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

Study design

An overview of our study is shown in Fig. 1. The participants were first allowed to install a dedicated smartphone app for monitoring the time they spend on the smartphone and the apps used. Upon installing the app, the participants were explicitly asked to consent to the data collection and the use of their data for research on digital well-being, anonymously. After app installation, the participants completed the 10-item Big Five Inventory (BFI-10) (Rammstedt & John, 2007) along with demographic information; education, gender, and age. In return, the participants were offered a premium version of the app as a token of appreciation for their participation. Upon consenting to take part in the study and completing the survey, the collection of smartphone usage data started. Upon consenting to take part in the study and completing the survey, the collection of smartphone usage data started. Then, the acquired data were processed to calculate the BFI-10 score and e-sleep variables. Based on the sleep-wake cycle, the sleep variables have been extracted from the usage data of the apps. Thus, we use the term “e-sleep” in this study. E-sleep represents the passive time of smartphones during sleeping hours. There are chances that the smartphone user might be awake but not using the smartphone during sleeping hours. The purpose of this study is to determine how much time users spend away from their smartphones and how it causes distraction during sleeping hours. All the extracted information was then stored in the e-sleep dataset. Finally, analysis was performed on the e-sleep dataset (N = 269) using JASP version 0.16.0 (Goss-Sampson, 2019) for multivariate linear regression and logistic regression analysis and SPSS version 28.1.1.1 (Hinton, McMurray & Brownlow, 2014) for multinomial logistic regression analysis.

Personality assessment

To assess the PT, we used Big Five Inventory-10 (BFI-10) (Rammstedt & John, 2007). This 10-item self-report questionnaire assesses extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience, the five personality traits. Participants were asked to rate how accurate statements were in describing their personalities. The 10 items are assessed on a 5-point Likert scale (1 = Strongly disagree to 5 = Strongly agree), with each dimension of personality corresponding to two items and, thus, having a value between 2 and 10. A higher score denotes a greater degree of a particular personality trait. The BFI-10 is reliable and valid across a wide range of sample groups (Rammstedt & John, 2007).

Data processing

A total of 602 participants agreed to take part in our study, which took place between October 2020 and April 2021. The participants’ explicit consent to data collection was obtained, and they also filled out a questionnaire on demographics and the BFI-10. Around 4.4 million records of app usage were acquired. Participants were excluded for having a low-quality sleep-wake cycle based on the usage of apps data if they: (i) had fewer than seven full nights’ worth of usage of apps; or (ii) had fewer than 16 h of recorded app usage within the 24 h maximum possible. The latter was made to exclude the case where data synchronization did not work correctly, e.g., when the internet was off for a long time, or when the app was forced to restart. Figure 2 depicts an example of app usage. The usage of apps was processed to extract the human sleep-wake cycle patterns, as shown in Fig. 3. From such patterns, we were able to identify the nighttime period that had the most uninterrupted inactive time (free of smartphone use). We implemented interactive software to process the data and visualize the usage for each user and each day to enable manual annotation of sleeping and wake-up times. After the annotation, daily usage of apps, e-sleep duration, sleeping time, wake-up time, and sleep distraction were calculated from the objectively recorded usage data. We defined smartphone-distracted e-sleep as having been interrupted due to smartphone usage during sleeping hours. Finally, we created a dataset called the e-sleep dataset containing the e-sleep variables, demographics, and the BFI-10 score for each user (N = 269), as shown in the flowchart in Fig. 4. The student received the approval of the research ethics committee of the authors’ institution.

