Objective

The primary objective was to develop a statistical models capable of assessing stress levels based on behavioural health data collected from smartphone sensors, accommodating the reality that most users do not possess wearable technology. By leveraging the widespread availability of smartphones, our approach offers a scalable approach to assess stress and its implications on health and behaviour within the general population, setting the stage for a broader understanding and potential interventions.

Methodology

The Depression Anxiety Stress Scale known as the DASS-21, is used as a clinical screening tool for the overall assessment of the three conditions. This study analysed the results from the Stress sub-score of the questionnaire from participants. All participants installed the Sahha research app which collected and analysed digital heath data from smartphone sensors and any other wearables. Participants were prompted with the DASS-21 screening questionnaire weekly which assessed their internal state. Data analysis and feature engineering was conducted to surface behavioural features that could discern between different levels of stress. These features were use for model development.

Data Collection:

• A total of 30,534 DASS-21 surveys were collected from 820 unique participants, providing a comprehensive dataset for analysis.
• Users were recruited through an online platform (Prolific) across the following countries: UK, USA, Ireland, Germany, France, Australia, Canada, Iceland, Israel, Japan, Korea, Netherlands, New Zealand, Singapore, Taiwan
• Age of recruitment was between 18-65
• Health data included physical activity (steps), sleep patterns, phone unlocks, etc., collected over a period of 6 months.

Model Development:

• The reliable and transparent statistical method logistic regression was employed for classification between healthy and stressed individuals.
• 5-fold cross-validation was used, with each user either only being part of training set or only part of test set, to ensure a good measure of generalisability and performance.
• To deal with heavy class imbalance, SMOTE (Synthetic Minority Over-sampling Technique) was used to oversample the training set in each fold.

Results

Data Analysis:

In-depth analysis of the collected dataset reveals significant insights into the behavioural patterns associated with varying levels of stress.

The following figure displays the average step count by hour for participants categorised by stress levels: normal, mild, moderate, severe, and extreme. Participants with 'normal' stress levels show a higher and more consistent step count, peaking in the late afternoon. Those with 'mild' to 'moderate' stress levels have a similar pattern but take fewer steps. 'Severe' and 'extreme' stress levels are associated with lower step counts and greater variability, with an unusual spike for 'extreme' stress late in the day. This suggests that higher stress may be linked to reduced physical activity throughout the day.