Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Dec 1.
Published in final edited form as: Fam Syst Health. 2018 Dec;36(4):451–470. doi: 10.1037/fsh0000376

Who Is Meeting the Healthy People 2020 Objectives?: Comparisons Between Racially/Ethnically Diverse and Immigrant Children and Adults

Jerica M Berge 1, Angela Fertig 1, Allan Tate 1,2, Amanda Trofholz 1, Dianne Neumark-Sztainer 2
PMCID: PMC6312388  NIHMSID: NIHMS997772  PMID: 30589321

Abstract

Introduction:

To determine the prevalence of reaching multiple Healthy People 2020 (HP 2020) objectives including nutrition and weight status, sleep health, physical activity, health-related quality of life, social determinants of health, and education among low-income, diverse children and adults.

Methods:

Children ages 5–7 years old (n=150; 47% female) and their parents (mean age = 35; 95% mothers) from six racial/ethnic and immigrant/refugee groups (n=25 from each; African American, Native American, Hispanic, Hmong, Somali, White) participated in this cross-sectional mixed-methods study.

Results:

Overall, the majority of HP 2020 objectives were not being met across this low-income, racially/ethnically diverse, and immigrant/refugee sample of children and adults. In particular, African American children and parents consistently fell below the majority of the HP 2020 targets, with only five of the twenty-four HP 2020 objectives being met. Additionally, immigrant children and parents met less than two-thirds of the HP 2020 objectives.

Discussion:

Concerted public health efforts are needed to address the disparities in reaching the HP 2020 objectives and informing the development of the future HP 2030 objectives among low-income, racially/ethnically diverse, and immigrant children and parents. In order to achieve and assess the current and future HP objectives in these diverse populations, changes may be needed in both interventions and assessment tools.

Keywords: Healthy People 2020, health disparities, immigrants, weight-related behaviors

INTRODUCTION

Healthy People 2020 (HP 2020) is a nationwide health promotion and disease prevention agenda that includes over 1,200 objectives in 42 focus areas (“Healthy People 2020 Objectives,” 2014). This initiative, developed by the United States Department of Health and Human Services (USDHHS), is a 10-year strategy aimed at improving the nation’s health. It highlights individual and societal determinants that affect the public’s health and contribute to health disparities from infancy through old age and provides strategic objectives to promote health and improve quality of life for all Americans (“Healthy People 2020 Objectives,” 2014). HP 2020 has four overarching goals: (1) Attain high-quality, longer lives free of preventable disease, disability, injury, and premature death; (2) Achieve health equity, eliminate disparities, and improve the health of all groups; (3) Create social and physical environments that promote good health for all; and (4) Promote quality of life, healthy development, and healthy behaviors across all life stages (“Healthy People 2020 Objectives,” 2014).

As 2020 draws closer, it is important to understand who is meeting these HP objectives. It is especially crucial to determine whether low-income, racially/ethnically diverse, and immigrant/refugee populations are meeting these objectives in order to identify disparities and to provide resources where needs are the greatest. In addition, by assessing the HP 2020 objectives in diverse populations it will be possible to see whether the HP 2020 objectives provide adequate measures for diverse racial/ethnic and immigrant/refugee populations and to inform the development of HP 2030 objectives. For example, if certain racial/ethnic groups are falling way below the HP 2020 objectives it may be important to adjust the HP 2030 objectives so that the objectives are not beyond reach for diverse populations, in addition to identifying measurement strategies that are more sensitive to diverse populations.

Furthermore, assessing who is meeting the HP 2020 objectives for both children and adults is important, especially within the same households, given family members are highly influenced by each other’s behaviors. Utilizing data from studies that comprehensively assess multiple HP 2020 targets with objective measures; that represent low-income, racially/ethnically diverse, and immigrant/refugee populations; and that include child and adult populations are needed to understand who is meeting the HP 2020 objectives and how to address disparities. It is uncommon for studies to include both racially/ethnically diverse and immigrant/refugee populations with measures that can assess multiple HP 2020 objectives. While large population-based studies of children (e.g., Youth Risk Behavior Study, Commonwealth Fund Study, Minnesota Adolescent Health Survey) and adults (e.g., Iowa Women’s Health Study) have the advantage of providing data on large samples, measures often need to be brief and may not adequately assess outcomes of relevance to HP 2020 objectives. Additionally, the measures collected in large population-based studies are often not objective or based on direct observational data (e.g., anthropometrics, accelerometry). Studies with more comprehensive assessments of HP 2020 objectives will greatly advance the field in understanding where resources need to be shifted to facilitate meeting the current HP 2020 objectives and developing the future HP 2030 objectives. In addition, studies with racially/ethnically diverse and immigrant/refugee samples would be critical to assess the HP 2020 objectives with, given these populations are at greatest risk for health disparities (Darmon, & Drewnowski, 2015; Larson, Eisenberg, Berge, Arcan, & Neumark-Sztainer, 2015).

The main objective of the current study is to utilize data from a unique data set that includes comprehensive measures addressing many of the HP 2020 objectives for children and their parents, within a low-income, racially/ethnically diverse, and immigrant sample to identify who is meeting the HP 2020 objectives. Results of this study will inform the development of future interventions to address gaps in meeting HP objectives for 2020 and future HP 2030 objectives. Additionally, healthcare providers will also be able to use study findings to shape the anticipatory guidance they give to parents and children related to weight and weight-related behaviors and health disparities.

METHODS

Data for the current study are from a National Institutes of Health funded study called Family Matters (Berge et al., 2017). Family Matters is a 5-year incremental (Phase I = 2014–2016.; Phase II = 2017–2019), mixed-methods (e.g., video-recorded tasks, ecological momentary assessment (EMA), interviews, surveys) study designed to identify novel risk and protective factors for childhood obesity in racially/ethnically diverse and primarily low-income households. Phase I is a cross-sectional study that uses mixed-methods to study the family home environment of diverse families (n=150). Phase II will be a longitudinal epidemiological cohort study with diverse families (n=1200). In-depth details regarding the study designs for both Phases of the study are published elsewhere (Berge et al., 2017).

Data for the current analysis are from Phase I of the Family Matters study. In Phase I, a mixed-methods analysis of the home environments of children ages 5–7 years old (n=150) from six racial/ethnic groups including, African American, Hispanic/Latino, Hmong, Native American, Somali, and White (n=25 from each racial/ethnic group) was conducted to identify individual, dyadic, and familial risk and protective factors for childhood obesity. The University of Minnesota’s Institutional Review Board Human Subjects Committee approved all protocols used in both phases of the Family Matters study.

Recruitment and Eligibility Criteria

Eligible children and their families were recruited from the Minneapolis/St. Paul, MN area between 2015–2016 via a letter sent to them by their family physician. Children were eligible to participate in the study if they were between the ages of 5–7 years old, had a sibling between the ages of 2–12 years old living in the same home, lived with their parent/primary guardian more than 50% of the time, shared at least one meal/day with the parent/primary guardian, and were from one of six racial/ethnic categories (African American, Hispanic/Latino, Hmong, Native American, Somali, and White). The sample was intentionally stratified by race/ethnicity and weight status (overweight/obese=BMI ≥85%ile; non-overweight=BMI >5%ile and <85%ile) of the child to identify potential weight- and/or race/ethnic-specific home environment factors related to obesity risk.

Procedures and Data Collection

In Phase I, a ten-day in-home observation was conducted with each family, including two in-home visits and an eight-day direct observational period in between home visits. The observational components relevant to the current study included: (1) EMA surveys measuring parent stress, depressed mood, parent modeling of eating and physical activity, and child dietary intake, physical activity, and sedentary behaviors (Shiffman, Stone, & Hufford, 2008); (2) child and parent accelerometry; (3) three 24-hour child dietary recalls; (4) objectively measured height and weight on all family members; and (5) a parent-completed online survey. All other study procedures are described in detail elsewhere (Berge et al., 2017). All study materials were translated into Spanish, Somali, and Hmong and bilingual staff were available at all home visits, allowing families to participate in their preferred language.

Sample demographics.

