Families face many barriers related to creating or maintaining a healthy lifestyle, which likely contributes to the prevalence of childhood obesity in the United States; however, no measure currently exists to examine these barriers. This study developed a quantitative measure of parents' perceptions of barriers to family healthy lifestyle.
Methods:
Parents of children between the ages of 7–17 were recruited using Amazon Mturk (n = 812). Exploratory and confirmatory factor analyses and preliminary convergent validity analyses were conducted.
Results:
Four factors emerged in the final measure: Parent Disengagement, Cost and Built Environment, Lack of Family Support, and Family Time Constraints, with the final 19-item measure having good initial psychometric properties, including reliability and validity.
Conclusions:
Future research is needed to examine whether this measure may be used in clinical practice to identify barriers to a healthy family lifestyle, to tailor interventions for families.
Get full access to this article
View all access options for this article.
References
1.
OgdenCL, CarrollMD, KitBK, FlegalKM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA, 2014; 311:806–814.
2.
KraakVA, LivermanCT, KoplanJP. Preventing Childhood Obesity: Health in the Balance. National Academies Press, Washington, DC, 2005.
3.
MurtaghJ, DixeyR, RudolfM. A qualitative investigation into the levers and barriers to weight loss in children: Opinions of obese children. Arch Dis Child, 2006; 91:920–923.
4.
UzarkKC, BeckerMH, DielmanT, et al.Perceptions held by obese children and their parents: Implications for weight control intervention. Health Educ Q, 1988; 15:185–198.
5.
BarlowSE, DietzWH. Management of child and adolescent obesity: Summary and recommendations based on reports from pediatricians, pediatric nurse practitioners, and registered dietitians. Pediatrics, 2002; 110(Supplement 1):236–238.
6.
EpsteinLH, KoeskeR, WingRR, ValoskiA. The effect of family variables on child weight change. Health Psychol, 1986; 5:1.
7.
EpsteinLH, PaluchRA, RoemmichJN, BeecherMD. Family-based obesity treatment, then and now: Twenty-five years of pediatric obesity treatment. Health Psychol, 2007; 26:381.
8.
EikenberryN, SmithC.Healthful eating: Perceptions, motivations, barriers, and promoters in low-income Minnesota communities. J Am Diet Assoc, 2004; 104:1158–1161.
9.
LeeH, HarrisKM, Gordon-LarsenP.Life course perspectives on the links between poverty and obesity during the transition to young adulthood. Popul Res Policy Rev, 2009; 28:505.
10.
FosterS, Giles-CortiB. The built environment, neighborhood crime and constrained physical activity: An exploration of inconsistent findings. Prev Med, 2008; 47:241–251.
11.
Gordon-LarsenP, NelsonMC, PageP, PopkinBM. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics, 2006; 117:417–424.
12.
LytleLA, HearstMO, FulkersonJ, et al.Examining the relationships between family meal practices, family stressors, and the weight of youth in the family. Ann Behav Med, 2011; 41:353–362.
13.
LucanSC, BargFK, LongJA. Promoters and barriers to fruit, vegetable, and fast-food consumption among urban, low-income African Americans—A qualitative approach. Am J Public Health, 2010; 100:631–635.
14.
ZabinskiMF, SaelensBE, SteinRI, et al.Overweight children's barriers to and support for physical activity. Obes Res, 2003; 11:238–246.
15.
PoseyKG.Household Income: 2015. U.S. Department of Commerce: Economics and Statistics Administration, Washington, DC, 2016; American Community Survey Briefs.
16.
BerinskyAJ, HuberGA, LenzGS. Evaluating online labor markets for experimental research: Amazon.com's Mechanical Turk. Pol Anal, 2012; 20:351–368.
17.
BuhrmesterM, KwangT, GoslingSD. Amazon's Mechanical Turk a new source of inexpensive, yet high-quality, data?. Perspect Psychol Sci, 2011; 6:3–5.
18.
MasonW, SuriS. Conducting behavioral research on Amazon's Mechanical Turk. Behav Res Methods, 2012; 44:1–23.
19.
KuczmarskiRJ, OgdenCL, Grummer-StrawnLM, et al.CDC growth charts: United States. Adv Data, 2000:1–27.
