Higher healthy lifestyle scores are associated with greater bone mineral density in middle-aged and elderly Chinese adults

An investigation into social determinants of health lifestyles of Canadians: a nationwide cross-sectional study on smoking, physical activity, and alcohol consumption

BACKGROUND

Health lifestyles exert a substantial influence on the quality of everyday life, primarily affecting health maintenance and enhancement. While health-related practices during the COVID-19 pandemic may have positively altered the health lifestyles of Canadians to a certain degree, government reports indicate that issues related to health behaviors, such as cigarette smoking, physical inactivity, and alcohol consumption, continue to pose challenges to the health of Canadians. Social determinants of these health behaviors thus hold significant academic value in the formulation of policy guidelines.

OBJECTIVE

The aim of this study is to scrutinize the social determinants of health with respect to social factors that have may have impacts on the health-related behaviors of Canadians. We tested health behaviors including cigarette use, alcohol consumption, and participation in physical exercise, which are integral to the promotion and improvement of individual health.

METHODS

To examine the social determinants of Canadians' health lifestyles, we utilized nationally representative data from the 2017-2018 Canadian Community Health Survey annual component. Our data analysis involved the bootstrapping method with two-level mixed-effect logistic regressions, ordered logistic regressions, and negative binomial regressions. Additionally, we conducted several robustness checks to confirm the validity of our findings.

RESULTS

The findings show that demographic background, socioeconomic status, social connections, and physical and mental health conditions all play a role in Canadians' smoking, physical activity, and drinking behaviors. Noticeably, the association patterns linking to these social determinants vary across specific health lifestyles, shedding light on the complex nature of the social determinants that may influence young and middle-aged Canadians' health lifestyles. Moreover, in the context of Canada, the health-region level demographic, socioeconomic, and working conditions are significantly linked to residents' health lifestyles.

CONCLUSIONS

Investigating the social determinants of health lifestyles is pivotal for policymakers, providing them with the necessary insights to create effective interventions that promote healthy behaviors among specific demographic groups. It is recommended that health education and interventions at the community level targeting smoking, physical inactivity, and alcohol consumption be introduced. These interventions should be tailored to specific subgroups, considering their demographic and socioeconomic characteristics, social networks, and health status. For instance, it is imperative to focus our attention on individuals with lower educational attainment and socioeconomic status, particularly in relation to their smoking habits and physical inactivity. Conversely, interventions aimed at addressing alcohol consumption should be targeted towards individuals of a higher socioeconomic status. This nuanced approach allows for a more effective and tailored intervention strategy.

Associations of combined lifestyle index with migraine prevalence and headache frequency: a cross-sectional study from the MECH-HK study

BACKGROUND

Prior research has shown that individual lifestyles were associated with migraine. Yet, few studies focused on combined lifestyles, particularly in Chinese populations. This cross-sectional study aimed to investigate the relationships of a combined lifestyle index with migraine in Hong Kong Chinese women.

METHODS

Baseline data from a cohort study named Migraine Exposures and Cardiovascular Health in Hong Kong Chinese Women (MECH-HK) were used for analysis. In total 3510 women aged 55.2 ± 9.1 years were included. The combined lifestyle index comprised eight lifestyle factors: smoking, physical activity, sleep, stress, fatigue, diet, body mass index, and alcohol. Each component was attributed a point of 0 (unhealthy) or 1 (healthy). The overall index was the sum of these points, ranging from 0 (the least healthy) to 8 points (the healthiest). Migraine was diagnosed by the International Classification of Headache Disorders 3rd edition. Additionally, for women with migraine, the data on migraine attack frequency (attacks/month) was collected.

RESULTS

A total of 357 women with migraine (10.2%) were identified. The prevalence of migraine for the 0-3-point, 4-point, 5-point, 6-point, and 7-8-point groups were 18.0% (162/899), 10.9% (86/788), 6.6% (51/776), 6.0% (38/636), and 4.9% (20/411), respectively. In the most-adjusted model, compared to the 0-3-point group, the odds ratios and 95% confidence intervals for the 4-point, 5-point, 6-point, and 7-8-point groups were 0.57 (0.43-0.75), 0.33 (0.24-0.46), 0.30 (0.21-0.44), and 0.25 (0.15-0.41), respectively (all p < 0.001). For each component, migraine was significantly associated with sleep, stress, fatigue, and diet; but was unrelated to smoking, physical activity, body mass index, and alcohol. Among women with migraine, per point increase in the combined lifestyle index was associated with a reduced migraine attack frequency (β = - 0.55; 95% confidence interval = - 0.82, - 0.28; p < 0.001).

CONCLUSIONS

A combined lifestyle index was inversely associated with migraine and migraine attack frequency in Hong Kong Chinese women. Adhering to a healthy lifestyle pattern might be beneficial to the prevention of migraine attacks. Conversely, it is also plausible that women with migraine might have a less healthy lifestyle pattern compared to those without headaches.

Combined Influence of Eight Lifestyle Factors on Metabolic Syndrome Incidence: A Prospective Cohort Study from the MECH-HK Study

Although previous studies have shown significant associations between individual lifestyles and metabolic syndrome, limited studies have explored the combined effect of lifestyles. The purpose of this study was to investigate whether a combined lifestyle score was associated with metabolic syndrome incidence in Hong Kong Chinese women. This prospective cohort study included 1634 women (55.9 ± 8.6 years) without baseline metabolic syndrome, diabetes, myocardial infarction, or stroke. Eight lifestyle factors (smoking, physical activity, sedentary time, sleep, stress, fatigue, diet, and alcohol) were included by assigning 0 (unhealthy) or 1 point (healthy). The overall score was the sum of these points, ranging from 0 (the least healthy) to 8 points (the healthiest). Metabolic syndrome was diagnosed by the joint interim statement. During a 1.16-year follow-up, 179 (11.0%) new metabolic syndrome cases were identified. The incidences for the 0-3-point, 4-point, 5-point, and 6-8-point groups were 12.8% (79/618), 11.5% (42/366), 9.4% (29/309), and 8.5% (29/341), respectively. Compared to the lowest combined lifestyle score group, the highest group had a 47% reduced metabolic syndrome incidence, with an adjusted odds ratio and 95% confidence interval of 0.53 (0.33-0.86) (p = 0.010). These findings indicate that a higher combined lifestyle score was associated with a lower metabolic syndrome incidence in this population.

Effect of healthy lifestyle index and lifestyle patterns on the risk of mortality: A community-based cohort study

Background

Limited evidence was available on the association of the integrated effect of multidimensional lifestyle factors with mortality among Chinese populations. This cohort study was to examine the effect of combined lifestyle factors on the risk of mortality by highlighting the number of healthy lifestyles and their overall effects.

Methods

A total of 11,395 participants from the Guangzhou Heart Study (GZHS) were followed up until 1 January 2020. Individual causes of death were obtained from the platform of the National Death Registry of China. The healthy lifestyle index (HLI) was established from seven dimensions of lifestyle, and lifestyle patterns were extracted from eight dimensions of lifestyle using principal component analysis (PCA). Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated using the Cox proportional hazard regression model.

Results

During 35,837 person-years of follow-up, 184 deaths (1.61%) were observed, including 64 from cardiovascular disease. After adjustment for confounders, HLI was associated with a 50% (HR: 0.50, 95% CI: 0.25–0.99) reduced risk of all-cause mortality when comparing the high (6–7 lifestyle factors) with low (0–2 lifestyle factors) categories. Three lifestyle patterns were defined and labeled as pattern I, II, and III. Lifestyle pattern II with higher factor loadings of non-smoking and low-level alcohol drinking was associated with a decreased risk of all-cause mortality (HR: 0.63, 95% CI: 0.43–0.92, P_–trend = 0.023) when comparing the high with low tertiles of pattern score, after adjustment for confounders. Every 1-unit increment of pattern II score was associated with a decreased risk (HR: 0.97, 95% CI: 0.95–0.99) of all-cause mortality. The other two patterns were not associated with all-cause mortality, and the association of cardiovascular mortality risk was observed with neither HLI nor any lifestyle pattern.

Conclusion

The results suggest that the more dimensions of the healthy lifestyle the lower the risk of death, and adherence to the lifestyle pattern characterized with heavier loading of non-smoking and low-level alcohol drinking reduces the risk of all-cause mortality. The findings highlight the need to consider multi-dimensional lifestyles rather than one when developing health promotion strategies.

Higher Healthy Lifestyle Score is associated with lower presence of non-alcoholic fatty liver disease in middle-aged and older Chinese adults: a community-based cross-sectional study

OBJECTIVE

Previous studies have reported inverse associations between certain healthy lifestyle factors and non-alcoholic fatty liver disease (NAFLD), but limited evidence showed the synergistic effect of those lifestyles. This study examined the relationship of a combination of lifestyles, expressed as Healthy Lifestyle Score (HLS), with NAFLD.

DESIGN

A community-based cross-sectional study. Questionnaires and body assessments were used to collect data on the six-item HLS (ranging from 0 to 6, where higher scores indicate better health). The HLS consists of non-smoking (no active or passive smoking), normal BMI (18·5-23·9 kg/m2), physical activity (moderate or vigorous physical activity ≥ 150 min/week), healthy diet pattern, good sleep (no insomnia or <6 months) and no anxiety (Self-rating Anxiety Scale < 50), one point each. NAFLD was diagnosed by ultrasonography.

SETTING

Guangzhou, China.

PARTICIPANTS

Two thousand nine hundred and eighty-one participants aged 40-75 years.

RESULTS

The overall prevalence of NAFLD was 50·8 %. After adjusting for potential covariates, HLS was associated with lower presence of NAFLD. The OR of NAFLD for subjects with higher HLS (3, 4, 5-6 v. 0-1 points) were 0·68 (95 % CI 0·51, 0·91), 0·58 (95 % CI 0·43, 0·78) and 0·35 (95 % CI 0·25, 0·51), respectively (P-values < 0·05). Among the six items, BMI and physical activity were the strongest contributors. Sensitivity analyses showed that the association was more significant after weighting the HLS. The beneficial association remained after excluding any one of the six components or replacing BMI with waist circumference.

CONCLUSIONS

Higher HLS was associated with lower presence of NAFLD, suggesting that a healthy lifestyle pattern might be beneficial to liver health.