Next Article in Journal
Lived Experiences of Everyday Memory in Adults with Dyslexia: A Thematic Analysis
Previous Article in Journal
Enhancing Subjective Wellbeing in Older Individuals with Amnestic Mild Cognitive Impairment: A Randomized Trial of a Positive Psychology Intervention
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Relationship between Problematic Smartphone Use, Sleep Quality and Bedtime Procrastination: A Mediation Analysis

Santiago Correa-Iriarte
Sergio Hidalgo-Fuentes
1,2 and
Manuel Martí-Vilar
Departamento de Psicología Básica, Facultad de Psicología y Logopedia, Universitat de València, 46010 Valencia, Spain
Departamento de Psicología y Salud, Facultad de Ciencias de la Salud y la Educación, Universidad a Distancia de Madrid (UDIMA), 28400 Madrid, Spain
Author to whom correspondence should be addressed.
Behav. Sci. 2023, 13(10), 839;
Submission received: 6 September 2023 / Revised: 2 October 2023 / Accepted: 11 October 2023 / Published: 13 October 2023


The purpose of this investigation was to study the relationship between sleep quality, problematic smartphone use (PSU) and bedtime procrastination, as well as to assess gender and age differences. A total of 313 participants, aged 18–60 (M = 30 ± 10.1; 53.2% males), completed an online survey between February and May 2023 in Spain. The Pittsburgh Sleep Quality Index, Smartphone Addiction Scale-Short Version and Bedtime Procrastination Scale were used to measure sleep quality, PSU and bedtime procrastination, respectively. Additionally, smartphone use habits were evaluated through self-report questions. Pearson correlations, independent samples t-tests, one-way ANOVA, Bonferroni’s post hoc tests and mediation analysis were conducted. Correlation analysis showed positive associations between the three main variables. Independent sample t-tests indicated females were more prone to PSU along with higher overall smartphone use. Post hoc analysis of one-way ANOVA exposed age differences between young adults (18–25 years old), adults (26–44 years old) and middle-aged adults (45–60 years old) in PSU and bedtime procrastination. Finally, mediation analysis revealed that PSU had indirect effects on sleep quality through bedtime procrastination, but no direct effects on sleep quality. Therefore, PSU, and especially bedtime procrastination, should be considered as targets in future campaigns or intervention programs to improve sleep quality among the young Spanish population.

1. Introduction

Sleep quality is a person’s subjective assessment of how well they feel they have slept [1]. In recent years, there has been growing concern about sleep-related problems (e.g., sleep efficiency, sleep latency or sleep quality) due to the importance of sleep in the overall health of the population, especially in mental health [2]. According to the Spanish Society of Neurology [3], more than 10% of the Spanish population will suffer from a severe chronic sleep disorder. In addition, 20–48% of Spanish adults and 20–25% of children have some difficulty initiating or maintaining sleep. There is no consensus on a gender difference in sleep quality, with some studies claiming that women have poorer sleep quality [4,5,6], while others do not find this relationship [7,8].
According to Stahl [9], disturbances in sleep–wake cycles are associated with an increase in mental disorders (e.g., major depression or anxiety disorders); immune system, cardiovascular and metabolic disorders (e.g., diabetes or stroke); neurological disorders (e.g., Alzheimer’s dementia or chronic pain); endocrine dysfunction (e.g., in the hypothalamic–pituitary–adrenal axis); cancer; and other derived economic costs (e.g., loss of productivity or cost of accident repair).
Considering the health risks associated with sleep disturbances, data on the increase in sleep problems since the global COVID-19 pandemic may be cause for concern. A study of 19,267 adults in 13 Asian, American and European countries [10] found a significant increase in sleep and mental health problems since the COVID-19 pandemic. Similar results have been found in studies conducted in China [11], where the prevalence of clinical insomnia has increased by 37% since the COVID-19 pandemic, or in Spain, where 23.9% of a sample of 15,070 people reported having problems initiating or maintaining sleep [12]. Considering that during the confinements the amount of time spent in front of screens increased for people of all age ranges [13], many health professionals have been interested in whether there is a link between increased screen use and sleep problems.
This becomes even more relevant when considering the widespread use of smartphones in the population and their suitability for use in bed [14]. In the case of Spain, and according to data from the National Institute of Statistics (INE) [15], 99.2% of people aged 16–74 used smartphones in the three months prior to the survey (conducted in November 2022). Smartme Analytics [16] reflects in its latest report that Spanish adults use, on average, a smartphone for 3 h and 40 min, a figure that increases to 4 h and 15 min a day in the case of young people between 18 and 24 years old.
Problematic smartphone use (PSU) is defined as the use of a smartphone associated with at least some element of dysfunctional use, such as anxiety when the smartphone is not available or neglect of other activities due to smartphone use [17]. These negative or dysfunctional effects can range from withdrawal to loss of control over phone use, decreased productivity, impaired daily functioning, detriment to social relationships or damage to physical health [18,19,20,21]. PSU is closely related, or even overlapping, with other phenomena such as problematic use of social networks, messaging apps or the internet [22,23,24]. However, there are some differences in terms of risk factors, for example, men tend to develop more problematic internet use, while women are more at risk of manifesting PSU [25]. The terminology related to behavioural addictions when researching smartphones is controversial, as some authors think that this may stigmatise smartphone users [21]. Moreover, PSU is not listed as an addiction in any of the main diagnostic manuals, neither the DSM-5 [26] nor the more recent ICD-11 [27]. Thus, in addition to “problematic smartphone use” and “smartphone addiction” other terms have been used to describe this type of relationship with smartphones: “excessive use”, “compulsive use” and “compensatory use” [28,29,30].
The prevalence of PSU in adults varies in different countries, for example, in Arabia it is 66.9% [31], in Bangladesh 61.4% [32] and in China estimates range from 65.8 to 52.8% [33]. Likewise, in Spain, several studies have placed the prevalence of PSU between 20.5 and 23.75% [34,35]. It is worth noting the difficulty in assessing and comparing the prevalence of PSU due to the inconsistency of diagnostic criteria and assessment methods [36]. Additionally, although some studies have found no sex differences [37], there is some consensus in the scientific evidence that women are at higher risk of developing PSU [38,39,40,41,42,43,44,45]. These differences could be caused by a different pattern of smartphone use, with women using smartphones for social purposes (i.e., social networking or instant messaging) and men for more varied purposes, such as video games, calls, and multimedia content [46,47]. In addition, women (especially younger women) may have a higher prevalence of PSU due to having more malleable and influenceable self-control in social situations than men [48,49].
Several negative consequences of PSU have been found, such as low productivity [50], poor academic performance [51,52], general procrastination [53], academic procrastination [54], low self-esteem [55], increased alcohol consumption [52], anxiety and depression [56,57,58], executive function deficiencies [59,60] and sleep problems [61,62,63,64,65].

1.1. Mechanisms by Which Smartphone Use Affects Sleep

1.1.1. Disruption of Circadian Rhythms

First, it has been proposed that smartphone use close to sleep time may alter the production of melatonin and/or cortisol, both of which are important hormones in the regulation of circadian rhythms.
Melatonin is a hormone secreted by the pineal gland and regulated by sleep-inducing light/dark cycles. The pineal gland is a neural structure related to the visual system and has retinohypothalamic connections with the suprachiasmatic nuclei that house the internal biological clock and play a crucial role in generating circadian rhythms. It has been shown that light exposure in the evening can delay the phase of the internal clock, resulting in sleep problems, while light exposure in the morning advances melatonin secretion [66].
Cortisol, on the other hand, is a steroid hormone produced in the adrenal cortex, related to sleep arousal and wakefulness [67]. Cortisol shows a circadian rhythm, with a peak at the transition between sleep and wakefulness. After awakening, secretion of the hormone decreases throughout the day, reaching a minimum around midnight, before gradually increasing again to reach a new peak the following morning. Thus, cortisol, like melatonin, serves as a marker of an organism’s circadian temporal structure, regulating the sleep/wake cycle.
LED-backlit displays (such as smartphones) emit 3.3 times more light in the blue range (440–470 nm) than non-LED-backlit displays (e.g., some eReaders or displays using cathode ray tubes), as reported by Cajochen et al. [68] This difference is relevant as studies indicate that human circadian physiology and alertness levels are particularly sensitive to short-wavelength light [69,70,71,72].
Night-time exposure to an LED-backlit computer screen has been shown to cause a decrease in salivary melatonin levels [69] and an increase in waking time, along with improved cognitive performance, sustained attention, and working and declarative memory [68,73]. However, any delay in the circadian release of melatonin has negative consequences for sleep induction [74]. Schmid et al. [75] compared the effect on melatonin, cortisol and sleep levels of reading before sleep on a smartphone versus reading a printed book. They found that melatonin and cortisol levels were found to be altered in the smartphone use condition, in addition to a reduction in slow wave sleep. According to a study by Wallenius et al. [76], school children who used digital media for three hours a day showed a decrease in the cortisol increase one hour after waking up, showing an alteration in the circadian rhythm that this hormone follows. In contrast, children who used digital media for less than three hours or not at all showed a typical increase in cortisol in the morning. Another study with children showed a greater increase in cortisol just after using DVD screens versus playing with wooden blocks [77].
It is worth noting that the closer-to-face use of smartphones compared to other traditional media such as television [78], which is usually placed at a greater distance from the face, can lead to greater exposure to shortwave light [79] and thus cause greater sleep disturbance compared to other types of screens. In fact, a study by Figueiro et al. [80] found that the light emitted by 70-inch LED-backlit LCD televisions located 1.8 to 2.7 metres from the subjects did not alter melatonin production in adults.
Regarding radiofrequency electromagnetic fields (RF-EMFs) emitted by smartphones, a review of the literature by Selmaoui and Touitou [81] states that no conclusive evidence has been found that this type of radiation alters melatonin or cortisol secretion. Instead, according to these authors, there are indications that melatonin may be a protective agent against the negative effects of RF-EMFs, such as oxidative stress and DNA damage, as well as having neuroprotective properties.

