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. 2026 May 10;13(5):e70568. doi: 10.1002/nop2.70568

Relationships Between Parental Phubbing, Mobile Phone Addiction and Well‐Being in Chinese Nursing Students: A Cross‐Sectional Study Using Structural Equation Modelling

Juan Guo 1, Qingmin Guo 2,✉, Hanbo Feng 3, Jinmei Zou 1
PMCID: PMC13158521  PMID: 42108785

ABSTRACT

Aim

This study aims to investigate the relationships between parental phubbing, mobile phone addiction and well‐being in nursing students.

Design

A cross‐sectional, correlational study design was employed.

Methods

A convenience sampling method was used, and a web‐based survey was conducted to obtain data. All variables were measured using standardised instruments. Structural equation modelling was used to test our hypothetical model that included parental phubbing, mobile phone addiction and well‐being in nursing students. The response rate was 98.6%.

Results

A total of 362 nursing students were included in this study. The structural equation model found relationships between parental phubbing, mobile phone addiction and well‐being in nursing students. Parental phubbing scores were positively related to mobile phone addiction scores (β = 0.54, p < 0.05). We also found that mobile phone addiction scores were positively and significantly associated with well‐being scores (β = 0.25, p < 0.05). Furthermore, the direct effect of parental phubbing on well‐being was non‐significant (β = 0.14, p = 0.10), and the total effect was significant and positive (β = 0.28, p < 0.05).

Conclusion

Parental phubbing behaviour may increase nursing students' mobile phone addiction, while nursing students' addiction to mobile phones may enhance their well‐being. The direct effect of parents' phubbing behaviour on nursing students' well‐being is non‐significant. These findings are helpful for college educators and related researchers to better understand the relationships between parental phubbing, mobile phone addiction and well‐being in nursing students. Practical and effective operational suggestions on mobile phone management and the increase of well‐being in nursing students are put forward.

Reporting Method

The study followed the STROBE checklist.

Patient or Public Contribution

None.

Keywords: mobile phone addiction, nursing students, parental phubbing, structural equation model, well‐being

1. Introduction

The use of mobile phones in daily life has been prevalent. According to the 53rd Statistical Report on China's Internet Development released by the China Internet Network Information Center, as of December 2023, the number of mobile phone users in China has reached 1.091 billion, and the proportion of Internet users using mobile phones has reached 99.9%. The average weekly online time is 26.1 h (China Internet Network Information Center (CINIC) website 2024). The frequent use of mobile phones will interfere with parent–child interaction in the family. Parents who look down at their mobile phones and have no time to take care of their children will make their children feel neglected and left out, which is called parental phubbing (Kılıman et al. 2024). Parental phubbing is a familiar microcosm in family life in the information age, and the family environment is crucial to the formation of children's mental health and behavioural habits. Previous studies have found that parents' phubbing behaviour is not only related to the negative emotions of generations such as anxiety and depression in children but also may lead to mobile phone addiction in children (Yang et al. 2022). As far as we know, there are currently no relevant studies on the impact of parental phubbing on nursing students. Social learning theory holds that an individual's social behaviour is derived from observation and imitation. When people lack relevant experience or knowledge, they seek alternative experiences from essential individuals in their lives (Yang et al. 2022). According to social learning theory, we propose that in the family context, parental phubbing serves as a directly observable behavioural model for nursing students. When parents frequently use mobile phones during parent–child interactions and ignore emotional communication, nursing students gradually internalise this pattern as a normative way to respond to interpersonal situations. Over time, repeated observation and imitation of parental phubbing behaviour increase the probability of excessive mobile phone use and eventually lead to mobile phone addiction. This theoretical pathway explains how parental phubbing may shape nursing students' mobile phone use behaviours. Therefore, grounded in social learning theory, hypothesis 1 is proposed: Parental phubbing is significantly and positively associated with mobile phone addiction among nursing students.

