Research Design: Leveraging AI and Wearable Technology for Health Insights

This repository contains research design developed for the project "BEWELL: Can AI and self-tracking improve your health and lifestyle?" outlines methods, and techniques used to create a robust research design that answers an important data science question.

Table of Contents

1. Introduction
2. Research Design Components
3. Research Design Methods and Techniques
4. Benefits of the Research Design

Introduction

The BEWELL research design aims to explore how AI and wearable technology can enhance health and lifestyle, particularly focusing on sleep quality. This research design addresses how to effectively combine user voice input and data from wearable devices to predict sleep quality. Our approach emphasizes creating a solid foundation for answering the research question through careful planning and methodical execution.

Research Question: Does the mixed use of user voice input and wearable technology data improve the prediction of sleep quality based on indicators such as physical activities, caffeine/alcohol intake, and screen time?

Research Design Components

The research design is structured around several key components:

1. Literature Review and Background Analysis:

   • Understanding the current landscape of wellness applications and their limitations.
   • Reviewing existing research on sleep disorders, particularly insomnia, and the effectiveness of wearable technology.

2. Study Design and Data Collection:

   • Defining the observational study design and determining the data collection methods.
   • Incorporating both qualitative (voice data) and quantitative (wearable data) aspects to create a comprehensive dataset.

3. Data Processing and Feature Engineering:

   • Processing raw data to ensure it is clean, accurate, and ready for analysis.
   • Creating new features from the collected data to better capture the relationships between different variables and sleep quality.

4. Statistical and Predictive Analysis:

   • Applying statistical methods to explore correlations and build predictive models.
   • Using machine learning techniques to analyze the data and generate insights.

5. Ethical Considerations and Privacy:

   • Ensuring data privacy and ethical handling of user information.
   • Addressing potential biases and risks in the data collection and analysis process.

Research Design Methods and Techniques

Step 1: Literature Review and Background Analysis

• Objective:

  • Identify the gaps in current wellness applications and the opportunities for using AI and wearable technology to enhance user health insights.

• Techniques:

  • Conducted a thorough literature review to understand the state of the art in sleep research and wearable technology.
  • Summarized key findings and identified areas where the BEWELL research design could contribute novel insights.

  "Understanding the limitations of existing wellness applications helped us define the scope of our research and identify key variables for our study."

Step 2: Study Design and Data Collection

• Objective:

  • Design an observational study that collects relevant data over time to understand the factors influencing sleep quality.

• Techniques:

  • Defined the study as a case-control design, focusing on comparing individuals with and without sleep issues.
  • Developed a protocol for data collection using both voice recordings and wearable devices to gather comprehensive data on user behaviors and sleep patterns.

"Our study design leverages both active data (voice inputs) and passive data (wearable sensors) to provide a holistic view of sleep quality."

Step 3: Data Processing and Feature Engineering

• Objective:

  • Prepare the collected data for analysis by ensuring it is clean and creating new features that can help in predictive modeling.

• Techniques:

  • Cleaned and standardized the data to handle missing values and ensure consistency.
  • Engineered new features such as categorizing sleep quality and quantifying the impact of screen time and physical activity.

# Example of data cleaning and feature engineering
data['sleep_quality'] = data['sleep_duration'].apply(lambda x: 'Good' if x >= 7 else 'Poor')
data['screen_time_category'] = pd.cut(data['screen_time'], bins=[0, 2, 4, 6, 8], labels=['Very Low', 'Low', 'Moderate', 'High', 'Very High'])

Step 4: Statistical and Predictive Analysis

• Objective:

  • Analyze the data to identify patterns and build models that predict sleep quality based on various indicators.

• Techniques:

  • Applied correlation analysis and regression models to understand the relationships between variables.

Step 5: Ethical Considerations and Privacy

• Objective:

  • Ensure that the research design respects user privacy and addresses ethical considerations.

• Techniques:

  • Implemented measures to anonymize user data and obtain consent for data collection.
  • Identified potential biases in the study and outlined strategies to mitigate them.

"We prioritized user privacy by ensuring all collected data was anonymized and users had full control over their participation."

Step 6: Research and Study Design

• Observational Study Design: Designed a case-control study to compare sleep quality between users with insomnia and those without.
• Quantitative Analysis: Focused on quantitative methods to understand the impact of various factors on sleep quality.

Benefits of the Research Design

1. Comprehensive Understanding of Sleep Patterns:

   • Provides insights into how different lifestyle factors affect sleep quality.
   • Helps users identify and modify behaviors that contribute to poor sleep.

2. Integration of AI and Wearable Technology:

   • Demonstrates the potential of combining AI and wearable sensors for health monitoring.
   • Shows how voice data and wearable technology can provide a complete view of user health.

3. Application of Research Design Skills:

   • Highlights the use of robust research design principles in creating a comprehensive study.
   • Serves as a practical example of how to apply research design skills to address a real-world data science question.