1.1.2. Increased Arousal

According to another line of research, increased arousal (especially cognitive and somatic arousal) before sleep may negatively influence sleep quality.
A study by Kheirinejad et al. [82] used the OURA wearable [83] to measure different components of sleep and the AWARE instrument [84] to assess smartphone usage by automatically collecting usage data. They concluded that the cognitive activation required during bedtime to perform different smartphone uses, such as conversing with other people or consuming images, text, video and audio, have a negative impact on sleep quality, but without a significant difference between them. On the other hand, Ong et al. [85] proposed a model in which two types of cognitive arousal, primary and secondary, contribute to the maintenance of insomnia. Secondary (metacognitive) cognitive arousal would encompass biases towards sleep-related thoughts and behaviours, rigidity in behavioural or sleep-related beliefs, and absorption in sleep problem solving. Primary cognitive arousal would consist of expectations about sleep, beliefs about the daytime consequences of sleep deprivation and, what concerns us in this section, increased mental activity at bedtime.
One of the main uses of smartphones is social media and communication applications, which sometimes require high cognitive functioning, which is not suitable for sleep induction or good sleep quality [86]. Specifically, of the 3 h and 40 min of smartphone time per day of Spanish adults, 1 h and 20 min are spent on social networks and another 40 min on instant messaging applications [16], which combined account for more than 50% of the total time spent on smartphones. It has been shown how increased arousal before sleep is a mediating variable between the negative effect of binge-watching TV series via smartphones [87] or the use of social networks [88] on sleep quality.
However, although correlational studies point to increased arousal as a possible cause of reduced sleep quality [89,90,91], an experimental study blocking the effect of blue light from smartphones conducted by Combertaldi et al. [92] found no such relationship. They did not observe empirical evidence of increased arousal from the use of social networks such as WhatsApp or Snapchat, nor a reduction in sleep quality. The authors hypothesise that the negative effect of smartphone use on sleep quality is due to the use of smartphones at bedtime (which usually exceeds 30 min) and its corresponding displacement of sleep time.

1.1.3. Bedtime Procrastination and Sleep Displacement

Self-regulation, according to Gillebaart [93], can be defined as the cognitive ability to monitor, plan and guide a person’s behaviour to facilitate goal attainment and inhibit disruptive emotions and behaviour. Proper self-regulation requires adequate functioning in the brain’s reward system and top-down control of the prefrontal cortex [94]. As Zhang and Wu [95] point out, it has been shown that addictive behaviours can alter brain circuits related to self-regulation such as prefrontal cortex functioning and top-down control [96,97,98,99,100]. A deficit in inhibitory control, a feature closely related to self-regulation, is present in individuals with a PSU [101]. Additionally, Rebetez et al. [102] suggested the depletion of self-regulatory resources and the failure of self-regulation as a source of procrastination. Thus, when talking about self-regulation and sleep, we must talk about bedtime procrastination, a relatively recent concept [103] that is defined as the action by which people deliberately delay going to bed without external interference, even though negative outcomes are anticipated.
A qualitative study by Nauts et al. [104] found three reasons why people procrastinate sleep: deliberate bedtime procrastination, unconscious bedtime procrastination and planned delay. The first refers to consciously delaying sleep time to perform tasks that could be done at another time or to have a moment to oneself after a long day of work. Unconscious bedtime procrastination occurs when, for example, people lose perception of time while absorbed in a task. Finally, strategic procrastination is when people make a conscious decision to delay sleep in order to avoid negative emotions related to rumination, long sleep latency or as a remedy for insomnia (accumulating “sleep pressure”). However, some authors argue that the belief held by these subjects that sleep delay benefits them (even if it does not) means that strategic delay is not considered bedtime procrastination per se [104,105,106].
Kroese and De Ridder [106] indicate how people with low self-regulation show higher bedtime procrastination as well as insufficient sleep. Another study by Ma et al. [107] involving 1550 university students found that bedtime procrastination is a strong predictor of prevalence and severity of poor sleep quality. Authors have proposed that smartphone use may be one of the causes of the so-called displacement theory. This theory is based on the idea that unstructured leisure use of electronic devices, such as smartphones, can displace other activities, such as sleep. Thus, smartphone use may delay sleep time (causing bedtime procrastination) and possibly reduce the amount of sleep, or even create an association between being in bed and being active [108,109,110]. In this regard, a study conducted in China involving 2741 university students found that a total of 57.5% of their sample used a smartphone in bed [111]. At the same time, a Danish study [112] found that 12% of participants used their smartphone for 3–5 h late at night. There is no consensus regarding gender differences in bedtime procrastination, as some studies find significant differences [113], while others do not [114].
In addition, procrastination using the smartphone can induce negative self-evaluations such as self-defeating thoughts [115]. Consequently, these self-evaluations may in turn cause stress [116], guilt [117] or feelings of self-condemnation [118], which manifest as sleep problems [115]. The same can be inferred to be true for bedtime procrastination, as it is a form of procrastination and has been directly linked to depression [119]. The opposite relationship has also been observed, where rumination and other forms of negative affect may increase bedtime procrastination and thus affect sleep quality [120]. Moreover, some studies suggest that smartphone use may be a form of experiential avoidance of negative emotions [121]. This could be explained by the bidirectional relationship between PSU, increased depressive or anxious symptoms, and vice versa [122,123]. In addition, it has also been shown that the ability of smartphones to impair sleep quality and sleep drift can increase symptoms of depression and stress [124]. Simultaneously, PSU itself may increase depression and anxiety, and thus impair sleep [61]. This impact could form a vicious cycle in which negative emotions (including depressive and anxious symptoms towards sleep quality or bedtime procrastination) lead to maintaining or increasing their smartphone use and fuel negative affectivity.
Recently, different authors have conducted different studies based on mediational analyses observing how the impact of smartphone use (problematic or not) in sleep quality is mediated by bedtime procrastination, in addition to other variables such as self-regulation [95], psychological detachment [125] or fear of missing out (or FoMO) [126]. A similar phenomenon has been observed in the impact of problematic internet use and poorer sleep quality, mediated by bedtime procrastination [127].
Given this information, we propose the following research questions:
RQ1: To what extent do Spanish adults have problems with sleep quality and bedtime procrastination, and make intensive use of smartphones (throughout the day and before going to sleep)?; RQ2: Are there gender differences in the above variables?; RQ3: Are there age differences in PSU, bedtime procrastination and sleep quality?; RQ4: Is it possible that bedtime procrastination has a mediational effect between PSU and sleep quality (as seen in Figure 1)?

2. Materials and Methods

2.1. Participants and Procedure

A total of 313 people participated in the study, between February and May 2023, using non-probability convenience sampling. Three participants with outliers, due to input errors (e.g., 26 h of sleep or 288 years using smartphones), were excluded, giving a final total of 310 subjects. All participants, who ranged in age from 18 to 60 years (M = 30; SD = 10.1; 46.8% female and 53.2% male), completed a questionnaire anonymously and without compensation via the online questionnaire platform Google Forms. After explaining the aim and scope of the study, the questionnaire included a specific item asking for the consent of the participants, informing them that their answers would be anonymous and only accessible by the researchers, and that the results would be displayed in an aggregated and anonymous form. The questionnaire was disseminated via the social networks WhatsApp and Instagram.

2.2. Instruments

2.2.1. Sleep Quality

The Pittsburgh Sleep Quality Index (PSQI) [128] includes 19 individual items about the subject’s sleep in the last month, divided into 7 components: subjective sleep quality (1 item), sleep latency (2 items), sleep duration (1 item), habitual sleep efficiency (3 items), sleep disturbances (9 items), use of sleep medication (1 item) and dysfunction during wakefulness (2 items). Each item is rated on a scale of 0–3, 0 being no difficulty and 3 being severe difficulty. Thus, the total score of the scale results from all components ranges from 0 to 21, where a higher score means worse sleep quality. In the original version, it is described that scores below 5 would be translated as “no sleep problems”, from 5 to 7 “deserves medical attention”, from 8 to 14 “deserves medical attention and treatment” and scores above 15 “severe sleep problems”. However, the Spanish validation used in the present study by Macías-Fernández and Royuela-Rico [129] only distinguishes between scores equal to or lower than 5 as “good sleep quality” and higher than 5 as “poor sleep quality”. In this study, the internal consistency of the PSQI was α = 0.69.

2.2.2. Bedtime Procrastination

To assess bedtime procrastination, the Bedtime Procrastination Scale (BPS), originally created by Kroese et al. [103], was used. It is especially used in research on sleep deprivation and health problems, as indicated by the authors of the translation and validation of the scale in the Spanish population [130]. It contains a total of 9 items. The items are scored on a Likert scale from 1 (almost never) to 5 (almost always). The scale ranges from 9 to 45. A higher score is a sign of greater bedtime procrastination. In this study, the reliability achieved by the BPS was α = 0.84.

2.2.3. Problematic Smartphone Use

PSU was measured with the Smartphone Addiction Scale-Short Version (SAS-SV) [17], a shortened version of the Smartphone Addiction Scale (SAS), developed by Kwon [131]. The SAS-SV consists of 10 items. These items are scored on a Likert scale from 1 (strongly disagree) to 6 (strongly agree), giving a total range of 10 to 60 points, with a higher score interpreted as a higher PSU. The original version placed the cut-off point for the PSU at 31 for women and 33 for men. In the Spanish validation of the SAS-SV questionnaire, conducted by Lopez-Feranandez [132], no gender differences were found, so 32 was chosen as the cut-off point for both genders. The reliability of the SAS-SV for this study was α = 0.85.

2.2.4. Smartphone Usage Pattern

In addition to the standardised tests, information was also collected on estimated daily smartphone use, smartphone use in bed and how long they had owned a smartphone, by means of an ad hoc questionnaire. To assess smartphone use in bed, 4 questions were asked: whether they used their smartphone in bed (yes/no), how many days a week they used their smartphone in bed, for how long (less than 15 min, 16–30 min, 31–60 min or more than 60 min) and, finally, what activity they did with their smartphone in bed (calling, texting, surfing the internet, checking social networks, watching multimedia content, playing video games or other).