The study sample included diverse families who were equally distributed across six racial/ethnic groups (i.e., n=25 each of African American, Hispanic, Hmong, Native American, Somali, White households) (see Table 1). Likewise, there were equal numbers in the three immigrant/refugee groups recruited for the study (i.e., Hispanic, Hmong, Somali). Additionally, families were from primarily low-income households, with over 70% of families earning less than $35,000 per year. About one quarter of families were on public assistance. The majority of primary caregivers were mothers (91%) who were approximately 35 years old (mean = 34.5; sd = 7.1) with children around 6 years old (mean = 6.4; sd= 0.8). Secondary caregivers were 38 years old (mean=38.2; sd = 8.4). About half of the primary caregivers were married and 64% of households had two parents. Over 40% of primary and secondary caregivers were foreign born, with over 30% of all caregivers foreign born and living in the US for ten years or more.

Table 1:

Demographic Characteristics of Sample by Race/Ethnicity

Total White Black Hmong Hispanic Native American Somali
Sample Child n=150 n=25 n=25 n=25 n=25 n=25 n=25
Female 47% 40% 60% 44% 40% 48% 52%
Age (years) 6.4 6.2 6.4 6.5 6.5 6.4 6.5
Foreign born 5% 4% 0% 0% 0% 0% 24%
Overweight 19% 28% 12% 20% 20% 16% 16%
Obese 30% 16% 36% 32% 32% 32% 32%
Family income < $20,000 34% 8% 50% 20% 36% 56% 32%
Family income $20,000–$34,999 37% 16% 38% 44% 52% 32% 40%
Family income $35,000–$49,999 11% 8% 0% 20% 4% 8% 24%
Family income $50,000–$74,999 8% 20% 4% 12% 8% 0% 4%
Family income >= $75,000 11% 48% 8% 4% 0% 4% 0%
Family receives public assistance 23% 4% 63% 8% 4% 32% 28%
Primary Caregiver n=150 n=28 n=25 n=25 n=22 n=25 n=25
Mom of sample child 87% 86% 96% 80% 86% 80% 92%
Dad of sample child 9% 14% 0% 20% 9% 0%* 8%
Not mom or dad 5% 0% 4% 0% 5% 20% 0%
Female 91% 86% 100% 80% 91% 100% 92%
Age (years) 34.5 39.0 30.3 30.9 35.8 35.0 35.9
Married 52% 89% 8% 64% 73% 8% 68%
Foreign born 42% 11% 0% 64% 86% 0% 100%
Foreign born & in US 10+ years 32% 7% 0% 64% 68% 0% 60%
Currently working 64% 71% 54% 68% 59% 48% 80%
Overweight 25% 21% 20% 44% 27% 20% 20%
Obese 51% 32% 76% 24% 50% 68% 60%
Secondary Caregiver n=78 n=20 n=9 n=16 n=16 n=9 n=8
Mom of sample child 15% 21% 0% 20% 13% 0% 25%
Dad of sample child 75% 68% 78% 80% 80% 67% 75%
Not mom or dad 11% 11% 22% 0% 7% 33% 0%
Female 18% 20% 11% 25% 13% 11% 25%
Age 38.2 40.1 37.1 34.7 37.8 39.7 40.7
Married 74% 95% 22% 75% 88% 33% 100%
Foreign born 46% 10% 0% 69% 94% 0% 100%
Foreign born & in US 10+ years 36% 5% 0% 63% 81% 0% 50%
Currently working 60% 70% 56% 63% 56% 44% 63%
Overweight 31% 30% 22% 38% 31% 11% 50%
Obese 51% 50% 67% 50% 44% 67% 38%
Open in a new tab

Notes: Overweight for children is defined as BMI percentile>=85 & <95. Obese for children is defined as BMI percentile>=95. Overweight for adults is defined as BMI>=25 & <30. Obese for adults is defined as BMI>=30.

Measures

The current study uses Family Matters data collected during in-home visits and an eight-day direct observational period including parent and child anthropometry and accelerometry data, dietary recalls on the child, ecological momentary assessment, and an online survey completed by parents. Table 2 provides an in-depth description of the measures used in the current study and provides details regarding how the study variables were operationalized to match the HP 2020 objectives.

Table 2:

Descriptions of Measures Developed using Family Matters In-Home Visit Components

In-Home Visit Measurement Procedure How Variable was Created to Match the Healthy People 2020 Objectives
3-day 24-hour Dietary Recalls—NDSR Using the Nutrition Data System for Research (NDSR), three 24-hour dietary recalls (McPherson, Hoelscher, Alexander, Scanlon, & Serdula, 2000b) were conducted with the primary caregiver to assess child dietary intake (2 weekdays and 1 weekend day) (Bollella et al., 1998; Collins, Watson, & Burrows, 2009; Livingston & Robson, 2000). Recalls were conducted in-person (first/last home visit) and over the phone (between visits) in the parent’s language with a certified staff member (McPherson, Hoelscher, Alexander, Scanlon, & Serdula, 2000a; McPherson, Hoelscher, Alexander, Scanlon, Serdula , 2002). The NDSR system aggregates foods into subgroups, and nutrient profiles are provided per day and per meal (Guenther et al., 2014). The nutrient profiles of three 24-hour periods are averaged to produce all measures of dietary intake.
 • Consumption of vegetables (cups/1000 calories) - The combined servings of all vegetables consumed in one 24-hour period divided by 2 (to convert servings to cups), and then divided by the Total Energy Intake in kilocalories for the day multiplied by 1000. The vegetables include dark-green vegetables (e.g. broccoli, spinach), deep-yellow vegetables (e.g. carrots, sweet potatoes), tomato, white potatoes, fried potatoes, other starchy vegetables (e.g. corn, peas), legumes (e.g. refried beans, bake beans), other vegetables (e.g. beets, cabbage), fried vegetables (e.g. onion rings), and 100% vegetable juice.
• Consumption of dark green, red & orange vegetables, and beans & peas (cups/1000 calories) - The combined servings of dark green, red & orange vegetables and beans & peas consumed in one 24-hour period divided by 2 (to convert servings to cups), and then divided by the Total Energy Intake in kilocalories for the day multiplied by 1000. The vegetables include dark-green vegetables (e.g. broccoli, spinach), deep-yellow vegetables (e.g. carrots, sweet potatoes), tomato, other starchy vegetables (e.g. corn, peas), and legumes (e.g. refried beans, bake beans).
• Consumption of whole grains (ounces/1000 calories) - The combined ounce equivalent of all whole grains consumed in one 24-hour period divided by the Total Energy Intake in kilocalories for the day multiplied by 1000. The whole grain items include whole grains, flour and dry mixes (e.g. oat bran, wheat germ); loaf-type whole grain bread and plain rolls; other whole grain breads (quick breads, corn muffins, tortillas); whole grain crackers; whole grain pasta; whole grain ready-to-eat cereals (presweetened and not presweetened); whole grain cakes, cookies, pies, pastries, Danish, doughnuts, and cobblers; whole grain snack bars; whole grain snack chips; popcorn; flavored popcorn; and baby food whole grain mixtures.
• Consumption of solid fats (% of calories) – The combined grams of solid fat consumed in one 24-hour period multiplied by 9 (to convert grams to calories) divided by the Total Energy Intake in kilocalories for the day multiplied by 1000.
• Consumption of added sugars (% of calories) – The combined grams of added sugar consumed in one 24-hour period multiplied by 3.84 (to convert grams to calories) divided by the Total Energy Intake in kilocalories for the day multiplied by 1000. The value in grams is equal to the available carbohydrate value of the added sugar.
• Consumption of solid fats and added sugars (% of calories) – The combined grams of solid fat consumed in one 24-hour period multiplied by 9 (to convert grams to calories) plus the grams of added sugar consumed in the same 24-hour period multiplied by 3.84 (to convert grams to calories) divided by the Total Energy Intake in kilocalories for the day multiplied by 1000. The value in grams is equal to the available carbohydrate value of the added sugar.
• Consumption of saturated fat (% of calories) - The combined grams of saturated fat consumed in one 24-hour period multiplied by 9 (to convert grams to calories) divided by the Total Energy Intake in kilocalories for the day multiplied by 1000.
• Consumption of sodium (mg) – The combined milligrams of sodium consumed in one 24-hour period. Sodium includes naturally occurring sodium in foods as well as that ded during food processing. It does not include sodium from salt added at the table.
• Consumption of calcium (mg) – The combined milligrams of calcium consumed in one 24-hour period.
Ecological Momentary Assessment (EMA) EMA (Shiffman et al., 2008; Zunker et al., 2011) measures were used to capture day-to-day fluctuations in parent stress, mood, and parenting practices and child sleep, physical activity, and eating behaviors. Items used in EMA data capture are based on validated (r=.80–.92) measures (Dunton, Intille, Wolch, & Pentz, 2012; Dunton, Liao, Intille, Spruijt-Metz, & Pentz, 2011). Using an iPad mini, parents received signal, event, or end-of-day contingent EMA text messages multiple times throughout the day that included a link to an online survey. Only signal contingent recordings were used in the current analysis. Signal contingent recordings were researcher-initiated and used in a stratified random manner so that each parent was prompted (via a beep or vibration) to fill out a survey four times a day, within a three-hour time block (e.g., 7–10am, 11–2pm, 3pm-6pm, 7–10pm) that would expire after 1 hour. All EMA responses were time-stamped. The minimum requirement was for parents to respond to at least 2 signal contingent, 1 event contingent, and 1 end-of-day EMA message (total=4) per day. • Proportion of children who have poor quality sleep – The fraction of children whose average hours of sleep over an 8–10 day observation window met the recommended number of hours of sleep given their age. The minimum recommended hours of sleep is 10 hours for children age 5, and 9 hours for children age 6–8. Children’s hours of sleep was reported by the primary caregiver at the first signal contingent EMA recording of each observation day.
• Proportion of adults who self-report good or better mental health – The fraction of primary caregivers who responded “moderately,” “quite a bit,” or “extremely” to the question “How sad or depressed are you feeling right now?” no more than one day in their 8–10 day observation window. Response options include “not at all,” “a little,” “moderately,” “quite a bit,” and “extremely.” The question is answered by the primary caregiver at each signal contingent EMA recording, which occurs 4 times per day in the 8 day observation window.
Online Survey Survey development procedures used in prior research (Berge et al., 2014; Creswell, Klassen, Plano Clark, & Clegg Smith, 2011; N. Larson et al., 2011; Larson, Neumark-Sztainer, Story, van den Berg, & Hannan, 2011) were used to create the Phase I survey. All survey items were drawn from preexisting standardized measures. Standardized measures of the home food environment (e.g., food security (Blumberg, Bialostosky, Hamilton, & Briefel, 1999); family meals (Appelhans, Waring, Schneider, & Pagoto, 2014; Crawford, Ball, Mishra, Salmon, & Timperio, 2007; Fulkerson, Neumark-Sztainer, & Story, 2006)); physical and sedentary activity, (Amireault & Godin, 2015; Berge et al., In Press); family emotional atmosphere, interpersonal relationships (e.g., parenting style), family structure, family stressors (e.g., work/family balance (Netemeyer, 1996)), and acculturation (Cuellar & Arnold, 1995; Garrett, Pichette, & Pichette, 2000; Gim Chung, Kim, & Abreu, 2004; Landrine & Klonoff, 1995; Phinney, 1992) were included. Questions regarding parents (e.g., mental health, sleep), significant other, siblings, family functioning, and target child were also included to allow for addressing household-level factors (Berge et al., 2014). • Proportion of children who view television, videos, or play video games for no more than 2 hours a day – The fraction of children ages 2–5 and 6–14 whose average combined hours of watching television/videos/DVDs and playing computer or video games (like Nintendo or Xbox) was less than or equal to 2 hours per day, as reported by the primary caregiver. The survey responses about weekday and weekend watching/playing were averaged together such that weekday responses received a weight of 5/7 and weekend responses received a weight of 2/7.
• Proportion of children who use a computer or play computer games outside of school (for non-school work) for no more than 2 hours a day – The fraction of children ages 2–5 and 6–14 whose average combined hours of playing video games or using the internet/email or other electronic media for leisure was less than or equal to 2 hours per day, as reported by the primary caregiver. The survey responses about weekday and weekend playing/using were averaged together such that weekday responses received a weight of 5/7 and weekend responses received a weight of 2/7.
• Proportion of households with children with very low food security – The fraction of households with a Household Food Security Scale of 5 or 6. The Household Food Security Scale23 sums a series of six questions about skipping meals, eating less, hunger, and affording balanced meals and ranges between 0 and 6. A higher score represents higher food insecurity.
• Proportion of households with food insecurity – The fraction of households with a Household Food Security Scale of 2 through 6. The Household Food Security Scale23 (Blumberg et al., 1999) sums a series of six questions about skipping meals, eating less, hunger, and affording balanced meals and ranges between 0 and 6. A higher score represents higher food insecurity.
• Proportion of adults who get sufficient sleep – The fraction of primary caregivers whose self-reported usual hours of sleep met the recommended number of hours of sleep. The minimum recommended hours of sleep is 7 hours for adults. The survey responses about usual weekday and weekend hours of sleep were averaged together such that weekday responses received a weight of 5/7 and weekend responses received a weight of 2/7.
• Proportion of adults who engage in no leisure-time physical activity – The fraction of primary caregivers who responded “none” to a question about how many hours they spend doing strenuous exercise (heart beats rapidly). Response options include “none,” “less than 1/2 an hour a week,” “1/2 an hour - 2 hours a week,” “2 1/2 hours - 4 hours a week,” “4 1/2 hours - 6 hours a week,” and “6+ hours a week.”
• Proportion of the population that completes high school education – The fraction of primary and secondary caregivers who responded “High School or GED,” “Vocational, technical, trade, etc.,” “Associate degree,” “Bachelor degree,” “Graduate or professional degree,” or “Other” to a question about their highest level of completed education. Other response options include “Middle school or junior high,” and “Some high school.”
Accelerometry Parent physical activity level was evaluated using an accelerometer (Actigraph GT1M model, Fort Walton Beach, FL) (Computer Science and Applications Inc., 1991; Eston, Rowlands, & Ingledew, 1998; Louie, Eston, Rowlands, & et al, 1999; Trost et al., 1998). The primary caregiver wore the accelerometer over the 8-day observation period (Sirard, 2001; Treuth et al., 2004). Standardized re-wear protocol from previous research was followed (i.e., minimum=4 days; 1 weekend, 3 weekdays; 8 waking hrs./day) (Sherwood et al., 2013). Accelerometers were set to collect data in 15 second epochs. Measurements produced from accelerometry data include total time in minutes of moderate physical activity, and total time in minutes of vigorous physical activity. •  Proportion of adults who engage in aerobic physical activity of at least moderate intensity for at least 150 minutes/week, or 75 minutes/week of vigorous intensity, or an equivalent combination – The fraction of primary caregivers whose accelerometer recorded moderate intensity equivalent exercise of at least 150 minutes/week. Average daily minutes of moderate intensity and vigorous intensity exercise is multiplied by 7 days. Moderate intensity equivalent exercise is calculated as the number of minutes in 7 days of moderate intensity exercise plus 2 times the number of minutes in 7 days of vigorous intensity exercise.
•  Proportion of adults who engage in aerobic physical activity of at least moderate intensity for at least 300 minutes/week, or 150 minutes/week of vigorous intensity, or an equivalent combination – The fraction of primary caregivers whose accelerometer recorded moderate intensity equivalent exercise of at least 300 minutes/week. Average daily minutes of moderate intensity and vigorous intensity exercise is multiplied by 7 days. Moderate intensity equivalent exercise is calculated as the number of minutes in 7 days of moderate intensity exercise plus 2 times the number of minutes in 7 days of vigorous intensity exercise.
Anthropometry Weight was measured on all family members in duplicate on a portable digital scale (Seca 869 model) and recorded to the nearest 0.1 kg. If the two measures differed by more than 0.5 kg, a third measure was obtained. Height was measured in duplicate, using a portable stadiometer (Seca 217 model) and recorded to the nearest 1.0 cm. If the two measures differed by more than 0.5 cm, a third measure was obtained (Lohman, Roche, & Martorell, 1988; Lohman, Roche, & Matorell, 1988). Adult heights and weights were converted to body mass index (BMI) based on CDC guidelines (dividing weight in kilograms by height in centimeters squared) (Centers for Disease Control and Prevention, 2015). • Proportion of adults who are at a healthy weight – The fraction of primary and secondary caregivers whose BMI is greater than 18.5 and less than 25.0.
• Proportion of adults who are obese – The fraction of primary and secondary caregivers whose BMI is greater than or equal to 30.0.
Open in a new tab

HP 2020 objectives.

