Comparison of Adolescent Obesity Risk Scale and Obesity Risk Scale to Determine Obesity Risk Knowledge in Parents

Tanna Woods1*, Mary A. Nies2

1College of Health Professions, Nursing, Western Governors University, Salt Lake City, Utah, United States
2College of Health, Idaho State University, Pocatello, Idaho, United States

*Corresponding author

*Tanna Woods, College of Health Professions, Nursing, Western Governors University, Salt Lake City,
Utah, United States


Background: Obesity is a health crisis affecting all ages. Among adults 20 and older in the United States, the prevalence of obesity is 41.9% while severe obesity is 9.2. Severe obesity increased from 4.7% to 9.2% between 2017 and March 2020. The number of children under 5 years old who are either overweight or obese has increased to 39 million worldwide in 2020.   Research shows that there is a knowledge deficit regarding how early life weight issues can increase the risk of health issues. However, limited research has evaluated whether parents of younger children understand health risks associated with obesity. Two scales have been developed to systematically evaluate knowledge of obesity health risks, the Obesity-risk scale (ORK-10) and the Adolescent Risk Knowledge Scale (AORK). No previous literature has compared AORK and the ORK-10 as measures of knowledge in a single study population.

Methods:  secondary analysis of a cohort of parents and preschool children was performed. The original cross-sectional study had parents answer both the ORK-10 and AORK knowledge scales, though only analysis of the ORK-10 scale was part of a cross-sectional study looking at factors affecting parental perception of child weight. For this secondary analysis, differences in respondent characteristics and their scores on the AORK and ORK-10 scales were examined using descriptive statistics, t-tests, one-way ANOVA and logistic regression.

Results: Parental obesity risk knowledge results showed that they scored higher on the AORK (M =6.99 (SD = 1.9)  than the ORK-10 (M = 3.96 (SD = 1.99). There was a significant difference between each parent’s score on the ORK-10 scale and the AORK scale (paired t-test: t = -25.13, df =  201, p = <.0001). Cronbach’s alpha showed an interitem variable of .122 and scale reliability coefficient of 0.70 for the ORK-10. The AORK had an average interitem covariance of .064 and a scale reliability coefficient of 0.61.

Conclusion: Results on the ORK-10 were similar to previous research, though the AORK scores were higher. Parents displayed low knowledge of risk and poor recognition of abnormal weights which can both be barriers to preventing and decreasing childhood obesity levels.

Keywords: Pediatric obesity, parents’ knowledge, obesity health risk, scales, AORK, ORK-10

What is currently known?

Obesity risk knowledge has been measured inconsistently in research. However, two scales using standardized assessment measures have been identified to measure knowledge of obesity risk. The Obesity-risk scale (ORK-10) was developed in 2006 while the Adolescent Risk Knowledge Scale (AORK) was developed in 2015. Only the ORK-10 has been used consistently.

What does this article add?
This research adds comparison of both the AORK and ORK-10 scales within a single population of parents of preschool-aged children. It helps to assess the use of each scale to measure parental knowledge of health risks.


Excess weight is an increasing public health issue affecting all ages worldwide, and it has potentially long-reaching, negative consequences if not treated. Understanding parental knowledge of obesity health risks has been identified as a potentially influential component in targeting obesity intervention in early ages when there is still fluidity in development of critical protective factors.

The number of individuals with obesity has tripled worldwide since 1975 affecting both children and adults (World Health Organization, 2021). The number of children under five years old who are either overweight or obese has increased from an estimated 38.2 million in 2019 to 39 million in 2020 (World Health Organization, 2021). Meanwhile, 29% of adults older than 18 were overweight in 2016 (World Health Organization, 2021). Among adults 20 and older in the United States, the prevalence of obesity is 41.9% while severe obesity is 9.2%  (Stierman et al., 2021). Severe obesity increased from 4.7% to 9.2% between 2017 and March 2020 (CDC, 2022).

As weight increases so does the risk for non-communicable diseases including cardiovascular problems like heart disease and stroke, diabetes, musculoskeletal issues like osteoarthritis, and various cancers, such as breast, gallbladder, kidney, and colon (World Health Organization, 2021; CDC, 2022). The medical cost of obesity in the United States was almost $173 billion in 2019 dollars, and adults with obesity have $1,861 more medical costs than people of normal weight (CDC, 2022).

