Next Article in Journal
Hip Fracture Patients in Geriatric Rehabilitation Show Poor Nutritional Status, Dietary Intake and Muscle Health
Next Article in Special Issue
Supplemental Nutrition Assistance Program-Education Improves Food Security Independent of Food Assistance and Program Characteristics
Previous Article in Journal
The Effect of Acute Hypohydration on Indicators of Glycemic Regulation, Appetite, Metabolism and Stress: A Systematic Review and Meta-Analysis
Previous Article in Special Issue
Caregiver’s Self-Confidence in Food Resource Management Is Associated with Lower Risk of Household Food Insecurity among SNAP-Ed-Eligible Head Start Families
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Prevalence and Predictors of Overweight and Obesity among Young Children in the Children’s Healthy Living Study on Guam

Rachael T. Leon Guerrero
L. Robert Barber
Tanisha F. Aflague
Yvette C. Paulino
Margaret P. Hattori-Uchima
Mark Acosta
Lynne R. Wilkens
2 and
Rachel Novotny
Office of Research & Sponsored Programs, University of Guam, Mangilao, Guam 96923, USA
University of Hawaii Cancer Center, Honolulu, HI 96813, USA
College of Tropical Agriculture and Human Resources, University of Hawaii At Manoa, Honolulu, HI 96822, USA
Author to whom correspondence should be addressed.
Nutrients 2020, 12(9), 2527;
Submission received: 12 July 2020 / Revised: 5 August 2020 / Accepted: 12 August 2020 / Published: 20 August 2020
(This article belongs to the Special Issue Nutrition among Vulnerable Populations)


This study is part of the Children’s Healthy Living program in U.S. Affiliated Pacific region. The objectives were to estimate overweight and obesity (OWOB) prevalence and identify possible related risk factors among ethnic groups in Guam. In 2013, 865 children (2–8 years) were recruited via community-based sampling from select communities in Guam. Children’s demographic and health behavior information; dietary intake; and anthropometric measurements were collected. Logistic regression, odds ratio, t-tests, and chi-square tests were used to determine differences and assess covariates of OWOB. The results indicate that 58% of children were living below the poverty level, 80% were receiving food assistance, and 51% experienced food insecurity. The majority of children surveyed did not meet recommendations for: sleep duration (59.6%), sedentary screen-time (83.11%), or fruit (58.7%) and vegetable (99.1%) intake, and consumed sugar sweetened beverages (SSB) (73.7%). OWOB affected 27.4% of children. Children affected by OWOB in this study were statistically more likely (p = 0.042) to suffer from sleep disturbances (p = 0.042) and consume marginally higher amounts (p value = 0.07) of SSB compared to children with healthy weight. Among Other Micronesians, children from families who considered themselves ‘integrated’ into the culture were 2.05 (CI 0.81–5.20) times more likely to be affected by OWOB. In conclusion, the OWOB prevalence among 2–8-year-olds in Guam was 27.4%; and compared with healthy weight children, children with OWOB were more likely to have educated caregivers and consume more SSBs. Results provide a basis for health promotion and obesity prevention guidance for children in Guam.

1. Introduction

Childhood overweight and obesity (OWOB) is a global epidemic affecting many countries [1,2,3,4,5], including the United States (US) and the US Affiliated Pacific region (USAP). In the US a high prevalence of OWOB among racial/ethnic minority groups for children 2–19 years is reported, specifically Non-Hispanic black (19.5%) and Hispanic (21.9%), yet Native Hawaiian or Other Pacific Islanders are not included [1,2]. Novotny and colleagues conducted a systematic review of childhood OWOB in the USAP region and estimated that the prevalence of OWOB for children 2–8 years was 21%, and that the prevalence increased to 39% by age 8 [6]. Similarly, the proportion of obese children increased from 10% at age 2 years to 23% at age 8 years, with the highest prevalence of obesity in both Guam and American Samoa [6]. The systematic review found that in Guam, OWOB prevalence (39%) among children ages 3 to 5 years [6] exceeded the US national average (23%) for children aged 2 to 5 years [1,2].
Guam is a U.S. Territory located in the northwestern Pacific Ocean, approximately 3700 miles west of Hawaii, 6000 miles west of California, and 1300 miles southeast of Japan. CHamorus are the original inhabitants of Guam [7,8], and are typically grouped with other Pacific Islanders. However, the current population of Guam is characterized by substantial ethnic variation [9]: 37% CHamoru, 26% Filipino, 12% other Pacific Islander, 11% other ethnicity, 7% White, and 7% other Asian. This ethnic diversity evolved through centuries of colonization and migration that continues today [10] and may modify the burden and predictors of OWOB among CHamorus. Factors related to OWOB are sleep, physical activity, psychosocial, life course exposure, SES, and diet [11,12] that for many racial/ethnic groups in Guam is influenced by this history [12,13]. OWOB prevalence among adults is higher among CHamorus compared to other ethnic groups on Guam [11]. In a study among adults in the two largest ethnic populations in Guam then (2008) and now, CHamorus and Filipinos, there was a significant difference in energy density of diets between the two groups, where CHamorus had a higher energy and added sugar intake [11]. In 2009 and 2010, prevalence of OWOB was highest among CHamorus followed by Other Micronesians reported in one of the first reports to disaggregate BRFSS data by ethnicity [14].
Children who are overweight or obese have a higher risk of being overweight or obese adults [15] with an increased risk of chronic diseases later in life [16]. However, there is limited information about young children in Guam, and data are needed to understand the burden of and determine the appropriate intervention for childhood OWOB. Therefore, the purpose of this study is to estimate the prevalence of OWOB among children in Guam and identify demographic or other risk factors targeted by the Children’s Healthy Living (CHL) program for overweight and obesity in CHamoru children.

2. Methods

The Children’s Healthy Living Program for Remote Underserved Minority Populations in the Pacific Region (CHL) program is a partnership of universities and local organizations across the remote USAP (Alaska, American Samoa, Commonwealth of the Northern Marianas, Federated States of Micronesia, Republic of Marshall Islands, Republic of Palau, Hawaii, and Guam) working to prevent childhood obesity in the Pacific. Detailed information on the study design of the CHL program can be found elsewhere [17,18]. Briefly, approximately 900 children (2–8 years) and their parents/caregivers from each participating USAP jurisdiction were recruited to participate in the study at both baseline and 24-month follow-up. On Guam, a total of 865 children were recruited at baseline (2013) and 696 children were recruited from the same communities at 24-month follow-up. This paper will focus on baseline data of the 865 children on Guam recruited from communities (Agana Heights, Sinajana, Agat, Santa Rita, Yigo, Yona, Talafofo, and Dededo) selected due to their size, representation of indigenous (CHamoru) residents, and isolation and cohesiveness (for purpose of intervention) [17]. Behavioral targets of the CHL program were increasing sleep, water intake, fruit and vegetable intake, physical activity and decreasing sugar sweetened beverage intake and sedentary behavior [17].
Child participants were recruited primarily at early childhood education centers (e.g., Guam Head Start program, daycares, and public elementary schools) and community-based settings (e.g., municipal centers and public housing areas) on Guam. Data collection occurred over a minimum of two visits at these recruitment sites. Further details on recruitment is described elsewhere [17,18]. Child participants with parents’ consent and who provided assent were assessed.
At the first visit, parents/caregivers completed surveys on demographic information (i.e., child age, place of birth, race/ethnicity, and sex; household composition and food security; parent/caregiver educational level and income); cultural identity of the parent/caregiver; and general health status, early life feeding behaviors, and sedentary screen-time and sleep behavior germane to the child. They also received instructions for completing a two-day food and activity log at home during the week. Sleep quality was measured with the Tayside Children’s Sleep Questionnaire (TCSQ) [19] and sleep duration [20] was reported by the parent/caregiver as hours asleep at night and during naps. Other surveys were adapted from previous studies [17] with some local terminology added for participant clarity.
Detailed information was collected in CHL on race/ethnic groups, allowing for multiple groups to be selected; classification prioritized ethnic groups indigenous to the jurisdiction. On Guam, the indigenous ethnic group is CHamoru [7,8]. If more than one race/ethnicity was indicated for the child and CHamoru was selected, the child was categorized as CHamoru. If a child identified as not CHamoru and as one of the other Micronesian Pacific Island ethnic groups, such as Chuukese, Palauan, Yapese, Carolinian, Marshallese, or Kosraean, that child was categorized as ‘Other Micronesian’, because the only other Pacific Island ethnic groups identified in the Guam sample were from Micronesia and not Polynesia (e.g., Native Hawaiians or Samoans). These other Micronesian Pacific Islander groups were grouped together as their numbers were small and they were all from neighboring Micronesian islands.
Food insecurity was determined by asking one question from the US Department of Agriculture’s Core Food Security Module: “In the past 12 months how often does money for food run out by the end of the month?” (never, seldom, sometimes, most times, always, don’t know, or no response [21]. In this study, options of don’t know or no response were treated as missing values. Household food insecurity was considered present if the respondent chose sometimes, most times, or always. Parent’s cultural identity, or “acculturation,” was determined using their responses reported on a cultural affiliation questionnaire, which assesses one of four modes of acculturation, from two cultural identity subscales (i.e., respondent’s ethnic group and the US): traditional (high ethnic and low US subscale scores), integrated (high ethnic and high US subscale scores), assimilated (low ethnic and high US subscale scores), or marginalized (low ethnic and low US scores) [22]. The same scoring system was used as described by Kaholokula and others [22].
After providing assent, each child was measured for weight, height, and waist circumference (WC) by trained and standardized research staff [23] and described in detail elsewhere [17]. These measures were used to compute Body Mass Index (BMI) [weight (kg)/height(m)2], WC (cm) to height (cm) ratios, and subsequent BMI z-score, BMI-for-age-percentiles, and waist circumference- for-age percentiles [24,25]. BMI percentiles and z-scores were calculated according to CDC reference data [26] and BMI categories were assigned accordingly: underweight (<5th percentile), healthy weight (5th–84th percentile), overweight (85th–94th percentile), and obese (≥95th percentile). Children were considered to have OWOB if their BMI percentile was greater than the 85th percentile for age and sex. Cutoff values for biologically implausible values defined as <−5 or >4 standard deviations (SD) for height-for-age z-score and <−4 or >8 SD for BMI z-score (according to CDC reference data) were removed from the analysis. Child participants were considered to have abdominal obesity if their WC was greater than the International Diabetes Federation [27] cut-point.
Fruit, vegetable, and beverage intake were estimated using data recorded in the child’s two-day food logs reported by the child’s parent/caregiver and data collection methods are described in detail elsewhere [17]. Parents/caregivers were asked to complete the food log of everything their children ate or drank for two randomly assigned non-consecutive days, which included weekdays and weekend days, between visit one and two, approximately 6 days apart. Assignment of recording days was based on the day of the child’s first visit (Monday–Saturday). Parents/caregivers were instructed in record keeping techniques with the aid of food models, service ware, and measuring utensils. During visit two, research staff reviewed the food log with the parents/caregivers (e.g., for completeness of food entries, portion size estimation, food preparation methods, and/or accuracy of recording data). Trained staff entered the food log data into the Pacific Tracker3 (PacTrac3), which includes a food composition database developed in collaboration with the University of Hawaii Cancer Center for use in the Pacific region [28,29,30]. For this study, PacTrac3 data were used to classify beverage type (i.e., sugar-sweetened beverages (SSB), milk, or water), fruit, and vegetable, as well as, calculate intake (e.g., cups/day, grams/day). All soft drinks, fruit drinks (fruit flavored or containing less than 100% juice), sports drinks, energy drinks, sweetened tea, and sweetened coffee reported were classified as SSB. Milk intake in cups was calculated for food log entries and included all fluid milk (cow and/or goat), chocolate milk, lactose-reduced milk, lactose-free milk, filled milk, dry milk, and evaporated milk. Fruit, vegetable, milk, water, and SSB intake per day was adjusted for within person variance and then averaged over the two days of records, weighted for weekday and weekend days.
Compensation for study participation was provided at visits one and two [17]. The CHL program was funded by the United States Department of Agriculture (USDA), Agriculture and Food Research Initiative. Ethical approval for this project was granted by both the University of Guam Committee on Human Research Subjects (IRB) (CHRS#12-74) and the University of Hawaii Institutional Review Board (CHS#18915); written consent was given by all parents and oral assent was given by all child participants prior to their inclusion, in accordance with the Declaration of Helsinki.
Statistical analyses were performed using IBM SPSS Statistics version 26 (IBM Corporation, Armonk, NY, USA). Data analysis included the calculation of percentages for ordinal and nominal data, and means and standard errors for the interval and continuous data. t-tests and chi-square tests were used to test for differences in continuous and categorical variables, respectively, between BMI groups and ethnic groups. Binary logistic regression models of OWOB assessed its relationship with several potential covariates, adjusted for sex, age, and ethnicity and with the variance corrected for clustering of children within communities. Odds ratios and 95% CI were the primary statistics reported from the models. ORs were calculated for each of the following child factors from all ethnic groups combined and in CHamoru and Other Micronesian children separately: child age, sex, ever breastfed, sleep, sedentary screen-time, vegetable intake, fruit intake, water intake, and SSB intake; and parent/caregiver education, acculturation and marital status; and household income, food insecurity, food assistance, and household size. Does-response was assessed using a trend variable assigned consecutive integers (1, 2, …) to each ordered category. These factors were selected among demographic and lifestyle-related variables because they have been reported to be risk factors for OWOB among children [17,25,31,32] and/or were behaviors of interest for the CHL Program. p values < 0.05 were considered statistically significant, whereas p values of 0.05 to 0.10 were described as borderline significant.

