Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors of Food Journalers

This repository contains two Jupyter notebooks used for reproducing the results published in our Digital Health 2018 paper:

Achananuparp, P., Lim, E.-P., & Abhishek, V. (2018). Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors of Food Journalers. In Proceedings of the 2018 International Conference on Digital Health - DH ’18 (pp. 35–44). New York, New York, USA: ACM Press. https://doi.org/10.1145/3194658.3194663

Please contact Aek if you have any questions or problems.

Requirements

The notebooks have been tested in Python 2.7 via Anaconda with the following packages:

• Pandas 0.23
• Matplotlib 2.2.3
• Seaborn 0.9
• Scikit-learn 0.20.2

See requirements.txt for a complete list.

Project Structure

By default, the project assumes the following directory structure:

project 
└───data  
│   │   profiles.csv
│   │   fv_servs.csv
│   │   protein_servs.csv
│   │   sugar_servs.csv
│   │   daily_calories_details.csv
│   │   lapses.csv
└───notebooks
│   │   1-exploratory-data-analysis.ipynb
│   │   2-stats-regression-analysis.ipynb
│   │   [optional]-streaks-lapses.ipynb
└───reports
│   └───figures

All CSV data files should be put in the data folder. All notebooks should be put in the notebooks folder. Any generated reports and figures will be put in the reports folder.

Pipeline

Step 0: Data import

Download the data and extract the CSV files to the data directory.

Step 1: Exploratory data analysis

Run the notebook 1-exploratory-data-analysis.ipynb to generate figures and additional aggregated food intake data.

Outputs: Several CSV files will be generated and stored in the data folder.

Step 2: Hypothesis testing and regression analysis

Run the notebook 2-stats-regression-analysis.ipynb to perform hypothesis testing and regression analysis of food intakes. The notebook requires data files from previous steps in the data folder.

Outputs: Several reports will be generated and stored in the reports folder.

[Optional] Analyzing streaks and lapses

Run the notebook [optional]-streaks-lapses.ipynb to compute logging streaks and lapses.

Outputs: Several CSV files will be generated and stored in the data folder.