Nightly sleep duration predicts grade point average in the first year of college.

Academic achievement in the first year of college is critical for setting students on a pathway toward long-term academic and life success, yet little is known about the factors that shape early college academic achievement. Given the important role sleep plays in learning and memory, here we extend this work to evaluate whether nightly sleep duration predicts change in end-of-semester grade point average (GPA). First-year college students from three independent universities provided sleep actigraphy for a month early in their winter/spring academic term across five studies. Findings showed that greater early-term total nightly sleep duration predicted higher end-of-term GPA, an effect that persisted even after controlling for previous-term GPA and daytime sleep. Specifically, every additional hour of average nightly sleep duration early in the semester was associated with an 0.07 increase in end-of-term GPA. Sensitivity analyses using sleep thresholds also indicated that sleeping less than 6 h each night was a period where sleep shifted from helpful to harmful for end-of-term GPA, relative to previous-term GPA. Notably, predictive relationships with GPA were specific to total nightly sleep duration, and not other markers of sleep, such as the midpoint of a student's nightly sleep window or bedtime timing variability. These findings across five studies establish nightly sleep duration as an important factor in academic success and highlight the potential value of testing early academic term total sleep time interventions during the formative first year of college.

Lack of Belonging Predicts Depressive Symptomatology in College Students.

Feeling a sense of belonging is a central human motivation that has consequences for mental health and well-being, yet surprisingly little research has examined how belonging shapes mental health among young adults. In three data sets from two universities (exploratory study: N = 157; Confirmatory Study 1: N = 121; Confirmatory Study 2: n = 188 in winter term, n = 172 in spring term), we found that lower levels of daily-assessed feelings of belonging early and across the academic term predicted higher depressive symptoms at the end of the term. Furthermore, these relationships held when models controlled for baseline depressive symptoms, sense of social fit, and other social factors (loneliness and frequency of social interactions). These results highlight the relationship between feelings of belonging and depressive symptoms over and above other social factors. This work underscores the importance of daily-assessed feelings of belonging in predicting subsequent depressive symptoms and has implications for early detection and mental health interventions among young adults.

Mindfulness Training and Physical Health: Mechanisms and Outcomes.

OBJECTIVE: There has been substantial research and public interest in mindfulness interventions, biological pathways, and health for the past two decades. This article reviews recent developments in understanding relationships between mindfulness interventions and physical health. METHODS: A selective review was conducted with the goal of synthesizing conceptual and empirical relationships between mindfulness interventions and physical health outcomes. RESULTS: Initial randomized controlled trials in this area suggest that mindfulness interventions can improve pain management outcomes among chronic pain populations, and there is preliminary evidence for mindfulness interventions improving specific stress-related disease outcomes in some patient populations (i.e., clinical colds, psoriasis, irritable bowel syndrome, posttraumatic stress disorder, diabetes, HIV). We offer a stress-buffering framework for the observed beneficial effects of mindfulness interventions and summarize supporting biobehavioral and neuroimaging studies that provide plausible mechanistic pathways linking mindfulness interventions with positive physical health outcomes. CONCLUSIONS: We conclude with new opportunities for research and clinical implementations to consider in the next two decades.

The (Un)reliability of Alibi Corroborators: Failure to Recognize Faces of Briefly Encountered Strangers Puts Innocent Suspects at Risk.

Some innocent suspects rely on the memory of strangers to corroborate their alibis. However, no research has examined whether such potential alibi corroborators can accurately recognize an innocent suspect with whom they previously interacted. We developed a novel alibi corroboration paradigm in which undergraduate students (representing innocent suspects who would later provide an alibi) interacted with naïve university employees (representing potential alibi corroborators). Each student briefly interacted with a different naïve university employee (n = 60), and were also each yoked to a different employee with whom they did not interact (n = 60). Employees were presented 24 hours later with either a single photograph of the student or a six-person array containing a photograph of the student and were asked if they recognized anyone. The majority of employees failed to make a correct recognition of the student. False recognitions, however, were rare. Students exhibited overconfidence that they would be recognized. Findings imply that innocent suspects who rely on strangers to corroborate their alibis may be at risk. Copyright © 2016 John Wiley & Sons, Ltd.

Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study.

BACKGROUND: Biobehavioral rhythms are biological, behavioral, and psychosocial processes with repeating cycles. Abnormal rhythms have been linked to various health issues, such as sleep disorders, obesity, and depression. OBJECTIVE: This study aims to identify links between productivity and biobehavioral rhythms modeled from passively collected mobile data streams. METHODS: In this study, we used a multimodal mobile sensing data set consisting of data collected from smartphones and Fitbits worn by 188 college students over a continuous period of 16 weeks. The participants reported their self-evaluated daily productivity score (ranging from 0 to 4) during weeks 1, 6, and 15. To analyze the data, we modeled cyclic human behavior patterns based on multimodal mobile sensing data gathered during weeks 1, 6, 15, and the adjacent weeks. Our methodology resulted in the creation of a rhythm model for each sensor feature. Additionally, we developed a correlation-based approach to identify connections between rhythm stability and high or low productivity levels. RESULTS: Differences exist in the biobehavioral rhythms of high- and low-productivity students, with those demonstrating greater rhythm stability also exhibiting higher productivity levels. Notably, a negative correlation (C=-0.16) was observed between productivity and the SE of the phase for the 24-hour period during week 1, with a higher SE indicative of lower rhythm stability. CONCLUSIONS: Modeling biobehavioral rhythms has the potential to quantify and forecast productivity. The findings have implications for building novel cyber-human systems that align with human beings' biobehavioral rhythms to improve health, well-being, and work performance.

College students' daily mind wandering is related to lower social well-being.

Objective: This study sought to examine how daily mind wandering is related to loneliness, felt connection to others, and school belonging among college students. Participants: Three samples (n = 209, n = 173, and n = 266) from two US campuses were recruited. Methods: Data were collected via ecological momentary assessment over the course of two academic quarters in one sample and an academic semester in two samples. Results: Social well-being declined throughout the academic term in all samples. Lower day-to-day mind wandering predicted lower loneliness at the next time point and was concurrently related to a higher felt connection to others and higher school belonging. Thoughts about the past and future were associated with lower social well-being than present-focused thoughts. Conclusions: This study supports the proposition that promoting present-centered attention can benefit college students' social well-being and alleviate their feelings of loneliness and isolation that they often experience.

Memory conformity affects inaccurate memories more than accurate memories.

After controlling for initial confidence, inaccurate memories were shown to be more easily distorted than accurate memories. In two experiments groups of participants viewed 50 stimuli and were then presented with these stimuli plus 50 fillers. During this test phase participants reported their confidence that each stimulus was originally shown. This was followed by computer-generated responses from a bogus participant. After being exposed to this response participants again rated the confidence of their memory. The computer-generated responses systematically distorted participants' responses. Memory distortion depended on initial memory confidence, with uncertain memories being more malleable than confident memories. This effect was moderated by whether the participant's memory was initially accurate or inaccurate. Inaccurate memories were more malleable than accurate memories. The data were consistent with a model describing two types of memory (i.e., recollective and non-recollective memories), which differ in how susceptible these memories are to memory distortion.

Intoxicated eyewitnesses: Better than their reputation?

According to law enforcement, many witnesses are intoxicated either at the time of the crime, the interview, or both (Evans et al., Public Policy Law 15(3):194-221, 2009). However, no study to date has examined whether intoxicated witnesses' recall is different from sober witnesses' and whether they are more vulnerable to misinformation using an ecologically valid experimental design. Intoxicated, placebo, and sober witnesses observed a live, staged theft, overheard subsequent misinformation about the theft, and took part in an investigative interview. Participants generally believed they witnessed a real crime and experienced a real interview. Intoxicated witnesses were not different from placebo or sober witnesses in the number of accurate details, inaccurate details, or "don't know" answers reported. All the participants demonstrated a misinformation effect, but there were no differences between intoxication levels: Intoxicated participants were not more susceptible to misinformation than sober or placebo participants. Results are discussed in the light of their theoretical and applied relevance.

Alcohol intoxication and memory for events: a snapshot of alcohol myopia in a real-world drinking scenario.

Alcohol typically has a detrimental impact on memory across a variety of encoding and retrieval conditions (e.g., Mintzer, 2007; Ray & Bates, 2006). No research has addressed alcohol's effect on memory for lengthy and interactive events and little has tested alcohol's effect on free recall. In this study 94 participants were randomly assigned to alcohol, placebo, or control groups and consumed drinks in a bar-lab setting while interacting with a "bartender". Immediately afterwards all participants freely recalled the bar interaction. Consistent with alcohol myopia theory, intoxicated participants only differed from placebo and control groups when recalling peripheral information. Expanding on the original hypervigilance hypothesis, placebo participants showed more conservative reporting behaviour than the alcohol or control groups by providing more uncertain and "don't know" responses. Thus, alcohol intoxication had confined effects on memory for events, supporting and extending current theories.

Mindfulness training and systemic low-grade inflammation in stressed community adults: Evidence from two randomized controlled trials.

Mindfulness interventions have garnered significant attention as a complementary health treatment for many physical and psychological conditions. While some research has shown that mindfulness training can decrease psychological and physiological stress responses, it remains unclear whether mindfulness training impacts inflammation-a predictor of poor health outcomes. In addition, little research has examined the active components of mindfulness that may drive health-related improvements. Here, we provide data from two 3-arm randomized controlled trials that examined the effect of mindfulness training on inflammation in stressed community adults. Specifically, we examined whether training individuals to have an accepting attitude towards present moment experiences is a key emotion regulation skill that can lead to decreases in inflammation. Both studies randomly assigned participants to one of three conditions: mindfulness training that taught both attention monitoring and acceptance skills (Monitor+Accept); mindfulness training teaching monitoring without the acceptance component (Monitor Only); or a control condition. Study 1 employed a novel 2-week smartphone-based intervention and Study 2 employed a standard 8-week Mindfulness-Based Stress Reduction (MBSR) intervention. We hypothesized that Monitor+Accept training would lead to reductions in the inflammatory biomarker C-Reactive Protein (CRP) compared to Monitor Only training and control groups. Contrary to this hypothesis, we found that Monitor+Accept mindfulness training did not lead to reductions in CRP. Exploratory analyses combining study subsamples, however, suggest that both mindfulness interventions may reduce CRP in populations at risk for systemic inflammation-midlife-to-older adults and individuals with high BMI. Overall, the present studies contribute significantly to the question of whether mindfulness interventions can reduce systemic markers of low-grade inflammation.

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data.

BACKGROUND: Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. OBJECTIVE: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns. METHODS: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner. RESULTS: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]). Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%). CONCLUSIONS: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals' health and well-being.