Reduced Health-Related Quality of Life Due to Mobile Phone Dependence in a Sample of Chinese College Students: The Mediating Role of Chronotype and Sleep Quality

Abstract

Purpose

Presenting a chain mediation model to investigate whether mobile phone dependence results in a reduction in health-related quality of life (HRQoL) among Chinese college students, through the mediating effect of chronotype and sleep quality.

Design and Setting

A cross-sectional survey was conducted on students from a Chinese university using a validated structured questionnaire.

Sample

2014 freshmen.

Measures

The study measured the students’ level of mobile phone dependence using the Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use. Chronotype and sleep quality were measured by the Chinese version of the Morningness-Eveningness Questionnaire (MEQ) and the Pittsburgh Sleep Quality Index (PSQI), respectively. HRQoL was evaluated using the five-level EuroQol five-dimensional questionnaire (EQ-5D-5L), including a descriptive system and a visual analog scale (VAS).

Analysis

Descriptive statistical analysis, correlation analysis, and mediation analysis.

Results

Mobile phone dependence had a significant negative effect on HRQoL as indicated by both the EQ-5D-5L index score and EQ-VAS score (P < .001 for both). Additionally, it was found to significantly predict chronotype (MEQ score) (β = −.546, P < .001) and sleep quality (PSQI score) (β = .163, P < .001). Chronotype negatively predict sleep quality (β = −.058, P < .001), and sleep quality was a significant negative predictor of HRQoL (EQ-5D-5L index score, β = −.008, P < .001; EQ-VAS score, β = −1.576, P < .001).

Conclusion

Mobile phone dependence negatively impacts students’ HRQoL through chronotype and sleep quality, and there is a chain mediating effect. Students should consider making lifestyle changes to improve their HRQoL and promote health.

Keywords

mobile phone dependence health-related quality of life chronotype sleep quality college students mediation analysis health promotion

Purpose

The rapid development of information and communication technology has led to the widespread popularity of mobile phones, which have gradually become an indispensable part of many people’s lives. However, some individuals may develop a dependence on their mobile phones. For instance, they may feel anxious if they do not check their phones for a while. Mobile phone dependence, also known as smartphone addiction or mobile phone addiction,¹ is becoming a global public health concern that is receiving increasing attention.²

Mobile phone dependence is a significant problem among young people, particularly college students. A report states that 25% of Chinese university students experience mobile phone addiction.³ Those who suffer from this addiction often experience mental health issues, increase their screen time and sedentary behavior, and decrease their physical exercise.^4,5 Additionally, studies have shown that mobile phone addiction or internet addiction can lead to shorter nighttime sleep duration or poor sleep quality.^6,7

Excessive use of mobile phones can increase the risk of mobile phone addiction, which can lead to symptoms similar to those of internet addiction.⁸ These addictive behaviors can affect people’s mood, sleep (both chronotype and sleep quality), and reduce their time to engage in other activities, thereby affecting their health-related quality of life (HRQoL), a multidimensional outcome measure that includes physical health, mental health, and social functioning.⁹ Given the high prevalence of mobile phone addiction and its negative impact on HRQoL, it is crucial to investigate the factors and mechanisms that influence HRQoL among college students.

Mobile Phone Dependence and HRQoL

Numerous studies have demonstrated that dependence on mobile phones can result in not only psychological distress, such as anxiety, irritability, sleep disorders, depression, and insomnia, but also an unhealthy lifestyle, thereby impairing individuals’ physical health, mental health, and social functioning.^10-13 A study conducted in Korea found a significant positive correlation between overuse of the internet, games, or smartphones (IGS) and stress, depression, and suicidal ideation.¹⁴ 11.23% of participants reported impairment in their daily activities due to IGS overuse, which led to a decline in the HRQoL of participants aged 19 to 64 years.¹⁴ The China Internet Network Information Center (CINIC) reported that there were 854 million internet users in Mainland China in 2019.¹⁵ Of these, 99.1% accessed the internet via mobile phones, with the highest proportion of users (24.6%) being aged between 20 and 29.¹⁵ On average, Chinese college students spend over 5 hours per day on their phones, and approximately 79% of students use their phones during class.¹⁶ A study conducted among Chinese university students found that excessive mobile phone use can have an adverse impact on their quality of life.¹⁷ Students who were addicted to mobile phone use had significantly lower scores in all domains of quality of life, including physical, psychological, social and environmental quality of life, as measured by the brief version of the World Health Organization Quality of Life.¹⁷ Similar findings were also reported in a study conducted among university students in India.¹⁸