3. Results

3.1. Demographics

The descriptive characteristics of child participants at baseline are summarized by ethnicity in Table 1. The average age of child participants was 5.79 years (sem 0.062). The majority of child participants were male (51.7%), 2–5 years old (53.8%), CHamoru ethnicity (64.8%), born on Guam (84.5%), from households where parents were not married (60.7%) or there were 3 or more children (76.2%), and considered as being ‘integrated’ (79.9%) as reported by parent/caregiver.
Overall, more than half of participating children (57.9%) came from families whose annual income was less than $20,000, which was slightly below the poverty level for a 3-person household in 2015 [33]. A significantly higher (p < 0.001) proportion of Other Micronesian children (82.9%) came from families whose annual income was less than $20,000 compared to CHamoru (54.2%), Filipino (43.24%) and Other (40%) children. Parents of Filipino children reported significantly higher (p < 0.001) education attainment levels compared to both CHamoru and Other Micronesians.

3.2. OWOB Prevalence

OWOB prevalence among child participants in this study is 27.4%. A significantly (p = 0.03) higher proportion of Other Micronesian children were affected by obesity (BMI ≥ 95th percentile) compared to CHamoru children (18.3% versus 11.0%) (Table 1).
Table 2 shows child participant characteristics by weight status for all ethnic groups combined, and for CHamorus and Other Micronesians separately. Children affected by OWOB reported statistically higher body size parameters as measured by height, weight, WC, BMI, and BMI z-scores, compared to healthy weight children. All children with abdominal obesity, as defined by the International Diabetes Federation [27], were OWOB. Among children in the OWOB weight status, 33.8% had abdominal obesity by this cut-point. It is worth noting that the International Diabetes Federation cut-point is for older children (6–10 years old) and is limited in identifying younger children (2–5 years) with abdominal obesity.
There were no significant differences in the presence of OWOB among children by sex, ethnicity, family income, food insecurity, food assistance, acculturation, screen-time, sleep duration, or intake of fruits, vegetables, water, or milk. OWOB children consumed marginally higher amounts (p value = 0.07) of SSB compared to healthy weight children. There were significant differences in the presence of OWOB among children by certain demographic characteristics, such as parent/caregiver education level, parent/caregiver marital status, and child birthplace (Table 2).
To explore further the relationship between the behavioral factors of interest and OWOB, we calculated ORs separately for CHamorus and Other Micronesians, the two largest ethnic groups in the study (Table 3). Among CHamoru children, those who were older (6–8 years) were 1.63 times more likely to be affected by OWOB than younger children (2–5 years). The highest prevalence of OWOB was seen among Filipino children (33.8%), followed by Other Micronesian children (28.9%), and CHamoru children (26.0%). OWOB was also more likely to occur among children ages 6–8 years (30.4%) versus children between the ages of 2–5 years (24.7%). No significant associations were found except that association between OWOB and SSB intake, and OWOB and parent education level.

3.3. Nutrition Factors

The majority of child participants (80.4%) were receiving some sort of food assistance, including USDA Supplemental Nutrition Assistance Program (SNAP) (67.8%), USDA Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC) (31.8%), free or reduced school lunch/breakfast (28.8%), and/or assistance from a local food bank (11.7%). Food insecurity was also prevalent as 51.5% of participants reported that food money ran out at the end of the month either always/most times (16.8%) or sometimes (34.7%). Significantly more (p < 0.001) Other Micronesian child participants (74.9%) reported that food money ran out at least sometimes compared to CHamoru (42.8%), Filipino (53.7%) and Other (50%) children.
As for OWOB risk factors and CHL related nutrition behaviors, majority of children did not meet recommendations for fruit (58.7%) and vegetable (99.1%) intake and consumed SSB (73.7%). Fruit and vegetable intake did not differ significantly by ethnicity (Table 1) or by the presence of OWOB (Table 2). Mean intake of vegetables was low (0.61 c/d) and only 6 children surveyed (0.09%) consumed the recommended amount of vegetables daily, which equates to ≥1 cup equivalent for children 2 years old and ≥1.5 cup equivalent for children 3–8 years old [34,35,36]. Mean intake of fruits was somewhat better (0.88 c/d) and more children (41.3%) reported consuming the recommended amount of fruits daily, which equates to ≥1.5 cup equivalent for children 2–8 years old [34].
When asked if the child participant was ever breastfed as a baby, about two-thirds (67%) of all parents/caregivers responded yes. Only 13.1% of children were exclusively breastfed (not shown in table), while 54.2% of children surveyed were fed both breastmilk and infant formula as an infant. CHamoru children (21.32) reported being significantly less likely to be ever breastfed compared to Other Micronesian children (37.2%). Of those children who were ever breastfed, Other Micronesian children were weaned at a significantly older age (12.48 m) compared to children from other ethnic groups. Breastfeeding during infancy, whether exclusive or mixed with infant formula feeding did not differ by the presence of OWOB. Age at weaning also did not differ by presence of OWOB. Among those who were exclusively breastfed, mean age at weaning was significantly higher compared to mixed fed infants (19.27 ± 1.19 m versus 7.56 ± 0.04 m). Risk of OWOB increased significantly with SSB intake. The relationship between presence of OWOB and increasing SSB intake was not monotonic, the ORs (95% CI) for SSB intake for the tertile groups for consumers compared to those with zero intake of SSB were 2.06 (1.02–4.16), 1.50 (0.90–2.48), and 1.82 (1.11–3.01). Only 25% of children surveyed consumed the recommended amount of “zero intake” of SSB [37], and one-third of child participants consumed at least 1.1 cups of SSB per day. Correlation analysis showed that SSB intake was positively and significantly associated with BMI and waist circumference. Among CHamoru children, the relationship between SSB intake and body size was stronger, as SSB intake among CHamoru children was positively and significantly associated with BMI (r = 0.18, p = 0.0002), waist circumference (r = 0.16, p = 0.0007), and BMI z-score (r = 0.11, p = 0.023). In Table 3, although SSB intake was not significantly associated with OWOB for Other Micronesian children, it was significant for CHamoru children. The relationship again was not monotonic, the ORs (95% CI) for SSB intake for the tertile groups of consumers compared to those with zero intake of SSB were 3.15 (1.1–9.08), 2.84 (1.28–6.30) and 3.19 (1.46–6.95).

