Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Cognitive Development During Adolescence

Cognitive Development During Adolescence

During adolescence, individuals experience significant cognitive development that enhances their understanding of others' emotions and thoughts, known as cognitive empathy. This period is marked by an increased ability to adapt to others' perspectives and a more nuanced understanding of others' mental states, a skill that is foundational for social problem-solving and conflict avoidance. The development of cognitive empathy relies heavily on the theory of mind — the...

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...

Interval Level of Measurement

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The Role of the Mitochondrial Permeability Transition Pore in Chronic Pain.

Current neuropharmacology·2026

Same author

Stand development shifts nitrogen cycling strategies and supply-demand balance in larch plantations.

BMC plant biology·2026

Same author

Flavokawain C alleviates dextran sulphate sodium-induced colitis through suppressing necroptosis via blocking RIPK1-RIPK3-MLKL axis.

International immunopharmacology·2026

Same author

Camptothecin Nanowires Induce the cGAS-STING Pathway to Remold Tumor-Associated Macrophages for Antitumor Immunity.

Pharmaceutics·2026

Same author

TRNAU1AP and PRPF39 establish integrated control over processing of most abundant human non-coding RNAs.

Nature communications·2026

Same author

Kinetic characteristics of Wushu Changbing PiJi: decoupling sex-specific adaptive strategies via functional principal component analysis.

Frontiers in sports and active living·2026

Search research articles

Related Experiment Video

Updated: Jan 21, 2026

Swimming Performance Assessment in Fishes

Swimming Performance Assessment in Fishes

Published on: May 20, 2011

Multivariate dynamic association analysis between sprint interval training and adolescent freestyle swimming

Xiaotong Chen^1,2, Yankang Jiang¹, Yupeng Shen³

¹Sports Engineering Center, School of Sports Science, South China Normal University, Guangzhou, China.

BMC Sports Science, Medicine & Rehabilitation

|January 20, 2026

Summary

Complex network modeling reveals how training variables dynamically impact swimming performance. Key factors like velocity, blood lactate, and perceived exertion become more central during high-intensity interval training.

Keywords:

100m freestyle Adolescent swimmers Complex network Sprint interval training

More Related Videos

A Real-World High-Intensity Interval Training Protocol for Cardiorespiratory Fitness Improvement

A Real-World High-Intensity Interval Training Protocol for Cardiorespiratory Fitness Improvement

Published on: February 22, 2022

Short Session High Intensity Interval Training and Treadmill Assessment in Aged Mice

Short Session High Intensity Interval Training and Treadmill Assessment in Aged Mice

Published on: February 2, 2019

Related Experiment Videos

Last Updated: Jan 21, 2026

Swimming Performance Assessment in Fishes

Swimming Performance Assessment in Fishes

Published on: May 20, 2011

A Real-World High-Intensity Interval Training Protocol for Cardiorespiratory Fitness Improvement

A Real-World High-Intensity Interval Training Protocol for Cardiorespiratory Fitness Improvement

Published on: February 22, 2022

Short Session High Intensity Interval Training and Treadmill Assessment in Aged Mice

Short Session High Intensity Interval Training and Treadmill Assessment in Aged Mice

Published on: February 2, 2019

Area of Science:

Sports Science
Physiology
Biomechanics

Background:

Complex network modeling is underutilized for analyzing dynamic training adaptations in sports.
Understanding multivariate relationships during high-intensity training is crucial for performance optimization.

Purpose of the Study:

To apply complex network modeling to analyze dynamic associations between kinematic, metabolic, and perceptual variables during a single swimming training session.
To investigate the impact of a 6x50m sprint interval training (SSIT) protocol on 100-meter freestyle performance and underlying physiological responses.

Main Methods:

Complex network analysis was used to examine dynamic relationships among stroke rate, stroke length, blood lactate, Rating of Perceived Exertion (RPE), and swimming velocity.
Data were collected from 16 adolescent swimmers during a 6x50m SSIT protocol.

Main Results:

Network topology remained stable, indicating collective contribution to performance enhancement (density increased from 42.65% to 49.17%, modularity from 0.2 to 0.24).
Nodal centrality of swimming velocity, blood lactate, and RPE significantly increased, highlighting their role as performance mediators.
Stroke rate influence decreased, while stroke length remained stable throughout the training session.

Conclusions:

Complex network modeling offers a novel approach to dynamically assess training processes and understand adaptive mechanisms in swimmers.
The study provides insights into how anaerobic capacity is shaped during high-intensity interval training, with specific variables playing dynamic roles.