scholarly journals Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach

2019 ◽  
Vol 55 (5) ◽  
pp. 599-610 ◽  
Author(s):  
Marco Helbich ◽  
Julian Hagenauer ◽  
Hannah Roberts
Keyword(s):  
Machine Learning ◽  
Low Income ◽  
Neighborhood Environment ◽  
Supervised Machine Learning ◽  
Relative Importance ◽  
Neighborhood Characteristics ◽  
Depression Severity ◽  
Cross Sectional ◽  
Neighborhood Environments ◽  
Low Educated

Abstract Purpose The physical and social neighborhood environments are increasingly recognized as determinants for depression. There is little evidence on combined effects of multiple neighborhood characteristics and their importance. Our aim was (1) to examine associations between depression severity and multiple perceived neighborhood environments; and (2) to assess their relative importance. Methods Cross-sectional data were drawn from a population-representative sample (N = 9435) from the Netherlands. Depression severity was screened with the Patient Health Questionnaire (PHQ-9) and neighborhood perceptions were surveyed. Supervised machine learning models were employed to assess depression severity-perceived neighborhood environment associations. Results We found indications that neighborhood social cohesion, pleasantness, and safety inversely correlate with PHQ-9 scores, while increasing perceived distance to green space and traffic were correlated positively. Perceived distance to blue space and urbanicity seemed uncorrelated. Young adults, low-income earners, low-educated, unemployed, and divorced persons were more likely to have higher PHQ-9 scores. Neighborhood characteristics appeared to be less important than personal attributes (e.g., age, marital and employment status). Results were robust across different ML models. Conclusions This study suggested that the perceived social environment plays, independent of socio-demographics, a role in depression severity. Contrasted with person-level and social neighborhood characteristics, the prominence of the physical neighborhood environment should not be overstated.

Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention

2022 ◽  
Vol 127 ◽  
pp. 108548
Author(s):  
Edward J. Camp ◽  
Robert J. Quon ◽  
Martha Sajatovic ◽  
Farren Briggs ◽  
Brittany Brownrigg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Self Management ◽  
Depression Severity ◽  
Management Intervention

Adults’ Daily Walking for Travel and Leisure: Interaction Between Attitude Toward Walking and the Neighborhood Environment

2016 ◽  
Vol 31 (5) ◽  
pp. 435-443 ◽  
Author(s):  
Yong Yang ◽  
Ana V. Diez-Roux
Keyword(s):  
New York ◽  
Los Angeles ◽  
Positive Attitude ◽  
Mail Survey ◽  
Neighborhood Environment ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Walking Behavior ◽  
Walk Score ◽  
Neighborhood Environments

Purpose: Studies on how the interaction of psychological and environmental characteristics influences walking are limited, and the results are inconsistent. Our aim is to examine how the attitude toward walking and neighborhood environments interacts to influence walking. Design: Cross-sectional phone and mail survey. Setting: Participants randomly sampled from 6 study sites including Los Angeles, Chicago, Baltimore, Minneapolis, Manhattan, and Bronx Counties in New York City, and Forsyth and Davidson Counties in North Carolina. Participants: The final sample consisted of 2621 persons from 2011 to 2012. Measures: Total minutes of walking for travel or leisure, attitude toward walking, and perceptions of the neighborhood environments were self-reported. Street Smart (SS) Walk Score (a measure of walkability derived from a variety of geographic data) was obtained for each residential location. Analysis: Linear regression models adjusting for age, gender, race/ethnicity, education, and income. Results: Attitude toward walking was positively associated with walking for both purposes. Walking for travel was significantly associated with SS Walk Score, whereas walking for leisure was not. The SS Walk Score and selected perceived environment characteristics were associated with walking in people with a very positive attitude toward walking but were not associated with walking in people with a less positive attitude. Conclusion: Attitudes toward walking and neighborhood environments interact to affect walking behavior.