About three-quarters of children who are overweight and obese will remain so into adulthood (Di Cesare, 2019). Given difficult treatment of obesity in adulthood (Singh et al., 2008) the need for treatment at the earliest possible age is crucial (Di Cesare, 2019). Modifiable risk factors for obesity have been identified as early as the in-utero period (Gillman et al., 2012; Taveras et al., 2013), though a lack of parental knowledge of the importance of this time period on future child weight status and health has also been noted (Baidal et al., 2015).

Young children also have an added sociocultural context that needs to be considered when examining obesity risk and intervention (Vittrup & McClure, 2018). Specifically, socialization to items like eating behaviors, exercise habits, attitudes toward health, activity, and eating, as well as the role modeling of health and exercise fall under parental responsibility (Vittrup & McClure, 2018; Holden, Vittrup, & Rosen, 2011). Researchers have noted the preschool age is a critical time period to prevent unhealthy weight and reverse obesity trends as children have more fluidity in development of important lifestyle components, such as habits, tastes, and food choices (McKee et al., 2016). Appropriate health risk knowledge is a key for informed decision-making regarding healthy lifestyle choices (Swift, Glazebrook, & Macdonald, 2006). Parents who recognize and understand the consequences of child weight issues are more likely to make needed lifestyle changes that can create healthy habits and prevent weight gain (Moreno, 2013).  However, it is unclear how well individuals can even perceive their own weight. Between 1997 and 2015, the proportion of individuals misperceiving their weight status has increased (Muttarak 2018).

Limited research has explored how to best target parent knowledge and behavior (Skouteris et al., 2011) or even what knowledge exists in parents related to health risk (Woods & Nies, 2020). A systematic search of literature showcased the inconsistency in how obesity risk knowledge is measured and identified that few studies have relied on standardized assessment tools to measure knowledge (Woods & Nies, 2020). Only he Adolescent Risk Knowledge Scale (AORK) and Obesity-risk scale (ORK-10) were identified as relevant scales measuring obesity risk knowledge. These scales have been evaluated using formal validation studies prior to use in a study to ensure they had desirable measurement properties and to ensure scores reflected valid indicators of knowledge (Woods & Nies, 2020).

Given the magnitude of the obesity epidemic and parental influence in youth, assessing knowledge of obesity health risks could provide key information to craft informed intervention strategies. No previous literature has compared results of the AORK and the ORK-10 as measures of knowledge in a single study population. The objective of this study was to describe parental knowledge regarding obesity health risks using two existing scales.



The Obesity-risk scale (ORK-10) was developed as a systematic way to evaluate knowledge of obesity risks using a 10-item questionnaire with three answer options of true, false, and don't know (Swift et al., 2006). Content includes obesity-related comorbidities like bowel cancer and high blood pressure as well as risks for specific locations of weight gain, i.e. abdomen versus thighs. ORK-10 was created as quick tool to assess knowledge of obesity with adult populations at a 12-13 year reading age in the United Kingdom (Swift et al., 2006). Each correct answer is assigned 1 point whereas incorrect and don’t know answers earn 0 points (Swift et al., 2006). A higher score reflects increased knowledge of obesity risks. The Cronbach’s alpha coefficient was .83 while all 10 items had a corrected item-total correlation of .3 or higher (Swift et al., 2006). The scale was tested with experts and non-experts.

The Adolescent Risk Knowledge Scale (AORK) was developed in the United States for use with adolescents and their families (Rutkowski & Connelly, 2016). It is a tool designed to help practitioners have a starting point to initiate discussions about weight (Rutkowski & Connelly, 2016). The scale was modified from the ORK-10 using a mixed-method design with qualitative focus groups, scientific advisory groups, and quantitative analysis. Like the ORK-10, there are 10 questions with answer choices of true, false, or don’t know. One point is given for correct answers and 0 points for incorrect or don’t know responses; a higher score indicates more awareness of obesity health risks. Cronbach’s alphas was .68 (Rutkowski & Connelly, 2016). Content included assessing factors that cause weight gain, risks with certain weight gain patterns, and  knowledge of known risks related to obesity, such as development of diabetes, heart issues, and breast cancer (Rutkowski & Connelly, 2016).

Study design and sampling

This study involved a post-hoc analysis of data from Health Belief Model Factors as Predictors of Parental Misclassification of the Weight of the Preschool Child (Woods & Nies, 2019). The original study used a cross-sectional survey approach to collect information from a written questionnaire of parents of 2- to 5-year-old children who were either enrolled in a preschool or a daycare with a preschool. The investigator also collected anthropometric measurements per standardized procedures to assess the height and weight of each enrolled child. The study examined parental classification of child weight and factors that may affect it, including knowledge of obesity health risk. Both ORK-10 and the AORK-10 were given to parent participants, though only the total score of the ORK-10 was used for analyses in the original study. Scores from the ORK-10 and AORK were classified as correct or incorrect (where both unknown and incorrect choices were counted as incorrect). The review board approved the original study, IRB-FY2018-249: Health Belief Model Factors as Predictors of Parental Misclassification of the Weight of the Preschool Child, which is the parent study. A full description of the protocol in this study is available in Woods and Nies (2019, 2020b).

This post-hoc analysis relied on data from the questionnaires of parents from the original study. The sample included 198 parents of preschool children at stand-alone preschools or a daycares with a preschool (n=17). An additional four participant results were included in the post-hoc analysis. They had not been used in the original study due to lacking child weight and height data. As the post-hoc analysis only used parent information, those four surveys were included. This study’s inclusion criteria were that the adult needed to have legal responsibility for a preschool child regardless of biological connection, ability to speak and read English, and sign written consent for child participation. Exclusion criteria included the child being diagnosed with a medical condition that could affect the child’s weight/size, parental inability to read/write English, and participation of another guardian.

When examining knowledge in the post-hoc analysis, data for the AORK and ORK-10 scales were coded to categorize answer choices as correct, incorrect, or unknown to account for the three possible answers. Scores were categorized as low (<4), medium (between 4 and 6), and high (7 or higher). Parents were asked to self-report height and weight, which were then converted to body mass index scores and categorized as underweight, healthy, overweight, and obese. Parents were also asked to self-assign a weight category.

The goal of this analysis was  to examine the ORK-10 and AORK-10 to answer these questions:

  1. What is the level of parental knowledge regarding obesity health risks as measured by both the ORK-10 and AORK scales?
  1. Is there a significant difference between the AORK scores and the ORK-10 scores in measuring parental knowledge regarding obesity health risks?3. What factors are associated with the parents’ knowledge of obesity health risk
  2. Do the continuous AORK or ORK-10 scores predict parental classification of weight (measured as correct or incorrect)?

Statistical analysis

All statistical analysis was performed with Stata 14.1 (Stata Corporation, College Station, TX, U.S.) with statistical significance set at p <0.05.

Demographic information including parent age, gender, race/ethnicity, body mass index (ascertained from parent self-report of height and weight), socioeconomic status as measured by income, and level of education were analyzed for descriptive statistics.

For question one, each scale was analyzed using descriptive statistics to ascertain items including mean, range, and standard deviation. With question two, a paired t-test was used to assess if there was a significant difference between parental scores on the AORK and ORK-10. A Cronbach’s alpha was used to assess the internal consistency reliability of each test. Question three used ANOVA to determine what parental factors affected both the ORK-10 and AORK mean scores. With question 4, the continuous ORK-10 and AORK scores (separately) were used as well as the parental classification of weight status (classified as correct or incorrect depending on if the parent report of weight status – unhealthy, healthy, overweight or obese, matched their actual status as determined by BMI). Correct classification was used as the referent category.


Demographics: The full reporting of sample characteristics of the original 198 participants is available in Woods and Nies (2020). This sample of  202 participants had mostly mothers (87.13%), though fathers (11.88%) and grandparents (0.99) were also included. The largest age group for parents was 30-39 (52%) followed by 21-29 (37.5%).  Most participants had some college but no degree (40.09 %) by followed by bachelor’s degree (27.23%) and high school equivalent (21.291%). The majority of the sample was white (83.17%) and made between $75,000-$99,999 (27.41%) or between $50,000 and $74,999 (23.35%).

Question 1: What is the level of parental knowledge regarding obesity health risks as measured by both the ORK-10 and AORK scales?

