Introduction Cooking at home is associated with better diet quality. This study examined the frequency of home-cooked dinners versus eating out in relation to the Healthy Eating Index (HEI), and food expenditures. Methods The Seattle Obesity Study used a stratified random sample of 437 King County adults. In-person computer-assisted interviews collected sociodemographic and behavioral data during 2011–2013. HEI-2010 and 2005 were computed using Food Frequency Questionnaires. Multivariable regression analyses, conducted in 2015, examined associations among HEI scores, food expenditures, and frequency of cooking at home versus eating out variables. Results Frequent home-cooked dinners were associated with being married, unemployed, larger households, presence of children aged <12 years, and lower frequency of eating out, but unrelated to education or income. In adjusted models, frequent at-home cooking was associated with higher HEI-2010 (β=7.4, p<0.001), whereas frequent eating out was associated with lower HEI-2010 (β= −6.6, p<0.001). Frequent home cooking was linked with reduced per capita food expenditures overall ($330/month among low vs $273/month among high cooking group, p<0.001), and reduced away-from-home expenditures ($133 and $65, respectively), without any significant increase in at-home food expenditures. However, frequent eating out was associated with significantly higher per capita food expenditures overall ($261 in low vs $364 among high eating out group, p=0.001), and higher away-from-home expenditures. Conclusions Home-cooked dinners were associated with greater dietary guideline compliance, without significant increase in food expenditures. By contrast, frequent eating out was associated with higher expenditures and lower compliance. Home cooking may be a component of nutrition resilience.

BackgroundThe built environment (BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet.MethodsThe Seattle Obesity Study (SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index (HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity (PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months’ exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors (HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally.ResultsNone of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict better diets and more physical activity. Better diets and more physical activity were associated with lower obesity prevalence at baseline and 12 mo, but did not predict weight change.ConclusionAny links between the BE and health outcomes critically depend on establishing appropriate behavioral pathways. In this study, home-centric BE measures, were not related to physical activity or to diet. Further studies will need to consider a broader range of BE attributes that may be related to diets and health.

ObjectiveLower socioeconomic status (SES) has been linked with higher obesity rates but not with weight gain. This study examined whether SES can predict short-term weight change.Design and MethodsThe Seattle Obesity Study II was based on an observational cohort of 440 adults. Weights and heights were measured at baseline and at 1 y. Self-reported education and incomes were obtained by questionnaire. Home addresses were linked to tax parcel property values from the King Co. tax assessor. Associations among SES variables, prevalent obesity, and 1 y weight change were examined using multivariable linear regressions.ResultsLow residential property values at tax parcel predicted prevalent obesity at baseline and at 1 y. Living in the top quartile of house prices reduced obesity risk by 80% at both time points. At 1 year, about 38% of the sample lost >1kg body weight; 32% maintained (± 1kg), and 30% gained >1kg. In adjusted models, none of the baseline SES measures had any impact on 1 y weight change.ConclusionsSES variables, including tax parcel property values predicted prevalent obesity but did not predict short-term weight change. These findings, based on longitudinal cohort data, suggest other mechanisms are involved in short-term weight change.

BackgroundSelf-reported weights and heights can be subject to gender, socio-economic, and other biases. On the other hand, obtaining measured anthropometric data can pose a significant respondent burden.MethodsSeattle Obesity Study II (SOS II) participants (n = 419) provided self-reported height, weight, and demographic data through an interviewer-assisted behavior survey. Participants were then weighed and measured by trained staff. The entire process was repeated 12 months later. At the follow up visit, participants were also asked to recall their weight from 12 months ago. The concordance between measured and self-reported data was assessed using Bland-Altman plots.ResultsSome weight underreporting by obese individuals was observed. Gender or socio-economic status (SES) did not affect self-reports. Bland-Altman plots provided 95 % limits of agreement of −3.13 to 5.83 for weight (kg), and 1.21 to 2.52 for BMI (kg/m2). The concordance between measured and self-reported BMI categories was excellent (Kappa = 0.82 for men, and 0.86 for women). At the follow up visit, participants estimated their weight 12 months ago more accurately than their current weight.ConclusionsSelf-reported heights and weights were highly correlated with objective measures at two points in time. No gender or SES biases were observed. Minor, yet statistically significant under-reporting (<1.5 kg) was observed for obese participants. Caution should be used when using self-reported data in obese populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s40608-016-0088-2) contains supplementary material, which is available to authorized users.