3.4. Sleep and Screen-Time

Less than half of all children surveyed (40.4%) met the sleep duration recommendations [20]; those recommendations being at least 8–11 h/day for children under the age of 5 years, and 8–10 h per day for children 5 years and older. Sleep duration did not differ by the presence of OWOB, yet we observed TCSQ sleep quality scores (Table 2) were significantly lower (p = 0.0421) among children affected by OWOB compared to children with healthy weight (5.59 ± 0.34 versus 6.55 ± 0.26, respectively), meaning that children with OWOB were statistically more likely to suffer from sleep disturbances. Only 30.58% of Other Micronesian children met sleep recommendations (Table 1), which was significantly (p = 0.014) less than children from other ethnic groups. Other Micronesian children reported TCSQ sleep quality scores that were lower (p = 0.056) compared to children from the other ethnic groups.
The recommended daily screen-time for children is less than 2 h per day [38]. Only one sixth of the children surveyed (16.9%) met this recommendation and meeting screen-time recommendations did not differ between healthy weight children and children affected by OWOB. Average screen-time was slightly more than 5 h per day. When comparisons were made across four ethnic groups, there was a significant difference (p = 0.007) in the proportion of children that met the screen-time recommendation: CHamorus (16.7%), Other Micronesian (22.5%), Filipinos (4.6%), and Other (0%). There was also a significant difference in the average daily screen-time among the ethnic groups: CHamorus (5.13 h), Other Micronesians (5.45 h), Filipinos (6.24 h), and Other (3.59 h) (Table 1).

3.5. Acculturation

The majority of study participants (80%) considered their cultural affiliation as integrated, meaning a bicultural orientation where the respondent retains their ethnic cultural identity at the same time moving to join the dominant society (i.e., the US) [22,39]. About 15% of both CHamorus and Other Micronesians considered their cultural affiliation traditional, where the respondent retains their ethnic identity and does not recognize or identify with cultural characteristics of the US [22,39]. The Other Micronesians (6.1%) were most likely of all ethnic groups represented in this study to report having marginalized cultural affiliation, meaning the respondent does not maintain his/her ethnic cultural characteristics and excludes or withdraws him/herself from the dominant society (i.e., the U.S.) [22,39]. Acculturation did not differ by the presence of OWOB (Table 2). However, we observed that among Other Micronesians, children from families who considered their cultural affiliation as integrated into the culture were 2.05 times more likely to be affected by OWOB (CI 0.81–5.20) (Table 3).

4. Discussion

Pacific Islanders were one of the fastest growing racial/ethnic groups in the US in 2000 to 2010 [40], but little is known about OWOB for young children (under 11 year) in the Pacific region [41]. The current prevalence of OWOB in the US is 25% among children between 2–5 years and 32.8% among children between 6–8 years [42]. Findings in this current study on Guam were consistent with previous reports [43] and similar to the US. Unlike previous reports, this study revealed ethnic differences in OWOB presence among children in Guam. Filipino and Other Micronesian had the highest OWOB (33%) and obesity (18%) prevalence compared to other children in Guam.
Some recent studies have shown that obesity among various Pacific Islander groups, such as Samoans, may be linked to specific genes influencing adiposity [40,41,44]; and Pacific Islanders of Polynesian, Micronesian, and Melanesian origin may all carry certain alleles associated with higher body weight, BMI, and risk of obesity [41,45]. Waist circumference-to-height ratio (WHR) and WC have been used as markers of body adiposity in adults and, in children and adolescents (8–18 years), WHR was determined to be better at predicting adiposity [46,47,48]. One third of this study’s participants (33.8%) were observed to have both, abdominal obesity and OWOB. On the other hand, healthy weight children did not have abdominal obesity (0%). This aligns with other studies that found adiposity indicators, like WC, were associated with being overweight [47]. This is alarming as the location and distribution of body fat are associated with cardiovascular and other disease risks that is likely to continue into adulthood [49,50,51].
Given the steady rise in obesity over the past few decades on a global scale, environmental (macro and micro) factors promoting obesity are a more likely predominant explanation for racial/ethnic disparities in childhood obesity [52], such as the increase in obesity seen on Guam and other islands in the Pacific. A majority of children in this study exhibited several behaviors that are obesity risk factors such as low intake of fruits and vegetables, high intake of SSB, low sleep duration, and high amounts of sedentary screen-time [52].
From the micro-environment of children, which includes their families and households, in this study, the majority of child participants came from households where their parent/caregivers were unmarried (60.7%) and with at least three or more children (76.2%). Those two factors, compounded by the fact that most of the parents in this study also reported low annual incomes, can lead to an unfavorable home environment. Research shows that family and home environments, whether directly or indirectly, may contribute to the development of child OWOB [53,54,55], possibly through maternal depression and stress. Further study and more specific questions regarding parent/caregiver psychological health is needed to determine impact of family environment on the presence of OWOB among children in Guam.
Surprisingly, 59.65% of the children surveyed did not meet the recommended sleep duration for their age. Research indicates that short sleep duration puts children at risk for obesity and other metabolic, cardiovascular, and behavior disorders [56,57,58]. This study did not find sleep duration to differ significantly between children with OWOB and those with healthy weight, possibly due to misinterpretation of the question on sleep duration. Given some of the very low reported levels of sleep hours per day for some of the children and limited English comprehension of the islander parents/caregivers, it is possible that nap times during the day may have been the reported time, rather than sleep over the 24-h period. Future studies should separate questions for nap time sleep from night sleep in determining USAP children’s sleep duration and include more qualitative non-subjective measurements for sleep duration. In terms of sleep quality, children affected by OWOB in this study were statistically more likely to suffer from sleep disturbances. This aligns with the research linking poor sleep quality with OWOB and an increased risk for cognitive and behavioral problems [56,57,58]. Research also links reduced sleep quality with high SSB intake, and in bed screen-time [59].
The proportion of children exceeding screen-time recommendation in this study (83%) was comparable to the 87% reported among a prospective birth cohort of children born in New York led by researchers from the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development [60]; however, the birth cohort included children below two years of age, which is an age group not represented in the Guam study. Additionally, while screen-time increased with preschool age in the birth cohort, the total hours decreased to below the recommended two hours by 7–8 years old possibly due to school activities [60].
Screen-time has been shown to be associated with other behaviors, especially sleep. Twenge and colleagues [61] found an inverse association, increased screen-time and reduced sleep duration among a population-based sample of 0–17 year-olds in the United States [61]. In the current study, the order of screen-time, from highest to lowest duration, by ethnic group was Filipino (6.24 h), Other Micronesians (5.45 h), CHamorus (5.13 h), and Other (3.59 h). Interestingly, this order was reversed with the TCSQ sleep quality score, where Filipinos reported the highest quality sleep (5.23), and Others reported the lowest quality (8.0), but the pattern was not the same with sleep duration. Researchers are beginning to better understand the impact of screen-time on sleep behaviors. For example, Guerrero and colleagues [62] found a relationship between increased screen-time and increased social problems (e.g., rule-breaking, social problems, aggressive behavior, and thought problems), and the relationship was mediated by sleep duration (every hour increase in sleep = 8.8% to 16.6% decrease in problem behaviors) among 9–10 year-old in the Adolescent Brain Cognitive Development study [63]. Behavioral problems, although complex, are real issues plaguing children and should be considered when building wellness models for healthy families and healthy communities. Further studies on screen-time among children in Guam are warranted.
Childhood behavioral problems have been linked to racial socialization and cultural resilience [64]. Similarly, cultural affiliation, or low acculturation (i.e., traditional), is associated with lower rates of obesity and sedentary behaviors in children from migrant populations [39]. Although CHamorus in this study are not likely migrants, the long history of colonization in Guam lends to multidimensional acculturation for CHamorus and Guamanians alike in the presence of the colonial society (i.e., the US). This considered, CHamoru children had the highest proportion of parents identifying as traditional and the lowest rates of obesity compared to other ethnic groups in this study. Additionally, Filipino children identified most as integrated had the highest average screen-time (hr/d) compared to other ethnic groups. These findings align with previous studies that have found traditional or low acculturation to be protective against obesity and related risk factors [39,65,66]. A greater acculturation (i.e., assimilation) has also shown to be a predictor of lower fruit and vegetable intake among adult populations [67,68]. Therefore, obesity interventions or health programs in Guam should include cultural values and practices to maintain or reinforce healthy cultural behaviors unique to each and shared across ethnic group(s).
Both fruit and vegetable consumption among child participants in this study were low and failed to meet the recommendations set forth by the USDA Dietary Guidelines [34]. Little is known about the food intake, particularly fruits and vegetables, of children on Guam. Pobocik and colleagues [69,70] studied the food intake of over 1000 elementary school-aged children on Guam and reported fruit and vegetable consumption was well below the recommended intake levels [69]. Leon Guerrero and Workman [71] studied health behaviors of adolescents on Guam and reported that only 24.7% of those surveyed reported consumption at least one serving of fruit and/or vegetable, and none reported consuming the recommendations for fruit and vegetable intake set forth by the Dietary Guidelines. Children on Guam are not unlike children from the US mainland. A recent study looking at diet quality by BMI category of US children found that in general, all children’s diets in the United States were poor and in need of improvement, regardless of BMI status, age, sex, race/ethnicity, and poverty-to-income ratio classification [72].
CHamorus and Filipinos were less likely to exclusively breastfeed and tended to breastfeed for shorter durations, compared to other Micronesians and Other ethnic groups; mixed feeding was common in all ethnic groups and associated with earlier weaning as would be expected. No relationship of feeding method with overweight and obesity was detected. Breastfeeding is the biologic norm, important for infant nutrition and healthy growth, and protective for child obesity [73,74,75], although not demonstrated here. Likely other socio-economic (e.g., income) and behavioral factors (e.g., SSB intake) not controlled in this comparison confound the association in this study.
Several studies have shown an association between SSB intake and adverse health outcomes in children, particularly overweight, obesity, unhealthy weight gain, and central adiposity [76,77,78]. Other longitudinal data indicated that children consuming as little as one SSB serving per day were 55% more likely to be OWOB compared to children with limited SSB consumption [79]. Wojcicki and colleagues looked at the effect of SSB consumption on leukocyte telomere length in preschool children [80]. After adjusting for age sex, and BMI, SSB intake among the preschool children was significantly related to shorter leukocyte length. Shorter telomere length has been associated with a number of adverse health outcomes such as type 2 diabetes, stroke, and myocardial infarction because shortened telomeres start the inflammatory cascade through increased production of cytokines, thus adversely impacting cellular health [80]. SSB consumption during early childhood may not lead immediately to OWOB, as there may be an age effect. It may take a number of years of SSB consumption before the development of OWOB or metabolic changes associated with obesity [77,80,81,82]. SSB intake may also be a marker of a food and beverage intake pattern and other lifestyle factors that are associated with obesity.
High intake of SSB is common on Guam, and SSBs are readily available in food stores. In a previous study looking at dietary intake of CHamoru and Filipino adults living on Guam, Leon Guerrero and colleagues [11] found that CHamoru adults reported consuming between 7–9% of food energy from SSBs (7% for women and 9% for men). In this same study [11], SSBs were the second largest contributor to energy intake for CHamoru adults; and third largest contributor to energy intake for Filipino adults.
Like most other islands in the Pacific, Guam relies heavily on imported processed foods. A 2013 survey of processed foods available in the Pacific Islands [83] showed that Guam recorded the highest number of available processed food products compared to the other Pacific islands surveyed, with over 2100 products available to Guam consumers. High intake of SSBs is not only a problem for children affected by OWOB. The child participants surveyed on Guam, whether or not they were affected by OWOB, consumed a high amount of SSBs, putting them at risk for developing adverse cardiometabolic outcomes as they grow older.
There were some limitations to this study. The small sample size of Other Micronesian and especially Filipino children made ethnic group comparisons difficult. Some parents/caregivers, Other Micronesians in particular, spoke English as a second language and had limited English comprehension; making it difficult to comprehend and respond to survey tools. This may have affected accuracy of results. Generalizations are limited due to non-randomized cluster sampling. However, recruitment of children at the community-based settings in Guam communities was designed to ensure appropriate representation by age, sex, ethnicity, and geographic location. Since this study reports cross-sectional data, causal association of OWOB with behavioral or demographic factors cannot be conclusively inferred; longitudinal examination is needed to confirm causality. Despite the limitations, we were able to recruit and assess a large sample of children (n = 865) from several communities across the island that were representative of the major ethnic groups on Guam. Anthropometric measurements of children surveyed provided a reliable assessment of OWOB status among children on Guam.
In conclusion, the child participants surveyed on Guam, whether or not they are currently affected by OWOB, are at a high risk for developing OWOB in adulthood, as well as other chronic diseases. Specifically, for OWOB risk factors and CHL behaviors, the majority of children surveyed did not meet recommendations for: sleep duration (59.6%), sedentary screen-time (83.11%), or fruit (58.7%) and vegetable (99.1%) intake and consumed SSB (73.7%). The study found children affected by OWOB in this study, were statistically more likely (p = 0.042) to suffer from sleep disturbances, and consumed marginally higher amounts (p value = 0.07) of SSB compared to children with healthy weight. The study identified indicators of potentially stressful home situations among participant families that include: high prevalence of the child participants being cared for by a single parent (60.7%), families with three or more children (76.2%), and high prevalence of some level of food insecurity (59.6%) among participant families. These family stressors may also contribute to OWOB among children on Guam. With respect to OWOB prevention guidance, the study found evidence supporting previous studies’ findings that traditional or low acculturation to be protective against obesity [39,59,60]. Healthy cultural behaviors, both unique to and shared across ethnic group(s), should be incorporated in future obesity interventions by highlighting healthy cultural values, practices and traditional foods. The obesogenic factors identified in the children in this study provide a basis for health promotion and obesity prevention guidance for children in Guam.

