There is growing interest in understanding which aspects of the local environment influence obesity. Using data from the longitudinal West of Scotland Twenty-07 study ( = 2040) we examined associations between residents' self-reported neighbourhood problems, measured over a 13-year period, and nurse-measured body weight and size (body mass index, waist circumference, waist⁻hip ratio) and percentage body fat. We also explored whether particular measures such as abdominal obesity, postulated as a marker for stress, were more strongly related to neighbourhood conditions. Using life course models adjusted for sex, cohort, household social class, and health behaviours, we found that the accumulation of perceived neighbourhood problems was associated with percentage body fat. In cross-sectional analyses, the strongest relationships were found for contemporaneous measures of neighbourhood conditions and adiposity. When analyses were conducted separately by gender, perceived neighbourhood stressors were strongly associated with central obesity measures (waist circumference, waist⁻hip ratio) among both men and women. Our findings indicate that chronic neighbourhood stressors are associated with obesity. Neighbourhood environments are modifiable, and efforts should be directed towards improving deleterious local environments to reduce the prevalence of obesity.
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http://dx.doi.org/10.3390/ijerph15091854 | DOI Listing |
Sleep Epidemiol
December 2024
Socio-Spatial Determinants of Health (SSDH) Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland.
Introduction: Research suggests that perceived neighborhood social environments (PNSE) may contribute to gender and race/ethnicity-based sleep disparities. Our study aimed to examine associations between PNSE factors and adolescents' sleep patterns. As a secondary aim, we examined how gender and race/ethnic groups might moderate these associations.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Health Administration, Yonsei University Graduate School, Wonju, Republic of Korea.
This study is the first to examine the determinants of future anxiety in South Korea using the Social Ecological Model (SEM). It aimed to show that, beyond individual factors, mezzo- and macro-level aspects, particularly those related to housing, may influence anxiety. Utilizing 2018 data from the Korean Health Panel Survey, we employed a three-level multilevel analysis to investigate how these factors contribute to the perception of future anxiety among Koreans.
View Article and Find Full Text PDFJ Frailty Aging
February 2025
Division of Geriatrics and Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, USA.
Background: Pre-frailty is highly prevalent and multimodal lifestyle interventions are effective for preventing transition to frailty. However, little is known about the potential for medical group visits (MGV) to prevent frailty progression.
Objectives: To assess the feasibility and acceptability of the MGV Age Self Care-Resilience.
J Frailty Aging
February 2025
National Center for Geriatrics and Gerontology, Aichi 474-8511, Japan.
Background: Skin tactile perception may indicate frailty in older adults. Although gait performance is crucial for diagnosing frailty, its association with skin tactile perception has not yet been explored.
Objectives: To examine the association between skin tactile perception and changes in step length, cadence, and gait speed in middle-aged and older adults.
Neural Netw
January 2025
Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China. Electronic address:
Entity alignment (EA) is a typical strategy for knowledge graph integration, aiming to identify and align different entity pairs representing the same real object from different knowledge graphs. Temporal Knowledge Graph (TKG) extends the static knowledge graph by introducing timestamps. However, since temporal knowledge graphs are constructed based on their own data sources, this usually leads to problems such as missing or redundant entity information in the temporal knowledge graph.
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