The Healthy People 2020 objectives were developed by the USDHHS. There are over 1200 “objectives” identified in Healthy People 2020, with specific “targets” for meeting each objective. Of those, the Family Matters study captured data on fourteen objectives focused on children and ten objectives focused on adults. While the USDHHS developed the HP objectives and offer some suggestions for measuring the objectives such as the health-related quality-of-life (HRQOL) scale, there is not a comprehensive list of measures for the HP 2020 objectives. Thus, in the current study, gold standard measures (e.g., three-day 24 hour dietary recalls for dietary intake and nutrients, accelerometry to measure physical activity and sedentary behavior, and anthropometry for measuring BMI) and other standardized measures (e.g., survey items on depressive symptoms) were utilized to assess the HP 2020 objectives. These measures are briefly described below and in more detail in Table 2.

The HP objectives around children’s nutrition were matched to data obtained via three-day 24 hour of dietary recalls. The HP objectives on children’s sleep and adults’ mental health were matched to survey measures collected over an 8-day observational period by EMA. The HP 2020 objectives on children’s physical activity (and one adult physical activity objective), household food insecurity, adults’ sleep health, and adults’ education were matched to responses to an online survey. Two HP objectives on adults’ physical activity were matched to data collected by accelerometry over an 8-day observational period and two objectives on adults’ weight status were matched to anthropometry data collected by research staff. A few of the children’s HP objectives were age-specific (e.g. age 2–5 and 6–14 for screen time). In those cases, only the sample children in the appropriate age range were used to create the measure. Table 2 provides an in-depth description of these measures and details regarding how the study variables were created to match the HP 2020 objectives.

Population level HP 2020 standardized measure.

In addition to the sample HP 2020 measures, a population level standardized measure for each HP 2020 target was created by re-weighting the sample to Minnesota’s population composition. Stratified and population standardized analyses explain who is healthy and how community groups contribute to overall population health. This approach informs how intervention activity could be directed. For example, if the state-level standardized measure indicates sleep targets are not being met, and the stratified analyses indicate a large-sized subgroup is completely meeting the target, then these two data points can be used to justify a more tailored intervention approach to address disparities in the remaining group needs (Kitagawa, 1964). In the current study, each participant was given a weight based on the number of people of their race/ethnicity and immigrant status as represented in Minnesota using population statistics from the 2011–2015 American Community Survey. Thus, the population level standardized measure is the mean we would expect if our sample of participants were representative of the state of Minnesota where 81.7% of the population is non-Hispanic white, 3.9% is native-born African-American, 4.4% is Asian, 5.0% is Hispanic, 1.0% is Native American, and 1.6% is foreign-born Black (e.g., Somali).

Statistical Analysis

For the current analysis, data collected on children and up to two parents from 150 diverse households in the Family Matters study were utilized. Data analysis was conducted during 2017. Households were equally divided among six racial/ethnic and immigrant groups: 25 White, 25 African-American, 25 Hmong, 25 Hispanic, 25 Native American, and 25 Somali. The primary caregiver provided survey data on the child, themselves, a second parent (n=78), and the overall household. A stratified analysis by racial/ethnic and immigrant group was performed to examine achievement of Healthy People 2020 objectives and targets.

One-sided t-tests were used to determine whether each group (including the standardized population measure) met or exceeded the target (versus failed to meet the target). When observations from multiple caregivers in a household are included in the estimate, significance tests are adjusted for correlations within households using clustered standard errors. We have chosen not to adjust the standard errors for multiplicity (e.g. Bonferroni correction) because many of the measures are highly correlated and thus would over-correct. Instead, we present all of the tests conducted and report sample sizes on each test for transparency.

RESULTS

Overall, considerable gaps were observed between most of the Healthy People 2020 objectives and targets and current patterns of prevalent health behaviors of children and adults from low-income, racially/ethnically diverse, and immigrant/refugee households in the Family Matters study. In addition, the standardized Minnesota population level measures also identified disparities between the racially/ethnically diverse and immigrant/refugee populations represented in the sample with regard to meeting the HP 2020 objectives.

Healthy People 2020 Objectives for Children

Nutrition and weight status HP 2020 objectives.

All children across low-income, racial/ethnic, and immigrant/refugee households were falling far below the HP 2020 target for total vegetable and specific vegetable intake (p < 0.005) (see Table 3). For example, the 2020 target for total vegetables is 1.16 cups per 1000 calories. All children in the current sample had between .26 and .57 cups per 1000 calories. All groups also fell significantly below the HP 2020 target for calcium. With regard to whole grain consumption and calories from solid fats, Hmong children were significantly lower (0.48 ounces/1000 calories; p < 0.025) on whole grain intake compared to the HP 2020 target of .66 ounces per 1000 calories, and African American (16.5%; p<0.005) children were significantly higher on consumption of calories from solid fats compared to the HP 2020 target (i.e., 14.2%). Children from all groups except Hmong and Somali children consumed more calories from added sugars than the HP 2020 target of 9.7%, while African-American and Hmong children consumed more calories from saturated fat than the HP 2020 target of 9.9%. Finally, African American (3097 mg; p<0.005) and Native American children (2710 mg; p<0.025) consume significantly more sodium that the HP 2020 target of 2300mg.

Table 3:

Healthy People 2020 Objectives and Measures Focused on Children by Race/Ethnicity

Healthy People (HP) 2020 Objectives for Children HP 2020 Target MN Population Level Std Measure§ White African American Hmong Hispanic Native American Somali
Nutrition and Weight Status -- Age 2 years and older (n=25) (n=25) (n=25) (n=25) (n=25) (n=25)
Increase consumption of vegetables (cups/1000 calories) 1.16 0.51** 0.52** 0.33** 0.40** 0.57** 0.53** 0.26**
(0.30) (0.31) (0.20) (0.23) (0.38) (0.30) (0.16)
Increase consumption of dark green, red & orange vegetables, and beans & peas to the diet (cups/1000 calories) 0.53 0.30** 0.31** 0.16** 0.20** 0.32** 0.26** 0.14**
(0.24) (0.25) (0.18) (0.12) (0.28) (0.19) (0.11)
Increase consumption of whole grains (ounces/1000 calories) 0.66 1.00 1.04 0.58 0.48* 1.18 0.58 1.18
(0.72) (0.75) (0.37) (0.38) (0.74) (0.52) (0.81)
Reduce consumption of solid fats (% of calories) 14.2% 14.6% 14.7% 16.5%** 14.6% 12.7% 14.6% 9.8%
(5.0%) (5.1%) (4.0%) (5.2%) (4.1%) (5.3%) (3.2%)
Reduce consumption of added sugars (% of calories) 9.7% 13.2%** 13.5%** 14.3%** 9.3% 12.9%** 12.7%* 11.0%
(4.8%) (4.8%) (4.9%) (4.2%) (5.7%) (6.0%) (3.6%)
Reduce consumption of solid fats and added sugars (% of calories) 25.5% 27.8% 28.2% 30.9%** 23.9% 25.5% 27.2% 20.8%
(7.7%) (7.9%) (5.4%) (7.5%) (7.5%) (7.9%) (4.4%)
Reduce consumption of saturated fat (% of calories) 9.9% 10.8%* 10.8% 11.9%** 11.3%* 9.9% 10.7% 9.3%
(2.4%) (2.4%) (2.5%) (3.0%) (2.4%) (2.4%) (1.9%)
Reduce consumption of sodium (mg) 2300 2465 2475 3097** 2405 2020 2710* 1767
(801) (785) (883) (1104) (590) (765) (510)
Increase consumption of calcium (mg) 1384 1061** 1088** 1015** 743** 932** 1006** 1063**
(361) (363) (416) (386) (239) (262) (299)
Early and Middle Childhood (n=25) (n=25) (n=25) (n=25) (n=25) (n=25)
Reduce the proportion of children who have poor quality sleep (% of children) N/A 67.6% 72.0% 48.0% 40.0% 36.0% 76.0% 60.0%
(47.4%) (45.8%) (51.0%) (50.0%) (49.0%) (43.6%) (50.0%)
Physical Activity -- Age 2 to 5 (n=16) (n=9) (n=8) (n=8) (n=8) (n=6)
Increase the proportion of children who view television, videos, or play video games for no more than 2 hours a day (% of age 5) 83.2% 72.3% 75.0% 22.2%** 50.0%* 50.0%* 62.5% 83.3%
(44.7%) (44.7%) (44.1%) (53.5%) (53.5%) (51.8%) (40.8%)
Increase the proportion of children who use a computer or play computer games outside of school (for non-school work) for no more than 2 hours a day (% of age 5) N/A 86.1% 87.5% 66.7% 62.5% 75.0% 87.5% 100.0%
(34.6%) (34.2%) (50.0%) (51.8%) (46.3%) (35.4%) (0.0%)
Physical Activity -- Age 6 to 14 (n=9) (n=15) (n=17) (n=17) (n=17) (n=19)
Increase the proportion of children who view television, videos, or play video games for no more than 2 hours a day (% of age 6–8) 86.8% 68.5% 77.8% 6.7%** 41.2%** 58.8%* 47.1%** 73.7%
(46.5%) (44.1%) (25.8%) (50.7%) (50.7%) (51.4%) (45.2%)
Increase the proportion of children who use a computer or play computer games outside of school (for non-school work) for no more than 2 hours a day (% of age 6–8) 100.0% 67.0%* 66.7% 33.3%** 64.7%** 88.2% 76.5%* 84.2%
(47.0%) (50.0%) (48.8%) (49.3%) (33.2%) (43.7%) (37.5%)
Open in a new tab
§

