Journal of Pediatric Nursing (2015) 30, e45–e52
Pilot Study of a Computer-Based Parental Questionnaire and Visual Profile of Obesity Risk in Healthy Preschoolers Marilyn A. Davies, PhD, RN a,⁎, Lauren Terhorst, PhD b , Peng Zhang, MS c , Amanda J. Nakonechny, BSN, RN d , Mary Patricia Nowalk, PhD e a
Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh PA Department of Occupational Therapy, University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, PA c Duquesne University, Pittsburgh, PA d ABC Family Pediatricians, Center Valley, PA e Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh PA b
Received 27 August 2014; revised 13 February 2015; accepted 13 February 2015
Key words: Prevention; Childhood obesity; Obesity risk profile; Preschool; Parental questionnaire
Purpose This group field-tested a computer-based, parental questionnaire entitled the Childhood Obesity Risk Questionnaire 2–5 (CORQ 2–5) designed to assess obesity risk in healthy preschoolers. COR 2–5 generates a profile of seven obesity risk factors. Results: Field studies provided good internal reliability data and evidence of discriminant validity for the CORQ 2–5. Pediatric nurse clinicians found the CORQ 2–5 profile to be clinically relevant. Conclusion: The CORQ 2–5 is a promising measure of obesity risk in preschoolers who attend communitybased health centers for their wellchild visits and who are not yet obese. CORQ 2–5 is intended to guide provider–parental obesity risk discussions. © 2015 Elsevier Inc. All rights reserved.
RECENT DATA FROM the 2011–2012 National Health and Nutrition Examination Survey (NHANES) indicate that 16.9% of youth (2–19 years of age) in the United States (U.S.) are obese (defined as a BMI greater than or equal to the age- and sex-specific 95th percentiles of the 2000 Center for Disease control (CDC) growth charts) (Ogden, Carroll, Kit, & Flegal, 2014). This information is a public health concern since the prevalence of obesity in youth has not changed significantly over the last decade (Ogden et al., 2014). However, within the youth population, the prevalence rate for 2–5 year old children (8.4%) indicates a significant decrease from 2003–2004 rate (13.9%) (Ogden et al., 2014), suggesting that efforts to prevent childhood obesity in preschoolers should continue (Wen et al., 2012). ⁎ Corresponding author: Marilyn A. Davies, PhD, RN. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.pedn.2015.02.008 0882-5963/© 2015 Elsevier Inc. All rights reserved.
From a public health perspective, childhood obesity remains a major burden on health care costs and resources as obese children have increased risk of disorders such as diabetes (Santoro, 2013), hypertension (Sorof & Daniels, 2002), orthopedic problems (Wills, 2004), and sleep-related disordered breathing (Wing et al., 2003). The annual U.S. obesity-attributable medical expenditures on childhood obesity were estimated at $75 billion (2003 dollars); without effective prevention programs, health-care costs attributable to childhood obesity are expected to double every decade until 2030 (Wang, Beydoun, Liang, Caballero, & Kumanyika, 2008). In 2010, the Surgeon General's Vision for a Healthy and Fit Nation stressed the important role that primary care providers have in preventing childhood obesity through comprehensive assessments and the use of best practice guidelines (Services, U. S. D. o. H. a. H., 2010). Unfortunately, a significant care gap exists between childhood obesity prevention practice recommendations,
e46 guideline awareness and clinical practice decisions among many primary care providers (Cook, Weitzman, Auinger, & Barlow, 2005; McDonald et al., 2012; O'Brien, Holubkov, & Reis, 2004; Tanda & Salsberry, 2013; Wethington, Sherry, & Polhamus, 2011). Reasons given for this gap include time constraints, lack of resources, lack of self-efficacy, and a belief that obesity prevention efforts should be performed at schools and communities rather than primary care offices (Tanda & Salsberry, 2013). Of relevance to this study are the findings of McDonald and associates (McDonald et al., 2012) who attribute this care gap to a lack of health information technology (HIT) that could be used to improve intervention strategies. However, a technological change does not guarantee improvements in primary prevention efforts. For example, the technological change of automatic calculations of body mass index (BMI) has not resulted in improvements in obesity counseling efforts in primary care centers (Shaikh, Nelson, Tancredi, & Byrd, 2010). Therefore, that challenge of finding HIT changes that result in improvements in primary prevention efforts still exists. This paper presents the development, initial field testing and clinical relevancy of an HIT tool designed to improve obesity prevention efforts of primary care providers who see children aged 2–5 years with BMIs that fall between the 75th–95th percentiles of the 2000 CDC growth charts for their age and gender. Notably, the children in our project were not obese. Their BMIs fell within the 75th–85th percentile (classified for this project as “above normal”), or they fell within the 85th–95th percentile (classified by the CDC as “overweight”). We selected to focus on children within these BMI ranges because we believe that primary care providers should assess obesity risk and initiate obesity prevention efforts well before a preschooler crosses over the BMI threshold of obesity. The technology for this project involves a computerbased questionnaire that asks parents of the healthy preschoolers their perceptions of multiple risk factors for childhood obesity. The questionnaire generates a unique, multi-factorial profile of obesity risk for each preschooler. The long-term goal for developing the profile is to provide a resource to primary care providers that assist them in their obesity prevention efforts during well-child visits (Elwyn et al., 2012; Vine, Hargreaves, Briefel, & Orfield, 2013).
Methods This project occurred from 2009 to 2012 and involved six steps: 1) selection of a vulnerable population, 2) literature review and development of a conceptual framework, 3) initial questionnaire construction and field testing, 4) questionnaire reformulation, 5) obesity risk profile construction, and 6) a clinical relevancy study.
Selection of a Vulnerable Population Preschoolers were selected for study, as many lifetime health behaviors and patterns are established during the preschool years (Andrews, Silk, & Eneli, 2010; Anzman, Rollins, & Birch, 2010; Brotman et al., 2012). In addition, because the project
M.A. Davies et al. focused on primary prevention efforts, the preschoolers were not obese; their BMIs fell between the 75th–95th percentiles of the 2000 CDC growth charts for their age and gender. Lastly, the project focused on preschoolers who attended a communitybased health center. Community-based health centers serve millions of children in the U.S., and children attending these centers represent low income populations, the uninsured, families experiencing homelessness, and those living in public housing (http://bphc.hrsa.gov/about/). The Pediatric Nutrition Surveillance System (PedNSS) 2009 data on over 3 million low-income children between the ages of 2–5 years in federally funded child health programs indicated that 16.4 percent were overweight, and almost 15 percent were obese (Anonymous, 2009). Settler and colleagues found that, regardless of race/ ethnic or geographic characteristics, children aged 2–5 years who used community-based health centers in the US had a much higher prevalence of childhood obesity compared with a representative sample from a national survey (Stettler, Elliott, Kallan, Auerbach, & Kumanyika, 2005).
Literature Review and Conceptual Framework The goal of the literature review was twofold: 1) to determine which risk factors were associated with overweight/ obesity in children aged 2–5 years, and 2) to determine which risk factors could be addressed by a primary care provider during a well-child visit. The primary literature search sites were Pub Med and the Cochrane database; dates of interest were years 2000–2012. Search keywords included childhood overweight, childhood obesity, prevention, risk for childhood obesity and risk for childhood overweight. Results indicated that childhood overweight and obesity had a multi-factorial etiology. Risk factors included genetic predisposition (Walley, Blakemore, & Froguel, 2006; Yamada et al., 2006), embryonic and fetal environment (Salsberry & Reagan, 2005, 2007), socio-demographic factors (i.e. ethnicity and gender) (James, 2005; Martin & Ferris, 2007; Thompson, Rafiroiu, & Sargent, 2003), socioeconomic factors (Kumanyika & Grier, 2006; Vieweg, Johnston, Lanier, Fernandez, & Pandurangi, 2007), history of breastfeeding (Goldfield, Epstein, Kilanowski, Paluch, & Kogut-Bossler, 2001; Koletzko, 2006), rapid growth between birth and age two years (Karaolis-Danckert et al., 2006; Monteiro & Victora, 2005), lifestyle factors (i.e., diet, physical activity, and sedentary activity) (Merchant, Dehghan, Behnke-Cook, & Anand, 2007; Dehghan, Akhtar-Danesh, & Merchant, 2005), child sleep habits (Cappuccio et al., 2008; Currie & Cappuccio, 2007), parental behaviors around eating (Agras, Hammer, McNicholas, & Kraemer, 2004; Faith & Kerns, 2005; Francis, Ventura, Marini, & Birch, 2007; Keller, Pietrobelli, Johnson, & Faith, 2006; Nelson, Gordon-Larsen, North, & Adair, 2006), child temperament (Wasser et al., 2011; Wu, Dixon, Dalton, Tudiver, & Liu, 2011), food security (Gooze, Hughes, Finkelstein, & Whitaker, 2012; Gundersen, Lohman, Garasky, Stewart, & Eisenmann, 2008), and the built environment (Oreskovic, Winickoff, Kuhlthau, Romm, & Perrin, 2009; Rahman, Cushing, & Jackson, 2011).
Parental Questionnaire and Visual Profile of Obesity Risk The research team then determined which risk factors could be best addressed during a primary care, well-child visit. The team developed a conceptual framework to guide this project (Figure 1), which was a modification of an ecological model for understanding overweight that was proposed by Swinburn and colleagues (Swinburn, Gill, & Kumanyika, 2005). In the model, risk factors for obesity are classified as either ‘obesogenic’ (promoting overweight) or ‘leptogenic’ (promoting healthy life choices). The model allows for biological, behavioral and environmental risk factors to work independently and in interaction with each other to influence a child's weight status during the preschool period of life.
