Related Experiment Video
Updated: Sep 6, 2025
13:35
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
10.7K
Applying classification and regression tree analysis to identify risks of developing sarcopenia in the older
Nga Thi Thuy Nguyen1,2, Huyen Thi Thanh Vu3,4, Huey Lan Hu5
1School of Nursing, Taipei Medical University, Taipei, Taiwan.
International Journal of Older People Nursing
|June 29, 2022
Summary
Interactions between age, body mass index (BMI), and walking habits significantly influence sarcopenia risk in older adults. Maintaining healthy weight and regular walking are key preventive strategies.
Area of Science:
- Gerontology
- Public Health
- Nutritional Science
Background:
- Sarcopenia risk factors vary, but their interactions are understudied in community-dwelling older adults.
- Understanding these interactions is crucial for targeted prevention strategies.
Purpose of the Study:
- To examine the interactions among various risk factors for sarcopenia.
- To identify specific pathways and groups at higher risk of developing sarcopenia in older adults.
Main Methods:
- A cross-sectional study involving 200 older adults was conducted.
- Data collected included demographics, dietary habits, lifestyle behaviors, and depression symptoms.
- A classification and regression tree (CART) model analyzed risk factor interactions and identified at-risk groups.
Main Results:
- The prevalence of sarcopenia was 38.5%.
- Low body mass index (BMI) (<18.5 kg/m²) was a primary risk factor for sarcopenia.
- Older adults (≥68 years) with normal BMI and no regular walking habit showed increased sarcopenia risk.
Conclusions:
- Interactions between older age, BMI, and physical activity (walking) influence sarcopenia development.
- Individuals with low or normal BMI and sedentary habits represent a key at-risk group.
- Maintaining healthy body weight and regular walking are recommended for sarcopenia prevention.
Related Concept Videos
01:19Survival Tree
154
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
154
01:15Receiver Operating Characteristic Plot
328
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
328
01:20Comparing the Survival Analysis of Two or More Groups
277
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...
277
01:25Statistical Methods for Analyzing Epidemiological Data
513
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:
513