With the widespread use of smartphones and the Internet, an increasing number of college students rely on smartphones, which have a direct or indirect impact on their well‐being (Koç and Turan 2021; Choi et al. 2023; Niu 2023; Yang 2018; Lou 2021; David and Roberts 2021). Numerous studies have shown that the more college students are addicted to mobile phones, the lower their subjective well‐being levels (Koç and Turan 2021; Choi et al. 2023). College students' addiction to mobile phones will occupy their limited study time, which may lead to delays in completing their academic tasks and generate negative emotions such as anxiety, thus reducing their subjective well‐being (Niu 2023). Yang (2018) proposed that the more time college students spend on mobile phones, the less time they spend on interpersonal communication and the less positive emotions and well‐being they experience. Lou (2021) conducted in‐depth research on post‐00s college students in China and found that mobile phone addiction is more severe among post‐00s college students. There is a negative correlation between mobile phone addiction and the subjective well‐being of post‐00s college students, which is consistent with most previous research results. However, contrary to the popular belief that using smartphones will reduce well‐being, David and Roberts's findings show that using smartphones may promote social connection and boost people's well‐being during the COVID‐19 pandemic (David and Roberts 2021). Cybulska et al. found that the more pathological the smartphone use, the higher the levels of anxiety and stress experienced by nursing students. Cybulska et al. found that the more pathological the smartphone use, the higher the levels of anxiety and stress experienced by nursing students (Cybulska et al. 2025). To the best of our knowledge, no prior research has explored the impact of mobile phone addiction on the well‐being of nursing students specifically. Nevertheless, the above‐mentioned abundant evidence demonstrates that mobile phone addiction exerts an influence on the well‐being of general college students. Therefore, hypothesis 2 is proposed in this study: There is a significant correlation between mobile phone addiction and the well‐being of nursing students.

The well‐being of nursing students is influenced by many factors. Huang et al. (2022) found that both perceived parenting style and self‐efficacy were positively correlated with subjective well‐being among nursing students. Wan et al. (2023) found that the prosocial behaviours of undergraduate nursing students had a positive predictive effect on subjective well‐being. Interpersonal sensitivity and maladaptive perfectionism were both significantly negatively associated with subjective well‐being in nursing students (Song et al. 2025; Lu et al. 2024). In addition, the higher the level of subjective well‐being of undergraduate nursing students, the stronger their interpersonal skills (Song et al. 2025). Although parental phubbing is pervasive in family environments, few scholars have examined whether and how parental phubbing affects the well‐being of nursing students. Social Exchange Theory regards interpersonal communication as an exchange process based on reciprocity (Jiang 2024). All social activities are based on exchange, and the social relations formed by people in social exchange also belong to a form of exchange (Jiang 2024). The social exchange between individuals includes not only the exchange of material goods but also the exchange of non‐material goods such as help, praise, and affection (Ahmad et al. 2023). From the perspective of social exchange, the influence of parents' phubbing behaviour on college students' well‐being can be seen as follows: parents pay less attention to college students and spend less time on them. Therefore, when faced with parents' phubbing behaviour, college students experience the feeling of being rejected, snubbed, and ignored. This interaction makes college students feel the asymmetry of the relationship, which violates the principle of reciprocity in interpersonal communication. It has a negative impact on college students and may make their well‐being index decline. Based on the above analysis and speculation, hypothesis 3 is proposed in this study: parental phubbing is significantly and negatively correlated with nursing students' well‐being.

Few studies have examined the relationships between parental phubbing, mobile phone addiction, and well‐being in nursing students. To address this gap, we propose the following hypothetical model (Figure 1) to explore the potential mechanisms of parental phubbing, mobile phone addiction, and well‐being in nursing students.

FIGURE 1.

FIGURE 1

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Hypothetical model. H = Hypothesis, MPA = Mobile Phone Addiction, PP = Parental Phubbing, WB = Well‐Being.

2. Methods

2.1. Study Design

A cross‐sectional study.

2.2. Setting and Participants

This study was conducted at the School of Nursing at a college in China. The nursing students who participated in the study were in their final year of school and had completed most nursing courses. Due to resource constraints, a convenient sampling method was adopted in this study. Three latent variables and 31 free parameters were included in this study. According to the principle that the ratio of the sample size to free parameters be 10:1 or greater (Liu et al. 2018; Kline 2015), the sample size of 362 was adequate for our structural equation modelling analysis, which required at least 310 cases.

2.3. Variables

2.3.1. Descriptive Information

Descriptive information consisted of 7 items, including age, home place, gender, only one child or not, family economic condition, willingness to engage in nursing careers or not, attitude towards nursing profession. Each question was self‐reported.

2.3.2. Parental Phubbing

Parents' phubbing behaviour was measured through the Chinese version of the Parental Phubbing Scale (PPS), which was revised and translated by Chinese researchers Ding et al. (2020). The scale is single‐dimensional, including nine questions (e.g., when my parents and I have dinner together, they use mobile phones). Each question is answered on a 5‐point Likert scale as follows: ‘never’ (1 point), ‘rarely’ (2 points), ‘sometimes’ (3 points), ‘often’ (4 points), and ‘always’ (5 points). The sum of the scores of each item is the total score, and a higher total score indicates a higher degree of Parents' phubbing behaviour. In this study, Cronbach's α value was 0.763.

2.3.3. Mobile Phone Addiction

Mobile phone addiction was evaluated by the Mobile Phone Addiction Index scale (MPA), which was initially developed by Liang in China (Leung 2008). The scale contains 17 items (e.g., I have difficulty turning off my phone) across four subscales: loss of control, avoidance, withdrawal, and inefficiency. Each item is answered on a 5‐point Likert scale as follows:’never’(1 point), ‘sometimes’ (2 points), ‘occasionally’ (3 points), ‘often’ (4 points), and ‘always’ (5 points). The sum of the scores of each item is the total score; the higher the score is, the more severe the subject's phone addiction is. In this study, Cronbach's α value was 0.804.

2.3.4. Well‐Being

The World Health Organization Five‐item Well‐Being Index (WHO‐5) scale was used to assess nursing students' Well‐being index (Topp et al. 2015). The scale is single‐dimensional, contains five questions (e.g., I have felt happy and in a good mood in the past 2 weeks), is short in structure, and is easy to use. The score of the five questions is added to the total score. The higher the total score is, the higher the nursing students' well‐being index is. In this study, Cronbach's α value was 0.993.

All three measurement instruments have been previously validated in Chinese populations. The Parental Phubbing Scale (PPS) was developed and validated among Chinese adolescents, with Cronbach's α ranging from 0.826 to 0.885 (Ding et al. 2020). The Mobile Phone Addiction Index (MPAI) has been extensively used in Chinese college student samples, reporting a Cronbach's α of 0.864 (An et al. 2025). The WHO‐5 Well‐Being Index has demonstrated cross‐cultural validity, including among Chinese university students, with a Cronbach's α of 0.85 (Fung et al. 2022). Thus, all scales are appropriate for assessing the target constructs in our nursing student cohort.

2.4. Data Collection and Ethical Consideration

Between April and May 2024, we collected data through an electronic questionnaire published by an online questionnaire platform. The researchers got support from the nursing students' teachers. The teacher showed the QR code of the questionnaire to the students during the break, and the nursing students scanned the QR code with their mobile phones to fill out the questionnaire. To ensure anonymity and data confidentiality, the following measures were implemented: (1) No personal identifiers (e.g., name, student ID number, or residential address) were collected; (2) IP addresses were not recorded by the online platform to prevent potential traceability; (3) All data were stored on a password‐protected computer accessible only to the research team; (4) Electronic informed consent was obtained separately from questionnaire responses, ensuring that consent forms could not be linked to individual survey data. The researchers emphasised that nursing students could voluntarily choose whether to participate, and unwillingness to participate would not affect their academic performance. All nursing students who agreed to participate in the study signed informed consent forms. This study was approved by the Medical Ethics Committee of China Medical University (Reference number: 2024‐141).