Initial Questionnaire Construction and Field Testing The research team decided on a parental questionnaire for data collection because parents usually spend most time with the preschool child, and they are primarily responsible for establishing and maintaining health practices for very young children (Olstad & McCargar, 2009). A search of the published literature yielded no parental questionnaire that collected information on all of the obesity risk factors of interest; therefore, this research team constructed a questionnaire for this project, entitled the Child Obesity Risk Questionnaire 2–5 (CORQ 2–5). Items for the CORQ 2–5 were selected from five well-established questionnaires and from one national survey (Table 1). All instruments demonstrated face validity for collecting information on a risk factor of interest. The questionnaires had adequate to good psychometric properties, and they showed evidence of administration to parents of young children. The questionnaires included: 1) Children's Eating Behavior Questionnaire (CEBQ) (Wardle, G., Sanderson, & Rapoport, 2001), 2) Children's Behavior Questionnaire (CBQ) (Putman & R. M., 2006), 3) the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (Colder & O'Connor, 2004), 4) Children's Sleep Habits Questionnaire (CSHQ) (Owens, Spirito, & McGuinn, 2000), and 4) Caregiver Feeding Style Questionnaire (CFSQ) (Patrick, Nicklas, Hughes, & Morales, 2005). The research team selected
Figure 1
e47 four questions from the U.S. Household Food Security Scale (Blumberg, Bialostosky, Hamilton, & Briefel, 1999), which was used in several national surveys and classified almost 98% of households correctly. Notably, the literature search produced no questionnaire that assessed parental perceptions of the built environment; therefore, the research team constructed seven questions that asked parents their perceptions of neighborhood traffic, stranger danger, road safety, recreational venues and access to public transportation. Approval from the university's IRB was obtained for two separately funded and consecutive studies to examine psychometric properties of the CORQ 2–5. Both studies were conducted at a federally qualified, hospital-based community health care center located in an urban neighborhood in Southwestern Pennsylvania. The purpose of the first field study was to examine the reliability of the instrument; the purpose of the second field study was to examine its validity. Free and informed consents were obtained from parents, who represented a convenience sample. Parents met the following inclusion criteria: 1) biological parent of a child aged 2–5 years (inclusive) who fell within the 75th–95th BMI percentile for their age and sex, 2) lived with the child in a low-income household, as defined by a household whose income does not exceed 80 percent of the median income for the area, as determined by housing and urban development (HUD), with adjustments for smaller or larger families; and 3) spoke English (questionnaire was in English). A parent was excluded if: 1) their child's medical record indicated a medical condition or psychological condition that interfered with sleep or eating and/or caused excess weight gain, and 2) the child was ever enrolled in a weight loss/weight management program. Statistical analyses for two field studies were conducted using SPSS 12 statistical package (2012). Results Field Study 1: Eight parents participated in this first study (Table 2). Results indicated a lack of variability for Child Activity as parents reported that all children were within a
Conceptual framework for CORQ 2–5.
e48 Table 1
M.A. Davies et al. Self-report instruments used to develop 45-item CORS and their psychometric properties.
Instrument/Construct
Reliability/Validity
Selected items
Children's Eating Behavior Questionnaire (CEBQ)[/satiety responsiveness
Good test–retest reliability (.52–.87); good internal reliability (Chronbach's alpha .4–.91) good concurrent validity (explained 33%–56% of variance of behavioral measures of eating) Adequate internal consistency (ranged .64–.92); convergent validity (parental agreement) mean 0.51; evidence of predictive validity with social and laboratory behaviors Adequate internal consistency (ranged for .69–.87)
5
Adequate internal consistency for both a community sample(p = 0.68) and a clinic sample (u = 0/78) [59]; alpha coefficients ranged from 0.36–0.93., test–retest reliability (0.62–0.79); good discriminant validity[59] Test–Retest reliability (.82–.85), internal consistency (.71–.86); good evidence of convergent validity A score was developed by adding up affirmative responses to six questions A score was developed by adding up affirmative responses to seven questions
1
Children's Behavior Questionnaire (CBQ)/activity level The Sensitivity to Punishment and Sensitivity to Reward Questionnaire/child temperament Children's Sleep Habits Questionnaire (CSHQ)/usual hours of sleep per night
Caregiver Feeding Style Questionnaire (CFSQ)/parental feeding practices US Household Food Security Scale ref/food security Built environment (no instrument found)
normal range of activity. Internal consistency reliability was evaluated by computing Cronbach's alpha representing five components of the CORQ 2–5 (Table 3). Four components (child temperament, child eating behaviors, parental behaviors at mealtime and food security) had good internal consistency (Nunnally & B., 1994); the lower estimate for the Built Environment subscale was most likely due to the small sample size. Results Field Study 2: A second field study was independently funded to examine the discriminant validity of the CORQ 2–5. Sixteen parents participated in this study (Table 2). Discriminant validity was established by correlating component scores of the CORQ 2–5 with the overall score of the Preschool Feelings Checklist (PFC) (Luby, H, Koenig-McNaught, Brown & Spitznagel, 2004), which is a 16-item parent report that identifies preschoolers Table 2
Demographics of parents in field testing studies.
VARIABLE Gender Male Female Marital status Married Divorced Never married Unknown Education Attended high school Completed high school Attended college Completed college Attended post grad school Unknown
STUDY 1 (N = 8)
STUDY 2 (N = 16)
n
%
n
%
2 6
25.00 75.00
2 14
12.50 87.50
2 0 6 0
25.00 0.00 75.00 0.00
2 2 11 1
12.50 12.50 68.75 6.25
2 2 2 1 1 0
25.00 25.00 25.00 12.50 12.50 0.00
4 3 3 2 2 2
25.00 18.75 18.75 12.50 12.50 12.50
7
7
19 6 7
(ages 3.0–5.6 years) with symptoms of depression (Table 4). The Child Activity and Built Environment components were excluded from the correlation analysis due to lack of response variability; all parents reported that their child was active and their built environment was obesogenic.
Questionnaire Reformulation Based on findings from the two field studies, a revised version CORQ 2–5 now contains 47-items with some items recoded for scoring (see Appendix A). Two items from the Built Environment component were excluded due to lack of variability in both field studies; all parents indicated a strong agreement with “There are few sporting venues within our local areas” and “Public transportation is limited in my area”. Also, given emerging evidence for the genetic predisposition to obesity, the research team constructed a Family History component, which includes three questions: 1) consenting parent's height, 2) consenting parent's weight, and (3) whether the other biological parent or any biological grandparent is overweight or obese (yes/no).
Obesity Risk Profile Construction The web application researcher (PZ) established a computer-based algorithm by which the 47 items of the CORQ 2–5 resulted in eight component risk factor scores Table 3
Internal consistency reliabilities of CORQ 2–5 components.
CORQ 2–5 component
Number of items
Internal consistency estimate
Child temperament Child eating behaviors Mealtime Food security Built environment Sleep
5 7 21 4 7 1
.879 .858 .936 .940 .614 –
Parental Questionnaire and Visual Profile of Obesity Risk
e49
Table 4 Correlations between CORQ 2–5 components and PFC score. Components
Correlation with PFC
p-value
Child temperament Child eating behaviors Mealtime Food security Sleep
.330 .301 .079 .113 − .014
.213 .257 .770 .676 .960
depending on how items were answered (Table 5). Next, the PI and the web application researcher established criteria for the presence of an obesogenic risk factor (promoting overweight) based on component scores. The web application researcher then programmed the computer to generate a visual image of an obesity risk profile (Figure 2). A diamond next to a risk factor indicated the presence of an obesogenic risk factor. Figure 2
Clinical Relevancy Study Approval from the university's IRB was obtained for this study. Three different risk profiles were presented to 52 volunteer, advanced practice nurses who attended the 2012 International Pediatric Nursing Conference. The nurses were asked to consider three clinical decisions after reviewing each profile: a) no action needed at this time, b) spend part of appointment time addressing area(s) of obesity risk, or c) referral for follow-up care to help manage obesity risk factors. Inter-rater reliability was assessed using joint probability agreement (i.e., percentages for each level of referral for each scenario) and intraclass correlation statistics for the three scenarios. Results indicated a high inter-rater reliability of .72, .80 and .82 respectively for the same clinical decision for the three profiles.
Conclusions The CORQ 2–5 and its visual profile move the field of childhood obesity prevention forward by: 1) challenging a “one size fits all” model of risk for childhood obesity, 2) incorporating evidence-based findings related to multiple
Table 5
Scoring algorithm for final CORQ 2–5.