2.3. Statistical Analysis

The sample size was calculated with the G*Power tool of Faul et al. [133] for a mean effect size, an alpha of 0.05 and a statistical power of 0.99 with two predictors, obtaining a minimum of 125 participants. For the rest of the analyses, the statistical package IBM SPSS for MacOS (Version 25, Chicago, IL, USA) was used. The internal consistency of the tests used was analysed using Cronbach’s alpha and descriptive statistics were calculated for the sociodemographic variables and the study variables. Normal distribution of the sample was analysed using skewness and kurtosis statistics for the different variables, which were found to be below ±1, indicating relatively normal distributions according to the criterion proposed by Bulmer [134]. Pearson’s correlation was used to examine the association between the variables under study.
In addition, independent sample t-tests were performed to compare means and analyse differences between genders. Effect size was calculated following Cohen’s d statistic, which was interpreted as follows: 0.2, small; 0.5, medium; and 0.8, large. One-way ANOVA was performed to compare the scores of the sleep quality, PSU and bedtime procrastination variables according to different age groups: young adults (aged 18–25 years), adults (aged 26–44 years) and middle-aged adults (aged 46–60 years). Additionally, the effect size of the one-way ANOVA was estimated using the η2 statistic, with a value of 0.01, 0.06 and 0.14 being a small, medium and large effect, respectively. To check which groups were different, post hoc comparisons were performed using the Bonferroni test.
Moreover, we employed the 5000-sample bootstrapping method of Hayes [135] using the PROCESS macro (version 4.2 for SPSS) to perform a simple mediation analysis and test the possible mediational effect of bedtime procrastination on the effect of PSU on sleep quality. We calculated the effects of PSU on bedtime procrastination (path a), of bedtime procrastination on sleep quality (path b) and direct effect of PSU on sleep quality (path c’). In addition, we analysed the indirect effect (path a*b) of the PSU on sleep quality through bedtime procrastination, and finally the total effect of the model (path c), the sum of paths c’ and a*b. The coefficients of the direct and indirect effects were calculated with a 95% confidence interval (CI).

3. Results

3.1. Descriptive Statistics

Socio-demographic characteristics of the participants can be found in Table 1. It is worth noting that 79% of the sample used the smartphone in bed and, of these, 16% used it for less than 15 min, 34.3% for 16–30 min, 34% for 31–60 min and 15.6% for more than 60 min.
Table 2 shows the descriptive statistics of the participants for the study variables. Regarding sleep quality, the overall mean of the participants was within the “requires medical attention” range proposed by the original PSQI, and in the “poor sleepers” category of the Spanish validation (M = 7.5, SD = 3.6). Mean PSU and bedtime procrastination were M = 28.9, SD = 10.0 and M = 25.2, SD = 3.7, respectively. Participants owned a smartphone for 11.9 years on average (SD = 4.3) and used it 5 h per day (SD = 2.8). In addition, they used the smartphone on an average of 5.9 days per week (SD = 1.9). The variable of time spent using the smartphone in bed is scored using a Likert-type scale (M = 2.5, SD = 0.9) and has a median of 3 (2–3).
Overall, 31.6% of the sample had good sleep quality compared to 68.4% who had poor sleep quality. With regard to the PSU, 37.2% of the participants showed signs of a PSU as measured by the SAS-SV scale, while 59.7% did not show these characteristics. A total of 69.7% of the women in the sample had a PSU, compared to 67.3% of the men.

3.2. Correlations

As shown in Table 3, statistically significant correlations between the three main study variables were found. The strongest correlation between these three is between PSU and bedtime procrastination. Moreover, poor sleep quality correlates positively with higher PSU and higher bedtime procrastination. In addition, spending more hours on the smartphone is also positively correlated with poorer sleep quality, PSU and bedtime procrastination. We found a positive correlation between a greater number of days using a smartphone in bed and a higher PSU, higher bedtime procrastination and higher smartphone overall use. Regarding the time of smartphone use during bedtime, this correlates positively with poorer sleep quality, higher PSU, higher bedtime procrastination, more daily smartphone hours and more days per week using smartphones in bed. We observed a negative correlation between more years of smartphone use and more time using smartphones during bedtime. Older age is negatively correlated with PSU, bedtime procrastination, daily hours of smartphone use and smartphone use in bed. However, no significant correlation was found between age and sleep quality.

3.3. Mean Comparison between Genders

As can be seen in Table 4, differences were found between men and women in PSU (t(308) = 3.248, p = 0.001). However, no differences were found in sleep quality (t(308) = 0.210, p = 0.834) and bedtime procrastination (t(308) = 1.031, p = 0.303). Finally, we observed that women used a smartphone for 1.1 h more on average per day than men (t(306) = 3.115, p = 0.002), while men owned smartphones for 1.4 years more than women (t(306) = −2.311, p = 0.021). For all statistically significant differences, the effect size is small.

3.4. Comparison between Age Groups

Results of one-way ANOVA are shown in Table 5. The ANOVA for sleep quality showed that there were no significant differences between any of the age groups (F(2, 306) = 0.60, p = 0.548). However, statistically significant differences were found for PSU (F(2, 306) = 8.73, p < 0.001) and bedtime procrastination (F(2, 306) = 7.15, p = 0.001), with a small, close to medium effect size (η2 = 0.05).
Bonferroni post hoc contrasts (Table 6) show that mean problematic smartphone use is significantly different (p < 0.05) between young adults (M = 31.6, SD = 9.5) and adults (M = 27.6, SD = 10.3), and between young adults and middle-aged adults (M = 25.4, SD = 9.3). There were no statistically significant differences between adults and middle-aged adults (p > 0.05). Similarly, we found statistically significant differences (p < 0.05) in mean bedtime procrastination between young adults (M = 30.7, SD = 7.6) and adults (M = 28.3, SD = 7.0), and between young adults and middle-aged adults (M = 26.2, SD = 6.3). We found no significant differences between adults and middle-aged adults (p > 0.05).

3.5. Mediation Analysis

As for the results of the mediation analysis, as can be seen in Table 7 and Figure 2, there is a statistically significant effect between PSU and bedtime procrastination (path a; B = 0.26, SE = 0.03, t = 6.83, p < 0.001), and between bedtime procrastination and worse sleep quality (path b; B = 0.14, SE = 0.03, t = 4.75, p < 0.001). Likewise, an indirect effect (i.e., the effect of PSU on sleep quality through bedtime procrastination) was found to be significant (B = 0.04, 95% CI [0.02, 0.05]). However, the direct effect of PSU on sleep quality is not significant (B = 0.02, SE = 0.02, t = 1.01, p = 0.312). Finally, we found a significant overall effect between PSU and sleep quality (B = 0.06, SE = 0.02, t = 2.84, p = 0.005), taking into account any indirect effect through bedtime procrastination. Therefore, we can conclude that bedtime procrastination has a full mediation effect (the direct effect c’ is not significant) on the impact of PSU on poorer sleep quality.

4. Discussion

Our study aimed to describe data on sleep quality problems, smartphone use (in general and during bedtime), PSU and bedtime procrastination in Spanish adults (RQ1); analyse possible gender differences in the variables mentioned above (RQ2); study disparities between age groups in terms of PSU, bedtime procrastination and sleep quality (RQ3); and, finally, analyse a possible mediational effect of bedtime procrastination on the relationship between PSU and sleep quality.
Thus, in terms of RQ1, we found that the majority (68.4%) of the sample had poor sleep quality, and that more than a third (37.2%) showed signs of presenting a PSU; although these are higher numbers, they are not very far from other studies involving Spanish adults [34,35]. Additionally, participants reported a daily smartphone usage of 5 h, which is 1 h and 20 min higher than the data from Smartme Analytics [16] in Spain. Furthermore, we observed that more Spanish adults use their smartphone in bed (78.9%) than in other countries such as China, where 57.5% use it [111]. Furthermore, 15.6% of the sample displaced their sleep time by more than one hour by using their smartphone in bed.
On the other hand, and responding to RQ2, we can see that women tend to use their smartphones for longer periods of time, which is one of the risk factors for PSU. Moreover, they show a higher PSU, which is consistent with numerous investigations [25,38,39,40,41,43,44,45]. As indicated by different authors [46,47], women’s smartphone use is predominantly social (e.g., social networking or instant messaging), compared to a more diverse pattern for men. Greater use of social networks has been found to be associated with a detriment in the perception of real social support [136]. Thus, it is possible that, by making their smartphone use pattern more social, women may feel less real social support and try to mitigate this by increasing their use of social networks, resulting in higher PSU. Additionally, we found no gender differences in bedtime procrastination, similar to previous research [113], but contradicting the direct effect of PSU on bedtime procrastination proposed by this research. It is possible that another unstudied variable is involved in this relationship, for example, greater responsibility in women [137]. As there are no differences in bedtime procrastination, according to the mediational model of this study that we will detail later, the finding of no differences in sleep quality is consistent with other research [7,8].
In relation to RQ3, we found differences in the PSU and bedtime procrastination variables in young adults (18–25 years) versus adults (24–44) and middle-aged adults (45–60), but no differences in sleep quality. The differences in PSU are consistent with the literature [138], and are possibly due to the fact that younger people spend more hours on their smartphone (both variables correlate significantly, as can be seen in Table 3) and that younger generations have grown up with this technology [139]. Regarding bedtime procrastination, these results are also consistent with previous research [140]. It is likely that young adults, having a higher PSU, delay tasks they have to perform during the day, and thus procrastinate at their sleep time as well. However, young adults, who show higher PSU and bedtime procrastination, do not show worse sleep quality. This result seems to be inconsistent with the indirect effect of PSU on sleep quality, mediated by bedtime procrastination, as proposed by the model of this study and discussed in RQ4. This may be due to the fact that a large proportion of young adults are still studying (85.1% of the sample), so they have greater flexibility in their schedules to compensate for a shift in sleep time caused by the PSU, for example, by not attending classes the morning after having procrastinated sleep or by sleeping more on days off (sleep duration is one of the components of the PSQI scale). Adults, being mostly workers (79.4% of the sample), generally have less flexible schedules and are therefore less able to compensate for sleep displacement. In fact, a study comparing sleep parameters of non-working versus working high school students found that the latter were sleepier during the day [141]. In addition, adults and middle-aged adults may generally have increased stress or health problems (e.g., pain or respiratory diseases) that affect sleep, apart from PSU and bedtime procrastination.
As for our RQ4, the mediation analysis shows that PSU is not directly related to poorer sleep quality, but affects sleep quality through the indirect effect of bedtime procrastination (i.e., the mediation of bedtime procrastination is total). These results are in agreement with previous research [125], and especially with Zhang and Wu [95], as they also found no direct relationship between PSU and sleep quality, unlike Huang et al. [126]. Considering the reasons for bedtime procrastination described by Nauts et al. [104], two possible causes of bedtime procrastination related to higher PSU may be receiving the gratification of taking time for oneself using the smartphone, or the loss of a sense of time when consuming content on smartphones. The latter possibly is supported by the nature of many smartphone apps, which are designed to retain the viewer as long as possible and form consumption habits [142]. In addition, as we have discussed above, higher PSU may cause the delay of completing activities during the day, as it has been related to general [58] and academic [54] procrastination, which would cause people to perform postponed tasks throughout the day and during the night, increasing bedtime procrastination, shifting sleep time and thus affecting sleep quality. Furthermore, as described above, PSU can elicit negative affectivity [115,116,117,118] that feeds back on itself by negative feelings due to bedtime procrastination and poor sleep quality [122,123], thus sustaining these phenomena.