Analysis

From the smartphone usage data, we extract three continuous variables, i.e., e-sleep duration (SDur), e-sleep distraction (SDist), and the daily average of smartphone usage (UsageOfAppsAvg), and four categorical variables, i.e., e-sleep duration quality (SDurQ), e-sleep distraction category (SDurC), e-sleeping time (STime), e-wake-up time. Our continuous variable SDur represents the average daily duration of sleeping time. From SDur, a categorical variable called SDurQ was created. SDurQ represents the quality of sleep in terms of quantity, i.e., poor, good, and over e-sleep. While sleeping, a smartphone could cause distraction. For example, users may check their phones in response to notifications, messages, or calls. They may also spend time on social media and messaging apps, where they are supposed to be continuing their sleep. We have visualized and scrutinized the smartphone usage data of each user to identify when such distraction happened and created the continuous variable SDist, which is the average daily time, in minutes, of sleep distraction caused by smartphones. From SDist, we created a categorical variable called SDistC consisting of two categories, i.e., distracted and not-distracted. From the smartphone usage data, we extracted the sleeping time and the wake-up time of each user and processed the first and derived two categorical variables based on whether the user sleeps at a regular time, i.e., STime (early sleep, regular sleep, delayed sleep, and poor sleep), and processed the second in the same way regarding the time the user wakes up, i.e., WTime (early wakeup, regular wakeup, delayed wakeup, and poor wakeup). Further explanation of these variables is shown in Table 1.

In this study, first, the Pearson’s correlation analysis was conducted to explore the associations among variables. We then used multivariate linear regression models to predict SDur through PT, UsageOfAppsAvg of smartphone apps usage, age, and gender. While considering age and gender as factors, binomial logistic regression was used to estimate the impacts of PT and UsageOfAppsAvg on the likelihood that participants have had distracted sleep, measured through SDistC. Through PT, UsageOfAppsAvg, age, and gender, a multinomial logistic regression analysis was used to predict E-Sleeping Time. Finally, PT, UsageOfAppsAvg, age, and gender were used to predict wakeup time in terms of early, regular, delayed, and poor wakeup time using multinomial logistic regression analysis.

Sample characteristics

Descriptive statistics of the participants’ demographics are shown in Tables 2, 3 and 4. Of the 269 participants, 41.03% were male, and 44.86% were emerging adults between the ages of 15 and 24. The participants were from different professions, with 34.94% being students. The participants are from ten countries (India, the United States, the United Kingdom, Canada, Australia, Germany, the Netherlands, Brazil, France, and Sweden). Table 3 shows the mean average for men, women, students, non-students, adults, and emerging adults. The SDur average for males was 465.61 min and for females 469.47 min and found no significant differences between males and females.

As shown in Table 3, the average sleeping hours for males were 7.7, and for females were 7.8 h. Approximately 24%, 58%, and 17% of the users showed insufficient poor sleep, good sleep, and oversleeping in terms of duration, respectively, as shown in Fig. 4. 60.13% of females and 56.31% of males were found to be good sleepers, whereas 27.18% of females and 23.64% of males were found to be bad sleepers. 70.94% of non-students, 56.38% of students, 60.68% of adults, and 55.93% of emerging adults were observed as good sleepers. The average daily smartphone usage of the participant was around 304.62 min (5.07 h). Among them, the males were 286.58 min (4.77 h) and the females were 317.52 min (5.29 h). Whereas the average usage of the smartphone by students was 339.26 min (5.65 h) non-students had 286.01 min (4.76 h), emerging adults had 337.28 min (5.62), and adults had 280.32 min (4.67). Regarding sleep distraction, as shown in Table 3, the average sleeping distraction time was found to be 13.38 min, and approximately 80% of participants were classified as having distracted sleep because of smartphone interrupts.

As indicated in Table 4, approximately 5%, 20%, 29%, and 46% of the users showed early, regular, delayed, and poor sleeping time, respectively. Likewise, users with early wake-up were 5%, regular wake-up was 20%, delayed wake-up was 29%, and poor wake-up was 46%. Males (49.51%) were found to have poor sleeping time compared to females (43.24%). The percentage of students (60.63%) and emerging adults (59.32%) were high to have poor sleeping time compared to non-students (38.85%) and adults (36.55%), respectively. Regarding the wake-up time, more females were found to have regular wake-up times compared to males.