Neighborhood Environment, Access to Places for Activity, and Leisure-Time Physical Activity in a Diverse North Carolina Population

2003 ◽  
Vol 18 (1) ◽  
pp. 58-69 ◽  
Author(s):  
Sara L. Huston ◽  
Kelly R. Evenson ◽  
Philip Bors ◽  
Ziya Gizlice
Keyword(s):  
Physical Activity ◽  
North Carolina ◽  
Environmental Factors ◽  
Leisure Time ◽  
Leisure Time Physical Activity ◽  
Heavy Traffic ◽  
Leisure Activity ◽  
Neighborhood Environment ◽  
Neighborhood Characteristics ◽  
Cross Sectional

Purpose. To examine associations between perceived neighborhood characteristics, access to places for activity, and leisure-time physical activity. Design. Cross-sectional telephone survey. Setting. Cabarrus, Henderson, Pitt, Robeson, Surry, and Wake counties in North Carolina. Subjects. Population-based sample of 1796 adults at least 18 years of age residing in the six counties. Measures. The 133-item questionnaire assessed self-reported leisure-time physical activity and perceptions of neighborhood characteristics (sidewalks, trails, heavy traffic, streetlights, unattended dogs, and safety from crime) and general access to places for physical activity. Results. Trails, streetlights, and access to places were positively associated with engaging in any leisure activity: unadjusted odds ratio (OR) (95% confidence interval [CI]); 1.62 (1.09–2.41), 1.57 (1.14–2.17), and 2.94 (1.91–4.52), respectively. Trails and access to places were positively associated with engaging in the recommended amount of leisure activity: 1.49 (1.00–2.22), and 2.28 (1.30–4.00), respectively). In multivariable logistic regression modeling including environmental factors and demographics, access to places was associated with any activity (2.23 [1.44–3.44]) and recommended activity (2.15 [1.23–3.77]), and trails were associated with recommended activity (1.51 [1.00–2.28]). Conclusion. Certain neighborhood characteristics, particularly trails, and access to places for physical activity may be associated with leisure activity levels. In this study, perceived neighborhood environmental factors and access to places for physical activity were strongly associated with race, education, and income.

scholarly journals Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample (Preprint)

2020 ◽  
Author(s):  
Dina Huang ◽  
Yuru Huang ◽  
Sahil Khanna ◽  
Pallavi Dwivedi ◽  
Natalie Slopen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Representative Sample ◽  
Public Health Research ◽  
Cross Sectional Study ◽  
Neighborhood Environment ◽  
Neighborhood Characteristics ◽  
Lower Prevalence ◽  
Cross Sectional ◽  
Individual Level ◽  
Nationally Representative

BACKGROUND Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. OBJECTIVE This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. METHODS We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter’s Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. RESULTS Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI –1.10 to –0.20) and 0.85 kg/m<sup>2</sup> (95% CI –1.48 to –0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively. CONCLUSIONS Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

scholarly journals The Role of Individual and Neighborhood Characteristics on Mental Health after a Period of Economic Crisis in the Lisbon Region (Portugal): A Multilevel Analysis

2019 ◽  
Vol 16 (15) ◽  
pp. 2647 ◽  
Author(s):  
Loureiro ◽  
Santana ◽  
Nunes ◽  
Almendra
Keyword(s):  
Mental Health ◽  
Economic Crisis ◽  
Cross Sectional Study ◽  
Poor Mental Health ◽  
Individual Characteristics ◽  
Neighborhood Environment ◽  
Neighborhood Characteristics ◽  
Multilevel Regression ◽  
Cross Sectional ◽  
The Individual

Mental health is an intrinsic dimension of health influenced by individual and contextual factors. This cross-sectional study analyzes the association between the individual, neighborhood characteristics, and one’s self-assessed mental health status in the Lisbon region after an economic crisis. Via the application of multilevel regression models, the study assesses the link between one’s neighborhood environment—deprivation, low self-assessed social capital, and low self-assessed satisfaction with the area of residence—and mental health regardless of one’s individual characteristics. Constraints related to the economic crisis play an important role in the explanation of poor mental health.

scholarly journals Neighborhood Environment Perceptions among Latinos in the U.S.