The mean score of the ORK-10 was 3.96 (SD = 1.99) with a range of 0 to 9. The mean AORK was 6.99 (SD = 1.9) with a range of 1 to 10. The most frequent scores on the AORK scale were 7 (n = 50, 24.75%) and 8 (n = 45, 22.28%). Meanwhile, the most frequent scores on the ORK-10 were 3 (n =39, 19.31%) and 4 (n = 35, 17.33%). There was a stark contrast in how parents scored on the ORK-10 versus the AORK. At the 50th percentile rank, the score on the ORK-10 was 4 while it was 7 on the AORK. The score only marginally increased at the 75th percentile rank with 5 on the ORK-10 and 8 on the AORK.

The breakdown of percentage of people answering correct versus answering incorrect and don’t know or incorrect is shown in Table 1.

Table 1 :Participant responses to AORK and ORK-10 questions

Question 2: Is there a significant difference between the AORK scores and the ORK-10 scores in measuring parental knowledge regarding obesity health risks?

There was a significant difference between each parent’s score on the ORK-10 scale and the AORK scale (paired t-test: t = -25.13, df =  201, p = <.0001). The mean difference in the scores on the ORK-10 were 3.96 (SD=1.99, 95% CL: 3.68 to 4.23) and 6.99 (SD=1.82, 95% CL: 6.73 to 7.24) on the AORK. Both knowledge scales had outliers with the AORK having two low outliers with scores of 0 and 2 while the ORK-10 had one high outliers with a score of 9.

The Cronbach’s alpha showed an interitem variable of .122 and scale reliability coefficient of 0.70 for the ORK-10. The AORK had an average interitem covariance of .064 and a scale reliability coefficient of 0.61. There is a strong, positive correlation between AORK scores and ORK-10 scores (r=0.58, p < 0.001).

The scores were broken into three categories to better show the distribution with low equaling 3 or less, medium equally 4 to 6, and high equaling 7 or higher. With the AORK scale, most people (n = 134, 66.34%) had a high rating while 60 people (29.70%) had a medium and 8 people (3.96%) had a low rating. For the ORK-10, few people (n = 25, 12.38%) had a high ranking while 94 (46.53%) had a medium ranking and 87 (43.07%) had a low ranking. The spread of how parents scored on the AORK and ORK-10 scale grouped by demographics is displayed in Table 2.

Table 2: AORK and ORK-10 scores by demographic variables

Question 3: What factors are associated with the parents’ knowledge of obesity health risk?

Does parental recognition of their own weight predict the continuous score of either the ORK-10 or AORK?

One-way ANOVA was used to determine the association between parental factors and the total scores on each scale. With the AORK scale, parental BMI classification (F (3.58, 664.39) = 0.36, p =0.78), parental sex (F (1.12, 666.9) = 0.34, p 0.56) and ethnicity (F (22.06, 645.92) = 1.11, p =0.36) were not significant. Meanwhile, income (F (56.31, 582.69) = 2.27, p =0.02), age (F (42.76, 600.19) = 3.49, p =0.009), and education (F (70.38, 597.59) = 5.8, p =0.0002) were significant.  For age, Tukey’s post-hoc test showed the mean value of AORK scores was significantly different between those making between $10,000 to $24,999 and those making $25,000 to $49,999 (p < 0.001). Tukey’s post-hoc test showed more significance between groups regarding the mean value of the AORK score and education. Statistical differences were identified between less than high school degree and some college but no degree (p <0.02), less than high school degree and bachelor’s degree (p<0.001), high school or equivalent and bachelor’s degree (p < 0.03), less than high school degree and graduate degree (p = 0.004), as well as high school or equivalent and graduate degree (p=0.03).

With the ORK scale, parental BMI classification (F (9.59, 788.09) = 0.80, p =0.49), parental sex (F (.82, 796.86) = 0.21, p = 0.65) and ethnicity (F (46.77, 750.92) = 2.02, p =0.06) were not significant. The one-way ANOVA between the ORK mean score and education (F(67.87, 729.82) = 4.58, p < 0.002), the mean ORK score and income (F(67.81, 712.17) = 2.24, p <0.03), and  the ORK mean score and parental was statistically significant (F(61.91, 720.01) = 4.21, p < 0.003). Tukey’s post-hoc test for education showed there was statistical significance between less than high school and bachelor’s degree (p < 0.03), high school or equivalent and bachelor’s degree (p =0.05), and less than high school and graduate degree (p < 0.03). Tukey’s post-hoc test for age showed there was statistical significance between those aged 21-29 and 30-39 (p < 0.001) and those between 21-29 and 40-49 (p < 0.02).