Predicting sleep duration

To predict the impact of the PT and UsageOfAppsAvg on SDur, a multiple linear regression analysis was performed. First, the assumptions were checked and verified. The dependent variable SDur was visually checked for being normally distributed. While performing the Shapiro–Wilk test, the p-value was not significant, i.e., 0.247, which signifies the hypothesis of normality. There were no notable outliers in the SDur that significantly differed from the model based on the Standardized Residuals, which did not exceed −3.29 and 3.29. The collinearity statistics confirmed no multicollinearity among the variables. VIF values were less than 5 (for all predictors), and Tolerance values were more than 0.2. Pearson’s correlation was also performed and showed no multicollinearity among the variables, as shown in Table 5. Durbin–Watson value, which should be between 1 and 3, was found to be 1.962, indicating the independence of predictors. The residuals’ histograms were fairly normally distributed, the numbers did not exceed three, and the residuals’ normality and homoskedasticity were fulfilled. The majority of the data points were on or near the line, according to the Q-Q Plot for residuals.

As shown in Table 6, PT, UsageOfAppsAvg, age, and gender were included in the multiple linear regression model as predictors and SDur as the dependent variable. The results revealed that the overall model contributed to the prediction of SDur with a variance of 26.2%. and adjusted R² of .238 (23.8%) where F (8, 240) = 10.658, and p <.001. We found that only UsageOfAppsAvg (B = −0.420, p < 0.001) and conscientiousness (B = 0.186, p = 0.002) were significant and positive predictors of SDur.

Predicting sleep distraction

Binomial logistic regression analysis was performed to predict the effects of PT, and UsageOfAppsAvg on the likelihood that participants had distracted sleep, represented through variable SDistC (distracted, non-distracted), while considering age and gender as factors in the model. The logistic regression model was statistically significant, χ²(390) = 57.278, p < .001 between the outcome and the predictor variables. The model explained 17.8% (Nagelkerke R²) of the variance in e-sleep distraction and correctly classified 63.0% of cases. Sensitivity and specificity were recorded at 64.5%, and 61.5%, respectively.

For distracted sleep relative to not distracted sleep, out of eight predictor variables, three were statistically significant: conscientiousness (p < .001, Exp(B) = 0.811), openness (p = .011, Exp(B) =0.867), and UsageOfAppsAvg (p = .002, Exp(B) = 1.002), as shown in Table 7. While looking at the value of the Exp(B), if a subject were to increase the conscientiousness score by one point, the log-odds of preferring not-distracted to distracted sleep would be expected to increase by 0.811 units while holding all other variables in the model constant. This suggests that lower conscientiousness levels are significantly related to an increased probability of having distracted sleep. If a subject were to increase the openness score by one point, the log-odds of preferring not-distracted to distracted sleep would be expected to increase by 0.867 units while holding all other variables in the model constant. This suggests that higher openness levels are significantly related to decreased probability of having distracted sleep. Increasing UsageOfAppsAvg is related to a significantly increased probability of distracted sleep. The agreeableness, extraversion, neuroticism, and factors (age and gender) were not found statistically significant. This relation of conscientiousness, openness, and UsageOfAppsAvg with the SDistC is visualized in Fig. 5.

Predicting sleeping time

Multinomial logistic regression analysis was performed to predict e-sleeping time in terms of early, regular, delayed, and poor sleeping time through PT, UsageOfAppsAvg, age, and gender. The full model, model fitting, was statistically significant (i.e., p < .001) and predicted the dependent variable better than the intercept-only model alone. The pseudo-R-square measures, i.e., Cox and Snell, Nagelkerke, and McFadden were found to be 0.360 (36.0%), 0.384 (38.4%), and 0.161 (16.1%), respectively. The Likelihood ratio test (Table 8) shows the contribution of each variable to the model. Conscientiousness (p < .001), UsageOfAppsAvg (p < 0.001), age (p < 0.001), and gender (p = 0.006) contributed significantly, but extraversion, agreeableness, and neuroticism did not.