Component
Number of items
Obesogenic risk factor
Family history Child temperament Child activity Child eating behaviors Family practices During mealtime Food security Built environment Hours of sleep Total
3 5 7 5 16
N1 N 5 and b 11 N 7 and b 15 N 5 and b 11 N 16 and b 33 N 4 and b 9 N 4 and b 9 N 4 and b 9 b 11
4 4 3 47
Example of a child's obesity risk profile.
biological, behavioral and environmental risk factors for childhood obesity, and 3) making use of information technology to create a computer-based, multi-factorial profile of obesity risk. To our knowledge, no assessment and profile of obesity risk in preschoolers include all of the risk factors in this study. A limitation of this project was that parents in the two field studies represented those who lived in the same low-income neighborhood, those who took their child to a hospital-based community health center for their health care, and those who were willing to participate in a study about factors associated with obesity in children. Also, fathers were underrepresented. Clearly, further field testing of the 47-item questionnaire is indicated to test and establish its psychometric properties before implementation in primary care settings. Further studies that include larger, more diverse samples from other inner-city neighborhoods would confirm the psychometric properties of the 47-item CORQ 2–5 and yield results that could be generalized to a wider population. Potentially, clinicians and researchers could incorporate the CORQ 2–5 and its profile in the design of longitudinal studies or quality improvement projects of obesity prevention efforts during the preschool period of life. Once established, a visual profile of a child's risk for obesity could offer providers an opportunity to educate parents of preschoolers about the risk factors for childhood obesity during this important developmental period of life and to engage parents in shared decision making related to managing the risk for obesity, depending on a child's current health status and family life circumstances.
Acknowledgments Funding Sources: Sigma Theta Tau International, Eta Chapter Perkins Kuehn Memorial Fund, University of Pittsburgh School of Nursing Central Research Development Fund, University of Pittsburgh.
e50
M.A. Davies et al.
Appendix A. Child Obesity Risk Questionnaire
2–5
(CORQ)
2–5.
Please circle your agreement on each item using the five-point scale.
Strongly agree
Agree
Disagree
Strongly disagree
1. 2. 3. 4. 5. 6. ⁎ 7. 8. ⁎9. ⁎ 10. 11. 12. ⁎
My child often does things for praise. My child enjoys being the center of attention. In a group, my child tries to stand out as the smartest or the funniest. When my child gets something he wants, he feels excited and energized. My child likes activities that involve a reward right away. My child seems to always be in a big hurry to get from one place to another. My child sometimes prefers to watch rather than join other children playing. My child has trouble sitting still before an exciting event. My child tends to run rather than walk from room to room. When my child is outside, he often sits quietly. My child prefers quiet activities to active games. My child is full of energy, even in the evening.
1 1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3 3
4 4 4 4 4 4 4 4 4 4 4 4
13. ⁎ 14. 15. ⁎ 16. ⁎ 17. ⁎
These next questions are about your child's eating behaviors. My child gets full easily. My child has a big appetite. My child leaves food on his plate at the end of the meal. My child gets full before his meal is finished. My child cannot eat a meal if he has had a snack just before.
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1
2
3
4
1
2
3
4
1
2
3
4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1
2
3
4
1
2
3
4
1 1
2 2
3 3
4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
2
3
4
18. 19. 20. 21. 22. 23. 24. 25. ⁎ 26. 27.
28. 29. 30. 31. 32. 33.
Please circle a response relating to your behavior. I physically struggle with my child to get him to eat. For example, I put my child in the chair so he or she will eat. I promise my child something other than food if he eats. For example, “If you eat your beans, we can play ball after dinner”. I encourage my child to eat by arranging the food to make it more interesting. For example, I make smiley faces on the pancakes. I ask my child questions about the food during dinner. I tell my child to eat at least a little bit of food on his plate. I reason with my child to get him to eat. For example, “Milk will make you strong”. I say something to show my disapproval of my child for not eating dinner. I allow my child to choose the foods he wants to eat for dinner from foods already prepared. I compliment my child for eating food. For example, “What a good boy! You are eating your beans”. I warn my child that I will take away something other than food if he does not eat. For example, “If you do not finish your meat, there will be no play time after dinner”. I tell my child to eat something on the plate. For example, “Eat your beans”. I warn my child that I will take a food away if my child does not eat. For example, “If you do not finish your vegetables, you will not get fruit”. I say something positive about the food my child is eating during dinner. I help my child to eat dinner. For example, I cut the food into smaller pieces. I encourage my child to eat something by using food as a reward. For example, “If you finish your vegetables, you will get some fruit”. I beg my child to eat dinner.
1 1 1
2 2 2
3 3 3
4 4 4
37.
Please circle your response regarding your food situation in the last 12 months I worried whether my food would run out before I got money to buy more. The food that I bought just did not last. I did not have money to get more. I bought a few kinds of low-cost food to feed my child because I was running out of money. I cannot feed my child a healthy meal because I cannot afford that.
1
2
3
4
38.
Please rate your neighborhood where you live: There is heavy traffic in our local streets.
1
2
3
4
34. 35. 36.
Parental Questionnaire and Visual Profile of Obesity Risk
e51
(continued)
39. 40. 41. 42.
45. 46. 47.
Please circle your agreement on each item using the five-point scale.
Strongly agree
Agree
Disagree
Strongly disagree
Stranger danger is a concern to me. Road safety is a concern in our area. There are no lights or crossings for my child to use. My child would have to cross several roads to get to the play areas.
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 4
Please indicate your child's usual time for: 43. Bedtime ____________PM 44. Awakening __________AM Please indicate your height: ____feet ____inches Please indicate your weight: _______pounds Is anyone in your child's immediate family (parents or grandparents) overweight or obese? Please circle Yes or No ⁎ Recode.
References Agras, W. S., Hammer, L. D., McNicholas, F., & Kraemer, H. C. (2004). Risk factors for childhood overweight: A prospective study from birth to 9.5 years. The Journal of Pediatrics, 145, 20–25. Andrews, K. R., Silk, K. S., & Eneli, I. U. (2010). Parents as health promoters: A theory of planned behavior perspective on the prevention of childhood obesity. Journal of Health Communication, 15, 95–107, http://dx.doi.org/10.1080/10810730903460567 (918961188 [pii]). Obesity prevalence among low-income, preschool-aged children - United States, 1998-2008. MMWR. Morbidity and Mortality Weekly Report, 58(2009)., 769–773 (doi:mm5828a1 [pii]). Anzman, S. L., Rollins, B. Y., & Birch, L. L. (2010). Parental influence on children's early eating environments and obesity risk: Implications for prevention. International Journal of Obesity, 34, 1116–1124, http://dx. doi.org/10.1038/ijo.2010.43 (ijo201043 [pii]). Blumberg, S. J., Bialostosky, K., Hamilton, W. L., & Briefel, R. R. (1999). The effectiveness of a short form of the Household Food Security Scale. American Journal of Public Health, 89, 1231–1234. Brotman, L. M., et al. (2012). Early childhood family intervention and long-term obesity prevention among high-risk minority youth. Pediatrics, 129, e621–e628, http://dx.doi.org/10.1542/peds.2011-1568 [peds.2011-1568 [pii]]. Cappuccio, F. P., et al. (2008). Meta-analysis of short sleep duration and obesity in children and adults. Sleep, 31, 619–626. Colder, C. R., & O'Connor, R. M. (2004). Gray's reinforcement sensitivity model and child psychopathology: Laboratory and questionnaire assessment of the BAS and BIS. Journal of Abnormal Child Psychology, 32, 435–451. Cook, S., Weitzman, M., Auinger, P., & Barlow, S. E. (2005). Screening and counseling associated with obesity diagnosis in a national survey of ambulatory pediatric visits. Pediatrics, 116, 112–116. Currie, A., & Cappuccio, F. P. (2007). Sleep in children and adolescents: A worrying scenario: can we understand the sleep deprivation-obesity epidemic? Nutrition, Metabolism, and Cardiovascular Diseases, 17, 230–232, http://dx.doi.org/10.1016/j.numecd.2006.10.007 (S09394753(06)00232-8 [pii]). Dehghan, M., Akhtar-Danesh, N., & Merchant, A. T. (2005). Childhood obesity, prevalence and prevention. Nutrition Journal, 4, 24, http://dx. doi.org/10.1186/1475-2891-4-24 (1475-2891-4-24 [pii]). Elwyn, G., et al. (2012). Shared decision making: A model for clinical practice. Journal of General Internal Medicine, 27, 1361–1367, http:// dx.doi.org/10.1007/s11606-012-2077-6.