Author Contributions

R.T.L.G. led study concept, acquisition, analysis, and interpretation of data, and writing of manuscript; oversaw and had primary responsibility for final manuscript. L.R.B. assisted in study concept, interpretation of data, and writing of manuscript. M.P.H.-U. assisted in acquisition of data, and providing critical review of manuscript. M.A. assisted in acquisition and entry of data and providing critical review of manuscript. T.F.A. assisted in acquisition, entry and analysis of data, and writing of manuscript. Y.C.P. assisted in acquisition, entry and analysis of data, and writing of manuscript. L.R.W. participated in overall study design, contributed to statistical analysis, interpreted data, and writing of manuscript. R.N. initially assisted in the study concept, participated in design, interpreted data, and writing of manuscript. All authors provided critical review of the manuscript and gave final approval. All authors have read and agreed to the published version of the manuscript.


This research was funded by the Agriculture and Food Research Initiative grant 2011-68001-30335 from the U.S. Department of Agriculture, National Institute of Food and Agricultural, Science Enhancement Coordinated Agricultural Program and the CHL Center of Excellence is currently funded by the USDA National Institute of Food and Agriculture, Agriculture and Food Research Institute Grant no. 2018-69001-27551.


We would like to thank the parents and children who participated in the Children’s Healthy Living Program (CHL), as well as our many community partners who assisted.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Ogden, C.L.; Carroll, M.D.; Kit, B.K.; Flegal, K.M. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA 2014, 311, 806–814. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Ogden, C.L.; Carroll, M.D.; Fryar, C.D.; Flegal, K.M. Prevalence of Obesity among Adults and Youth: United States, 2011–2014. NCHS Data Brief 2015, 219, 1–8. [Google Scholar]
  3. Yang, L.; Colditz, G.A. Prevalence of Overweight and Obesity in the United States, 2007–2012. JAMA Intern. Med. 2015, 175, 1412–1413. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Afshin, A.; Forouzanfar, M.H.; Reitsma, M.B.; Sur, P.; Estep, K.; Lee, A.; Marczak, L.; Mokdad, A.H.; Moradi-Lakeh, M.; Naghavi, M.; et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N. Engl. J. Med. 2017, 377, 13–27. [Google Scholar] [CrossRef]
  5. Ng, M.; Fleming, T.; Robinson, M.; Thomson, B.; Graetz, N.; Margano, C.; Mullany, C.E.; Biryukov, S.; Abbafati, C.; Abera, F.S.; et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014, 384, 766–781. [Google Scholar] [CrossRef] [Green Version]
  6. Novotny, R.; Fialkowski, M.K.; Li, F.; Paulino, Y.S.N.; Vargo, D.; Jim, R.; Coleman, P.; Bersamin, A.; Nigg, C.R.; Leon Guerrero, R.T.; et al. Systematic review of prevalence of young child overweight and obesity in the United States Affiliated Pacific region compared to the 48 contiguous states: A the Children’s Health Living Program. Am. J. Pub. Health 2015. [Google Scholar] [CrossRef]
  7. Underwood, J. The native origins of the neo-Chamorros of the Mariana Islands. Micronesia 1976, 12, 203–209. [Google Scholar]
  8. Vilar, M.G.; Chan, C.W.; Santos, D.R.; Lynch, D.; Spathis, R.; Garruto, R.M.; Lum, J.K. The origins and genetic distinctiveness of the Chamorros of the Marianas Islands: An mtDNA perspective. Am. J. Hum. Biol. 2013, 25, 116–122. [Google Scholar] [CrossRef]
  9. Central Intelligence Agency: The World Factbook. Available online: (accessed on 30 June 2020).
  10. Asian and Pacific Islander American Health Forum Health Briefs: Chamorros in the United States. Asian and Pacific Islander American Health Forum. 2006. Available online: (accessed on 25 June 2020).
  11. Guerrero, R.T.L.; Paulino, Y.C.; Novotny, R.; Murphy, S.P. Diet and obesity among Chamorro and Filipino adults on Guam. Asia Pac. J. Clin. Nutr. 2008, 17, 216–222. [Google Scholar]
  12. Hankin, J.; Reed, D.; Labarthe, D.; Nichaman, M.; Stallones, R. Dietary and Disease Patterns among Micronesians. Am. J. Clin. Nutr. 1970, 23, 346–357. [Google Scholar] [CrossRef]
  13. Pollock, N. Food habits in Guam over 500 years. Pac. Viewp. 1986, 27, 120–143. [Google Scholar] [CrossRef]
  14. Uncangco, A.; Badowski, G.; David, A.; Ehlert, M.; Haddock, R.; Paulino, Y. First Guam BRFSS Report 2007–2010; Guam Department of Public Health & Social Services: Mangilao, Guam, USA, 2012.
  15. Singh, A.S.; Mulder, C.; Twisk, J.W.; van Mechelen, W.; Chinapaw, M.J. Tracking of childhood overweight into adulthood: A systematic review of the literature. Obes. Rev. 2008, 9, 474–488. [Google Scholar] [CrossRef] [PubMed]
  16. Barker, D.J.; Osmond, C.; Forsen, T.J.; Kajantie, E.; Eriksson, J.G. Trajectories of growth among children who have coronary events as adults. N. Engl. J. Med. 2005, 353, 1802–1809. [Google Scholar] [CrossRef] [PubMed]
  17. Wilken, L.R.; Novotny, R.; Fialkowski, M.K.; Boushey, C.J.; Nigg, C.; Paulino, Y.; Leon Guerrero, R.; Bersamin, A.; Vargo, D.; Kim, J.; et al. Children’s Healthy Living (CHL) Program for remote underserved minority populations in the Pacific region: Rationale and design of a community randomized trial to prevent early childhood obesity. BMC Pub. Health 2013, 13, 944. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Novotny, R.; Fialkowski, M.K.; Areta, A.A.; Bersamin, A.; Braun, K.; DeBaryshe, B.; Deenik, J.; Dunn, M.; Hollyer, J.; Kim, J.; et al. The Pacific Way to Child Wellness: The Children’s Healthy Living Program for Remote Underserved Minority Populations of the Pacific Region (CHL). Hawaii J. Med. Pub. Health 2013, 72, 406–408. [Google Scholar]
  19. McGreavey, J.A.; Donnan, P.T.; Pagliari, H.C.; Sullivan, F.M. The tayside children’s sleep questionnaire: A simple tool to evaluate sleep problems in young children. Child Care Health Dev. 2005, 31, 539–544. [Google Scholar] [CrossRef]
  20. Hirshkowitz, M.; Whiton, K.; Albert, S.M.; Alessi, C.; Bruni, O.; DonCarlos, L.; Hazen, N.; Herman, J.; Katz, E.S.; Kheirandish-Gozal, L.; et al. National Sleep Foundation’s sleep time duration recommendations: Methodology and results summary. Sleep Health 2015, 1, 40–43. [Google Scholar] [CrossRef]
  21. Coleman-Jensen, A.; Gregory, C.; Singh, A. Household Food Security in the United States in 2013; US Department of Agriculture, Economic Research Service, Ed.; USDA Economic Research Service: Washington, DC, USA, 2014.
  22. Kaholokula, J.K.; Iwane, M.K.; Nacapoy, A.H. Effects of perceived racism and acculturation on hypertension in Native Hawaiians. Hawaii Med. J. 2010, 69, 11–15. [Google Scholar]
  23. Li, F.; Wilkens, L.R.; Novotny, R.; Fialkowski, M.; Paulino, Y.C.; Nelson, R.; Bersamin, A.; Martin, U.; Deenik, J.; Boushey, C. Anthropometric Measurement Standardization in the US-Affiliated Pacific: Report from the Children’s Healthy Living Program. Am. J. Hum. Biol. 2016, 28, 364–371. [Google Scholar] [CrossRef] [Green Version]
  24. Cook, S.; Auinger, P.; Huang, T.T. Growth curves for cardio-metabolic risk factors in children and adolescents. J. Pediatr. 2009, 155, e15–e26. [Google Scholar] [CrossRef] [Green Version]