For early sleep relative to regular sleep, as shown in Table 9, conscientiousness (Exp(B) = 1.472 (95% CI 1.232 to 1.759), p < 0.001, Wald = 18.115), and age (Exp(B) = 2.779 (95% CI 1.508 to 5.121), p < 0.001, Wald = 10.740) were statistically significant. The multinomial log-odds of choosing regular to early sleep should fall by 0.472 units whenever a subject’s conscientiousness score increases by one point, according to the conscientiousness Exp(B) value, while maintaining all other model variables constant. More generally, it can be inferred that if a subject were to increase their conscientiousness score, we would expect them to be more likely in early sleep over regular sleep. For age (Exp(B) = 2.779, p < 0.001), emerging adults compared to adults were more likely to be early sleepers. Conversely, we can say that adults were more prone to be regular sleepers, Exp(B) = 0.359 (given by the reciprocal of 2.779).

For delayed relative to regular sleeping time, the UsageOfAppsAvg (Exp(B) = 1.008 (95% CI 1.005 to 1.011), p < 0.001, Wald =29.187), and gender (Exp(B) = 0.439 (95% CI .241 to 0.797), p = 0.007, Wald = 7.309) were found statistically significant. The multinomial log-odds of preferring delayed to regular sleeping time would be expected to decrease by 0.008 units while holding all other variables in the model constant, according to the UsageOfAppsAvg’s Exp(B) value. This is because if a subject were to raise their UsageOfAppsAvg score by one point. More generally, it can be inferred that if a subject were to increase their UsageOfAppsAvg score, we would expect them to be more likely in the delayed sleeping time compared to regular sleep. For gender (Exp(B) = .439, p = 0.007), males compared to females were more likely to be delayed sleepers. Conversely, we can say that females were more prone to be regular sleepers, Exp(B) = 0.359 (given by the reciprocal of 2.779).

Likewise, for poor relative to regular sleeping time, the UsageOfAppsAvg (Exp(B) = 1.011 (95% CI 1.008 to 1.014), p < 0.001, Wald = 52.229), and age (Exp(B) = 2.449 (95% CI 1.292 to 4.645), p = 0.006, Wald = 7.527) were found statistically significant. The Exp(B) value for UsageOfAppsAvg indicates that if a subject were to increase the UsageOfAppsAvg score by one point, the multinomial log-odds of preferring poor to regular sleeping time would be expected to increase by 0.011 units while holding all other variables in the model constant. For age, emerging adults compared to adults were more likely to fall in the poor sleeping time category compared to regular sleeping.

Predicting wakeup time

Multinomial logistic regression analysis was performed to predict wakeup time in terms of early, regular, delayed, and poor wakeup time through PT, UsageOfAppsAvg, age, and gender. The full model was statistically significant (i.e., p < .001) and predicted the dependent variable better than the intercept-only model alone. The pseudo-R-square measures, i.e., Cox and Snell, Nagelkerke, and McFadden were found 0.254 (25.4%), 0. 273 (27.3%), and 0.110 (11.0%), respectively. The Likelihood Ratio Test (Table 10) shows the contribution of each variable to the model. Extraversion (p < .001), Agreeableness (p < .001), Conscientiousness (p = .022), Neuroticism (p = .003), age (p < .001), and gender (p < .001) contributed significantly, but not openness, and UsageOfAppsAvg.

