Figure 1: DeepTake uses data from multiple sources (pre-driving survey, vehicle data, non-driving related tasks (NDRTs) information, and driver biometrics) and feeds the preprocessed extracted features into deep neural network models for the prediction of takeover intention, time and quality

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Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbeing. To date, many researchers have employed a variety of methods for extracting mental health-centric features from digital text communication (DTC) data, including natural language processing, social network analysis, and extraction of temporal discourse patterns. However, none have explored a hierarchical framework for extracting features from private messages with the goal of unifying approaches across methodological domains. Furthermore, while analyses of large, public corpora abound in existing literature, limited work has been done to explore the relationship between of private textual communications, personality traits, and symptoms of mental illness. We present a framework for constructing rich feature spaces from digital text communications. We then demonstrate the efficacy of our framework by applying it to a dataset of private Facebook messages in a college student population (N=103). Our results reveal key individual differences in temporal and relational behaviors, as well as language usage in relation to validated measures of trait-level anxiety, loneliness, and personality. This work represents a critical step forward in linking features of private social media messages to validated measures of mental health, wellbeing, and personality.

Background: Effective emotion regulation (ER) is important to long-term healthy functioning, but little is known about what constitutes effective ER in the moment or how social anxiety symptoms and different strategies influence short-term effectiveness outcomes.Methods: Intensive ecological momentary data from N = 124 college students illustrate how different ways of operationalizing ER effectiveness leads to different conclusions about the short-term effectiveness of different strategies in daily life.Results: When effectiveness is operationalized as the degree to which participants judged that their ER attempts made them feel better, social anxiety severity was negatively associated with effectiveness, and avoidance-oriented strategies were judged to be less effective than engagement-oriented strategies. In contrast, when effectiveness is operationalized as the degree of change in self-reported affect following ER attempts, social anxiety severity was not related to effectiveness, and avoidance-oriented strategies were more effective than engagement-oriented strategies. Social anxiety and ER strategy type did not interact in either model, regardless of how effectiveness was measured. Conclusions:The study highlights discrepancies when examining two common but distinct ways of measuring the same overarching effectiveness construct, and raises intriguing questions about how forms of psychopathology that are intimately tied to emotion dysregulation, like social anxiety, moderate different ways of measuring the effectiveness of ER attempts.

The present study assessed target engagement, preliminary efficacy, and feasibility as primary outcomes of a free multi-session online cognitive bias modification of interpretation (CBM-I) intervention for anxiety in a large community sample. High trait anxious participants (N = 807) were randomly assigned to a CBM-I condition: 1) Positive training (90% positive-10% negative); 2) 50% positive-50% negative training; or 3) no-training control. Further, half of each CBM-I condition was randomized to either an anxious imagery prime or a neutral imagery prime. Due to attrition, results from six out of eight sessions were analyzed using structural equation modeling of latent growth curves. Results for the intent-to-treat sample indicate that for target engagement, consistent with predictions, decreases in negative interpretations over time were significantly greater among those receiving positive CBM-I training compared to no-training or 50-50 training, and vice-versa for increases in positive interpretations. For intervention efficacy, the decrease in anxiety symptoms over time was significantly greater among those receiving positive CBM-I training compared to no-training. Interaction effects with imagery prime were more variable with a general pattern of stronger results for those completing the anxious imagery prime. Findings indicate that online CBM-I positive training is feasible and shows some promising results, although attrition rates were very high for later training sessions.

Poor adherence to long-term therapies for chronic diseases, such as cancer, compromises effectiveness of treatment and increases the likelihood of disease progression, making medication adherence a critical issue in population health. While the field has documented many eers to adherence to medication, it has also come up with few efficacious solutions to medication adherence, indicating that new and innovative approaches are needed. In this paper, we evaluate medication-taking behaviors based on social cognitive theory (SCT), presenting patterns of adherence stratified across SCT constructs in 33 breast cancer survivors over an 8-month period. Findings indicate that medication adherence is a very personal experience influenced by many simultaneously interacting factors, and a deeper contextual understanding is needed to understand and develop interventions targeting non-adherence.

Heavy traffic consequences in crowded cities can be extremely reduced by using mass transportation. Recent extensive studies on Tehran subway system, as a representative of crowded cities, show that ever increasing commutation demand results in rapid decline in service quality and satisfaction level, system capacity wastage, and poorer system performance. Since passenger boarding/alighting period is noticeable compared to the inter-station travel time, it seems that passenger boarding/alighting management would play a significant role in system performance improvement. Aiming at increasing satisfaction level and service success rate, while reducing travel time, different boarding/alighting strategies are proposed. Passengers behaviors are carefully simulated based on a microscopic model, through introducing an inclination function which governs a passengers movement in a two-dimensional queue. Simulation results, in terms of three aforementioned measures of performance, show that in less crowded stations, the first strategy, expectedly, outperforms the other two. However, in crowded stations (e.g. interchange stations) the third strategy outperforms the others significantly.