JMIR Publications

ABSTRACT

Background:

Loneliness significantly affects the quality of life and physical and mental health. In addition, recent studies have shown high levels of loneliness across various populations ranging from older adults to college students. Detection of loneliness through passive sensing on personal devices can lead to a better understanding of measurable behaviors that low and high loneliness individuals exhibit and can thus lead to explore these behaviors in interventions aimed at decreasing rates of loneliness among college students.

Objective:

We aimed to explore the potential of using smartphone and wearable sensors to infer the severity of loneliness and to identify its corresponding behavioral patterns.

Methods:

Data was collected from smartphones and Fitbits of 160 college students over a semester. Participants completed the UCLA loneliness questionnaire at the beginning and at the end of the semester. The scores were categorized to high (questionnaire score>40) and low (<=40) loneliness. Daily features were extracted from mobile phone and Fitbit sensors to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then aggregated to generate semester-level features. We used three analytic methods: 1) statistical analysis to provide an overview of loneliness in college students, 2) data mining using the Apriori algorithm to extract combined behavior patterns associated with loneliness, and 3) machine learning classification using an ensemble of Gradient Boosting and Logistic Regression algorithms with feature selection in a leave-one-student-out cross-validation manner to infer the level of loneliness.

Results:

The average loneliness score from both the pre-surveys and post-surveys was high, above 43, and the majority of participants fell into the high loneliness category, with 63.8% in pre-survey and 58.5% in post-survey. Severe loneliness (scores greater than one standard deviation above the mean) increased from 38.2% at the beginning of the semester to 39.4% at the end of the semester. 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 loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that (1) spending less time outside of campus during evening hours on weekends and (2) spending less time in Greek houses for social events in the evening and night on weekdays were common behavior patterns in students with high loneliness. The analysis also indicated more activity and less sedentary behavior especially in the evening and night was associated with a decrease in loneliness.

Conclusions:

Passive sensing has the potential for detecting loneliness in college students and identifying the associated daily behavioral patterns, including the intensity of activity especially during night hours and the amount of time spent outside of campus. These findings highlight opportunities for interventions through mobile technology to reduce the impact of loneliness and social isolation on individuals’ health and wellbeing.