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Objective: Extreme sport participation and injury rates have increased in recent decades. This study aimed to investigate sub-dimensions of impulsivity and sensation seeking that contribute to participation and injury risk in extreme sports. Participants: Data included cross-sectional survey responses from 7,109 college students (Mage=19.68, SD = 2.31). Methods: This study utilized path analysis to investigate sub-dimensions of sensation seeking and impulsivity as predictors of extreme sport participation and injury across 3 models. Results: Results of the final model identify risk seeking and lack of perseverance as the two strongest predictors of extreme sports injury, risk seeking, experience seeking, and lack of premeditation as the strongest positive predictors of extreme sports participation, and lack of perseverance as the strongest negative predictor of extreme sports participation. Conclusions: These results will contribute to targeted prevention and intervention efforts for extreme sports injury among young adults based on identified individual personality factors.

Background: Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. Methods: Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. Results: ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7–8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. Conclusions: Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months. [ABSTRACT FROM AUTHOR]

The dual systems theory of health risk behaviors posits that maladaptive behaviors during late adolescence and early adulthood are the result of differential developmental trajectories of a cognitive control system and socioemotional system. The divergence or "gap" between these two systems was modeled as two Latent Gap Scores (LGS) in a sample of college undergraduates (N = 7,109) and associations with health risk behavior were tested. Robust associations were found between these latent scores and engagement in substance misuse, risky sexual behavior, and non-suicidal self-injury. These results provide support for dual systems theory and represent a novel assessment of personality factors with respect to engagement in health-risk behaviors. [ABSTRACT FROM AUTHOR]

Sensation seeking and emotion dysregulation have been identified as contributing factors for consequences associated with cannabis use, although findings have been mixed. The study seeks to clarify relations between sensation seeking, emotion dysregulation, and cannabis-related consequences. Cannabis users (N = 2,128) were assessed for facets of sensation seeking, emotion dysregulation, and cannabis-related consequences. Results revealed a positive association between risk seeking and cannabis-related consequences, as well as between emotion dysregulation and cannabis-related consequences. A negative association was observed between experience seeking and cannabis-related consequences. An interaction was found between emotion dysregulation and risk seeking. Risk seeking appears to be a risk factor for cannabis-related consequences at lower levels of emotion dysregulation. At high levels, the effect of emotion dysregulation superseded that of risk seeking. These findings have implications for the prioritization of clinical concerns in individuals seeking treatment for problematic cannabis use. [ABSTRACT FROM AUTHOR]

Until recently, marijuana research has often relied on self-reported frequency of use (e.g., days used per month). These estimations may oversimplify use at times, as they can only infer the quantity that one uses, rather than directly measure it. While some studies have estimated quantity (e.g., number of joints, grams), research has supported that user estimates of marijuana weight are often inaccurate and overestimate the true quantity. Since weight misestimation varies by participant, it may be important to identify individual difference factors (e.g., personality) that influence weight estimation. One such factor may be impulsivity, as it is known to relate to other marijuana-use measures (e.g., frequency, consequences). However, research has yet to examine the specific relation between impulsivity and misestimation of marijuana weights. The present study investigated impulsivity as a predictor of marijuana quantity estimation, using objectively weighed quantity data. We hypothesized that impulsivity facets would relate to marijuana quantity misestimation. We asked attendees at a marijuana- related event to roll a joint or pack a bowl, and then take, among a battery of measures, the SUPPS-P Impulsive Behavior Scale. We found that negative urgency, positive urgency, and lack of perseverance were not significantly associated with misestimation, while lack of premeditation was significantly negatively related to misestimation. Findings indicated that individuals who lacked in premeditation made more accurate quantity estimates than those higher in premeditation. Future directions should investigate the relation between impulsivity, marijuana intoxication, and marijuana quantity estimation., (© 2022 Authors et al.)