The role of objectively recorded smartphone usage and personality traits in sleep quality

Abstract

Purpose. The proliferation of smartphones, accompanied by internet facilities, has contributed to a decrease in sleep quality over the last decades. It has been revealed that excessive internet usage impacts the physical and mental health of smartphone users, while personality traits (PT) could play a role in developing internet addictions and preventing their negative effects. The objective of the present study is to assess the role of PT and smartphone usage in sleep quality. Method. The sample comprised 269 participants, 55% females, within the age range of 15-64 years. We objectively collected one-week smartphone apps usage data from the participants. They also responded to demographics and the PT (BFI-10) questionnaires. The usage data of smartphone apps were processed to calculate smartphone usage amounts and sleep variables, including sleep duration, sleep distraction, sleeping time, and wake-up time. The data were analyzed using the correlation coefficient and regression analyses. Results. The results indicated that more smartphone usage was associated with reduced sleep duration, increased sleep distraction, and later bedtime. Furthermore, smartphone users with the conscientiousness trait had a longer sleep duration, earlier sleeping time, less sleep distraction, and earlier wakeablity. Sleep distraction was positively associated with openness. Extraversion and neuroticism were found to be positive predictors of early wakeablity. Neuroticism had a negative association with early wakeablity. Finally, the implications of the study have been discussed. Conclusion. Our study's usage of data that was acquired objectively has strong methodological qualities. The present study is the first to contribute to the literature on the role of PT and objectively measured smartphone usage in the prediction of sleep quality.Wefound that smartphone use and sleep variables are associated with PT. Other scholars can use our dataset for benchmarking and future comparisons