  25. Barlow, S. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: Summary report. Pediatrics 2007, 120 (Suppl. 4), S164–S192. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Centers for Disease Control and Prevention A SAS Program for the 2000 CDC Growth Charts (Ages 0 to <20 Years); Centers for Disease Control and Prevention; 2014. Available online: (accessed on 6 April 2020).
  27. Zimmet, P.; Alberti, K.G.; Kaufman, F.; Tajima, N.; Silink, M.; Arslanian, S.; Wong, G.; Bennett, P.; Shaw, J.; Caprio, S.; et al. The metabolic syndrome in children and adolescents—An IDF consensus report. Pediatr. Diabetes 2007, 8, 299–306. [Google Scholar] [CrossRef] [PubMed]
  28. Novotny, R.; Nigg, C.; McGlone, K.; Renda, G.; Jung, N.; Matsunaga, M.; Karanja, N. Pacific tracker 2—Expert system (PacTrac2-ES) behavioural assessment and intervention tool for the pacific kids DASH for health (PacDASH) study. Food Chem. 2013, 140, 471–477. [Google Scholar] [CrossRef] [PubMed]
  29. Martin, C.L.; Murphy, S.P.; Leon Guerrero, R.T.; Davison, N.; Jung, Y.O.; Novotny, R. The Pacific Tracker (PacTrac): Development of a dietary assessment instrument for the Pacific. J. Food Compost. Anal. 2008, 21, S103–S108. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Murphy, S.; Blitz, C.; Novotny, R. Pacific tracker (PacTrac): An interactive dietary assessment program at the CRCH website. Hawaii Med. J. 2006, 65, 175–178. [Google Scholar] [PubMed]
  31. Rolland-Cachera, M.F.; Akrout, M.; Peneau, S. Nutrient Intakes in Early Life and Risk of Obesity. Int. J. Environ. Res. Public Health 2016, 13, 564. [Google Scholar] [CrossRef] [Green Version]
  32. Weihrauch-Blüher, S.; Wiegand, S. Risk Factors and Implications of Childhood Obesity. Curr. Obes. Rep. 2018, 7, 254–259. [Google Scholar] [CrossRef]
  33. 2015 Poverty Guidelines. Office of the Assistant Secretary for Planning and Evaluation. US Department of Public Health and Social Services. Available online: (accessed on 9 July 2020).
  34. U.S. Department of Health and Human Services and U.S. Department of Agriculture. 2015–2020 Dietary Guidelines for Americans, 8th ed.; December 2015. Available online: (accessed on 22 April 2020).
  35. Bowman, S.; Friday, J.; Moshfegh, A. MyPyramid Equivalents Database, 2.0 for USDA Survey Foods, 2003–2004 [Online]; Food Surveys Research Group, Beltsville Human Nutrition Research Center, Agricultural Research Service, U.S., Ed.; Department of Agriculture: Beltsville, MD, USA, 2008.
  36. Friday, J.; Bowman, S. MyPyramid Equivalents Database for USDA Survey Food Codes, 1994–2002 Version 1.0. [Online]; USDA, Agricultural Research Service, Beltsville Human Nutrition Research Center, Ed.; Community Nutrition Research Group: Beltsville, MD, USA, 2006.
  37. Popkin, B.M.; Armstrong, L.E.; Bray, G.M.; Caballero, B.; Frei, B.; Willett, W.C. A new proposed guidance system for beverage consumption in the United States. Am. J. Clin. Nutr. 2006, 83, 529–542. [Google Scholar] [CrossRef]
  38. American Academy of Pediatrics. Children, adolescents, and television. Pediatrics 2001, 107, 423–426. [Google Scholar] [CrossRef] [Green Version]
  39. Renzaho, A.M.; Swinburn, B.; Burns, C. Maintenance of traditional cultural orientation is associated with lower rates of obesity and sedentary behaviours among African migrant children to Australia. Int. J. Obes. 2008, 32, 594–600. [Google Scholar] [CrossRef] [Green Version]
  40. Hixson, L.; Hepler, B.B.; Kim, M.O. The Native Hawaiian and other Pacific Islander Population: 2010; US Census Bureau, Ed.; US Census Bureau: Washington, DC, USA, 2012.
  41. Wate, J. Chapter 2: The Obesity Pandemic in the Pacific. In Wealthy but Unhealthy: Overweight and Obesity in Asia and the Pacific: Trends, Costs, and Policies for Better Health; Helble, M., Sato, A., Eds.; Asian Development Bank Institute: Tokyo, Japan, 2018. [Google Scholar]
  42. Skinner, A.C.; Ravanbakht, S.N.; Skelton, J.A.; Perrin, E.M.; Armstrong, S.C. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics 2018. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Paulino, Y.C.; Guerrero, R.T.; Uncangco, A.A.; Rosadino, M.G.; Quinene, J.C.; Natividad, Z.N. Overweight and obesity prevalence among public school children in Guam. J. Health Care Poor Underserved 2015, 26 (Suppl. 2), 53–62. [Google Scholar] [CrossRef] [PubMed]
  44. Minster, R.L.; Hawley, N.L.; Su, C.T.; Sun, G.; Kershaw, E.E.; Cheng, H.; Buhule, O.D.; Lin, J.; Reupena, M.S.; Viali, S.; et al. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat. Genet. 2016, 48, 1049–1054. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Furusawa, T.; Naka, I.; Yamauchi, T.; Natsuhara, K.; Kimura, R.; Nakazawa, M.; Ishida, T.; Inaoka, T.; Matsumura, Y.; Ataka, Y.; et al. The Q223R polymorphism in LEPR is associated with obesity in Pacific Islanders. Hum. Genet. 2010, 127, 287–294. [Google Scholar] [CrossRef]
  46. Brambilla, P.; Bedogni, G.; Heo, M.; Pietrobelli, A. Waist circumference-to-height ratio predicts adiposity better than body mass index in children and adolescents. Int. J. Obes. 2013, 37, 943–946. [Google Scholar] [CrossRef] [Green Version]
  47. Vieira, S.A.; Ribeiro, A.Q.; Hermsdorff, H.H.M.; Pereira, P.F.; Priore, S.E.; Franceschini, S. Waist-to-height ratio index or the prediction of overweight in children. Rev. Paul. Pediatr. 2018, 36, 7. [Google Scholar]
  48. Rakić, R.; Pavlica, T.; Bjelanović, J.; Vasiljević, P. Predictive ability of waist-to-hip-ratio and waist-to-height-ratio in relation to overweight/obesity in adolescents from Vojvodina (the Republic of Serbia) predictive ability of waist-to-hip-ratio and waist-to-height-ratio: Predictive Ability of Waist-to-Hip-Ratio and Waist-to-Height-Ratio. Prog. Nutr. 2019, 24, 992–998. [Google Scholar]
  49. Staiano, A.E.; Gupta, A.K.; Katzmarzyk, P.T. Cardiometabolic risk factors and fat distribution in children and adolescents. J. Pediatr. 2014, 164, 560–565. [Google Scholar] [CrossRef] [Green Version]
  50. Freedman, D.S.; Dietz, W.H.; Srinivasan, S.R.; Berenson, G.S. The relation of overweight to cardiovascular risk factors among children and adolescents: The Bogalusa Heart Study. Pediatrics 1999, 103 Pt 1, 1175–1182. [Google Scholar] [CrossRef]
  51. Kahn, H.S.; Imperatore, G.; Cheng, Y.J. A population-based comparison of BMI percentiles and waist-to-height ratio for identifying cardiovascular risk in youth. J. Pediatr. 2005, 146, 482–488. [Google Scholar] [CrossRef]
  52. Dixon, B.; Peña, M.M.; Taveras, E.M. Lifecourse approach to racial/ethnic disparities in childhood obesity. Adv. Nutr. 2012, 3, 73–82. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Epstein, L.H.; Myers, M.D.; Anderson, K. The association of maternal psychopathology and family socioeconomic status with psychological problems in obese children. Obes. Res. 1996, 4, 65–74. [Google Scholar] [CrossRef] [PubMed]
  54. Davis, M.; Young, L.; Davis, S.P.; Moll, G. Parental depression, family functioning and obesity among African American children. J. Cult. Divers. 2008, 15, 61–65. [Google Scholar] [PubMed]
  55. McConley, R.L.; Mrug, S.; Gilliland, M.J.; Lowry, R.; Elliott, M.N.; Schuster, M.A.; Bogart, L.M.; Franzini, L.; Escobar-Chaves, S.L.; Franklin, F.A. Mediators of maternal depression and family structure on child BMI: Parenting quality and risk factors for child overweight. Obesity 2011, 19, 345–352. [Google Scholar] [CrossRef]
  56. Pacheco, S.R.; Miranda, A.M.; Coelho, R.; Monteiro, A.C.; Braganca, G.; Loureiro, H.C. Overweight in youth and sleep quality: Is there a link? Arch. Endocrinol. Metab. 2017. [Google Scholar] [CrossRef]