Faith, M. S., & Kerns, J. (2005). Infant and child feeding practices and childhood overweight: The role of restriction. Maternal & Child Nutrition, 1, 164–168, http://dx.doi.org/10.1111/j.1740-8709.2005.00024.x (MCN24 [pii]). Francis, L. A., Ventura, A. K., Marini, M., & Birch, L. L. (2007). Parent overweight predicts daughters' increase in BMI and disinhibited overeating from 5 to 13 years. Obesity (Silver Spring), 15, 1544–1553, http://dx.doi.org/10.1038/oby.2007.183 (15/6/1544 [pii]). Goldfield, G. S., Epstein, L. H., Kilanowski, C. K., Paluch, R. A., & Kogut-Bossler, B. (2001). Cost-effectiveness of group and mixed family-based treatment for childhood obesity. International Journal of Obesity and Related Metabolic Disorders, 25, 1843–1849, http://dx.doi. org/10.1038/sj.ijo.0801838. Gooze, R. A., Hughes, C. C., Finkelstein, D. M., & Whitaker, R. C. (2012). Obesity and food insecurity at the same table: How head start programs respond. Preventing Chronic Disease, 9, E132 (doi:E132 [pii]). Gundersen, C., Lohman, B. J., Garasky, S., Stewart, S., & Eisenmann, J. (2008). Food security, maternal stressors, and overweight among lowincome US children: Results from the National Health and Nutrition Examination Survey (1999-2002). Pediatrics, 122, e529–e540, http:// dx.doi.org/10.1542/peds.2008-0556 (122/3/e529 [pii]). James, W. P. (2005). The policy challenge of coexisting undernutrition and nutrition-related chronic diseases. Maternal & Child Nutrition, 1, 197–203. Karaolis-Danckert, N., Buyken, A. E., Bolzenius, K., deFaria, C. P., Lentze, M. J., & Kroke, A. (2006). Rapid growth among term children whose birth weight was appropriate for gestational age has a longer lasting effect on body fat percentage than on body mass index. American Journal of Clinical Nutrition, 84, 1449–1455. Keller, K. L., Pietrobelli, A., Johnson, S. L., & Faith, M. S. (2006). Maternal restriction of children's eating and encouragements to eat as the 'non-shared environment': A pilot study using the child feeding questionnaire. International Journal of Obesity, 30, 1670–1675. Koletzko, B. (2006). Long-term consequences of early feeding on later obesity risk. Nestlé Nutrition Workshop Series. Paediatric Programme, 58, 1–18. Kumanyika, S., & Grier, S. (2006). Targeting interventions for ethnic minority and low-income populations. Future of Children, 16, 187–207. Luby, J. L., H, A., Koenig-McNaught, A. L., Brown, K., & Spitznagel, E. (2004). The Preschool Feelings Checklist: A brief and sensitive screening measure for depression in young children. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 708–717. Martin, K. S., & Ferris, A. M. (2007). Food insecurity and gender are risk factors for obesity. Journal of Nutrition Education and Behavior, 39, 31–36.
e52 McDonald, J., et al. (2011). Health information technology to guide pediatric obesity management. Clinical Pediatrics, 50, 543–549. Merchant, A. T., Dehghan, M., Behnke-Cook, D., & Anand, S. S. (2007). Diet physical activity, and adiposity in children in poor and rich neighbourhoods: A cross-sectional comparison. Nutrition Journal, 6, http://dx.doi.org/10.1186/1475-2891-6-1 (1475-2891-6-1 [pii]). Monteiro, P. O., & Victora, C. G. (2005). Rapid growth in infancy and childhood and obesity in later life–A systematic review. Obesity Reviews, 6, 143–154. Nelson, M. C., Gordon-Larsen, P., North, K. E., & Adair, L. S. (2006). Body mass index gain, fast food, and physical activity: Effects of shared environments over time. Obesity (Silver Spring), 14, 701–709, http:// dx.doi.org/10.1038/oby.2006.80 (14/4/701 [pii]). Nunnally, J., & B., I. (1994). Psychometric theory (3rd ed.). McGraw Hill. O'Brien, S. H., Holubkov, R., & Reis, E. C. (2004). Identification, evaluation, and management of obesity in an academic primary care center. Pediatrics, 114, e154–e159. Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA, 311, 806–814, http://dx.doi.org/10.1001/jama.2014.732 (1832542 [pii]). Olstad, D. L., & McCargar, L. (2009). Prevention of overweight and obesity in children under the age of 6 years. Applied Physiology, Nutrition and Metabolism, 34, 551–570, http://dx.doi.org/10.1139/H09-016 (h09-016 [pii]). Oreskovic, N. M., Winickoff, J. P., Kuhlthau, K. A., Romm, D., & Perrin, J. M. (2009). Obesity and the built environment among Massachusetts children. Clinical pediatrics, 48, 904–912, http://dx.doi.org/10.1177/ 0009922809336073 (0009922809336073 [pii]). Owens, J. A., Spirito, A., & McGuinn, M. (2000). The Children's Sleep Habits Questionnaire (CSHQ): Psychometric properties of a survey instrument for school-aged children. Sleep, 23, 1043–1051. Patrick, H., Nicklas, T. A., Hughes, S. O., & Morales, M. (2005). The benefits of authoritative feeding style: Caregiver feeding styles and children's food consumption patterns. Appetite, 44, 243–249. Putman, S. P., & R. M. (2006). Development of short and very short forms of the children’s behavior questionnaire. Journal of Personality Assessment, 87, 102–112. Rahman, T., Cushing, R. A., & Jackson, R. J. (2011). Contributions of built environment to childhood obesity. Mount Sinai Journal of Medicine, 78, 49–57, http://dx.doi.org/10.1002/msj.20235. Salsberry, P. J., & Reagan, P. B. (2005). Dynamics of early childhood overweight. Pediatrics, 116, 1329–1338. Salsberry, P. J., & Reagan, P. B. (2007). Taking the long view: The prenatal environment and early adolescent overweight. Research in Nursing and Health, 30, 297–307. Santoro, N. (2013). Childhood obesity and type 2 diabetes: The frightening epidemic. World Journal of Pediatrics, 9, 101–102, http://dx.doi.org/ 10.1007/s12519-013-0410-8. Services, U. S. D. o. H. a. H. (2010). The Surgeon General's vision for a healthy and fit. Nation, 10. Shaikh, U., Nelson, R., Tancredi, D., & Byrd, R. S. (2010). Presentation of body mass index within an electronic health record to improve weight assessment and counselling in children and adolescents. Informatics in Primary Care, 18, 235–244. Sorof, J., & Daniels, S. (2002). Obesity hypertension in children: A problem of epidemic proportions. Hypertension, 40, 441–447.
M.A. Davies et al. Stettler, N., Elliott, M. R., Kallan, M. J., Auerbach, S. B., & Kumanyika, S. K. (2005). High prevalence of overweight among pediatric users of community health centers. Pediatrics, 116, e381–e388, http://dx.doi. org/10.1542/peds.2005-0104 (116/3/e381 [pii]). Swinburn, B., Gill, T., & Kumanyika, S. (2005). Obesity prevention: A proposed framework for translating evidence into action. Obesity Reviews, 6, 23–33. Tanda, R., & Salsberry, P. (2013). The Impact of the 2007 Expert Committee Recommendations on Childhood Obesity Preventive Care in Primary Care Settings in the United States. Journal of Pediatric Health Care, http://dx. doi.org/10.1016/j.pedhc.2013.05.009 (S0891-5245(13)00196-X [pii]). Thompson, S. H., Rafiroiu, A. C., & Sargent, R. G. (2003). Examining gender, racial, and age differences in weight concern among third, fifth, eighth, and eleventh graders. Eating Behaviors, 3, 307–323 (doi: S1471015302000934 [pii]). Vieweg, V. R., Johnston, C. H., Lanier, J. O., Fernandez, A., & Pandurangi, A. K. (2007). Correlation between high risk obesity groups and low socioeconomic status in school children. Southern Medical Journal, 100, 8–13. Vine, M., Hargreaves, M. B., Briefel, R. R., & Orfield, C. (2013). Expanding the role of primary care in the prevention and treatment of childhood obesity: A review of clinic- and community-based recommendations and interventions. Journal of Obesity, 172035, http://dx.doi. org/10.1155/2013/172035. Walley, A. J., Blakemore, A. I., & Froguel, P. (2006). Genetics of obesity and the prediction of risk for health. Human Molecular Genetics, 15, R124–R130, http://dx.doi.org/10.1093/hmg/ddl215 (Spec No 2, 15/ suppl_2/R124 [pii]). Wang, Y., Beydoun, M. A., Liang, L., Caballero, B., & Kumanyika, S. K. (2008). Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring), 16, 2323–2330, http://dx.doi.org/10.1038/oby.2008.351 (oby2008351 [pii]). Wardle, J., G., C., Sanderson, S., & Rapoport, L. (2001). Development of the Children's Eating Behaviour Questionnaire. Journal of Child Psychology and Psychiatry, 42, 963–970. Wasser, H., et al. (2011). Infants perceived as "fussy" are more likely to receive complementary foods before 4 months. Pediatrics, 127, 229–237, http:// dx.doi.org/10.1542/peds.2010-0166 (peds.2010-0166 [pii]). Wen, X., Gillman, M. W., Rifas-Shiman, S. L., Sherry, B., Kleinman, K., & Taveras, E. M. (2012). Decreasing prevalence of obesity among young children in Massachusetts from 2004 to 2008. Pediatrics, 129, 823–831, http://dx.doi.org/10.1542/peds.2011-1833 (peds.2011-1833 [pii]). Wethington, H. R., Sherry, B., & Polhamus, B. (2011). Physician practices related to use of BMI-for-age and counseling for childhood obesity prevention: A cross-sectional study. BMC Family Practice, 12, 80. Wills, M. (2004). Orthopedic complications of childhood obesity. Pediatric Physical Therapy, 16, 230–235, http://dx.doi.org/10.1097/01.PEP. 0000145911.83738.C600001577-200401640-00007. Wing, Y. K., et al. (2003). A controlled study of sleep related disordered breathing in obese children. Archives of Disease in Childhood, 88, 1043–1047. Wu, T., Dixon, W. E., Jr., Dalton, W. T., III, Tudiver, F., & Liu, X. (2011). Joint effects of child temperament and maternal sensitivity on the development of childhood obesity. Maternal and Child Health Journal, 15, 469–477, http://dx.doi.org/10.1007/s10995-010-0601-z. Yamada, Y., et al. (2006). Genetic factors for obesity. International Journal of Molecular Medicine, 18, 843–851.