5. Conclusions

Although gender and age differences in PSU had small effect sizes, these results may help to identify the population profiles most vulnerable to PSU and its negative effects, especially considering the novelty of the topic and the lack of a total consensus regarding gender differences in the literature. The practical utility of detecting gender differences in PSU is observed in publications by other authors, which suggest the need to develop gender-specific interventions. More specifically, training in self-awareness and self-control in adolescent females has been proposed [143]. On the other hand, results of age differences not only let us know that younger adults have a higher risk of PSU, but also suggest the existence of possible distinct PSU effects in different age groups [144]. Younger adults with PSU tend to show greater interpersonal and intrapersonal conflicts than older adults. The latter also show fewer physical and psychological withdrawal symptoms, but these are of greater weight. Taking into account these age and gender differences may be helpful in designing more tailored interventions to each population profile.
From this study we conclude that the smartphone is a source of problematic use (especially for young people and women), which can displace users’ sleep through bedtime procrastination (consciously or not) and thus negatively impact sleep quality. To our knowledge, no study has been carried out involving the Spanish population exploring gender and age differences in bedtime procrastination. Nor are we aware of any research carried out involving Spanish adults that relates PSU, sleep quality and bedtime procrastination (in addition to its mediational effect on the relationship between the first two).
Finally, we must highlight the limitations of the present study. Firstly, the cross-sectional design and the accidental non-probabilistic sampling limit the generalisability of the results. On the other hand, the measurement of the variables, being based on self-report tests, may not fully conform to reality (e.g., due to social desirability effects or difficulties in understanding the questions), although the anonymous and voluntary nature of the study minimises this risk. Furthermore, the effect of other latent variables (such as self-regulation or rumination) or reciprocal relationships between the study variables have not been explored. Thus, future studies could consider a more complex analysis (e.g., based on structural equation modelling), controlling for possible confounding factors by including demographic variables such as socioeconomic status in the analysis, and a longitudinal design and random sampling, so that causal relationships between these variables could be established in the Spanish population, something that has not been done to date.
The above results and conclusions point to the PSU, and especially to bedtime procrastination, as potential treatment targets to improve sleep quality. Thus, efforts could be made to raise awareness among the Spanish population (especially women and young adults) and to try to implement interventions, such as smartphone apps, that prevent the negative effects of PSU, bedtime procrastination and poor sleep quality.