For “early wakeup” relative to “regular wakeup”, as shown in Table 11, the extraversion (Exp(B) = 1.228 (95% CI 1.084 to 1.391), p < 0.001, Wald = 10.44), agreeableness (Exp(B) = 0.678 (95% CI .586 to 0.784), p < 0.001, Wald = 27.20), conscientiousness (Exp(B) = 1.183 (95% CI 0.740 to 1.004), p = 0.057, Wald = 3.62), neuroticism (Exp(B) = 1.183 (95% CI 1.041 to 1.345), p = 0.010, Wald = 6.59), and gender (Exp(B) = 3.913 (95% CI 2.233 to 6.857), p < 0.001, Wald = 22.71) were statistically significant. The Exp(B) value for extraversion indicates that if a subject were to increase the extraversion score by one point, the multinomial log-odds of preferring “regular wakeup” to “early wakeup” would be expected to decrease by 0.228 units while holding all other variables in the model constant. The Exp(B) value for neuroticism indicates that if a subject were to increase the neuroticism score by one point, the multinomial log-odds of preferring “regular wakeup” to “early wakeup” would be expected to decrease by 0.183 units while holding all other variables in the model constant. More generally, it can be inferred that if a subject were to increase their extraversion and/or neuroticism score, we would expect them to be more likely in the early wakeup over regular wakeup. The Exp(B) value for conscientiousness indicates that if a subject were to increase the conscientiousness score by one point, the multinomial log-odds of preferring “regular wakeup” to “early wakeup” would be expected to increase by 0.862 units. The Exp(B) value for agreeableness indicates that if a subject were to increase the agreeableness score by one point, the multinomial log-odds of preferring “regular wakeup” to “early wakeup” would be expected to increase by 0.678 units. For males relative to females, the relative risk of preferring “early wakeup” to “regular wakeup” would be expected to increase by a factor of 0.913 given the other variables in the model are held constant. In other words, males are more likely than females to prefer “early wakeup” to “regular wakeup”, Exp(B) = 0.255 (given by the reciprocal of 3.913). The multinomial log-odds of choosing “regular wakeup” over “early wakeup” is projected to fall by 0.183 units if a subject’s neuroticism score were to rise by one point, according to the neuroticism score’s Exp(B) value, while holding all other model variables constant. More broadly, it may be deduced that we would anticipate a subject to be more likely in the early wakeup than ordinary wakeup if their extraversion and/or neuroticism scores increased. According to the conscientiousness score’s Exp(B) value, if a subject’s conscientiousness level were to rise by one point, the multinomial log-odds that they would choose “normal wakeup” over “early wakeup” were predicted to rise by 0.862 units. According to the agreeableness score’s Exp(B) value, the multinomial log-odds of choosing “normal wakeup” over “early wakeup” should rise by 0.678 units if the agreeableness score were raised by one point. Given that the other factors in the model are maintained constant, it would be predicted that males are more likely than females to choose “early wakeup” over ”normal wakeup”. In other words, Exp(B) = 0.255 indicates that males are more likely than females to choose ”early wakeup” over ”normal wakeup” (given by the reciprocal of 3.913).

For “delayed wakeup” relative to “regular wakeup”, the second section of Table 11, agreeableness (Exp(B) = 0.793 (95% CI 0.685 to 0.919), p = 0.002, Wald = 9.583), conscientiousness (Exp(B) = 0.814 (95% CI 0.700 to 0.946), p = 0.007, Wald = 7.240), and neuroticism (Exp(B) = 1.127 (95% CI 0.999 to 1.271), p = 0.053, Wald = 3.755) were statistically significant. The Exp(B) value for agreeableness indicates that if a subject were to increase the agreeableness score by one point, the multinomial log-odds of preferring “regular wakeup” to “delayed wakeup” would be expected to decrease by 0.793 units while holding all other variables in the model constant. The Exp(B) value for conscientiousness indicates that if a subject were to increase the conscientiousness score by one point, the multinomial log-odds of preferring “regular wakeup” to “delayed wakeup” would be expected to decrease by 0.183 units while holding all other variables in the model constant. It can be inferred that if a subject were to increase their agreeableness and/or conscientiousness score, we would expect them to be more likely in the “delayed wakeup” over “regular wakeup”. The Exp(B) value for neuroticism indicates that if a subject were to increase the neuroticism score by one point, the multinomial log-odds of preferring “regular wakeup” to “delayed wakeup” would be expected to increase by 0.862 units.