  57. Navarro-Solera, M.; Carrasco-Luna, J.; Pin-Arboledas, G.; Gonzalez-Carrascosa, R.; Soriano, J.M.; Codoner-Franch, P. Short Sleep Duration Is Related to Emerging Cardiovascular Risk Factors in Obese Children. J. Pediatr. Gastroenterol. Nutr. 2015, 61, 571–576. [Google Scholar] [CrossRef]
  58. Sakamoto, N.; Gozal, D.; Smith, D.L.; Yang, L.; Morimoto, N.; Wada, H.; Maruyama, K.; Ikeda, A.; Suzuki, Y.; Nakayama, M.; et al. Sleep duration, snoring prevalence, obesity, and behavioral problems in a large cohort of primary school students in Japan. Sleep 2017, 40. [Google Scholar] [CrossRef] [Green Version]
  59. Morrissey, B.; Allender, S.; Strugnell, C. Dietary and activity factors influence poor sleep and the sleep-obesity nexus among children. Int. J. Environ. Res. Public Health 2019, 16, 1778. [Google Scholar] [CrossRef] [Green Version]
  60. Trinh, M.H.; Sundaram, R.; Robinson, S.L.; Lin, T.C.; Bell, E.M.; Ghassabian, A.; Yeung, E.H. Association of trajectory and covariates of children’s screen media time. JAMA Pediatr. 2019, 174, 71–78. [Google Scholar] [CrossRef]
  61. Twenge, J.M.; Hisler, G.C.; Krizan, Z. Associations between screen time and sleep duration are primarily driven by portable electronic devices: Evidence from a population-based study of U.S. children ages 0–17. Sleep Med. 2019, 56, 211–218. [Google Scholar] [CrossRef]
  62. Guerrero, M.D.; Barnes, J.D.; Chaput, J.P.; Tremblay, M.S. Screen time and problem behaviors in children: Exploring the mediating role of sleep duration. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 105. [Google Scholar] [CrossRef] [PubMed]
  63. Aflague, T.; Boushey, C.; Leon Guerrero, R.; Ahmad, Z.; Kerr, D.; Delp, E. Feasibility and use of the mobile food record for capturing eating occasions among children ages 3–10 years in Guam. Nutrients 2015, 7, 4403–4415. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Burt, C.H.; Simons, R.L.; Gibbons, F.X. Racial discrimination, ethnic-racial socialization, and crime: A micro-sociological model of risk and resilience. Am. Sociol. Rev. 2012, 77, 648–677. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Delavari, M.; Sønderlund, A.L.; Swinburn, B.; Mellor, D.; Renzaho, A. Acculturation and obesity among migrant populations in high income countries—A systematic review. BMC Public Health 2013, 13, 458. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Lind, C.; Mirchandani, G.G.; Castrucci, B.C.; Chávez, N.; Handler, A.; Hoelscher, D.M. The effects of acculturation on healthy lifestyle characteristics among Hispanic fourth-grade children in Texas public schools, 2004–2005. J. School Health 2012, 82, 166–174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Bolstad, A.L.; Bungum, T. Diet, acculturation, and BMI in Hispanics living in southern Nevada. Am. J. Health Behav. 2013, 37, 218–226. [Google Scholar] [CrossRef]
  68. Lee, S.; Sobal, J.; Frongillo, E. Acculturation, food consumption, and diet-related factors among Korean Americans. J. Nutr. Educ. 1999, 31, 321–330. [Google Scholar] [CrossRef]
  69. Pobocik, R.S.; Richer, J.J.; Hentges, D.L. Food sources of macronutrients in the diets of fifth grade children on Guam. Asian Am. Pac. Isl. J. Health 1999, 7, 25–37. [Google Scholar]
  70. Pobocik, R.S.; Richer, J.J. Estimated intake and food sources of vitamin, A.; folate, vitamin, C.; vitamin, E.; calcium, iron, and zinc for Guamanian children aged 9 to 12. Pac. Health Dialog 2002, 9, 193–202. [Google Scholar]
  71. LeonGuerrero, R.T.; Workman, R.L. Physical activity and nutritional status of adolescents on Guam. Pac. Health Dialog 2002, 9, 177–185. [Google Scholar]
  72. Thomson, J.L.; Landry, A.S.; Tussing-Humphreys, L.M.; Goodman, M.H. Diet quality of children in the United States by body mass index and sociodemographic characteristics. Obes. Sci. Pract. 2020, 6, 84–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Koletzko, B.; Godfrey, K.M.; Poston, L.; Szajewska, H.; van Goudoever, J.B.; de Waard, M.; Brands, B.; Grivell, R.M.; Deussen, A.R.; Dodd, J.M.; et al. Nutrition during pregnancy, lactation and early childhood and its implications for maternal and long-term child health: The early nutrition project recommendations. Ann. Nutr. Metab. 2019, 74, 93–106. [Google Scholar] [CrossRef] [PubMed]
  74. Koletzko, B.; Brands, B.; Grote, V.; Kirchberg, F.F.; Prell, C.; Rzehak, P.; Uhl, O.; Weber, M. Long-term health impact of early nutrition: The power of programming. Ann. Nutr. Metab. 2017, 70, 161–169. [Google Scholar] [CrossRef] [PubMed]
  75. Horta, B.L.; Loret de Mola, C.; Victora, C.G. Long-term consequences of breastfeeding on cholesterol, obesity, systolic blood pressure and type 2 diabetes: A systematic review and meta-analysis. Acta. Paediatr. 2015, 104, 30–37. [Google Scholar] [CrossRef]
  76. Keller, A.; Bucher Della Torre, S. Sugar-sweetened beverages and obesity among children and adolescents: A review of systematic literature reviews. Child. Obes. 2015, 11, 338–346. [Google Scholar] [CrossRef]
  77. Scharf, R.J.; DeBoer, M.D. Sugar-Sweetened Beverages and Children’s Health. Annu. Rev. Public Health 2016, 37, 273–293. [Google Scholar] [CrossRef] [Green Version]
  78. Frantsve-Hawley, J.; Bader, J.D.; Welsh, J.A.; Wright, J.T. A systematic review of the association between consumption of sugar-containing beverages and excess weight gain among children under age 12. J. Public Health Dent. 2017, 77 (Suppl. 1), S43–S66. [Google Scholar] [CrossRef] [Green Version]
  79. Laverty, A.A.; Magee, L.; Monteiro, C.A.; Saxena, S.; Millett, C. Sugar and artificially sweetened beverage consumption and adiposity changes: National longitudinal study. Int. J. Behav. Nutr. Phys. Act. 2015, 12, 137. [Google Scholar] [CrossRef] [Green Version]
  80. Wojcicki, J.M.; Medrano, R.; Lin, J.; Epel, E. Increased cellular aging by 3 years of age in latino, preschool children who consume more sugar-sweetened beverages: A pilot study. Child. Obes. 2018, 14, 149–157. [Google Scholar] [CrossRef]
  81. Macintyre, A.K.; Marryat, L.; Chambers, S. Exposure to liquid sweetness in early childhood: Artificially-sweetened and sugar-sweetened beverage consumption at 4–5 years and risk of overweight and obesity at 7–8 years. Pediatr. Obes. 2018, 13, 755–765. [Google Scholar] [CrossRef] [Green Version]
  82. Dubois, L.; Farmer, A.; Girard, M.; Peterson, K. Regular sugar-sweetened beverage consumption between meals increases risk of overweight among preschool-aged children. J. Am. Diet. Assoc. 2007, 107, 924–934. [Google Scholar] [CrossRef] [PubMed]
  83. Snowdon, W.; Raj, A.; Reeve, E.; Guerrero, R.; Fesaitu, J.; Cateine, K.; Guignet, C. Processed foods available in the Pacific Islands. Glob. Health 2013, 9, 53. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table 1. Characteristics * of child participants from Guam in the Children’s Healthy Living (CHL) Program by ethnicity.
Table 1. Characteristics * of child participants from Guam in the Children’s Healthy Living (CHL) Program by ethnicity.
TotalCHamoru (n = 561)Other Micronesians (n = 223)Filipino (n = 81)Other (n = 10)p Value
Child Participant
Age, years5.79 ± 0.0625.80 ± 0.0785.66 ± 0.1235.99 ± 0.0626.24 ± 0.670
2–5 years old465 (53.76%)296 (52.76%)123 (57.76%)42 (51.85%)4 (40%)0.418
