
==== Front
Digit Health
Digit Health
DHJ
spdhj
Digital Health
2055-2076
SAGE Publications Sage UK: London, England

10.1177/20552076241283338
10.1177_20552076241283338
Original Research Article
Relationship between problematic smartphone use and sleep problems: The roles of sleep-related compensatory health beliefs and bedtime procrastination
An Yandong 12
https://orcid.org/0000-0003-1648-732X
Zhang Meng Xuan 12
1 Department of Medical Humanities, School of Humanities, 12579 Southeast University , Nanjing, Jiangsu, China
2 Psychological Research & Education Center, School of Humanities, 12579 Southeast University , Nanjing, Jiangsu, China
YA and MXZ are co-first authors.

Meng Xuan Zhang, Department of Medical Humanities, School of Humanities, Southeast University, Nanjing, 211189, Jiangsu, China. Email: zhangmengxuan@seu.edu.cn
12 9 2024
Jan-Dec 2024
10 2055207624128333817 4 2024
28 8 2024
© The Author(s) 2024
2024
SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits any use, reproduction and distribution of the work as published without adaptation or alteration, provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Objective

Concerns regarding sleep problems in emerging adults and their antecedents, such as problematic smartphone use (PSU), have been growing. This study tested the association between PSU and sleep problems and further investigated the mechanisms of this relationship based on the theory of compensatory health beliefs (CHBs).

Methods

This study included 999 participants (74.87% female) in China, aged 17 to 25 years (M = 21.16; standard deviation = 1.60), who voluntarily filled in an anonymous survey.

Results

The findings showed positive correlations between sleep problems and PSU, sleep-related CHBs, and bedtime procrastination (rs = .25–.52, p < .001). Furthermore, the positive link between PSU and sleep problems was mediated by bedtime procrastination alone (β=.21, 95% confidence interval (CI) [.17, .26]) or a serial path of sleep-related CHBs and bedtime procrastination (β=.04, 95% CI [.02, .05]).

Conclusion

This study provides a new perspective to understand the internal mechanism underlying the PSU-sleep problem link. Interventions for sleep disorders ought to consider the theoretical guidelines of the CHBs model to reduce the risk of bedtime procrastination and sleep disorders in emerging adults.

Compensatory health beliefs
sleep problem
problematic smartphone use
bedtime procrastination
emerging adult
Liberal Arts Enhancement Program of Southeast University 4060692203/003 Fundamental Research Funds for the Central Universities https://doi.org/10.13039/501100012226 3213002308A2 3213002406A2 4013002306 typesetterts19
cover-dateJanuary-December 2024
==== Body
pmcIntroduction

The increasing prevalence of sleep problems in recent years has attracted public concerns. 1 Sleep health in emerging adults should also be paid attention to as their sleep patterns dramatically change during the developmental transition from adolescence to adulthood (e.g. delays in falling asleep), 2 with the potential risks of sleep problems. Previous studies have shown that sleep problems have been prevalent and increasing in emerging adults over the past decade.3–5 Sleep problems constitute a more holistic view of sleep, and mainly include poor subjective sleep quality, short sleep duration, difficulty falling asleep, and low sleep efficiency.3,6 Furthermore, sleep problems can exert detrimental effects on physical health, potentially leading to an elevated risk of weight gain and cardiovascular disease,7,8 as well as mental health, including depression and anxiety. 3 Therefore, it is necessary to identify sleep problems and their related risk factors among emerging adults.

With an increasing number of emerging adults possessing and immersing themselves in smartphones, problematic smartphone use (PSU) has become prevalent and negatively impacts health, including sleep health, among emerging adults. 9 A systematic review reported the increased risk of poor sleep among young adults with PSU. 10 Although many studies explored the relationship between PSU and sleep problems, limited research investigated the mechanisms underlying this relationship. Therefore, this study aimed to examine whether and how PSU is associated with sleep problems based on the theory of compensatory health beliefs (CHBs) 11 among Chinese emerging adults, with the mediating effects of sleep-related CHBs and bedtime procrastination. This study hypothesized that sleep-related CHBs and bedtime procrastination mediate the relationship between PSU and sleep problems among emerging adults. The findings of this study may offer valuable insights for devising potential interventions to enhance sleep health among emerging adults.

PSU and sleep problems

PSU (i.e. excessive and addictive smartphone use) is regarded as a generalized type of problematic internet use. 12 Individuals with PSU and other specific types of problematic internet use, such as problematic gaming and problematic social media use, have similar symptoms of impulse control problems, excessive use, withdrawal, and negative physical or mental consequences.13–16 PSU is prevalent among emerging adults, who are the primary group of smartphone users. 17 For example, a systematic review reported that the prevalence of PSU was 36.5% to 67% (average 52%) among emerging adults. 18 An empirical study also found that 46.6% of British university students showed symptoms of PSU. 19 PSU is not only associated with psychological factors, such as fear of missing out, emotional dysregulation, and neurotic personality traits,20,21 but also physical and mental health problems. For example, multiple studies found that individuals with PSU are more likely to have depression, anxiety, loneliness, and sleep problems in both the general population and clinical samples (e.g. people with attention deficit hyperactivity disorder).22–24

As sleep health is garnering increasing attention, 1 many studies have investigated the relationship between PSU (or specific types of problematic use) and sleep problems (e.g. poor sleep quality). 25 For example, a previous longitudinal research found that PSU and problematic social media use predicted poor sleep quality in people with schizophrenia.26,27 A significant relationship is also observed between PSU (or problematic internet use) and poor sleep quality in individuals with substance use disorders. 28 However, these studies were conducted in clinical populations.

Studies in recent years also showed that PSU has increasingly emerged as a risk factor for compromised sleep health among emerging adults. 10 For example, Sohn et al. 29 showed that 38.9% of young adults from a UK sample suffered from PSU, 68.7% of whom had poor sleep. Ozcan and Acimis 9 discovered that the rate of PSU among university students in Turkey was 34.6%, and that students with PSU experienced a significantly higher frequency of sleep problems. Moreover, a study found that the prevalence of PSU was 38.63% in China, with a higher risk of sleep problems in university students. 30 Additionally, a longitudinal study unveiled that the prevalence of PSU was 26.6% in Chinese university students, and 52.5% 1 year later; the results also showed that PSU would predict sleep problems 1 year later. 31 Concerning specific characteristics of sleep problems, previous studies have found that PSU is associated with lower subject sleep quality, longer sleep latency, and shorter sleep duration. 32 Therefore, this study hypothesized that PSU is positively associated with sleep problems in emerging Chinese adults (H1).

Regarding the possible mechanism underlying the relationship between PSU and sleep problems, blue light and electromagnetism from smartphones at night may disturb sleep. 33 Cortisol and melatonin could also influence sleep. 34 Additionally, other emotional factors, including depression and anxiety, have been observed. 10 According to the CHBs model, sleep-related CHBs and bedtime procrastination may play mediating roles underlying PSU–sleep problems relationship.

Applying the CHBs model to the PSU–sleep problems relationship

As suggested by Knäuper et al., 11 CHBs refer to beliefs that the negative consequences of unhealthy behaviors would be compensated or neutralized through conducting health behaviors. The model was proposed by Rabia et al. 35 explaining how and why people employ CHBs when faced with temptation. Specifically, when an individual's desire to engage in tempting yet unhealthy behavior conflicts with their health-related goals, the result is a state of internal distress. These inconsistencies can lead to irrational or maladaptive outcomes. Chronic activation of CHBs can become an automatic response to help deal with dissonance. 36 However, several studies have demonstrated a positive association between CHBs and unhealthy outcomes, such as unhealthy eating, 37 and alcohol consumption. 38 Therefore, we proposed that individuals with higher sleep-related CHBs experience more sleep problems (H2). In addition, CHBs have been associated with addictive behaviors (e.g. binge-watching). 39 PSU, a form of addictive behavior, has not been studied previously. To bridge this gap, we propose that sleep-related CHBs serve as mediators between PSU and sleep problems (H3).

Emerging adults who suffer from PSU are more likely to spend time on a smartphone, even delaying their bedtime. 40 The health goal of sleeping on time or keeping adequate sleep time would conflict with the desire to use a smartphone, and CHBs would be activated. However, individuals with sleep-related CHBs like to spend more time on their smartphones, even before or during sleep time, which may lead to bedtime procrastination with a greater tendency for sleep problems. 41 Therefore, bedtime procrastination should be further investigated.

The role of bedtime procrastination

Bedtime procrastination refers to delaying bedtime for no external reasons. 42 Individuals engaging in bedtime procrastination go to bed later than planned. Bedtime procrastination can be regarded as a health-interfering behavior that negatively affects sleep health.42,43 A study of meta-analysis showed a positive association between bedtime procrastination and sleep problems in many populations, including emerging adults. 44 Furthermore, bedtime procrastination was found to be related to PSU among emerging adults in both cross-sectional and longitudinal research.32,45

Previous research has also investigated the relationship among PSU, bedtime procrastination, and sleep quality. For example, two studies with samples of young people from Turkey and Spain found that bedtime procrastination had a mediating effect on the relationship between PSU and poor sleep quality.46,47 Similarly, Zhang and Wu 48 identified this connection among Chinese college students. However, poor sleep quality was measured using three indicators of sleep problems (i.e. subjective sleep quality, sleep latency, and sleep duration), which are limited to representing only sleep problems. Therefore, it was proposed that bedtime procrastination served as a mediation in the link between PSU and sleep problems (H4). Furthermore, CHBs were shown to be positively associated with procrastination (i.e. academic procrastination). 39 Accordingly, we further hypothesized the serial mediation of sleep-related CHBs and bedtime procrastination underlying PSU–sleep problems relationship (H5). The conceptual model is presented in Figure 1.

Figure 1. The conceptual model. CHBs: compensatory health beliefs; PSU: problematic smartphone use; .

Material and methods

Participants and procedure

This study utilized a cross-sectional design. In October 2022, 1017 emerging adults without any mental disorders from mainland China were recruited via a popular online platform (Credamo; https://www.credamo.com) using convenience sampling. Data from 18 participants were excluded (they responded in a set pattern or filled in the questionnaire abnormally) and 999 participants (74.87% female) aged 17 to 25 years (Mage = 21.16, standard deviation (SD) = 1.60) were included in the data analyses. After filling in a written consent form (electronic version) with the study's objectives and their rights (e.g. anonymous responses and withdrawal at any time without any punishment), all participants completed the online questionnaire. Participants received a compensation of RMB 10 (approximately USD 1.45) after completing the questionnaire. Ethical approval was granted by the Ethics Committee of the corresponding author's department.

Measures

Sleep problems

Sleep problems were assessed using the Pittsburgh Sleep Quality Index (PSQI), 49 which has been validated for the Chinese population and shows good reliability and validity. 50 The scale includes nine self-rated questions (e.g. “During the past month, how would you rate your sleep quality overall?”), with seven dimensions: subjective sleep quality, sleep duration, sleep latency, habitual sleep efficiency, step disturbances, use of sleep medication, and daytime dysfunction. Each dimension was scored on a scale of 0 to 3 points, with a higher total score (ranging from 0 to 21) indicating more severe sleep problems. In the current study, the Cronbach's alpha coefficient for the scale was 0.72.

PSU

It was assessed by Smartphone Addiction Scale-Short Version (SAS-SV). 51 This scale has been validated in Chinese samples. 52 Participants answered 10 items (e.g. “I miss planned work due to smartphone use”) on a 6-point Likert scale, with 1 = strongly disagree to 6 = strongly agree. The score was summed, ranging from 10 to 60, and a higher total score indicated a more severity of PSU. This scale had a good reliability in this study (α= 0.90).

Sleep-related CHBs

In this study, two items from the Compensatory Health Beliefs Scale were used to measure sleep-related CHBs. 11 The Chinese version of this scale also demonstrated good reliability and validity. 53 Participants reported their sleep-related CHBs, such as “Too little sleep during the week can be compensated for by sleeping in on the weekends” and “It is okay to go to bed late if one can sleep longer the next morning (only the number of hours counts),” using a five-point Likert scale (1 = Never to 5 = Always). A higher total score indicated a stronger inclination towards sleep-related CHBs.

Bedtime procrastination

Bedtime procrastination was assessed using the Bedtime Procrastination Scale, 42 which has been validated for Chinese samples. 54 The scale has been widely used in previous research conducted in China. 43 Participants answered nine items (e.g. “I usually go to bed later than I planned”) on a five-point Likert scale (1 = never to 5 = always). A higher total score indicates a higher level of bedtime procrastination. The internal reliability (Cronbach's alpha coefficient) of the scale was 0.91 in this study.

Demographic information

Information on the sex (male = 1 and female = 2) and age (years) was recorded.

Data analysis

There was no missing data in the dataset. The simr package was used to perform Monte Carlo simulation analysis to calculate the minimum sample size in R. 55 Parameters were set according to the medium effect size standards. The results indicated that at least 600 participants are required to achieve a statistical power of 0.95 (α = 0.01),56,57 suggesting that our sample size meets this requirement. The descriptive statistics and correlational coefficients were calculated using SPSS version 22.0 in this study. The conceptual model was tested by R software with the lavaan package. 58 Model fit was assessed by Schreiber et al., 59 using the following indices: chi-square test (χ2/df), comparative fit index (CFI;  > .90), Tucker-Lewis index (TLI;  > .90), root mean square error of approximation (RMSEA;  < .08), and standardized root mean square residual (SRMR;  < .08). Additionally, the standardized coefficients of direct and indirect effects were calculated and a 95% confidence interval (CI) used the bias-corrected percentile method with 5000 bootstrap samples.

Results

Demographic and correlational findings

Descriptive statistics for each dimension of sleep problems among emerging Chinese adults are presented in Table 1. Table 2 illustrates the correlations between PSU and seven sleep problem dimensions. PSU was significantly and positively correlated with most sleep problems (rs = .08 to .47, p < .01) except for the use of sleep medication (r = .06, p = .05).

Table 1. Sleep characteristics of emerging adults (n = 999).

	% (N)	
Subjective sleep quality		
Very good	12.6 (126)	
Fairly good	57.2 (571)	
Fairly bad	27.6 (276)	
Very bad	2.6 (26)	
Sleep latency		
<=15 minutes	30.0 (300)	
16–30 minutes	43.7 (437)	
31–60 minutes	15.9 (159)	
> 60 minutes	10.3 (103)	
Sleep duration		
>7 hours	79.4 (793)	
6–7 hours	17.8 (178)	
5–6 hours	2.5 (25)	
<5 hours	0.3 (3)	
Habitual sleep efficiency		
>85%	94.7 (946)	
75–84%	4.9 (49)	
65–74%	0.2 (2)	
<65%	0.2 (2)	
Use of sleep medication		
Not during the past month	95.0 (949)	
Less than once a week	2.8 (28)	
Once or twice a week	1.2 (12)	
Three or more times a week	1.0 (10)	
Daytime dysfunction		
0	5.8 (58)	
1	18.6 (186)	
2	41.8 (418)	
3	33.7 (337)	
Step disturbances		
0	21.5 (215)	
1	53.6 (535)	
2	16.0 (160)	
3	8.9 (89)	
Note: The range of daytime dysfunction and step disturbances refers to better (0) to worst (3).

Table 2. Correlation between PSU and different dimensions of sleep problems.

	1	2	3	4	5	6	7	8	
1. PSU	–								
2. Subjective sleep quality	0.35***	–							
3. Sleep latency	0.16***	0.41***	–						
4. Sleep duration	0.12***	0.21***	0.10**	–					
5. Habitual sleep efficiency	0.08**	0.14***	0.06	0.29***	–				
6. Step disturbances	0.27***	0.62***	0.41***	0.12***	0.12***	–			
7. Use of sleep medication	0.06	0.19***	0.14***	0.16***	0.18***	0.23***	–		
8. Daytime dysfunction	0.47***	0.42***	0.17***	0.18***	0.08*	0.33***	0.12***	–	
Note: *p < .05; **p < .01; ***p < .001.

The higher the score on each dimension of sleep problems, the severer in this sleep problem.

PSU: problematic smartphone use.

The results of preliminary analyses (i.e. M and SD) and correlational coefficients of all the variables are shown in Table 3. PSU and bedtime procrastination were positively associated with sleep problems (r = .41 and .52, p < .001), thus supporting H1. Sleep-related CHBs had positive correlations with sleep problems, bedtime procrastination, and PSU (r = .25 to .41, p < 0.001), meaning that H2 was supported.

Table 3. Mean, standard deviation, and intercorrelation coefficients of the major variables.

	M	SD	1	2	3	4	5	6	
1. Sleep problems	5.80	2.78	–						
2. Bedtime procrastination	29.30	7.71	.52***	–					
3. Sleep-related CHBs	6.94	1.71	.25***	.41***	–				
4. PSU	40.23	9.66	.41***	.59***	.38***	–			
5. Sex#	–	–	.07*	.13***	.04	.13***	–		
6. Age	21.16	1.60	.01	−.02	−.09**	−.02	−.11***	–	
Note: *p < .05; ***p < .001. #1= male, 2 = female.

CHBs: compensatory health beliefs; PSU: problematic smartphone use.

Sex was positively associated with sleep problems (r = .07, p = .02), bedtime procrastination (r = .13, p < .001), and PSU (r = .13, p < .001), but not with sleep-related CHBs (r = .04, p = .23). Age was negatively associated with sleep-related CHBs levels (r = −.09, p < .05).

Model testing

Path analysis, in which PSU was the independent variable and sleep problems constituted the dependent variables, demonstrated the mediating roles of sleep-related CHBs and bedtime procrastination. The effects of gender and age were controlled for during model testing. The results of the path analysis showed a good model fit: χ2(4) = 16.47, χ2/df = 4.12, p < .01, CFI = .99, TLI = .96, RMSEA = .06, 90% CI [.03, .09], SRMR = .02. In this model (see Figure 2), all standardized coefficients of the hypothesized relationships were statistically significant, except for the direct path from sleep-related CHBs to sleep problems (β = .02, p = .43).

Figure 2. The final model. Note: *p < .05; ***p < .001. The path coefficient is a standardized coefficient. CHBs: compensatory health beliefs; PSU: problematic smartphone use.

As shown in Table 4, PSU had a significant direct effect on sleep problems (β = .15, 95% CI [.08, .22]). The relationship between PSU and sleep problems was mediated by bedtime procrastination (β = .21, 95% CI [.17, .26]), but not by sleep-related CHBs (β = .01, 95% CI [−.01, .03]), thus supporting H4. Moreover, the indirect effect of PSU on sleep problems via both sleep-related CHBs and bedtime procrastination was statistically significant (β = .04, 95% CI [.02, .05]), thus supporting H5.

Table 4. Results of path analysis.

Path	β	95% CI		
Lower	Upper	
Direct effect:				
PSU → sleep problems	.15***	.08	.22	
Indirect effect:				
PSU → sleep-related CHBs → sleep problems	.01	−.01	.03	
PSU → bedtime procrastination → sleep problems	.21***	.17	.26	
PSU → sleep-related CHBs → bedtime procrastination → sleep problems	.04***	.02	.05	
Note: ***p < .001; β is the standardized coefficient.

CHBs: compensatory health beliefs; CI: confidence interval; PSU: Problematic smartphone use.

Discussion

Sleep problems among emerging adults are currently prevalent and can co-occur or lead to physical and mental disorders, owing to which they have attracted increasing public attention. 1 This study tested the status quo of sleep problems from a comprehensive health-oriented perspective and investigated the correlation between PSU and sleep problems. Furthermore, we examined the mediation model of sleep-related CHBs and bedtime procrastination in the link between PSU and sleep problems based on the CHBs model.

Nearly one-third of this sample of emerging adults reported poor subjective sleep quality. This finding aligns with the results of prior research conducted with a sample of Chinese university students aged 18 to 26 years. 48 Regarding sleep latency, 26.2% of the participants reported longer than 30 minutes of latency before sleep, as the findings in a previous meta-analysis. 60 Whereas 20.6% slept less than 7 hours, which was far below the percentage for sleep duration of less than 7 hours (46%) in the study by Li et al. 61 A possible reason for this difference is the influence of protective measures (e.g. suspended offline teaching) during the COVID-19 pandemic. Previous studies also found that young adults, including university students, had longer sleep durations, whereas they continuously suffered from poor sleep quality and insomnia in the COVID-19 pandemic. 62 Chen et al. 5 argued that sleep problems are the most severe mental health problem among university students in mainland China, with an increasing trend over the past decade. To produce effective interventions, attention should be paid to the risk factors and underlying mechanisms of sleep problems.

PSU has also been identified as a vital factor in sleep problems among emerging adults. 10 This study found that PSU was positively associated with the summed score of sleep problems; specifically, emerging adults who had higher levels of PSU exhibited more sleep problems. Furthermore, we found a positive relationship between most characteristics of sleep problems (e.g. poor sleep quality, short sleep duration, long sleep latency, and daytime dysfunction) and PSU, except for the use of medicine. Regardless of the overall score for each characteristic of sleep problems, its relationship with PSU was consistent with previous research,30,31 providing more comprehensive empirical evidence for the positive relationship between PSU and sleep problems in emerging adults. However, there was no significant association between PSU and the use of medicine (a dimension of sleep problems). One possible reason for this is that the current study focused on a health-oriented perspective of sleep; therefore, we collected data from a general population of emerging adults, rather than those diagnosed with sleep problems or disorders. The general population of emerging adults tends to have mild or moderate sleep problems, with these people not usually resorting to medication to promote sleep.

This empirical study is the first to demonstrate a positive relationship between sleep-related CHBs and sleep problems in emerging Chinese adults. Emerging adults with higher sleep-related CHBs reported more sleep problems, which strongly supports the assumption that CHBs are related to unhealthy outcomes in daily life, 37 including sleep in the present study. Moreover, there is also a positive relationship between CHBs and addictive behaviors (i.e. PSU), as previous research by Paulus and Aziz 39 that CHBs have been associated with binge-watching. The possible reason is that individuals with PSU would reduce self-efficacy, which leads to the activation of CHBs and further engaging in tempting activities. 35 More importantly, this is the first study to apply the CHBs model to explain the mechanisms underlying PSU–sleep problems relationship. According to the CHBs model, 35 CHBs constitute a coping strategy to deal with uncomfortable feelings of cognitive dissonance when individuals with long-term health goals engage in unhealthy behaviors. Emerging adults with PSU tend to spend more time on their smartphones, especially when they have sleep conflicts. The cognitive dissonance between smartphone use and sleep activates sleep-related CHBs. Compensatory beliefs negatively impact sleep behaviors, with procrastination during sleep time (attention diverted to other affairs) and more sleep problems in the long run.

The findings of this study demonstrated that bedtime procrastination serves as a mediator between PSU and sleep problems. Previous research has identified the role of bedtime procrastination in the relationship between PSU and sleep quality. However, these studies either involved nonemerging adult samples from Western countries46,47 or had certain limitations in the measurement of sleep problems. 48 Our study used the complete PSQI with seven dimensions. Emerging adults with higher PSU prefer to spend more time on their smartphones, which makes them unable to control their use patterns successfully, even before or during bedtime. 48 Therefore, they tend to develop a higher level of bedtime procrastination (for smartphone use), which further contributes to sleep problems. Additionally, this study found that the relationship between PSU and sleep problems is mediated through a serial path of sleep-related CHBs and bedtime procrastination; this highlights the underlying mechanism of the relationship between PSU and sleep problems. Emerging adults exhibiting PSU are more likely to develop sleep-related CHBs, which allows them to indulge in more smartphone use before bedtime, with longer procrastination, as they prefer to believe that the negative effects of bedtime procrastination related to PSU may be compensated for by getting up late the next day or sleeping more during weekends. However, compensatory sleep behaviors occur less or the negative consequences of bedtime procrastination are difficult to compensate for. 11 As a result, these individuals exhibit higher levels of bedtime procrastination and are more susceptible to experiencing additional sleep problems.

This study has theoretical implications, as it extends the applications of the CHBs model to explain the risk factors of sleep problems. This study further identified risk factors associated with sleep problems, including PSU, bedtime procrastination, and sleep-related CHBs. Specifically, it clarified the mediating influence of sleep-related CHBs and bedtime procrastination on the relationship between PSU and sleep problems in emerging adults. In terms of practical implications, the findings not only strongly support the positive correlation between PSU and sleep problems among emerging adults, but also reveal the effects of sleep-related CHBs and bedtime procrastination. Interventions related to lower bedtime procrastination will be effective in decreasing sleep problems among emerging adults (e.g. using mental contrasts with implementation intentions). 63 And interventions can be considered to influence the development of CHBs according to the CHBs model (e.g. improving self-efficacy), 35 decreasing compensatory beliefs or facilitating the implementation of compensatory behaviors (e.g. making action plans). 64 Moreover, cognitive behavioral therapy for insomnia is a possible method for treating sleep problems. 65

This study had certain limitations. First, there was subjective bias in the self-reported method for measuring PSU and sleep problems. Further studies should consider other objective methods (e.g. smartphone program recording). Second, the sample in this study was recruited by convenience sampling, which limits the generalizability of the findings. Therefore, future research should test findings in more samples from emerging adults. Third, this cross-sectional study design is limited to testing reciprocal or causal effects of PSU on sleep-related beliefs, behaviors, or problems; thus, longitudinal or experimental research ought to be conducted to explore the interrelationships or causal relationships among these variables. Lastly, one study showed that children aged 3 to 5 years had a PSU tendency with adverse consequences for cognitive development and mental health. 66 Future studies on PSU should also expand studies on multiple samples, including children.

