Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros

Bases de dados
País/Região como assunto
Ano de publicação
Tipo de documento
País de afiliação
Intervalo de ano de publicação
1.
Int J Health Geogr ; 20(1): 19, 2021 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-33941196

RESUMO

INTRODUCTION: Individuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one's neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health. METHODS: In this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety. RESULTS: In this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (ß = - 2.34, p = 0.017), depression scores (ß = - 4.22, p = 0.006), and BMI (ß = - 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots. CONCLUSION: Future research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity-via stress reduction and concomitant effects on cardiometabolic health.


Assuntos
Doenças Cardiovasculares , Caminhada , Adulto , Emoções , Exercício Físico , Humanos , Saúde Mental , Michigan/epidemiologia , Características de Residência , Segurança
2.
Health Place ; 52: 240-246, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30015181

RESUMO

We systematically reviewed the current use of Google Street View (GSV) in health research and characterized major themes, strengths and weaknesses in order to highlight possibilities for future research. Of 54 qualifying studies, we found that most used GSV to assess the neighborhood built environment, followed by health policy compliance, study site selection, and disaster preparedness. Most studies were conducted in urban areas of North America, Europe, or New Zealand, with few studies from South America or Asia and none from Africa or rural areas. Health behaviors and outcomes of interest in these studies included injury, alcohol and tobacco use, physical activity and mental health. Major strengths of using GSV imagery included low cost, ease of use, and time saved. Identified weaknesses were image resolution and spatial and temporal availability, largely in developing regions of the world. Despite important limitations, GSV is a promising tool for automated environmental assessment for health research. Currently untapped areas of health research using GSV include identification of sources of air, soil or water pollution, park design and usage, amenity design and longitudinal research on neighborhood conditions.


Assuntos
Pesquisa Biomédica/métodos , Sistemas de Informação Geográfica , Características de Residência , Big Data , Planejamento Ambiental , Exposição Ambiental , Monitoramento Ambiental , Comportamentos Relacionados com a Saúde , Humanos , Software , População Urbana
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA