Mobile phone addiction among nursing interns and its related influencing factors: a cross-sectional network analysis

Abstract

Background

As mobile phones become increasingly indispensable in modern life, problematic mobile phone use among nursing interns has gained growing attention from nursing educators and clinical practitioners. Time management disposition and family care have been identified as key factors associated with addictive behaviors. The primary objective of this study is to investigate, from a cross-sectional perspective, the interrelationships among time management disposition, family care, and mobile phone addiction in nursing interns using a network analysis approach.

Methods

A total of 291 nursing interns (246 females and 45 males) were recruited using convenience sampling in this cross-sectional study between May and July 2025. An online questionnaire was administered via the “Wenjuanxing” platform to collect demographic information and data regarding mobile phone addiction, time management disposition, and family care. Descriptive statistical analyses were performed using SPSS 25.0. A network structure model was established in RStudio, with relevant algorithms employed to compute node relationships and centrality indices.

Results

The mean score on the Mobile Phone Addiction Index (MPAI) among nursing interns was 49.94 ± 8.58. Overall, 27.49% of participants reported some degree of family dysfunction. The constructed network consisted of 12 nodes and 40 non-zero edges, with a network density of 0.606. The node with the highest expected influence (EI = 1.18) was “Sense of Time Control”, whereas “Growth” acted as the bridging node linking the three domains: mobile phone addiction, time management disposition, and family care. Stability analysis confirmed that the network exhibited high accuracy and robust stability, with a node strength centrality stability (CS) coefficient of 0.67.

Conclusions

Nursing interns in this sample showed a higher likelihood of mobile phone addiction than the general student population, and a considerable proportion reported some level of family dysfunction. Within the network, sense of time control emerged as a central node, while family growth functioned as a key bridging node. These findings may suggest potential targets for future longitudinal or experimental research aimed at understanding the maintenance of this network and developing interventions to promote rational mobile phone use among nursing interns.

Clinical trial number

Not applicable.

Introduction

Nursing interns represent a vital emerging force driving the sustainable development of the nursing profession and constitute an important component of the future nursing workforce. During clinical internships, nursing interns face numerous challenges as they undergo significant adjustments to adapt to the clinical environment, which often results in negative emotions and psychological stress [1, 2]. Studies have shown that 27% of nursing students experience mental health problems [3], and 72.3% of interns report poor sleep quality [4]. Additionally, 43.6% of students exhibit symptoms of job burnout after completing their internships [5]. These issues may negatively impact academic performance and may also contribute to the development of certain addictive behaviors, such as excessive reliance on social media.

With the rapid advancement of information technology, young people—especially students—who frequently use electronic devices for social interaction are increasingly vulnerable to mobile phone addiction [6, 7]. Among medical students, the prevalence of mobile phone addiction has been reported to reach 41.93% [8]. One survey indicated that 72% of nursing students experience moderate to severe symptoms of mobile phone-related anxiety [9]. Globally, mobile phone addiction is emerging as a significant public health concern. A meta-analysis of 44 studies from 16 countries, involving over 147,943 participants, found that mobile phone addiction is associated with decreased learning engagement and academic achievement among college students, as well as impaired cognitive abilities and essential learning skills [10]. Excessive mobile phone use may also result in psychological problems such as anxiety and depression, and can interfere with normal sleep patterns, leading to sleep disorders [11–14]. Consequently, nursing educators and practitioners should develop and implement targeted prevention and intervention strategies to reduce excessive mobile phone use among nursing interns.

Effective time management skills enable individuals to achieve valued goals while mitigating inefficient time use, such as academic procrastination and anxiety resulting from excessive mobile phone use [15–17]. Time management disposition refers to an individual’s psychological and behavioral characteristics in the utilization of time [18]. Notably, over the past few decades, the tendency of college students to manage their time has shown a continuous downward trend, and their sense of time control has also raised concerns [19, 20]. As nursing students transition to clinical practice, time management becomes critical to balancing work, study, and life. Emerging evidence links nursing interns’ time management disposition to problematic mobile phone use [21]. Thus, assessing time management capabilities and exploring its relationship with mobile phone addiction is essential for developing targeted nursing interventions.

The family, as the cornerstone of human culture and economic life, plays a pivotal role in shaping individuals’ psychological and social development. Research indicates that medical students from different family functioning backgrounds exhibit varying levels of caring capacity, empathy, and compassion [22]. Additionally, college students who grow up in supportive family environments have a lower risk of developing mobile phone addiction [23]. The family care index, defined as an individual’s perception of mutual care and support among family members, reflects overall satisfaction with family functioning [24, 25]. Assessing this index among nursing interns serves two main purposes: first, to clarify their family functional status for more precise support; second, to explore the relationship between family care and mobile phone addiction, thereby providing a theoretical foundation for effective interventions.

However, most prior studies focused on isolated variables or linear relationships. This approach fails to capture the complex interactions between time management disposition, family care, and mobile phone addiction dimensions. Investigating these variables in isolation is insufficient. They also need to be regarded as a single “network” for study, so as to fully understand the interconnections among them. Within such networks, identifying central and bridge nodes can generate hypotheses for future longitudinal or experimental research. This helps prioritize specific symptoms and clarifies their role in sustaining network structure [26, 27]. Therefore, this study aims to utilize network analysis methods to comprehensively explore the interrelationships among time management disposition, family care, and mobile phone addiction dimensions. It further seeks to identify core and bridging symptoms within these networks, providing a scientific basis for research on mobile phone addiction among nursing interns. Specific research questions (RQs) are as follows:

RQ1: What are the current levels of mobile phone addiction, time management disposition, and family care among nursing interns?

RQ2: In the network structure regarding the mobile phone addiction among nursing interns and its related influencing factors, which node represents the core node of the intervention measures?

RQ3: In the network structure related to mobile phone addiction, which nodes can serve as bridge indicators for intervening and providing care for nursing interns with mobile phone addiction?

Methods

Study design

This study adopted an observational cross-sectional research design. Data on the mobile phone addiction, time management disposition, and family care status of the trainee nurses were collected through online questionnaire surveys. Network analysis was used to explore the complex relationships among these variables.

Setting

This research was conducted in a tertiary general hospital in Anhui Province, China. This is a comprehensive teaching hospital and is a designated internship hospital for many nursing colleges. The study procedure was illustrated in Fig. 1.

Fig. 1.

Flowchart of the study procedure

Participants

A convenience sampling approach was used to enroll nursing interns from a tertiary general hospital in Anhui Province. Participants were selected based on the following inclusion criteria: (1) full-time nursing students who had completed their on-campus coursework and were currently in the clinical internship phase; (2) showed interest in participating. The exclusion criteria were as follows: (1) internship duration of less than two weeks; (2) absence from internship for more than one month; (3) current participation in further education or standardized training programs.

Sample size estimation

As no standardized method for calculating the minimum sample size for network analysis has been established, we followed recommendations from similar studies, which suggest that the sample size should be at least 20 times the number of nodes [28]. This study aimed to construct a network structure comprising 12 nodes (four for mobile phone addiction, three for time management disposition, and five for family care); therefore, a minimum sample size of 240 participants was deemed necessary. Accounting for a 20% non-response rate, the target sample size was set at 240 × (1 + 20%) = 288 cases. A total of 306 questionnaires were distributed, and 291 valid responses were retrieved, yielding an effective response rate of 95.10%.

Ethical considerations

This study was conducted in strict accordance with the relevant provisions of the Declaration of Helsinki and the Chinese Health Research Law. The study protocol was reviewed and approved by the Ethics Committee of The First Affiliated Hospital of Anhui Medical University (Approval Number: PJ2025-06-44). Informed consent was obtained from all participants prior to questionnaire completion, and participants were informed of their right to withdraw from the study at any time without any adverse consequences. All research data were anonymized and properly stored to protect the privacy of the participants.

Measures

General information questionnaire

Participants were asked to provide general demographic information using a questionnaire developed by the researchers. The survey items primarily included gender, age, education level, family residence, whether they were the only child, parents’ marital status, willingness to pursue advanced degrees, and how long they had been in the internship.

Mobile Phone Addiction Index (MPAI)

We determined whether nursing interns exhibited a tendency toward mobile phone addiction based on their scores on the Mobile Phone Addiction Index. The scale was originally developed by Professor Liang from the Chinese University of Hong Kong [29] and has been validated for reliability and validity across large student populations [30, 31]. The questionnaire consists of 17 items. Representative examples include: “Your friends and family complain about your cell phone use”; “You want to spend less time on your phone, but you can’t”; and “When you have other priorities, you find yourself addicted to your phone, and that causes problems.” Each item is rated on a 5-point Likert scale ranging from 1 (“never”) to 5 (“always”). As all items were positively worded, the total scale score was computed as the sum of responses across all 17 items, with a possible range of 17 to 85. Higher scores correspond to more severe mobile phone addiction tendencies. In this study, the scale demonstrated excellent internal consistency, with a Cronbach’s α of 0.971.