The Potential Mediating Role of Chronotype

Chronotype is an important indicator in evaluating an individual’s circadian rhythm.¹⁹ It reflects the periodic changes in human biological and physiological processes over approximately 24 hours and is essential for human well-being.^20,21 A study conducted among Russian adolescents found that those who were addicted to the internet experienced significant disturbances in the quality of their nighttime sleep and excessive sleepiness during the day, suggesting that internet addiction can affect the circadian rhythm and chronotype.⁶ Morning-oriented adolescents reported significantly higher HRQoL and fewer symptoms of insomnia compared to evening-oriented chronotypes.²² Therefore, chronotype may mediate the impact of mobile phone dependence on HRQoL in college students.

The Potential Mediating Role of Sleep Quality

Sleep quality and quantity are crucial for an individual’s health.²³ According to a meta-analysis, college students who suffer from mobile phone addiction are more likely to experience high levels of anxiety, depression, impulsivity, and poor sleep quality.²⁴ A population-based study conducted in Korea found that the group with poor sleep quality had significantly lower mean EQ-5D-3L index scores than the group with good sleep quality, indicating a close association between sleep quality and HRQoL.²⁵ Additionally, good sleep quality has been reported to be moderately correlated with high values of general health and HRQoL.²⁶ Therefore, sleep quality may also act as a mediating variable in the impact of mobile phone dependence on HRQoL among college students.

The Potential Chain Mediating Role of Chronotype and Sleep Quality

Previous studies have indicated that individuals with an evening chronotype experience lower sleep quality. A study conducted among undergraduates found a significant relationship between chronotypes and sleep quality.²⁷ Individuals who preferred evening-type experienced worse sleep quality compared to morning and intermediate types.²⁷ The average Pittsburgh Sleep Quality Index (PSQI) total score of the evening chronotype students was higher than that of the intermediate and morning chronotypes, indicating that their sleep was disturbed.²⁸ Therefore, it is assumed that the relationship between mobile phone dependence and HRQoL might be influenced first by chronotype and then by sleep quality, with a possible chain mediating effect.

Purpose of the Current Study

Previous studies have suggested that mobile phone dependence may have a negative impact on HRQoL. However, the mechanism behind this relationship has not been fully elucidated. The aim of this study is to explore the relationship between these 4 variables in greater depth and provide a theoretical framework for understanding how mobile phone dependence affects HRQoL. The study proposes a theoretical model (Figure 1) with 4 hypotheses: (1) mobile phone dependence can negatively predict HRQoL of college students; (2) mobile phone dependence can indirectly predict HRQoL of college students through the mediating role of chronotype; (3) mobile phone dependence can indirectly predict HRQoL of college students through the mediating role of sleep quality; (4) mobile phone dependence can indirectly predict HRQoL of college students through the chain mediation of chronotype and sleep quality, implying that it first affects chronotype, followed by sleep quality, and ultimately affects HRQoL.

Figure 1.

Hypothesized model of the chain mediation between mobile phone dependence, chronotype, sleep quality and HRQoL.

Methods

Design

This study was conducted in 2021 at Dali University in Yunnan, China. Yunnan is a province located in southwestern China and has the largest number of ethnic groups in the country, demonstrating excellent ethnic diversity. A structured questionnaire was designed to investigate demographic and health-related behavioral information, mobile phone dependence, chronotype, sleep quality, and HRQoL of participating students. To conduct the survey, a staff manual was developed in consultation with relevant experts from Soochow University.²⁹ Trained investigators were then required to conduct the survey in accordance with the manual. The investigators were also required to have sufficient expertise to deal with any emergencies that may arise on site. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Affiliated Hospital of Yunnan University (No. 2021040).

Sample

The study participants were 2698 freshmen at the University. Each participant was sent a link to an online questionnaire by the investigators at the campus hospital. The first page of the questionnaire provided a brief description of the study and informed consent. Participants were given the option to participate in the survey and could withdraw at any stage. The study’s inclusion criteria comprised of students who provided consent for participation and were under the age of 26. The quality of the data was thoroughly checked to exclude those with missing or incomplete answers, as well as contradictory responses. Ultimately, the study included a total of 2014 freshmen who completed a questionnaire meeting the quality standards. The database was created using EpiData 3.1 software and a double-entry method was employed to ensure input accuracy.

Measures

Demographic and Health-Related Behavioral Characteristics

The demographic characteristics of the participants collected included gender (ie, male or female), age, ethnic group (ie, Han or non-Han), registered residence (ie, town or village), and self-reported family financial situation (ie, bad, normal, or good). We also investigated their smoking and drinking alcohol status in the past month (ie, whether they smoked at least 1 day or drank alcohol at least once).

Mobile Phone Dependence

The level of mobile phone dependence was measured using the Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use. It consists of 13 items: 6 for mobile phone withdrawal symptoms, 3 for mobile phone craving, and 4 for the physical and psychological effects of mobile phone dependence.³⁰ Each item provides 2 response options: yes (1) or no (0). The total score ranges from 0 to 13, with a higher total score indicating a higher level of mobile phone dependence. The reliability and validity of the questionnaire has been demonstrated in China,³⁰ and it also showed good reliability (Cronbach’s α = .848) in the study sample.

Chronotype

The study participants’ chronotype preferences were measured using the Chinese version of the Morningness-Eveningness Questionnaire (MEQ),³¹ which has been shown to have good reliability and validity in the Chinese population.³² The questionnaire consists of 19 items that assess participants’ habitual wakefulness and sleep time, preference time for physical and mental performance, subjective alertness upon awakening, and before initiating sleep.³³ The total score ranges from 16 to 86, with a higher or lower score indicating a preference for morningness or eveningness, respectively.³⁴ Based on the total score, chronotype can be classified into 3 categories: evening chronotype (16-41), intermediate chronotype (42-58), and morning chronotype (59-86).²⁹ The Cronbach’s α in this study was .675.

Sleep Quality

The sleep quality of the participants was assessed using the Chinese version of the PSQI, which has been validated and proven reliable in China.³⁵ The PSQI measures self-reported sleep quality and disturbances experienced by participants in the month prior to the survey.³⁶ It consists of 19 items distributed across 7 components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disorders, use of medication to sleep, and daytime dysfunction. Each component is scored on a range of 0 to 3, with a total score range of 0 to 21. A lower total score indicates better sleep quality. The Cronbach’s α for this study was .788.

Health-Related Quality of Life

The five-level EuroQol five-dimensional questionnaire, ie, EQ-5D-5L (5L) was used to measure the participants’ HRQoL. It comprises a descriptive system and a visual analog scale (VAS).³⁷ The descriptive system consists of 5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension has 5 functioning levels: no problems, mild problems, moderate problems, severe problems, and very severe problems. The study converted participants’ responses to the 5 dimensions into 5L index scores using the Chinese 5L value set. The resulting 5L index scores range from −.391 (the worst state) to 1 (full health).³⁸ The EQ-VAS is a vertical visual analogue scale that provides a quantitative description of participants’ overall health perceptions.³⁷ A score of zero indicates the worst health status, while a score of 100 indicates the best health status that the participant could imagine. Both the 5L index score and the EQ-VAS score can be used to assess the current HRQoL of the participants. The reliability and validity of the Chinese version of the 5L were found to be good in the Chinese population.^39,40 The Cronbach’s α for the 5L in this study was .696.

Analysis

Data analysis was performed using SPSS 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistical analyses were conducted on all variables, including means and standard deviations (SD) for quantitative variables and percentages for qualitative variables. Pearson correlation analysis was used to explore the associations among mobile phone dependence, chronotype, sleep quality, and HRQoL (5L index score/EQ-VAS score). To test the hypotheses, mediation analysis was carried out using SPSS PROCESS version 3.5 with model 6. To examine the mediating role of chronotype and sleep quality in the relationship between mobile phone dependence and the 5L index score or the EQ-VAS score, 2 models (model 1 and model (2) were constructed. The total indirect effect comprised 3 indirect paths: path 1 - mobile phone dependence → chronotype → HRQoL, path 2 - mobile phone dependence → sleep quality → HRQoL, and path 3 - mobile phone dependence → chronotype → sleep quality → HRQoL. Indirect effects were calculated using a bias-corrected bootstrapping procedure. If the 95% confidence interval (CI) did not include 0, it indicated a significant mediation effect.⁴¹ In order to control the possible impact of demographic factors (ie, gender, age, ethnic group, registered residence and self-reported family financial situation) and health-related behaviors (ie, smoking and drinking alcohol status) on the relationships, these were included as covariates in the models. To diagnose multicollinearity in the regression models, we calculated the Variance Inflation Factor (VIF) values of the variables. We also calculated the coefficients of determination (R²) for the regression models to indicate the degree to which the data fit the models.

Results

Participant Characteristics

Table 1 presents the demographic characteristics of the participants. The majority of participants were female (68.4%) and belonged to the Han ethnic group (74.9%). The mean (SD) age was 19.0 (.9), ranging from 15 to 25 years. 34.4% of the participants were from towns, and the rest were from villages. More than half of the participants (56.2%) considered their family’s financial situation to be at a normal level. Only a small proportion of participants reported smoking at least 1 day (5.9%) or drinking alcohol at least once (17.7%) in the past month.

Table 1.

Demographic and Health-Related Behavioral Characteristics of Participants.

Variable	n = 2014	%
Gender
Male	637	31.6
Female	1377	68.4
Ethnic group
Han	1509	74.9
Non-Han	505	25.1
Age (years)
15∼17	220	10.9
18∼20	1742	86.5
21∼25	52	2.6
Registered residence
Town	693	34.4
Village	1321	65.6
Self-reported family financial situation
Bad	821	40.8
Normal	1131	56.2
Good	62	3.1
Smoking^a
Yes	118	5.9
No	1896	94.1
Drinking alcohol^a
Yes	357	17.7
No	1657	82.3

^aSmoking and drinking alcohol refer to whether the participants have smoked at least 1 day or drunk alcohol at least once during the past month.

Descriptive Analysis and Correlations Between the Variables

Table 2 shows the descriptive statistics and bivariate correlation data for the study variables. The mean total scores for mobile phone dependence, chronotype (as measured by the MEQ score), sleep quality (as measured by the PSQI score), the 5L index score, and the EQ-VAS score were 3.2 ± 3.2, 52.2 ± 7.3, 4.5 ± 2.4, .96 ± .08, and 82.0 ± 17.0, respectively. All variables are significantly correlated with each other. The study found that mobile phone dependence had a negative correlation with chronotype (MEQ score) (r = −.239) and HRQoL (5L index score, r = −.262; EQ-VAS score, r = −.211), and a positive correlation with sleep quality (PSQI score) (r = .277). Additionally, chronotype was found to have a negative association with sleep quality (r = −.230).

Table 2.

Correlation Analysis Among the Study Variables.

	Mean (SD)	1	2	3	4	5
1. Mobile phone dependence	3.2 (3.2)	1.000
2. Chronotype (MEQ score)	52.2 (7.3)	−.239***	1.000
3. Sleep quality (PSQI score)	4.5 (2.4)	.277***	−.230***	1.000
4. EQ-5D-5L index score	.96 (.08)	−.262***	.084***	−.281***	1.000
5. EQ-VAS score	82.0 (17.0)	−.211***	.134***	−.275***	.229***	1.000

***P < .001.

Analysis of Chain Mediating Effects

Table 3 and Figures 2 and 3 present the results of the mediating effects of chronotype and sleep quality on the relationship between mobile phone dependence and HRQoL, while controlling for covariates. The determination coefficients (R²) of the regression models ranged from .050 to .129. Furthermore, there is no issue of multicollinearity as all variable VIF values were less than 1.5. Mobile phone dependence had a significant negative predictive effect on HRQoL (5L index score, β = −.007; EQ-VAS score, β = −1.100, P < .001 for both), which supported hypothesis 1. When chronotype and sleep quality were included in the regression equation, mobile phone dependence was found to significantly predict chronotype (β = −.546, P < .001) and sleep quality (β = .163, P < .001). Chronotype significantly predicted sleep quality (β = −.058, P < .001) and had a positive predictive effect on the EQ-VAS score (β = .132, P = .014), but there was no statistically significant effect on the 5L index score. Additionally, sleep quality was found to be a negative predictor of HRQoL (5L index score, β = −.008, P < .001; EQ-VAS score, β = −1.576, P < .001). At this point, the impact of mobile phone dependence on HRQoL is diminished (5L index score, β = −.005, P < .001; EQ-VAS score, β = −.722, P < .001). These results indicated that mobile phone dependence can significantly negatively predict the EQ-VAS score through the mediating effect of chronotype and sleep quality, as well as the chain mediating effect of chronotype to sleep quality, confirming hypotheses 2-4. However, the single mediating effect of chronotype was not found to be significant for the 5L index score.

Table 3.

Regression Analysis of the Relationship Between Mobile Phone Dependence and HRQoL Assessed by EQ-5D-5 L Index Score and EQ-VAS Score.

Outcome variable	Predictive Variable	R	R ²	F	β	t	P	VIF
EQ-5D-5L index score	Mobile phone dependence	.286	.082	22.338	−.0068	−11.708	<.001	1.019
EQ-VAS score	Mobile phone dependence	.224	.050	13.189	−1.1004	−9.309	<.001	1.019
Chronotype	Mobile phone dependence	.339	.115	32.424	−.5464	−11.219	<.001	1.019
Sleep quality	Mobile phone dependence	.358	.129	32.820	.1626	9.963	<.001	1.083
	Chronotype				−.0584	−8.048	<.001	1.129
EQ-5D-5L index score	Mobile phone dependence	.356	.127	29.046	−.0054	−8.995	<.001	1.136
	Chronotype				−.0003	−1.087	.277	1.166
	Sleep quality				−.0081	−10.113	<.001	1.147
EQ-VAS score	Mobile phone dependence	.317	.100	22.354	−.7217	−5.938	<.001	1.136
	Chronotype				.1318	2.457	.014	1.166
	Sleep quality				−1.5764	−9.707	<.001	1.147

Note: VIF, variance inflation factor. Controlling for gender, age, ethnic group, registered residence, self-reported family financial situation, smoking and drinking alcohol status. Chronotype and sleep quality were assessed using the MEQ score and the PSQI score, respectively.

Figure 2.

Model 1: Chain mediating effect of chronotype and sleep quality on the relationship between mobile phone dependence and HRQoL (EQ-5D-5 L index score). ***P < .001.

Figure 3.

Model 2: Chain mediating effect of chronotype and sleep quality on the relationship between mobile phone dependence and HRQoL (EQ-VAS score). *P < .05. ***P < .001.

The mediating effect values of the indirect paths in the 2 models were shown in Table 4. For model 1 (5L index score), the total indirect effect value was −.0014, accounting for 20.6% of the total effect value (−.0068). The ratios of the 3 indirect path effects to the total effect were −2.9%, 19.1%, and 4.4%, respectively. However, the indirect effect for path 1 did not reach a significant level. For model 2 (EQ-VAS score), the total indirect effect value (−.3786) accounted for 34.4% of the total effect value (−1.1004), with the ratios of the 3 indirect path effects to the total effect being 6.5%, 23.3%, and 4.6%, respectively. All of them were statistically significant.

Table 4.

Mediating Effect Analysis.

	Effect	Boot SE	Boot LLCI	Boot ULCI	% Of the Total Effect (%)
Model 1 (EQ-5D-5L index score)
Total indirect effect	−.0014	.0003	−.0020	−.0009	20.6
Indirect effect 1	.0002	.0001	−.0001	.0004	−2.9
Indirect effect 2	−.0013	.0002	−.0018	−.0009	19.1
Indirect effect 3	−.0003	.0001	−.0004	−.0002	4.4
Model 2 (EQ-VAS score)
Total indirect effect	−.3786	.0520	−.4886	−.2819	34.4
Indirect effect 1	−.0720	.0311	−.1328	−.0120	6.5
Indirect effect 2	−.2563	.0425	−.3469	−.1802	23.3
Indirect effect 3	−.0503	.0101	−.0725	−.0327	4.6

Note: Indirect effect 1: Mobile phone dependence→Chronotype→HRQoL;Indirect effect 2: Mobile phone dependence→Sleep quality→HRQoL;Indirect effect 3: Mobile phone dependence→Chronotype→Sleep quality→HRQoL. Boot SE: Bootstrap standard error; Boot LLCI and Boot ULCI: Lower limit and upper limit of 95% bootstrap confidence interval, respectively.

Discussion

This study employed a chain mediation model to investigate whether mobile phone dependence results in a reduction in HRQoL among college students in China. To the authors’ knowledge, this is the first study to consider the mediating role of both chronotype and sleep quality in this relationship, as well as to explore the chain mediating role between them. The study found that mobile phone dependence negatively predicted the HRQoL of the students. This effect was observed not only through the single mediating effect of chronotype and sleep quality but also through their chain mediating effect. However, the study did not find significant influence on the 5L index score through the single mediating effect of chronotype.

The study first confirmed that dependence on mobile phones affects the chronotype of college students and subsequently their HRQoL. The study found that students with a higher level of mobile phone dependence tended to develop an evening chronotype, which is consistent with a study conducted at a university in the United Arab Emirates.⁴² Compared to morning chronotype, evening chronotype would reduce vitality of adolescents, impact their physical well-being, psychological well-being, body image, relations with parents and teachers, and overall health.⁴³ The study found a positive association between chronotype and the EQ-VAS score, suggesting that a morning chronotype is more conducive to better HRQoL. The study has not yet found statistical significance in the effect of chronotype on the 5L index score, possibly due to the high ceiling effect (ie, 63.4%) of the 5L index score. This difference in effect on the EQ-VAS score and the EQ-5D index score has also been observed in other researches.^44,45

The study also analyzed the mediating role of sleep quality in mobile phone dependence and HRQoL. Sleep quality is a crucial aspect of HRQoL.⁴⁶ Previous studies have shown that mobile phone dependence can have a negative impact on sleep quality.^47,48 This may be due to the tendency of mobile phone-dependent individuals to stay up late, the interference of blue light, and the emotionally or physiologically stimulating effects of messages.⁴⁹ The current study confirms that mobile phone dependence is positively correlated with PSQI scores, indicating that high dependence on mobile phones can lead to sleep disturbance. The study found a negative correlation between PSQI scores and HRQoL. The mediating effect of sleep quality accounted for a larger proportion of the total indirect effect, indicating that decreased sleep quality is an important factor in the decrease of HRQoL caused by mobile phone dependence. This finding also supports the hypothesis that mobile phone dependence can predict the reduce of HRQoL.

Additionally, a chain mediating effect of chronotype and sleep quality on the association between mobile phone dependence and HRQoL was also demonstrated. Previous studies have shown a strong correlation between chronotype and sleep quality.²⁷ The evening chronotype has been found to result in poorer sleep quality among students compared to the morning chronotype.⁵⁰ This was confirmed in the study, which found a significant negative association between MEQ scores and PSQI scores. The chain mediating model suggests that mobile phone dependence may cause poor sleep quality due to staying up late, which can disrupt the sleep-wake schedule and affect the synthesis and secretion of melatonin in the brain.^51,52 When students rely heavily on mobile phones, they are more likely to develop an evening chronotype. This, in turn, can negatively impact their sleep quality, ultimately impairing their HRQoL.

In summary, this study has demonstrated the chain mediating role of chronotype and sleep quality in the reduction of HRQoL caused by mobile phone dependence among the students. The results provide a theoretical basis for college students to adjust their lifestyle and improve their HRQoL amidst the growing problem of mobile phone dependence. However, there are some limitations to this study. Firstly, the cross-sectional design used in this study did not allow for direct analysis of causal relationships between variables. Nevertheless, previous researches have shown a negative impact of mobile phone dependence on HRQoL.^14,53 Other studies investigating chronotype and sleep quality have found that mobile phone dependence may lead to a tendency to develop an evening chronotype and poorer sleep quality.^54,55 At the same time, chronotype and sleep quality were reported to have an important relationship with a person’s HRQoL,^22,25 and there may also be a strong association between them.²⁷ Hence, it can be hypothesized that chronotype and sleep quality may play a unidirectional chain mediating role in the decline in HRQoL due to mobile phone dependence among college students. Future longitudinal studies are still warranted to verify the causality. Secondly, the questionnaires used in this study were self-reported. More objective and sensitive indicators can be used for evaluation in future studies. Thirdly, all participants were freshmen from 1 university, which may limit the generalizability of the findings to other populations. Follow-up studies can use a larger scope of survey populations to make the findings more generalizable.

So What?

What is Already Known on This Topic?

Mobile phone dependence is a growing global public health concern, particularly among young people, especially college students. Previous studies have shown that mobile phone dependence can affect health-related quality of life (HRQoL) and may result in a reduction in HRQoL. However, the mechanism behind this has not been fully elucidated.

What Does This Article Add?

This study presents a chain mediation model and verifies the chain mediation role of chronotype and sleep quality in the relationship between mobile phone dependence and HRQoL among Chinese college students.

What are the Implications for Health Promotion Practice or Research?

The study indicates that chronotype and sleep quality mediate the reduction of HRQoL among the Chinese college students due to mobile phone dependence. Mobile phone dependence can cause the college students to develop an evening chronotype, which affects their sleep quality and ultimately leads to a decrease in HRQoL. This study presents a theoretical framework for understanding the impact of mobile phone dependence on HRQoL and provides recommendations for college students to modify their lifestyle, improve HRQoL, and promote health.

Supplemental Material

Supplemental Material - Reduced Health-Related Quality of Life Due to Mobile Phone Dependence in a Sample of Chinese College Students: The Mediating Role of Chronotype and Sleep Quality

Supplemental Material for Reduced Health-Related Quality of Life Due to Mobile Phone Dependence in a Sample of Chinese College Students: The Mediating Role of Chronotype and Sleep Quality by Zhi-Qi Ying, BSc, Dan-Lin Li, MSc, Gang Liang, MD, Zhi-Jian Yin, MSc, Yue-Zu Li, MSc, Rong Ma, MSc, Yu Qin, MSc, Ya-Jie Zheng, MBBS, Pei Wang, PhD, and Chen-Wei Pan, PhD in American Journal of Health Promotion.

Footnotes

Acknowledgments

The authors are deeply grateful to acknowledge all the students who participated in the study, as well as other staff members who contributed to data collection.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by The National Natural Science Foundation of China (Grant No. 82160204); and Joint Key Project of Yunnan Provincial Department of Science and Technology and Kunming Medical University on Applied Basic Research (Grant No. 202301AY070001-017).

Ethical Statement

ORCID iD

Zhi-Qi Ying

Supplemental Material

Supplemental material for this article is available online.

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