Smartphone use and personality: Their effects on sleep quality across groups using mediation analysis

Abstract

Purpose: The rapid rise in smartphone use has led to declining sleep quality. Excessive internet use has been linked to negative impacts on physical and mental health, and individual personality traits (PT) may contribute to internet addiction and mitigate its harmful effects. This study aims to: (1) examine whether PT mediate the relationship between smartphone use and sleep quality, and (2) investigate whether the relationship between smartphone use and sleep quality varies across different gender and age groups.

Methods: There were 269 participants in the dataset. The daily averages for sleep duration, sleep distraction, and smartphone use were extracted from the usage data acquired through a dedicated smartphone application. Structural equation modeling was employed to investigate the mediating role of personality in the relationship of smartphone use and sleep quality.

Results: Results indicated that PT partially mediated the relationship, with a significant negative indirect impact of smartphone use on sleep quality. Age differences were observed, showing distinct patterns between younger and older participants, while no significant gender differences emerged.

Conclusions: This study found that excessive smartphone use, coupled with low personality indicators, leads to poor sleep quality. Positive personality traits improve sleep outcomes, with age influencing the impact of smartphone use on sleep. Our findings support and contribute to existing concerns about technology overuse and highlight the need for targeted interventions to promote more beneficial technology design and usage patterns.

Keywords: Smartphone use; digital well-being; mediation analysis‌; personality traits; sleep quality.

Figure 1.

Conceptual model: the impact of TMU on SleepQ with the mediating effect of PT while considering the gender and age differences.

Figure 2.

An abstract view of the research study.

Figure 3.

An abstract view of different apps usage by different participants.

Figure 4.

(a) illustrates the 24-hours human sleep-wake cycle. The use of applications in relation to the sleep-wake cycle is highlighted in (b). The usage patterns and the absence of activity during the sleeping hours are shown in (b). Sleep duration refers to this idle period. “Usage of apps/hour” in (b) refers to the real smartphone use activities a user completes in a single hour. For instance, by 12:00 pm, the user had spent about 25 of the 60 minutes on his smartphone.

Figure 5.

Flowchart showing the steps involved in gathering and processing the e-sleep dataset. The cylinder shape represents data storage, and the rectangular shape represents automated processing.

Figure 6.

Resulted model with path coefficients where “a” is the standardized-β for path: TMU to PT, “b” is β for path: PT to SleepQ, c is the β of direct, and d is the β for indirect effect.

Figure 7.

(a) is the resulted model for males and (b) for females with path coefficients where “a” is the standardized-β for path: TMU to PT, “b” is β for path: PT to SleepQ, “c” is the β of direct, and “d” is the β for indirect effect.

Figure 8.

(a) is the resulted model for young and (b) for adults with path coefficients where “a” is the standardized-β for path: TMU to PT, “b” is β for path: PT to SleepQ, “c” is the β of direct, and “d” is the β for indirect effect.

Figure A1.

E-sleep-wakeup cycle pattern detection and annotation software interface.