As a digital health researcher, you are likely familiar with using statistical packages such as SPSS, SAS, or Stata for your data analysis and cleaning needs. While these programs have a relatively low initial learning curve, they may not provide the versatility, scalability, and ease of use you need to tackle complex and large data sets, such as those required to evaluate mHealth engagement.
On the other hand, Python offers a range of benefits that make it a valuable tool for researchers working in digital health research. The learning curve for getting started with Python may be steeper than for other statistical packages, but the benefits of investing in learning the language are well worth it.
In this blog post we will review some of the common uses of Python and discuss how to get started.
Benefits of using Python in digital health research
Python offers many benefits to researchers working with digital health data:
- Integrating data from multiple sources: Python has many libraries and packages specifically designed to retrieve data from almost any platform you use in your research. This means you can automate the process of collecting data from all the systems you use in your study and integrating them into a single data set for analysis. For example, you can use the QulatricsAPI package to retrieve and match survey data with individual user data from your digital health platform (e.g., Twilio or cloud backends such as AWS or Firebase). This is an essential step in the process of evaluating how engagement is influencing your intervention outcomes.
- Data analysis and visualization: You can use Python to load data, clean and transform it, and create informative visualizations to communicate findings effectively. For instance, the Pandas package enables helpful operations such as grouping, pivoting, and merging data. Python also has many libraries and packages for data visualization, including Matplotlib, Seaborn, and Plotly, that make it easy to create a wide range of visualizations, from simple bar charts and line graphs to more complex visualizations such as heatmaps and interactive plots.
- Real-time dashboards: Python can create interactive and dynamic dashboards for real-time monitoring of trends and patterns. This can help you quickly identify patterns and respond to emerging issues in your data. Real-time dashboards are also valuable for creating a shared understanding of user engagement with stakeholders and collaborators. We often use the Streamlit package, as it is quick and easy to implement. Other popular dashboard packages include Dash and Voila.
- Statistical tests: Python has a plethora of libraries and packages that provide a wide range of statistical tests and functions, including NumPy, SciPy, and StatsModels. This means that you can integrate statistical tests into your workflow or dashboard, from simple tests like t-tests and chi-squared tests to more complex tests like regression analysis and machine learning algorithms.
- Machine learning: Machine learning has become an increasingly important tool in digital health research, allowing researchers to analyse large and complex data sets, identify patterns and relationships, and make data-driven decisions in their research. You can use Python to implement machine learning algorithms using libraries and packages such as scikit-learn, TensorFlow, and PyTorch.
- Reproducibility: Python provides tools and workflows that make it easier to ensure that your data analysis is reproducible. For example, you can use Jupyter notebooks to record your code, data, and results and share your work with others.
In the realm of digital health research, Python is a powerful and flexible tool that is both accessible and user-friendly. Regardless of your programming experience, using code-based workflows can streamline data retrieval, cleaning, and analysis, ultimately saving you valuable time.
Stay tuned in the coming weeks for more insights on how to utilize Python to optimize your digital research. And if you have specific questions or concerns, don’t hesitate to reach out – we’re here to help.