Question 4: Do the continuous AORK or ORK-10 scores predict parental classification of weight (measured as correct or incorrect)?

Parental self-assessment of their BMI category was compared to their actual BMI category determined by self-reported height and weight (κ = .51, p <.0001). Binary logistic regression was used to determine the predication ability of the AORK and ORK-10 scores with parental classification of their weight (0 = incorrect, 1=correct). The percentage of correct assessment of weight was 63.9% (n = 129). No significance was found with either the AORK or the ORK-10. The binary logistic regression for the ORK-10 was -2 Log Likelihood =264.28, c2(5, n = 202) = .2.68, p = .75. The Nagelkerke pseudo R2 = .00 indicates the model accounted for no variance in classification. The binary logistic regression for the AORK was -2 Log Likelihood =263.75, c2(5, n = 202) = 3.58, p = .61. The Nagelkerke pseudo R2 = .004 indicates the model accounted for 0.4%.


The purpose of this study was to describe parental knowledge regarding obesity health risks using two existing scales. This study showed that parents had higher scores on the AORK with a mean of 6.99 (SD=1.82) than the ORK-10 with a mean of  3.96 (SD=1.99). The ORK-10 has been used in other research with similar performance in non-expert populations (Alasmari et al., 2017, Gormley & Melby, 2020; Rurkowski & Connelly, 2011; Thaher et al., 2018). However, the AORK score in this study was higher than that of the original, validating study as well as one other identified study (Rutkowski et al., 2016; Omotola, 2017). No previous literature has compared results of the AORK and the ORK-10 as measures of knowledge in a single study population.

The number of parents who responded “don’t know” was higher on the ORK-10 scale with three questions having more than 50% selecting that choice: “Obesity increases the risk of bowel cancer” (58.59%), “An obese person who gets diabetes needs to lose at least 40% of their body weight for clear health benefits” (61.62%), and “Obesity increases the risk of getting a food allergy” (57.07%). Only one question, “Obesity increases the risk of getting breast cancer in women,” had more than 50% of participants selecting “don’t know” (53.03%). While obesity does not pose an allergy risk, it is highly related to other health conditions, including type 2 diabetes, cardiovascular disease, arthritis, psychological problems, and cancer (Winter & Wuppermann, 2013). Participants in the this study had more knowledge of heart issues and diabetes related to obesity than cancer. Given that cancer is the second most common death in the world (Krupa-Kotara & Dominkia, 2021), the lack of knowledge relating to obesity and increase of cancer risk is concerning. Obesity is known as a major risk factor for 16 types of cancer including endometrial, breast, and all major gastrointestinal cancers (Karczewski et al., 2019; Krupa-Kotara & Dominkia, 2021, Haggerty et al., 2017). While smoking is the first leading cause of cancer, obesity is the second (Karczewski et al., 2019). This study highlights that recognition between obesity and cancer is inadequate. With stomach or bowel cancer, only 28.79% and 31.31% of the participants recognized the risk of stomach cancer on the AORK and ORK-10 scales respectively. Meanwhile, the risk of breast cancer was only recognized by 27.78% and 19.19% of participants. It is important to perceive conditions as severe or as a severe risk as necessary to increase the likelihood of action to counteract it (Visscher et al., 2017).

This study showed that participants with more education did achieve higher scores on both knowledge scales. This significance was found for both the AORK and the ORK-10 in relationship to those holding a bachelor’s degree versus less than high school education and those holding a graduate degree versus less than high school education. Parental knowledge of obesity risks demonstrated in this study and the identification of its relationship to education highlights the importance of parental knowledge. Past research has already shown that interventions including knowledge interventions have proven successful in weight loss (Cadzow, Chambers, & Sandell, 2015; Mazloomy-Mahmoodabad et al., 2017). Education has also previously been identified as important to recognition of obesity as a chronic disease (Mauro et al., 2008) and to improvements on parental awareness of obesity prevention strategies (Renales, Whitted, & Lennen, 2021).  Further, parents who recognize weight as a health problem have been linked to increased readiness to implement changes (Rhee et al., 2005). Parents with higher education have also been linked to having lower barriers to behavior change than parents with less education (White et al., 2016).