The population level standardized measure is the mean value when the sample of participants from all race/ethnic/immigrant groups are combined and weighted to be representative of the state of Minnesota.

Standard deviations shown in parentheses.

Results of one-sided significance tests relative to Target:

**

significantly below or above Target at p<0.005;

*

p<0.025.

Intake is average of three 24-hour dietary recalls

With regard to the standardized population level results, the finding of .51 cups of vegetables/1000 calories (see Table 3) indicates that at a population level in Minnesota, the HP 2020 target (i.e., 1.16 cups/1000 calories) was not being met and further highlighted the racial/ethnic and immigrant/refugee disparities in nutrition/diet quality HP 2020 objectives. The standardized population level results followed this same pattern for most of the nutrition HP 2020 targets, except whole grains, solid fats, solid fats and added sugars combined, and sodium.

Early and middle childhood HP 2020 objectives.

Sleep quality is a HP 2020 objective, however specific targets for hours of sleep are not identified; rather the objective is to “reduce poor sleep quality”. Over 60% of Somali, Native American, and White children were meeting the American Academy of Pediatrics—AAP (“American Academy of Pediatrics Childhood Sleep Guidelines,” 2016) recommended 10–13 hours of sleep per night for children ages 3–5 years or 9–12 hours of sleep per night for children ages 6–12 years, whereas less than half of African American, Hmong, and Hispanic children were meeting this recommendation (see Table 3). The standardized population level measure indicated that about 68% of Minnesota children were meeting the sleep HP 2020 target, which masked the disparities by race/ethnicity. For example, Hispanic children were falling significantly below the AAP recommended hours of sleep, with only 36% of children meeting this recommendation.

Physical activity HP 2020 objectives.

The majority of the HP physical activity objectives focus on sedentary behavior targets. There were large racial/ethnic disparities in meeting the 2020 target for sedentary behavior. For the 2020 target of increasing the proportion of children watching TV/videos/video games for no more than two hours/day, African American children fell far below the 2020 target, with only 22% of children ages 2–5 years (p<0.005) and 7% of children ages 6–14 years meeting the target (p <0.005) (see Table 3). Additionally, African-American, Hmong, and Native American children ages 6 to 14 years fell far below the 2020 target of 100% of children in this age group using computers/computers games outside of school no more than two hours/day (p<0.025), with African American children having the lowest prevalence of 33% meeting the HP 2020 target. The standardized population level measure indicated that between 67–86% of Minnesota children were meeting the HP 2020 targets related to sedentary behavior, which highly masked the disparities by race/ethnicity.

Healthy People 2020 Objectives for Adults and Households

Social determinants of health HP 2020 objectives.

Within the social determinants of health HP 2020 objectives there are targets related to reducing household-level food insecurity and eliminating very low food insecurity. In the current sample, African American, Hmong, and Native American families were not meeting the HP target of only 6% of households being food insecure; instead 20–32% fell into the food insecure category (see Table 4). With regard to very low food insecurity, all households except Native American households were meeting the target of only 0.2% being very low food insecure (p<0.025), with a range of 0–20% falling in the very low food insecurity category. The standardized population level measure indicated that most of Minnesota households were meeting the food insecurity HP 2020 targets, which masked the disparities by race/ethnicity.

Table 4:

Healthy People 2020 Objectives and Measures Focused on Adults/Households by Race/Ethnicity

Healthy People (HP) 2020 Objectives for Adults HP 2020 Target MN Population Level Sth Measure§ White African American Hmong Hispanic Native American Somali
Social Determinants of Health (n=25) (n=24) (n=25) (n=25) (n=25) (n=25)
Eliminate very low food security among children (% of households) 0.2% 7.8% 8.0% 8.3% 12.0% 0.0% 20.0%* 4.0%
(26.9%) (27.7%) (28.2%) (33.2%) (0.0%) (40.8%) (20.0%)
Reduce household food insecurity (% of households) 6.0% 10.0% 8.0% 20.8%** 32.0%** 12.0% 32.0%** 4.0%
(30.0%) (27.7%) (41.5%) (47.6%) (33.2%) (47.6%) (20.0%)
Health-Related Quality of Life and Well-Being (n=28) (n=24) (n=25) (n=22) (n=25) (n=25)
Increase the proportion of adults who self-report good or better mental health (% of primary caregivers) 80.1% 77.9% 82.1% 48.0%** 56.0%* 68.2% 68.0% 32.0%**
(41.5%) (39.0%) (51.0%) (50.7%) (47.7%) (47.6%) (47.6%)
Sleep Health (n=28) (n=24) (n=25) (n=22) (n=25) (n=25)
Increase the proportion of adults who get sufficient sleep (% of primary caregivers with 7+ hours) 70.8% 65.2% 64.3% 72.0% 60.0% 72.7% 56.0% 92.0%
(47.6%) (48.8%) (45.8%) (50.0%) (45.6%) (50.7%) (27.7%)
Physical Activity -- Adults (n=26) (n=23) (n=25) (n=22) (n=25) (n=25)
Increase the proportion of adults who engage in aerobic activity of at least moderate intensity for at least 150 minutes/week, or 75 minutes/week of vigorous intensity, or an equivalent combination (% of primary caregivers) 47.9% 55.8% 61.5% 17.4%** 20.0%** 40.9% 44.0% 28.0%*
(49.7%) (49.6%) (38.8%) (40.8%) (50.3%) (50.7%) (45.8%)
Increase the proportion of adults who engage in aerobic physical activity of at least moderate intensity for more than 300 minutes/week, or more than 150 minutes/week of vigorous intensity, or an equivalent combination (% of primary caregivers) 31.3% 6.8%** 7.7%** 0.0%** 8.0%** 0.0%** 4.0%** 0.0%**
(27.2%) (27.2%) (0.0%) (27.7%) (0.0%) (20.0%) (0.0%)
Reduce the proportion of adults who engage in no leisure-time physical activity (% of primary caregivers who self-report no strenuous exercise) 32.6% 34.0% 32.1% 37.5% 40.0% 54.5% 32.0% 40.0%
(47.4%) (47.6%) (49.5%) (50.0%) (51.0%) (47.6%) (50.0%)
Educational and Community-Based Programs (n=47) (n=32) (n=41) (n=38) (n=32) (n=33)
Increase the proportion of the population that completes high school education (% of primary+secondary caregivers)^ 97.9% 95.1%* 100.0% 84.4% 75.6%** 47.4%** 90.6% 57.6%**
(21.7%) (0.0%) (36.9%) (43.5%) (50.6%) (29.6%) (50.2%)
Adult Weight Status (n=48) (n=32) (n=34) (n=37) (n=30) (n=32)
Increase the proportion of adults who are at a healthy weight (% of primary+secondary caregivers >18.5 and <25.0 BMI)^ 33.9% 31.4% 33.3% 6.3%** 29.4% 24.3% 16.7%* 18.8%
(46.4%) (47.6%) (24.6%) (46.2%) (43.5%) (37.9%) (39.7%)
Reduce the proportion of adults who are obese (% of primary+secondary caregivers ≥ 30.0 BMI)^ 30.5% 40.8% 39.6% 71.9%** 20.6% 45.9% 63.3%** 53.1%*
(49.2%) (49.4%) (45.7%) (41.0%) (50.5%) (49.1%) (50.7%)
Open in a new tab
§

The population level standardized measure is the mean value when the sample of participants from all race/ethnic/immigrant groups are combined and weighted to be representative of the state of Minnesota.