Conclusions

This current study found the status quo of sleep problems and a positive link between PSU and sleep problems among Chinese emerging adults. Based on the CHBs model, this study discovered that sleep-related CHBs were negatively correlated with sleep problems and further investigated the psychological mechanisms between PSU and sleep problems, specifically bedtime procrastination alone or a serial path of sleep-related CHBs and bedtime procrastination underlying the PSU–sleep problem relationship. This study extends the application of the CHBs model to unhealthy behaviors (i.e. sleep problems). To promote sleep health in emerging adults, more interventions should be employed to reduce bedtime procrastination and sleep-related CHBs, or to promote the implementation of compensatory behaviors. Furthermore, cognitive behavioral therapy for insomnia is a possible treatment method for sleep problems.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076241283338 - Supplemental material for Relationship between problematic smartphone use and sleep problems: The roles of sleep-related compensatory health beliefs and bedtime procrastination

Supplemental material, sj-docx-1-dhj-10.1177_20552076241283338 for Relationship between problematic smartphone use and sleep problems: The roles of sleep-related compensatory health beliefs and bedtime procrastination by Yandong An and Meng Xuan Zhang in DIGITAL HEALTH

Acknowledgment

The authors would like to thank for the researchers’ permission regarding the questionnaires used in this study.

Authors’ contribution: YA contributed to formal analysis, methodology, and writing—original draft. MXZ was involved in conceptualization, writing—review & editing, supervision, and project administration. Both the authors contributed to and approved the final manuscript.

Data availability statement: Data can be downloaded here: https://osf.io/tx3d5/?view_only=e1a71afbc93242338ab2f94608eb1fba.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethic statement: Ethical approval was granted by the Ethics Committee of the Department of Medical Humanities (#20220902).

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was supported by the research grants from the Fundamental Research Funds for the Central Universities (4013002306, 3213002308A2, and 3213002406A2), and Liberal Arts Enhancement Program of Southeast University (4060692203/003).

ORCID iD: Meng Xuan Zhang https://orcid.org/0000-0003-1648-732X

Supplemental material: Supplemental material for this article is available online.
==== Refs
References

1 Bruce ES Lunt L McDonagh JE . Sleep in adolescents and young adults. Clin Med 2017; 17 : 424.
2 Wolfson AR . Adolescents and emerging adults’ sleep patterns: new developments. J Adolesc Health 2010; 46 : 97–99.20113914
3 Becker SP Jarrett MA Luebbe AM , et al. Sleep in a large, multi-university sample of college students: sleep problem prevalence, sex differences, and mental health correlates. Sleep Health 2018; 4 : 174–181.29555131
4 Hwang E Shin S . Prevalence of sleep disturbance in Korean university students: a systematic review and meta-analysis. Korean J Health Promot 2020; 20 : 49–57.
5 Chen YM Zhang YL Yu GL . A meta-analysis of the detection rates of mental health problems among college students in mainland China from 2010 to 2020. Adv Psychol Sci 2022; 5 : 991–1004.
6 Buysse DJ . Sleep health: can we define it? Does it matter? Sleep 2014; 37 : 9–17.24470692
7 Gawlik K Melnyk BM Tan A , et al. Heart checks in college-aged students link poor sleep to cardiovascular risk. J Am Coll Health 2019; 67 : 113–122.29652617
8 Sa J Choe S Cho B , et al. Relationship between sleep and obesity among U.S. and South Korean college students. BMC Public Health 2020; 20 : 96.31969131
9 Ozcan B Acimis NM . Sleep quality in Pamukkale university students and its relationship with smartphone addiction. Pak J Med Sci 2021; 37 : 206–211.33437278
10 Yang J Fu X Liao X , et al. Association of problematic smartphone use with poor sleep quality, depression, and anxiety: a systematic review and meta-analysis. Psychiatry Res 2020; 284 : 112686.31757638
11 Knäuper B Rabiau M Cohen O , et al. Compensatory health beliefs: scale development and psychometric properties. Psychol Health 2004; 19 : 607–624.
12 Ruckwongpatr K Chirawat P Ghavifekr S , et al. Problematic internet use (PIU) in youth: a brief literature review of selected topics. Curr Opin Behav Sci 2022; 46 : 101150.
13 Huang YT Ruckwongpatr K Chen JK , et al. Specific internet disorders in university students in Taiwan and Hong Kong: psychometric properties with invariance testing for the traditional Chinese version of the assessment of criteria for specific internet-use disorders (ACSID-11). Int J Ment Health Addict 2024: 1–34.
14 Yang YN Su JA Pimsen A , et al. Validation of the Thai assessment of criteria for specific internet-use disorders (ACSID-11) among young adults. BMC Psychiatry 2023; 23 : 819.37940885
15 Elhai JD Dvorak RD Levine JC , et al. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disord 2017; 207 : 251–259.27736736
16 Panova T Carbonell X . Is smartphone addiction really an addiction? J Behav Addict 2018; 7 : 252–259.29895183
17 Tan CNL . Toward an integrated framework for examining the addictive use of smartphones among young adults. Asian J Soc Health Behav 2023; 6 : 119–125.
18 Candussi CJ Kabir R Sivasubramanian M . Problematic smartphone usage, prevalence and patterns among university students: a systematic review. J Affect Disord Rep 2023; 14 : 100643.
19 Candussi C Sivasubramanian M Parsa AD , et al. Estimating prevalence and patterns of problematic smartphone use among nursing and public health students: a cross-sectional investigation. J Popul Ther Clin Pharmacol 2023; 30 : 1417–1427.
20 Quaglieri A Biondi S Roma P , et al. From emotional (Dys) regulation to internet addiction: a mediation model of problematic social media use among Italian young adults. J Clin Med 2021; 11 : 188.35011929
21 Varchetta M González-Sala F Mari E , et al. Psychosocial risk factors of technological addictions in a sample of Spanish university students: the influence of emotional (dys) regulation, personality traits and fear of missing out on internet addiction. Psychiatry Res 2023; 329 : 115518.37826975
22 Alimoradi Z Broström A Potenza MN , et al. Associations between behavioral addictions and mental health concerns during the COVID-19 pandemic: a systematic review and meta-analysis. Curr Addict Rep 2024; 11 : 1–23.
23 Chen CY Lee KY Fung XC , et al. Problematic use of internet associates with poor quality of life via psychological distress in individuals with ADHD. Psychol Res Behav Manage 2024; 17 : 443–455.
24 Phetphum C Keeratisiroj O Prajongjeep A . The association between mobile game addiction and mental health problems and learning outcomes among Thai youths classified by gender and education levels. Asian J Soc Health Behav 2023; 6 : 196–202.
25 Wong HY Mo HY Potenza MN , et al. Relationships between severity of internet gaming disorder, severity of problematic social media use, sleep quality and psychological distress. Int J Environ Res Public Health 2020; 17 : 1879.32183188
26 Chang KC Chang YH Yen CF , et al. A longitudinal study of the effects of problematic smartphone use on social functioning among people with schizophrenia: mediating roles for sleep quality and self-stigma. J Behav Addict 2022; 11 (2 ): 567–576.35394922
27 Saffari M Chang KC Chen JS , et al . Sleep quality and self-stigma mediate the association between problematic use of social media and quality of life among people with schizophrenia in Taiwan: a longitudinal study. Psychiatry Investig 2023; 20 :1034.
28 Saffari M Chen HP Chang CW , et al. Effects of sleep quality on the association between problematic internet use and quality of life in people with substance use disorder. BJPsych Open 2022; 8 (5 ): e155.
29 Sohn SY Krasnoff L Rees P , et al. The association between smartphone addiction and sleep: a UK cross-sectional study of young adults. Front Psychiatry 2021; 12 : 629407.33737890
30 Lane HY Chang CJ Huang CL , et al. An investigation into smartphone addiction with personality and sleep quality among university students. Int J Environ Res Public Health 2021; 18 : 7588.34300037
31 Zhang J Yuan G Guo H , et al. Longitudinal association between problematic smartphone use and sleep disorder among Chinese college students during the COVID-19 pandemic. Addict Behav 2023; 144 : 107715.37059002
32 Zhang MX Zhou H Yang HM , et al. The prospective effect of problematic smartphone use and fear of missing out on sleep among Chinese adolescents. Curr Psychol 2023; 42 : 5297–5305.
33 Oh JH Yoo H Park HK , et al. Analysis of circadian properties and healthy levels of blue light from smartphones at night. Sci Rep 2015; 5 : 11325.26085126
34 Chang AM Aeschbach D Duffy JF , et al. Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc Natl Acad Sci U S A 2015; 112 : 1232–1237.25535358
35 Rabia M Knäuper B Miquelon P . The eternal quest for optimal balance between maximizing pleasure and minimizing harm: the compensatory health beliefs model. Br J Health Psychol 2006; 11 : 139–153.16480560
36 Radtke T Kaklamanou D Scholz U , et al. Are diet-specific compensatory health beliefs predictive of dieting intentions and behaviour? Appetite 2014; 76 : 36–43.24472827
37 Amrein MA Scholz U Inauen J . Compensatory health beliefs and unhealthy snack consumption in daily life. Appetite 2021; 157 : 104996.33058952
38 Matley FAI Davies EL . Resisting temptation: alcohol specific self-efficacy mediates the impacts of compensatory health beliefs and behaviours on alcohol consumption. Psychol Health Med 2018; 23 : 259–269.28793802
39 Paulus AM Aziz A . Binge watching, compensatory health beliefs and academic procrastination among university students. J Behav Sci 2023; 33 : 25.
40 Feng B Sun W . Bedtime procrastination and fatigue in Chinese college students: the mediating role of mobile phone addiction. Int J Ment Health Addict 2023; 21 : 3362–3375.
41 Chung SJ An H Suh S . What do people do before going to bed? A study of bedtime procrastination using time use surveys. Sleep 2020; 43 : 267.
42 Kroese FM De Ridder DTD Evers C , et al. Bedtime procrastination: introducing a new area of procrastination. Front Psychol 2014; 5 : 611.24994989
43 Ma X Meng D Zhu L , et al. Bedtime procrastination predicts the prevalence and severity of poor sleep quality of Chinese undergraduate students. J Am Coll Health 2022; 70 : 1104–1111.32669056
44 Hill VM Rebar AL Ferguson SA , et al. Go to bed! A systematic review and meta-analysis of bedtime procrastination correlates and sleep outcomes. Sleep Med Rev 2022; 66 : 101697.36375334
45 Cui G Yin Y Li S , et al. Longitudinal relationships among problematic mobile phone use, bedtime procrastination, sleep quality and depressive symptoms in Chinese college students: a cross-lagged panel analysis. BMC Psychiatry 2021; 21 : 449.34507561
46 Bozkurt A Demirdöğen EY Akıncı MA . The association between bedtime procrastination, sleep quality, and problematic smartphone use in adolescents: a mediation analysis. Eurasian J Med 2024; 56 (1 ): 69–75.39128061
47 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 : 839.37887489
48 Zhang MX Wu AMS . Effects of smartphone addiction on sleep quality among Chinese university students: the mediating role of self-regulation and bedtime procrastination. Addict Behav 2020; 111 : 106552.32717501
49 Buysse DJ Reynolds CF Monk TH , et al. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res 1989; 28 : 193–213.2748771
50 Liu X Tang M Hu L , et al. Reliability and validity study of the Pittsburgh Sleep Quality Index in Chinese. Chin J Psychiatry 1996; 2 : 103–107.
51 Kwon M Kim DJ Cho H , et al. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS ONE 2013; 8 (12 ): e83558.
52 Luk TT Wang MP Shen C , et al. Short version of the Smartphone Addiction Scale in Chinese adults: psychometric properties, sociodemographic, and health behavioral correlates. J Behav Addict 2018; 7 : 1157–1165.30418073
53 Zhang H Zhang K Zhang S . Reliability and validity of the Chinese version of the Health Compensation Beliefs Scale. Psychol Technol Appl 2022; 11 : 663–672.
54 Ma X Zhu L Guo J , et al. Reliability and validity of the Chinese version of the Sleep Procrastination Scale in college students. Chin J Clin Psychol 2021; 4 : 717–720.
55 Arend MG Schäfer T . Statistical power in two-level models: a tutorial based on Monte Carlo simulation. Psychol Methods 2019; 24 : 1.30265048
56 Thoemmes F MacKinnon DP Reiser MR . Power analysis for complex mediational designs using Monte Carlo methods. Struct Equ Model 2010; 17 : 510–534.
57 Cohen J . Statistical power analysis for the behavioral sciences. New York: Routledge, 2013.
58 Rosseel Y . Lavaan: an R package for structural equation modeling. J Stat Softw 2012; 48 : 1–36.
59 Schreiber JB Nora A Stage FK , et al. Reporting structural equation modeling and confirmatory factor analysis results: a review. J Educ Res 2006; 99 : 323–338.
60 Li L Wang YY Wang SB , et al. Sleep duration and sleep patterns in Chinese university students: a comprehensive meta-analysis. J Clin Sleep Med 2017; 13 : 1153–1162.28859718
61 Li L Lok KI Mei SL , et al. Sleep duration and self-rated health in Chinese university students. Sleep Breath 2019; 23 : 1351–1356.31152382
62 Zhou SJ Wang LL Yang R , et al. Sleep problems among Chinese adolescents and young adults during the coronavirus-2019 pandemic. Sleep Med 2020; 74 : 39–47.32836185
63 Valshtein TJ Oettingen G Gollwitzer PM . Using mental contrasting with implementation intentions to reduce bedtime procrastination: two randomised trials. Psychol Health 2019; 35 : 1–27.31204484
64 Storm V Reinwand D Wienert J , et al. Brief report: compensatory health beliefs are negatively associated with intentions for regular fruit and vegetable consumption when self-efficacy is low. J Health Psychol 2017; 22 : 1094–1100.26826167
65 Alimoradi Z Jafari E Broström A , et al. Effects of cognitive behavioral therapy for insomnia (CBT-I) on quality of life: a systematic review and meta-analysis. Sleep Med Rev 2022; 64 : 101646.35653951
66 Abdulla F Hossain MM Huq MN , et al. Prevalence, determinants and consequences of problematic smartphone use among preschoolers (3–5 years) from Dhaka, Bangladesh: a cross-sectional investigation. J Affect Disord 2023; 329 : 413–427.36858268
