Social and demographic factors predictive of change over seven years in CHD-related behaviours in men aged 18–49 years

Boniface,; Cottee, MJ; Neal, Diane; Skinner, Adam

doi:10.1038/sj.ph.1900762

Cited by 10 publications

(16 citation statements)

References 26 publications

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“…Three that have been described previously reported a positive effect of education on changes in LTPA [60,70] and habitual physical activity [66], although the size and consistency of the effect across educational groups varied and was influenced by age in the latter study [66]. Further, Kaplan and Lazarus [54] excluded education from the full regression model.…”

Section: As Illustrated Inmentioning

confidence: 99%

“…Boniface et al [66] found that occupational class measured using two different scales was not a significant predictor of uptake or maintenance of physical activity in British men. Kaplan and Lazarus [54] reported that being in a white-versus bluecollar occupation was positively associated with physical activity change in basic regression analyses (adjusting for gender, age, BMI, smoking and baseline physical activity), although income was used as the sole socio-economic variable in the full model.…”

Section: Outcomes For Occupational Social Classmentioning

confidence: 99%

“…Neither longitudinal study reporting occupational class [54,66] was of high quality (QR4). Boniface et al [66] found that occupational class measured using two different scales was not a significant predictor of uptake or maintenance of physical activity in British men.…”

Section: Outcomes For Occupational Social Classmentioning

confidence: 99%

“…Twenty-eight cross-sectional and five longitudinal studies conducted in ten different countries met the inclusion criteria: America (n=16) [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54], Australia (n=5) [55][56][57][58][59], Canada (n=3) [60][61][62], Spain (n=1) [63], England (n=3) [64][65][66], Finland (n=1) [67], Sweden (n=1) [68], France (n =1) [69], the Netherlands (n=1) [70] and Greece (n=1) [71]. Two cross-sectional studies by Crespo et al [46,47] reported different analyses of data from the same sample and are treated as a single study (n=32).…”

Section: Country Of Originmentioning

confidence: 99%

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A systematic review of the relationship between socio-economic position and physical activity

Gidlow

Johnston

Crone

et al. 2006

Health Education Journal

356

288

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Objective The aim of the present review was to examine epidemiological evidence to determine if there is strong evidence of a positive gradient of increasing physical activity across the socio-economic strata, and how relationships are affected by socio-economic measurement. Design Systematic review. Method A search of major databases was conducted to identify published studies that reported physical activity in relation to socio-economic position (SEP) in adults. Results Twenty-eight cross-sectional and five longitudinal studies met the inclusion criteria. Approximately half of these were American. Consequently education and income were most commonly used to represent SEP. The majority of studies were secondary analyses of existing health survey data, which could explain the generally large sample sizes and methodological weaknesses in physical activity and SEP measurement. There was consistent evidence of a higher prevalence or higher levels of leisure-time or moderate-vigorous intensity physical activity in those at the top of the socio-economic strata compared with those at the bottom. Evidence for positive gradients across the socio-economic strata was less consistent. Education produced the most stable relationships, less susceptible to confounding effects of ethnicity and the environment. Conclusion Those at the top of the socio-economic scale appear to perform more leisure-time activity than those at the bottom. Diverse and often crude physical activity and socio-economic measurement made it difficult to distinguish between artefact and true effect in a relationship with so many potential confounding influences. Further studies using up-to-date methods of socio-economic and physical activity measurement are necessary to further explore this relationship and its confounders.

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Section: As Illustrated Inmentioning

confidence: 99%

Section: Outcomes For Occupational Social Classmentioning

confidence: 99%

Section: Outcomes For Occupational Social Classmentioning

confidence: 99%

Section: Country Of Originmentioning

confidence: 99%

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A systematic review of the relationship between socio-economic position and physical activity

Gidlow

Johnston

Crone

et al. 2006

Health Education Journal

356

288

View full text Add to dashboard Cite

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“…Bizim çalışmamıza benzer sonuçlar Birleşik Krallık'ta ve Fransa'da elde edilmiştir. Bu çalışmalarda öğrenim durumunun fiziksel aktiviteyi etkilemediği belirlenmiştir (31,32). Sigara kullanımında olduğu gibi, sedanter yaşamın gelişmiş ülkelerde düşük sosyal sınıfların, az gelişmiş ülkelerde yüksek sosyal sınıfların bir sorunu olduğuna ilişkin genel bir görüş bulunmaktadır (13).…”

Section: Sosyal Eşitsizlikler Ve Fiziksel Inaktiviteunclassified

Effects of social inequalities on coronary heart disease risk factors: a population-based, cross-sectional study in İzmir