Pilot Study of a Computer-Based Parental Questionnaire and Visual Profile of Obesity Risk in Healthy Preschoolers Marilyn A. Davies, PhD, RN a,⁎, Lauren Terhorst, PhD b , Peng Zhang, MS c , Amanda J. Nakonechny, BSN, RN d , Mary Patricia Nowalk, PhD e a
Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh PA Department of Occupational Therapy, University of Pittsburgh School of Health and Rehabilitation Sciences, Pittsburgh, PA c Duquesne University, Pittsburgh, PA d ABC Family Pediatricians, Center Valley, PA e Department of Family Medicine, University of Pittsburgh School of Medicine, Pittsburgh PA b
Received 27 August 2014; revised 13 February 2015; accepted 13 February 2015
Key words: Prevention; Childhood obesity; Obesity risk profile; Preschool; Parental questionnaire
Purpose This group field-tested a computer-based, parental questionnaire entitled the Childhood Obesity Risk Questionnaire 2–5 (CORQ 2–5) designed to assess obesity risk in healthy preschoolers. COR 2–5 generates a profile of seven obesity risk factors. Results: Field studies provided good internal reliability data and evidence of discriminant validity for the CORQ 2–5. Pediatric nurse clinicians found the CORQ 2–5 profile to be clinically relevant. Conclusion: The CORQ 2–5 is a promising measure of obesity risk in preschoolers who attend communitybased health centers for their wellchild visits and who are not yet obese. CORQ 2–5 is intended to guide provider–parental obesity risk discussions. © 2015 Elsevier Inc. All rights reserved.
RECENT DATA FROM the 2011–2012 National Health and Nutrition Examination Survey (NHANES) indicate that 16.9% of youth (2–19 years of age) in the United States (U.S.) are obese (defined as a BMI greater than or equal to the age- and sex-specific 95th percentiles of the 2000 Center for Disease control (CDC) growth charts) (Ogden, Carroll, Kit, & Flegal, 2014). This information is a public health concern since the prevalence of obesity in youth has not changed significantly over the last decade (Ogden et al., 2014). However, within the youth population, the prevalence rate for 2–5 year old children (8.4%) indicates a significant decrease from 2003–2004 rate (13.9%) (Ogden et al., 2014), suggesting that efforts to prevent childhood obesity in preschoolers should continue (Wen et al., 2012). ⁎ Corresponding author: Marilyn A. Davies, PhD, RN. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.pedn.2015.02.008 0882-5963/© 2015 Elsevier Inc. All rights reserved.
From a public health perspective, childhood obesity remains a major burden on health care costs and resources as obese children have increased risk of disorders such as diabetes (Santoro, 2013), hypertension (Sorof & Daniels, 2002), orthopedic problems (Wills, 2004), and sleep-related disordered breathing (Wing et al., 2003). The annual U.S. obesity-attributable medical expenditures on childhood obesity were estimated at $75 billion (2003 dollars); without effective prevention programs, health-care costs attributable to childhood obesity are expected to double every decade until 2030 (Wang, Beydoun, Liang, Caballero, & Kumanyika, 2008). In 2010, the Surgeon General's Vision for a Healthy and Fit Nation stressed the important role that primary care providers have in preventing childhood obesity through comprehensive assessments and the use of best practice guidelines (Services, U. S. D. o. H. a. H., 2010). Unfortunately, a significant care gap exists between childhood obesity prevention practice recommendations,
e46 guideline awareness and clinical practice decisions among many primary care providers (Cook, Weitzman, Auinger, & Barlow, 2005; McDonald et al., 2012; O'Brien, Holubkov, & Reis, 2004; Tanda & Salsberry, 2013; Wethington, Sherry, & Polhamus, 2011). Reasons given for this gap include time constraints, lack of resources, lack of self-efficacy, and a belief that obesity prevention efforts should be performed at schools and communities rather than primary care offices (Tanda & Salsberry, 2013). Of relevance to this study are the findings of McDonald and associates (McDonald et al., 2012) who attribute this care gap to a lack of health information technology (HIT) that could be used to improve intervention strategies. However, a technological change does not guarantee improvements in primary prevention efforts. For example, the technological change of automatic calculations of body mass index (BMI) has not resulted in improvements in obesity counseling efforts in primary care centers (Shaikh, Nelson, Tancredi, & Byrd, 2010). Therefore, that challenge of finding HIT changes that result in improvements in primary prevention efforts still exists. This paper presents the development, initial field testing and clinical relevancy of an HIT tool designed to improve obesity prevention efforts of primary care providers who see children aged 2–5 years with BMIs that fall between the 75th–95th percentiles of the 2000 CDC growth charts for their age and gender. Notably, the children in our project were not obese. Their BMIs fell within the 75th–85th percentile (classified for this project as “above normal”), or they fell within the 85th–95th percentile (classified by the CDC as “overweight”). We selected to focus on children within these BMI ranges because we believe that primary care providers should assess obesity risk and initiate obesity prevention efforts well before a preschooler crosses over the BMI threshold of obesity. The technology for this project involves a computerbased questionnaire that asks parents of the healthy preschoolers their perceptions of multiple risk factors for childhood obesity. The questionnaire generates a unique, multi-factorial profile of obesity risk for each preschooler. The long-term goal for developing the profile is to provide a resource to primary care providers that assist them in their obesity prevention efforts during well-child visits (Elwyn et al., 2012; Vine, Hargreaves, Briefel, & Orfield, 2013).
Methods This project occurred from 2009 to 2012 and involved six steps: 1) selection of a vulnerable population, 2) literature review and development of a conceptual framework, 3) initial questionnaire construction and field testing, 4) questionnaire reformulation, 5) obesity risk profile construction, and 6) a clinical relevancy study.
Selection of a Vulnerable Population Preschoolers were selected for study, as many lifetime health behaviors and patterns are established during the preschool years (Andrews, Silk, & Eneli, 2010; Anzman, Rollins, & Birch, 2010; Brotman et al., 2012). In addition, because the project
M.A. Davies et al. focused on primary prevention efforts, the preschoolers were not obese; their BMIs fell between the 75th–95th percentiles of the 2000 CDC growth charts for their age and gender. Lastly, the project focused on preschoolers who attended a communitybased health center. Community-based health centers serve millions of children in the U.S., and children attending these centers represent low income populations, the uninsured, families experiencing homelessness, and those living in public housing (http://bphc.hrsa.gov/about/). The Pediatric Nutrition Surveillance System (PedNSS) 2009 data on over 3 million low-income children between the ages of 2–5 years in federally funded child health programs indicated that 16.4 percent were overweight, and almost 15 percent were obese (Anonymous, 2009). Settler and colleagues found that, regardless of race/ ethnic or geographic characteristics, children aged 2–5 years who used community-based health centers in the US had a much higher prevalence of childhood obesity compared with a representative sample from a national survey (Stettler, Elliott, Kallan, Auerbach, & Kumanyika, 2005).
Literature Review and Conceptual Framework The goal of the literature review was twofold: 1) to determine which risk factors were associated with overweight/ obesity in children aged 2–5 years, and 2) to determine which risk factors could be addressed by a primary care provider during a well-child visit. The primary literature search sites were Pub Med and the Cochrane database; dates of interest were years 2000–2012. Search keywords included childhood overweight, childhood obesity, prevention, risk for childhood obesity and risk for childhood overweight. Results indicated that childhood overweight and obesity had a multi-factorial etiology. Risk factors included genetic predisposition (Walley, Blakemore, & Froguel, 2006; Yamada et al., 2006), embryonic and fetal environment (Salsberry & Reagan, 2005, 2007), socio-demographic factors (i.e. ethnicity and gender) (James, 2005; Martin & Ferris, 2007; Thompson, Rafiroiu, & Sargent, 2003), socioeconomic factors (Kumanyika & Grier, 2006; Vieweg, Johnston, Lanier, Fernandez, & Pandurangi, 2007), history of breastfeeding (Goldfield, Epstein, Kilanowski, Paluch, & Kogut-Bossler, 2001; Koletzko, 2006), rapid growth between birth and age two years (Karaolis-Danckert et al., 2006; Monteiro & Victora, 2005), lifestyle factors (i.e., diet, physical activity, and sedentary activity) (Merchant, Dehghan, Behnke-Cook, & Anand, 2007; Dehghan, Akhtar-Danesh, & Merchant, 2005), child sleep habits (Cappuccio et al., 2008; Currie & Cappuccio, 2007), parental behaviors around eating (Agras, Hammer, McNicholas, & Kraemer, 2004; Faith & Kerns, 2005; Francis, Ventura, Marini, & Birch, 2007; Keller, Pietrobelli, Johnson, & Faith, 2006; Nelson, Gordon-Larsen, North, & Adair, 2006), child temperament (Wasser et al., 2011; Wu, Dixon, Dalton, Tudiver, & Liu, 2011), food security (Gooze, Hughes, Finkelstein, & Whitaker, 2012; Gundersen, Lohman, Garasky, Stewart, & Eisenmann, 2008), and the built environment (Oreskovic, Winickoff, Kuhlthau, Romm, & Perrin, 2009; Rahman, Cushing, & Jackson, 2011).