Standard deviations shown in parentheses.

Results of one-sided significance tests relative to Target:

**

significantly below or above Target at p<0.005;

*

p<0.025.

^

Significance tests account for multiple observations from each household using clustered standard errors.

Health-related quality of life HP 2020 objectives.

One of the HP 2020 objectives for health-related quality of life is reporting “good or better” mental health. In the current sample, Somali (32%) and Hmong (56%) immigrant parents and African American (48%) parents fell far below the 2020 mental health target of 80% of adults reporting good mental health (p<0.025) (see Table 4). The standardized population level measure indicated that about 78% of Minnesota adults were meeting the HP 2020 mental health target, which highly masked the disparities by race/ethnicity.

Sleep health HP 2020 objectives.

Regarding sleep health, all parents across racial/ethnic and immigrant/refugee groups were meeting or exceeding the HP 2020 sleep target, with over 90% of Somali parents meeting the HP target (see Table 4). However, only 56% of Native American parents met the HP 2020 sleep target. The standardized population level measure indicated that about 65% of Minnesota adults were meeting the sleep HP 2020 objective, which was close to the target of 70%.

Physical activity HP 2020 objectives.

All racial/ethnic groups were meeting the HP 2020 target of reducing the proportion of adults who engage in no leisure-time physical activity (see Table 4). All racial/ethnic groups were significantly below the HP 2020 target of 300 minutes per week of moderate intensity physical activity or vigorous intensity for more than 150 minutes per week (p<0.005). Additionally, there were large disparities in meeting the 2020 target of engaging in either 150 minutes per week of moderate aerobic physical activity or 75 minutes per week of vigorous aerobic physical activity. The standardized population level measure indicated between 7–56% of Minnesota adults were meeting sedentary activity HP 2020 targets, which highlights gaps that exist in meeting these targets.

Educational and community-based programs HP 2020 objectives.

One of the objectives within the educational and community-based HP 2020 target is increasing the proportion of adults that graduate from high school to 98%. In the current sample, Hmong, Hispanic, and Somali parents were significantly below the 2020 objective of 98% high school graduation (p<0.005; Table 4). Only 47% of Hispanic parents, 58% of Somali parents, and 76% of Hmong parents had graduated from high school. The standardized population level measure indicated that about 95% of Minnesota adults were meeting the education HP 2020 target, which highly masked the disparities across racial/ethnic groups.

Adult weight status HP 2020 objectives.

The weight status HP 2020 target for adults includes objectives for both increasing the proportion of healthy weight adults and reducing the proportion of adults who are obese. In the current sample, African American, Native American, and Somali parents did not meet these two 2020 targets (p<0.025) (see Table 4). For example, 72% of African American parents, 63% of Native American parents, and 53% of Somali parents had body mass indexes (BMIs) that put them in the obese (i.e., BMI ≥30) category. The standardized population level measure indicated that about 31% of Minnesota adults were within the healthy weight HP 2020 target and 41% of adults were obese, which were both lower than HP 2020 target and thus masked the disparities by racial/ethnic group.

DISCUSSION

Results of the current study indicate large gaps among low-income, racially/ethnically diverse, and immigrant/refugee children and adults in meeting the HP 2020 objectives and targets. The large disparities are concerning, particularly for African American children and parents, who consistently fell far below the majority of the 2020 objectives. Findings from the current study also clearly show that immigrant/refugee children and parents are falling below many of the HP 2020 objectives and targets. These gaps are important to address given past research showing that unhealthy dietary and physical activity patterns developed in childhood contribute to obesity, unhealthy weight control behaviors and eating disorders, and increased risk of multiple chronic diseases in adulthood (Biro & Wien, 2010; Garg, Maurer, Reed, & Selagamsetty, 2014; Neumark-Sztainer et al., 2007; Reilly & Kelly, 2011; Reilly et al., 2003; Sullivan, Morrato, Ghushchyan, Wyatt, & Hill, 2005; Thompson, Humbert, & Mirwald, 2003; Thompson et al., 2007; Vucenik & Stains, 2012; Winter et al., 2008). In addition, prior research shows unhealthy weight and weight-related behaviors, mental health problems, and household food insecurity are associated with adult overweight, obesity, chronic disease, and early mortality (Bruening, MacLehose, Loth, Story, & Neumark-Sztainer, 2012; Sullivan et al., 2005; Winter et al., 2008). Furthermore, weight, weight-related behaviors, and mental health disparities create an enormous societal and economic burden (Darmon & Drewnowski, 2015; Trasande, Liu, Fryer, & Weitzman, 2009; Withrow & Alter, 2011).

The population level standardized measure also suggested that at a population level in Minnesota, the HP 2020 targets were not being met, and highlighted race and ethnic disparities. For example, for the HP 2020 target of reducing household food insecurity to less than six percent, the population standard measure suggested that the target was nearly met (10%), however there were large disparities with African American (21%), Hmong (32%), and Native American (32%) being far from the HP 2020 target. While the standardized measure might suggest that food insecurity ranks lower as a population health issue in Minnesota, substantial disparities were masked by subpopulation differences in food security for which there may be a critical public health need. Thus, more needs to be done to help racially/ethnically diverse and immigrant/refugee populations meet the HP 2020 and future 2030 objectives.

For example, findings from the current study suggest the importance of investigating social and environmental factors contributing to the reasons for not meeting the 2020 objectives. It would be important for interventions to address immigrant-specific needs related to meeting the HP 2020 targets. For example, developing interventions that take into account differing social norms regarding eating and physical activity patterns, support systems, and other specific racial/ethnic factors (e.g., body shape). Furthermore, the large gaps in nutritional 2020 objectives found for all children are important to address. Interventions may need to be developed that focus on these specific objectives (i.e., vegetables, added sugars, calcium) in order to see movement towards meeting the 2020 objectives and the future development of 2030 objectives. To date, many child and adult health-focused interventions take a general approach to healthy eating and that focuses on increasing fruits as well as vegetables. It may be important for future research to create interventions that focus solely on increasing vegetable and calcium intake and reducing sodium intake.

To achieve the HP 2020 objectives it may also be necessary to alter assessment tools. For example, it would be important for future research to examine the validity of measures for diverse racial/ethnic and immigrant households. It may be the case that assessment tools are not accurately measuring the food intake or mental health of these cultures, or that assessment measures currently utilized with other racial/ethnic groups are not salient to immigrant households. One specific example of this is that in Hmong and Somali there is no specific translatable word for “depression”. Furthermore, it would be important to consider whether the HP 2020 and future 2030 objectives are reasonable for groups that are already falling so far below the Healthy People objectives. If the goal is really for all people to achieve the HP 2020 and future 2030 objectives, strong efforts will be needed to meet the needs of these high-risk and diverse populations.

Healthcare providers may also be able to use study findings to shape the anticipatory guidance they give parents and children related to weight and weight-related behaviors and health disparities. Specifically, within family medicine/primary care settings, physicians often see patients and families from low-income and minority populations and it may be important for physicians to tailor their messages to these populations related to the weight and weight-related HP 2020 objectives gaps found in the current study.

A final contribution is the study benefits researchers planning future studies. The means and standard deviations provided in this study may inform power analyses for future studies with diverse populations.

