Analysis code for the research paper: "Associations Between Physical Activity Intensity and Experience, Self-Regulation, and Self-Reported Interoceptive Accuracy and Attention"

Preprint: https://www.medrxiv.org/content/10.1101/2025.05.06.25326015v1

Authors: J. Mulder, J.C. Kiefte-de Jong, J.D. de Vries, M.T. Elferink-Gemser

This analysis examines associations between:

1. Physical activity intensities (walking, moderate, vigorous exercise)
2. Years of PA experience
3. Self-regulation abilities (SSRQ)
4. Self-reported interoceptive accuracy (IAS) and attention (IATS)

• Python 3.7+
• Anaconda/Spyder environment (recommended)
• Standard scientific Python libraries (see requirements.txt)

1. Clone this repository:

git clone https://github.com/yourusername/interoception-physical-activity.git cd interoception-physical-activity

2. Install required packages:

pip install -r requirements.txt

1. Prepare your data: Place your dataset file in the data/ directory
2. Run the analysis:

python analysis.py

3. View results: Check the output/ directory for generated files

• Complete analysis: ~5-10 minutes
• Generates 8 output files including statistical results and visualizations

interoception-physical-activity/ ├── README.md # This file ├── LICENSE # MIT License ├── .gitignore # Git ignore rules ├── requirements.txt # Python dependencies ├── analysis.py # Main analysis script ├── data/ # Data directory │ └── README.md # Data information ├── output/ # Results directory │ └── README.md # Output information └── docs/ # Documentation └── README.md # Additional documentation 

The analysis produces the following files in output/:

• reliability_results.csv - Internal consistency (Cronbach's α) for all scales
• lognorm_results.xlsx - Main regression analyses (univariate, multivariate, stepwise)
• lognorm_interaction_results.xlsx - Analyses with PA × self-regulation interactions
• lognorm_results_by_SSRQ.xlsx - Sensitivity analyses by self-regulation levels
• descriptive_table.xlsx - Comprehensive descriptive statistics

• continuous_variables_histograms.png - Distribution plots for continuous variables
• categorical_variables_histograms.png - Distribution plots for categorical variables
• correlation_matrix.png - Correlation heatmap for all continuous measures

1. Data Loading & Preprocessing

   • Import SPSS dataset with final variable selection
   • Handle inactive participants and experience variables
   • Data quality checks and validation

2. Missing Data Analysis

   • Missing At Random (MAR) assessment using logistic regression
   • Multiple imputation using MICE (5 datasets, 20 iterations)
   • Post-imputation validation

3. Outlier Detection & Removal

   • Z-score based outlier detection (±3 SD threshold)
   • Applied to key outcome and predictor variables

4. Internal Consistency Analysis

   • Cronbach's alpha for IAS (21 items), IATS (21 items), SSRQ (31 items)
   • Reliability interpretation and reporting

5. Descriptive Statistics

   • Comprehensive summary statistics for all variables
   • Age, sex, and socioeconomic status distributions
   • Correlation analysis between continuous measures

6. Main Regression Analyses

   • Log-normalization of all continuous variables
   • Univariate models: Each predictor vs. IAS/IATS separately
   • Model 1: Core PA variables (walking, moderate, vigorous hours + experience + self-regulation)
   • Model 2: Stepwise addition of demographic variables (sex, age, SES)

7. Interaction Analyses

   • PA intensity × self-regulation interaction terms
   • Enhanced model testing for moderation effects

8. Sensitivity Analyses

   • Median split based on self-regulation scores
   • Separate analyses for high vs. low self-regulation groups

• IAS_Totaal: Interoceptive Accuracy Scale total score
• IATS_Totaal: Interoceptive Attention Scale total score

• Wandel_UrenWeek: Walking hours per week
• Moderate_UrenWeek: Moderate intensity PA hours per week
• Vigorous_UrenWeek: Vigorous intensity PA hours per week
• IPAQ_SportErvaring: Years of sport/PA experience
• SSRQ_Totaal: Short Self-Regulation Questionnaire total score

• Geslacht: Sex (1=male, 2=female)
• Leeftijd: Age in years
• SES: Socioeconomic status (1=low, 2=high)

Important: Raw data is not included in this repository to protect participant privacy and comply with ethical guidelines. The analysis script is provided for transparency and reproducibility of statistical methods.

For data access requests or replication purposes, please contact the corresponding author.

If you use this code in your research, please cite:

Mulder, J., Elferink-Gemser, M. T., de Vries, J. D., & Kiefte-de Jong, J. C. (2025). Associations Between Physical Activity Intensity and Experience, Self-Regulation, and Self-Reported Interoceptive Accuracy and Attention. medRxiv. https://doi.org/https://doi.org/10.1101/2025.05.06.25326015 

This project is licensed under the MIT License - see the LICENSE file for details.

Keywords: interoception, physical activity, self-regulation, regression analysis, Python, reproducible research