Findings in this study showed that those with higher education better understood obesity risk while those with a high school equivalent or less more often struggled. This is troubling as understanding health impacts can be crucial to shifting behavior (LoRe, Leung, Brenner, & Suskind, 2019). Being overweight and obese is also more prevalent in lower socioeconomic groups (Juliusson et al., 2010), and these groups are associated with lower intake of fruits and vegetables as well as less physical activity (Ovrum, 2011; Ovrum, Gustavsen & Rickersten, 2014).

            However, it is important to emphasize that only the measure reliability score for the ORK-10 met the minimum  benchmark of 0.7 to show reliability (Cohen, 1988). The Cronbach’s alpha showed an interitem variable of .122 and scale reliability coefficient of 0.70 for the ORK-10. In the original validation study, the alpha was 0.83 (Swift, Glazebrook, & Macdonald, 2006). In this current study, one scale question related to obesity risk in people from South Asia versus Europe that only 3.03% of individuals answered correctly. There was only 1 of 10 questions where at least 75% of all participants answered correctly. This could reflect knowledge that is not readily discussed in the United States and impacts the reliability of the scale. Meanwhile, the AORK scale in this study had an alpha of 0.61 compared to the alpha of 0.68 in the original study validating the scale (Rutkowski & Connelly 2011). This could be related to some AORK questions that were too easy as there were four questions where more than 85% of participants answered correctly. There were 7 of 10 questions where participants scored 75% or higher. Data was also positively skewed on the AORK whereas the ORK-10 scores were more variable and resembled a standard bell curve.


This was a secondary analysis of a previously reported cross-sectional study, where the analysis and study size estimations were based on evaluating factors affecting parental classification of child weight. The study therefore is limited to information already collected in the original study. The sample size in the original study was adequate for this study’s question, but the population was largely homogenous in terms of race and sex, which can limit the generalizability of the study’s findings. Further, the original study relied on self-reported data from samples of parents of preschool children in one region of a single state, thus limiting the generalizability of the results.


Findings suggest that the AORK scale may not be as valid as the ORK-10 scale.  Parental results on the ORK-10 were similar to previous research and representative of a non-expert samples while this study’s sample had higher average AORK scores than previous research. However, at least one question on the ORK-10 had extremely low performance in this study population, which could signal a need for revision of the scale to suit the American population. The original scale and many subsequent findings have been examined mainly in European populations. More research and potential revision of the ORK-10 scale is needed to see if revision of that question would yield more appropriate assessments of parental knowledge.

With 33.19% of parents on the AORK scale and 88.38 on the ORK-10 scoring medium or low for obesity knowledge risk scores, it is clear  that knowledge of obesity health risks could be improved. Research suggests that parental influence is significantly influential on childhood obesity (Hansen et al., 2014; Moore et al., 2012, Rune et al., 2015). Further research is needed to determine if a short scale, such as the AORK and ORK-10, can be beneficial as a way to identify educational needs in parents.