Author Contributions

Conceptualization, S.C.-I. and S.H.-F.; methodology, S.C.-I. and S.H.-F.; software, S.C.-I. and M.M.-V.; validation, S.C.-I. and S.H.-F.; formal analysis, S.C.-I. and S.H.-F.; investigation, S.C.-I. and M.M.-V.; resources, S.H.-F. and M.M.-V.; data curation, S.C.-I. and M.M.-V.; writing—original draft preparation, S.C.-I.; writing—review and editing, S.H.-F. and M.M.-V.; visualization, S.C.-I. and S.H.-F.; supervision, S.H.-F. and M.M.-V. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, approved by the Commission for Ethics in Experimental Research of the University of Valencia, procedure number 1040164.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Buysse, D.J. Sleep Health: Can We Define It? Does It Matter? Sleep 2014, 37, 9–17. [Google Scholar] [CrossRef] [PubMed]
  2. Scott, A.J.; Webb, T.L.; Martyn-St James, M.; Rowse, G.; Weich, S. Improving Sleep Quality Leads to Better Mental Health: A Meta-Analysis of Randomised Controlled Trials. Sleep Med. Rev. 2021, 60, 101556. [Google Scholar] [CrossRef] [PubMed]
  3. Spanish Society of Neurology. Los Problemas Del Sueño Amenazan la Salud Y la Calidad de Vida de Hasta El 45% De la Población Mundial. 2021. Available online: (accessed on 18 May 2023).
  4. Galland, B.C.; Gray, A.R.; Penno, J.; Smith, C.; Lobb, C.; Taylor, R.W. Gender Differences in Sleep Hygiene Practices and Sleep Quality in New Zealand Adolescents Aged 15 to 17 Years. Sleep Health 2017, 3, 77–83. [Google Scholar] [CrossRef]
  5. Fatima, Y.; Doi, S.A.R.; Najman, J.M.; Mamun, A.A. Exploring Gender Difference in Sleep Quality of Young Adults: Findings from a Large Population Study. Clin. Med. Res. 2016, 14, 138–144. [Google Scholar] [CrossRef] [PubMed]
  6. Tang, J.; Liao, Y.; Kelly, B.C.; Xie, L.; Xiang, Y.-T.; Qi, C.; Pan, C.; Hao, W.; Liu, T.; Zhang, F.; et al. Gender and Regional Differences in Sleep Quality and Insomnia: A General Population-Based Study in Hunan Province of China. Sci. Rep. 2017, 7, 43690. [Google Scholar] [CrossRef] [PubMed]
  7. João, K.A.D.R.; de Jesus, S.N.; Carmo, C.; Pinto, P. The Impact of Sleep Quality on the Mental Health of a Non-Clinical Population. Sleep Med. 2018, 46, 69–73. [Google Scholar] [CrossRef] [PubMed]
  8. Madrid-Valero, J.J.; Kirkpatrick, R.M.; González-Javier, F.; Gregory, A.M.; Ordoñana, J.R. Sex Differences in Sleep Quality and Psychological Distress: Insights from a Middle-Aged Twin Sample from Spain. J. Sleep Res. 2023, 32, e13714. [Google Scholar] [CrossRef]
  9. Stahl, S.M. Stahl’s Essential Psychopharmacology: Neuroscientific Basis and Practical Applications, 1st ed.; Cambridge University Press: Cambridge, UK, 2022; ISBN 978-1-00-905336-5. [Google Scholar]
  10. Merikanto, I.; Kortesoja, L.; Benedict, C.; Chung, F.; Cedernaes, J.; Espie, C.A.; Morin, C.M.; Dauvilliers, Y.; Partinen, M.; De Gennaro, L.; et al. Evening-Types Show Highest Increase of Sleep and Mental Health Problems during the COVID-19 Pandemic-Multinational Study on 19 267 Adults. Sleep 2022, 45, zsab216. [Google Scholar] [CrossRef]
  11. Lin, L.-Y.; Wang, J.; Ou-Yang, X.-Y.; Miao, Q.; Chen, R.; Liang, F.-X.; Zhang, Y.-P.; Tang, Q.; Wang, T. The Immediate Impact of the 2019 Novel Coronavirus (COVID-19) Outbreak on Subjective Sleep Status. Sleep Med. 2021, 77, 348–354. [Google Scholar] [CrossRef]
  12. Dal Santo, F.; González-Blanco, L.; Rodríguez-Revuelta, J.; Marina González, P.A.; Paniagua, G.; García-Álvarez, L.; De La Fuente-Tomás, L.; Sáiz, P.A.; García-Portilla, M.P.; Bobes, J. Early Impact of the COVID-19 Outbreak on Sleep in a Large Spanish Sample. Behav. Sleep. Med. 2022, 20, 100–115. [Google Scholar] [CrossRef]
  13. Trott, M.; Driscoll, R.; Iraldo, E.; Pardhan, S. Changes and Correlates of Screen Time in Adults and Children during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis. eClinicalMedicine 2022, 48, 101452. [Google Scholar] [CrossRef] [PubMed]
  14. Lapierre, M.A.; Zhao, P.; Custer, B.E. Short-Term Longitudinal Relationships Between Smartphone Use/Dependency and Psychological Well-Being Among Late Adolescents. J. Adolesc. Health 2019, 65, 607–612. [Google Scholar] [CrossRef] [PubMed]
  15. National Institute of Statistics. Encuesta sobre Equipamiento y Uso de Tecnologías de Información y Comunicación (TIC) en los Hogares. 2022. Available online: (accessed on 18 May 2023).
  16. Smartme Analytics Digital Consumer by Generation. Available online: (accessed on 18 May 2023).
  17. Kwon, M.; Kim, D.-J.; Cho, H.; Yang, S. The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. PLoS ONE 2013, 8, e83558. [Google Scholar] [CrossRef] [PubMed]
  18. Billieux, J.; Maurage, P.; Lopez-Fernandez, O.; Kuss, D.J.; Griffiths, M.D. Can Disordered Mobile Phone Use Be Considered a Behavioral Addiction? An Update on Current Evidence and a Comprehensive Model for Future Research. Curr. Addict. Rep. 2015, 2, 156–162. [Google Scholar] [CrossRef]
  19. Horwood, S.; Anglim, J. Personality and Problematic Smartphone Use: A Facet-Level Analysis Using the Five Factor Model and HEXACO Frameworks. Comput. Hum. Behav. 2018, 85, 349–359. [Google Scholar] [CrossRef]
  20. Lepp, A.; Li, J.; Barkley, J.E. College Students’ Cell Phone Use and Attachment to Parents and Peers. Comput. Hum. Behav. 2016, 64, 401–408. [Google Scholar] [CrossRef]
  21. Panova, T.; Carbonell, X. Is Smartphone Addiction Really an Addiction? J. Behav. Addict. 2018, 7, 252–259. [Google Scholar] [CrossRef] [PubMed]
  22. Montag, C.; Wegmann, E.; Sariyska, R.; Demetrovics, Z.; Brand, M. How to Overcome Taxonomical Problems in the Study of Internet Use Disorders and What to Do with “Smartphone Addiction”? J. Behav. Addict. 2021, 9, 908–914. [Google Scholar] [CrossRef]
  23. Servidio, R. Self-Control and Problematic Smartphone Use among Italian University Students: The Mediating Role of the Fear of Missing out and of Smartphone Use Patterns. Curr. Psychol. 2021, 40, 4101–4111. [Google Scholar] [CrossRef]
  24. Sha, P.; Sariyska, R.; Riedl, R.; Lachmann, B.; Montag, C. Linking Internet Communication and Smartphone Use Disorder by Taking a Closer Look at the Facebook and Whatsapp Applications. Addict. Behav. Rep. 2019, 9, 100148. [Google Scholar] [CrossRef]
  25. Lee, S.-Y.; Lee, D.; Nam, C.R.; Kim, D.Y.; Park, S.; Kwon, J.-G.; Kweon, Y.-S.; Lee, Y.; Kim, D.J.; Choi, J.-S. Distinct Patterns of Internet and Smartphone-Related Problems among Adolescents by Gender: Latent Class Analysis. J. Behav. Addict. 2018, 7, 454–465. [Google Scholar] [CrossRef]
  26. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Arlington, VA, USA, 2013; ISBN 978-0-89042-555-8. [Google Scholar]
  27. World Health Organization. International Classification of Diseases 11th Revision (ICD-11). Available online: (accessed on 8 August 2023).
  28. Thomée, S. Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure. Int. J. Environ. Res. Public Health 2018, 15, 2692. [Google Scholar] [CrossRef]
  29. Kardefelt-Winther, D. A Conceptual and Methodological Critique of Internet Addiction Research: Towards a Model of Compensatory Internet Use. Comput. Hum. Behav. 2014, 31, 351–354. [Google Scholar] [CrossRef]
  30. Widyanto, L.; Griffiths, M. ‘Internet Addiction’: A Critical Review. Int. J. Ment. Health Addict. 2006, 4, 31–51. [Google Scholar] [CrossRef]
  31. Abo-Ali, E.A.; Al-Ghanmi, A.; Hadad, H.; Etaiwi, J.; Bhutta, K.; Hadad, N.; Almilaibary, A.; Ghareeb, W.A.; Sanad, A.; Zaytoun, S. Problematic Smartphone Use: Prevalence and Associated Factors Among Health Sciences Students in Saudi Arabia. J. Prev. 2022, 43, 659–671. [Google Scholar] [CrossRef] [PubMed]
  32. Ratan, Z.A.; Parrish, A.-M.; Alotaibi, M.S.; Hosseinzadeh, H. Prevalence of Smartphone Addiction and Its Association with Sociodemographic, Physical and Mental Well-Being: A Cross-Sectional Study among the Young Adults of Bangladesh. Int. J. Environ. Res. Public Health 2022, 19, 16583. [Google Scholar] [CrossRef] [PubMed]
  33. Wang, A.; Wang, Z.; Zhu, Y.; Shi, X. The Prevalence and Psychosocial Factors of Problematic Smartphone Use Among Chinese College Students: A Three-Wave Longitudinal Study. Front. Psychol. 2022, 13, 877277. [Google Scholar] [CrossRef]
  34. Ballestar-Tarín, M.L.; Simó-Sanz, C.; Chover-Sierra, E.; Saus-Ortega, C.; Casal-Angulo, C.; Martínez-Sabater, A. Self-Perception of Dependence as an Indicator of Smartphone Addiction. Establishment of a Cut-off Point in the SPAI-SP Inventory. Int. J. Environ. Res. Public Health 2020, 17, 3838. [Google Scholar] [CrossRef] [PubMed]
  35. De-Solá, J.; Talledo, H.; de Fonseca, F.R.; Rubio, G. Prevalence of Problematic Cell Phone Use in an Adult Population in Spain as Assessed by the Mobile Phone Problem Use Scale (MPPUS). PLoS ONE 2017, 12, e0181184. [Google Scholar] [CrossRef] [PubMed]
  36. Winkler, A.; Jeromin, F.; Doering, B.K.; Barke, A. Problematic Smartphone Use Has Detrimental Effects on Mental Health and Somatic Symptoms in a Heterogeneous Sample of German Adults. Comput. Hum. Behav. 2020, 113, 106500. [Google Scholar] [CrossRef]
  37. Chen, B.; Liu, F.; Ding, S.; Ying, X.; Wang, L.; Wen, Y. Gender Differences in Factors Associated with Smartphone Addiction: A Cross-Sectional Study among Medical College Students. BMC Psychiatry 2017, 17, 341. [Google Scholar] [CrossRef] [PubMed]
  38. Amador-Licona, N.; Carpio-Mendoza, J.J.; Guízar-Mendoza, J.M.; Rodríguez-Sánchez, P. Auto-percepción del Uso Problemático del Teléfono Móvil en Estudiantes Universitarios de Acuerdo a su Sexo. Cuad. Hispanoam. Psicol. 2019, 19, 1–16. [Google Scholar] [CrossRef]
  39. Choi, S.-W.; Kim, D.-J.; Choi, J.-S.; Ahn, H.; Choi, E.-J.; Song, W.-Y.; Kim, S.; Youn, H. Comparison of Risk and Protective Factors Associated with Smartphone Addiction and Internet Addiction. J. Behav. Addict. 2015, 4, 308–314. [Google Scholar] [CrossRef] [PubMed]
