Related Experiment Video

Updated: Jun 4, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Gender differences in dietary patterns and physical activity: an insight with principal component analysis (PCA)

Alessandra Feraco^1,2, Stefania Gorini^1,2, Elisabetta Camajani^1,2

¹Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Open University, Via di Val Cannuta, 247, Rome, 00166, Italy.

Journal of Translational Medicine

|December 19, 2024

Abstract

Background:

Gender differences in dietary patterns and physical activity are known to influence metabolic health, but research exploring these differences using principal component analysis (PCA) is limited. This study aims to identify distinct patterns of eating behaviour, body composition and physical activity between men and women, in order to develop tailored interventions.

Methods:

A cross-sectional study was conducted on 2,509 adults attending a metabolic health centre. Data on eating habits, physical activity and body composition were collected by means of questionnaires and bioimpedance analysis. PCA was used to identify patterns of eating behaviour and physical activity. Statistical analyses were performed to explore gender-specific differences.

Results:

Based on the PCA, five distinct groups of participants were identified: Balanced Eaters, Focused on Home Cooking, Routine Eaters, Restaurant Lovers and Varied Eaters. Significant gender differences in food preferences were observed, with men consuming more meat and women more vegetables. Men also reported greater participation in strength and endurance sports, while women showed a more structured eating routine.

Conclusions:

This study, using principal component analysis (PCA), revealed gender-specific patterns in diet, physical activity and body composition. PCA identified five distinct behavioural groups, revealing that men tended to consume more meat and engage in strength training, while women adhered to structured, vegetable-rich diets. The application of PCA provided more insight than traditional analysis, highlighting the complexity of gender-specific behaviour. These results emphasize the need for tailored interventions, focusing on increasing vegetable intake in men and encouraging strength training in women. Future research should exploit PCA to explore behavioural patterns longitudinally for more refined and personalised health strategies.

Clinical Trials Registered:

This study is registered on ClinicalTrials.gov (NCT06654674).

Keywords:

Anthropometric analysis BMI Body composition Dietary patterns Eating behaviors Fat mass Gender differences Physical activity Principal component analysis (PCA)Sport

More Related Videos

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

Physical Activity Measurement in Children Accepting Table Tennis Training

Physical Activity Measurement in Children Accepting Table Tennis Training

Published on: July 27, 2022

Related Concept Videos

Lifestyle Factors and Health

Lifestyle Factors and Health

Lifestyle factors play a critical role in maintaining overall health and preventing chronic diseases. Key elements, such as regular physical activity, a nutritious diet, and abstinence from smoking, can significantly enhance physical, mental, and emotional well-being while reducing the risk of several life-threatening conditions.
Benefits of Physical Activity
Physical activity, whether through structured exercise or casual activities like walking, biking, or dancing, is a cornerstone of a...

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...

Assessment of the Gastrointestinal System II: Health Perception Pattern

Assessment of the Gastrointestinal System II: Health Perception Pattern

Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health Perception Patterns
Health perception patterns offer valuable insights into a patient's lifestyle habits and how they may impact their GI health. These patterns include:

Panic Disorder

Panic Disorder

Panic disorder is an anxiety disorder characterized by recurrent and sudden minutes-long episodes of intense fear, known as panic attacks. These attacks may feel like heart attacks and often happen without warning or a specific cause. They can include symptoms such as rapid heart rate, shortness of breath, chest pain, trembling, sweating, dizziness, and a sense of helplessness. During a panic attack, individuals may feel as though they are experiencing a heart attack or are in a...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

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

Preliminary pre-post changes in neuromuscular performance after application of a wearable photobiomodulation device in young tennis athletes.

European journal of translational myology·2026

Physical exercise as an adjunctive therapy in obesity-associated cancer: From biological rationale to a pragmatic clinical framework.

Seminars in cancer biology·2026

Adipose Tissue-Central Nervous System Axis in Obesity: Molecular Mechanisms, Inflammation, and Nutritional and Technological Implications.

Metabolites·2026

Efficacy and Cost-Effective Treatment of Isometric Resistance Training for Blood Pressure Control: A Narrative Review in Healthy Individuals and People with Cardiovascular Diseases.

Journal of functional morphology and kinesiology·2026

Dietary bioactive compounds targeting insulin resistance: mechanisms and preventive potential.

Frontiers in nutrition·2026

Correlations in Full-Body Postural Morphology: A Cross-Sectional Exploratory Pilot Study Using a Dual-Camera Structured Light System.

Diagnostics (Basel, Switzerland)·2026

See all related articles