Adolescence time management disposition scale (ATMD)

Time management ability was measured using the ATMD, developed by Chinese scholars Huang and Zhang [18]. This self-report instrument includes 44 items grouped into three core dimensions: Sense of Time Value (e.g., “No matter what I do, time is my first consideration”), Sense of Time Control (e.g., “I usually schedule my daily activities”), and Sense of Time Efficiency (e.g., “I think I divide my time reasonably between study and extracurricular activities”). All items are rated on a 5-point Likert scale, with scores ranging from 1 (“completely inconsistent”) to 5 (“completely consistent”) [32]. Among the 44 items, five (items 9, 17, 27, 30, and 41) were reverse-scored (1→5, 2→4, 3→3, 4→2, 5→1) before summation. The total score, calculated as the sum of all 44 items, ranges from 44 to 220; higher scores correspond to better time management capacity. In this study, the scale demonstrated good internal consistency, with a Cronbach’s α coefficient of 0.927.

Family APGAR index (APGAR)

Family care was assessed using the Family APGAR Index, developed by Smilkstein in 1978 to evaluate an individual’s level of family care [33]. A key advantage of this tool is its ability to quickly and easily quantify family functioning. The Chinese version has been widely used in related studies [34]. The scale comprises five dimensions: Adaptation (“ability to cope with family changes”), Partnership (“mutual support between family members”), Growth (“promotion of individual development within the family”), Affection (“emotional connection among family members”), and Resolve (“commitment to solving family problems”). Each item is rated on a 3-point scale (0 = “almost never”, 1 = “sometimes”, 2 = “usually”). All items were positively scored. The total score was calculated by summing the scores of the five items, yielding a possible range of 0 to 10, with lower scores indicating greater family dysfunction. Specifically, scores of 7–10 suggest good family function, 4–6 indicate moderate dysfunction, and 0–3 reflect severe dysfunction. In this study, the scale demonstrated acceptable internal consistency, with a Cronbach’s α of 0.623.

Procedure

Data collection

Data were collected from May to July 2025 from nursing interns at a tertiary general hospital in Anhui Province. After obtaining approval from the hospital’s nursing department, the research team distributed the questionnaire link to WeChat groups of internship team leaders, who then forwarded it to members of their respective internship groups for completion. Concurrently, the link was shared in WeChat groups of nurse managers and clinical instructors to encourage and guide eligible nursing interns to complete the survey. The survey took approximately 20 min to complete.

A pilot study was first conducted with 10 questionnaires distributed and collected. In the main study, 306 questionnaires were distributed; 15 were excluded due to failure to meet the inclusion criteria, leaving 291 valid questionnaires for subsequent statistical analysis. The data collection process is presented in Fig. 2.

Fig. 2.

Flowchart of the data collection process

Quality control

A multi-stage quality control protocol was implemented to ensure data reliability and validity:

1. Questionnaire Design: We developed the questionnaire using CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines to remove redundant items and reduce response fatigue.

2. Pilot Test: A pre-survey was conducted with 10 nursing interns (who were not part of the formal sample) to refine and finalize the questionnaire.

3. Online Submission Monitoring: The survey platform incorporated safeguards, including IP-based frequency limits to prevent duplicate submissions, prompts for missing items to avoid overlooking important information, and built-in logical checks to flag inconsistent responses.

4. Data Cleaning: After data collection, data were cleaned according to strict exclusion criteria: completion time < 120 s, more than 5% missing items, or logical inconsistencies. This resulted in the exclusion of 15 questionnaires, leaving 291 valid responses for analysis.

5. Data Entry and Missing Values: Data were double-entered independently by two researchers. Missing data were handled using multiple imputation. Prior to network analysis, we additionally screened for missing values using the “mice” package in R to ensure data integrity.

Data analysis

Descriptive statistical analysis was conducted using SPSS 25.0, and network analysis was performed using RStudio (version 4.5.2). The EBICglasso network structure model was estimated using the qgraph package, with each variable represented as a node [35]. The EBIC hyperparameter (γ) was set to the default value of 0.5, which provides a good balance between sensitivity and specificity in network estimation [36]. Edges between nodes represent partial correlations among variables, with edge thickness and color indicating the magnitude and direction of associations [37]. Node strength was calculated using the centralityPlot function to assess the relative importance of each node, while bridge strength was computed using the networktools package to identify nodes connecting the three symptom clusters. Network stability was evaluated using the bootnet package, employing the correlation stability coefficient (CS-coefficient), with values exceeding 0.5 indicating good stability [38–40].

