Previous studies have provided inconsistent findings on smoking among migrants, and very limited data exist on their second-hand smoke exposure. This study aims to investigate internal migrants’ smoking prevalence, second-hand smoke exposure among non-smokers, and knowledge of the health hazards of smoking in 12 major migrant provinces in China in 2013. Data from the 2013 Migrant Dynamics Monitoring Survey in China published by the National Commission of Health and Family Planning was used in this study. Descriptive analysis, Chi-square analysis, and sex-stratified multivariate logistic regression analysis were used to explore the determinants of current smoking and second-hand smoke exposure. Among 7200 migrants, 34.1% (55% male, 4% female) were current smokers. For males, factors associated with current smoking were education year (aOR = 0.95, 95% CI: 0.93–0.98), duration of stay (aOR = 1.01, 95% CI: 1.00–1.03) and occupation (aOR = 1.25, 95% CI: 1.03–1.53). For females, household registration status (aOR = 1.70, 95% CI: 1.04–2.80) was the most important factor associated with current smoking. Sixty five percent of non-smokers were exposed to second-hand smoke. Factors associated with exposure to second-hand smoke were duration of stay (aOR = 1.01, 95% CI: 1.00–1.02), divorced/widowed marital status (aOR = 0.48, 95% CI: 0.25–0.91), occupation (aOR = 1.29, 95% CI: 1.05–1.58) and the nature of employer (aOR = 0.77, 95% CI: 0.60–0.97). About 95% of participants were aware that lung cancer is one of the hazards of smoking. Non-current smokers had a better knowledge of fertility reduction and accelerated aging as hazards of smoking than current smokers (p < 0.01). Knowledge of the impact of smoking on cardiovascular diseases was relatively low compared with knowledge of other smoking-related hazards (26.1–44.3%). Current smoking and exposure to second-hand smoke among internal migrants in China is high. Socio-demographic characteristics and migration status were strongly associated with current smoking and second-hand smoke exposure. We recommend specifically targeted tobacco control interventions to help to address these risk factors, such as focusing on divorced/widowed women.

Background: Previous research examined the associations between social support and physical activity. However, little is known about the associations between social support change and trajectories of physical activity during the transition from late adolescence to young adulthood. Methods: The current study sought to examine these issues among 434 Chinese college students (M_age = 19.15, SD_age = 0.61; 46.1% male), who completed questionnaires regarding demographics, physical activity, family support change, and peer support change across three waves (the data from one of the waves was retrospective). Results: After controlling for covariates, the findings revealed that: (a) there was an increase in overall physical activity and duration, but a decrease in frequency during the transition from late adolescence (the second year of high school) to young adulthood (the third year of college); (b) family support change did not contribute to trajectories of physical activity, while peer support change significantly predicted the trajectory of overall physical activity, duration, and frequency. Conclusions: The findings extend the literature on physical activity from a developmental perspective by revealing different trends among physical activity duration and frequency, and unpacking different effects of family and peer support change on trajectories of physical activity.

This cross-sectional study classified the factors related to falls among residents ≥ 60 years old in China in order to provide evidence for the prevention of falls in the elderly. A total of 2994 participants were enrolled, and the correlations between social demography, physical health, self-health management awareness, family support, and fall risk were analyzed. Factors influencing falls were classified by location, cause, and the activity during falls. Suffering from osteoarthropathy (OR = 1.761, 95% CI: 1.234–2.513, p < 0.05), depression or anxiety (OR = 1.896, 95% CI: 1.331–2.700, p < 0.001), household size > 2 (OR = 1.806, 95% CI: 1.042–3.130, p < 0.05), and poor self-assessed health (OR = 1.478, 95% CI: 1.107–1.972, p < 0.01) were risk factors. Higher participation in community health programs (OR = 0.522, 95% CI: 0.298–0.912, p < 0.05) and spousal support (OR = 0.909, 95% CI: 0.841–0.981, p < 0.05) were protective factors. Falls were divided into the following categories: stairs/hallway (vision, attention problems), bath/toilet (vision, attention problems, slipping), indoor housework (dizziness, leg weakness), and outdoor activities (attention, surface problems). While acknowledging that the personal physical and mental health of the elderly may lead to falls, community support, accelerated transformation of housing, and the construction of barrier-free environments play an important role in reducing the risk of falls.

To analyze and assess health risk awareness of youth migrants in China and the factors that influence it, and to provide evidence for making health promotion interventions and decreasing health risks among Chinese youth migrants. This was a cross-sectional survey conducted in 2009 among rural-to-urban migrants aged 15–24 years in Tianjin and Xi’an, China. A total of 1,838 youth migrants were enrolled by the stratified cluster sampling method. An anonymous questionnaire was self-administered to investigate health risk awareness. The t test and χ² test were used to analyze differences between different groups. Logistic regression analysis was used to test the influence of various sociodemographic, living condition, and occupational factors. The smoking rate of men (66.8%) was higher than that of women (6.8%; P < 0.05), the rate of sexual intercourse in men was higher than in women (56.8 vs 27.7%; P < 0.05), and 75.7% of participants had written into medical care systems with 40.4% of them having undergone a physical examination during the last year. Only 438 of the participants (26% of 1,647) were considered to have a satisfactory level of health risk awareness [273 (32.4% of 958) from Tianjin and 165 (28.8% of 689) from Xi’an]. No significant difference was found between the youth migrant populations of the two cities. The percentage of youth migrants with a satisfactory level of health risk awareness who thought they had a good health status was higher than that with an unsatisfactory health risk awareness who thought they had a good health status (P < 0.05). Logistic regression analysis showed that gender, age, education, reading the newspaper, and occupation significantly influenced on health risk awareness. Youth migrants in China have a low health risk awareness. Combined and targeted health education interventions should be promoted to increase their health risk awareness.

Background: Numerous studies have clarified that family socioeconomic status (SES) is positively associated with health. However, the mechanism of family SES on health needs to be further investigated from a social epidemiological perspective. This study aims to analyze the relationships among family SES, family social capital, and adult general health and tests whether gender-based differences exist in the relationship between family social capital and general health. Methods: A cross-sectional survey was used to collect data from 4187 representative households in six Chinese provinces. Family SES was conceptualized based on household income, family education, and family occupational status. Family social capital was measured by using family cohesion and health-related family support. General health was assessed by using five general health perception items of the Health Survey Short Form. Structural equation modeling (SEM) was applied to examine the relationships among family SES, family social capital, and general health, and a linear regression model was used to test gender-based differences. Results: The SEM showed that the direct effects of family SES, family cohesion, and health-related family support on health were 0.08 (P < 0.001), 0.17 (P < 0.001), and 0.10 (P < 0.001), respectively. Family SES had indirect effect (β = 0.05, P < 0.01) on general health via health-related family support. The total effect of family social capital (β = 0.27, P < 0.001) on general health was greater than that of family SES (β = 0.13, P < 0.001). Besides, the regression showed that the effect of health-related family support on general health was greater for women (β = 0.13, P < 0.001) than men (β = 0.04, P > 0.05). Conclusions: The results provide strong support for the positive association between family SES, family social capital, and adult health. Family intervention programs should focus on establishing a harmonious family relationship to mobilize family support, particularly for the families with low cohesion and low SES.

Background: Health behaviors developed in the college years tend to persist in adulthood. However, distinct changing patterns of food choices and physical activity (PA) and their predictors are still less clear among college students. The current study sought to explore changes of food choices and PA, as well as the effects of personal and interpersonal factors.Method: Two-wave longitudinal data was collected from a sample of 431 Chinese college students (Mean baseline age = 19.15 ± 0.61 years; 45.7% male). A validated self-reported food frequency questionnaire was used to assess the frequency of food choices. The Chinese revised version of physical activity rating scale was used to assess physical activity. Latent profile analysis, latent transition analysis, and multinomial logistic regression analysis were used to analyze the data.Results: Two profiles of food choices, i.e.,Avoiding staples(5.1% at Time 1) andVaried diet(94.9% at Time 1), were identified at both timepoints. 90.9% remained the same profiles over time, 63.6% participants in theAvoiding staplesprofile shifted to theVaried dietprofile, and only 6.3% of those in theVaried dietprofile shifted to theAvoiding staplesprofile. Negative body shape-related belief was related to the translation from theVaried dietprofile to theAvoiding staplesprofile. Further, four profiles of PA, i.e.,Inactives(51.0% at Time 1),Low activies(26.0% at Time 1),Moderate activies(15.3% at Time 1), andActivies(7.7% at Time 1), were identified at both timepoints. 50.8% remained the same profiles over time, 38.6%Inactiviesshifted to the other profiles, and 48.5%Activiesshifted to the other profiles over time. Participants with higher self-efficacy showed an increase in PA over time, and those with lower self-efficacy and lower peer support showed a decrease in PA over time.Conclusions: Overall, most of college students remained the same food choices profiles, and body shape-related belief contributed to changes in food choices profiles. About half of college students experienced changes in PA, and the predictors of such changes were peer support and self-efficacy. The findings extend the understanding of the personal and interpersonal predictors of health behaviors among college students from a dynamic perspective.