Parental Questionnaire and Visual Profile of Obesity Risk The research team then determined which risk factors could be best addressed during a primary care, well-child visit. The team developed a conceptual framework to guide this project (Figure 1), which was a modification of an ecological model for understanding overweight that was proposed by Swinburn and colleagues (Swinburn, Gill, & Kumanyika, 2005). In the model, risk factors for obesity are classified as either ‘obesogenic’ (promoting overweight) or ‘leptogenic’ (promoting healthy life choices). The model allows for biological, behavioral and environmental risk factors to work independently and in interaction with each other to influence a child's weight status during the preschool period of life.
Initial Questionnaire Construction and Field Testing The research team decided on a parental questionnaire for data collection because parents usually spend most time with the preschool child, and they are primarily responsible for establishing and maintaining health practices for very young children (Olstad & McCargar, 2009). A search of the published literature yielded no parental questionnaire that collected information on all of the obesity risk factors of interest; therefore, this research team constructed a questionnaire for this project, entitled the Child Obesity Risk Questionnaire 2–5 (CORQ 2–5). Items for the CORQ 2–5 were selected from five well-established questionnaires and from one national survey (Table 1). All instruments demonstrated face validity for collecting information on a risk factor of interest. The questionnaires had adequate to good psychometric properties, and they showed evidence of administration to parents of young children. The questionnaires included: 1) Children's Eating Behavior Questionnaire (CEBQ) (Wardle, G., Sanderson, & Rapoport, 2001), 2) Children's Behavior Questionnaire (CBQ) (Putman & R. M., 2006), 3) the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (Colder & O'Connor, 2004), 4) Children's Sleep Habits Questionnaire (CSHQ) (Owens, Spirito, & McGuinn, 2000), and 4) Caregiver Feeding Style Questionnaire (CFSQ) (Patrick, Nicklas, Hughes, & Morales, 2005). The research team selected
Figure 1
e47 four questions from the U.S. Household Food Security Scale (Blumberg, Bialostosky, Hamilton, & Briefel, 1999), which was used in several national surveys and classified almost 98% of households correctly. Notably, the literature search produced no questionnaire that assessed parental perceptions of the built environment; therefore, the research team constructed seven questions that asked parents their perceptions of neighborhood traffic, stranger danger, road safety, recreational venues and access to public transportation. Approval from the university's IRB was obtained for two separately funded and consecutive studies to examine psychometric properties of the CORQ 2–5. Both studies were conducted at a federally qualified, hospital-based community health care center located in an urban neighborhood in Southwestern Pennsylvania. The purpose of the first field study was to examine the reliability of the instrument; the purpose of the second field study was to examine its validity. Free and informed consents were obtained from parents, who represented a convenience sample. Parents met the following inclusion criteria: 1) biological parent of a child aged 2–5 years (inclusive) who fell within the 75th–95th BMI percentile for their age and sex, 2) lived with the child in a low-income household, as defined by a household whose income does not exceed 80 percent of the median income for the area, as determined by housing and urban development (HUD), with adjustments for smaller or larger families; and 3) spoke English (questionnaire was in English). A parent was excluded if: 1) their child's medical record indicated a medical condition or psychological condition that interfered with sleep or eating and/or caused excess weight gain, and 2) the child was ever enrolled in a weight loss/weight management program. Statistical analyses for two field studies were conducted using SPSS 12 statistical package (2012). Results Field Study 1: Eight parents participated in this first study (Table 2). Results indicated a lack of variability for Child Activity as parents reported that all children were within a
Conceptual framework for CORQ 2–5.
e48 Table 1
M.A. Davies et al. Self-report instruments used to develop 45-item CORS and their psychometric properties.
Instrument/Construct
Reliability/Validity
Selected items
Children's Eating Behavior Questionnaire (CEBQ)[/satiety responsiveness
Good test–retest reliability (.52–.87); good internal reliability (Chronbach's alpha .4–.91) good concurrent validity (explained 33%–56% of variance of behavioral measures of eating) Adequate internal consistency (ranged .64–.92); convergent validity (parental agreement) mean 0.51; evidence of predictive validity with social and laboratory behaviors Adequate internal consistency (ranged for .69–.87)
5
Adequate internal consistency for both a community sample(p = 0.68) and a clinic sample (u = 0/78) [59]; alpha coefficients ranged from 0.36–0.93., test–retest reliability (0.62–0.79); good discriminant validity[59] Test–Retest reliability (.82–.85), internal consistency (.71–.86); good evidence of convergent validity A score was developed by adding up affirmative responses to six questions A score was developed by adding up affirmative responses to seven questions
1
Children's Behavior Questionnaire (CBQ)/activity level The Sensitivity to Punishment and Sensitivity to Reward Questionnaire/child temperament Children's Sleep Habits Questionnaire (CSHQ)/usual hours of sleep per night
Caregiver Feeding Style Questionnaire (CFSQ)/parental feeding practices US Household Food Security Scale ref/food security Built environment (no instrument found)
normal range of activity. Internal consistency reliability was evaluated by computing Cronbach's alpha representing five components of the CORQ 2–5 (Table 3). Four components (child temperament, child eating behaviors, parental behaviors at mealtime and food security) had good internal consistency (Nunnally & B., 1994); the lower estimate for the Built Environment subscale was most likely due to the small sample size. Results Field Study 2: A second field study was independently funded to examine the discriminant validity of the CORQ 2–5. Sixteen parents participated in this study (Table 2). Discriminant validity was established by correlating component scores of the CORQ 2–5 with the overall score of the Preschool Feelings Checklist (PFC) (Luby, H, Koenig-McNaught, Brown & Spitznagel, 2004), which is a 16-item parent report that identifies preschoolers Table 2
Demographics of parents in field testing studies.
VARIABLE Gender Male Female Marital status Married Divorced Never married Unknown Education Attended high school Completed high school Attended college Completed college Attended post grad school Unknown
STUDY 1 (N = 8)
STUDY 2 (N = 16)
n
%
n
%
2 6
25.00 75.00
2 14
12.50 87.50
2 0 6 0
25.00 0.00 75.00 0.00
2 2 11 1
12.50 12.50 68.75 6.25
2 2 2 1 1 0
25.00 25.00 25.00 12.50 12.50 0.00
4 3 3 2 2 2
25.00 18.75 18.75 12.50 12.50 12.50
7
7
19 6 7
(ages 3.0–5.6 years) with symptoms of depression (Table 4). The Child Activity and Built Environment components were excluded from the correlation analysis due to lack of response variability; all parents reported that their child was active and their built environment was obesogenic.
Questionnaire Reformulation Based on findings from the two field studies, a revised version CORQ 2–5 now contains 47-items with some items recoded for scoring (see Appendix A). Two items from the Built Environment component were excluded due to lack of variability in both field studies; all parents indicated a strong agreement with “There are few sporting venues within our local areas” and “Public transportation is limited in my area”. Also, given emerging evidence for the genetic predisposition to obesity, the research team constructed a Family History component, which includes three questions: 1) consenting parent's height, 2) consenting parent's weight, and (3) whether the other biological parent or any biological grandparent is overweight or obese (yes/no).
Obesity Risk Profile Construction The web application researcher (PZ) established a computer-based algorithm by which the 47 items of the CORQ 2–5 resulted in eight component risk factor scores Table 3
Internal consistency reliabilities of CORQ 2–5 components.
CORQ 2–5 component
Number of items
Internal consistency estimate
Child temperament Child eating behaviors Mealtime Food security Built environment Sleep
5 7 21 4 7 1
.879 .858 .936 .940 .614 –
Parental Questionnaire and Visual Profile of Obesity Risk
e49
Table 4 Correlations between CORQ 2–5 components and PFC score. Components
Correlation with PFC
p-value
Child temperament Child eating behaviors Mealtime Food security Sleep
.330 .301 .079 .113 − .014
.213 .257 .770 .676 .960
depending on how items were answered (Table 5). Next, the PI and the web application researcher established criteria for the presence of an obesogenic risk factor (promoting overweight) based on component scores. The web application researcher then programmed the computer to generate a visual image of an obesity risk profile (Figure 2). A diamond next to a risk factor indicated the presence of an obesogenic risk factor. Figure 2
Clinical Relevancy Study Approval from the university's IRB was obtained for this study. Three different risk profiles were presented to 52 volunteer, advanced practice nurses who attended the 2012 International Pediatric Nursing Conference. The nurses were asked to consider three clinical decisions after reviewing each profile: a) no action needed at this time, b) spend part of appointment time addressing area(s) of obesity risk, or c) referral for follow-up care to help manage obesity risk factors. Inter-rater reliability was assessed using joint probability agreement (i.e., percentages for each level of referral for each scenario) and intraclass correlation statistics for the three scenarios. Results indicated a high inter-rater reliability of .72, .80 and .82 respectively for the same clinical decision for the three profiles.
Conclusions The CORQ 2–5 and its visual profile move the field of childhood obesity prevention forward by: 1) challenging a “one size fits all” model of risk for childhood obesity, 2) incorporating evidence-based findings related to multiple
Table 5
Scoring algorithm for final CORQ 2–5.