  1. Di Cesare, M., Soric, M., Bovet, P., Miranda, J., Bhutta, Z., Stevens, G., Laxmaiah, A., Kengne, A., Bentham, J. (2019). The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Medicine, 17, 212.
  2. World Health Organization (2021). Obesity and overweight. Retrieved from
  3. : Stierman, B., Afful, J., Carroll, M.Chen, T., Davy, O., Fink, S., Fryar, C., Gu, Q., Hales, C., Hughes, J., Ostchega, Y., Storandt, R., Akinbami, L. (2021).  National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files Development of Files and Prevalence Estimates for Selected Health Outcomes. National Health Statistics Reports. Retrieved from
  4. Centers for Disease Control and Prevention (CDC) (2022). Adult obesity facts. Retrieved from
  5. Gillman, M., Rifas-Shiman, S., Kleinman, K., Oken, E., Rich-Edwards, J., & Taveras, E. (2012). Developmental origins of childhood overweight: Potential public health impact. Obesity, 16(7), 1651-1656. doi: 10.1038/oby.2008.260.
  6. Taveras, E., Gillman, M., Kleinman, K., Rich-Edwards, J., Rifas-Shiman, S. (2013). Reducing racial/ethnic disparities in childhood obesity: the role of early life risk factors. JAMA Pediatrics, 167(8).
  7. Baidal, J., Criss, S., Goldman, R., Perkins, M., Cunningham, C., & Taveras, E. (2015). Reducing Hispanic children’s obesity risk factors in the first 1000 days of life: A Qualitative Analysis. Journal of Obesity, 215(31). doi: 10.1155/2015/945918.
  8. Vittrup, B., McClure, D. (2018). Barriers to Childhood Obesity Prevention: Parental Knowledge and Attitudes. Pediatric Nursing, 44(2), 81-94.
  9. Holden, G., Vittrup, B., & Rosen L. (2011). Families, parenting, and discipline. In M.K. Underswood & L.H. Rosen (Eds). Social development: Relationships in infancy, childhood, and adolescence (pp. 127-152). Guilford Press.
  10. Singh AS, Mulder C, Twisk JWR, Van Mechelen W, Chinapaw MJM. Tracking of childhood overweight into adulthood: A systematic review of the literature. Obesity Reviews, 2008; 9:474–88. doi: 10.1111/j.1467-789X.2008.00475.x.
  11. Swift, J.A., Glazebrook, C. and Macdonald, I.    (2006)  Validation  of  a  Brief,  Reliable Scale  to  Measure  Knowledge  about  the  Health  Risks  Associated  with  Obesity.  In-International Journal of Obesity, 30, 661-668.
  12. Skouteris, H., McCabe, M., Swinburn, B., Newgreen, V., Sacher, P., Chadwick, P. (2011). Parental influence and obesity prevention in preschoolers: a systematic review of interventions. Obesity Reviews, 12(5), 315-328. doi: 10.1111/j.1467-789X.2010.00751.x.
  13. Moreno, N. (2013). Parental perception of child weight: a concept analysis. Journal of Advanced Nursing, 70(1), 34-45. Doi: 10.1111/jan.12143.
  14. Woods, T., & Nies, M. (2020). Protocol to examine important weight issues with preschool children and their parents. Journal of Integrated Health Science, 4(20): 1-3. doi: 10.0000/JHSE.1000184.
  15. Muttarak, R. (2018). Normalization of plus size and the danger of unseen overweight and obesity in England. Obesity, 26(7): 1125-1129. Doi:10.1002/oby.22204.
  16. Rutkowski, E. M., & Connelly, C. D. (2016). Adolescent Obesity Risk Knowledge (AORK): Let the Discussion Begin. Journal for Specialists in Pediatric Nursing, 21(1), 37–43.
  17. Cole, T.J., Bellizzi, M.C., Flegal, K.M., et al. Prevalence of Obesity and Knowledge of Health Risk Associated with Obesity among Female Adolescents in Jordan.
  18. Woods, T., & Nies, M. (2019). Most appropriate mechanism to understand the parental classification of child’s weight status. Annals of Nursing Research and Practice, 4(1), 1-8. ISSN: 2572-9403.
  19. Woods, T., & Nies, MA (2020b). Examination of parental knowledge of child weight status and associated potential health risks. Journal of Education and Health Promotion, 9(1). doi: 10.4103/jehp.jehp_43_19.
  20. Alasmari, H., Al-Shehri, A., Aljuaid, T., Alzaidi, B., & Alswat, K. (2017). Relationship between body mass index and obesity awareness in school students. Journal of Clinical Medicine Research, 9(6), 520-524.
  21. Gormley, N., & Melby, V. (2020). Nursing students’ attitudes towards obese people, knowledge of obesity risk, and self-disclosure of own health behaviors: An exploratory survey. Nurse Education Today, 84.
  22. Rutkowski, E. M., & Connelly, C. D. (2011). Obesity Risk Knowledge and Physical Activity in Families of Adolescents. Journal of Pediatric Nursing, 26(1), 51–57.
