尿道癌患者的体力活动,酒精和烟草使用,诊断以来的时间
Bartosz Bogusz Adamczak1, Zofia Kuźnik1, Szymon Makles1
1Student Scientific Association, Department of Physical Education and Sport, Wroclaw Medical University, 51-601 Wroclaw, Poland.
Healthcare (Basel, Switzerland)
|January 11, 2024
概括
泌尿癌患者在诊断后体力活动显著下降. 问题饮酒和吸烟在第一年是最高的,需要进一步调查有针对性的干预措施.
科学领域:
- 在瘤学瘤学.
- 公共卫生 公共卫生
- 行为科学 行为科学
背景情况:
- 泌尿系统癌症对全球健康构成重大负担.
- 改变生活方式和行为对于癌症患者的康复和福祉至关重要.
- 了解这些变化对于有效的治疗和支持至关重要.
研究的目的:
- 调查尿道癌患者体力活动,问题饮酒 (AUDIT) 和吸烟 (FTND) 的变化.
- 分析这些变化在诊断后的三年内以及在三年前诊断的患者中.
- 确定尿道癌症护理中生活方式干预的关键时期.
主要方法:
- 一项涉及128名泌尿癌患者的纵向研究.
- 评估身体活动,酒精使用障碍识别测试 (AUDIT) 成绩和尼古丁依赖法格斯特罗姆测试 (FTND) 成绩.
- 在诊断后的三年内,在四个不同的时间间隔收集数据.
主要成果:
- 观察到体力活动的显著下降,在第一和第二次评估之间从69%下降到45% (p < 0.0001).
- 问题性物质使用的最高平均得分是在诊断后的第一年记录的 (AUDIT: 4.20,p = 0.01;FTND: 4.83,p = 0.08).
- 在诊断后的最初几年中,人们发现了生活方式和行为变化的显著趋势.
结论:
- 身体活动的下降需要进一步研究促成因素和有针对性的干预措施.
- 高度的初始物质使用需要调查,以确定它是应对机制还是行为改变的先驱.
- 了解这些动态是为泌尿癌患者开发有效,有针对性的干预措施的关键.
相关概念视频
01:21Cancer Survival Analysis
353
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
353
02:59Cancer Prevention
6.2K
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
6.2K
01:25Statistical Methods for Analyzing Epidemiological Data
367
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:
367
01:11Observational Studies
8.6K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
8.6K
01:26Stress Prevention and Stress Management Techniques IV
29
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,...
29
01:20Comparing the Survival Analysis of Two or More Groups
195
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
195