Component
Number of items
Obesogenic risk factor
Family history Child temperament Child activity Child eating behaviors Family practices During mealtime Food security Built environment Hours of sleep Total
3 5 7 5 16
N1 N 5 and b 11 N 7 and b 15 N 5 and b 11 N 16 and b 33 N 4 and b 9 N 4 and b 9 N 4 and b 9 b 11
4 4 3 47
Example of a child's obesity risk profile.
biological, behavioral and environmental risk factors for childhood obesity, and 3) making use of information technology to create a computer-based, multi-factorial profile of obesity risk. To our knowledge, no assessment and profile of obesity risk in preschoolers include all of the risk factors in this study. A limitation of this project was that parents in the two field studies represented those who lived in the same low-income neighborhood, those who took their child to a hospital-based community health center for their health care, and those who were willing to participate in a study about factors associated with obesity in children. Also, fathers were underrepresented. Clearly, further field testing of the 47-item questionnaire is indicated to test and establish its psychometric properties before implementation in primary care settings. Further studies that include larger, more diverse samples from other inner-city neighborhoods would confirm the psychometric properties of the 47-item CORQ 2–5 and yield results that could be generalized to a wider population. Potentially, clinicians and researchers could incorporate the CORQ 2–5 and its profile in the design of longitudinal studies or quality improvement projects of obesity prevention efforts during the preschool period of life. Once established, a visual profile of a child's risk for obesity could offer providers an opportunity to educate parents of preschoolers about the risk factors for childhood obesity during this important developmental period of life and to engage parents in shared decision making related to managing the risk for obesity, depending on a child's current health status and family life circumstances.
Acknowledgments Funding Sources: Sigma Theta Tau International, Eta Chapter Perkins Kuehn Memorial Fund, University of Pittsburgh School of Nursing Central Research Development Fund, University of Pittsburgh.
e50
M.A. Davies et al.
Appendix A. Child Obesity Risk Questionnaire
2–5
(CORQ)
2–5.
Please circle your agreement on each item using the five-point scale.
Strongly agree
Agree
Disagree
Strongly disagree
1. 2. 3. 4. 5. 6. ⁎ 7. 8. ⁎9. ⁎ 10. 11. 12. ⁎
My child often does things for praise. My child enjoys being the center of attention. In a group, my child tries to stand out as the smartest or the funniest. When my child gets something he wants, he feels excited and energized. My child likes activities that involve a reward right away. My child seems to always be in a big hurry to get from one place to another. My child sometimes prefers to watch rather than join other children playing. My child has trouble sitting still before an exciting event. My child tends to run rather than walk from room to room. When my child is outside, he often sits quietly. My child prefers quiet activities to active games. My child is full of energy, even in the evening.
1 1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3 3
4 4 4 4 4 4 4 4 4 4 4 4
13. ⁎ 14. 15. ⁎ 16. ⁎ 17. ⁎
These next questions are about your child's eating behaviors. My child gets full easily. My child has a big appetite. My child leaves food on his plate at the end of the meal. My child gets full before his meal is finished. My child cannot eat a meal if he has had a snack just before.
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1
2
3
4
1
2
3
4
1
2
3
4
1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4
1
2
3
4
1
2
3
4
1 1
2 2
3 3
4 4
1 1 1
2 2 2
3 3 3
4 4 4
1
2
3
4
18. 19. 20. 21. 22. 23. 24. 25. ⁎ 26. 27.
28. 29. 30. 31. 32. 33.
Please circle a response relating to your behavior. I physically struggle with my child to get him to eat. For example, I put my child in the chair so he or she will eat. I promise my child something other than food if he eats. For example, “If you eat your beans, we can play ball after dinner”. I encourage my child to eat by arranging the food to make it more interesting. For example, I make smiley faces on the pancakes. I ask my child questions about the food during dinner. I tell my child to eat at least a little bit of food on his plate. I reason with my child to get him to eat. For example, “Milk will make you strong”. I say something to show my disapproval of my child for not eating dinner. I allow my child to choose the foods he wants to eat for dinner from foods already prepared. I compliment my child for eating food. For example, “What a good boy! You are eating your beans”. I warn my child that I will take away something other than food if he does not eat. For example, “If you do not finish your meat, there will be no play time after dinner”. I tell my child to eat something on the plate. For example, “Eat your beans”. I warn my child that I will take a food away if my child does not eat. For example, “If you do not finish your vegetables, you will not get fruit”. I say something positive about the food my child is eating during dinner. I help my child to eat dinner. For example, I cut the food into smaller pieces. I encourage my child to eat something by using food as a reward. For example, “If you finish your vegetables, you will get some fruit”. I beg my child to eat dinner.
1 1 1
2 2 2
3 3 3
4 4 4
37.
Please circle your response regarding your food situation in the last 12 months I worried whether my food would run out before I got money to buy more. The food that I bought just did not last. I did not have money to get more. I bought a few kinds of low-cost food to feed my child because I was running out of money. I cannot feed my child a healthy meal because I cannot afford that.
1
2
3
4
38.
Please rate your neighborhood where you live: There is heavy traffic in our local streets.
1
2
3
4
34. 35. 36.
Parental Questionnaire and Visual Profile of Obesity Risk
e51
(continued)
39. 40. 41. 42.
45. 46. 47.
Please circle your agreement on each item using the five-point scale.
Strongly agree
Agree
Disagree
Strongly disagree
Stranger danger is a concern to me. Road safety is a concern in our area. There are no lights or crossings for my child to use. My child would have to cross several roads to get to the play areas.
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 4
Please indicate your child's usual time for: 43. Bedtime ____________PM 44. Awakening __________AM Please indicate your height: ____feet ____inches Please indicate your weight: _______pounds Is anyone in your child's immediate family (parents or grandparents) overweight or obese? Please circle Yes or No ⁎ Recode.
References Agras, W. S., Hammer, L. D., McNicholas, F., & Kraemer, H. C. (2004). Risk factors for childhood overweight: A prospective study from birth to 9.5 years. The Journal of Pediatrics, 145, 20–25. Andrews, K. R., Silk, K. S., & Eneli, I. U. (2010). Parents as health promoters: A theory of planned behavior perspective on the prevention of childhood obesity. Journal of Health Communication, 15, 95–107, http://dx.doi.org/10.1080/10810730903460567 (918961188 [pii]). Obesity prevalence among low-income, preschool-aged children - United States, 1998-2008. MMWR. Morbidity and Mortality Weekly Report, 58(2009)., 769–773 (doi:mm5828a1 [pii]). Anzman, S. L., Rollins, B. Y., & Birch, L. L. (2010). Parental influence on children's early eating environments and obesity risk: Implications for prevention. International Journal of Obesity, 34, 1116–1124, http://dx. doi.org/10.1038/ijo.2010.43 (ijo201043 [pii]). Blumberg, S. J., Bialostosky, K., Hamilton, W. L., & Briefel, R. R. (1999). The effectiveness of a short form of the Household Food Security Scale. American Journal of Public Health, 89, 1231–1234. Brotman, L. M., et al. (2012). Early childhood family intervention and long-term obesity prevention among high-risk minority youth. Pediatrics, 129, e621–e628, http://dx.doi.org/10.1542/peds.2011-1568 [peds.2011-1568 [pii]]. Cappuccio, F. P., et al. (2008). Meta-analysis of short sleep duration and obesity in children and adults. Sleep, 31, 619–626. Colder, C. R., & O'Connor, R. M. (2004). Gray's reinforcement sensitivity model and child psychopathology: Laboratory and questionnaire assessment of the BAS and BIS. Journal of Abnormal Child Psychology, 32, 435–451. Cook, S., Weitzman, M., Auinger, P., & Barlow, S. E. (2005). Screening and counseling associated with obesity diagnosis in a national survey of ambulatory pediatric visits. Pediatrics, 116, 112–116. Currie, A., & Cappuccio, F. P. (2007). Sleep in children and adolescents: A worrying scenario: can we understand the sleep deprivation-obesity epidemic? Nutrition, Metabolism, and Cardiovascular Diseases, 17, 230–232, http://dx.doi.org/10.1016/j.numecd.2006.10.007 (S09394753(06)00232-8 [pii]). Dehghan, M., Akhtar-Danesh, N., & Merchant, A. T. (2005). Childhood obesity, prevalence and prevention. Nutrition Journal, 4, 24, http://dx. doi.org/10.1186/1475-2891-4-24 (1475-2891-4-24 [pii]). Elwyn, G., et al. (2012). Shared decision making: A model for clinical practice. Journal of General Internal Medicine, 27, 1361–1367, http:// dx.doi.org/10.1007/s11606-012-2077-6.