  40. De-Solá, J.; Rubio, G.; Talledo, H.; Pistoni, L.; Van Riesen, H.; Rodríguez de Fonseca, F. Cell Phone Use Habits Among the Spanish Population: Contribution of Applications to Problematic Use. Front. Psychiatry 2019, 10, 883. [Google Scholar] [CrossRef] [PubMed]
  41. Hsieh, H.-F.; Hsu, H.-T.; Lin, P.-C.; Yang, Y.-J.; Huang, Y.-T.; Ko, C.-H.; Wang, H.-H. The Effect of Age, Gender, and Job on Skin Conductance Response among Smartphone Users Who Are Prohibited from Using Their Smartphone. Int. J. Environ. Res. Public Health 2020, 17, 2313. [Google Scholar] [CrossRef] [PubMed]
  42. Lee, H.; Kim, J.W.; Choi, T.Y. Risk Factors for Smartphone Addiction in Korean Adolescents: Smartphone Use Patterns. J. Korean Med. Sci. 2017, 32, 1674–1679. [Google Scholar] [CrossRef] [PubMed]
  43. Lopez-Fernandez, O.; Losada-Lopez, J.L.; Honrubia-Serrano, M.L. Predictors of Problematic Internet and Mobile Phone Usage in Adolescents. Aloma Rev. Psicol. Ciènc. Educ. Esport 2015, 33, 49–58. [Google Scholar] [CrossRef]
  44. Nayak, J.K. Relationship among Smartphone Usage, Addiction, Academic Performance and the Moderating Role of Gender: A Study of Higher Education Students in India. Comput. Educ. 2018, 123, 164–173. [Google Scholar] [CrossRef]
  45. Randler, C.; Wolfgang, L.; Matt, K.; Demirhan, E.; Horzum, M.B.; Beşoluk, Ş. Smartphone Addiction Proneness in Relation to Sleep and Morningness–Eveningness in German Adolescents. J. Behav. Addict. 2016, 5, 465–473. [Google Scholar] [CrossRef]
  46. Chen, C.; Zhang, K.Z.K.; Gong, X.; Zhao, S.J.; Lee, M.K.O.; Liang, L. Examining the Effects of Motives and Gender Differences on Smartphone Addiction. Comput. Hum. Behav. 2017, 75, 891–902. [Google Scholar] [CrossRef]
  47. De-Solá, J.; Rodríguez de Fonseca, F.; Rubio, G. Cell-Phone Addiction: A Review. Front. Psychiatry 2016, 7, 175. [Google Scholar] [CrossRef]
  48. Jo, Y.; Bouffard, L. Stability of Self-Control and Gender. J. Crim. Justice 2014, 42, 356–365. [Google Scholar] [CrossRef]
  49. Jo, Y.; Zhang, Y. Parenting, Self-Control, and Delinquency: Examining the Applicability of Gottfredson and Hirschi’s General Theory of Crime to South Korean Youth. Int. J. Offender Ther. Comp. Criminol. 2014, 58, 1340–1363. [Google Scholar] [CrossRef] [PubMed]
  50. Duke, É.; Montag, C. Smartphone Addiction, Daily Interruptions and Self-Reported Productivity. Addict. Behav. Rep. 2017, 6, 90–95. [Google Scholar] [CrossRef] [PubMed]
  51. Amez, S.; Baert, S. Smartphone Use and Academic Performance: A Literature Review. Int. J. Educ. Res. 2020, 103, 101618. [Google Scholar] [CrossRef]
  52. Grant, J.E.; Lust, K.; Chamberlain, S.R. Problematic Smartphone Use Associated with Greater Alcohol Consumption, Mental Health Issues, Poorer Academic Performance, and Impulsivity. J. Behav. Addict. 2019, 8, 335–342. [Google Scholar] [CrossRef] [PubMed]
  53. Rozgonjuk, D.; Kattago, M.; Täht, K. Social Media Use in Lectures Mediates the Relationship between Procrastination and Problematic Smartphone Use. Comput. Hum. Behav. 2018, 89, 191–198. [Google Scholar] [CrossRef]
  54. Hidalgo-Fuentes, S. Uso Problemático Del Smartphone y Procrastinación En El Ámbito Académico: Un Meta-Análisis. Electron. J. Res. Educ. Psychol. 2022, 20, 449–468. [Google Scholar] [CrossRef]
  55. Casale, S.; Fioravanti, G.; Bocci Benucci, S.; Falone, A.; Ricca, V.; Rotella, F. A Meta-Analysis on the Association between Self-Esteem and Problematic Smartphone Use. Comput. Hum. Behav. 2022, 134, 107302. [Google Scholar] [CrossRef]
  56. Elhai, J.D.; Dvorak, R.D.; Levine, J.C.; Hall, B.J. Problematic Smartphone Use: A Conceptual Overview and Systematic Review of Relations with Anxiety and Depression Psychopathology. J. Affect. Disord. 2017, 207, 251–259. [Google Scholar] [CrossRef]
  57. Elhai, J.D.; Rozgonjuk, D.; Alghraibeh, A.M.; Yang, H. Disrupted Daily Activities from Interruptive Smartphone Notifications: Relations With Depression and Anxiety Severity and the Mediating Role of Boredom Proneness. Soc. Sci. Comput. Rev. 2021, 39, 20–37. [Google Scholar] [CrossRef]
  58. Rozgonjuk, D.; Levine, J.C.; Hall, B.J.; Elhai, J.D. The Association between Problematic Smartphone Use, Depression and Anxiety Symptom Severity, and Objectively Measured Smartphone Use over One Week. Comput. Hum. Behav. 2018, 87, 10–17. [Google Scholar] [CrossRef]
  59. Hartanto, A.; Chua, Y.J.; Quek, F.Y.X.; Wong, J.; Ooi, W.M. Problematic Smartphone Usage, Objective Smartphone Engagement, and Executive Functions: A Latent Variable Analysis. Atten. Percept. Psychophys. 2023. [Google Scholar] [CrossRef] [PubMed]
  60. Lim, J. The Effect of Adult Smartphone Addiction on Memory Impairment: Focusing on the Mediating effect of Executive Function Deficiencies. J. Digit. Converg. 2018, 16, 299–308. [Google Scholar] [CrossRef]
  61. Demirci, K.; Akgönül, M.; Akpinar, A. Relationship of Smartphone Use Severity with Sleep Quality, Depression, and Anxiety in University Students. J. Behav. Addict. 2015, 4, 85–92. [Google Scholar] [CrossRef] [PubMed]
  62. Hughes, N.; Burke, J. Sleeping with the Frenemy: How Restricting ‘Bedroom Use’ of Smartphones Impacts Happiness and Wellbeing. Comput. Hum. Behav. 2018, 85, 236–244. [Google Scholar] [CrossRef]
  63. Panda, A.; Jain, N.K. Compulsive Smartphone Usage and Users’ Ill-Being among Young Indians: Does Personality Matter? Telemat. Inform. 2018, 35, 1355–1372. [Google Scholar] [CrossRef]
  64. Volungis, A.M.; Kalpidou, M.; Popores, C.; Joyce, M. Smartphone Addiction and Its Relationship with Indices of Social-Emotional Distress and Personality. Int. J. Ment. Health Addict. 2020, 18, 1209–1225. [Google Scholar] [CrossRef]
  65. Yang, Z.; Asbury, K.; Griffiths, M.D. “A Cancer in the Minds of Youth?” A Qualitative Study of Problematic Smartphone Use among Undergraduate Students. Int. J. Ment. Health Addict. 2021, 19, 934–946. [Google Scholar] [CrossRef]
  66. Lewy, A.J. Melatonin and Human Chronobiology. Cold Spring Harb. Symp. Quant. Biol. 2007, 72, 623–636. [Google Scholar] [CrossRef]
  67. Oster, H.; Challet, E.; Ott, V.; Arvat, E.; De Kloet, E.R.; Dijk, D.-J.; Lightman, S.; Vgontzas, A.; Van Cauter, E. The Functional and Clinical Significance of the 24-Hour Rhythm of Circulating Glucocorticoids. Endocr. Rev. 2017, 38, 3–45. [Google Scholar] [CrossRef]
  68. Cajochen, C.; Frey, S.; Anders, D.; Späti, J.; Bues, M.; Pross, A.; Mager, R.; Wirz-Justice, A.; Stefani, O. Evening Exposure to a Light-Emitting Diodes (LED)-Backlit Computer Screen Affects Circadian Physiology and Cognitive Performance. J. Appl. Physiol. 2011, 110, 1432–1438. [Google Scholar] [CrossRef] [PubMed]
  69. Cajochen, C.; Münch, M.; Kobialka, S.; Kräuchi, K.; Steiner, R.; Oelhafen, P.; Orgül, S.; Wirz-Justice, A. High Sensitivity of Human Melatonin, Alertness, Thermoregulation, and Heart Rate to Short Wavelength Light. J. Clin. Endocrinol. Metab. 2005, 90, 1311–1316. [Google Scholar] [CrossRef] [PubMed]
  70. Fisk, A.S.; Tam, S.K.E.; Brown, L.A.; Vyazovskiy, V.V.; Bannerman, D.M.; Peirson, S.N. Light and Cognition: Roles for Circadian Rhythms, Sleep, and Arousal. Front. Neurol. 2018, 9, 56. [Google Scholar] [CrossRef] [PubMed]
  71. Höhn, C.; Schmid, S.R.; Plamberger, C.P.; Bothe, K.; Angerer, M.; Gruber, G.; Pletzer, B.; Hoedlmoser, K. Preliminary Results: The Impact of Smartphone Use and Short-Wavelength Light during the Evening on Circadian Rhythm, Sleep and Alertness. Clocks Sleep 2021, 3, 66–86. [Google Scholar] [CrossRef] [PubMed]
  72. Van Der Lely, S.; Frey, S.; Garbazza, C.; Wirz-Justice, A.; Jenni, O.G.; Steiner, R.; Wolf, S.; Cajochen, C.; Bromundt, V.; Schmidt, C. Blue Blocker Glasses as a Countermeasure for Alerting Effects of Evening Light-Emitting Diode Screen Exposure in Male Teenagers. J. Adolesc. Health 2015, 56, 113–119. [Google Scholar] [CrossRef] [PubMed]
  73. Taillard, J.; Capelli, A.; Sagaspe, P.; Anund, A.; Akerstedt, T.; Philip, P. In-Car Nocturnal Blue Light Exposure Improves Motorway Driving: A Randomized Controlled Trial. PLoS ONE 2012, 7, e46750. [Google Scholar] [CrossRef] [PubMed]
  74. Chinoy, E.D.; Duffy, J.F.; Czeisler, C.A. Unrestricted Evening Use of Light-Emitting Tablet Computers Delays Self-Selected Bedtime and Disrupts Circadian Timing and Alertness. Physiol. Rep. 2018, 6, e13692. [Google Scholar] [CrossRef] [PubMed]
  75. Schmid, S.R.; Höhn, C.; Bothe, K.; Plamberger, C.P.; Angerer, M.; Pletzer, B.; Hoedlmoser, K. How Smart Is It to Go to Bed with the Phone? The Impact of Short-Wavelength Light and Affective States on Sleep and Circadian Rhythms. Clocks Sleep 2021, 3, 558–580. [Google Scholar] [CrossRef]
  76. Wallenius, M.; Hirvonen, A.; Lindholm, H.; Rimpela, A.; Nygård, C.-H.; Saarni, L.; Punamäki, R.-L. Salivary Cortisol in Relation to the Use of Information and Communication Technology (ICT) in School-Aged Children. Psychology 2010, 1, 88–95. [Google Scholar] [CrossRef]
  77. Christakis, D.A.; Liekweg, K.; Garrison, M.M.; Wright, J.A. Infant Video Viewing and Salivary Cortisol Responses: A Randomized Experiment. J. Pediatr. 2013, 162, 1035–1040. [Google Scholar] [CrossRef] [PubMed]
  78. Twenge, J.M. More Time on Technology, Less Happiness? Associations Between Digital-Media Use and Psychological Well-Being. Curr. Dir. Psychol. Sci. 2019, 28, 372–379. [Google Scholar] [CrossRef]
  79. Calamaro, C.J.; Mason, T.B.A.; Ratcliffe, S.J. Adolescents Living the 24/7 Lifestyle: Effects of Caffeine and Technology on Sleep Duration and Daytime Functioning. Pediatrics 2009, 123, e1005–e1010. [Google Scholar] [CrossRef] [PubMed]
  80. Figueiro, M.G.; Wood, B.; Plitnick, B.; Rea, M.S. The Impact of Watching Television on Evening Melatonin Levels: Impact of Watching Television on Evening Melatonin. J. Soc. Inf. Disp. 2013, 21, 417–421. [Google Scholar] [CrossRef]
