Smartphone addiction, work duration and job burnout in nursing interns: a multicenter cross-sectional study

Smartphone addiction, work duration and job burnout in nursing interns: a multicenter cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Smartphone addiction, work duration and job burnout in nursing interns: a multicenter cross-sectional study Jun Liu, Hongyuan Peng, Linfei Dou, Xinghua Yang, Zheng Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7345965/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in BMC Nursing → Version 1 posted 11 You are reading this latest preprint version Abstract Background Job burnout prevalence among healthcare workers is high, posing significant threats to physical or mental health, care quality, and patient safety. While work duration is an established risk factor for burnout, the impact of smartphone addiction and its potential synergistic effect with prolonged work hours on job burnout remains unclear. Methods In this multicenter cross-sectional study, a total of 1,055 nursing interns were recruited from hospitals in Hunan Province, China. Smartphone addiction was measured with the Smartphone Addiction Scale-Short Version, and job burnout was evaluated using the Maslach Burnout Inventory-General Survey (MBI-GS). Logistic regression was used to estimate the associations between smartphone addiction, work duration and job burnout. Additive interactions were examined using relative excess risk due to interaction (RERI) and attributable proportion (AP) metrics. Results Among 1,055 nursing interns, job burnout prevalence was higher with smartphone addiction (47.5% vs 38.5%; P = 0.012). Each 1-unit increase in addiction score elevated MBIGS by 0.471 (95% CI: 0.360–0.583), while addiction status showed borderline association with job burnout (OR = 1.33, 95% CI: 0.99–1.78). Additionally, participants with work duration more than 50 hours had significant increased odds of job burnout as compared to those with work duration less than 40 hours (OR = 1.97, 95% CI: 1.22–3.19). Notably, smartphone addiction combined with prolonged work duration synergistically exhibited a markedly increased odds of job burnout (OR = 3.24, 95% CI: 1.63–6.51; RERI = 1.948, 95% CI: 0.240–3.656; AP = 0.601, 95% CI: 0.293–0.908). Conclusions This study suggested that both smartphone addiction and prolonged work duration are independent risk factors for job burnout among nursing interns, and they exhibit synergistic effects on burnout. job burnout smartphone addiction work duration nursing intern 1.Introduction Job burnout is a syndrome resulting from chronic excessive work stress, characterized by extreme emotional, mental, and physical exhaustion, with primary manifestations of emotional exhaustion, depersonalization, and reduced personal accomplishment. The World Health Organization (WHO) has included it in the International Classification of Diseases, Eleventh Revision (ICD-11) [ 1 ] and identified it as a priority public health crisis [ 2 ]. A recent study focusing on burnout among healthcare workers revealed that its prevalence in this population is alarmingly high and shows a worsening trend[ 3 ], highlighting the urgency of exploring emerging risk factors for effective early intervention to reduce burnout risk at all stages. Nursing staff are a high-risk group for burnout[ 4 – 6 ]. For example, a meta-analysis showed that the overall prevalence of burnout among nurses globally is 11.23% [ 7 ]. With the increasing burden of complex and chronic diseases, enhancing the appeal of the nursing profession to address staff shortages and declining enrollment has become a consensus, while improving care quality and reducing talent loss have become key strategies for healthcare systems worldwide. Multiple studies indicate that burnout is a significant factor driving increased nurse turnover rates and reduced workforce stability[ 8 , 9 ]. Particularly under the impact of the COVID-19 pandemic, global healthcare systems face unprecedented pressure. Research on occupational health issues among nurses—as the frontline workforce—has consequently intensified. Previous studies demonstrate that burnout risk factors span individual and organizational levels, including sociodemographic and work-related factors[ 10 , 11 ]. A theoretical review incorporating 91 studies on nursing burnout summarized its predictors as: low/inadequate nurse staffing levels, ≥ 12-hour shifts, high work intensity and psychological demands, role conflict, poor interpersonal relationships and support systems, and lack of job security[ 9 ]. Burnout leads to adverse consequences for both individuals and organizations: initial psychological effects gradually progress to negative physical health and behavioral changes (e.g., pain, cardiovascular diseases, infections, insomnia, chronic fatigue) [ 12 ]; further consequences include reduced care quality, compromised patient safety, and increased medical errors and complication rates[ 13 , 14 ] . Job burnout manifests even during early career stages. Multiple studies investigating burnout rates among medical students[ 15 ] reported prevalence ranging from 7.0–75.2%[ 16 ]. However, data on nursing students—particularly those in the critical clinical internship phase—remain scarce[ 17 ]. As a transitional group from students to registered nurses, nursing interns face significant challenges when moving from campus to clinical settings. During this phase, they endure dual pressures from national nursing licensing examinations and graduation requirements, while also confronting persistent stressors (e.g., patient suffering and death, work-life imbalance) and developmental stressors (e.g., medical error risks, disputes, personal life events) [ 8 ], alongside common clinical training stressors (e.g., heavy workloads, high demands, patient care responsibilities) [ 18 ]. As future backbone of the nursing workforce and vital members of hospital nursing teams, nursing interns bear critical responsibilities for patient safety and care quality. Their burnout not only harms personal health but may also increase career-change intentions, leading to academic discontinuation and loss of nursing talent reserves[ 19 , 20 ]. Therefore, investigating emerging risk factors in this population for early intervention is imperative. With internet proliferation and exponential growth in smartphone users, smartphone addiction has become increasingly prominent as a research focus [ 21 ]. It features compulsive smartphone use—often termed problematic smartphone use [ 22 , 23 ] —and is closely associated with negative health outcomes like poor sleep quality, depression, anxiety, and stress[ 24 – 26 ]. Meta-analyses indicate average smartphone addiction prevalence of approximately 23% among Chinese university students[ 23 ] and 21.7% among medical students [ 27 ]. Nursing interns may face higher addiction risks due to younger age, ongoing psychological maturation, relatively weaker self-regulation abilities, and potential use of smartphones to alleviate workplace distress. Nevertheless, whether smartphone addiction affects burnout occurrence in nursing interns remains unclear. Moreover, in high-intensity work environments, interns may frequently use smartphones to relieve stress or divert attention, but excessive use may worsen burnout by reducing sleep quality and increasing psychological stress. Notably, excessive work hours are established significant risk factors for burnout, as they increase physical/mental load and reduce recovery and self-regulation time. This suggests potential synergistic effects between smartphone addiction and excessive work hours on burnout development—though empirical evidence supporting this hypothesis remains lacking. Consequently, this study aims to examine: 1) Whether smartphone addiction and work duration correlate with early-career burnout in nursing interns. 2)Whether synergistic effects exist between smartphone addiction and work duration on burnout. Findings from the current study may provide new insights and intervention targets for preventing and alleviating job burnout during the early career stage. 2. Methods 2.1.Study Design and Participants This study adopted a multi-center cross-sectional survey design. The participants were recruited through convenience sampling from multiple tertiary hospitals in Hunan Province. The eligibility criteria included: (1) currently engaged in clinical practice as nursing interns at these hospitals; (2) possessed Chinese cognitive and literacy skills enabling them to fully comprehend and complete the questionnaire; (3) voluntarily provided written informed consent before participation .Exclusion criteria encompassed individuals: (1) with a history of severe mental disorders potentially impairing cognitive function; (2) providing incorrect answers to quality control items(e.g., “What is 5 times 12 times 5?”). Incorrectly ultimately 1105 nursing interns were initially recruited 50 invalid questionnaires were excluded resulting in 1055 valid questionnaires for statistical analysis. This study followed the guidelines for strengthening epidemiological observational research reports and was approved by the Ethics Committee of Hunan Provincial Maternal and Child Health Hospital (Approval Number: 2023-S189). Each participant signed a written informed consent form, fully understanding the purpose, procedures, risks and their rights of the study. All collected data were anonymized and strictly confidential. 2.2 Measurements 2.2.1. Smartphone Addiction Scale-Short Version (SAS-SV) Smartphone addiction was assessed using the Smartphone Addiction Scale - Short Version (SAS-SV). This scale comprises 10 items carefully selected by Kwon et al. from the original scale, which are highly relevant to the core characteristics of smartphone addiction. It employs a 6-point Likert scoring system (1="strongly disagree" to 6="strongly agree"), with total scores ranging from 10 to 60. Higher scores indicate more severe smartphone addiction. This study defined smartphone addiction using recommended cutoff values (≥ 31 for males, ≥ 33 for females) [ 28 ].The Cronbach's α coefficient for this scale in our study was 0.919. 2.2.2. Work duration The 40-hour standard working week stipulated in the Chinese Labor Law (People's Republic of China, 2024), based on the distribution of participants, work duration information was collected through a single question: "How many hours do you typically work per week?" with three response options provided: ≤40hours, 40–50 hours, and > 50 hours[ 29 ]. 2.2.3. Job burnout The Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS) was used for assessment. This Chinese version was revised and validated by Professors Li Chaoping and Shi Kan from the Chinese Academy of Sciences, suitable for the Chinese population[ 30 ]. Its reliability and validity have been published in Acta Psychologica Sinica and are freely available for academic research [ 31 ]. The scale comprises 15 items using a 7-point scoring system from 0 ("never") to 6 ("daily").It includes three subscales: exhaustion (5 items), cynicism (4 items), professional efficacy (6 items). Burnout manifests as high scores in emotional exhaustion and cynicism, and low scores in professional efficacy. The cutoff values for burnout are set at 20 for exhaustion, 16 for cynicism, and 24 for professional efficacy. Burnout positive criterion: If any dimension score meets or exceeds its corresponding cutoff value, burnout is determined[ 32 ]. Additionally, this study calculated the MBI-GS total score (referred to as MBIGS score) for partial analyses. The Cronbach's α coefficient for the full scale was 0.90, while for the three subscales they were 0.944, 0.928, and 0.923 respectively. 2.2.4. Covariates In all multivariable analyses, we adjusted for a priori defined potential confounders affecting the smartphone addiction-burnout and work duration-burnout associations[ 9 , 15 , 18 , 33 ].These covariates included key sociodemographic and biobehavioral factors. Socio-demographic information including age and sex was collected by a self-designed general information questionnaire. The negative events that occurred in the past year include experiencing family upheaval, whether having experienced family upheaval, hospitalization experiences, failure in exams, and failure in relationships. The health status includes five options: very good, good, average, poor, and very poor. Smokers were defined as participants who had smoked one or more cigarettes in the past 30 days, consistent with standard definitions used in previous studies [ 34 ]. Current drinkers were defined as those who had consumed at least one drink in the past 30 days, following the criteria established by the National Institute on Alcohol Abuse and Alcoholism [ 35 ]. Physical activity was measured by the International Physical Activity Questionnaire Short Form (IPAQ-SF), with levels of physical activity including low, moderate, and high, based on the standard critical levels of metabolic equivalent (MET) values [ 36 ]. Body Mass Index (BMI) is calculated by dividing weight by the square of height. Sleep quality was assessed with the question "In the past month, how would you overall rate the quality of your sleep?", offering four response options (Very good, fairly good, fairly poor, and very poor) [ 37 ]. 2.3. Statistical analysis All statistical analyses were performed using R software version 4.3.3 where continuous variables were expressed as mean ± standard deviation and categorical variables as frequency with percentage in brackets. Group comparisons applied chi-square tests for categorical variables and independent samples t-tests for continuous variables. Association analyses utilized linear regression models to examine relationships between smartphone addiction status and work duration categories with MBIGS total scores, reporting regression coefficients with 95% confidence intervals (CIs), while logistic regression models assessed associations between these predictors and burnout outcome, generating odds ratios (ORs) with corresponding 95% confidence intervals (CIs). For joint effect evaluation, we calculated additive interaction effects between smartphone addiction and prolonged work duration using the epiR package in R version 4.3.3, specifically deriving the Relative Excess Risk due to Interaction (RERI) and Attributable Proportion (AP) due to interaction metrics both accompanied by 95% confidence intervals. All tests employed two-sided approaches with statistical significance determined at P < 0.05. 3. Results Table 1 presents the baseline characteristics of the study population, comprising 1,055 nursing interns (mean age, 20.8 years). Regarding smartphone addiction, participants with burnout reported a significantly higher mean smartphone addiction score (31.2) compared to those without burnout (29.6), with a significant P-value of 0.048. The prevalence of smartphone addiction was also higher among participants with burnout (47.5%) compared to those without burnout (38.5%), with a statistically significant difference (P = 0.012). Concerning other characteristics, significant differences were observed in health status (P < 0.001), with 41.5% of the burnout group reporting poor health compared to 24.6% in the non-burnout group. Work duration also showed a significant difference (P = 0.0087), with more participants in the burnout group working over 50 hours per week (15.8%) compared to the non-burnout group (9.1%). In terms of sleep quality, a higher proportion of participants with burnout reported poor sleep quality (P < 0.001). Table 2 summarizes the association between smartphone addiction and job burnout in nursing interns. In Model 0 (crude model), with each unit increase in the smartphone addiction score corresponding to 0.573 increase in MBIGS score (β = 0.573, 95% CI: 0.461–0.685). After adjusting for age and sex (Model 1), the association remained significant, with a slight reduction in the coefficient for MBIGS score (β = 0.557, 95% CI: 0.444–0.670). Further adjustment for additional variables such as BMI, negative events during the past year, health status, sleep quality, smoking, drinking, and physical activity (Model 2) led to a slight reduction in the association, but it remained statistically significant (β = 0.471, 95% CI: 0.360–0.583). Additionally, smartphone addiction was significantly associated with increased MBIGS score (β = 7.271, 95% CI:5.102–9.440), but its association with job burnout was borderline significant (OR = 1.33; 95% CI:0.99–1.78, P = 0.055) In terms of work duration, nursing interns working longer duration had significantly higher odds of burnout. Specifically, interns working over 50 hours per week had an OR of 1.97 (95% CI: 1.22–3.19) for burnout. This association remained consistent even after adjusting for potential confounders in Model 2 (OR = 1.86; 95% CI: 1.13–3.04) (Table 3 ). Table 4 investigates the joint association of smartphone addiction and work duration with job burnout in nursing interns. In the fully adjusted model, participants working over 50 hours per week with smartphone addiction had significantly higher odds of burnout (OR = 3.24, 95% CI: 1.63–6.51) compared to those with no smartphone addiction and shorter work duration. The relative excess risk due to interaction (RERI) was positive (RERI = 1.948, 95% CI: 0.239–3.656), indicating a significant synergistic effect, with the additive proportion (AP) also suggesting a significant joint effect (AP = 0.601, 95% CI: 0.293–0.908). This finding indicates a synergistic effect of long work duration and smartphone addiction on burnout. Table 1 The baseline characteristics of the study population (N = 1055) Characteristics without burnout (n = 790) with burnout (n = 265) P Age, y, mean (SD) 20.9 (1.5) 20.7 (1.2) 0.127 Female, n(%) 714 (90.4) 236 (89.1) 0.614 BMI, kg/m2, mean (SD) 20.8 (3.2) 20.8 (3.3) 0.991 Smartphone addiction score, mean (SD) 29.6 (8.7) 31.2 (12) 0.048 Smartphone addiction, n(%) 304 (38.5) 126 (47.5) 0.012 Negative events past year, ever, n(%) 82 (10.4) 33 (12.5) 0.410 Poor health status, n (%) 194 (24.6) 110 (41.5) 50hours/week 72 (9.1) 42 (15.8) Sleep quality, n(%) < 0.001 very good 183 (23.2) 61 (23) fairly good 427 (54.1) 126 (47.5) fairly poor 157 (19.9) 51 (19.2) very poor 23 (2.9) 27 (10.2) Smoking, yes, n(%) 49 (6.2) 30 (11.3) 0.009 Drinking, yes, n(%) 142 (18) 51 (19.2) 0.711 Physical activity, n(%) 0.009 low 245 (31) 109 (41.1) medium 423 (53.5) 118 (44.5) high 122 (15.4) 38 (14.3) MBIGS score, mean (SD) 46.7 (15.1) 71.3 (16.2) < 0.001 Table 2 Association between smartphone addiction and job burnout in nursing interns Smartphone addiction Model 0 Model 1 Model 2 MBIGS score (β[95%CI]) Burnout (OR[95%CI]) MBIGS score (β[95%CI]) Burnout (OR[95%CI]) MBIGS score (β[95%CI]) Burnout (OR[95%CI]) Smartphone addiction score, each unit increases 0.573 (0.461–0.685) 1.02 (1.00-1.03) 0.557 (0.444–0.670) 1.02 (1.00-1.03) 0.471 (0.360–0.583) 1.01 (1.00-1.03) Smartphone addiction no REF REF REF REF REF REF yes 8.959 (6.722–11.196) 1.45 (1.09–1.92) 8.676 (6.436–10.916) 1.44 (1.08–1.90) 7.271 (5.102–9.440) 1.33 (0.99–1.78) Note: Model 0, crude model; Model 1, adjustment for age and sex; Model 2, adjustment for age, sex, BMI, negative event during the past year, health status, sleep quality, smoking, drinking, physical activity, and work duration; Table 3 Association between work duration and job burnout in nursing interns Work duration Model 0 Model 1 Model 2 MBIGS score (β[95%CI]) Burnout (OR[95%CI]) MBIGS score (β[95%CI]) Burnout (OR[95%CI]) MBIGS score (β[95%CI]) Burnout (OR[95%CI]) ≤40hours/week REF REF REF REF REF REF 40 ~ 50hours/week 1.987 (-0.692-4.665) 1.07 (0.76–1.51) 1.889 (-0.779-4.558) 1.07 (0.76–1.52) 1.138 (-1.416-3.693) 1.02 (0.73–1.46) >50hours/week 8.200 (4.088–12.312) 1.97 (1.22–3.19) 8.106 (4.011–12.201) 1.97 (1.21–3.18) 6.675 (2.753–10.597) 1.86 (1.13–3.04) Note: Model 0, crude model; Model 1, adjustment for age and sex; Model 2, adjustment for age, sex, BMI, negative event during the past year, health status, sleep quality, smoking, drinking, and physical activity; Table 4 joint association of smartphone addiction and work duration with job burnout in nursing interns Work duration Full adjusted Model RERI for burnout AP for burnout MBIGS score (β[95%CI]) Burnout (OR [95%CI]) ≤40hours/week no smartphone addiction 0 [Ref] 1 [Ref] 0 [Ref] 1 [Ref] with smartphone addiction 8.130 (3.754–12.506) 1.11 (0.60–2.03) 40 ~ 50hours/week no smartphone addiction 2.171 (-1.106-5.449) 1.00 (0.63–1.61) with smartphone addiction 8.406 (4.864–11.928) 1.19 (0.73–1.97) 0.091(-0.528,0.711) 0.077(-0.448,0.601) >50hours/week no smartphone addiction 5.233 (0.065–10.402) 1.19 (0.58–2.40) with smartphone addiction 16.102 (10.572–21.632) 3.24 (1.63–6.51) 1.948(0.240,3.656) 0.601(0.293,0.908) Note: adjustment for age, sex, BMI, negative event during the past year, health status, sleep quality, smoking, drinking, and physical activity; RERI: Relative excess risk due to interaction; AP: Attributable proportion due to interaction. 4. Discussion This multicenter study revealed that smartphone addiction and prolonged work duration was associated with increased prevalence of job burnout. In addition, a significant synergistic effect of smartphone addiction and excessive work duration on the increased odds of job burnout was observed among nursing interns. These findings provide new perspectives for understanding multifactorial interaction mechanisms of job burnout and offer scientific evidence for developing targeted interventions. The prevalence of job burnout observed in the present study (25.1%) aligns with global reported data, which range from 19–41% [ 18 ],Among them, Costa Rica (18.8%) and Brazil (24.74%) have ratios of 41% and 46.4% respectively. It is notable that two other independent studies reported that the rate of job burnout among Saudi nursing interns has significantly increased to 65% [ 2 ], while the rate for healthcare workers in Belgium is 50% [ 38 ]. Even considering methodological factors (such as the differences in the MBI-GS, cultural adaptation) and the protective mechanisms within the Chinese nursing education system [ 37 ], the relatively high prevalence of job burnout among nursing interns still deserves attention. The reasons for this phenomenon are complex. This might be due to the cumulative stress effect and the increase in clinical responsibilities [ 10 , 39 , 40 ]; as well as the mismatch between individuals and their careers [ 40 ]. Additionally, according to Maslach's three-dimensional theory [ 9 ], the emotional exhaustion (20.3%), depersonalization (12.3%), and reduced occupational efficacy (21.2%) of the intern nurses were consistent with the three-dimensional survey results of 1004 nurses, indicating a higher degree of emotional exhaustion and reduced personal accomplishment[ 41 ], which might be related to the fatigue experienced by intern nurses when they first enter the workplace and the lack of respect from their colleagues [ 42 ]. Despite growing academic interest in the smartphone-burnout nexus—evidenced by associations identified among osteopathic students[ 43 ], procrastination-mediated cynicism in new graduate nurses [ 44 ], and the mediating role of psychological capital between life satisfaction and academic burnout in Chinese medical postgraduates [ 45 ]—research specifically targeting nursing interns remains scarce, predominantly focused on academic burnout. Our study reveals a high smartphone addiction prevalence among interns (SAS-SV = 29.99; 40.8% meeting diagnostic thresholds), surpassing Serbian medical students (24.69) [ 27 ] yet paralleling nurses' internet addiction rates (40.9%)[ 24 ]. Crucially, a statistically significant dose-response relationship emerged between smartphone addiction and job burnout, aligning with findings in Chinese students [ 23 ] and novice nurses [ 25 ]. Mechanistically, excessive smartphone use may disrupt melatonin secretion and circadian rhythms [ 26 , 42 ], exacerbate anxiety[ 24 , 25 ], and impair self-control [ 40 ], manifesting as sleep disturbances, compromised emotional regulation [ 46 ], entertainment-driven escapism[ 23 ], and attentional deficits that collectively elevate burnout risk. Regarding the relationship between working hours and job burnout, this survey found that 10.8% of participants worked over 50 hours weekly, a group demonstrating significantly higher job burnout likelihood with 1.97-times greater incidence compared to those working under 40 hours. These individuals concurrently reported poor sleep quality, a critical observation given established sleep deprivation-burnout links [ 47 ]. Our results confirm prior findings connecting prolonged working hours to job burnout [ 1 , 48 ], including direct relationships between workload/time pressure and emotional exhaustion plus depersonalization [ 9 ]. Adverse job characteristics—high workload, low staffing levels, extended shifts—link to nursing job burnout [ 9 ], consistent with demand-resources theory where burnout stems from time-resource imbalances [ 49 ]. This study further revealed that smartphone addiction and extended working hours (> 50 weekly) jointly elevate job burnout risk, demonstrating significant synergistic effects where combined exposure substantially increased burnout likelihood. We speculate this operates through burnout's three core dimensions via interconnected pathways. Excessive work impairs sleep quality and amplifies negative emotions like fatigue-anxiety, while smartphone blue light exposure simultaneously suppresses melatonin secretion and disrupts circadian rhythms - together establishing a self-perpetuating cycle of sleep deprivation and emotional fatigue that triggers exhaustion. Additionally, prolonged workers' craving for work disengagement synergizes with smartphone addicts' virtual interaction dependency to intensify detachment. Finally, overtime workers' preference for low-effort digital consumption (e.g., short videos, entertainment news) fragments attention and increases errors, progressively eroding professional efficacy through diminished achievement perception.This synergistic effect intensifies burnout through interconnected pathways, highlighting the need for targeted interventions. The present study carry significant theoretical and practical implications by showing the significant associations of smartphone addiction and excessive working hours with the odds of job burnout among nursing interns. First, it is crucial to address smartphone addiction through collaborative intervention strategies. Nursing educators and administrators should implement person-centered approaches, such as promoting physical and mental health, to reduce smartphone usage [ 23 ], alongside organizational solutions [ 50 ]. Additionally, given the direct impact of extended working hours on burnout rates, ensuring adequate rest is a necessary preventive measure. This can be achieved by optimizing shift scheduling and sleep monitoring in nursing schools and hospitals to maximize continuous rest periods [ 51 ]. Lastly, preventing burnout during the internship phase not only contributes to the well-being and professional success of the interns but also helps sustain the professional spirit of nursing and the quality of healthcare [ 52 ],Therefore, targeted prevention of smartphone addiction and adjustments to working hours are essential for long-term employee resilience and the provision of excellent care. 5. Limitations Although our research yielded valuable findings, there is still several limitations warrant attention. Firstly, the cross-sectional design limits causal inference, requiring validation through prospective cohorts. Secondly, self-reported data collection risks social desirability bias, potentially compromising validity and reliability, and the use of a single-item self-report for working hours may introduce potential bias affecting result interpretation. Thirdly, convenience sampling may limit generalizability, and caution is needed when extrapolating to primary care institutions despite multicenter sampling. Lastly, although major sociodemographic confounders were adjusted for, unmeasured confounding parameters may affect results. 6. Conclusion This study demonstrates that both smartphone addiction and prolonged work hours are independent risk factors for burnout among nursing interns, and they exhibit synergistic effects on burnout. This suggests that dual-path intervention strategies targeting smartphone addiction reduction and work hour limitation may effectively reduce burnout risk in future clinical practice. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, approved by the institutional review board at Maternal and Child Health Hospital of Hunan Province (2023-S189). Written informed consent was obtained from each participant, with participants being fully informed about the study’s purpose, procedures, risks, and their rights. All data collected was anonymized to maintain confidentiality and ensure the privacy of participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this article and its accompanying supplementary information files. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The study was funded through grants from the Natural Science Foundation of Hunan Province (2025JJ80661, 2023JJ40801). CRediT authorship contribution statement L.J.: Writing – original draft, Methodology, Investigation, Data curation, Formal analysis, Conceptualization. X.M.: Writing-original draft, Supervision, Methodology, Investigation, Data curation, Conceptualization. P.H.: Investigation, Investigation, Data curation, Conceptualization. D.L.: Methodology, Data curation, Conceptualization. Y.X.: Methodology, Formal analysis, Conceptualization. X.N.: Methodology, Formal analysis, Conceptualization. Z.Z.: Methodology, Data curation. W.M.: Writing-review & editing, Methodology, Investigation, Data curation, Formal analysis, Funding acquisition, Conceptualization. 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Exploring Burnout among Nursing Students in Bangalore: A t-Distributed Stochastic Neighbor Embedding Analysis and Hierarchical Clustering in Cross-Sectional Data. Nursing Reports 2024, 14(3):1693-1705. Van Hoek G, Portzky M, Franck E. The influence of socio-demographic factors, resilience and stress reducing activities on academic outcomes of undergraduate nursing students: A cross-sectional research study. Nurse Education Today 2019, 72:90-96. Ratan Z, Parrish A-M, Zaman S, Alotaibi M, Hosseinzadeh H. Smartphone Addiction and Associated Health Outcomes in Adult Populations: A Systematic Review. International Journal of Environmental Research and Public Health 2021, 18(22). Lin C-Y, Ratan ZA, Pakpour AH. Collection of smartphone and internet addiction. BMC Psychiatry 2023, 23(1). Zhu L, Hou J, Zhou B, Xiao X, Wang J, Jia W. Physical activity, problematic smartphone use, and burnout among Chinese college students. PeerJ 2023, 11. Toth G, Kapus K, Hesszenberger D, Pohl M, Kosa G, Kiss J, Pusch G, Fejes E, Tibold A, Feher G: Internet Addiction and Burnout in A Single Hospital. Is There Any Association? International Journal of Environmental Research and Public Health 2021, 18(2). Stimpfel AW, Fatehi F, Kovner C. Nurses' sleep, work hours, and patient care quality, and safety. Sleep Health 2020, 6(3):314-320. Hale L, Guan S. Screen time and sleep among school-aged children and adolescents: A systematic literature review. Sleep Medicine Reviews 2015, 21:50-58. Nikolic A, Bukurov B, Kocic I, Vukovic M, Ladjevic N, Vrhovac M, Pavlović Z, Grujicic J, Kisic D, Sipetic S. Smartphone addiction, sleep quality, depression, anxiety, and stress among medical students. Frontiers in Public Health 2023, 11. Choi D-S, Kwon M, Kim D-J, Cho H, Yang S. The Smartphone Addiction Scale: Development and Validation of a Short Version for Adolescents. PLoS ONE 2013, 8(12). Che H, Wu H, Qiao Y, Luan B, Zhao Q, Wang H. Association between long working hours and mental health among nurses in China under COVID-19 pandemic: based on a large cross-sectional study. BMC Psychiatry 2023, 23(1). Li Chaoping, Kan S. The influence of distributive justice and procedural justice on job burnout. Acta Physiol Sinica. 2003;35(5):677–84. Shi L, Ren F, Xin S, Sun Q, Li D-n, Li K, Wang Y. Prevalence of burnout among military personnel in the plateau region of China: a cross-sectional survey. BMC Public Health 2024, 24(1). Hou, Caiyun, et al. A study on the correlation between nurse burnout and empathy ability. Psychol Monthly. 2024;19(13):25–7. Shah MK, Gandrakota N, Cimiotti JP, Ghose N, Moore M, Ali MK. Prevalence of and Factors Associated With Nurse Burnout in the US. JAMA Network Open 2021, 4(2). Pulvers K, Scheuermann TS, Romero DR, Basora B, Luo X, Ahluwalia JS. Classifying a Smoker Scale in Adult Daily and Nondaily Smokers. Nicotine & Tobacco Research 2014, 16(5):591-599. Midanik LT, Ye Y, Greenfield TK, Kerr W. Missed and inconsistent classification of current drinkers: results from the 2005 US National Alcohol Survey. Addiction 2012, 108(2):348-355. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. International Journal of Behavioral Nutrition and Physical Activity 2011, 8(1). Caminati G, Cappelli L, Ferri P, et al. Emotional impact of clinical practice in Burns Unit among nursing students. a qualitative study. Acta Biomedica. 2021, 92(S2).e2021008. Baudewyns V, Bruyneel A, Smith P, Servotte JC, Dancot J. Prevalence and factors associated with academic burnout risk among nursing and midwifery students during the COVID‐19 pandemic: A cross‐sectional study. Nursing Open 2022, 10(5):3232-3242. Fu X, You L, Liu X, Zheng J, Gubrud-Howe P, Liu J, Li M, Wan L. Developing trends of initial nursing education in China from 2006 to 2017: A descriptive analysis based on national-level data. Nurse Education Today 2022, 110. Wang S, Luo G, Ding X, Ma X, Yang F, Zhang M, Sun G, Wang F, Zhu L, Wang S et al. Factors associated with burnout among frontline nurses in the post-COVID-19 epidemic era: a multicenter cross-sectional study. BMC Public Health 2024, 24(1). Kakemam E, Chegini Z, Rouhi A, Ahmadi F, Majidi S. Burnout and its relationship to self‐reported quality of patient care and adverse events during COVID‐19: A cross‐sectional online survey among nurses. Journal of Nursing Management 2021, 29(7):1974-1982. Brubaker JR, Swan A, Beverly EA. A brief intervention to reduce burnout and improve sleep quality in medical students. BMC Medical Education 2020, 20(1). Brubaker JR, Beverly EA. Burnout, Perceived Stress, Sleep Quality, and Smartphone Use: A Survey of Osteopathic Medical Students. Journal of Osteopathic Medicine 2020, 120(1):6-17. Ma H, Zou Jm, Zhong Y, He Jq. The influence of mobile phone addiction and work procrastination on burnout among newly graduated Chinese nurses. Perspectives in Psychiatric Care 2021, 57(4):1798-1805. Hu Q, Yang N, Huang Q, Cheng C, Xiao L, Gao X, Zhang F. Mobile Phone Addiction and Psychological Capital Mediates the Relationship Between Life Satisfaction and Learning Burnout in Chinese Medical Postgraduate Students: A Structural Equation Model Analysis. Psychology Research and Behavior Management 2024, Volume 17:3169-3180. Yang G-H, Cao X-X, Fu Y-Y, Wang N-D, Lian S-L. Mobile phone addiction and academic burnout: the mediating role of technology conflict and the protective role of mindfulness. Frontiers in Psychiatry 2024, 15. Jung FU, Bodendieck E, Bleckwenn M, Hussenoeder FS, Luppa M, Riedel-Heller SG. Burnout, work engagement and work hours – how physicians’ decision to work less is associated with work-related factors. BMC Health Services Research 2023, 23(1). Bakker AB, Demerouti E: Job demands–resources theory. Taking stock and looking forward. Journal of Occupational Health Psychology 2017, 22(3):273-285. Stewart NH, Arora VM. The Impact of Sleep and Circadian Disorders on Physician Burnout. Chest 2019, 156(5):1022-1030. West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. Journal of Internal Medicine 2018, 283(6):516-529. Mendelsohn D, Despot I, Gooderham PA, Singhal A, Redekop GJ, Toyota BD. Impact of work hours and sleep on well‐being and burnout for physicians‐in‐training: the Resident Activity Tracker Evaluation Study. Medical Education 2018, 53(3):306-315. Eltaybani S, Yamamoto-Mitani N, Ninomiya A, Igarashi A. The association between nurses’ burnout and objective care quality indicators: a cross-sectional survey in long-term care wards. BMC Nursing 2021, 20(1). Additional Declarations No competing interests reported. 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Province","correspondingAuthor":false,"prefix":"","firstName":"Hongyuan","middleName":"","lastName":"Peng","suffix":""},{"id":528113689,"identity":"55641b1c-1d2a-4e77-a2e1-544bdbfaefd7","order_by":2,"name":"Linfei Dou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Linfei","middleName":"","lastName":"Dou","suffix":""},{"id":528113690,"identity":"2e4d1c99-6c23-43d1-8725-1157331acf54","order_by":3,"name":"Xinghua Yang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xinghua","middleName":"","lastName":"Yang","suffix":""},{"id":528113691,"identity":"6375ff21-7228-4cc2-8d9b-3df150800e96","order_by":4,"name":"Zheng Zhang","email":"","orcid":"","institution":"Central South 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15:59:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":368102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7345965/v1/65787908-ffb9-4348-8cfd-e82fa6ede8a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSmartphone addiction, work duration and job burnout in nursing interns: a multicenter cross-sectional study\u003c/p\u003e","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eJob burnout is a syndrome resulting from chronic excessive work stress, characterized by extreme emotional, mental, and physical exhaustion, with primary manifestations of emotional exhaustion, depersonalization, and reduced personal accomplishment. The World Health Organization (WHO) has included it in the International Classification of Diseases, Eleventh Revision (ICD-11) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and identified it as a priority public health crisis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A recent study focusing on burnout among healthcare workers revealed that its prevalence in this population is alarmingly high and shows a worsening trend[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], highlighting the urgency of exploring emerging risk factors for effective early intervention to reduce burnout risk at all stages.\u003c/p\u003e\u003cp\u003eNursing staff are a high-risk group for burnout[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, a meta-analysis showed that the overall prevalence of burnout among nurses globally is 11.23% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the increasing burden of complex and chronic diseases, enhancing the appeal of the nursing profession to address staff shortages and declining enrollment has become a consensus, while improving care quality and reducing talent loss have become key strategies for healthcare systems worldwide. Multiple studies indicate that burnout is a significant factor driving increased nurse turnover rates and reduced workforce stability[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Particularly under the impact of the COVID-19 pandemic, global healthcare systems face unprecedented pressure. Research on occupational health issues among nurses\u0026mdash;as the frontline workforce\u0026mdash;has consequently intensified. Previous studies demonstrate that burnout risk factors span individual and organizational levels, including sociodemographic and work-related factors[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A theoretical review incorporating 91 studies on nursing burnout summarized its predictors as: low/inadequate nurse staffing levels, \u0026ge;\u0026thinsp;12-hour shifts, high work intensity and psychological demands, role conflict, poor interpersonal relationships and support systems, and lack of job security[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Burnout leads to adverse consequences for both individuals and organizations: initial psychological effects gradually progress to negative physical health and behavioral changes (e.g., pain, cardiovascular diseases, infections, insomnia, chronic fatigue) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; further consequences include reduced care quality, compromised patient safety, and increased medical errors and complication rates[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eJob burnout manifests even during early career stages. Multiple studies investigating burnout rates among medical students[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported prevalence ranging from 7.0\u0026ndash;75.2%[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, data on nursing students\u0026mdash;particularly those in the critical clinical internship phase\u0026mdash;remain scarce[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As a transitional group from students to registered nurses, nursing interns face significant challenges when moving from campus to clinical settings. During this phase, they endure dual pressures from national nursing licensing examinations and graduation requirements, while also confronting persistent stressors (e.g., patient suffering and death, work-life imbalance) and developmental stressors (e.g., medical error risks, disputes, personal life events) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], alongside common clinical training stressors (e.g., heavy workloads, high demands, patient care responsibilities) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As future backbone of the nursing workforce and vital members of hospital nursing teams, nursing interns bear critical responsibilities for patient safety and care quality. Their burnout not only harms personal health but may also increase career-change intentions, leading to academic discontinuation and loss of nursing talent reserves[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, investigating emerging risk factors in this population for early intervention is imperative.\u003c/p\u003e\u003cp\u003eWith internet proliferation and exponential growth in smartphone users, smartphone addiction has become increasingly prominent as a research focus [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It features compulsive smartphone use\u0026mdash;often termed problematic smartphone use [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] \u0026mdash;and is closely associated with negative health outcomes like poor sleep quality, depression, anxiety, and stress[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Meta-analyses indicate average smartphone addiction prevalence of approximately 23% among Chinese university students[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and 21.7% among medical students [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nursing interns may face higher addiction risks due to younger age, ongoing psychological maturation, relatively weaker self-regulation abilities, and potential use of smartphones to alleviate workplace distress. Nevertheless, whether smartphone addiction affects burnout occurrence in nursing interns remains unclear. Moreover, in high-intensity work environments, interns may frequently use smartphones to relieve stress or divert attention, but excessive use may worsen burnout by reducing sleep quality and increasing psychological stress. Notably, excessive work hours are established significant risk factors for burnout, as they increase physical/mental load and reduce recovery and self-regulation time. This suggests potential synergistic effects between smartphone addiction and excessive work hours on burnout development\u0026mdash;though empirical evidence supporting this hypothesis remains lacking.\u003c/p\u003e\u003cp\u003eConsequently, this study aims to examine: 1) Whether smartphone addiction and work duration correlate with early-career burnout in nursing interns. 2)Whether synergistic effects exist between smartphone addiction and work duration on burnout. Findings from the current study may provide new insights and intervention targets for preventing and alleviating job burnout during the early career stage.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1.Study Design and Participants\u003c/h2\u003e\u003cp\u003eThis study adopted a multi-center cross-sectional survey design. The participants were recruited through convenience sampling from multiple tertiary hospitals in Hunan Province. The eligibility criteria included: (1) currently engaged in clinical practice as nursing interns at these hospitals; (2) possessed Chinese cognitive and literacy skills enabling them to fully comprehend and complete the questionnaire; (3) voluntarily provided written informed consent before participation .Exclusion criteria encompassed individuals: (1) with a history of severe mental disorders potentially impairing cognitive function; (2) providing incorrect answers to quality control items(e.g., \u0026ldquo;What is 5 times 12 times 5?\u0026rdquo;). Incorrectly ultimately 1105 nursing interns were initially recruited 50 invalid questionnaires were excluded resulting in 1055 valid questionnaires for statistical analysis.\u003c/p\u003e\u003cp\u003e This study followed the guidelines for strengthening epidemiological observational research reports and was approved by the Ethics Committee of Hunan Provincial Maternal and Child Health Hospital (Approval Number: 2023-S189). Each participant signed a written informed consent form, fully understanding the purpose, procedures, risks and their rights of the study. All collected data were anonymized and strictly confidential.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurements\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Smartphone Addiction Scale-Short Version (SAS-SV)\u003c/h2\u003e\u003cp\u003eSmartphone addiction was assessed using the Smartphone Addiction Scale - Short Version (SAS-SV). This scale comprises 10 items carefully selected by Kwon et al. from the original scale, which are highly relevant to the core characteristics of smartphone addiction. It employs a 6-point Likert scoring system (1=\"strongly disagree\" to 6=\"strongly agree\"), with total scores ranging from 10 to 60. Higher scores indicate more severe smartphone addiction. This study defined smartphone addiction using recommended cutoff values (\u0026ge;\u0026thinsp;31 for males, \u0026ge;\u0026thinsp;33 for females) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].The Cronbach's α coefficient for this scale in our study was 0.919.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Work duration\u003c/h2\u003e\u003cp\u003eThe 40-hour standard working week stipulated in the Chinese Labor Law (People's Republic of China, 2024), based on the distribution of participants, work duration information was collected through a single question: \"How many hours do you typically work per week?\" with three response options provided: \u0026le;40hours, 40\u0026ndash;50 hours, and \u0026gt;\u0026thinsp;50 hours[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Job burnout\u003c/h2\u003e\u003cp\u003eThe Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS) was used for assessment. This Chinese version was revised and validated by Professors Li Chaoping and Shi Kan from the Chinese Academy of Sciences, suitable for the Chinese population[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Its reliability and validity have been published in Acta Psychologica Sinica and are freely available for academic research [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The scale comprises 15 items using a 7-point scoring system from 0 (\"never\") to 6 (\"daily\").It includes three subscales: exhaustion (5 items), cynicism (4 items), professional efficacy (6 items). Burnout manifests as high scores in emotional exhaustion and cynicism, and low scores in professional efficacy. The cutoff values for burnout are set at 20 for exhaustion, 16 for cynicism, and 24 for professional efficacy. Burnout positive criterion: If any dimension score meets or exceeds its corresponding cutoff value, burnout is determined[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, this study calculated the MBI-GS total score (referred to as MBIGS score) for partial analyses. The Cronbach's α coefficient for the full scale was 0.90, while for the three subscales they were 0.944, 0.928, and 0.923 respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Covariates\u003c/h2\u003e\u003cp\u003eIn all multivariable analyses, we adjusted for a priori defined potential confounders affecting the smartphone addiction-burnout and work duration-burnout associations[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].These covariates included key sociodemographic and biobehavioral factors. Socio-demographic information including age and sex was collected by a self-designed general information questionnaire. The negative events that occurred in the past year include experiencing family upheaval, whether having experienced family upheaval, hospitalization experiences, failure in exams, and failure in relationships. The health status includes five options: very good, good, average, poor, and very poor. Smokers were defined as participants who had smoked one or more cigarettes in the past 30 days, consistent with standard definitions used in previous studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Current drinkers were defined as those who had consumed at least one drink in the past 30 days, following the criteria established by the National Institute on Alcohol Abuse and Alcoholism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Physical activity was measured by the International Physical Activity Questionnaire Short Form (IPAQ-SF), with levels of physical activity including low, moderate, and high, based on the standard critical levels of metabolic equivalent (MET) values [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Body Mass Index (BMI) is calculated by dividing weight by the square of height. Sleep quality was assessed with the question \"In the past month, how would you overall rate the quality of your sleep?\", offering four response options (Very good, fairly good, fairly poor, and very poor) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software version 4.3.3 where continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical variables as frequency with percentage in brackets. Group comparisons applied chi-square tests for categorical variables and independent samples t-tests for continuous variables. Association analyses utilized linear regression models to examine relationships between smartphone addiction status and work duration categories with MBIGS total scores, reporting regression coefficients with 95% confidence intervals (CIs), while logistic regression models assessed associations between these predictors and burnout outcome, generating odds ratios (ORs) with corresponding 95% confidence intervals (CIs). For joint effect evaluation, we calculated additive interaction effects between smartphone addiction and prolonged work duration using the epiR package in R version 4.3.3, specifically deriving the Relative Excess Risk due to Interaction (RERI) and Attributable Proportion (AP) due to interaction metrics both accompanied by 95% confidence intervals. All tests employed two-sided approaches with statistical significance determined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the study population, comprising 1,055 nursing interns (mean age, 20.8 years). Regarding smartphone addiction, participants with burnout reported a significantly higher mean smartphone addiction score (31.2) compared to those without burnout (29.6), with a significant P-value of 0.048. The prevalence of smartphone addiction was also higher among participants with burnout (47.5%) compared to those without burnout (38.5%), with a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.012). Concerning other characteristics, significant differences were observed in health status (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 41.5% of the burnout group reporting poor health compared to 24.6% in the non-burnout group. Work duration also showed a significant difference (P\u0026thinsp;=\u0026thinsp;0.0087), with more participants in the burnout group working over 50 hours per week (15.8%) compared to the non-burnout group (9.1%). In terms of sleep quality, a higher proportion of participants with burnout reported poor sleep quality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the association between smartphone addiction and job burnout in nursing interns. In Model 0 (crude model), with each unit increase in the smartphone addiction score corresponding to 0.573 increase in MBIGS score (β\u0026thinsp;=\u0026thinsp;0.573, 95% CI: 0.461\u0026ndash;0.685). After adjusting for age and sex (Model 1), the association remained significant, with a slight reduction in the coefficient for MBIGS score (β\u0026thinsp;=\u0026thinsp;0.557, 95% CI: 0.444\u0026ndash;0.670). Further adjustment for additional variables such as BMI, negative events during the past year, health status, sleep quality, smoking, drinking, and physical activity (Model 2) led to a slight reduction in the association, but it remained statistically significant (β\u0026thinsp;=\u0026thinsp;0.471, 95% CI: 0.360\u0026ndash;0.583). Additionally, smartphone addiction was significantly associated with increased MBIGS score (β\u0026thinsp;=\u0026thinsp;7.271, 95% CI:5.102\u0026ndash;9.440), but its association with job burnout was borderline significant (OR\u0026thinsp;=\u0026thinsp;1.33; 95% CI:0.99\u0026ndash;1.78, P\u0026thinsp;=\u0026thinsp;0.055)\u003c/p\u003e\u003cp\u003eIn terms of work duration, nursing interns working longer duration had significantly higher odds of burnout. Specifically, interns working over 50 hours per week had an OR of 1.97 (95% CI: 1.22\u0026ndash;3.19) for burnout. This association remained consistent even after adjusting for potential confounders in Model 2 (OR\u0026thinsp;=\u0026thinsp;1.86; 95% CI: 1.13\u0026ndash;3.04) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e investigates the joint association of smartphone addiction and work duration with job burnout in nursing interns. In the fully adjusted model, participants working over 50 hours per week with smartphone addiction had significantly higher odds of burnout (OR\u0026thinsp;=\u0026thinsp;3.24, 95% CI: 1.63\u0026ndash;6.51) compared to those with no smartphone addiction and shorter work duration. The relative excess risk due to interaction (RERI) was positive (RERI\u0026thinsp;=\u0026thinsp;1.948, 95% CI: 0.239\u0026ndash;3.656), indicating a significant synergistic effect, with the additive proportion (AP) also suggesting a significant joint effect (AP\u0026thinsp;=\u0026thinsp;0.601, 95% CI: 0.293\u0026ndash;0.908). This finding indicates a synergistic effect of long work duration and smartphone addiction on burnout.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe baseline characteristics of the study population (N\u0026thinsp;=\u0026thinsp;1055)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewithout burnout (n\u0026thinsp;=\u0026thinsp;790)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ewith burnout (n\u0026thinsp;=\u0026thinsp;265)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, y, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.9 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.7 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e714 (90.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e236 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m2, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.8 (3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.8 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmartphone addiction score, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.6 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.2 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmartphone addiction, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304 (38.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative events past year, ever, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor health status, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194 (24.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork duration, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;40hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40-50hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e522 (66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;50hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep quality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003every good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efairly good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e427 (54.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efairly poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003every poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, yes, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (11.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking, yes, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.711\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical activity, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245 (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e423 (53.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBIGS score, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.7 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.3 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between smartphone addiction and job burnout in nursing interns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSmartphone addiction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBurnout (OR[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBurnout (OR[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBurnout (OR[95%CI])\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmartphone addiction score, each unit increases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.573 (0.461\u0026ndash;0.685)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.557 (0.444\u0026ndash;0.670)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.471 (0.360\u0026ndash;0.583)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.01 (1.00-1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.959 (6.722\u0026ndash;11.196)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.09\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.676 (6.436\u0026ndash;10.916)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.44 (1.08\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.271 (5.102\u0026ndash;9.440)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.33 (0.99\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 0, crude model;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1, adjustment for age and sex;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 2, adjustment for age, sex, BMI, negative event during the past year, health status, sleep quality, smoking, drinking, physical activity, and work duration;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between work duration and job burnout in nursing interns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWork duration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBurnout (OR[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBurnout (OR[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBurnout (OR[95%CI])\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;40hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eREF\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026thinsp;~\u0026thinsp;50hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.987 (-0.692-4.665)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07 (0.76\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.889 (-0.779-4.558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07 (0.76\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.138 (-1.416-3.693)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.02 (0.73\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;50hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.200 (4.088\u0026ndash;12.312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.97 (1.22\u0026ndash;3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.106 (4.011\u0026ndash;12.201)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.97 (1.21\u0026ndash;3.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.675 (2.753\u0026ndash;10.597)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.86 (1.13\u0026ndash;3.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 0, crude model;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1, adjustment for age and sex;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 2, adjustment for age, sex, BMI, negative event during the past year, health status, sleep quality, smoking, drinking, and physical activity;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ejoint association of smartphone addiction and work duration with job burnout in nursing interns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWork duration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFull adjusted Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRERI for burnout\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAP for burnout\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBIGS score (β[95%CI])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBurnout (OR [95%CI])\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;40hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno smartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 [Ref] 1 [Ref]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 [Ref] 1 [Ref]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewith smartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.130 (3.754\u0026ndash;12.506)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11 (0.60\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026thinsp;~\u0026thinsp;50hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno smartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.171 (-1.106-5.449)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.63\u0026ndash;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewith smartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.406 (4.864\u0026ndash;11.928)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19 (0.73\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.091(-0.528,0.711)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.077(-0.448,0.601)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;50hours/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eno smartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.233 (0.065\u0026ndash;10.402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19 (0.58\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewith smartphone addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e16.102 (10.572\u0026ndash;21.632)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.24 (1.63\u0026ndash;6.51)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.948(0.240,3.656)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.601(0.293,0.908)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: adjustment for age, sex, BMI, negative event during the past year, health status, sleep quality, smoking, drinking, and physical activity; RERI: Relative excess risk due to interaction; AP: Attributable proportion due to interaction.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis multicenter study revealed that smartphone addiction and prolonged work duration was associated with increased prevalence of job burnout. In addition, a significant synergistic effect of smartphone addiction and excessive work duration on the increased odds of job burnout was observed among nursing interns. These findings provide new perspectives for understanding multifactorial interaction mechanisms of job burnout and offer scientific evidence for developing targeted interventions.\u003c/p\u003e\u003cp\u003eThe prevalence of job burnout observed in the present study (25.1%) aligns with global reported data, which range from 19\u0026ndash;41% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],Among them, Costa Rica (18.8%) and Brazil (24.74%) have ratios of 41% and 46.4% respectively. It is notable that two other independent studies reported that the rate of job burnout among Saudi nursing interns has significantly increased to 65% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], while the rate for healthcare workers in Belgium is 50% [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Even considering methodological factors (such as the differences in the MBI-GS, cultural adaptation) and the protective mechanisms within the Chinese nursing education system [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the relatively high prevalence of job burnout among nursing interns still deserves attention. The reasons for this phenomenon are complex. This might be due to the cumulative stress effect and the increase in clinical responsibilities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]; as well as the mismatch between individuals and their careers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, according to Maslach's three-dimensional theory [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the emotional exhaustion (20.3%), depersonalization (12.3%), and reduced occupational efficacy (21.2%) of the intern nurses were consistent with the three-dimensional survey results of 1004 nurses, indicating a higher degree of emotional exhaustion and reduced personal accomplishment[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which might be related to the fatigue experienced by intern nurses when they first enter the workplace and the lack of respect from their colleagues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite growing academic interest in the smartphone-burnout nexus\u0026mdash;evidenced by associations identified among osteopathic students[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], procrastination-mediated cynicism in new graduate nurses [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and the mediating role of psychological capital between life satisfaction and academic burnout in Chinese medical postgraduates [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u0026mdash;research specifically targeting nursing interns remains scarce, predominantly focused on academic burnout. Our study reveals a high smartphone addiction prevalence among interns (SAS-SV\u0026thinsp;=\u0026thinsp;29.99; 40.8% meeting diagnostic thresholds), surpassing Serbian medical students (24.69) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] yet paralleling nurses' internet addiction rates (40.9%)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Crucially, a statistically significant dose-response relationship emerged between smartphone addiction and job burnout, aligning with findings in Chinese students [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and novice nurses [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Mechanistically, excessive smartphone use may disrupt melatonin secretion and circadian rhythms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], exacerbate anxiety[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and impair self-control [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], manifesting as sleep disturbances, compromised emotional regulation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], entertainment-driven escapism[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and attentional deficits that collectively elevate burnout risk.\u003c/p\u003e\u003cp\u003eRegarding the relationship between working hours and job burnout, this survey found that 10.8% of participants worked over 50 hours weekly, a group demonstrating significantly higher job burnout likelihood with 1.97-times greater incidence compared to those working under 40 hours. These individuals concurrently reported poor sleep quality, a critical observation given established sleep deprivation-burnout links [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Our results confirm prior findings connecting prolonged working hours to job burnout [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], including direct relationships between workload/time pressure and emotional exhaustion plus depersonalization [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Adverse job characteristics\u0026mdash;high workload, low staffing levels, extended shifts\u0026mdash;link to nursing job burnout [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], consistent with demand-resources theory where burnout stems from time-resource imbalances [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study further revealed that smartphone addiction and extended working hours (\u0026gt;\u0026thinsp;50 weekly) jointly elevate job burnout risk, demonstrating significant synergistic effects where combined exposure substantially increased burnout likelihood. We speculate this operates through burnout's three core dimensions via interconnected pathways. Excessive work impairs sleep quality and amplifies negative emotions like fatigue-anxiety, while smartphone blue light exposure simultaneously suppresses melatonin secretion and disrupts circadian rhythms - together establishing a self-perpetuating cycle of sleep deprivation and emotional fatigue that triggers exhaustion. Additionally, prolonged workers' craving for work disengagement synergizes with smartphone addicts' virtual interaction dependency to intensify detachment. Finally, overtime workers' preference for low-effort digital consumption (e.g., short videos, entertainment news) fragments attention and increases errors, progressively eroding professional efficacy through diminished achievement perception.This synergistic effect intensifies burnout through interconnected pathways, highlighting the need for targeted interventions.\u003c/p\u003e\u003cp\u003eThe present study carry significant theoretical and practical implications by showing the significant associations of smartphone addiction and excessive working hours with the odds of job burnout among nursing interns. First, it is crucial to address smartphone addiction through collaborative intervention strategies. Nursing educators and administrators should implement person-centered approaches, such as promoting physical and mental health, to reduce smartphone usage [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], alongside organizational solutions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Additionally, given the direct impact of extended working hours on burnout rates, ensuring adequate rest is a necessary preventive measure. This can be achieved by optimizing shift scheduling and sleep monitoring in nursing schools and hospitals to maximize continuous rest periods [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Lastly, preventing burnout during the internship phase not only contributes to the well-being and professional success of the interns but also helps sustain the professional spirit of nursing and the quality of healthcare [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e],Therefore, targeted prevention of smartphone addiction and adjustments to working hours are essential for long-term employee resilience and the provision of excellent care.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eAlthough our research yielded valuable findings, there is still several limitations warrant attention. Firstly, the cross-sectional design limits causal inference, requiring validation through prospective cohorts. Secondly, self-reported data collection risks social desirability bias, potentially compromising validity and reliability, and the use of a single-item self-report for working hours may introduce potential bias affecting result interpretation. Thirdly, convenience sampling may limit generalizability, and caution is needed when extrapolating to primary care institutions despite multicenter sampling. Lastly, although major sociodemographic confounders were adjusted for, unmeasured confounding parameters may affect results.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study demonstrates that both smartphone addiction and prolonged work hours are independent risk factors for burnout among nursing interns, and they exhibit synergistic effects on burnout. This suggests that dual-path intervention strategies targeting smartphone addiction reduction and work hour limitation may effectively reduce burnout risk in future clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, approved by the institutional review board at Maternal and Child Health Hospital of Hunan Province (2023-S189). Written informed consent was obtained from each participant, with participants being fully informed about the study\u0026rsquo;s purpose, procedures, risks, and their rights. All data collected was anonymized to maintain confidentiality and ensure the privacy of participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article and its accompanying supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded through grants from the Natural Science Foundation of Hunan Province (2025JJ80661, 2023JJ40801).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCRediT authorship contribution statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eL.J.: Writing \u0026ndash; original draft, Methodology, Investigation, Data curation, Formal analysis, Conceptualization. X.M.: Writing-original draft, Supervision, Methodology, Investigation, Data curation, Conceptualization. P.H.: Investigation, Investigation, Data curation, Conceptualization. D.L.: Methodology, Data curation, Conceptualization. Y.X.: Methodology, Formal analysis, Conceptualization. X.N.: Methodology, Formal analysis, Conceptualization. Z.Z.: Methodology, Data curation. W.M.: Writing-review \u0026amp; editing, Methodology, Investigation, Data curation, Formal analysis, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all of participants who give their time for this survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical Trial number\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEd\u0026uacute;-Valsania S, Lagu\u0026iacute;a A, Moriano JA. 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BMC Nursing 2021, 20(1).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"job burnout, smartphone addiction, work duration, nursing intern","lastPublishedDoi":"10.21203/rs.3.rs-7345965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7345965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eJob burnout prevalence among healthcare workers is high, posing significant threats to physical or mental health, care quality, and patient safety. While work duration is an established risk factor for burnout, the impact of smartphone addiction and its potential synergistic effect with prolonged work hours on job burnout remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this multicenter cross-sectional study, a total of 1,055 nursing interns were recruited from hospitals in Hunan Province, China. Smartphone addiction was measured with the Smartphone Addiction Scale-Short Version, and job burnout was evaluated using the Maslach Burnout Inventory-General Survey (MBI-GS). Logistic regression was used to estimate the associations between smartphone addiction, work duration and job burnout. Additive interactions were examined using relative excess risk due to interaction (RERI) and attributable proportion (AP) metrics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 1,055 nursing interns, job burnout prevalence was higher with smartphone addiction (47.5% vs 38.5%; P\u0026thinsp;=\u0026thinsp;0.012). Each 1-unit increase in addiction score elevated MBIGS by 0.471 (95% CI: 0.360\u0026ndash;0.583), while addiction status showed borderline association with job burnout (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 0.99\u0026ndash;1.78). Additionally, participants with work duration more than 50 hours had significant increased odds of job burnout as compared to those with work duration less than 40 hours (OR\u0026thinsp;=\u0026thinsp;1.97, 95% CI: 1.22\u0026ndash;3.19). Notably, smartphone addiction combined with prolonged work duration synergistically exhibited a markedly increased odds of job burnout (OR\u0026thinsp;=\u0026thinsp;3.24, 95% CI: 1.63\u0026ndash;6.51; RERI\u0026thinsp;=\u0026thinsp;1.948, 95% CI: 0.240\u0026ndash;3.656; AP\u0026thinsp;=\u0026thinsp;0.601, 95% CI: 0.293\u0026ndash;0.908).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study suggested that both smartphone addiction and prolonged work duration are independent risk factors for job burnout among nursing interns, and they exhibit synergistic effects on burnout.\u003c/p\u003e","manuscriptTitle":"Smartphone addiction, work duration and job burnout in nursing interns: a multicenter cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 23:39:48","doi":"10.21203/rs.3.rs-7345965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T09:05:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T18:53:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T02:14:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332589815704961932035436337986603538192","date":"2025-11-13T07:08:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150707757541967671281133097753200239382","date":"2025-11-12T12:39:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T14:01:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110437240593752588734470885603508860105","date":"2025-10-01T17:49:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-01T14:26:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T11:55:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T11:55:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-08-11T11:36:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c847684-6093-4ca0-b50b-3e0da22fbafd","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T15:59:16+00:00","versionOfRecord":{"articleIdentity":"rs-7345965","link":"https://doi.org/10.1186/s12912-026-04681-1","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2026-04-27 15:57:23","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-10-14 23:39:48","video":"","vorDoi":"10.1186/s12912-026-04681-1","vorDoiUrl":"https://doi.org/10.1186/s12912-026-04681-1","workflowStages":[]},"version":"v1","identity":"rs-7345965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7345965","identity":"rs-7345965","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}