The third section of Table 11 shows the relation of “poor wakeup” to “regular wakeup”. Agreeableness (Exp(B) = 0.815 (95% CI .680 to.977), p = 0.027, Wald = 4.911), conscientiousness (Exp(B) = 0.815 (95% CI 0.676 to 0.982), p = .032, Wald = 4.603), and age (Exp(B) = 4.922 (95% CI 2.520 to 9.615), p < 0.001, Wald = 21.760) were statistically significant. The Exp(B) value for agreeableness indicates that if a subject were to increase the agreeableness score by one point, the multinomial log-odds of preferring “regular wakeup” to “poor wakeup” would be expected to decrease by 0.815 units while holding all other variables in the model constant. The Exp(B) value for conscientiousness indicates that if a subject were to increase the conscientiousness score by one point, the multinomial log-odds of preferring “regular wakeup” to “delayed wakeup” would be expected to decrease by 0.815 units while holding all other variables in the model constant. It can be inferred that if a subject were to increase their agreeableness and/or conscientiousness score, we would expect them to be more likely in the “regular wakeup” over “poor wakeup”. For age, emerging adults compared to adults were more likely to fall in the “poor wakeup” time category compared to “regular wakeup”.

Sleep duration prediction

As indicated in Table 3, the average sleeping hours for males were 7.7, and for females were 7.8 h. Approximately 24%, 58%, and 17% of our participants showed poor sleep, good sleep, and oversleeping in terms of duration, respectively (Table 4). Previous research found a 42.4% to 60% prevalence of poor sleep duration (Ayala et al., 2017; Costa, Patrão & Machado, 2019; Pensuksan et al., 2016; Yazdi et al., 2016). This difference might be due to recall bias in self-reported sleep data in previous research, or to the fact that we collected our data during the COVID-19 period when they had fewer sleep restrictions, e.g., on waking up time (Yuksel et al., 2021). Another reason might be our reliance on smartphone usage to estimate sleep time, whereas people may be away from their phones but not asleep. Females (60.13%) were found to be good sleepers compared to males (56.31%), which is consistent with the findings of Yazdi et al. (2016). Non-students (70.94%) and adults (60.68%) were found to be good sleepers compared to students (56.38%) and emerging adults (55.93%). It shows that non-students and adults can keep themselves away from their smartphones during the recommended sleep time.

In the correlation analysis, Table 5, the personality traits of agreeableness (r = 0.14; p < 0.05) and conscientiousness (r = 0.27; p < 0.001) were found to have a significant positive relationship with sleep duration. In addition to the correlational analysis, the regression analysis revealed the significant contribution of conscientiousness and usage of smartphones in the prediction of sleep duration, as shown in Table 6. These findings are consistent with those in Stephan et al. (2018), i.e., low conscientiousness was associated with a worsening of quality sleep duration. Our second finding, i.e., smartphone usage related to sleep duration, supports the findings in Cain & Gradisar (2010), Mohammadbeigi et al. (2016), Tamura et al. (2017) and Orzech et al. (2016), i.e., participants who spent more time using digital media, had less sleep duration.

Sleeping distraction prediction

Regarding sleep distraction (Table 3), the average sleeping distraction time was found to be 13.38 min, and approximately 80% of participants had distracted sleep due to smartphone interrupts. The authors in Jniene et al. (2019) conducted a study before COVID-19 and found that 60% of the participants had sleep distractions due to smartphone usage. The distraction was almost uniform across gender, profession, and age. Table 6 shows that the personality trait of conscientiousness (r = −0.196; p < 0.001) had a significant negative relationship with sleep distraction due to smartphone usage. Regression analysis revealed that conscientiousness, openness, and smartphone usage amount were all significant predictors of sleep distraction, as shown in Table 7. This association between smartphone overuse and sleep distraction is consistent with findings in Cain & Gradisar (2010) and Foundation (2011).