2.5. Statistical Analysis

Descriptive analyses were performed using IBM Statistical Package for the Social Sciences(SPSS)26.0. Harman's single‐factor test was used to test for common method bias (CMB). Continuous variables were described by mean ± standard deviation (x ± s), categorical variables were described by frequency and percentage (%). Cronbach's α (Cronbach's α > 0.7), composite reliability (CR > 0.7), and average variance extracted (AVE > 0.5) were used to evaluate local fit of the constructs (Hair et al. 2011). The P–P plots indicate the data showed an approximately normal distribution. Pearson's correlation was used to analyse the relationship between two continuous variables. To examine whether demographic characteristics influence nursing students' well‐being, a two‐step analytical approach was adopted. First, univariate analyses were conducted: Pearson correlation was used for continuous variable (age); independent samples t‐test for binary variables (gender, home place, only one child or not, willingness to engage in nursing careers); one‐way ANOVA for family economic condition (three categories: poverty, ordinary, good); and Spearman correlation for attitude towards nursing profession (five ordinal levels: like very much, like, normal, dislike, dislike very much). Second, all demographic variables were entered simultaneously into a multiple linear regression model (enter method) with well‐being score as the dependent variable. The Analysis of Moment Structures (AMOS) 26.0 was utilised to verify the hypothetical model of parental phubbing, mobile phone addiction and well‐being in nursing students. A minimum value of 0.3 for item factor loading was set to confirm the proposed constructs (Ayob et al. 2017). The model fit was assessed based on the chi‐square test (χ 2)/degree of freedom (df), the goodness‐of‐fit index (GFI), the adjusted goodness‐of‐fit index (AGFI), Tucker Lewis index (TLI), comparative fit index (CFI) and root means square error of approximation (RMSEA). We accepted the model if χ 2/df ≤ 5, GFI > 0.9, AGFI > 0.9, TLI > 0.9, CFI > 0.9 and RMSEA < 0.08 (Browne and Cudeck 1992; Hu and Bentler 1999; Schermelleh‐Engel et al. 2003). To examine the mediating effects of mobile phone addiction, the bootstrapping method was used. Statistical significance is set at p < 0.05 (two‐tailed).

3. Results

3.1. Common Method Bias Test

To assess the potential threat of common method bias due to the exclusive use of self‐reported data, Harman's single‐factor test was conducted. All items from the three scales (parental phubbing, mobile phone addiction, and well‐being) were entered into an unrotated exploratory factor analysis. The results of Harman's single‐factor test showed 7 factors with eigenvalues greater than 1 and the first component explained 24.98% (< 40%) of the total variance (Podsakoff et al. 2003), suggesting that common method bias did not substantially distort the findings.

3.2. Participant Information

A total of 367 questionnaires were collected for this study. We removed 5 questionnaires that had identical scores on all items. Finally, the effective response rate was 98.6% (Sammut et al. 2021). Among the 362 nursing students, the mean and standard deviation of age were 18.34 ± 0.70, ranging from 17 to 22 years old. Most were female (89.0%), not only one child (82.0%), and intended to become nurses (86.5%). Table 1 summarises demographic and socio‐economic characteristics.

TABLE 1.

Demographic and socio‐economic characteristics of the study subjects (n = 362).

Characteristics n %
Age (years) Range: 17–22 M ± SD: 18.34 ± 0.70
Home place Urban 155 42.8%
Rural 207 57.2%
Gender Male 40 11.0%
Female 322 89.0%
Only one child or not Yes 65 18.0%
No 297 82.0%
Family economic condition Poverty 71 19.6%
Ordinary 284 78.5%
Good 7 1.9%
Willingness to engage in nursing careers Yes 313 86.5%
No 49 13.5%
Attitude towards nursing profession Like very much 15 4.1%
Like 85 23.5%
Normal 233 64.4%
Dislike 23 6.4%
Dislike very much 6 1.7%
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3.3. Effects of Demographic Factors on Well‐Being

Univariate analysis was conducted to examine the relationship between each demographic variable and well‐being. The results showed that age was not significantly correlated with well‐being (r = −0.032, p = 0.543). No significant differences in well‐being scores were found between groups of home place (t = −0.451, p = 0.652), gender (t = −1.943, p = 0.053), only one child or not (t = −0.394, p = 0.694), or willingness to engage in nursing careers (t = −1.027, p = 0.309). One‐way ANOVA indicated that family economic status had no significant effect on well‐being (F = 2.359, p = 0.096). Notably, Spearman's rank correlation analysis revealed a significant positive correlation between attitude towards the nursing profession and well‐being (r = 0.240, p < 0.001). A multiple linear regression model was constructed to further explore the independent effects of demographic factors on well‐being. The overall model was statistically significant (F = 5.248, p < 0.001), with an R 2 of 0.106, meaning the model explained 10.6% of the variance in nursing students' well‐being. As shown in Table 2, only attitude towards the nursing profession was a significant independent predictor of well‐being (β = 0.294, p < 0.001). No other demographic variables had a significant predictive effect on well‐being (all *p > 0.05).

TABLE 2.

Multiple linear regression analysis of demographic factors on well‐being (n = 362).

Variable Unstandardised B Standard error Standardised β t p VIF
Constant 6.212 4.781 — 1.299 0.195 —
Age −0.105 0.233 −0.023 −0.451 0.653 1.0
Gender 1.013 0.527 0.099 1.924 0.055 1.0
Home place 0.071 0.346 0.011 0.204 0.838 1.1
Only one child or not 0.107 0.434 0.013 0.246 0.806 1.1
Willingness to engage in nursing careers −0.062 0.52 −0.007 −0.119 0.905 1.2
Family economic status: Poor 1.951 1.289 0.241 1.513 0.131 10.0
Family economic status: Average 0.77 1.227 0.098 0.627 0.531 9.7
Attitude towards nursing profession 1.361 0.252 0.294 5.391 < 0.001 1.2
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Note: Dependent variable: Well‐being. Reference group for family economic status: Good.

3.4. Construct Reliability and Convergent Validity

Table 3 shows the statistical data of factor loading, Cronbach's α composite reliability(CR), and average variance extracted (AVE). According to Hair, the value of the Cronbach's α coefficient should be above 0.7. In this study, the Cronbach's α coefficient was between 0.763 and 0.993, indicating the high reliability of the questionnaires (Hair et al. 2011). The composite reliability value ranged from 0.768 to 0.934, which was higher than 0.7, indicating the good convergent validity of the questionnaire (Hair et al. 2011). In addition, the average variance extracted was greater than 0.5, indicating that the observed items explain the variance more than the error term and that the model aggregation validity is relatively high (Hair et al. 2011). The study variables had factor loadings ranging from 0.625 to 0.911, indicating acceptable to satisfactory values (Ayob et al. 2017).

TABLE 3.

The Cronbach's α, average variances extracted, composite reliability and item factor loading of scales (n = 362).

Variable Items S.E. C.R. p Factor loading SMC Cronbach's α AVE CR
WB WB1 0.871 0.759 0.933 0.738 0.934
WB2 0.044 24.582 *** 0.906 0.821
WB3 0.044 24.838 *** 0.911 0.830
WB4 0.052 18.481 *** 0.777 0.604
WB5 0.046 20.477 *** 0.824 0.679
PP PP1 0.732 0.536 0.763 0.528 0.768
PP2 0.077 10.181 *** 0.625 0.391
PP3 0.101 11.396 *** 0.810 0.656
MPA MPA1 0.663 0.440 0.804 0.524 0.813
MPA2 0.139 11.224 *** 0.712 0.507
MPA3 0.138 10.610 *** 0.663 0.440
MPA4 0.126 12.339 *** 0.843 0.711
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Abbreviations: AVE = Average Variances Extracted, CR = Composite Reliability, MPA = Mobile Phone Addiction, PP = Parental Phubbing, SMC = Square Multiple Correlations, WB = Well‐Being.

***

p < 0.001.

3.5. Correlations Between Variables

The correlations among the study variables are presented in Table 4. There was a positive correlation between well‐being and parental phubbing (r = 0.241, p < 0.01), well‐being and mobile phone addiction (r = 0.299, p < 0.01), and parental phubbing and mobile phone addiction (r = 0.428, p < 0.01).

TABLE 4.

Correlation between mobile phone addiction, parental phubbing, and well‐being (n = 362).

M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. WB 11.20 3.221 1
2. PP 24.23 5.258 0.241** 1
3. MPA 46.23 10.181 0.299** 0.428** 1
4. PP1 2.85 0.721 0.222** 0.826** 0.382** 1
5. PP2 2.68 0.695 0.149** 0.776** 0.314** 0.431** 1
6. PP3 2.55 0.698 0.220** 0.866** 0.359** 0.595** 0.522** 1
7. WB1 2.10 0.702 0.891** 0.228** 0.292** 0.195** 0.159** 0.207** 1
8. WB2 2.23 0.729 0.909** 0.200** 0.232** 0.173** 0.121* 0.196** 0.813** 1
9. WB3 2.27 0.736 0.920** 0.187** 0.261** 0.193** 0.081 0.182** 0.770** 0.839** 1
10. WB4 2.36 0.755 0.846** 0.206** 0.248** 0.201** 0.122* 0.183** 0.639** 0.674** 0.731** 1
11. WB5 2.24 0.705 0.875** 0.250** 0.298** 0.222** 0.183** 0.211** 0.744** 0.712** 0.740** 0.697** 1
12. MPA1 2.55 0.613 0.282** 0.345** 0.806** 0.315** 0.273** 0.264** 0.305** 0.229** 0.240** 0.202** 0.283** 1
13. MPA2 2.73 0.852 0.196** 0.324** 0.812** 0.283** 0.228** 0.287** 0.185** 0.170** 0.192** 0.128* 0.198** 0.439** 1
14. MPA3 3.10 0.891 0.184** 0.297** 0.742** 0.258** 0.190** 0.280** 0.151** 0.116* 0.148** 0.231** 0.169** 0.383** 0.559** 1
15. MPA4 2.72 0.849 0.286** 0.412** 0.817** 0.372** 0.312** 0.333** 0.267** 0.208** 0.243** 0.259** 0.294** 0.588** 0.584** 0.546** 1
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Abbreviations: M = Mean, MPA = Mobile Phone Addiction, PP = Parental Phubbing, SD = Standard Deviation, WB = Well‐Being.

*

p < 0.05.

**

p < 0.01.

3.6. Structural Equation Model

A structural equation model was performed to identify the path relationship among parental phubbing, mobile phone addiction, and well‐being. The model (see Figure 2) exhibited excellent model‐fitting indices: χ 2/df = 2.5, GFI = 0.943, AGFI = 0.913, CFI = 0.968, TLI = 0.959, RMSEA = 0.065. In the SEM, parental phubbing scores were positively related to mobile phone addiction scores (β = 0.54, p < 0.05); mobile phone addiction scores were significantly associated with well‐being scores (β = 0.25, p < 0.05). Hence, H1 and H2 were supported. Bootstrapping mediation analysis with 2000 iterations was adopted to examine the mediating role of mobile phone addiction on parental phubbing and well‐being.

FIGURE 2.

FIGURE 2

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Pathway relations and effect values for the hypothetical model. The solid lines represent p < 0.05 and the dotted line represents p > 0.05. d = residual term, e = error term, MPA = Mobile Phone Addiction, PP = Parental Phubbing, WB = Well‐Being.

As shown in Table 5, the first direct effect refers to the path from parental phubbing to well‐being. The indirect effect (parental phubbing → mobile phone addiction → well‐being) and the total effect of parental phubbing on well‐being are presented subsequently. The second direct effect, presented under the same heading, includes two separate paths: parental phubbing → mobile phone addiction and mobile phone addiction → well‐being. The direct effect of parental phubbing on well‐being was non‐significant (β = 0.14, p = 0.10), and the total effect was significant and positive (β = 0.28, p < 0.05) (see Table 5). Therefore, H3 was not supported.

TABLE 5.

Standardised coefficients of all effects in the hypothetical model (n = 362).

Paths Effect p 95% CI [Lower, Upper]
Direct effect PP → WB 0.139 0.095 [−0.023, 0.292]
Indirect effect PP → MPA → WB 0.136 0.009 [0.043, 0.232]
Total effect PP → WB 0.275 < 0.001 [0.152, 0.406]
Direct effect PP → MPA 0.536 < 0.001 [0.423, 0.638]
MPA → WB 0.254 0.012 [0.072, 0.413]
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Abbreviations: Cl = bias‐corrected confidence interval, MPA = Mobile Phone Addiction, PP = Parental Phubbing, WB = Well‐Being.

4. Discussion

This study explored the relationships between parental phubbing, mobile phone addiction, and well‐being among nursing students, and our two hypotheses were confirmed. As far as we know, this is the first study to simultaneously examine parental phubbing, mobile phone addiction, and well‐being within a single structural equation model in a nursing student population. The findings provide novel evidence for the mediating pathway linking family environment to student mental health. With the advent of the 5G era, whether work and study, online shopping, or getting news, it is inseparable from convenient and lightweight mobile phones. Both parents and nursing students use mobile phones widely. Therefore, this research has important theoretical and practical significance in the current social background.

Our results show that there is a positive correlation between parental phubbing and nursing students' mobile phone addiction (β = 0.54, p < 0.05), supporting the hypothesis that parental phubbing will increase nursing students' mobile phone addiction, which is consistent with previous evidence (Yang et al. 2022; Sui and Liu 2024). Parents frequently look at their mobile phones, which has a negative impact on parent–child interaction. Nursing students who feel neglected and left out by their parents' phubbing behaviour are very likely to respond in the same way by being addicted to their phones. This research result also confirms the view of social learning theory (Yang et al. 2022). Individuals' social learning mainly comes from their parents. Nursing students learn socially by observing and imitating what their parents say and do. The results of this study provide implications for nursing education. When dealing with the problem of nursing students' mobile phone addiction, we should consider the influence of family factors and guide students to use mobile phones reasonably. For example, nursing schools could distribute short educational videos on parental phubbing to families and encourage parents to refrain from browsing other content on their phones and instead focus attentively on communicating with their children during parent–child calls.

Given that Chinese nursing students typically reside on campus and have reduced daily face‐to‐face contact with their parents, an important question arises: does parental phubbing remain influential under such circumstances? Possible explanations are as follows. First, the shift from offline to online parent–child communication in the digital era means that physical separation does not eliminate the influence of parental behaviours. In contemporary China, frequent virtual interactions (e.g., WeChat chats, video calls) have become the primary modality for maintaining parent–child bonds among college students. Crucially, parental phubbing is not limited to physical co‐presence; it manifests vividly in online interactions as well—such as parents being distracted by their phones, providing delayed or perfunctory responses, or failing to engage attentively during conversations. Perceived parental inattention in these virtual exchanges is sufficient to elicit feelings of being ignored or devalued, thereby may damaging the quality of the parent–child relationship. This disruption of emotional connection, whether online or offline, serves as a psychological stressor that may drive adolescents and young adults to seek compensatory comfort and belonging through excessive mobile phone use. Second, according to previous studies, early family experiences shape long‐term behavioural patterns through socialisation processes (Berger and Jammal‐Abboud 2025; Xia 2022). Individuals acquire behavioural repertoires through observing and imitating significant others, particularly parents. These learned patterns become internalised and persist into young adulthood (Berger and Jammal‐Abboud 2025). Nursing students who experienced frequent parental phubbing before university may have already developed mobile phone addiction as a maladaptive coping mechanism that continues during college. Therefore, parental phubbing may be conceptualised as an enduring family environmental risk factor rather than a temporary influence. Future longitudinal research should examine how the strength of this association changes as parent–child contact frequency declines over the university years, and whether interventions targeting family communication patterns can mitigate these effects.