  23. Thaher, L., Alasad, J., Maharmeh, M., & Salami, I. (2018). Prevalence of obesity knowledge of health risk associated with obesity among female adolescents in Jordan. Scientific Research Publishing, 8, 60-68. Https://
  24. Omotola, A. (2017). Knowledge and attitude towards obesity among secondary school students of Royal Crystal College, Ile-Ife, Nigeria. Texila International Journal of Public Health, 5(1), 1-14. doi: 10.21522/TIJPH.201305.013Art015
  25. Etelson, D., Brand, D., Patrick, P., & Shirali, A. (2003). Childhood obesity: Do parents recognize health risk? Obesity Research, 11(11), 1362-1368. doi: 10.1038/oby.2003.184
  26. Renales, F., Whitted, K., & Lennen, N. (2021). Assessing parental perceptions on childhood obesity: An educational intervention. Pediatric Nursing, 47(1), 23-29.
  27. Winter, J. & Wuppermann, A. (2013). Do they know what is at risk? Health risk perception among the obese. Health Economics, 23(5), 564-585. Doi: 10.1002hec.2933.
  28. Krupa-Kotara, K., & Dakowska, D. (2021). Impact of obesity on risk of cancer. Central European Journal of Public Health, 29(1): 38-44.
  29. Karczewski, J., Beiger-Krasinska, B., Staszewski, R., Poplawska E., Gulczynska-Elhadi, K., & Dobrowolska, A. (2019). Obesity and the risk of gastrointestinal cancers. Digestive Diseases and Sciences, 64: 2740-2749.
  30. Haggerty, A., Sarwer, D., Schmitz, K., Ko, E., Allison, K., & Chu, C. (2017). Obesity and endometrial cancer: A lack of knowledge but opportunity for intervention. Nutrition and Cancer, 69(7): 990-995.
  31. Visscher, T., Lakerveld, J., Olsen, N., Kupers, L., Ramalho, S., Keaver, L., Brei, C., Bjune, J., Ezquerro, S., & Yumuk, V. (2017). Perceived Health Status: Is Obesity Perceived as a Risk Factor and Disease. Obesity Facts, 10(1), 52-60. Doi: 10.1159/000457958.
  32. Cadzow, R., Chambers, M., & Sandell, A. (2015). School-based obesity intervention associated with three-year decrease in student weight status in low-income school district. Journal of Community Health, 40: 709-713. doi: 10.1007/s10900-015-9989-0.
  33. Mazloomy-Mahmoodabad, S., Navabi, Z. S., Ahmadi, A., & Askarishahi, M. (2017). The effect of educational intervention on weight loss in adolescents with overweight and obesity: Application of the theory of planned behavior. ARYA Atherosclerosis, 13(4), 176–183. Https://
  34. Mauro M, Taylor V, Wharton S, Sharma AM (2008). Barriers to obesity treatment. European Journal of Internal Medicine, 19: 173-180.
  35. Rhee KE, De Lago CW, Arscott-Mills T, Mehta SD, Davis RK (2005) Factors associated with parental readiness to make changes for overweight children. Pediatrics, 116: e94-101. doi. 10.1542/peds/2004-2479
  36. White DA, Rofey DL, Kriska AM, Venditti EM, Gibbs BB, et al. (2016) Parental Influences on Child Weight: Perception, Willingness to Change, and Barriers. Journal of Obesity and Weight Loss Therapy, 6:293. doi:10.4172/2165-7904.1000293.
  37. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  38. Hansen, A.R., Duncan, D.T., Tarasenko, Y.N., Yan, F., & Zhang, J. (2014). Generational shift in parental perceptions of overweight among school-aged children. Pediatrics, 134(3), 481-488.
  39.  Moore, L.C., Harris, C.V., & Bradlyn, A.S. (2012). Exploring the relationship between parental concern and the management of childhood obesity. Maternal & Child Health Journal, 16, 902-908.
  40.  Rune, K.T., Mulgrew, K., Sharman, R., & Lovell, G.P. (2015). Effect of an obesity pamphlet on parental perception and knowledge of excess weight on their children: Results of a randomized controlled trial. Health Promotion Journal of Australia, 26(2), 129- 132. doi: 10.1071/HE14060
  41. LoRe, D., Leung, C., Brenner, L., & Suskind, D. (2019). Parent-directed intervention in promoting knowledge of pediatric nutrition and healthy lifestyle among low-SES families with toddlers: A randomized controlled trial. Child Care Health Development, 45, 518-522. doi: 10.1111/cch.12682.
  42. Juliusson PB, Eide GE, Roelants M, Waaler PE, Hauspie R, Bjerknes R. (2010). Overweight and obesity in Norwegian children: prevalence and socio-demographic risk factors. Acta Paediatrica, 99(6): 900–5. doi:10.1111/j.1651-2227.2010.01730x
  43. Øvrum A. (2011). Socioeconomic status and lifestyle choices: evidence from latent class analysis. Health Economics, 20: 971–84. 9. doi: 10.1002/hec.1662
  44. Øvrum A, Gustavsen G, Rickertsen K. (2014). Age and socioeconomic inequalities in health: Examining the role of lifestyle choices. Advances in Life Course Research, 19:1–1. doi:10.1016/j.alcr.2013.10.002