Conscientious people are accountable, trustworthy, motivated, and may be driven to get enough sleep to get more done throughout the day. Higher levels of conscientiousness (self-control) may enable people to better manage their impulses, refrain from excessive smartphone use and other sleep-impairing activities, and obtain a decent night’s sleep. Most studies have also found a relationship between conscientiousness and better sleep quality (Duggan et al., 2014; Gray & Watson, 2002; Hintsanen et al., 2014; Kim et al., 2015; Williams & Moroz, 2009), although two studies did not find support for this association (Allen, Magee & Vella, 2016; Cellini, Duggan & Sarlo, 2017). Openness has generally been found to be unrelated to global measures of sleep quality (Duggan et al., 2014; Hintsanen et al., 2014; Huang et al., 2016; Kim et al., 2015), but one study found that higher openness is related to poorer sleep quality (Allen, Magee & Vella, 2016). Unlike existing work, we found that lower openness is related to higher sleep distraction when considering smartphone usage. In this context, the study by Takao (2014) found that openness has a negative correlation with problematic smartphone use. Moreover, smartphone overuse is associated with poor sleep quality (Yang et al., 2020). People with high openness are curious about new things, ideas and people, eager to learn, and have new experiences. Consequently, a high level of openness may be less dependent on smartphones, which could lead to quality sleep. We also found evidence that agreeableness (p < .068, Exp(B) = 1.112) is almost significant and associated with low peer influence resistance, and this can interpret why people distract from their sleep, perhaps to check and reply to messages. However, this requires further analysis of the apps that were used when a person was distracted.

Sleeping and wakeup time prediction

Regression analysis revealed that conscientiousness, smartphone usage, age, and gender have a significant contribution to the prediction of sleeping time. A high level of conscientiousness could lead to early or /regular sleeping time. We discovered that increased smartphone usage is associated with less sleep, which is not surprising given past research linking digital media usage and sleep (Orzech et al., 2016; Smyth, 2007; Wolfe et al., 2014). Emerging adults with a high level of conscientiousness would prefer early sleep compared to adults. Emerging adults with a high level of smartphone usage would fall into poor sleep compared to adults.

Regarding waking-up time prediction, extraversion, neuroticism, conscientiousness, agreeableness, age, and gender were found to be significant predictors. Smartphone daily average usage has shown no relation with wake-up time. However, an increase in smartphone usage leads to poor sleep duration, distraction, and sleeping time. In the regression analyses, conscientiousness remained a significant predictor of sleep quality in terms of duration. High levels of conscientiousness and agreeableness resulted in people being more likely to have regular wake-up times, whereas increasing extraversion and neuroticism led to poor wake-up time. In the regression analysis, when analyzing the impact of PT and smartphone usage on WTime, we found that males prefer early wake-up compared to females, and emerging adults were more likely to fall into poor wake-up time compared to adults.

These results demonstrate that the extraversion personality trait has an impact on controlling and maintaining a wake-up schedule. This is aligned with recent research that looked at the vital role of extraversion in morningness (Pljusnin, 1993; Tankova, Adan & Buela-Casal, 1994). The results are also consistent with Eyesenck’s personality theory, which states that extraverts focus their attention on the outer world. They most likely pay closer attention, actively seek out external stimuli, and also have a more positive perspective on the situation, and perceive less threat in what is happening (Rosswurm, Pierson & Woodward, 2001). Positive affect is more prevalent in people with higher extraversion traits. According to empirical data, positive people have more stable circadian rhythms (Kim, 2013). Extroverts’ reactions in a given scenario are significantly influenced by this positive psychological condition. Therefore, even when using a smartphone, users with higher extraversion were able to maintain healthy sleep habits.

We also found that an increase in neuroticism leads to delayed wake-up. People with high neuroticism do not have a specific wake and sleep times and are unable to sleep for an adequate amount of time (Kaur & Kaur, 2021). Similar results were achieved by Kaur & Kaur (2021), who reported that internet users who possess the neuroticism personality trait are more likely to have wake-up delays. According to Eysenck (1991) and Muris et al. (2005), neurotics have higher levels of anxiety, which may be related to higher levels of arousal that cause them to stay awake longer before falling asleep. Additionally, those who exhibit this tendency are susceptible to daily stressors (Bolger & Schilling, 1991) and dwell on negative concerns that might cause sleep disruptions, such as difficulty going to sleep (Norlander, Johansson & Bood, 2005). Positive affect strengthens the association between extraversion and improved sleep quality, whereas the tendency to experience positive affect mediates the relationship between neuroticism and sleep problems (Allen, Magee & Vella, 2016). People high in neuroticism and low in conscientiousness have poor sleep quality because these personality traits are associated with difficulty regulating emotions and behavior (Duggan et al., 2014).