20.
BickelG, NordM, PriceC, et al.Guide to measuring household food security. US Department of Agriculture, Food and Nutrition Service, Office of Analysis, Nutrition, and Evaluation. 2000. Available at www fns usda gov/fsec/FILES/Guide% 20to% 20Measuring% 20Household% 20Food% 20Security (3-23-00) pdf (last accessed October1, 2017).
21.
PintoAM, FavaJL, RaynorHA, et al.Development and validation of the weight control strategies scale. Obesity, 2013; 21:2429–2436.
22.
RogersVW, MotykaE. 5-2-1-0 goes to school: A pilot project testing the feasibility of schools adopting and delivering healthy messages during the school day. Pediatrics, 2009; 123(Supplement 5):S272–S276.
23.
SothernMS, HunterS, SuskindRM, et al.Motivating the obese child to move: The role of structured exercise in pediatric weight management. South Med J, 1999; 92:577–584.
24.
OgdenCL, LambMM, CarrollMD, FlegalKM. Obesity and socioeconomic status in children and adolescents: United States, 2005–2008. NCHS Data Brief, 2010; 51:1–8.
25.
GolanM, CrowS. Targeting parents exclusively in the treatment of childhood obesity: Long‐term results. Obesity, 2004; 12:357–361.
26.
JabsJ, DevineCM. Time scarcity and food choices: An overview. Appetite, 2006; 47:196–204.
EisingaR, Te GrotenhuisM, PelzerB. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown?. Int J Public Health, 2013; 58:637–642.
29.
MuthénLK, MuthénBO. Mplus users guide, version 7. Statistical Analysis with Latent Variables. Los Angeles, CA, 2007, p. 3.
30.
HuLt, BentlerPM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling, 1999; 6:1–55.
31.
KlineRB.Principles and Practice of Structural Equation Modeling. Guilford Publications, New York, NY, 2015.
32.
BentlerPM.Comparative fit indexes in structural models. Psychol Bull. 1990; 107:238.
33.
BrowneMW, CudeckR, BollenKA, LongJS. Alternative ways of assessing model fit. Sage Focus Editions, 1993; 154:136.
34.
OsborneJW, CostelloAB. Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Pan-Pac Manage Rev, 2009; 12:131–146.
35.
BritoC, PearlJ. A new identification condition for recursive models with correlated errors. Struct Equ Modeling, 2002; 9:459–474.
36.
ByrneBM.Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming: Routledge, New York, NY, 2013.
37.
LittleTD.Longitudinal Structural Equation Modeling. Guilford Press, New York, NY, 2013.
38.
LanceCE, ButtsMM, MichelsLC. The sources of four commonly reported cutoff criteria what did they really say?. Organ Res Methods, 2006; 9:202–220.
39.
CaseyPH, SzetoK, LensingS, et al.Children in food-insufficient, low-income families: Prevalence, health, and nutrition status. Arch Pediatr Adolesc Med, 2001; 155:508–514.
40.
SallisJF, GlanzK. Physical activity and food environments: Solutions to the obesity epidemic. Milbank Q, 2009; 87:123–154.
41.
GellarLA, SchraderK, NanselTR. Healthy eating practices perceptions, facilitators, and barriers among youth with diabetes. Diabetes Educ, 2007; 33:671–679.
42.
MulvaneySA, HoodKK, SchlundtDG, et al.Development and initial validation of the barriers to diabetes adherence measure for adolescents. Diabetes Res Clin Pract, 2011; 94:77–83.
43.
GlasgowRE, ToobertDJ, GilletteCD. Psychosocial barriers to diabetes self-management and quality of life. Diabetes Spectr, 2001; 14:33–41.
44.
RushMC, PhillipsJS, PanekPE. Subject recruitment bias: The paid volunteer subject. Percept Mot Skills, 1978; 47:443–449.
45.
KuczmarskiMF, KuczmarskiRJ, NajjarM. Effects of age on validity of self-reported height, weight, and body mass index: Findings from the Third National Health and Nutrition Examination Survey, 1988–1994. J Am Diet AssocJan2001; 101:28–34; quiz 35–36.
