Michael J. Tumminia

TL;DR: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns and these findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.

TL;DR: A new method to extract contextually filtered features from passively collected, time-series mobile data via association rule mining and its best model uses contextually-filtered features to significantly outperform a standard model that uses only unimodal features.

TL;DR: In this article, a machine learning approach was used to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity.

TL;DR: In this article, a memory-based learning algorithm is proposed to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students, which leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week.

TL;DR: There is suggestive evidence that individual differences in mindfulness, and in particular nonjudgmental acceptance, prospectively predict less negative affect through lower rumination.