6–8 years old400 (46.24%)265 (47.23%)90 (42.25%)39 (48.14%)6 (60%)
Birthplace 0.0001
Guam723 (84.46%)488 (88.09%)170 (80.19%)62 (76.54%)3 (33.33%)
U.S. Mainland52 (6.08%)36 (6.50%)9 (4.25%)3 (3.70%)4 (44.44%)
Saipan, CNMI34 (3.97%)28 (5.05%)5 (2.36%)1 (1.24%)0 (0%)
Other Islands in Micronesia28 (3.27%)0 (0%)28 (13.21%)0 (0%)0 (0%)
Philippines15 (1.75%)0 (0%)0 (0%)15 (18.52%)0 (0%)
Other4 (0.47%)2 (0.36%)0 (0%)0 (0%)2 (22.22%)
Ever Breastfed? 0.0006
Yes557 (67.19%)341 (62.80%)155 (78.68%)53 (67.09%)8 (80%)
No272 (32.81%)202 (37.20%)42 (21.32%)26 (32.91%)2 (20%)
Child fed both breastmilk & infant formula 0.8223
Yes443 (54.23%)291 (54.09%)102 (53.40%)46 (58.23%)4 (44.45%)
No374 (45.77%)247 (45.91%)89 (46.60%)33 (41.77%)5 (55.55%)
Child age (months) when weaned from breastmilk9.76 ± 0.4388.63 ± 0.51912.48 ± 0.9588.511 ± 1.16518.0 ± 3.928
Average Sleep Duration (h/d)8.61 ± 0.0788.86 ± 0.086 7.75 ± 0.2008.99 ± 0.206 9.05 ± 0.2730.0001
TCSQ Sleep Score6.34 ± 0.2056.583 ± 0.2466.011 ± 0.46215.231 ± 0.5548.00 ± 3.386
Met Sleep Recommendations Ƒ 0.0136
Yes343 (40.35%)240 (43.09%)63 (30.58%)35 (43.75%)5 (50%)
No507 (59.65%)317 (56.91%)143 (69.42%)45 (65.25%)5 (50%)
Average Screen-time (hr/d)5.29 ± 0.1325.13 ± 0.154 §5.45 ± 0.3086.24 ± 0.4253.59 ± 0.550
Met Screen-time Recommendations Ω 0.0069
Yes124 (16.89%)82 (16.73%)39 (22.54%)3 (4.61%)0 (0%)
No610 (83.11%)408 (83.27%)134 (77.46%)62 (95.39%)6 (100%)
Height (cm)110.56 ± 0.46109.07 ± 0.59111.31 ± 1.71112.98 ± 1.22114.75 ± 5.03
Weight (kg)21.05 ± 0.2520.78 ± 0.3121.50 ± 0.5421.55 ± 0.7222.4 ± 2.44
Body Mass Index (kg/m2)16.84 ± 0.1116.77 ± 0.1417.09 ± 0.4316.65 ± 0.2716.42 ± 0.61
BMI z-score0.402 ± 0.0390.352 ± 0.048 0.544 ± 0.080.395 ± 0.1320.189 ± 0.2840.0397
BMI Categories ß
Underweight25 (2.96%)18 (32.91%)4 (1.93%)3 (3.75%)0 (0%)0.6945
Healthy weight595 (70.49%)392 (71.66%)144 (69.56%)51 (63.75%)8 (80%)0.4523
Overweight, % (≥85th%ile)113 (13.39%)77 (14.08%)21 (10.14%)14 (17.50%)1 (10%)0.3385
Obesity, % (≥95th%ile)111 (13.15%)60 (10.97%)38 (18.36%)12 (15.0%)1 (10%)0.0314
OWOB by Age
2–5 years old107 (23.94%)59 (20.85%)32 (26.89%)16 (39.02%)0 (0%)0.0378
6–8 years old117 (29.47%)78 (29.54%)27 (30.68%)10 (25.64%)2 (33.33%)0.9441
Waist Circumference (cm)55.07 ± 0.3054.76 ± 0.3855.44 ± 0.5856.104 ± 0.9555.64 ± 2.50
Abdominal Obesity 0.6782
Yes76 (8.94%)48 (8.77%)17 (7.98%)10 (12.50%)1 (10%)
No774 (91.06%)499 (91.23%)196 (92.02%)70 (87.5%)9 (90%)
Consumed SSB? 0.0001
Yes497 (74.74%)361 (81.49%)95 (61.29%)36 (59.01%)5 (50%)
No168 (25.26%)82 (18.51%)60 (38.71%)25 (40.99%)5 (50%)
Beverage Consumption ‡,¶
Water (c/d)1.47 ± 0.0481.306 ± 0.451.298 ± 0.661.401 ± 0.1051.522 ± 0.429
Milk (c/d)1.241 ± 0.0241.280 ± 0.029 †,§1.028 ± 0.0451.454 ± 0.081 1.660 ± 0.139
Sugar-sweet drinks (c/d)0.845 ± 0.0290.958 ± 0.036 †,§,‡0.560 ± 0.050 Ø0.727 ± 0.1011.095 ± 0.269
Fruit Consumption (c/d)0.882 ± 0.0210.88 ± 0.0250.846 ± 0.0450.956 ± 0.0801.153 ± 0.240
Vegetable Consumption ‡,¶ (c/d)0.609 ± 0.0130.648 ± 0.0150.472 ± 0.0260.651 ± 0.0410.850 ± 0.139
Met Recommendation for:
Fruit Intake257 (41.35%)182 (41.08%)63 (40.65%)27 (44.26%)3 (50%)0.9312
Vegetable Intake6 (0.90%)5 (1.13%)1 (0.65%)0 (0%)0 (0%)0.8064
Annual Family Income Level 0.0001
<$20,000348 (57.91%)220 (54.18%)92 (82.88%)32 (43.24%)4 (40%)
$20,000–$34,999182 (14.81%)59 (14.53%)9 (8.11%)17 (22.97%)4 (40%)
$35,000–$59,99993 (15.47%)71 (17.49%)5 (4.51%)17 (22.97%)0 (0%)
>$60,00071 (11.81%)56 (13.80%)5 (7.20%)8 (10.82%)2 (20%)
Receiving any Food Assistance 0.0001
Yes676 (80.38%)447 (81.57%)176 (86.70%)47 (58.75%)6 (60%)
No165 (19.62%)101 (18.43%)27 (13.30%)33 (41.25%)4 (40%)
Type of Food Assistance
SNAP 585 (67.85%)401 (71.61%)146 (69.19%)33 (40.74%)5 (50%)0.0001
Local Food Bank101 (11.72%)69 (12.32%)28 (13.27%)4 (4.94%)0 (0%)0.1319
WIC 274 (31.79%)162 (28.93%)86 (40.76%)22 (27.16%)4 (40%)0.0114
Free School Lunch/Breakfast248 (28.77%)202 (36.07%)33 (15.64%)10 (12.35%)3 (30%)0.0001
Food Insecurity δ 0.0001
Always/most times127 (16.82%)86 (17.20%)32 (18.71%)7 (9.46%)2 (20%)
Sometimes262 (34.70%)128 (25.6%)96 (56.16%)35 (47.29%)3 (30%)
Seldom/never366 (48.48%)286 (57.20%)43 (25.15%)32 (43.24%)5 (50%)
Parent/Caregiver Education 0.0001
<12th Grade281 (32.48%)184 (32.80%)79 (37.08%)17 (20.98%)1 (10%)
12th Grade/GED351 (40.58%)237 (42.24%)97 (45.54%)13 (16.05%)4 (40%)
Some college or higher233 (26.94%)140 (24.96%)37 (17.38%)32 (62.97%)5 (50%)
Parent Marital Status 0.0001
Married340 (39.31%)190 (33.89%)90 (42.25%)55 (67.90%)5 (50%)
Not Married525 (60.69%)371 (66.13%)123 (57.75%)26 (32.10%)5 (50%)
Number Children in Household4.19 ± 0.0774.29 ± 0.096 §4.35 ± 0.164 §3.14 ± 0.1513.4 ± 0.3060.0001
Household Size 0.008
1–2 children205 (23.78%)127 (22.72%)48 (22.64%)29 (35.80%)1 (1%)
3–4 children341 (39.56%)220 (39.36%)75 (35.38%)39 (48.15%)7 (70%)
5 or more children316 (36.66%)212 (37.92%)89 (41.98%)13 (16.05%)2 (20%)
Acculturation γ 0.0565
Integrated572 (79.89%)388 (80.15%)123 (75.00%)55 (88.71%)6 (100%)
Traditional101 (14.11%)74 (15.29%)24 (14.63%)3 (4.84%)0
Assimilated17 (2.37%)7 (1.45%)7 (4.27%)3 (4.83%)0
Marginalized26 (3.63%)15 (3.10%)10 (6.10%)1 (1.61%)0
* Frequency number (%), Mean ± standard error, Consumption in cups/day, weighted for weekday/weekend days and adjusted for within person variance, Significantly (p < 0.05) different from Other Micronesians § Significantly (p < 0.05) different from Filipino, Ø Significantly (p < 0.05) different from Other, Ƒ Sleep Recommendations: at least 11 h/d for 2-year-olds, at least 10 h/d for 3 to 5 year-olds, and at least 9 h/d for 6 to 10 year-olds, Ω Screen-time Recommendation of 2 h/d or less, ß BMI percentiles calculated according to CDC reference data and categories assigned as: underweight (<5th percentile), healthy weight (5th–84th percentile), overweight (85th–94th percentile), and obese (95th percentile), Abdominal obesity defined as waist circumference greater than the International Diabetes Federation cut-point (>90.2 percentile for age and sex), USDA Supplemental Nutrition Assistance Program (SNAP), USDA Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC), δ Question from USDA Core Food Security Module “In the past 12 months how often does money for food run out by the end of the month? γ What category of acculturation does family consider themselves?
Table 2. Characteristics of child participants from Guam in the Children’s Healthy Living (CHL) Program stratified by BMI status.
Table 2. Characteristics of child participants from Guam in the Children’s Healthy Living (CHL) Program stratified by BMI status.
Participant & Family CharacteristicsTotal Mean ± SE or n(%)Healthy Weight * Mean ± SE or n (%)OWOB * Mean ± SE or n (%)p Value
Age 0.069
2–5 years old437 (53.2%)329 (55.1%)108 (48%)
6–8 years old385 (46.8%)268 (44.9%)117 (52%)
Sex 0.917
Boys425 (51.7%)308 (51.6%)117 (52.0%)
Girls397 (48.3%)289 (48.4%)108 (48.0%)
Child Ethnicity 0.455
CHamoru528 (64.55%)392 (65.88%)137 (61.16%)
Other Micronesians203 (24.82%)144 (24.20%)59 (26.34%)
Filipino77 (9.41%)51 (8.57%)26 (11.61%)
Other10 (1.22%)8 (1.34%)2 (0.89%)
Annual Family Income Level 0.473
<$20,000331 (57.6%)234 (56.9%)97 (59.1%)
$20,000–$34,99986 (15%)63 (15.3%)23 (14%)
$35,000–$59,99989 (15.5%)60 (14.6%)29 (17.7%)
>$60,00069 (12%)54 (13.1%)15 (9.1%)
Receiving any Food Assistance 0.768
Yes645 (78.5%)470 (78.7%)175 (77.8)
No177 (21.5%)127 (21.3%)50 (22.2%)