2019 ◽  
Vol 16 (17) ◽  
pp. 3062 ◽  
Author(s):  
Lilian G. Perez ◽  
John M. Ruiz ◽  
David Berrigan
Keyword(s):  
Survey Design ◽  
Age Factors ◽  
Cross Sectional Study ◽  
Neighborhood Environment ◽  
Foreign Born ◽  
Cross Sectional ◽  
Environmental Interventions ◽  
Low Education ◽  
Neighborhood Environments ◽  
The U.S

In the U.S., immigrants and racial/ethnic minorities (e.g., Latinos) often report unfavorable neighborhood environments, which may hinder physical activity (PA). Among Latinos, PA levels are disproportionately lower in foreign-born, female, older, and low-education individuals. It is unclear whether these subgroups, including those from multiple disadvantaged backgrounds (e.g., low education, foreign-born), perceive worse neighborhood environments for PA. This cross-sectional study aimed to examine differences in neighborhood environment perceptions among Latinos in the 2015 National Health Interview Survey (N = 4643; 59% foreign-born). Logistic regression models examined nativity—and its interactions with age, gender, and education—in relation to the perceived presence of transportation infrastructure (two items) and destinations (four items), controlling for self-reported walking. Models used sample weights and accounted for the complex survey design. Nativity was not significantly associated with neighborhood environment perceptions. However, nativity interactions with age and education showed the greatest inequities (lowest perceptions) of neighborhood infrastructure (e.g., fewer sidewalks) or destinations (e.g., fewer places to relax) among disadvantaged U.S.-born (older or low education) and advantaged foreign-born (higher education) Latinos. Findings suggest neighborhood perceptions are shaped by complex interactions of nativity with structural (education) and contextual (age) factors. Additional research is needed to complement our findings and inform environmental interventions targeting Latinos.

scholarly journals Ambiente percebido do bairro e atividade física no lazer em adultos de Curitiba, Brasil

2017 ◽  
Vol 19 (5) ◽  
pp. 596 ◽  
Author(s):  
Adriano Akira Ferreira Hino ◽  
Cassiano Ricardo Rech ◽  
Priscila Bezerra Gonçalves ◽  
Rodrigo Siqueira Reis
Keyword(s):  
Physical Activity ◽  
Leisure Time ◽  
Leisure Time Physical Activity ◽  
Vigorous Physical Activity ◽  
Public Spaces ◽  
Neighborhood Environment ◽  
Neighborhood Characteristics ◽  
Cross Sectional ◽  
Moderator Effect ◽  
Binary Model

The aim of this study was to analyze the association between perceived neighborhood characteristics and leisure time physical activity (PA) and the moderator effect of gender, age, schooling and time spent working/studying on perceived environment and leisure PA in adults. This is a cross-sectional study conducted with 699 adults (53.1% women), distributed from 32 census tracts selected according to walkability and neighborhood income characteristics in Curitiba. Perceived neighborhood characteristics were assessed using the Brazilian version of the Neighborhood Environment Walkability Scale-Abbreviated (A-NEWS). Leisure time PA was evaluated through the long-version IPAQ and walking and moderate to vigorous physical activity (MVPA) were analyzed separately. PA classification considered ≥10 minutes/week and ≥150 minutes/week of walking or MVPA. Associations were tested using a multilevel logistic binary model. After adjusting for potential confounders, aesthetics perception was associated with ≥10 minutes/week of walking. Additionally, access to public places for leisure remained associated with ≥10 minutes/week and ≥150 minutes/week of MVPA. The relationship between perceived access to public spaces, walking and MVPA were stronger in women and younger adults. It is concluded that a better perception of neighborhood aesthetics was associated with the practice of walking and access to public spaces with the practice of MVPA, respectively.