There were both strengths and limitations of the current study to take into consideration when interpreting the results of the study. Strengths of the current study include the use of objective measures and gold standard measures for the health disparities field such as NDSR for dietary recalls, accelerometry for measuring physical activity, anthropometry for measuring BMI, and EMA for measuring sleep and mental health (i.e., stress, mood) at multiple time points within and across days over an eight-day period. This approach minimizes the possibility of common method bias (Podsakoff, MacKenzie, & Podsakoff, 2012). In addition, the sample includes a large proportion of low-income, racially/ethnically diverse, and immigrant/refugee children and parents. Furthermore, standardized population level measures were used in comparing racial/ethnic and immigrant/refugee households to the HP 2020 targets. Using both our sample-level and population-level results may be useful for other states within the U.S. that are becoming more diverse as more immigrant and refugee populations settle in the U.S.

One limitation of the study is that because children were pre-stratified on weight status (nonoverweight vs. overweight/obese), we could not calculate the prevalence of child overweight/obese for the HP 2020 objectives. A second limitation is that because the statistics presented in this analysis are all unadjusted for factors like income or education, it is not possible based on these results to offer potential explanations for why some subgroups appear to have better population health than others. Another limitation of the study is the small sample size. While the smaller sample size allowed for comprehensive and more objective measures to be collected, each subgroup sample of 25 families may not be representative of their subgroup population and the measures created from these data exhibit greater variability than if the sample were larger. Thus, caution should be taken when generalizing findings from this sample. Last, because the current study examines multiple HP 2020 outcomes, the familywise error rate may be inflated resulting in inflated type I error. In sum, because the observed effect sizes were large in magnitude, the statistics were unadjusted, and the sample strata were small, we encourage future research examining these topics to strengthen confidence in these findings by replicating our approach in larger samples and conducting multivariate analyses. Results of the current study should be treated as exploratory and as a basis for improving on current study limitations.

CONCLUSIONS

Results of the current study indicated that there are many gaps in meeting the HP 2020 objectives at both the population level and for low-income, racially/ethnically diverse, and immigrant children and adults. Additionally, African American and immigrant households may be the most at risk of not meeting the HP 2020 objectives. Concerted public health efforts are needed to address the disparities in reaching the HP 2020 objectives and informing the HP 2030 objectives within low-income, racially/ethnically diverse, and immigrant/refugee children and parents.

Acknowledgments:

The Family Matters study is truly a team effort and could not have been accomplished without the dedicated staff who carried out the home visits, including: Awo Ahmed, Nimo Ahmed, Rodolfo Batres, Carlos Chavez, Mia Donley, Michelle Draxten, Carrie Hanson-Bradley, Sulekha Ibrahim, Walter Novillo, Alejandra Ochoa, Luis “Marty” Ortega, Anna Schulte, Hiba Sharif, Mai See Thao, Rebecca Tran, Bai Vue, and Serena Xiong.

Funding Source: Research is supported by grant number R01HL126171 from the National Heart, Lung, and Blood Institute (PI: Jerica Berge). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung and Blood Institute or the National Institutes of Health.

Footnotes

Financial disclosure: Authors have no financial disclosures to report.

Conflict of interest: Authors have no conflicts of interest to report.