Faith, M. S., & Kerns, J. (2005). Infant and child feeding practices and childhood overweight: The role of restriction. Maternal & Child Nutrition, 1, 164–168, http://dx.doi.org/10.1111/j.1740-8709.2005.00024.x (MCN24 [pii]). Francis, L. A., Ventura, A. K., Marini, M., & Birch, L. L. (2007). Parent overweight predicts daughters' increase in BMI and disinhibited overeating from 5 to 13 years. Obesity (Silver Spring), 15, 1544–1553, http://dx.doi.org/10.1038/oby.2007.183 (15/6/1544 [pii]). Goldfield, G. S., Epstein, L. H., Kilanowski, C. K., Paluch, R. A., & Kogut-Bossler, B. (2001). Cost-effectiveness of group and mixed family-based treatment for childhood obesity. International Journal of Obesity and Related Metabolic Disorders, 25, 1843–1849, http://dx.doi. org/10.1038/sj.ijo.0801838. Gooze, R. A., Hughes, C. C., Finkelstein, D. M., & Whitaker, R. C. (2012). Obesity and food insecurity at the same table: How head start programs respond. Preventing Chronic Disease, 9, E132 (doi:E132 [pii]). Gundersen, C., Lohman, B. J., Garasky, S., Stewart, S., & Eisenmann, J. (2008). Food security, maternal stressors, and overweight among lowincome US children: Results from the National Health and Nutrition Examination Survey (1999-2002). Pediatrics, 122, e529–e540, http:// dx.doi.org/10.1542/peds.2008-0556 (122/3/e529 [pii]). James, W. P. (2005). The policy challenge of coexisting undernutrition and nutrition-related chronic diseases. Maternal & Child Nutrition, 1, 197–203. Karaolis-Danckert, N., Buyken, A. E., Bolzenius, K., deFaria, C. P., Lentze, M. J., & Kroke, A. (2006). Rapid growth among term children whose birth weight was appropriate for gestational age has a longer lasting effect on body fat percentage than on body mass index. American Journal of Clinical Nutrition, 84, 1449–1455. Keller, K. L., Pietrobelli, A., Johnson, S. L., & Faith, M. S. (2006). Maternal restriction of children's eating and encouragements to eat as the 'non-shared environment': A pilot study using the child feeding questionnaire. International Journal of Obesity, 30, 1670–1675. Koletzko, B. (2006). Long-term consequences of early feeding on later obesity risk. Nestlé Nutrition Workshop Series. Paediatric Programme, 58, 1–18. Kumanyika, S., & Grier, S. (2006). Targeting interventions for ethnic minority and low-income populations. Future of Children, 16, 187–207. Luby, J. L., H, A., Koenig-McNaught, A. L., Brown, K., & Spitznagel, E. (2004). The Preschool Feelings Checklist: A brief and sensitive screening measure for depression in young children. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 708–717. Martin, K. S., & Ferris, A. M. (2007). Food insecurity and gender are risk factors for obesity. Journal of Nutrition Education and Behavior, 39, 31–36.
e52 McDonald, J., et al. (2011). Health information technology to guide pediatric obesity management. Clinical Pediatrics, 50, 543–549. Merchant, A. T., Dehghan, M., Behnke-Cook, D., & Anand, S. S. (2007). Diet physical activity, and adiposity in children in poor and rich neighbourhoods: A cross-sectional comparison. Nutrition Journal, 6, http://dx.doi.org/10.1186/1475-2891-6-1 (1475-2891-6-1 [pii]). Monteiro, P. O., & Victora, C. G. (2005). Rapid growth in infancy and childhood and obesity in later life–A systematic review. Obesity Reviews, 6, 143–154. Nelson, M. C., Gordon-Larsen, P., North, K. E., & Adair, L. S. (2006). Body mass index gain, fast food, and physical activity: Effects of shared environments over time. Obesity (Silver Spring), 14, 701–709, http:// dx.doi.org/10.1038/oby.2006.80 (14/4/701 [pii]). Nunnally, J., & B., I. (1994). Psychometric theory (3rd ed.). McGraw Hill. O'Brien, S. H., Holubkov, R., & Reis, E. C. (2004). Identification, evaluation, and management of obesity in an academic primary care center. Pediatrics, 114, e154–e159. Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA, 311, 806–814, http://dx.doi.org/10.1001/jama.2014.732 (1832542 [pii]). Olstad, D. L., & McCargar, L. (2009). Prevention of overweight and obesity in children under the age of 6 years. Applied Physiology, Nutrition and Metabolism, 34, 551–570, http://dx.doi.org/10.1139/H09-016 (h09-016 [pii]). Oreskovic, N. M., Winickoff, J. P., Kuhlthau, K. A., Romm, D., & Perrin, J. M. (2009). Obesity and the built environment among Massachusetts children. Clinical pediatrics, 48, 904–912, http://dx.doi.org/10.1177/ 0009922809336073 (0009922809336073 [pii]). Owens, J. A., Spirito, A., & McGuinn, M. (2000). The Children's Sleep Habits Questionnaire (CSHQ): Psychometric properties of a survey instrument for school-aged children. Sleep, 23, 1043–1051. Patrick, H., Nicklas, T. A., Hughes, S. O., & Morales, M. (2005). The benefits of authoritative feeding style: Caregiver feeding styles and children's food consumption patterns. Appetite, 44, 243–249. Putman, S. P., & R. M. (2006). Development of short and very short forms of the children’s behavior questionnaire. Journal of Personality Assessment, 87, 102–112. Rahman, T., Cushing, R. A., & Jackson, R. J. (2011). Contributions of built environment to childhood obesity. Mount Sinai Journal of Medicine, 78, 49–57, http://dx.doi.org/10.1002/msj.20235. Salsberry, P. J., & Reagan, P. B. (2005). Dynamics of early childhood overweight. Pediatrics, 116, 1329–1338. Salsberry, P. J., & Reagan, P. B. (2007). Taking the long view: The prenatal environment and early adolescent overweight. Research in Nursing and Health, 30, 297–307. Santoro, N. (2013). Childhood obesity and type 2 diabetes: The frightening epidemic. World Journal of Pediatrics, 9, 101–102, http://dx.doi.org/ 10.1007/s12519-013-0410-8. Services, U. S. D. o. H. a. H. (2010). The Surgeon General's vision for a healthy and fit. Nation, 10. Shaikh, U., Nelson, R., Tancredi, D., & Byrd, R. S. (2010). Presentation of body mass index within an electronic health record to improve weight assessment and counselling in children and adolescents. Informatics in Primary Care, 18, 235–244. Sorof, J., & Daniels, S. (2002). Obesity hypertension in children: A problem of epidemic proportions. Hypertension, 40, 441–447.
M.A. Davies et al. Stettler, N., Elliott, M. R., Kallan, M. J., Auerbach, S. B., & Kumanyika, S. K. (2005). High prevalence of overweight among pediatric users of community health centers. Pediatrics, 116, e381–e388, http://dx.doi. org/10.1542/peds.2005-0104 (116/3/e381 [pii]). Swinburn, B., Gill, T., & Kumanyika, S. (2005). Obesity prevention: A proposed framework for translating evidence into action. Obesity Reviews, 6, 23–33. Tanda, R., & Salsberry, P. (2013). The Impact of the 2007 Expert Committee Recommendations on Childhood Obesity Preventive Care in Primary Care Settings in the United States. Journal of Pediatric Health Care, http://dx. doi.org/10.1016/j.pedhc.2013.05.009 (S0891-5245(13)00196-X [pii]). Thompson, S. H., Rafiroiu, A. C., & Sargent, R. G. (2003). Examining gender, racial, and age differences in weight concern among third, fifth, eighth, and eleventh graders. Eating Behaviors, 3, 307–323 (doi: S1471015302000934 [pii]). Vieweg, V. R., Johnston, C. H., Lanier, J. O., Fernandez, A., & Pandurangi, A. K. (2007). Correlation between high risk obesity groups and low socioeconomic status in school children. Southern Medical Journal, 100, 8–13. Vine, M., Hargreaves, M. B., Briefel, R. R., & Orfield, C. (2013). Expanding the role of primary care in the prevention and treatment of childhood obesity: A review of clinic- and community-based recommendations and interventions. Journal of Obesity, 172035, http://dx.doi. org/10.1155/2013/172035. Walley, A. J., Blakemore, A. I., & Froguel, P. (2006). Genetics of obesity and the prediction of risk for health. Human Molecular Genetics, 15, R124–R130, http://dx.doi.org/10.1093/hmg/ddl215 (Spec No 2, 15/ suppl_2/R124 [pii]). Wang, Y., Beydoun, M. A., Liang, L., Caballero, B., & Kumanyika, S. K. (2008). Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring), 16, 2323–2330, http://dx.doi.org/10.1038/oby.2008.351 (oby2008351 [pii]). Wardle, J., G., C., Sanderson, S., & Rapoport, L. (2001). Development of the Children's Eating Behaviour Questionnaire. Journal of Child Psychology and Psychiatry, 42, 963–970. Wasser, H., et al. (2011). Infants perceived as "fussy" are more likely to receive complementary foods before 4 months. Pediatrics, 127, 229–237, http:// dx.doi.org/10.1542/peds.2010-0166 (peds.2010-0166 [pii]). Wen, X., Gillman, M. W., Rifas-Shiman, S. L., Sherry, B., Kleinman, K., & Taveras, E. M. (2012). Decreasing prevalence of obesity among young children in Massachusetts from 2004 to 2008. Pediatrics, 129, 823–831, http://dx.doi.org/10.1542/peds.2011-1833 (peds.2011-1833 [pii]). Wethington, H. R., Sherry, B., & Polhamus, B. (2011). Physician practices related to use of BMI-for-age and counseling for childhood obesity prevention: A cross-sectional study. BMC Family Practice, 12, 80. Wills, M. (2004). Orthopedic complications of childhood obesity. Pediatric Physical Therapy, 16, 230–235, http://dx.doi.org/10.1097/01.PEP. 0000145911.83738.C600001577-200401640-00007. Wing, Y. K., et al. (2003). A controlled study of sleep related disordered breathing in obese children. Archives of Disease in Childhood, 88, 1043–1047. Wu, T., Dixon, W. E., Jr., Dalton, W. T., III, Tudiver, F., & Liu, X. (2011). Joint effects of child temperament and maternal sensitivity on the development of childhood obesity. Maternal and Child Health Journal, 15, 469–477, http://dx.doi.org/10.1007/s10995-010-0601-z. Yamada, Y., et al. (2006). Genetic factors for obesity. International Journal of Molecular Medicine, 18, 843–851.
