Television Viewing Time and Stroke Risk: Australian Diabetes Obesity and Lifestyle Study (1999-2012)

Association between daily screen time and risk of stroke among middle-aged and elderly people: research based on China health and nutrition survey

Background

We aimed to explore the independent associations between screen time and the risk of stroke among Chinese adults based on the China Health and Nutrition Survey (CHNS).

Methods

Data on Chinese adults aged older than 40 years from the CHNS in during 2004-2009 were selected. A total of 4,587 individuals were included in 2009, including screen time and the risk of stroke. Simultaneously, we traced the previous screen time to 2004 for those with outcome measures in 2009 (n = 2,100). Basic information, lifestyle, and screen behavior were obtained through face-to-face interviews and self-completed questionnaires. Anthropometric data collected included blood pressure, body weight, height, hip circumference, and waist circumference. Fasting blood was obtained for measurements of lipid and glucose levels. Cross-sectional analysis and cohort analysis were both performed using multivariate logistic regression.

Results

Of all participants, 3,004 (65.49%) participants spent more than 2 h per day on screen time. Taking the men who spent less than 2 h on screen per day as reference, the crude odds ratio (OR) of the high risk of stroke was 1.53 [95% confidence interval (CI), 1.20-1.95] for the men who spent 2-3 h per day on screen and 2.37 (95% CI, 1.78-3.16) for the men who spent more than 3 h per day on screen. This difference remained significant after adjusting for confounding factors. No association was observed among women. However, in the cohort analysis with screen time in 2006 as the independent variable, the association between screen time and stroke risk was found both in men [OR, 1.83 (95% CI, 1.19-2.82)] and women [OR, 1.48 (95% CI, 1.10-1.99)]).

Conclusion

We found that the high screen time was associated with an increased stroke risk, which was pronounced in men, warranting a universal need to limit screen time in order to improve health.

Association of Accelerometer-Measured Sedentary Time and Physical Activity With Risk of Stroke Among US Adults

IMPORTANCE

The amount and intensity of physical activity required to prevent stroke are yet to be fully determined because of previous reliance on self-reporting measures. Furthermore, the association between objectively measured time spent being sedentary as an independent risk factor for stroke is unknown.

OBJECTIVE

To investigate the associations of accelerometer-measured sedentary time and physical activity of varying intensity and duration with the risk of incident stroke.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study involved participants who were enrolled in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study from February 5, 2003, to October 30, 2007. Accelerometer data were collected from 7607 Black and White adults 45 years or older in the contiguous US between May 12, 2009, and January 5, 2013. Data on other races and ethnicities were not collected for scientific and clinical reasons. By design, Black adults and residents of the southeastern US stroke belt and stroke buckle were oversampled. Data were analyzed from May 5, 2020, to November 11, 2021.

EXPOSURES

Sedentary time, light-intensity physical activity (LIPA), and moderate- to vigorous-intensity physical activity (MVPA) were measured using a hip-mounted accelerometer worn for 7 consecutive days and stratified by tertile for the analyses.

MAIN OUTCOMES AND MEASURES

Incident stroke.

RESULTS

Among 7607 participants, the mean (SD) age was 63.4 (8.5) years; 4145 participants (54.5%) were female, 2407 (31.6%) were Black, and 5200 (68.4%) were White. A total of 2523 participants (33.2%) resided in the stroke belt, and 1638 (21.5%) resided in the stroke buckle. Over a mean (SD) of 7.4 (2.5) years of follow-up, 286 incident stroke cases (244 ischemic [85.3%]) occurred. The fully adjusted hazard ratios (HRs) for incident stroke in the highest tertile compared with the lowest tertile were 0.74 (95% CI, 0.53-1.04; P = .08) for LIPA and 0.57 (95% CI, 0.38-0.84; P = .004) for MVPA. Higher sedentary time was associated with a 44% greater risk of incident stroke (HR, 1.44; 95% CI, 0.99-2.07; P = .04). When comparing the highest with the lowest tertile, mean sedentary bout duration was associated with a significantly greater risk of incident stroke (HR, 1.53; 95% CI, 1.10-2.12; P = .008). After adjustment for sedentary time, the highest tertile of unbouted MVPA (shorter bouts [1-9 minutes]) was associated with a significantly lower risk of incident stroke compared with the lowest tertile (HR, 0.62; 95% CI, 0.41-0.94; P = .02); however, bouted MVPA (longer bouts [at least 10 minutes]) was not (HR, 0.78; 95% CI, 0.53-1.15; P = .17). When expressed as continuous variables, sedentary time was positively associated with incident stroke risk (HR per 1-hour/day increase in sedentary time: 1.14; 95% CI, 1.02-1.28; P = .02), and LIPA was negatively associated with incident stroke risk (HR per 1-hour/day increase in LIPA: 0.86; 95% CI, 0.77-0.97; P = .02).

CONCLUSIONS AND RELEVANCE

In this cohort study, objectively measured LIPA, MVPA, and sedentary time were significantly and independently associated with incident stroke risk. Longer sedentary bout duration was also independently associated with an increased risk of incident stroke. These findings suggest that replacing sedentary time with LIPA, or even very short bouts of MVPA, may lower stroke risk, supporting the concept of moving more and sitting less as a beneficial stroke risk reduction strategy among adults.

Sedentary time and its association with risk of cardiovascular diseases in adults: an updated systematic review and meta-analysis of observational studies

BACKGROUND

Epidemiological studies assessing the association between sedentary time and cardiovascular diseases (CVD) risks have been published at a rapid pace in recent years, which makes the periodic review of knowledge essential. Furthermore, much of the early and ongoing work used screen time as a marker of total sedentary time, which may weaken the association between sedentary time and CVD risks.

OBJECTIVE

To update evidence on CVD risks associated with different types of sedentary time, especially total sedentary time and screen time, and to explore as a marker of total sedentary time, whether screen time had similar CVD risks with total sedentary time.

METHODS

PRISMA guideline was followed for the performing and reporting of this systematic review and meta-analysis. Three independent researchers searched eight electronic databases and two clinical trial registries for all studies published between January 2015 and December 2021 that assessed the association between sedentary time and CVD risks in adults. A standardized form was used for data extraction and collection. Wilmot and colleagues' modified tool was used for quality assessment. The categorical association was assessed by comparing the pooled effect sizes for CVD risks associated with the highest and the lowest sedentary time categories across included studies. Stata 16.0 and Review Manager 5.3 were used for all statistical analyses, P ≤ 0.05 was considered as statistically significant.

RESULTS

Seventeen prospective cohort studies and two cross-sectional studies with 145,1730 participants and over 48,668 CVD cases and deaths were included. Two included studies measured sedentary time with the accelerometer, 16 studies with self-reported questions, and one study with both the accelerometer and self-reported questions. CVD outcomes were self-reported in two included studies and objectively adjudicated through medical records or death certifications in 17 studies. Compared with the lowest total sedentary time category (median duration, 2.75 h/d), participants in the highest category (median duration, 10.5 h/d) had an increased risk of CVD morbidity (pooled RR, 1.24; 95% CI, 1.21-1.27). Compared with the lowest total sedentary time category (median duration, 2.98 h/d), participants in the highest category (median duration, 10.2 h/d) had an increased risk of CVD mortality (pooled HR, 1.29; 95% CI, 1.13-1.47). The association between screen time and CVD risks was similar to total sedentary time with the cut-off point of 5-6 h/d. The associations between occupational sitting time, leisure sedentary time, and CVD risks stayed inconclusive.

CONCLUSION

Total sedentary time and screen time are both associated with cardiovascular health. As a marker of total sedentary time, screen time over 5-6 h/d had similar CVD risks with total sedentary time over 10-11 h/d.

Genetic Liability to Sedentary Behavior in Relation to Stroke, Its Subtypes and Neurodegenerative Diseases: A Mendelian Randomization Study

Objective: To investigate the causal association of domain-specific sedentary behaviors with cerebrovascular diseases and neurodegenerative diseases, and the potential mediators among these associations. Methods: Genetic instruments were identified for television watching, computer use and driving behavior from a genome-wide association study including 408,815 subjects. Mendelian randomization (MR) analysis was used to estimate the causal effect of sedentary behaviors on the cerebrovascular diseases and neurodegenerative diseases. Multivariable MR analysis was applied to adjust potential confounding factors, and mediation analysis was conducted to explore potential mediators. Results: Genetically predisposition to 1.5 h/day increase in leisure time watching television was associated with increased risk of all-cause stroke [odds ratio (OR) = 1.32, 95% confidence interval (CI) = 1.15-1.52, p-value for MR-Egger method (P _Egger) = 0.11, I ² = 37%, Cochrane's Q = 212, p-value for Cochran Q test (P _Q) < 0.001], and ischemic stroke (OR = 1.28, 95%CI = 1.10-1.49, P _Egger = 0.04, I ² = 35%, Cochrane's Q = 206, P _Q = 0.002). Interestingly, television watching may decrease the risk of Parkinson's disease (OR = 0.65, 95%CI = 0.50-0.84, P _Egger = 0.47, I ² = 19%, Cochrane's Q = 157, P _Q = 0.04). Television watching was a detrimental factor of cognitive performance (estimate = -0.46, 95%CI = -0.55 - -0.37, P _Egger = 0.001, I ² = 85%, Cochrane's Q = 862, P _Q < 0.001). Sensitivity analyses using leave out method and MR-PRESSO method suggested weak evidence of pleiotropy. Conclusion: We provided genetic evidence for the causal association of television watching with increased risk of all-cause stroke and ischemic stroke, decreased risk of Parkinson's disease, and worse cognitive performance. The results should be interpreted with caution considering the pleiotropy.

Influences of Daily Life Habits on Risk Factors of Stroke Based on Decision Tree and Correlation Matrix

Purpose

To explore the influences of smoking, alcohol consumption, drinking tea, diet, sleep, and exercise on the risk of stroke and relationships among the factors, present corresponding knowledge-based rules, and provide a scientific basis for assessment and intervention of risk factors of stroke.

Methods

The decision tree C4.5 algorithm was optimized and utilized to establish a model for stroke risk assessment; then, the main risk factors of stroke (including hypertension, dyslipidemia, diabetes, atrial fibrillation, body mass index (BMI), history of stroke, family history of stroke, and transient ischemic attack (TIA)) and daily habits (e.g., smoking, alcohol consumption, drinking tea, diet, sleep, and exercise) were analyzed; corresponding knowledge-based rules were finally presented. Establish a correlation matrix of stroke risk factors and analyze the relationship between stroke risk factors.

Results

The accuracy of the established model for stroke risk assessment was 87.53%, and the kappa coefficient was 0.8344, which was superior to that of the random forest and Logistic algorithm. Additionally, 37 knowledge-based rules that can be used for prevention of risk factors of stroke were derived and verified. According to in-depth analysis of risk factors of stroke, the values of smoking, exercise, sleep, drinking tea, alcohol consumption, and diet were 6.00, 7.00, 8.67, 9.33, 10.00, 10.60, and 10.75, respectively, indicating that their influence on risk factors of stroke was reduced in turn; on the one hand, smoking and exercise were strongly associated with other risk factors of stroke; on the other hand, sleep, drinking tea, alcohol consumption, and diet were not firmly associated with other risk factors of stroke, and they were relatively tightly associated with smoking and exercise.

Conclusions

Establishment of a model for stroke risk assessment, analysis of factors influencing risk factors of stroke, analysis of relationships among those factors, and derivation of knowledge-based rules are helpful for prevention and treatment of stroke.