  81. Selmaoui, B.; Touitou, Y. Association Between Mobile Phone Radiation Exposure and the Secretion of Melatonin and Cortisol, Two Markers of the Circadian System: A Review. Bioelectromagnetics 2021, 42, 5–17. [Google Scholar] [CrossRef] [PubMed]
  82. Kheirinejad, S.; Visuri, A.; Ferreira, D.; Hosio, S. “Leave Your Smartphone out of Bed”: Quantitative Analysis of Smartphone Use Effect on Sleep Quality. Pers. Ubiquitous Comput. 2023, 27, 447–466. [Google Scholar] [CrossRef] [PubMed]
  83. Altini, M.; Kinnunen, H. The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring. Sensors 2021, 21, 4302. [Google Scholar] [CrossRef]
  84. Ferreira, D.; Kostakos, V.; Dey, A.K. AWARE: Mobile Context Instrumentation Framework. Front. ICT 2015, 2, 6. [Google Scholar] [CrossRef]
  85. Ong, J.C.; Ulmer, C.S.; Manber, R. Improving Sleep with Mindfulness and Acceptance: A Metacognitive Model of Insomnia. Behav. Res. Ther. 2012, 50, 651–660. [Google Scholar] [CrossRef]
  86. Bodas, M.; Siman-Tov, M.; Peleg, K.; Solomon, Z. Anxiety-Inducing Media: The Effect of Constant News Broadcasting on the Well-Being of Israeli Television Viewers. Psychiatry 2015, 78, 265–276. [Google Scholar] [CrossRef]
  87. Exelmans, L.; Van Den Bulck, J. Binge Viewing, Sleep, and the Role of Pre-Sleep Arousal. J. Clin. Sleep Med. 2017, 13, 1001–1008. [Google Scholar] [CrossRef] [PubMed]
  88. Harbard, E.; Allen, N.B.; Trinder, J.; Bei, B. What’s Keeping Teenagers Up? Prebedtime Behaviors and Actigraphy-Assessed Sleep Over School and Vacation. J. Adolesc. Health 2016, 58, 426–432. [Google Scholar] [CrossRef] [PubMed]
  89. Mauri, M.; Cipresso, P.; Balgera, A.; Villamira, M.; Riva, G. Why Is Facebook So Successful? Psychophysiological Measures Describe a Core Flow State While Using Facebook. Cyberpsychology Behav. Soc. Netw. 2011, 14, 723–731. [Google Scholar] [CrossRef] [PubMed]
  90. Van den Bulck, J. Text Messaging as a Cause of Sleep Interruption in Adolescents, Evidence from a Cross-Sectional Study. J. Sleep Res. 2003, 12, 263. [Google Scholar] [CrossRef] [PubMed]
  91. Woods, H.C.; Scott, H. #Sleepyteens: Social Media Use in Adolescence Is Associated with Poor Sleep Quality, Anxiety, Depression and Low Self-Esteem. J. Adolesc. 2016, 51, 41–49. [Google Scholar] [CrossRef] [PubMed]
  92. Combertaldi, S.L.; Ort, A.; Cordi, M.; Fahr, A.; Rasch, B. Pre-Sleep Social Media Use Does Not Strongly Disturb Sleep: A Sleep Laboratory Study in Healthy Young Participants. Sleep Med. 2021, 87, 191–202. [Google Scholar] [CrossRef] [PubMed]
  93. Gillebaart, M. The ‘Operational’ Definition of Self-Control. Front. Psychol. 2018, 9, 1231. [Google Scholar] [CrossRef] [PubMed]
  94. Heatherton, T.F.; Wagner, D.D. Cognitive Neuroscience of Self-Regulation Failure. Trends Cogn. Sci. 2011, 15, 132–139. [Google Scholar] [CrossRef]
  95. Zhang, M.X.; Wu, A.M.S. Effects of Smartphone Addiction on Sleep Quality among Chinese University Students: The Mediating Role of Self-Regulation and Bedtime Procrastination. Addict. Behav. 2020, 111, 106552. [Google Scholar] [CrossRef]
  96. Goldstein, R.Z.; Volkow, N.D. Dysfunction of the Prefrontal Cortex in Addiction: Neuroimaging Findings and Clinical Implications. Nat. Rev. Neurosci. 2011, 12, 652–669. [Google Scholar] [CrossRef]
  97. Lewis, M. Addiction and the Brain: Development, Not Disease. Neuroethics 2017, 10, 7–18. [Google Scholar] [CrossRef]
  98. Noël, X.; Brevers, D.; Bechara, A. A Neurocognitive Approach to Understanding the Neurobiology of Addiction. Curr. Opin. Neurobiol. 2013, 23, 632–638. [Google Scholar] [CrossRef]
  99. Volkow, N.D.; Wang, G.-J.; Fowler, J.S.; Tomasi, D. Addiction Circuitry in the Human Brain. Annu. Rev. Pharmacol. Toxicol. 2012, 52, 321–336. [Google Scholar] [CrossRef] [PubMed]
  100. Volkow, N.D.; Koob, G.F.; McLellan, A.T. Neurobiologic Advances from the Brain Disease Model of Addiction. N. Engl. J. Med. 2016, 374, 363–371. [Google Scholar] [CrossRef] [PubMed]
  101. Chen, J.; Liang, Y.; Mai, C.; Zhong, X.; Qu, C. General Deficit in Inhibitory Control of Excessive Smartphone Users: Evidence from an Event-Related Potential Study. Front. Psychol. 2016, 7, 511. [Google Scholar] [CrossRef] [PubMed]
  102. Rebetez, M.M.L.; Rochat, L.; Barsics, C.; Van Der Linden, M. Procrastination as a Self-Regulation Failure: The Role of Inhibition, Negative Affect, and Gender. Personal. Individ. Differ. 2016, 101, 435–439. [Google Scholar] [CrossRef]
  103. Kroese, F.M.; De Ridder, D.T.D.; Evers, C.; Adriaanse, M.A. Bedtime Procrastination: Introducing a New Area of Procrastination. Front. Psychol. 2014, 5, 611. [Google Scholar] [CrossRef] [PubMed]
  104. Nauts, S.; Kamphorst, B.A.; Stut, W.; De Ridder, D.T.D.; Anderson, J.H. The Explanations People Give for Going to Bed Late: A Qualitative Study of the Varieties of Bedtime Procrastination. Behav. Sleep. Med. 2019, 17, 753–762. [Google Scholar] [CrossRef] [PubMed]
  105. Chowdhury, S.F.; Pychyl, T.A. A Critique of the Construct Validity of Active Procrastination. Personal. Individ. Differ. 2018, 120, 7–12. [Google Scholar] [CrossRef]
  106. Kroese, F.M.; de Ridder, D.T.D. Health Behaviour Procrastination: A Novel Reasoned Route towards Self-Regulatory Failure. Health Psychol. Rev. 2016, 10, 313–325. [Google Scholar] [CrossRef]
  107. Ma, X.; Meng, D.; Zhu, L.; Xu, H.; Guo, J.; Yang, L.; Yu, L.; Fu, Y.; Mu, L. Bedtime Procrastination Predicts the Prevalence and Severity of Poor Sleep Quality of Chinese Undergraduate Students. J. Am. Coll. Health 2022, 70, 1104–1111. [Google Scholar] [CrossRef] [PubMed]
  108. Exelmans, L.; Van Den Bulck, J. Bedtime Mobile Phone Use and Sleep in Adults. Soc. Sci. Med. 2016, 148, 93–101. [Google Scholar] [CrossRef] [PubMed]
  109. Joshi, S.C.; Woodward, J.; Woltering, S. Nighttime Cell Phone Use and Sleep Quality in Young Adults. Sleep Biol. Rhythm. 2022, 20, 97–106. [Google Scholar] [CrossRef]
  110. Moulin, K.L.; Chung, C.-J. Technology Trumping Sleep: Impact of Electronic Media and Sleep in Late Adolescent Students. J. Educ. Learn. 2016, 6, 294. [Google Scholar] [CrossRef]
  111. Liu, H.; Zhou, Z.; Huang, L.; Zhu, E.; Yu, L.; Zhang, M. Prevalence of Smartphone Addiction and Its Effects on Subhealth and Insomnia: A Cross-Sectional Study among Medical Students. BMC Psychiatry 2022, 22, 305. [Google Scholar] [CrossRef]
  112. Rod, N.H.; Dissing, A.S.; Clark, A.; Gerds, T.A.; Lund, R. Overnight Smartphone Use: A New Public Health Challenge? A Novel Study Design Based on High-Resolution Smartphone Data. PLoS ONE 2018, 13, e0204811. [Google Scholar] [CrossRef]
  113. Alshammari, T.K.; Rogowska, A.M.; Basharahil, R.F.; Alomar, S.F.; Alseraye, S.S.; Al Juffali, L.A.; Alrasheed, N.M.; Alshammari, M.A. Examining Bedtime Procrastination, Study Engagement, and Studyholism in Undergraduate Students, and Their Association with Insomnia. Front. Psychol. 2023, 13, 1111038. [Google Scholar] [CrossRef]
  114. Hammoudi, S.F.; Mreydem, H.W.; Ali, B.T.A.; Saleh, N.O.; Chung, S.; Hallit, S.; Salameh, P. Smartphone Screen Time Among University Students in Lebanon and Its Association With Insomnia, Bedtime Procrastination, and Body Mass Index During the COVID-19 Pandemic: A Cross-Sectional Study. Psychiatry Investig. 2021, 18, 871–878. [Google Scholar] [CrossRef]
  115. Carney, C.E.; Harris, A.L.; Moss, T.G.; Edinger, J.D. Distinguishing Rumination from Worry in Clinical Insomnia. Behav. Res. Ther. 2010, 48, 540–546. [Google Scholar] [CrossRef]
  116. Meier, A.; Reinecke, L.; Meltzer, C.E. “Facebocrastination”? Predictors of Using Facebook for Procrastination and Its Effects on Students’ Well-Being. Comput. Hum. Behav. 2016, 64, 65–76. [Google Scholar] [CrossRef]
  117. Reinecke, L.; Hartmann, T.; Eden, A. The Guilty Couch Potato: The Role of Ego Depletion in Reducing Recovery Through Media Use. J. Commun. 2014, 64, 569–589. [Google Scholar] [CrossRef]
  118. Stainton, M.; Lay, C. Trait Procrastinators and Behavior/Trait-Specific Cognitions. J. Soc. Behav. Personal. 2000, 15, 297. [Google Scholar]
  119. Guo, J.; Meng, D.; Ma, X.; Zhu, L.; Yang, L.; Mu, L. The Impact of Bedtime Procrastination on Depression Symptoms in Chinese Medical Students. Sleep Breath. 2020, 24, 1247–1255. [Google Scholar] [CrossRef] [PubMed]
  120. You, Z.; Li, X.; Ye, N.; Zhang, L. Understanding the Effect of Rumination on Sleep Quality: A Mediation Model of Negative Affect and Bedtime Procrastination. Curr. Psychol. 2023, 42, 136–144. [Google Scholar] [CrossRef]
  121. Kim, J.-H.; Seo, M.; David, P. Alleviating Depression Only to Become Problematic Mobile Phone Users: Can Face-to-Face Communication Be the Antidote? Comput. Hum. Behav. 2015, 51, 440–447. [Google Scholar] [CrossRef]
  122. Van Den Eijnden, R.J.J.M.; Meerkerk, G.-J.; Vermulst, A.A.; Spijkerman, R.; Engels, R.C.M.E. Online Communication, Compulsive Internet Use, and Psychosocial Well-Being among Adolescents: A Longitudinal Study. Dev. Psychol. 2008, 44, 655–665. [Google Scholar] [CrossRef] [PubMed]
  123. Yen, J.-Y.; Cheng-Fang, Y.; Chen, C.-S.; Chang, Y.-H.; Yeh, Y.-C.; Ko, C.-H. The Bidirectional Interactions between Addiction, Behaviour Approach and Behaviour Inhibition Systems among Adolescents in a Prospective Study. Psychiatry Res. 2012, 200, 588–592. [Google Scholar] [CrossRef] [PubMed]
  124. Lemola, S.; Perkinson-Gloor, N.; Brand, S.; Dewald-Kaufmann, J.F.; Grob, A. Adolescents’ Electronic Media Use at Night, Sleep Disturbance, and Depressive Symptoms in the Smartphone Age. J. Youth Adolesc. 2015, 44, 405–418. [Google Scholar] [CrossRef]
