Wellbeing

To predict which users are at risk of suicide based on a small dataset of online posts, we leverage pre-trained sentence embeddings from large language models, and achieve high F1 scores (.83-.92). We further analyze users’ posts to determine which topics are most associated with suicidal users.

We train personalized hierarchical Bayes models to predict individual’s next-day stress, happiness, and health, and examine the effect of including features related to sleep in the model. Including sleep features significantly improves performance when predicting happiness.

Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.

To help promote behavior change, we leverage the power of social obligation, and conduct an experiment in which participants are paired together and asked to design a Behavioral Intervention Technology (BIT) customized to suit their partner’s behavior change goal.

Machine learning applied to nightly data from sensors and smartphones, shows value for predicting college student’s mood the following evening. Using multi-task learning to simultaneously predicted related wellbeing factors like health, energy, stress, and alertness improves performance.

The Challenge is a tool aimed at promoting social connections and decreasing sedentary activity in a workplace environment. Participants are paired with a partner to complete short physical challenges, leveraging social obligation and social consensus to drive behavior change.

Wellbeing is a complex internal state consisting of several related dimensions, such as happiness, stress, energy, and health. We use Multi-task Multi-kernel learning to classify them simultaneously, leading to significant performance approvements.

SmileTracker is an app that uses facial expression recognition to take a screenshot of the user’s screen whenever they smile. The screenshot and image of the user’s face are saved, to help them remember positive content they encountered during the day. Users can opt to share their images to a public gallery.