References

  1. American Academy of Pediatrics Childhood Sleep Guidelines. (2016). Retrieved from Overall, considerable gaps were seen between the targeted Healthy People 2020 objectives and current patterns of behavior/prevalences by children and adults from diverse racial/ethnic and immigrant groups
  2. Amireault S, & Godin G (2015). The Godin-Shephard leisure-time physical activity questionnaire: validity evidence supporting its use for classifying healthy adults into active and insufficiently active categories. Percept Mot Skills, 120(2), 604–622. [DOI] [PubMed] [Google Scholar]
  3. Appelhans BM, Waring ME, Schneider KL, & Pagoto SL (2014). Food preparation supplies predict children’s family meal and home-prepared dinner consumption in low-income households. Appetite, 76, 1–8. 10.1016/j.appet.2014.01.008 [DOI] [PubMed] [Google Scholar]
  4. Berge J, Maclehose R, Loth K, Eisenberg M, Fulkerson J, & Neumark-Sztainer D (2012). Parent-adolescent conversations about eating, physical activity and weight: Prevalences across sociodemographic characteristsics and associations with adolescent weight and weight-realted behaviors. Journal of Behavioral Medicine, 35(5):500–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Berge JM, Rowley S, Trofholz A, Hanson C, Rueter M, MacLehose RF, & Neumark-Sztainer D (2014). Childhood obesity and interpersonal dynamics during family meals. Pediatrics, 134(5), 923–932. 10.1542/peds.2014-1936 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Berge JM, Trofholz A, Tate A, Beebe M, Fertig A, Miner M, … Neumark-Sztainer D (2017). Examining unanswered questions about the home environment and childhood obesity disparities using an incremental, mixed-methods, longitudinal study design: The Family Matters study. Contemp Clin Trials 10.1016/j.cct.2017.08.002 [DOI] [PMC free article] [PubMed]
  7. Biro F, & Wien M (2010). Childhood obesity and adult morbidities. Journal of Clinical Nutrition, 91(5), 1499S–1505S. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Blumberg SJ, Bialostosky K, Hamilton WL, & Briefel RR (1999). The effectiveness of a short form of the Household Food Security Scale. Am J Public Health, 89(8), 1231–1234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bollella M, Boccia L, Nicklas T, Lefkowitz K, Pittman B, Zang E, & Williams C (1998). Assessing dietary intake in preschool children: The healthy start project-New York. Nutrition Research, 19(1), 11. [Google Scholar]
  10. Bruening M, MacLehose R, Loth K, Story M, & Neumark-Sztainer D (2012). Feeding a family in a recession: food insecurity among Minnesota parents. Am J Public Health, 102(3), 520–526. 10.2105/ajph.2011.300390 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Centers for Disease Control and Prevention. (2015). Healthy Weight. Assessing your Weight Retrieved from http://www.cdc.gov/healthyweight/assessing/
  12. Collins C, Watson J, & Burrows T (2009). Measuring dietary intake in children and adolescents in the context of overweight and obesity. International Journal of Obesity, 10.1038/ijo.2009.241. [DOI] [PubMed]
  13. Computer Science and Applications Inc. (1991). Wrist activity monitor technical manual Shalimar, FL: Computer Science and Applications Inc. [Google Scholar]
  14. Crawford D, Ball K, Mishra G, Salmon J, & Timperio A (2007). Which food-related behaviours are associated with healthier intakes of fruits and vegetables among women? Public Health Nutr, 10(3), 256–265. 10.1017/S1368980007246798 [DOI] [PubMed] [Google Scholar]
  15. Creswell JW, Klassen AC, Plano Clark VL, & Clegg Smith K (2011). Best Practices for Mixed Methods Research in the Health Sciences https://obssr-archive.od.nih.gov/mixed_methods_research/. Retrieved from https://obssr-archive.od.nih.gov/mixed_methods_research/
  16. Cuellar I, Arnold B, & R M (1995). Acculturation rating scale for Mexican Americans-II: A revision of the original ARSMA Scale. Hispanic Journal of Behavioal Sciences, 17(3), 275–304. [Google Scholar]
  17. Darmon N, & Drewnowski A (2015). Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: a systematic review and analysis. Nutr Rev, 73(10), 643–660. 10.1093/nutrit/nuv027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Dunton GF, Intille SS, Wolch J, & Pentz MA (2012). Children’s perceptions of physical activity environments captured through ecological momentary assessment: a validation study. Prev Med, 55(2), 119–121. 10.1016/j.ypmed.2012.05.015 [DOI] [PubMed] [Google Scholar]
  19. Dunton GF, Liao Y, Intille SS, Spruijt-Metz D, & Pentz M (2011). Investigating children’s physical activity and sedentary behavior using ecological momentary assessment with mobile phones. Obesity (Silver Spring), 19(6), 1205–1212. 10.1038/oby.2010.302 [DOI] [PubMed] [Google Scholar]
  20. Eston RG, Rowlands AV, & Ingledew DK (1998). Validity of heart rate, pedometry, and accelerometry for predicting the energy cost of children’s activities. J Appl Physiol, 84(1), 362–371. [DOI] [PubMed] [Google Scholar]
  21. Fulkerson JA, Neumark-Sztainer D, & Story M (2006). Adolescent and parent views of family meals. J Am Diet Assoc, 106(4), 526–532. 10.1016/j.jada.2006.01.006 [DOI] [PubMed] [Google Scholar]
  22. Garg SK, Maurer H, Reed K, & Selagamsetty R (2014). Diabetes and cancer: two diseases with obesity as a common risk factor. Diabetes Obes Metab, 16(2), 97–110. 10.1111/dom.12124 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Garrett M, Pichette G, & Pichette F (2000). Red as an apple: Native American acculturation and counseling with or without reservation. Journal of Counseling and Development, 78(1), 3–13. [Google Scholar]
  24. Gim Chung R, Kim B, & Abreu J (2004). Asian American Multidimensional Acculturation Scale: Development, factor analysis, reliability, and validity. Cultural Diversity and Ethnic Minority Psychology, 10(1), 66–80. [DOI] [PubMed] [Google Scholar]
  25. Guenther PM, Kirkpatrick SI, Reedy J, Krebs-Smith SM, Buckman DW, Dodd KW, … Carroll RJ (2014). The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 Dietary Guidelines for Americans. J Nutr, 144(3), 399–407. 10.3945/jn.113.183079 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Healthy People 2020 Objectives. (2014). Retrieved from https://www.healthypeople.gov/2020/topics-objectives
  27. Kitagawa E (1964). Standardized comparisons in population research. Demography, 1(1), 296–315. [Google Scholar]
  28. Landrine H, & Klonoff E (1995). The African American Acculturation Scale II: Cross-Validation and Short Form. Journal of Black Psychology, 21(2), 124–152. [Google Scholar]
  29. Larson N, Neumark-Sztainer D, Harwood EM, Eisenberg ME, Wall M, & Hannan PJ (2011). Do young adults participate in surveys that ‘go green’? Response rates to a web and mailed survey of weight-related behaviors. International Journal of Child Health and Human Development, 4(2), 225–231. [PMC free article] [PubMed] [Google Scholar]
  30. Larson NI, Neumark-Sztainer D, Story M, van den Berg P, & Hannan PJ (2011). Identifying correlates of young adults’ weight behavior: Survey development. American Journal of Health Behavior, 35, 712–725. [PMC free article] [PubMed] [Google Scholar]
  31. Livingston M, & Robson P (2000). Measurement of dietary intake in children. Proceedings of the Nutrition Society, 59(2), 14. [DOI] [PubMed] [Google Scholar]
  32. Lohman T, Roche AF, & Martorell R (Eds.). (1988). Anthropometric Standardization Reference Manual Champaign, IL: Human Kinetics Books. [Google Scholar]
  33. Lohman TG, Roche AF, & Matorell R (1988). Anthropometic standardization reference manual In.
  34. Louie L, Eston RG, Rowlands AV, & et al. (1999). Validity of heart rate, pedometry, and accelerometry for estimating the energy cost of activity in Hong Kong Chinese boys. Pediatr Exerc Sci, 11, 229–239. [Google Scholar]
  35. McPherson RS, Hoelscher DM, Alexander M, Scanlon KS, & Serdula MK (2000a). Dietary assessment methods among school-aged children: Validity and reliability. Prev Med, 31, S11–S33. [Google Scholar]
  36. McPherson RS, Hoelscher DM, Alexander M, Scanlon KS, & Serdula MK (2000b). Dietary Assessment Methods among School-Aged Children: Validity and Reliability Preventive Medicine, 31(2), S11–S33. [Google Scholar]
  37. McPherson S, Hoelscher D, Alexander M, Scanlon K, Serdula M. (2002). Validity and reliability of dietary assessment in school-age children. In C B(Ed.), Handbook of Nutrition and Foods (pp. 495–522). New York: CRC Press. [Google Scholar]
  38. Netemeyer RBJMR (1996). Development and Validation of Work-Family Conflict and Family-Work Conflict Scales. Journal of Applied Psychology, 81(4), 400–410. [Google Scholar]
  39. Neumark-Sztainer D, Wall MM, Haines JI, Story MT, Sherwood NE, & van den Berg PA (2007). Shared risk and protective factors for overweight and disordered eating in adolescents. American Journal of Preventive Medicine, 33(5), 359–369. 10.1016/j.amepre.2007.07.031 [DOI] [PubMed] [Google Scholar]
  40. Phinney J (1992). The Multigroup Ethnic Identity Measure: A new scale for use with diverse groups. Journal of Adolescnet Research, 7(2), 156–176. [Google Scholar]
  41. Podsakoff PM, MacKenzie SB, & Podsakoff NP (2012). Sources of method bias in social science research and recommendations on how to control it. Annu Rev Psychol, 63, 539–569. 10.1146/annurev-psych-120710-100452 [DOI] [PubMed] [Google Scholar]
  42. Reilly JJ, & Kelly J (2011). Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond), 35(7), 891–898. 10.1038/ijo.2010.222 [DOI] [PubMed] [Google Scholar]
  43. Reilly JJ, Methven E, McDowell ZC, Hacking B, Alexander D, Stewart L, & Kelnar CJ (2003). Health consequences of obesity. Arch Dis Child, 88(9), 748–752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sherwood NE, French SA, Veblen-Mortenson S, Crain AL, Berge J, Kunin-Batson A, … Senso M (2013). NET-Works: Linking families, communities and primary care to prevent obesity in preschool-age children. Contemp Clin Trials, 36(2), 544–554. 10.1016/j.cct.2013.09.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Shiffman S, Stone AA, & Hufford MR (2008). Ecological momentary assessment. Annu Rev Clin Psychol, 4, 1–32. [DOI] [PubMed] [Google Scholar]
  46. Sirard JR (2001). Physical activity assessment in children and adolescents. Sports Medicine, 31(6), 439–454. [DOI] [PubMed] [Google Scholar]
  47. Sullivan PW, Morrato EH, Ghushchyan V, Wyatt HR, & Hill JO (2005). Obesity, inactivity, and the prevalence of diabetes and diabetes-related cardiovascular comorbidities in the U.S., 2000–2002. Diabetes Care, 28(7), 1599–1603. [DOI] [PubMed] [Google Scholar]
  48. Thompson AM, Humbert ML, & Mirwald RL (2003). A longitudinal study of the impact of childhood and adolescent physical activity experiences on adult physical activity perceptions and behaviors. Qualitative Health Research, 13(3), 358–377. [DOI] [PubMed] [Google Scholar]
  49. Thompson D, Obarzanek E, Franko D, Barton B, Morrison J, Biro F, … Streigel-Moore R (2007). Childhood overweight and cardiovascular diesase risk factors: The National Health, Lung, and Blood Institute Growth and Health Study. Journal of Pediatrics, 150(1), 18–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Trasande L, Liu Y, Fryer G, & Weitzman M (2009). Effects of childhood obesity on hospital care and costs, 1999–2005. Health Aff (Millwood), 28(4), w751–760. 10.1377/hlthaff.28.4.w751 [DOI] [PubMed] [Google Scholar]
  51. Treuth MS, Sherwood NE, Baranowski T, Butte NF, Jacobs DR Jr., McClanahan B, … Obarzanek E (2004). Physical activity self-report and accelerometry measures from the Girls health Enrichment Multi-site Studies. Prev Med, 38 Suppl, S43–49. [DOI] [PubMed] [Google Scholar]
  52. Trost SG, Ward DS, Moorehead SM, Watson PD, Riner W, & Burke JR (1998). Validity of the computer science and applications (CSA) activity monitor in children. Med Sci Sports Exerc, 30(4), 629–633. [DOI] [PubMed] [Google Scholar]
  53. Vucenik I, & Stains JP (2012). Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann N Y Acad Sci, 1271, 37–43. 10.1111/j.1749-6632.2012.06750.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Winter Y, Rohrmann S, Linseisen J, Lanczik O, Ringleb PA, Hebebrand J, & Back T (2008). Contribution of obesity and abdominal fat mass to risk of stroke and transient ischemic attacks. Stroke, 39(12), 3145–3151. 10.1161/strokeaha.108.523001 [DOI] [PubMed] [Google Scholar]
  55. Withrow D, & Alter DA (2011). The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obes Rev, 12(2), 131–141. 10.1111/j.1467-789X.2009.00712.x [DOI] [PubMed] [Google Scholar]
  56. Zunker C, Peterson CB, Crosby RD, Cao L, Engel SG, Mitchell JE, & Wonderlich SA (2011). Ecological momentary assessment of bulimia nervosa: does dietary restriction predict binge eating? Behav Res Ther, 49(10), 714–717. 10.1016/j.brat.2011.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES