相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
06:04

Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome

Published on: September 27, 2024

792

在墨西哥城前性研究中,生活方式风险因素与死亡率之间的关联

Paloma Ferrero-Hernández1, Gary O'Donovan2, Fanny Petermann-Rocha3

  • 1Escuela de Ciencias de la Actividad Física, el Deporte y la Salud, Universidad de Santiago de Chile (USACH), Santiago, Chile.

Scientific reports
|January 2, 2025
PubMed
概括

墨西哥成年人有多种不健康的生活方式行为面临着明显更高的过早死亡风险. 吸烟,饮食不良和不活动等风险因素的综合得分强烈预测了各种疾病的死亡率.

关键词:
生活方式 生活方式死亡率 死亡率 死亡率非传染性疾病非传染性疾病反向因果关系的反向因果关系.风险因素 风险因素 风险因素

更多相关视频

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

4.4K
Phosphorus-31 Magnetic Resonance Spectroscopy: A Tool for Measuring In Vivo Mitochondrial Oxidative Phosphorylation Capacity in Human Skeletal Muscle09:40

Phosphorus-31 Magnetic Resonance Spectroscopy: A Tool for Measuring In Vivo Mitochondrial Oxidative Phosphorylation Capacity in Human Skeletal Muscle

Published on: January 19, 2017

11.7K

相关概念视频

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
299
Stress Prevention and Stress Management Techniques IV01:26

Stress Prevention and Stress Management Techniques IV

Stress often leads to unhealthy habits like smoking, excessive drinking, and overeating, which offer short-term relief but ultimately increase long-term health risks. These behaviors create a cycle that temporarily lowers stress levels but can result in severe long-term health consequences. Breaking these habits is essential to reduce the risk of chronic diseases and improve overall well-being. Three primary changes that support better health include quitting smoking, reducing alcohol intake,...
26
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
95
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
75
Psychoneuroimmunology: Cardiovascular Disease01:27

Psychoneuroimmunology: Cardiovascular Disease

Psychoneuroimmunology (PNI) is a multidisciplinary field that examines how psychological factors, particularly stress, interact with the immune system and impact physical health. Research in PNI has shown that chronic or traumatic stress can disrupt both the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. These disruptions contribute to serious health conditions, including cardiovascular diseases.
A key area of focus in PNI is the relationship between stress and coronary...
25
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
90