Search

Objective To investigate what social, psychological, personality, and behavioral factors affect overtime heart rate changes of college students. Participants: The daily heart rates of over 600 undergraduates at the University of Notre Dame were unobtrusively recorded via Fitbit devices from August 16, 2015, to May 13, 2017. Method: Latent Growth-Curve modeling strategy is utilized to examine how daily mean heart rate and its standard deviation change over time, and what foregoing factors predict observed changes. Results: The mean heart rate increased and its standard deviation stayed the same over the 637 days. Heart rate levels go up with that of social contacts, an indicator of peer influence. Both daily heart rate levels and changes are also affected by multiple external factors. Conclusion: Human heart rate is not only a physiological phenomenon but also a social-psychological one, as it is systematically affected by peer networks, social contexts, and human activities. [ABSTRACT FROM AUTHOR]

Fitbit wearable devices provide users with objective data on their physical activity and sleep habits. However, little is known about how users develop their usage patterns and the key mechanisms underlying the development of such patterns. In this article, we report results from a longitudinal analysis of Fitbit usage behavior among a sample of college students. Survey and Fitbit data were collected from 692 undergraduates at the University of Notre Dame across two waves. We use a structural equation modeling strategy to examine the relationships among three dimensions of Fitbit usage behavior corresponding to three elements of the habit loop model: trust in the accuracy of Fitbit physical activity and sleep data (cue), intensity of Fitbit device use (routine), and adjustment of physical activity and sleep behaviors based on Fitbit data (reward). More than 75 percent of participants trusted the accuracy of Fitbit data and nearly half of the participants reported they adjusted their physical activities based on the data reported by their devices. Participants who trusted the Fitbit physical activity data also tended to trust the sleep data, and those who intensively used Fitbit devices tended to adjust both their physical activities and then sleep habits. Psychological states and traits such as depression, extroversion, agreeableness, and neuroticism help predict multiple dimensions of Fitbit usage behaviors. However, we find little evidence that trust, Fitbit usage, or perceived adjustment of activity or sleep were associated with actual changes in levels of sleep and activity. We discuss the implications of these findings for understanding when and how this new monitoring technology results in changes in people's behavior. [ABSTRACT FROM AUTHOR]

Objectives: To determine whether first-year college students cluster in networks based on subjective perceptions of loneliness. Participants: 492 first-year Notre Dame students completed surveys across two semesters and provided communication data used to reconstruct their social networks. Methods: Subjective perceptions of loneliness are measured using the Social and Emotional Loneliness Scale for Adults (SELSA). Correlations between an individual's loneliness and the average loneliness of their alters are compared to associations in random networks created using a rewiring algorithm to determine statistical significance. Results: During their first semester, students are more likely than chance to form ties with other students with similar levels of family and romantic loneliness. In their second semester, students cluster on romantic loneliness but not on family or social loneliness. Conclusions: Students are more likely than chance to form ties with people with similar self-perceived levels of loneliness, but only for certain types of loneliness and during certain periods. [ABSTRACT FROM AUTHOR]