Type of Food Assistance
SNAP556 (67.9%)408 (68.7%)148 (65.8%)0.426
Local Food Bank99 (12.15%)72 (72.7%)27 (27.3%)0.977
WIC258 (31.5%)196 (33.0%)62 (27.6%)0.135
Free School Lunch/Breakfast238 (29.1%)169 (28.5%)69 (30.7%)0.533
Food Insecurity 0.566
Always/most times120 (16.7%)88 (16.8%)32 (16.3%)
Sometimes253 (35.2%)178 (34%)75 (38.3%)
Seldom/never346 (48.1%)257 (49.1%)89 (45.4%)
Parent/Caregiver Education 0.052
<12th Grade269 (32.7%)207 (34.7%)62 (27.6%)
12th Grade/GED331 (40.3%)241 (40.4%)90 (40%)
Some college or higher222 (27%%)149 (25%)73 (32.4%)
Parent Marital Status 0.06
Married326 (39.7%)225 (37.7%)101 (44.9%)
Not Married496 (60.3%)372 (62.3%)124 (55.1%)
Number Children in Household4.19 ± 0.0774.19 ± 0.0944.22 ± 0.1520.838
Household Size 0.491
1–2 children197 (24.1%)145 (24.4%)52 (23.1%)
3–4 children319 (38.9%)224 (37.7%)95 (42.2%)
5 or more children303 (37%)225 (37.9%)78 (34.7%)
Birthplace 0.0070
Guam685 (84.46%)505 (85.45%)180 (81.45%)
U.S. Mainland52 (6.41%)37 (6.26%)15 (6.79%)
Saipan, CNMI31 (3.82%)20 (3.38%)13 (5.88%)
Other Islands in Micronesia25 (3.08%)21 (3.55%)4 (1.81%)
Philippines13 (1.60%)8 (1.35%)5 (2.26%)
Other4 (0.49%)1 (0.17%)4 (1.82%)
Acculturation 0.2913
Integrated542 (80.30%)394 (79.60%)148 (82.22%)
Traditional94 (13.78%)72 (14.54%)21 (12.14%)
Assimilated17 (2.52%)10 (2.02%)7 (3.89%)
Marginalized23 (3.41%)19 (3.84%)4 (2.22%)
Ever Breastfed? 0.2140
Yes529 (64.59%)373 (66.13%)156 (70.59%)
No256 (35.41%)191 (33.87%)65 (29.41%)
Exclusively Breastfed? 0.5655
Yes97 (13.13%)68 (12.69%)29 (14.29%)
No642 (86.87%)468 (87.13%)174 (85.71%)
Child age (mos) when weaned from breastmilk9.76 ± 0.438
(n = 495)
10.22 ± 0.5429.23 ± 0.8440.3248
Child fed both breastmilk & infant formula 0.5135
Yes421 (54.39%)300 (53.67%)121(56.28%)
No353 (45.61%)259 (44.33%)94 (43.72%)
Average Sleep Duration (h/d)8.62 ± 0.088.64 ± 0.0968.59 ± 0.1420.7122
TCSQ Sleep Score6.34 ± 0.2056.55 ± 0.2575.59 ± 0.3410.0421
Met Sleep Recommendations 0.725
Yes327 (40.4%)236 (40%)91 (41.4%)
No483 (59.6%)354 (60%)129 (58.6%)
Average Screen-time (h/d)5.26 ± 0.1325.15 ± 0.1505.55 ± 0.2700.1743
Met Screen-time Recommendations 0.444
Yes117 (16.8%)82 (16.2%)35 (18.6%)
No578 (83.2%)425 (83.8%)153 (81.4%)
Height (cm)110.52 ± 0.47108.71 ± 0.528115.34 ± 0.9120.0001
Weight (kg)21.26 ± 0.2618.77 ± 0.18527.91 ± 0.6350.0001
BMI, kg/m216.95 ± 0.1115.64 ± 0.03820.45 ± 0.2800.0001
BMI z-score0.485 ± 0.0360.007 ± 0.0261.79 ± 0.0450.0001
Waist Circumference (cm)55.07 ± 0.45751.93 ±0.18864.40 ± 0.7070.0001
Abdominal Obesity 0.0001
Yes75 (9.2%)0 (0%)75 (33.60%)
No741 (90.8%)591 (100%)148 (66.40%)
Beverage Consumption
Water (c/d)1.31 ± 0.0351.32 ± 0.0431.28 ± 0.0700.5793
Milk (c/d)1.24 ± 0.241.25 ± 0.0281.21 ± 0.0480.5994
Sugar-sweetened drinks (c/d)0.84 ± 0.030.81 ± 0.340.93 ± 0.0610.0676
Vegetable Intake (c/d)0.61 ± 0.0130.60 ± 0.0150.63 ± 0.0270.2535
Fruit Intake (c/d)0.88 ± 0.0210.87 ± 0.0250.89 ± 0.0400.7324
Met Recommendation for:
Fruit Intake257 (41.35%)185 (39.96%)79 (46.20%)0.1571
Vegetable Intake6 (0.90%)4 (0.86%)2 (1.17%)0.7242
* Healthy weight defined as BMI between 5th–84th percentile for age and sex; ‘OWOB’ defined as BMI greater than 85th percentile for age and sex, Consumption in cups/day, weighted for weekday/weekend days and adjusted for within person variance.
Table 3. Prevalence and Odds of OWOB among child participants from Guam in the Children’s Healthy Living (CHL) Program.
Table 3. Prevalence and Odds of OWOB among child participants from Guam in the Children’s Healthy Living (CHL) Program.
Variablen% OWOB (95% CI)Adjusted OR †† (95% CI)
Overall82227.4 (24.3–30.4)
Child Characteristics
Males42527.5 (23.3–31.8)Referent
Females39727.2 (22.8–31.6)1.001 (0.734–1.366)
2–5 years43724.7 (20.7–28.8)Referent
6–8 years38530.4 (25.8–35.0)1.337 (0.982–1.821)
CHamoru53126.0 (22.3–29.7)Referent
Other Micronesians20428.9 (22.7–35.2)1.186 (0.824–1.706)
Filipino7733.8 (23.0–44.6)1.454 (0.870–2.430)
Other1020.0 (−10.2–50.2)0.693 (0.144–3.329)
Child was breastfed?
Yes53029.4 (25.5–33.3)1.212 (0.861–1.708)
No25825.6 (20.2–30.9)Referent
Child met sleep standard for his/her age group?
Yes32727.8 (23.0–32.7)0.994 (0.717–1.378)
No48326.7 (22.8–30.7)Referent
Child met recommendation for screen-time of ≤ 2 h/day?
Yes11729.9 (21.5–38.3)1.184 (0.757–1.851)
No57826.5 (22.9–30.1)Referent
Child met recommendation for vegetable intake for his/her age group?
Yes633.3 (−20.9–87.5)1.342 (0.236–7.649)
No62826.9 (23.4–30.4)Referent
Child met recommendation for fruit intake for his/her age group?
Yes26429.9 (24.4–35.5)1.274 (0.89–1.823)
No37024.9 (20.4–29.3)Referent
SSB beverage intake (cups/day)
(zero intake—referent)16021.3 (14.8–27.7)Referent
Tertile 1 (≤0.42)5433.3 (20.4–46.3)2.064 (1.024–4.160)
Tertile 2 (0.42–1.09)20826.4 (20.4–32.5)1.495 (0.90–2.485)
Tertile 3 (≥1.09)21230.2 (24.0–36.4)1.824 (1.106–3.007)
p-value for trend 0.022
Water beverage intake (cups/day)
(zero intake—referent)2630.8 (11.8–49.8)Referent
Tertile 1 (≤0.92)19526.2 (19.9–32.4)0.827 (0.333–2.057)
Tertile 2 (0.92–1.62)20128.4 (22.1–34.6)0.965 (0.388–2.401)
Tertile 3 (≥1.62)21225.9 (20.0–31.9)0.819 (0.330–2.033)
p-value for trend 0.597
Parent/Caregiver/Household Characteristics
Education Level
Less than 12th grade (use this as reference)26923.1 (18.0–28.1)Referent
12th grade/GED or higher55329.5 (25.7–33.3)1.415 (1.004–1.994)
Marital Status
Married32631.0 (25.9–36.0)Referent
Not married49625.0 (21.2–28.8)0.776 (0.564–1.067)
Annual household income
<$20,00033129.3 (24.4–34.2)Referent
$20,000–$34,9998626.7 (17.2–36.3)0.867 (0.501–1.502)
$35,000–$59,9998932.6 (22.7–42.5)1.121 (0.664–1.892)
$60,000+6921.7 (11.8–31.7)0.600 (0.315–1.143)
Food Insecurity
Yes12026.7 (18.6–34.7)0.982 (0.627–1.539)
No59927.4 (23.8–31.0)Referent
Receiving any food assistance
Yes64527.1 (23.4–30.6)1.027 (0.699–1.509)
No17728.3 (21.6–35.0)Referent
Number of children in household
1–2 children19726.4 (20.2–32.6)Referent
3–4 children31929.8 (24.7–34.8)1.18 (0.789–1.766)
5 or more children30325.7 (20.8–30.7)0.948 (0.622–1.446)
Acculturation—Family considers themselves ‘integrated’ into culture
Yes54527.3 (23.6–31.1)1.162 (0.747–1.81)
No13424.6 (17.2–32.0)Referent
‘ Underweight’ children excluded from analysis, †† Adjusted for clustering by community, age, sex, and ethnicity, Consumption in cups/day, weighted for weekday/weekend days and adjusted for within person variance.

Share and Cite

MDPI and ACS Style

Leon Guerrero, R.T.; Barber, L.R.; Aflague, T.F.; Paulino, Y.C.; Hattori-Uchima, M.P.; Acosta, M.; Wilkens, L.R.; Novotny, R. Prevalence and Predictors of Overweight and Obesity among Young Children in the Children’s Healthy Living Study on Guam. Nutrients 2020, 12, 2527.

AMA Style

Leon Guerrero RT, Barber LR, Aflague TF, Paulino YC, Hattori-Uchima MP, Acosta M, Wilkens LR, Novotny R. Prevalence and Predictors of Overweight and Obesity among Young Children in the Children’s Healthy Living Study on Guam. Nutrients. 2020; 12(9):2527.

Chicago/Turabian Style

Leon Guerrero, Rachael T., L. Robert Barber, Tanisha F. Aflague, Yvette C. Paulino, Margaret P. Hattori-Uchima, Mark Acosta, Lynne R. Wilkens, and Rachel Novotny. 2020. "Prevalence and Predictors of Overweight and Obesity among Young Children in the Children’s Healthy Living Study on Guam" Nutrients 12, no. 9: 2527.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop