在大学生中,每日步骤与WHO-5之间的反向U关联:非线性建模和稳健性检查
Huakai Zhang1,2, Shiguang Wang1, Yongchao Huang1,2
1Medical College, Zhengzhou University of Industrial Technology, Zhengzhou, Henan, China.
Frontiers in behavioral neuroscience
|November 10, 2025
概括
每天更高的步骤提高了学生的福祉,达到大约8,000-12,000步,之后的好处是高原. 这项研究支持加强大学生心理健康的逐步步骤指南.
科学领域:
- 运动科学 运动科学
- 心理健康研究 心理健康研究
- 公共卫生 公共卫生
背景情况:
- 身体活动被认为对心理健康有好处,但确切的剂量反应关系尚未完全理解.
- 不同体力活动水平对主观幸福感 (SWB) 的影响需要进一步研究,特别是在学生群体中.
研究的目的:
- 调查中国大学生每日步数与主观幸福感 (SWB) 之间的非线性关联.
- 确定跨越SWB收益开始平稳的步骤值.
主要方法:
- 一项涉及820名大学生的横截面研究,他们戴着加速度计7天来测量每天的步骤.
- 使用WHO-5问卷评估了主观幸福感.
- 使用受限立方线模型来分析剂量反应关系,并对潜在的混因素进行调整.
主要成果:
- 在每日步骤和SWB之间观察到显著的非线性关联 (p<0.05).
- 随着步数的增加,SWB的步数增加到大约8,650步/天,之后在19300步/天左右停滞不前.
- 没有测试的步数明显超过了与4000步/天相比,临床上重要的最小差异.
结论:
- 每日步数与大学生改善的SWB有关,好处在8,000-12,000步之间平衡.
- 这些发现表明,基于范围的,渐进的步骤建议可以支持学生的心理健康.
- 该研究强调,超过一定的步数后,回报率下降,这表明较高的活动水平没有危害.
相关概念视频
Correlation and Regression
3.0K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.0K
Confounding in Epidemiological Studies
565
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
565
Calibration Curves: Linear Least Squares
4.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
For data that follow a straight line, the standard method for fitting is the linear...
4.1K
Residuals and Least-Squares Property
9.0K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.0K
Clearance Models: Noncompartmental Models
237
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
237
Multiple Regression
3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K