According to the results of SEM, there is a significant positive correlation between mobile phone addiction and the well‐being of nursing students (β = 0.25, p < 0.05), which means that the more addicted nursing students are to mobile phones, the higher their well‐being, which contradicts the results of previous studies (Koç and Turan 2021; Choi et al. 2023; Niu 2023; Yang 2018; Lou 2021). However, this is consistent with the findings of research conducted by David and Roberts (2021) during the COVID‐19 pandemic. We made an in‐depth analysis of three possible reasons: (1) As the school in the study was relocated to the suburbs far away from the bustling city center, there were minimal entertainment places to play around, forcing nursing students to increase the possibility of relaxing through mobile phone Internet, which indirectly contributed to the positive correlation between mobile phone addiction and well‐being; (2) Due to the digital transformation of nursing education, a large number of exciting learning resources (such as serious games) are displayed in students' mobile phones. Students can achieve the dual purpose of learning and entertainment through mobile phones, which reduces learning pressure and improves well‐being; (3) Nursing students have a heavy learning burden and must complete many course learning tasks weekly. In limited and sporadic spare time, mobile phones become the best way to relax (Nie and Zhang 2020), indirectly improving nursing students' well‐being. We should treat the impact of mobile phones on nursing students dialectically. While avoiding the adverse effects that mobile phones may bring to nursing students, the positive effects of mobile phones should also be considered. Specifically, nursing educators could integrate gamified learning apps (e.g., nursing quiz games, virtual patient simulations) into the curriculum. This approach can transform phone use into an engaging educational tool that enhances both learning motivation and well‐being. Additionally, structured positive phone use challenges can be promoted as healthy phone habits. Examples include daily gratitude journaling via mobile apps and participation in online peer support groups for nursing students. These concrete methods harness the entertainment and social connectivity functions of mobile phones to improve well‐being while preventing addictive patterns.

Contrary to our hypothesis, parental phubbing did not significantly reduce nursing students' well‐being. The reason for this finding needs to be clarified. Well‐being is not determined by a single factor but by a combination of factors, including personality traits, coping styles, personal goals, psychological needs, etc. Well‐being is an essential manifestation of mental health (Jiang 2024). Paying attention to and striving to improve the well‐being of nursing students can not only help nursing students resist negative emotions such as stress and anxiety but also help them predict various positive results and better achieve self‐development.

4.1. Limitations

Nevertheless, the findings of this study have to be interpreted with respect to several limitations. First, the applicability of our findings requires careful consideration. Our sample comprised only final‐year nursing students from a single college in China, with a predominantly female composition (89.0%). This restricts generalisation to male nursing students, lower‐grade students, or other medical majors. Cross‐regional, gender‐balanced investigations are needed to verify the universality of the observed relationships. Second, the research adopts a cross‐sectional design, which is the primary limitation for causal inference. Cross‐sectional data can only reveal the correlation and potential mechanism among variables at a single time point, failing to clarify the dynamic causal relationship and temporal sequence. Longitudinal or experimental research designs are needed in future studies to verify the causal direction of the proposed model. Third, all data were collected via self‐report questionnaires, which may introduce measurement bias. Although Harman's single‐factor test indicated that common method bias was not a major concern in this study, self‐reported data remain inherently limited. Future research should consider incorporating objective measures or employing longitudinal designs with multi‐wave data collection to minimise potential bias.

5. Conclusions

In conclusion, the present study adopted an SEM approach to examine the relationships between parental phubbing, mobile phone addiction, and well‐being among nursing students. The findings contributed to broader nursing research. Specifically, there are positive correlations between parental phubbing and mobile phone addiction scores (β = 0.54, p < 0.05). Further, mobile phone addiction scores are significantly associated with well‐being scores (β = 0.25, p < 0.05). Considering the significant relationships, when managing the problem of nursing students' mobile phone addiction in the future, it is necessary to consider the influence of parents' phubbing behaviour and establish a family‐school joint education mechanism. In addition, mobile phone tools should be used scientifically and rationally to play a positive role and improve the well‐being of nursing students while avoiding the adverse effects that mobile phones may bring. However, more work using a longitudinal design and a larger sample across various settings may be needed to provide an in‐depth understanding of the relationships between parental phubbing, mobile phone addiction, and well‐being.

Author Contributions

Juan Guo: writing – original draft, methodology, formal analysis, investigation, funding acquisition. Qingmin Guo: writing – formal analysis, review and editing, funding acquisition, methodology, supervision. Hanbo Feng: methodology, formal analysis, writing – original draft. Jinmei Zou: methodology, supervision.

Funding

This project was supported by the Philosophy and Social Science Foundation Project of Sichuan Province (SCJJ24ND290) and the National Social Science Fund of China (25CFX083).

Disclosure

The views expressed in the submitted article are the authors' own and not an official position of the institution and funder.

Ethics Statement

This study was approved by the Medical Ethics Committee of China Medical University (Reference number: 2024‐141).

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgements

We are grateful to all the research assistants as well as students who participated in the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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