  125. Liu, H.; Ji, Y.; Dust, S.B. “Fully Recharged” Evenings? The Effect of Evening Cyber Leisure on next-Day Vitality and Performance through Sleep Quantity and Quality, Bedtime Procrastination, and Psychological Detachment, and the Moderating Role of Mindfulness. J. Appl. Psychol. 2020, 106, 990. [Google Scholar] [CrossRef]
  126. Huang, T.; Liu, Y.; Tan, T.C.; Wang, D.; Zheng, K.; Liu, W. Mobile Phone Dependency and Sleep Quality in College Students during COVID-19 Outbreak: The Mediating Role of Bedtime Procrastination and Fear of Missing Out. BMC Public Health 2023, 23, 1200. [Google Scholar] [CrossRef]
  127. You, Z.; Mei, W.; Ye, N.; Zhang, L.; Andrasik, F. Mediating Effects of Rumination and Bedtime Procrastination on the Relationship between Internet Addiction and Poor Sleep Quality. J. Behav. Addict. 2021, 9, 1002–1010. [Google Scholar] [CrossRef] [PubMed]
  128. Buysse, D.J.; Reynolds, C.F.; Monk, T.H.; Berman, S.R.; Kupfer, D.J. The Pittsburgh Sleep Quality Index: A New Instrument for Psychiatric Practice and Research. Psychiatry Res. 1989, 28, 193–213. [Google Scholar] [CrossRef] [PubMed]
  129. Royuela-Rico, A.; Macías-Fernández, J. Propiedades Clinimetricas de La Versión Castellana Del Cuestionario de Pittsburgh. Vigilia-Sueño 1997, 9, 81–94. [Google Scholar]
  130. Brando-Garrido, C.; Montes-Hidalgo, J.; Limonero, J.T.; Gómez-Romero, M.J.; Tomás-Sábado, J. Spanish Version of the Bedtime Procrastination Scale: Cross-Cultural Adaptation and Psychometric Evaluation in a Sample of Nursing Students. Psychol. Rep. 2022, 125, 1765–1779. [Google Scholar] [CrossRef] [PubMed]
  131. Kwon, M.; Lee, J.-Y.; Won, W.-Y.; Park, J.-W.; Min, J.-A.; Hahn, C.; Gu, X.; Choi, J.-H.; Kim, D.-J. Development and Validation of a Smartphone Addiction Scale (SAS). PLoS ONE 2013, 8, e56936. [Google Scholar] [CrossRef] [PubMed]
  132. Lopez-Fernandez, O. Short Version of the Smartphone Addiction Scale Adapted to Spanish and French: Towards a Cross-Cultural Research in Problematic Mobile Phone Use. Addict. Behav. 2017, 64, 275–280. [Google Scholar] [CrossRef] [PubMed]
  133. Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.-G. Statistical Power Analyses Using G*Power 3.1: Tests for Correlation and Regression Analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef] [PubMed]
  134. Bulmer, M.G. Principles of Statistics; Courier Corporation: New York, NY, USA, 1979; ISBN 978-0-486-63760-0. [Google Scholar]
  135. Hayes, A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach; Guilford Press: New York, NY, USA, 2013; pp. xvii, 507. ISBN 978-1-60918-230-4. [Google Scholar]
  136. Meshi, D.; Ellithorpe, M.E. Problematic Social Media Use and Social Support Received in Real-Life versus on Social Media: Associations with Depression, Anxiety and Social Isolation. Addict. Behav. 2021, 119, 106949. [Google Scholar] [CrossRef]
  137. Verbree, A.-R.; Hornstra, L.; Maas, L.; Wijngaards-de Meij, L. Conscientiousness as a Predictor of the Gender Gap in Academic Achievement. Res. High. Educ. 2023, 64, 451–472. [Google Scholar] [CrossRef]
  138. Busch, P.A.; McCarthy, S. Antecedents and Consequences of Problematic Smartphone Use: A Systematic Literature Review of an Emerging Research Area. Comput. Hum. Behav. 2021, 114, 106414. [Google Scholar] [CrossRef]
  139. Szymkowiak, A.; Melović, B.; Dabić, M.; Jeganathan, K.; Kundi, G.S. Information Technology and Gen Z: The Role of Teachers, the Internet, and Technology in the Education of Young People. Technol. Soc. 2021, 65, 101565. [Google Scholar] [CrossRef]
  140. Sirois, F.M.; Nauts, S.; Molnar, D.S. Self-Compassion and Bedtime Procrastination: An Emotion Regulation Perspective. Mindfulness 2019, 10, 434–445. [Google Scholar] [CrossRef]
  141. Teixeira, L.R.; Lowden, A.; Turte, S.L.; Nagai, R.; Moreno, C.R.; Latorre, M.D.; Marina Fischer, F. Sleep and Sleepiness among Working and Non-Working High School Evening Students. Chronobiol. Int. 2007, 24, 99–113. [Google Scholar] [CrossRef]
  142. Hynes, M. The Smartphone: A Weapon of Mass Distraction. In The Social, Cultural and Environmental Costs of Hyper-Connectivity: Sleeping Through the Revolution; Emerald Publishing Limited: Bingley, UK, 2021; pp. 71–84. ISBN 978-1-83909-976-2. [Google Scholar]
  143. Chun, J. Conceptualizing Effective Interventions for Smartphone Addiction among Korean Female Adolescents. Child. Youth Serv. Rev. 2018, 84, 35–39. [Google Scholar] [CrossRef]
  144. Csibi, S.; Griffiths, M.D.; Demetrovics, Z.; Szabo, A. Analysis of Problematic Smartphone Use Across Different Age Groups within the ‘Components Model of Addiction’. Int. J. Ment. Health Addict. 2021, 19, 616–631. [Google Scholar] [CrossRef]
Figure 1. Hypothetical mediation model of bedtime procrastination between PSU and poor sleep quality.
Figure 1. Hypothetical mediation model of bedtime procrastination between PSU and poor sleep quality.
Behavsci 13 00839 g001
Figure 2. Mediation model of bedtime procrastination between PSU and poor sleep quality. Note. The dashed line indicates a non-significant direct effect (p > 0.05).
Figure 2. Mediation model of bedtime procrastination between PSU and poor sleep quality. Note. The dashed line indicates a non-significant direct effect (p > 0.05).
Behavsci 13 00839 g002
Table 1. Socio-demographic variables of the sample.
Table 1. Socio-demographic variables of the sample.
Age group
   Young adult (18–25 years)13443.4
   Adult (26–44 years)13644.0
   Middle-aged adult (45–60 years)
Level of education
   Early childhood education10.3
   Primary education41.3
   Secondary education10131.6
   Higher education20766.8
Marital status
Use smartphone in bed
Time spent using a smartphone in bed
   Less than 15 min4216.2
   Between 16 and 30 min9033.2
   Between 31 and 60 min8934.0
   More than 60 min4116.6
Smartphone activity in bed
   Internet browsing5922.4
   Social networks8933.8
   Multimedia entertainment8231.2
Table 2. Descriptive statistics of the study variables.
Table 2. Descriptive statistics of the study variables.
1. Sleep quality (PSQI)7.53.6
2. Problematic smartphone use (SAS-SV)29.110.1
3. Bedtime procrastination (BPS)29.17.3
4. Years with a smartphone12.04.3
5. Daily hours of smartphone use5.02.8
6. Days per week using the smartphone in bed5.91.9
7. Time spent using a smartphone in bed2.51.0
Table 3. Pearson’s correlation coefficients of the study variables.
Table 3. Pearson’s correlation coefficients of the study variables.
Variable 1234567
1. Sleep quality (PSQI)Coef.
2. Problematic smartphone use (SAS-SV)Coef.0.160 **
3. Bedtime procrastination (BPS)Coef.0.298 ***0.363 ***
4. Years with a smartphoneCoef.−0.065−0.034−0.140 *
5. Daily hours of smartphone useCoef.0.222 **0.358 ***0.186 **−0.040
6. Days per week of smartphone use in bedCoef.−0.0060.201 **0.175 *−0.0170.183 ***
7. Time spent using a smartphone in bedCoef.0.173 **0.286 ***0.347 ***−0.172 **0.275 ***0.288 ***
8. AgeCoef.−0.063−0.202 ***−0.229 ***0.548 ***−0.171 **−0.230 ***−0.221 ***
Note: Bilateral significance, * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4. Descriptive statistics and gender differences.
Table 4. Descriptive statistics and gender differences.
VariableMenWomentglpCohen’s d
1. Sleep quality (PSQI)
2. Problematic smartphone use (SAS-SV)27.49.931.010.03.2483080.0010.36
3. Bedtime procrastination (BPS)
4. Years with a smartphone12.74.311.33.8−2.3113060.021−0.26
5. Daily hours of smartphone use4.
6. Days per week using the smartphone in bed6.
7. Time spent using a smartphone in bed2.
Note: Homogeneity of variances tested by Levene’s test (p > 0.05).
Table 5. One-way ANOVA for the variables under study according to age groups.
Table 5. One-way ANOVA for the variables under study according to age groups.
VariableYoung Adults (18–25 Years)Adults
(26–44 Years)
Middle-Aged Adults
(45–60 Years)
F(2, 306)pη2
Sleep quality (PSQI)
Problematic smartphone use (SAS-SV)31.69.527.610.325.49.38.73<0.0010.05
Bedtime procrastination (BPS)30.77.628.
Note: Homogeneity of variances tested by Levene’s test (p > 0.05).
Table 6. Bonferroni post hoc contrasts between age groups for the PSU and bedtime procrastination variables.
Table 6. Bonferroni post hoc contrasts between age groups for the PSU and bedtime procrastination variables.
VariableComparisonMean Diff.SDp95% CI
Lower LimitUpper Limit
Problematic smartphone use (SAS-SV)YA vs. A4.01.20.0031.126.90
YA vs. MAA6.21.80.0021.9210.55
A vs. MAA2.21.80.651−2.096.52
Bedtime procrastination (BPS)YA vs. A2.30.90.0220.264.47
YA vs. MAA4.41.30.0021.307.60
A vs. MAA2.11.30.333−1.065.23
Note: YA = young adults (18–25 years), A = adults (26–44 years), MAA = middle-aged adults (45–60 years).
Table 7. Mediation analysis of the model.
Table 7. Mediation analysis of the model.
EffectBSE (B)tp95% CI
Lower LimitUpper Limit
a: PSU → BP0.260.036.83<0.0011.553.20
b: BP → PSQ0.140.034.75<0.0010.110.31
c (total):
PSU → PSQ−0.021.47
c’ (direct):
PSU → PSQ−0.521.00
a*b (indirect): PSU → BP → PSQ0.04 0.020.05
Note: PSU = problematic smartphone use; BP = bedtime procrastination; PSQ = poor sleep quality.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Correa-Iriarte, S.; Hidalgo-Fuentes, S.; Martí-Vilar, M. Relationship between Problematic Smartphone Use, Sleep Quality and Bedtime Procrastination: A Mediation Analysis. Behav. Sci. 2023, 13, 839.

AMA Style

Correa-Iriarte S, Hidalgo-Fuentes S, Martí-Vilar M. Relationship between Problematic Smartphone Use, Sleep Quality and Bedtime Procrastination: A Mediation Analysis. Behavioral Sciences. 2023; 13(10):839.

Chicago/Turabian Style

Correa-Iriarte, Santiago, Sergio Hidalgo-Fuentes, and Manuel Martí-Vilar. 2023. "Relationship between Problematic Smartphone Use, Sleep Quality and Bedtime Procrastination: A Mediation Analysis" Behavioral Sciences 13, no. 10: 839.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop