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.

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.

How is Mindfulness Linked to Negative and Positive Affect? Rumination as an Explanatory Process in a Prospective Longitudinal Study of Adolescents.

Research shows greater mindfulness is associated with less negative affect and more positive affect. Fewer studies have examined the mediating psychological processes linking mindfulness to these outcomes in adolescents. This three-wave, prospective longitudinal study examines rumination-the tendency to engage in repetitive and negative self-focused thinking-as one potential explanatory process. High school students (N = 599, Mage = 16.3 years; 49% girls) completed a short-form version of the Five Facet Mindfulness Questionnaire, in addition to self-report measures of rumination and negative and positive affect three times over the course of a school year. Autoregressive, cross-lagged panel models tested reciprocal, prospective associations between mindfulness, rumination, and negative and positive affect, while accounting for prior levels of each construct, within-wave covariances, and gender and grade level. The results showed that the nonjudgment mindfulness facet (and the total mindfulness score) predicted cross-wave reductions in rumination, that in turn predicted cross-wave reductions in negative affect. No evidence for mediation was found for positive affect, or for any of the other mindfulness facets (describe, acting with awareness, and nonreactivity). This study provides suggestive evidence that individual differences in mindfulness, and in particular nonjudgmental acceptance, prospectively predict less negative affect through lower rumination.

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.

Changes in brain connectivity following exposure to bilateral eye movements.

The present research assessed how engaging in bilateral eye movements influences brain activity. Participants had their resting-state brain activity recorded with electroencephalography (EEG) before and after they performed 30 s of bilateral eye movements or a center-control manipulation. We assessed differences in change scores for absolute power and coherence between the eye-movement and center-control conditions. A main effect for handedness was present for EEG power in the theta and beta frequency bands, with inconsistent-handed participants displaying a greater increase than consistent-handed participants in both frequency bands. For theta, the increase in power for inconsistent handers was specific to participants in the bilateral eye-movement condition, whose increase in theta power exceeded the increase in theta power for consistent-handed participants regardless of condition. In contrast, for coherence, a main effect for condition was present for the delta frequency band, with participants in the control condition exhibiting a significant drop in posterior delta coherence pre to post. We suggest that the maintenance of posterior delta coherence over time may be an important factor in sustaining attention. Further, the malleability of EEG power for inconsistent-handed participants reveals the importance of individual-differences variables in the potential for behavioral manipulations to change brain activity.