Şimşek¹,

Günay²,

Uçku³

2010

Anadolu Kardiyol Derg

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ÖZETAmaç: İzmir'de Balçova Belediyesi'nin bir mahallesinde yaşayan 30 yaş ve üzeri bireylerde, sosyal eşitsizliklerin koroner kalp hastalığı risk etmenlerine etkisinin belirlenmesidir. Yöntemler: Kesitsel tipte planlanan araştırmanın evrenini 30 yaş ve üzeri 4409 kişi oluşturmaktadır. Örnek seçilmeyip tüm evrene ulaşmak hedeflenmiştir. Çalışmanın bağımlı değişkenleri; sigara kullanımı, fiziksel inaktivite, sağlıksız beslenme, şişmanlık, hipertansiyon ve riski, diyabet ve riski, kan yağlarındaki bozukluktur. Bağımsız değişken sosyal eşitsizliktir. Sosyal eşitsizlik değişkenleri olarak öğrenim durumu, gelir ve sosyal sınıf alınmıştır. Karıştırıcı etmenler yaş, cinsiyet ve aile öyküsüdür. Veri evde ve semtevinde olmak üzere iki aşamalı toplanmıştır. Veri Ki-kare testi ve lojistik regresyon analizi ile çözümlenmiştir. Bulgular: Sigara kullanımı riski kadınlarda ve erkeklerde düşük sosyal sınıflarda; sağlıksız beslenme riski kendi hesabına çalışan erkeklerde (OR=9.24, %95 GA=1.14-74.81, p=0.037) ve düşük öğrenimli (OR=1.53, %95 GA=1.02-2.30, p=0.040), düşük gelirli ya da işsiz-çalışmayan kadınlarda (OR=3.43, %95 GA=1.28-9.14, p=0.014); şişmanlık riski düşük öğrenimli kadınlarda (OR=1.89, %95 GA=1.37-2.59, p<0.001); yüksek-dansiteli lipoprotein (HDL) riski düşük gelirli erkeklerde (OR=2.21, %95 GA= 1.20-4.07, p=0.011) daha yüksektir. Erkeklerde düşük öğrenim düzeyine sahip olmak (OR=0.61, %95 GA=0.38-0.98, p=0.039), beyaz yakalı-yüksek nitelikli (OR=0.37, %95 GA=0.16-0.89, p=0.027) ya da mavi yakalı-niteliksiz hizmet işçisi olmak (OR=0.35, %95 GA=0.15-0.82, p=0.016) şişmanlık açısından; beyaz yakalı-yüksek nitelikli çalışan olmak (OR=0.53, %95 GA=0.29-0.95, p=0.033) düşük-dansiteli lipoprotein riski açısından koruyucudur. Sonuç: Çalışma bölgesinde yaşayan 30 yaş ve üzeri kişilerde koroner kalp hastalığının riskleri yüksektir. Erkekler kadınlardan daha risklidir. Sosyal eşitsizlikler arttıkça erkeklerde HDL riski; kadınlarda sigara kullanımı, sağlıksız beslenme, şişmanlık artmaktadır. ABSTRACTObjective: Aim of the study was to determine effects of social inequalities on coronary heart disease risk factors in individuals living in an urban district of İzmir; namely Balçova. Methods: In this cross-sectional study, no sampling was done and it was aimed to reach all residents living in that area aged 30 years and over (n=4409). Dependent variables were smoking, physical inactivity, unhealthy diet, obesity, hypertension, diabetes, dyslipidemia where social inequality was the independent variable. Educational status, income and social class were considered as the components of social inequality. Age, gender, family health histories were the confounding factors. Data were collected at home and in the neighborhood-house. For data analyses Chi-square test, and logistic regression analysis were used. Results: Smoking is higher in men and women in lower socio economic class, where unhealthy diet is higher in self-employed men (OR=9.24, 95% CI=1.14-74.81, p=0.037) and in women who have a lower education (OR=1.53, 95% CI=1.02-2.30...

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OBSOLETE: Socioeconomic Factors and CVD

Kabayama¹,

Kamide²

2018

Reference Module in Biomedical Sciences

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Social and demographic factors predictive of change over seven years in CHD-related behaviours in men aged 18–49 years

Cited by 10 publications

References 26 publications

A systematic review of the relationship between socio-economic position and physical activity

A systematic review of the relationship between socio-economic position and physical activity

Effects of social inequalities on coronary heart disease risk factors: a population-based, cross-sectional study in İzmir

OBSOLETE: Socioeconomic Factors and CVD

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