scholarly journals Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample

10.2196/17969 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e17969
Author(s):  
Dina Huang ◽  
Yuru Huang ◽  
Sahil Khanna ◽  
Pallavi Dwivedi ◽  
Natalie Slopen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Representative Sample ◽  
Public Health Research ◽  
Cross Sectional Study ◽  
Neighborhood Environment ◽  
Neighborhood Characteristics ◽  
Lower Prevalence ◽  
Cross Sectional ◽  
Individual Level ◽  
Nationally Representative

Background Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. Objective This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. Methods We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter’s Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. Results Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI –1.10 to –0.20) and 0.85 kg/m2 (95% CI –1.48 to –0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively. Conclusions Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

Malnutrition in all its forms and social inequalities in Brazil

2019 ◽  
Vol 23 (S1) ◽  
pp. s29-s38
Author(s):  
Daniela Silva Canella ◽  
Ana Clara Duran ◽  
Rafael Moreira Claro
Keyword(s):  
Short Stature ◽  
Low Income ◽  
Social Inequalities ◽  
Mixed Race ◽  
Cross Sectional Study ◽  
Excess Weight ◽  
White Adolescents ◽  
Cross Sectional ◽  
Race Ethnicity ◽  
Low Educated

AbstractObjective:To describe malnutrition (undernutrition and excess weight) by income, education and race/ethnicity in the Brazilian population.Design:Cross-sectional study.Setting:Brazil.Participants:Children aged <5 years (n 14 580), adolescents aged 11–19 years (n 31 892) and adults aged 20–49 years (n 84 660).Results:Among children, prevalence of excess weight, wasting and stunting was 16·9, 2·8 and 6·0 %, respectively. Differences related to income, education and race/ethnicity were verified, except for prevalence of wasting by education level. Girls and boys presented 18·4 and 20·5 % of excess weight, 2·8 and 3·7 % of underweight and 5·5 and 7·3 % of stunting, respectively. Prevalence of excess weight was lower among poorer, lower-educated (only for boys) and white adolescents, while stunting was lower among higher-income, higher-educated and white adolescents. Over three-quarters of women and almost half of men presented excess weight. Among adults, 3·9 % of women and 1·7 % of men were underweight, and 5·7 % of women and 0·2 % of men presented short stature. Prevalence of excess weight for women was higher among lower education and black, while for men it was higher among higher income and education and white. Short stature was more prevalent among black and mixed-race, low-educated and low-income women. Underweight prevalence was higher among low-educated, black and mixed-race women.Conclusions:In Brazil, the prevalence of excess weight was at least threefold higher than that of undernutrition for children and adolescents and at least sevenfold higher for adults. Social inequalities were observed in the distribution of malnutrition across the lifespan and by gender.

scholarly journals Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroki Kaneko ◽  
Hironobu Umakoshi ◽  
Masatoshi Ogata ◽  
Norio Wada ◽  
Norifusa Iwahashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
General Practice ◽  
Primary Aldosteronism ◽  
Blood Test ◽  
Characteristic Curve ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cross Sectional ◽  
Increased Risk

AbstractPrimary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Overall, 91 patients with unilateral and 138 patients with bilateral PA were randomly assigned to the training and test cohorts. Four supervised machine learning classifiers; logistic regression, support vector machines, random forests (RF), and gradient boosting decision trees, were used to develop predictive models from 21 clinical variables. The accuracy and the area under the receiver operating characteristic curve (AUC) for predicting of subtype diagnosis of PA in the test cohort were compared among the optimized classifiers. Of the four classifiers, the accuracy and AUC were highest in RF, with 95.7% and 0.990, respectively. Serum potassium, plasma aldosterone, and serum sodium levels were highlighted as important variables in this model. For feature-selected RF with the three variables, the accuracy and AUC were 89.1% and 0.950, respectively. With an independent external PA cohort, we confirmed a similar accuracy for feature-selected RF (accuracy: 85.1%). Machine learning models developed using blood test can help predict subtype diagnosis of PA in general practice.

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