Results

General information and scale scores of nursing interns

A total of 291 nursing interns were included in the final analysis. Their ages ranged from 17 to 24 years, with a mean age of 20.55 ± 1.23 years. Regarding educational attainment, 139 (47.77%) participants held a college degree or below, 140 (48.11%) had a bachelor’s degree, and 12 (4.12%) possessed a graduate degree. In terms of family background, 193 (66.32%) participants were from rural areas, 40 (13.75%) were the only child, and 32 (11.00%) were from single-parent families. As for internship duration, 285 (97.94%) participants had been in the internship for no more than three months, while 6 (2.06%) had been in the internship for more than three months. With respect to willingness to pursue further education, 89 (30.58%) participants reported no intention, and 202 (69.42%) expressed an intention to pursue advanced study. The scores for each dimension of the Mobile Phone Addiction Index Scale, Adolescent Time Management Disposition Scale, and Family APGAR Index are presented in Table 1.

Table 1.

Descriptive statistics for the MPAI, ATMD and APGAR index. Among nursing interns (n = 291)

Variable	Min	Max	M	SD	Skewness	Kurtosis
MPAI	26	74	49.94	8.58	0.127	0.036
Inability to Control Craving	8	35	19.22	4.02	− 0.141	0.596
Anxiety and Feeling Lost	4	20	11.53	2.83	0.225	0.089
Withdrawal or Escape	4	15	9.45	2.28	− 0.066	− 0.374
Productivity Loss	3	15	9.74	2.02	− 0.225	0.298
ATMD	90	220	172.60	24.57	−0.319	0.609
Sense of Time Value	20	50	40.14	5.71	− 0.492	0.372
Sense of Time Control	48	120	93.27	14.16	− 0.286	0.529
Sense of Time Efficacy	20	50	39.19	5.87	− 0.154	0.465
APGAR	5	10	7.54	1.57	−0.214	−1.047
Adaptation	1	2	1.60	0.49	− 0.402	-1.852
Partnership	1	2	1.53	0.50	− 0.132	-1.996
Growth	1	2	1.48	0.50	0.062	-2.010
Affection	1	2	1.44	0.50	0.258	-1.947
Resolve	1	2	1.49	0.50	0.035	-2.013

Network structure

Figure 3 illustrates the network associations among mobile phone addiction, time management, and family care in nursing interns. The network structure consisted of 12 nodes and 40 non-zero edges, with a network density of 0.606. Edge weights ranged from − 0.13 to 0.70, and the network included 24 cross-community edges. In the visualization, red edges represent negative correlations between nodes, whereas green edges denote positive correlations. Corresponding edge weights are detailed in Table 2.

Fig. 3.

Network structure of mobile phone addiction, time management disposition and family care

Table 2.

Edge weights of the network model for mobile phone addiction, time management disposition and family care

Node	MPAI 1	MPAI 2	MPAI 3	MPAI 4	ATMD 1	ATMD 2	ATMD 3	APGAR 1	APGAR 2	APGAR 3	APGAR 4	APGAR 5
MPAI 1		0.39	0.19	-	-	−0.01	-	−0.08	-	-	-	-
MPAI 2	0.39		0.25	0.17	-	0.01	0.02	−0.04	−0.02	−0.02	−0.05	-
MPAI 3	0.19	0.25		0.22	-	−0.01	−0.01	-	−0.10	−0.13	−0.05	-
MPAI 4	-	0.17	0.22		-	−0.03	−0.01	-	−0.05	−0.03	−0.09	-
ATMD 1	-	-	-	-		0.33	0.24	-	-	-	-	-
ATMD 2	−0.01	0.01	−0.01	−0.03	0.33		0.70	0.02	-	0.09	0.09	-
ATMD 3	-	0.02	−0.01	−0.01	0.24	0.70		-	0.03	0.04	0.02	-
APGAR 1	−0.08	−0.04	-	-	-	0.02	-		0.13	-	0.02	0.32
APGAR 2	-	−0.02	−0.10	−0.05	-	-	0.03	0.13		0.23	0.07	0.13
APGAR 3	-	−0.02	−0.13	−0.03	-	0.09	0.04	-	0.23		0.13	-
APGAR 4	-	−0.05	−0.05	−0.09	-	0.09	0.02	0.02	0.07	0.13		0.01
APGAR 5	-	-	-	-	-	-	-	0.32	0.13	-	0.01

Note: “-” indicates no association between two nodes

Among the cross-community edges, the largest positive partial correlations were observed between ATMD-2 (Sense of Time Control) and APGAR-3 (Growth) (weight = 0.09) and between ATMD-2 and APGAR-4 (Affection) (weight = 0.09), representing modest associations. For negative associations, the most pronounced edge was between MPAI-3 (Withdrawal or Escape) and APGAR-3 (Growth) (weight = -0.13), followed by the edge between MPAI-3 and APGAR-2 (Partnership) (weight = -0.10).

Centrality analysis

Centrality analysis results are presented in Fig. 4. Node strength reflects a node’s overall connectedness within the network, while bridge strength identifies nodes that connect different communities [41, 42]. In this network structure, ATMD-2 (Sense of Time Control) demonstrated the strongest expected influence (EI = 1.18), suggesting it may be a centrally important node within the network. Bridge strength analysis identified APGAR-3 (Growth) as the node with the highest bridge centrality, indicating its role in connecting the mobile phone addiction, time management, and family care domains.

Fig. 4.

Network centrality indicators

Stability analysis

The non-parametric bootstrap method was employed to evaluate the stability of inter-node edge weights. As shown in Fig. 5(a), the red curve represents the estimate derived from the overall sample, while the gray curve represents the bootstrap sampling estimate. The substantial overlap between the two curves indicates that the edge weights obtained through bootstrap sampling are highly consistent with those from the overall sample, suggesting excellent stability of the inter-node edge weights within the model. Stability testing further revealed a correlation stability (CS) coefficient of 0.67 for node strength, which exceeds the recommended threshold of 0.5, demonstrating good stability of the network’s node centrality (Fig. 5(b)).

Fig. 5.

Network stability analysis

Discussion

Analysis of the current status of mobile phone addiction, time management disposition, and family care among nursing interns

Consistent with our first research question, the results indicate a high-risk profile for this population. The mean MPAI score for nursing interns was 49.94 ± 8.58, considerably higher than the average score of 30.62 ± 11.92 reported for general student populations [43]. This elevated risk may be attributed to the unique stressors and negative psychological experiences encountered during clinical internship. Nursing interns are susceptible to anxiety and fatigue stemming from academic, professional, and interpersonal demands [44–47], and lower mental health levels are closely linked to mobile phone addiction [48]. These findings underscore the need for educators to address both excessive phone use and the underlying mental health of interns during this critical period.

The mean ATMD score in this study was higher than that reported by Ding et al. (154.89 ± 19.26) [21], suggesting that nursing interns demonstrated a higher level of time management ability during their clinical internships. This difference may be attributable to the heavy clinical workload, which necessitates efficient time management for task completion. Notably, the MPAI score in our study was lower than that reported in the aforementioned study, confirming a significant negative association between time management disposition and mobile phone addiction. Stronger time management competencies serve as a protective factor against mobile phone addiction and negative emotional outcomes in college students [49]. Effective time management relies on core competencies, including planning, priority-setting, maintaining a clear sense of purpose, organizing one’s environment, minimizing distractions, and using technology appropriately [50]. These findings underscore the critical need to integrate time management training into nursing education curricula. As nursing educators, we will implement targeted training to help students master these skills and cultivate sustainable time management habits.

According to the family function classification criteria [51], the overall family functioning of nursing interns in this study was generally satisfactory. However, over one-quarter (27.49%) of participants exhibited signs of family dysfunction, a finding that merits serious attention. Family support is negatively correlated with mobile phone addiction among adolescents [52]. Healthy family functioning, marked by emotional bonding and internal cohesion, may reduce the likelihood of addictive behaviors. In contrast, adolescents lacking adequate family support may turn to the internet for emotional fulfillment, thereby increasing their susceptibility to internet addiction [53]. These findings underscore the importance for clinical educators to identify and support nursing interns who may have limited family care resources. Providing targeted guidance and emotional support to this subgroup could be a valuable strategy for mitigating the risk of mobile phone dependence.

The central role in the network structure: sense of time control

This study found that ATMD-2 (Sense of Time Control) was the core node of the overall network. While previous research has documented associations between overall time management ability and addictive behaviors, the specific dimensions driving these associations have remained unclear [54, 55]. Our network analysis revealed that, within the time management-mobile phone addiction subnetwork, the edge between sense of time control and productivity loss showed the largest absolute weight (-0.03), suggesting that time control may be particularly relevant to mobile phone-related productivity impairment.

Additionally, sense of time control showed modest positive partial correlations with two family care nodes: Growth (0.09) and Affection (0.09). These associations, while small in magnitude, are consistent with previous research indicating that positive family functioning and social support are negatively correlated with adolescent addictive behaviors [56, 57]. The present findings extend this literature by identifying specific dimensions through which family functioning and time management may interconnect.

Based on these hypothesis-generating findings, future longitudinal research could usefully investigate whether time management skills—particularly sense of control over time—represent a potential mechanism linking family environment to mobile phone use patterns. If such temporal relationships are established, enhancing time management competencies might emerge as a plausible target for preventive interventions.

The bridging effect of APGAR-3 (Growth) between mobile phone addiction and time management disposition

The node “Growth” demonstrated the highest bridge centrality, suggesting it may serve as an important connector among the three domains. This hypothesis-generating finding indicates that family growth could be a valuable target for future research exploring the mechanisms linking family environment, time management, and mobile phone addiction.

Family care reflects an individual’s subjective satisfaction with family functions and is also a positive factor for personal growth and development. The normal operation of the family and the closeness of family members’ relationships have significant predictive effects on adolescents’ problem behaviors. Adverse childhood experiences (referring to traumatic events that occurred before the age of 18, mainly including abuse, neglect, and abnormal family functions) are associated with mobile phone addiction [58, 59]. The main reason for medical students’ mobile phone addiction is unmet potential psychological needs [60]. A supportive family environment can meet the psychological needs of family members and alleviate their negative psychological emotions [61]. At the same time, family conditions and parents’ behaviors also affect the mobile phone addiction and the time management ability of college students [62, 63].

These converging lines of evidence indicate that family growth—defined as the family’s active promotion of individual development—functions as a key bridging node linking these constructs. Future longitudinal research should investigate whether family growth acts as a mediator between the family environment and individual outcomes, including time management ability and mobile phone addiction. For nursing interns struggling with mobile phone dependence and time management challenges, family-centered, growth-focused interventions merit rigorous investigation in future intervention studies.

Limitations

Several limitations of this study should be noted. First, the cross-sectional design precludes causal inferences, as network analysis captures associations rather than directional or causal effects. Although centrality metrics identify potentially influential nodes, they do not confirm that intervening on these nodes would produce downstream changes; thus, longitudinal and experimental studies are needed to validate the causal hypotheses generated herein. Second, the Family APGAR scale demonstrated suboptimal internal consistency in this sample (α = 0.623), falling below the conventional acceptability threshold. This low reliability may have attenuated observed correlations and biased network structure estimation; therefore, findings related to APGAR subscales should be interpreted with caution, and replication using more reliable measures is warranted. Third, potential confounders (e.g., self-control, adverse childhood experiences, and other psychological factors) were not assessed, which may have influenced the observed relationships; future research should incorporate these variables to strengthen causal inference. Fourth, the measurement tools (MPAI and ATMD) were primarily validated in Chinese student populations, limiting the cross-cultural generalizability of the findings. Finally, the use of convenience sampling may have introduced selection bias, and replication in diverse populations is needed to enhance external validity.

Conclusions

This study employed network analysis to examine associations among mobile phone addiction, time management disposition, and family care in nursing interns. In this research, nursing interns showed a higher likelihood of mobile phone addiction than the general student population, and a considerable proportion reported some level of family dysfunction. Within the network, sense of time control emerged as a centrally important node, while family growth functioned as a key bridging node connecting the three domains. These hypothesis-generating findings may suggest potential targets for future longitudinal or experimental research aimed at understanding mechanisms underlying these associations. If causal relationships are established, interventions targeting time management skills and family growth could potentially be developed to promote healthy mobile phone use among nursing interns.

Author contributions

SZQ and XMM were responsible for the writing and revision of the manuscript. XL and LJX participated in the research design, data collection, and statistical analysis. GYM participated in the language polishing the process and also reviewed the final draft. All authors read and approved the final manuscript.

Funding

Not applicable.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted in strict accordance with the relevant provisions of the Declaration of Helsinki and the Chinese Health Research Law. The study protocol was reviewed and approved by the Ethics Committee of The First Affiliated Hospital of Anhui Medical University (Approval Number: PJ2025-06-44). Informed consent was obtained from all participants prior to questionnaire completion, and participants were informed of their right to withdraw from the study at any time without any adverse consequences. All research data were anonymized and properly stored to protect the privacy of the participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.