Cumulative risk factors, Problematic smartphone use and Depressive symptoms in Nursing Students：a cross-sectional study

doi:10.21203/rs.3.rs-3431987/v1

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Cumulative risk factors, Problematic smartphone use and Depressive symptoms in Nursing Students：a cross-sectional study

https://doi.org/10.21203/rs.3.rs-3431987/v1

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Background

No single risk factor is decisive in shaping an individual's healthy development. This study was to analyze that depressive symptoms are related to a cumulative risk index composed of individual, family and social variables among nursing students.

Methods

We recruited 1716 Chinese nursing students from 3 universities via a paperless survey that measured a set of individual, family and social risk factors of depressive symptoms.

Results

Regardless of the combination of factors, as the number of risk factors increased, the mean of depressive symptom scores also increased, with statistically significant difference, and the proportion of the normal group became lower, the proportion of different depressive symptoms severity groups showed an increasing trend(P <0.001). A high risk cluster of poor sleep quality combined with problematic smartphone use was associated with high levels of depressive symptom (P < 0.001).

Conclusion

The depressive symptoms of nursing students were positively related to the cumulative risk of individual, family and social variables. Among them, a cluster with poor sleep quality combined with problematic smartphone had the highest risk.

cumulative risk factors

depressive symptoms

cluster analysis

nursing students

Depression is a global public health problem that affects people of all ages around the world. The World Health Organization [1] has declared it to be the leading cause of disability in the world. Emerging adulthood (18-29 years), as a new life stage between adolescence and young adulthood, is a critical period for physical and psychological growth, which clearly increases the risk of mental health problems, including depressive symptoms from mild to diagnosed depression [2]. Nursing students is a population exhibiting risk factors for depressive symptoms, they are mostly female and aged 18 to 29 years [1,3]. McDermott et al. [4] recently found that approximately 20% of undergraduate nursing students across the United States screened positive for probable depression. The prevalence of depressive symptoms among nursing students in Hong Kong was 35.8% [5]. A recent longitudinal study of Chinese nursing students showed that the prevalence of depressive symptoms after 3 and 9.5 months of shift work was 24.1% and 22.7% respectively [6]. This rate was higher than for the general population of Chinese adolescents [7]. A meta-analysis of 27 cross-sectional studies from around the world revealed a high combined prevalence of depressive symptoms among nursing students at 34.0% [8]. The prevalence of depressive symptoms varied across age groups and geographical regions, with younger and Asian nursing students having a higher prevalence [8]. The onset of depressive symptoms in adolescence and emerging adulthood is associated with the persistence of depression in adulthood and the development of other comorbid psychiatric disorders throughout the life course [9]. For these reasons, studying risk factors for depressive symptoms in nursing students has major public health interest.

The advent of the digital age has made the association between smartphone use and depressive symptoms among emerging adults a hot topic of research in recent years [10-12]. It is difficult to avoid the conclusion from prior research that a positive association between problematic smartphone use (PSU) and depressive symptoms among college students exists [13-15]. PSU, also known as smartphone dependence or smartphone addiction, refers to the use of smartphones as a vehicle for individuals to become excessively addicted to certain activities. This leads to a persistent and intense sense of craving for and dependence on smartphones as well as to more pronounced problems with psychological and social functions [16]. PSU has a high detection rate among children and young people, ranging from 14.0% to 31.2% [17].

The mechanism of the association between PSU and depression is not clear. Is it the PSU itself, or is it the accumulation of PSU and other adverse individual, family and social factors found in all groups of mobile phone users? Adolescents’ behavior has typically been studied in terms of single causal models, in which the hypothesis is that a single variable is more or less the only determinant of the outcome. However, no single risk factor is decisive in shaping an individual's healthy development, and the effectiveness of interventions targeting only a single risk is greatly reduced [18]. A number of studies across a range of domain, such as cardiovascular disease [19] and heart disease studies [20] have found that combinations of risk factors best predict outcome variables, rather than their nature. Nevertheless, such multiple risk models have not been applied to the study of depressive symptoms in emerging adulthood.

In this study we intend to examine the relationship between a set of other individual, family and social risk factors for depression and depressive symptoms including PSU. Based on the literature review, these risk factors includes age [21], gender [21-23], monthly cost of living(<1000RMB, 1 000-1 500RMB, ≥1501RMB) [21,22], physical activity(low/mid/high) [24], sleep quality(good/bad) [25], and severe negative experience in the past year (facing big family trouble / be hospitalized / examination failure / break up in love) [26].

In the present study, for a sample of 1716 nursing freshmen from three medical universities in Anhui Province, China, we examined whether depressive symptom was best explained as only one function or by a compound score of individual, family and social risk factors. These variables are described in the method part.

1.1 Study design and participants

In this cross sectional study, also the first data wave in a longitudinal study, of Chinese nursing students, we obtained permissions from three universities’ management, then provided information documents including study descriptions, guidance for completing the questionnaire, consent forms, and a QR code for the paperless questionnaires to student counselors, who then distributed these to students. At the same time, volunteers were recruited in each class to help with the promotion of the project. The voluntary nature of participation without any conflict of interest were emphasized to participants. The time needed for completing the questionnaire was 10~20 minutes. All participants provided written informed consent.

1.2 Measurements/Instruments

1.2.1 Depressive symptoms

Depressive symptoms were measured by the Depression Anxiety Stress Scale 21 (DASS 21, validated Chinese version) [27]. This 21-item DASS assessed three dimensions of mental health symptoms over the past week, including depressive symptoms with 7 items focus on low self-esteem, low mood and a poor outlook for the future. Each item was scored on a Likert scale ranging from 0 to 3. (0 = did not apply at all over the last week, 1= applied to some degree, or some of the time; 2 = applied a considerable degree, or a good part of time; 3 = applied very much or most of the time), with a significant cutoff score of ≥ 10 for depressive symptoms. The summing scores of depression was categorized into the 5 severity levels, i.e. normal (0-9), mild (10-13), moderate (14-20), severe (21-27), and extremely severe (28+) [28]. The instrument is frequently used for assessing medical students [29] and Cronbach’s alpha of the Chinese version is 0.83 for the depression subscale [30]. For this study, the Cronbach’s coefficients alpha is 0.89.

1.2.2 Individual, family and social risk factors

Each risk factor was chosen based on the criteria that there was an empirical basis in the literature validating the variable’s potential impact on depressive symptoms outcomes. Potential risk factors included gender(male/female), number of friends (<3 / 3~5 / >5), and household income(low/medium/high), academic performance(poor/medium/excellent), critical negative life events in the last year (changes in family [yes/no] / broken up in love [yes/no] / hospitalizing [yes/no] / examination failure [yes/no]).

We adopted a standardized questionnaire—Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU) —found appropriate for evaluating PSU in adolescents [31]. SQAPMPU is focused on feelings or attitudes related to mobile phone use with each item describing a potential problem. It comprises 13 items in total, and cover three dimensions including withdrawal symptoms, craving and physical and mental health status—e.g., “I feel anxious if I have not checked for messages or switched on my mobile phone for some time”; “I need to spend more time on my mobile phone to feel satisfied”; “My productivity has decreased as a direct result of the time I spend on the mobile phone”. Responses to each item are on a five-point Likert scale (1 = not true at all, 2 = slightly true, 3 = moderately true, 4 = very true, and 5 = extremely true). The scores ranged between 13 and 65, with the 75th percentile (tell score here) used as the cutoff point.

The Chinese version of the Pittsburgh Sleep Quality Index (PSQI) [32] was used to assess the quality of sleep of university students in the last month.The PSQI consists of 19 entries covering sleep quality, time to fall asleep, sleep duration, sleep efficiency, sleep disorders, hypnotic medication use and daytime dysfunction. Each dimension is scored from 0 to 3. The total score ranges from 0 to 21, with a score of ≥8 indicating poor sleep quality [32]. The Cronbach alpha coefficient was 0.87 in the present study.

1.3 Statistical analysis

Data cleaning was performed before the data analysis, considering both range and consistence checks. A standard statistical package (SPSS 23 for windows, SPSS Inc.® headquarter, Chicago, USA) was used for analyses.

All of the risk factors were used in a prior study by our team [33].In this study, each of the eight risks variables was categorized into the high- and low-risk groups using the cut of scores. Among the eight discontinuous variables, household economic level as low, numbers of friends as less than 3, academic performance as poor, sleeping quality as bad, physical activity as low, PSU, having experienced hospitalization and breakup in the past year all were defined as having high risk. and the others as a low risk for each factor. Binary logistic regression was used to identify risk factors for depressive symptoms that were statistically significant.

Multiple risk analysis [34] was performed with the six factors that had a significant relationship with depressive symptoms to create a multiple risk score for the individual based on the total number of high-risk factors. This was done to produce a single, easily interpreted score that could group the participants for comparison on the outcome measure, i.e level of depressive symptoms. The scores of all high-risk factors were then summed to obtain a multiple risk score. Test for trend was used to ascertain the relation between the multiple risk classification and the depressive symptoms individually. Finally, a 2-step cluster analysis was used according to the Jaccard method [35] and χ² tests was used to test the relation between the different clusters and the level of depressive symptoms.

2.1 Study Population and the number of Risk Factors

The age of the participants ranged from 16 to 26 years (M = 18.90 ± 1.39), including 292 males (17.0%) and 1 424 females (83.0%).The overall prevalence of mild and above depressive symptom (10 and above) was 18.4%.

The definition of the high-risk group for each of eight variable is described below (Table 1).

Table 1

Variables used for calculating cumulative risk scores for participants
Risk Factors	Low Risk, n(%)	High Risk, n (%)
Household income	Medium and above, 1 292(75.3)	Low, 424(24.7)
Numbers of friends	≥ 3,1504(87.6)	< 3, 212(12.4)
Academic performance	Medium and above, 1 449(84.4)	Poor, 267(15.6)
Sleeping quality	Good, 1 538(89.6)	Bad, 178(10.4)
Physical activity level	Medium and above, 1 178(68.6)	Low, 538(31.4)
PSU	No, 1 247(72.7)	Yes, 469(27.3)
Be hospitalized in past 1 year	No, 1 550(90.3)	Yes, 166(9.7)
Break up in love in past 1 year	No, 1 370(79.8)	Yes, 346(20.2)

After binary logistic regression, six risk factors were screened for depressive symptoms in nursing students. Physical activity level and be hospitalized in past 1 year were not associated to depressive symptoms.

Table 2

Binary logistic regression analysis among variables influencing depressive symptoms
	β	Standard error	Wald χ²	OR(95%CI)^a
Constant	-1.84	1.06	3.01	0.16
Household income	0.47	0.15	9.92	1.60(1.20–2.15)^**
Numbers of friends	0.68	0.18	13.62	1.97(1.38–2.83)^***
Academic performance	0.63	0.17	14.30	1.88(1.36–2.61))^***
Sleep quality	1.35	0.18	55.79	3.87(2.71–5.51)^***
Physical activity level	0.21	0.14	2.04	1.23(0.93–1.63)
PSU	1.30	0.14	85.34	3.65(2.78–4.81)^***
Be hospitalized in past 1 year	-0.36	0.21	3.00	0.70(0.46–1.05)
Break up in love in past 1 year	0.37	0.16	5.32	1.45(1.06–1.98)^*
Abbreviations: PSU, problematic smartphone use. ^a Adjusted for gender and age. ^* P < 0.05 ^* P < 0.01 ^* P < 0.001

2.2 The relationship between number of risk factors and depressive symptoms

From the binary logistic regression analysis, a multiple risk score was created for each individual that was the total number of high-risk factors. The actual scores ranged from 0 to 5. The sample was well distributed between scores of 0 and 4. Only 6(0.3%) individual had a multiple risk score of 5, and they were combined in all analyses with those whose score was 4. The largest resulting group contained 607 participants (0 risk factors), and the smallest group had 44 participants (4 risk factors) .

The relation between the multiple risk classification and the depressive symptoms score is summarized graphically in Fig. 1. A linear trend analysis was significant(F = 322.229, df = 1, P < .001). As the number of risk factors increased, depressive symptoms score increased for participants.

The performance of the trend in mean depressive symptom score masked the individual effects of multiple risks. To describe this individual effect, the same data was analyzed again according to prevalence of different depressive symptoms levels. The results are shown in Fig. 2. Regardless of the combination of factors, as the number of risk factors increased, the proportion of the normal group became lower, the proportion of other three levels of depressive symptoms showed an increasing trend.

3 The relationship between components of risk and depressive symptoms

With the help of a 2-step cluster analysis individuals were divided into seven clusters with different sets of high-risk factors (including 607 participants without any risk factors), as listed in Table 3. This table presents all different combinations of risk factors with an incidence of more than 50% within a cluster, and these risk factors are ranked from highest to lowest frequency within that cluster. Risk factors within cluster with a prevalence of less than 50% are not displayed. There were some differences in the way risk factors showed, the prevalence of depressive symptoms among individuals in the seven clusters differ in the chi-square analysis. From this, it can be seen that the problematic smartphone use combined with sleep problems are the main high risk predictors of depression in nursing students.

Table 3

2-Step Cluster Analyses of all Individuals, n(%), Only variables with high risk from the clusters are displayed in the table
Cluster Description		Normal	Mild	Moderate	Severe and above
Cluster 1
	Sleep quality	61(55.0)	11(9.9)	21(18.9)	18(16.2)
	PSU	61(55.0)	11(9.9)	21(18.9)	18(16.2)
Cluster 2
	Number of friends	144(71.6)	26(12.9)	23(11.4)	6(4.0)
Cluster 3
	Academic performance	114(76.0)	17(11.3)	15(10.0)	4(2.7)
Cluster 4
	Break up in love in past year	181(75.7)	16(6.7)	33(13.8)	9(3.8)
Cluster 5
	Household income	210(82.7)	21(8.3)	17(6.7)	6(2.4)
Cluster 6
	PSU	120(77.9)	15(9.7)	14(9.1)	5(3.2)
Cluster 7
	No risk factors	570(93.9)	22(3.6)	13(2.1)	2(0.3)
^chi-square analysis, χ² = 188.16, P* < 0.001

To some extent, efforts to prevent mental illness in university students have shifted from a primary focus on biological factors to a broader focus on certain behavioral characteristics of the students. Based on the data from this study, we believe that attention to the complex pattern of individual, family and social factors of university students should be further expanded. However it is especial important to consider individuals with severe depressive symptoms. No single variable was a very strong determinant of the outcome of depressive symptoms (see Table2). Only for individuals with multiple risk factors were the student's depressive symptom more pronounced (severe/extremely severe). Poor sleep quality combined with PSU is a strong determinant pattern (see cluster 1).

Our data are consistent with findings from other areas of research that the accumulation of risk factors, rather than the role of specific factors, contributes to morbidity in several areas[20, 36]. In previous studies, many scholars have identified mobile phone addiction as a significant risk factor for depression among university students [13–15]. We therefore sought to identify the predictive value of individual, family and social risk measures for depressive symptoms in university students in addition to PSU. In the present study, the trend of the proportion of different levels of depressive symptoms was significant. Less than 10% of students in the low multiple risk group had depressive symptoms, while nearly 65% of students in the high multiple risk group had varying levels of depressive symptoms, with 13.64% having severe or higher symptoms. This set of six risk variables can be used to identify students with depressive symptoms and help design more targeted interventions and prevention strategies for students at risk of depressive symptoms. Of these risk factors, some can be prevented early such as number of friends, academic performance, and sleep quality. University administrators are advised to educate students about friendship, academic and routine at group level at the beginning of their studies. However, both household income and broken up in love experience are not easy to change. To these students, early identification and targeted interventions are needed at the individual level.

As can be seen in Table 3 PSU primarily adds to explained variance when it comes to students with severe levels of depression. The proportion of students in this cluster that exhibit severe depressive symptom is markedly higher than for the other clusters. Why severe depression is more strongly related to PSU remains to be explained. In this study we used the first data wave in a longitudinal study- It means that what we in cluster 1 see is a group of individuals that already from start have high levels of PSU. How there trajectories of PSU and depressive symptoms evolve over time is the next step in our research.

Strengths and Limitations

Our research includes a large, representative sample of nursing students; data were collected using the same protocols, and response rate was high. On the other hand, our research also has limitations. First, because of the nature of the cross-sectional study design, it is not possible to establish causal relationships between risk factors and students’ depressive symptoms. Second, some of the variables asked about, e. g number of friends, break up in love and PSU requires self-ratings. Finally, although we included several risk factors for depressive symptoms, we cannot rule out the possibility of residual confounding.

What can be seen from our data when taking a larger perspective involving individual, family and social influences on the students is that the more risk factors a student have the higher is the probability for depressive symptoms. Having PSU problems in it self does not explain a large degree of the variance but when occurring with multiple risk indicators the predictive power increase, especially for the development of severe depressive symptoms. As professionals we are limited to our own domains of expertise. However, as advocates for a healthier society, we must recognize the breadth of the problems and the multiplicity of social forces that affect university students.

Ethics approval and consent to participate

The study received ethical approval from the local ethics committee on human experimentation (Anhui Medical University Ethic Committee, No. 20170219). Written informed consent was obtained from all students participating in the study.

Consent for publication

Consent for publication have been obtained from the participants. You may request to see a copy at any stage (including after publication).

Availability of data and materials

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This research was funded by the Research and Practice on the Construction of National First-class Nursing Majors in Local Universities (NO: 2020jyxm0853).

Authors' contributions

All authors participated in study design and provided academic contributions to the development of this manuscript.

Yajuan Yang (First Author): Conceptualization, Methodology, statistical analyses, Investigation, Formal Analysis, Writing - Original Draft;

Mats Granlund: Statistical analyses, Contribution to interpretation of the results, Supervision;

Fang-biao Tao: Conceptualization, Methodology;

Shu-man Tao: Resources ;

Li-wei Zou: Software, Validation

Xiao-yan Wu (Co-Corresponding Author): Conceptualization, Methodology, Writing - Review & Editing

Karin Enskär: Contribution to interpretation of the results, Supervision;

Jing-Fang Hong (Corresponding Author): Conceptualization, Resources, Supervision, Writing - Review & Editing.

Acknowledgments

The authors thank Chuanying Zhang and Li Wang for their helpful contribution to data collection.

World Health Organization., Depression. [https://www.who.int/en/news-room/fact-sheets/detail/depression] Accessed on March 31, 2023.
Arnett JJ, Zukauskiene R, Sugimura K. The new life stage of emerging adulthood at ages 18–29 years: Implications for mental health. Lancet Psychiat. 2014;1(7):569–76. 10.1016/S2215-0366(14)00080-7.
Van de Velde S, Bracke P, Levecque K. Gender differences in depression in 23 European countries. Cross-national variation in the gender gap in depression. Soc Sci Med(1982). 2010;71(2):305–13.
McDermott RC, Fruh SM, Williams S, Hauff C, Sittig S, Wright T, et al. Characteristics of Negative and Positive Mental Health Among Nursing Students in the United States. J Am Psychiat Nurses. 2021;27(1):44–53.
Cheung T, Wong SY, Wong KY, Law LY, Ng K, Tong MT, et al. Depression, Anxiety and Symptoms of Stress among Baccalaureate Nursing Students in Hong Kong: A Cross-Sectional Study. Int J Env Res Pub He. 2016;13(8):1–1.
Xu S, Ouyang X, Shi X, Li Y, Chen D, Lai Y, et al. Emotional exhaustion and sleep-related worry as serial mediators between sleep disturbance and depressive symptoms in student nurses: A longitudinal analysis. J Psychosom Res. 2020;129:109870.
Guo L, Deng J, He Y, Deng X, Huang J, Huang G, et al. Prevalence and correlates of sleep disturbance and depressive symptoms among Chinese adolescents: a cross-sectional survey study. BMJ open. 2014;4(7):e005517.
Tung YJ, Lo KKH, Ho RCM. ,et al.Prevalence of depression among nursing students: A systematic review and meta-analysis. Nurs Educ Today. 2018;63:119–29. 10.1016/j.nedt.2018.01.009.
Pine DS, Cohen E, Cohen P, et al. Adolescent depressive symptoms as predictors of adult depression: moodiness or mood disorder? Am J Psychiatry. 1999;156(1):133–5.
Elhai JD, Dvorak RD, Levine JC, Hall BJ. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. J Affect Disorders. 2017;207:251–9.
Hwang KH, Yoo YS, Cho OH. Smartphone overuse and upper extremity pain, anxiety, depression, and interpersonal relationships among college students. J Korea Contents Association. 2012;12(10):365–75.
Demírcí K, Akgönül M, Akpinar A. Relationship of Smartphone Use Severity with Sleep Quality, Depression, and Anxiety In University Students. J Behav Addict. 2015;4(2):85–92.
Abid U, Khan TJ, Sheikh A, et al. The relationship between smartphone addiction and depression among university students in Karachi: a cross-sectional study. Int J Community Med Public Health. 2020;7(9):3472. 10.18203/2394-6040.ijcmph20203909.
Ithnain N, Ghazali SE, Jaafar N. Relationship between smartphone addiction with anxiety and depression among undergraduate students in Malaysia. Int J Health Sci Res. 2018;8(1):163–71.
Yang KM. The effect of self-esteem and depression on smartphone addiction among university students. J Korea Convergence Soc. 2016;7(1):113–23.
Elhai JD, Levine JC, Dvorak RD, Hall BJ. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput Hum Behav. 2017;69:75–82.
Sohn SY, Rees P, Wildridge B, Kalk NJ, Carter B. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry. 2019;19(1):356–6.
Evans GW, Li D, Whipple SS. Cumulative Risk and Child Development. Psychol Bull. 2013;139(6):1342–96.
Berenson GS. Childhood risk factors predict adult risk associated with subclinical cardiovascular disease: The Bogalusa Heart Study. The Am J Cardiol. 2002. 10.1016/S0002-9149(02)02953-3.
Canto JG, Kiefe CI, Rogers WJ, et al. Number of coronary heart disease risk factors and mortality in patients with first myocardial infarction. JAMA. 2011;19:306.
Long J, Liu TQ, Liao YH, Qi C, He HY, Chen SB, et al. Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. BMC Psychiatry. 2016;16(1):408–8.
Assari S. Social Determinants of Depression: The Intersections of Race, Gender, and Socioeconomic Status. Brain Sci. 2017;7(12):156.
Ngasa SN, Sama CB, Dzekem BS, Nforchu KN, Tindong M, Aroke D, et al. Prevalence and factors associated with depression among medical students in Cameroon: a cross-sectional study. BMC Psychiatry. 2017;17(1):216–6.
Hosen I, Al Mamun F, Sikder MT, Abbasi AZ, Zou L, Guo T, et al. Prevalence and Associated Factors of Problematic Smartphone Use During the COVID-19 Pandemic: A Bangladeshi Study. Risk Manag Healthc P. 2021;14:3797–805.
Awasthi AA, Taneja N, Maheshwari S, Gupta T, Bhavika. Prevalence of Internet Addiction, Poor Sleep Quality, and Depressive Symptoms Among Medical Students: A Cross-Sectional Study. Osong Public Health Res Perspect. 2020;11(5):303–8. 10.24171/j.phrp.2020.11.5.06.
Guang Y, Feng Z, Yang G, Yang Y, Wang L, Dai Q, et al. Depressive symptoms and negative life events: What psycho-social factors protect or harm left-behind children in China? BMC Psychiatry. 2017;17(1):402–2.
Gong X, Xie X, Xu R, Luo Y. Psychometric properties of the Chinese versions of DASS-21 in Chinese college students. Chin J Clin Psychol. 2010;18(4):443–6.
Lovibond PF, Lovibond SH. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav res and ther. 1995;33(3):335–43.
Rotenstein LS, Ramos MA, Torre M, Segal JB, Mata DA. Prevalence of depression, depressive symptoms, and suicidal ideation among medical students: a systematic review and meta-analysis. JAMA. 2016;316(21):2214. 10.1001/jama.2016.17324.
Wang K, Shi HS, Geng FL, Zou LQ, Tan SP, Wang Y, Neumann DL, Shum DHK, Chan RCK. Cross-cultural validation of the Depression Anxiety Stress Scale–21 in China. Psychol Assess. 2016;28(5):e88–e100. https://doi.org/10.1037/pas0000207.
Tao S, Wu X, Zhang S, Tong S, Hao J, Tao F. Association of alcohol use with problematic mobile phone use and depressive symptoms among college students in Anhui, China. J Public Health. 2017;25(1):103–12.
Liu XC, Tang MQ. Reliability and validity of the Pittsburgh Sleep Quality Index. Chin J Psychiatry. 1996;29(2):29103–7.
Yang Y, Granlund M, Tao F, Tao S, Zou L, Wu X, et al. Patterns of smartphone usage associated with depressive symptoms in nursing students. Front Psychiatry. 2023;14:1136126–6.
Wade M, Moore C, Astington JW, Frampton K, Jenkins JM. Cumulative contextual risk, maternal responsivity, and social cognition at 18 months. Dev and Psychopathol. 2015;27(1):189–203.
Everitt B, Landau S, Leese M. Cluster Analysis. 5th edition. Chicester: Wiley; 2010.
Inoue H, Nozawa T, Hirai T, Iwasa A, Okumura K, Lee JD, et al. Accumulation of Risk Factors Increases Risk of Thromboembolic Events in Patients With Nonvalvular Atrial Fibrillation. Circ J. 2006;70(6):651–6.

No competing interests reported.

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Cumulative risk factors, Problematic smartphone use and Depressive symptoms in Nursing Students：a cross-sectional study

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Abstract

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0 Introduction

1 Materials and methods

1.3 Statistical analysis

2 Results

3 Discussion

Strengths and Limitations

Conclusion

Declarations

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgments

References

Additional Declarations

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