Unknown or not available 0.46%
Maternal Race (%)
    American Indian 0.46%
    Asian 0.46%
    Black or African American 87.04%
    White 12.04%
Current BMI (kg/m2) 33.59 ± 9.43
Systolic Blood Pressure (mmHg) 118.43 ± 13.90
Diastolic Blood Pressure (mmHg) 78.07 ± 7.87
Household size N (%)1
   Homes with ≤ 5 178 (82.95%)
   Homes with ≥ 6 37 (16.79%)
Employment N (% Employed) 144 (66.98)
Marital Status N (% Married)1 69 (32.09%)
Household income (%)1
   Less than $25k 47.91%
   $25–34,999k 22.79%
   $35–49,999k 8.84%
   $50–74,999k 4.65%
   $75–99,999k 6.51%
   $100–149,999k 2.79%
   $150k or more 4.65%
   Unknown or not available 1.86%
Total Energy Intake (kcals) 1835.74 ± 639.29
Sodium intake (mg) 3246.66 ± 1119.76
Children 216
Age (years) 6.96 ± 2.07
Child BMI z-score 0.60 ± 1.28
Systolic blood pressure percentile (%)3 0.72 ± 0.23
 Diastolic blood pressure percentile (%)3 0.76 ± 0.19
Total Energy Intake (kcals) 1597.58 ± 490.93
Sodium Intake (mg) 2601.14 ± 857.40
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1

N=215

2

N=213

3

N=208

In unadjusted correlations, household disorder was positively associated with maternal systolic BP (r=0.14, p<0.05; Figure 1A) and there was a trend for a positive association with diastolic BP (r=0.15 p=0.07: Figure 1B). After adjusting for demographic characteristics (Model 1) the association of household disorder with systolic BP remained statistically significant (partial r=0.15, p<0.05), but the association of household disorder with diastolic BP weakened (partial r= 0.11, p=0.10). When sodium intake (Model 2) was added to the model, the results for the association of household disorder and systolic (partial r=0.15, p<0.05) and diastolic BP (partial r=0.11, p=0.10) remained unchanged. Similarly, the addition of BMI (Model 3) to the model did not change the association of household disorder with systolic (partial r=0.15, p<0.05) or diastolic (partial r=0.11, p=0.10) BP for the mothers. Among children, household disorder was not associated with BP percentiles for systolic (r=0.03, p=0.64) or diastolic (r=0.04, p=0.53) BP in the unadjusted and adjusted models (not shown).

Figure 1.

Figure 1

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A. Correlation between systolic blood pressure and household disorder in mothers, unadjusted p<0.05 and B. Correlation between diastolic blood pressure in mothers, unadjusted p=0.07.

Discussion

The purpose of this study was to determine whether household disorder was associated with BP in mothers and their children and whether these associations remained after adjusting for demographic characteristics, lifestyle factors, and BMI. This question is important because neighborhood factors such as traffic and aircraft noise have consistently been associated with BP and CVD risk in children and adults. However, previous studies have not explicitly investigated noise and disorder within the household. Our results showed that household disorder was modestly associated with systolic BP for mothers but was not associated with the BP percentiles of their children. These findings suggest that interventions to reduce household disorder could lead to a reduction in systolic BP for mothers. Further, future longitudinal research in children will be important to investigate whether household disorder has a stronger impact on children’s BP as they age.

The observed association of household disorder with maternal systolic BP is consistent with prior research reporting that household disorder is associated with poorer health outcomes among adults with myocardial infractions, and poorer glycemic control for women with diabetes (Colicchia et al., 2016; Ganasegeran & Rashid, 2018; Zullig et al., 2013). Although we did not find that the association of household disorder and BP was influenced by sodium intake, it is possible that other diet-related factors could be important mediators. For instance, others have reported that greater household disorder was associated with poor family dietary behaviors for parents and their children such as a higher consumption of sugar sweetened beverages, a greater availability of salty/fatty snacks, and less frequent family meals (Martin-Biggers et al., 2018). In one study, investigators documented that greater household disorder was associated with barriers to preparing homemade meals, poorer quality food in their home environment, and fewer meals consumed as a family (Fulkerson et al., 2019). Several cross-sectional and longitudinal studies have shown that the consumption of ultra-processed foods, which is a hallmark of poor quality diets, is associated with greater risk for high BP, hypertension, and cardiovascular disease (Alsabieh et al., 2019; Lane et al., 2021; Martinez Steele et al., 2017; Mendonca et al., 2017; Scaranni et al., 2021). Consequently, in the current study, it is possible that the association of household disorder with BP in mothers may have been at least partly mediated via dietary factors unrelated to sodium such as poor quality and ultra-processed foods and low intake of homemade meals. Future research should consider a more holistic approach to assess dietary intake when investigating whether diet could contribute to the association of household disorder and BP, by considering the quality of food consumed as well as the source of food, it’s preparation, and the consistency of meal patterns.

We did not observe a significant association between household disorder and children’s systolic and diastolic BP percentiles within this cohort. Much of the literature assessing the effects of noise on children’s BP has been contradictory. While some studies have reported increases in childhood BP with respect to traffic and aircraft noise, others have reported null results (Bullinger et al., 1999; Cohen et al., 1980; van Kempen et al., 2006). In a recent study, positive associations among noise annoyance, diastolic BP and mean arterial BP were reported in children (ages 7–18; mean age 12)(Badihian et al., 2020). Another study reported that when noise levels were elevated by 10 decibels, it was accompanied by a 1 mm Hg and 0.6 mm Hg increase of systolic and diastolic BP, respectively, in 8–14-year-old children (Babisch et al., 2009). It is possible that these associations were carried by the older children enrolled in these studies, who may have been more aware and impacted by the chaotic home environment as compared to younger children like those in the present study. Further, the young children within our cohort may have been less impacted by a chaotic home environment because they had adapted to it or might not recognize it as chaotic. This hypothesis is consistent with prior research documenting that children residing in homes with higher household disorder block out overstimulation (Matheny et al., 1995).

The limitations of this study include using a single BP measurement in mothers and children, although the BP measurement was repeated if it was above 180/110 mmHg in women or above the 99th percentile in children. Further, the homogeneity of the study cohort limits the generalizability. The cross-sectional design and observational nature of this study limits our ability to evaluate causal relationships. Despite these limitations, the study was strengthened by the concurrent assessment of mothers and their children in the context of a single study, the use of standardized clinical procedures to measure BMI and BP, and the inclusion of dietary recalls to assess sodium intake.

The results of this study suggest that household disorder should be explored as a potential target for intervention to reduce BP in mothers. Given that much of the literature showing BP differences and neighborhood noise levels include teenage children, longitudinal research is needed to investigate if and when children’s BP shows an association with household factors like household disorder. Future research should also perform more comprehensive phenotyping of BP across multiple study visits and investigate the potential for household disorder to mediate the association between underlying risk for CVD and measured BP.

Author Note:

We report how we determined our sample size, all data exclusions, all manipulations, and all measures of the study. Data were analyzed using SAS (version 9.4, SAS Institute Inc. Cary, NC). Participants of this study did not agree for their data to be shared publicly. Thus, supporting data will not be made available. This study’s design, analyses, and codes were not pre-registered due to ongoing work being performed by other investigators on the study team.

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