A cross-sectional analysis of physical activity and weight misreporting in diverse populations: The Seattle Obesity Study III

Investigating the effects of rurality on stress, subjective well-being, and weight-related outcomes

Purpose

Rates of obesity are significantly higher for those living in a rural versus urban setting. High levels of stress and low levels of subjective well-being (SWB) have been linked to poor weight-related behaviors and outcomes, but it is unclear if these relationships differ as a function of rurality. This study investigated the extent to which living in a rural versus urban county ("rurality") moderated associations between stress / subjective wellbeing (predictors) and diet quality, dietary intake of added sugars, physical activity, and BMI (outcomes).

Methods

Participants were recruited from urban (n = 355) and rural (n = 347) counties in Washington State and self-reported psychological, demographic, and food frequency questionnaires while physical activity behavior was measured objectively.

Findings

After controlling for relevant covariates, levels of stress were positively associated with added sugar intake for those living in the urban county while this relationship was non-significant for those residing in the rural county. Similarly, SWB was negatively associated with added sugar intake, but only for urban residents. County of residence was also found to moderate the relationship between SWB and BMI. Higher SWB was inversely associated with BMI for those living in the urban county while no relationship was observed for rural county residents.

Conclusions

These findings support the hypothesis that the relationships between stress / SWB and weight function differentially based on the rurality of the residing county. This work adds to the growing body of literature highlighting the role stress and SWB play in the rural obesity disparity.

Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults

This scoping review identified observational studies of adults that utilized accelerometry to assess physical activity and sedentary behavior. Key elements on accelerometry data collection were abstracted to describe current practices and completeness of reporting. We searched three databases (PubMed, Web of Science, and SPORTDiscus) on June 1, 2021 for articles published up to that date. We included studies of non-institutionalized adults with an analytic sample size of at least 500. The search returned 5686 unique records. After reviewing 1027 full-text publications, we identified and abstracted accelerometry characteristics on 155 unique observational studies (154 cross-sectional/cohort studies and 1 case control study). The countries with the highest number of studies included the United States, the United Kingdom, and Japan. Fewer studies were identified from the continent of Africa. Five of these studies were distributed donor studies, where participants connected their devices to an application and voluntarily shared data with researchers. Data collection occurred between 1999 to 2019. Most studies used one accelerometer (94.2%), but 8 studies (5.2%) used 2 accelerometers and 1 study (0.6%) used 4 accelerometers. Accelerometers were more commonly worn on the hip (48.4%) as compared to the wrist (22.3%), thigh (5.4%), other locations (14.9%), or not reported (9.0%). Overall, 12.7% of the accelerometers collected raw accelerations and 44.6% were worn for 24 hours/day throughout the collection period. The review identified 155 observational studies of adults that collected accelerometry, utilizing a wide range of accelerometer data processing methods. Researchers inconsistently reported key aspects of the process from collection to analysis, which needs addressing to support accurate comparisons across studies.

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

Objective

To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1- and 2-year changes in body mass index (BMI).

Methods

The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1–3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education.

Results

Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity.

Conclusion

Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1- and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

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Strong, inverse cross-sectional relationships between the built environment, residential property values (a proxy for individual socioeconomic status), and measured BMI were observed.

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Measures of the built environment and residential property values showed modest and inconsistent associations with 1- and 2-year BMI change.

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There was suggestive evidence that age may moderate the association between urban density and 1- and 2-year BMI change while education may moderate the association between residential property values and 2-year BMI change.

Infancy Dietary Patterns, Development, and Health: An Extensive Narrative Review

Correct dietary patterns are important for a child’s health from birth to adulthood. Understanding a child’s health as a state of entire physical, mental, and social well-being is essential. However, reaching adulthood in a complete health proper state is determined by feeding and dietary habits during preconception, pregnancy, or children infancy. Different factors, such as the mother’s lifestyle, culture, or socioeconomic status, are crucial during all these phases. In this review, we aimed to assess the long-term associations between infancy dietary patterns and health and their influence on development and growth. To reach this objective, a consensus critical review was carried out using primary sources such as scientific articles, and secondary bibliographic indexes, databases, and web pages. PubMed, SciELO, and Google Scholar were the tools used to complete this research. We found that high-income countries promote high-calorie foods and, consequently, obesity problems among children are rising. However, undernutrition is a global health issue concerning children in low- and middle-income countries; thus, parental socioeconomic status in early life is essential to children’s health and development, showing that biological, social, and environmental influences are increased risk factors for chronic diseases. This narrative review is aimed to collect evidence for early nutritional intervention and future disease prevention.

Associations between socioeconomic status and physical activity among older adults: cross-sectional results from the OUTDOOR ACTIVE study

BACKGROUND

Regular physical activity (PA) is an important strategy for healthy ageing. Socioeconomic status was found to be a key determinant of PA, however, evidence on associations between socioeconomic status and PA among older adults is limited. The aim of this study was to contribute to research on the associations of socioeconomic status and PA among older adults by including self-reported and objectively measured PA data. Furthermore, we examined the self-reported PA data more closely by looking at the activities separately.

METHODS

Cross-sectional data of 1507 participants (52.5% female) of the OUTDOOR ACTIVE study between 65 and 75 years, residing in Bremen, Germany, were included in the analyses. Self-reported PA was assessed via questionnaire and comprised all organised and non-organised activities. For analyses, mean hours per week of total and moderate to vigorous PA, and mean metabolic equivalents per week were used. Objectively measured PA was assessed using accelerometers over seven consecutive days. Socioeconomic status was included as an additive social class index containing education, income, and occupation. To test for associations between PA and socioeconomic status, linear regressions were carried out.

RESULTS

Self-reported PA showed significant negative associations with socioeconomic status for both men and women. Objectively measured PA was positively associated with socioeconomic status, which was significant in men but not in women. When examining physical activities separately, time spent on housework, gardening, biking, and walking decreased with increasing socioeconomic status. Women in the second SES quintile and men in the third quintile reported the most, and women in the first quintile and men in the fifth quintile the least hours per week spent on exercise.

CONCLUSIONS

The results of this study contributed to the existing research gap on the associations of socioeconomic status and PA among older adults. Moreover, we provided information on both self-reported and objectively measured PA, and showed the discrepancies in the two methods' results. These findings can help to develop PA promotion interventions targeting specific socioeconomic status groups and to develop accurate, valid, and reliable self-reported and objective measurements of PA for older adults.

From the clinic to the community: Can health system data accurately estimate population obesity prevalence?

OBJECTIVE

Health system data were assessed for how well they can estimate obesity prevalence in census tracts.

METHODS

Clinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from the Behavioral Risk Factor Surveillance System (BRFSS). The health system data were geocoded to identify each patient's tract of residence, and the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005 to 2006 and the concordance between University of Washington Medicine and BRFSS from 2012 to 2016 were assessed.

RESULTS

The spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). The University of Washington Medicine estimates of rank order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine versus 20% for BRFSS, Wilcoxon rank sum test p < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment.

CONCLUSIONS

Health system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.

Some Ultra-Processed Foods Are Needed for Nutrient Adequate Diets: Linear Programming Analyses of the Seattle Obesity Study

Typical diets include an assortment of unprocessed, processed, and ultra-processed foods, along with culinary ingredients. Linear programming (LP) can be used to generate nutritionally adequate food patterns that meet pre-defined nutrient guidelines. The present LP models were set to satisfy 22 nutrient standards, while minimizing deviation from the mean observed diet of the Seattle Obesity Study (SOS III) sample. Component foods from the Fred Hutch food frequency questionnaire comprised the market basket. LP models generated optimized 2000 kcal food patterns by selecting from all foods, unprocessed foods only, ultra-processed foods only, or some other combination. Optimized patterns created using all foods contained less fat, sugar, and salt, and more vegetables compared to the SOS III mean. Ultra-processed foods were the main sources of added sugar, saturated fat and sodium. Ultra-processed foods also contributed most vitamin E, thiamin, niacin, folate, and calcium, and were the main sources of plant protein. LP models failed to create optimal diets using unprocessed foods only and ultra-processed foods only: no mathematical solution was obtained. Relaxing the vitamin D criterion led to optimized diets based on unprocessed or ultra-processed foods only. However, food patterns created using unprocessed foods were significantly more expensive compared to those created using foods in the ultra-processed category. This work demonstrates that foods from all NOVA categories can contribute to a nutritionally adequate diet.

Inedible Food Waste Linked to Diet Quality and Food Spending in the Seattle Obesity Study SOS III

Americans waste about a pound of food per day. Some of this is represented by inedible food waste at the household level. Our objective was to estimate inedible food waste in relation to diet quality and participant socio-economic status (SES). Seattle Obesity Study III participants (n = 747) completed the Fred Hutch Food Frequency Questionnaire (FFQ) and socio-demographic and food expenditure surveys. Education and geo-coded tax-parcel residential property values were measures of SES. Inedible food waste was calculated from diet records. Retail prices of FFQ component foods (n = 378) were used to estimate individual-level diet costs. The NOVA classification was used to identify ultra-processed foods. Multivariable linear regressions tested associations between inedible food waste, SES, food spending, Nutrient Rich Food (NRF_9.3) and Healthy Eating Index (HEI-2015) scores. Inedible food waste was estimated at 78.7 g/d, mostly from unprocessed vegetables (32.8 g), fruit (30.5 g) and meat, poultry, and fish (15.4 g). Greater inedible food waste was associated with higher HEI-2015 and NRF_9.3 scores, higher food expenditures and lower percent energy from ultra-processed foods. In multivariable models, more inedible food waste was associated with higher food expenditures, education and residential property values. Higher consumption of unprocessed foods were associated with more inedible food waste and higher diet costs. Geo-located estimates of inedible food waste can provide a proxy index of neighborhood diet quality.