Analysis of sedentariness in women from a gender and equity perspective.

ABSTRACTThis cross-sectional study aimed to analyse the social and gender-related factors influencing sedentariness in women, including occupation and family size (FS). We included women aged 45-70 years participating in the Breast Cancer Screening Programme of the Valencia Region (BCSP-VR) between November 2018 and October 2019 (n = 121,988). The response variable was sedentariness measured by sitting time in hours/day (h/day) (<3 h/day and ≥3 h/day). The explanatory variables were age, educational level, country of origin, living alone, childcare responsibilities, FS, disability, body mass index (BMI) and smoking status. Logistic regression models were adjusted for the whole sample and were stratified by occupation and FS. The variables that increased the odds of sedentariness were age ≥65 years (OR = 1.28; CI = 1.20-1.36), high educational level (OR = 1.39; CI = 1.31-1.47), non-manual occupation (OR = 2.38; CI = 2.27-2.48), living alone (OR = 1.11; CI = 1.05-1.17), disability (OR = 1.37; CI = 1.20-1.56) and BMI ≥ 30 (OR = 1.33; CI = 1.28-1.38). The variables decreasing the odds were large (OR = 0.90; CI = 0.87-0.96) and medium FS (OR = 0.93; CI = 0.90-0.97). Older age, high educational level and high BMI conferred a higher odd of sedentary lifestyle, independently of occupation and FS with statistical differences. Protective factors were childcare responsibilities in non-working women (OR = 0.86; CI = 0.74-0.99), large FS in women with manual occupations (OR = 0.88; CI = 0.80-0.97) and medium FS in housewives (OR = 0.91; CI = 0.86-0.97). This study identified social and gender-related inequalities in the factors influencing sedentariness, which were related to the type of occupation and FS.Highlights There are social and gender inequalities in the factors influencing sedentarinessNon-manual occupation increases the risk of sedentary lifestyleBelonging to a medium or large family decreases the risk of sedentary behaviourChildcare responsibilities are related to lower sitting time throughout the day.

Social mobility and healthy behaviours from a gender perspective in the Spanish multicase-control study (MCC-Spain).

There is evidence for the influence of socioeconomic status (SES) on healthy behaviours but the effect of social mobility (SM) is not yet well known. This study aims to analyse the influence of origin and destination SES (O-SES and D-SES) and SM on healthy behaviours and co-occurrence, from an integrated gender and age perspective. Data were obtained from the controls of MCC-Spain between 2008-2013 (3,606 participants). Healthy behaviours considered: healthy diet, moderate alcohol consumption, non-smoking and physical activity. SM was categorized as stable high, upward, stable medium, downward or stable low. Binary and multinomial logistic regression models were adjusted. Those aged <65, with a low O-SES, D-SES and stable low SM are less likely to have healthy behaviours in the case of both women (physically active: OR = 0.65 CI = 0.45-0.94, OR = 0.71 CI = 0.52-0.98, OR = 0.61 CI = 0.41-0.91) and men (non-smokers: OR = 0.44 CI = 0.26-0.76, OR = 0.54 CI = 0.35-0.83, OR = 0.41 CI 0.24-0.72; physically active: OR = 0.57 CI = 0.35-0.92, OR = 0.64 CI = 0.44-0.95, OR = 0.53 CI = 0.23-0.87). However, for those aged ≥65, this probability is higher in women with a low O-SES and D-SES (non-smoker: OR = 8.09 CI = 4.18-15.67, OR = 4.14 CI = 2.28-7.52; moderate alcohol consumption: OR = 3.00 CI = 1.45-6.24, OR = 2.83 CI = 1.49-5.37) and in men with a stable low SM (physically active: OR = 1.52 CI = 1.02-1.26). In the case of men, the same behaviour pattern is observed in those with a low O-SES as those with upward mobility, with a higher probability of co-occurring behaviours (three-to-four behaviours: OR = 2.00 CI = 1.22-3.29; OR = 3.13 CI = 1.31-7.48). The relationship of O-SES, D-SES and SM with healthy behaviours is complex and differs according to age and gender.

Compositional analysis of dietary patterns.

Instead of looking at individual nutrients or foods, dietary pattern analysis has emerged as a promising approach to examine the relationship between diet and health outcomes. Despite dietary patterns being compositional (i.e. usually a higher intake of some foods implies that less of other foods are being consumed), compositional data analysis has not yet been applied in this setting. We describe three compositional data analysis approaches (compositional principal component analysis, balances and principal balances) that enable the extraction of dietary patterns by using control subjects from the Spanish multicase-control (MCC-Spain) study. In particular, principal balances overcome the limitations of purely data-driven or investigator-driven methods and present dietary patterns as trade-offs between eating more of some foods and less of others.