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. 2025 Jul 9;15:24696. doi: 10.1038/s41598-025-09864-5

Trialing a simple mobile phone dependency intervention strategy among Chinese college students

Lu Li 1,2, Hengte Wang 3, Rugang Liu 3,4,5,6,✉, Stephen Nicholas 7,8, Elizabeth Maitland 9
PMCID: PMC12241638  PMID: 40634420

Abstract

A global problem, mobile phone dependence causes physical and psychological problems for college students. We trial and assess a simple intervention strategy, the daily recording of mobile phone use to mitigate mobile phone dependency among Chinese college students. A randomized controlled trial was conducted among 110 college students, with the daily recording of mobile phone use over two weeks in the intervention group. Using Wilcoxon rank sum test and multivariate linear regression analysis, the effect of the daily recording intervention on reducing mobile phone dependence was assessed. After the daily recording intervention, the mobile phone dependence score of the intervention group decreased from 30.64 ± 9.9 to 24.44 ± 10.31, significantly lower than the control group (32.11 ± 8.51, P < 0.01). The total mobile phone using time declined from 6.22 ± 2.81 to 5.05 ± 2.85 h and was also lower than that of the control group (6.83 ± 2.98, P < 0.05). The mobile phone time watching videos and playing games dropped from 1.96 ± 1.70 to 1.09 ± 1.35 h and was lower than that of the control group (1.96 ± 1.4, P < 0.01). The results of linear regression indicate that daily recording intervention and regular exercise reduced mobile phone dependence significantly (P < 0.05). Daily recording of mobile phone use was a simple, but highly effective, effective intervention strategy reducing mobile phone dependence among Chinese college students. Daily exercise also reduced mobile phone dependence.

Keywords: Mobile phone dependence, Intervention, Daily recording, Randomized controlled trial

Introduction

Mobile phone use, including watching videos, playing games, taking pictures, and paying by mobile phone, can cause over-dependence1, headaches2, tinnitus3, meningioma risk4 and road injuries5, with insomnia6, social fear7, anxiety, depression8 and declining academic performance9. Mobile phone dependence typically refers to an individual’s excessive reliance on their phone, characterized by an inability to control usage time and frequency, leading to psychological and behavioral abnormalities, and impairing physiological, psychological, and social functioning10. The prevalence of internet and smartphone addiction has been increasing in different countries and cultures11,12, ranging from 3% to 26.8% in Hong Kong13 and reaching 41.93% among Asian medical students14. A meta-analysis encompassing 44 studies involving 147,943 university students from 16 countries revealed that smartphone addiction exerted a negative impact on students’ learning and overall academic performance, undermining the skills and cognitive abilities essential for academic achievement and learning15. These adverse impacts were confirmed in two meta-analysis of mobile phone dependence and procrastination among university students that revealed a significantly positive correlation16,17. In a cross-temporal meta-analysis of mobile phone addiction in Chinese college students based on data from 42 independent studies, Lyu et al. (2024) found compelling evidence of the rising trend of mobile phone dependence, influenced by multiple factors, including gender, anxiety, depression, loneliness, stress, well-being, social support, and resilience, as well as national-level indicators such as internet penetration rate and GDP.

To address mobile phone over-dependence and related problems, studies have explored different intervention methods including psychotherapy, pharmacotherapy, sports intervention, internet-based intervention and multicomponent interventions1. Psychotherapy focused on stimulating individuals’ positive thinking and cognition, encouraging positive behavior1. Cognitive behavioral therapy, reality therapy, mindfulness intervention, group counseling, and motivational interviewing have been shown to be highly effective and widely used in reducing internet and mobile phone dependence18,19. A systematic literature review of randomized controlled trials on exercise and psychological interventions for smartphone addiction found exercise and psychological interventions could reduce smartphone addiction20. Taking exercise or physical activity, such as tai chi, basketball, badminton, dance, running, and cycling, have positive effects on reducing mobile phone dependence levels as have complementary therapies21,22. Given that mobile phone dependence is often linked to various mental health issues, pharmacotherapy has served as an intervention to treat depression and attention deficit hyperactivity disorder18,23. Emerging web-based interventions have shown promising results and widespread acceptance, although less established than psychotherapy or pharmacotherapy24–26. Previous studies indicated that integrative therapies combining techniques was more effective in reducing mobile phone dependence than single method interventions20,27,28.

Self-control is an important predictor variable of substance and behavioral addiction, such as drinking, smoking, and internet over-dependence29. Individuals’ self-control resources are limited and easily depleted but improve with repeated practice30,31. We test the daily recording of mobile phone use as a simple and feasible intervention strategy to motivate self-control and address mobile phone dependency31–33.

Methods

Participants

We used the following standard formula to calculate sample size:

graphic file with name d33e411.gif

Setting the confidence level at 95%, Z = 1.96, standard deviation σ = 20, and margin of error δ = 5, the number of participants was 62 or greater. Given an estimated valid questionnaire response rate of 80%, we set our minimum required sample size as 78 or greater.

We recruited 115 freshmen majoring in health management at Nanjing Medical University by providing invitation letters at management course classes. Since enrolled students were recruited, the only exclusion criterion was not having a mobile device. All participants were informed about the purpose, content and process of the study and gave informed consent before recruitment in November 2023. The questionnaire was in Chinese. The effective success rate was 96.65%, yielding 110 valid responses. Nanjing Medical University gave ethics approval for the study.

Measure of mobile phone dependence and other variables

There were three dependence variables: the mobile phone dependence score; total minutes of mobile phone use; and minutes watching videos and playing games. Table 1 shows that the mobile phone dependence score was calculated by using the smartphone addiction scale-simplified version (SAS-SV)34, consisting of 10 Likert 6-point items ranging from 1 (strongly disagree) to 6 (strongly agree). The range of the dependency score was between no dependence (0) and total dependence (60). The Cronbach’s α was 0.882, indicating good reliability of the questionnaire and the 0.845 Kaiser–Meyer–Olkin measure of sampling adequacy (KMO) value also demonstrated good validity of the questionnaire.

Table 1.

The items of mobile phone dependence.

Sequence number Items
1 I delayed my planned study plan or things because I use my mobile phone
2 I have concentrating trouble when I study because I use my mobile phone
3 I feel pain in my hands, wrists, neck, shoulders and so on when I use my mobile phone
4 I can’t bear to live without my mobile phone
5 I feel anxious when my mobile phone is not around
6 My mind is always on my mobile phone or something related to it
7 I have no plans to give up my mobile phone, even if it seriously interferes with my life
8 I always check my mobile phone to make sure I wouldn’t miss some information
9 I always use my mobile phone longer than expected
10 People around me think I spend too much time on my mobile phone
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Following previous studies1, we collected participants’ sex, age, ethnicity (Han or other), parent annual household income, student living expense per month, regular exercise (yes–no), parents’ occupation and parents’ education level as control variables.

Intervention and procedures

The intervention asked participants to record their daily mobile phone use, including total mobile use time, time watching videos and playing video games, and study time. Detailed instructions were included in the on-line questionnaire and the form for daily mobile phone dependency. The questionnaire and form data were input electronically by the students, before being transferred by the researchers into password protected data files. At the end of each day, participants in the established WeChat group for the intervention group were reminded to complete their daily forms.

Figure 1 depicts the randomized controlled trial procedure. Step 1 divided the 110 participants randomly into a control group (55 participants) and an intervention group (55 participants) based on the parity (odd or even) of the last digit of a generated random number. Observation 1a and 1b Step 2 involved an online questionnaire survey to measure mobile phone dependence and to collect the control variables. In Step 3, the intervention group recorded in an online form their daily mobile phone use over a two week period, while the control group received no intervention. Finally, in observation 2a and 2b Step 4, the mobile phone dependence of participants was measured through the questionnaire survey again and the effect of the intervention was evaluated by comparing the changes before and after the intervention between the intervention group and control group.

Fig. 1.

Fig. 1

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The procedures of randomized controlled trial.

Statistical analysis

The chi-square test analyzed the differences between the control group and intervention group. The Wilcoxon rank sum test and multivariate linear regression analysis were used to analyze the effect of the daily recording intervention on reducing mobile phone dependence. All analyses were conducted by using STATA 15.0.

Results

Characteristics of participants

As shown in Table 2, 32.7% of 110 participants were male and 67.3% were female, which roughly matched the gender ratio of students majoring in health management in China. The average age was 18.31 ± 0.67, with most Han ethnicity (84.55%) and from urban families (71.82%). The average annual household income was RMB248963.6 ± 254,572.4; students’ average living expense per month was RMB2113.33 ± 993.37; one third of the students’ parents recorded private employment (33.64%); half of the parents (50.91%) had high school and below education level; and 61.82% of the students took exercise regularly.

Table 2.

Characteristics of participants.

Variables Total Control group Intervention group Chi2 P
N % N % N %
Sex Male 36 32.73 16 29.09 20 36.36 0.6607 0.416
Female 74 67.27 39 70.91 35 63.64
Age  ≤ 18 78 70.91 35 63.64 43 78.18 2.8205 0.093
 ≥ 19 32 29.09 20 36.36 12 21.82
Ethnicity Han 93 84.55 45 81.82 48 87.27 0.6262 0.429
Others 17 15.45 10 18.18 7 12.73
Family residence Urban 79 71.82 40 72.73 39 70.91 0.0449 0.832
Rural 31 28.18 15 27.27 16 29.09
Parents’ annual household income  ≤ RMB120k 37 33.64 20 36.36 17 30.91 2.6967 0.260
 ≤ RMB250k– > 120 k 37 33.64 21 38.18 16 29.09
 > RMB250k 36 32.73 14 25.45 22 40.00
Student living expense  < RMB2k 34 30.91 19 34.55 15 27.27 0.7906 0.673
 = RMB2k 50 45.45 23 41.82 27 49.09
 > RMB2k 26 23.64 13 23.64 13 23.64
Father’s occupation Farmer 10 9.09 5 9.09 5 9.09 2.1793 0.703
Factory worker 23 20.91 9 16.36 9 16.36
Civil servant 16 14.55 7 12.73 7 12.73
Private employment 37 33.64 21 38.18 21 38.18
Others 24 21.82 13 23.64 13 23.64
Mother’s occupation Farmer 10 9.09 5 9.09 5 9.09 1.7176 0.788
Factory worker 17 15.45 7 12.73 7 12.73
Civil servant 15 13.64 6 10.91 6 10.91
Private employment 34 30.91 19 34.55 19 34.55
Others 34 30.91 18 32.73 18 32.73
Father’s education Middle school and below 37 33.64 18 32.73 19 34.55 0.1537 0.926
High school 19 17.27 9 16.36 10 18.18
Above high school 54 49.09 28 50.91 26 47.27
Mother’s education Middle school and below 35 31.82 35 31.82 20 36.36 3.5574 0.169
High school 24 21.82 24 21.82 8 14.55
Above high school 51 46.36 51 46.36 27 49.09
Student regular exercise No 42 38.18 19 34.55 23 41.82 0.6162 0.432
Yes 68 61.82 36 65.45 32 58.18
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The results of chi-square test show that there was no difference between the control and intervention group based on the characteristics of participants (P > 0.05), which indicates that the randomization was effective.

The mobile phone dependence

Before the intervention, Fig. 2 shows the average mobile phone dependence score was 30.18 ± 9.74; the average total mobile phone use time was 6.7 ± 3.12 h; and the average mobile phone time watching videos and playing games was 1.94 ± 1.61 h. We found there was no statistical difference between male and female mobile phone dependency average scores. After the intervention, the average mobile phone dependence score fell to 28.27 ± 10.17; the average total mobile phone use time declined to 5.94 ± 3.03 h; and the average mobile phone watching videos and playing games use time fell to 1.53 ± 1.44 h.

Fig. 2.

Fig. 2

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The mobile phone dependence situation.

Intervention effect test

Tables 3, 4 and 5 show the Wilcoxon rank sum intervention effect test results. Before the intervention, there was no difference between the control group and intervention group mobile phone dependence score, total mobile phone use time and time watching videos and playing games. After the intervention, there were significant differences between control (32.11 ± 8.51, P < 0.0001) and intervention group (24.44 ± 10.31, P < 0.0001) in the mobile phone dependence score, the total mobile phone use time (P = 0.0004) and time watching videos and playing games (P = 0.0002). The total mobile phone use time (5.05 ± 2.85 h) and time watching videos and playing games (1.09 ± 1.35 h) for the intervention group were less than that of control group (6.83 ± 2.98 h mobile time use time and 1.96 ± 1.40 h watching videos and playing games).

Table 3.

Mobile phone dependence score (mean ± standard deviation).

Before intervention After intervention Z P
Control group 29.73 ± 9.65 32.11 ± 8.51  − 1.391 0.1641
Intervention group 30.64 ± 9.90 24.44 ± 10.31 3.263 0.0011
Z  − 0.733 4.383
P 0.4636  < 0.0001
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Table 4.

Total mobile phone use time (mean ± standard deviation).

Before intervention After intervention Z P
Control group 7.18 ± 3.35 6.83 ± 2.98 0.181 0.8567
Intervention group 6.22 ± 2.81 5.05 ± 2.85 2.565 0.0103
Z 1.643 3.527
P 0.1003 0.0004
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Table 5.

Watching videos and playing games mobile phone use time (mean ± standard deviation).

Before intervention After intervention Z P
Control group 1.91 ± 1.52 1.96 ± 1.40  − 0.426 0.6701
Intervention group 1.96 ± 1.70 1.09 ± 1.35 3.354 0.0008
Z  − 0.003 3.746
P 0.9976 0.0002
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Between observation 1b and observation 2b for the control group, there was no difference in the mobile phone dependence score, total mobile phone use time and time watching videos and playing games (P > 0.05). For the intervention group, there was a significant fall in the mobile phone dependence score (30.64 ± 9.90 to 24.44 ± 10.3, P = 0.0011), significant fall in total mobile phone use time (6.22 ± 2.81 to 5.05 ± 2.85 h, P = 0.0103); and significant fall in time watching videos and playing games (1.96 ± 1.70 to 1.09 ± 1.35 h, P = 0.0008) before (observation 1a) and after (observation 1b) the intervention. After the intervention, the Cohen’s D value between the control and intervention group was 0.811 for mobile phone dependence score (large intervention effect), 0.61 for total mobile phone use time (medium intervention effect) and 0.633 for watching videos and playing games mobile phone use time (medium intervention effect).

Table 6 reports the results of multivariate linear regression analysis. The dependent variable of model 1 was the mobile phone dependence score, for model 2 total mobile phone use time and for model 3 time watching videos and playing games. The Shapiro–Wilk test indicated that all three dependent variables followed a normal distribution (P = 0.002, P = 0.021, P < 0.001). The R-squared value was 0.3823 (model 1), 0.2251 (model 2) and 0.3517 (model 3) and the mean variance inflation factor (VIF) was 5.99, which indicates an acceptable level of model robustness and collinearity. The results indicate that the recording intervention significantly reduced the mobile phone dependence score (β = -8.32, P < 0.001), total mobile phone use time (β = -1.95, P = 0.002) and time watching videos and playing games (β = -0.83, P = 0.004). Taking regular exercise also significantly reduced the mobile phone dependence score (β = -5.89, P = 0.003) and time watching videos and playing games (β = -0.57, P = 0.049). No other demographic socio-economic control variable was significant in Table 5.

Table 6.

The results of multivariate linear regression analysis.

Variables Model 1 Model 2 Model 3
β P β P β P
Intervention No (Reference group)
Yes  − 8.32  < 0.001  − 1.95 0.002  − 0.83 0.004
Control variables
Sex Male (Reference group)
Female 3.44 0.096 0.50 0.459 0.41 0.180
Age  ≤ 18 (Reference group)
 ≥ 19  − 2.44 0.256  − 0.96 0.177 0.11 0.722
Ethnicity Han (Reference group)
Others 3.72 0.196 0.13 0.887 0.06 0.893
Family residence Urban (Reference group)
Rural  − 3.43 0.204 1.19 0.185 0.40 0.321
Parents’ annual household income  ≤ RMB120k (Reference group)
 ≤ RMB250k– > 120 k  − 2.66 0.290 0.23 0.785 0.05 0.903
 > RMB250k 1.00 0.725  − 0.13 0.892  − 0.32 0.455
Students’ living expenses  < RMB2k (Reference group)
 = RMB2k 0.88 0.714 0.33 0.681 0.39 0.282
 > RMB2k 0.12 0.968 0.75 0.437 0.02 0.960
Father’s occupation Farmer (Reference group)
Factory worker 4.44 0.541 0.96 0.691 1.13 0.300
Civil servant  − 2.79 0.717 3.51 0.170 1.00 0.383
Private employment 0.71 0.924 2.72 0.266 2.16 0.052
Others 1.94 0.798 3.15 0.213 1.47 0.198
Mother’s occupation Farmer (Reference group)
Factory worker  − 5.82 0.464 1.77 0.501  − 1.20 0.315
Civil servant  − 2.69 0.725  − 0.32 0.899  − 1.74 0.131
Private employment  − 5.50 0.478  − 0.01 0.996  − 2.15 0.066
Others  − 3.46 0.657 0.24 0.925  − 2.27 0.054
Father’s education Middle school and below (Reference group)
High school 0.05 0.988 0.97 0.340  − 0.14 0.767
Above high school  − 0.51 0.873  − 0.55 0.602 0.26 0.582
Mother’s education Middle school and below (Reference group)
High school 0.80 0.792  − 0.82 0.419  − 0.13 0.777
Above high school  − 3.66 0.264  − 0.80 0.462 0.14 0.783
Student regular exercise No (Reference group)
Yes  − 5.89 0.003  − 0.58 0.360  − 0.57 0.049
mean VIF = 5.99 mean VIF = 5.99 mean VIF = 5.99
R-squared = 0.3823 R-squared = 0.2251 R-squared = 0.3517
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Discussion

The increasing prevalence of mobile phone dependence among college students poses significant challenges to attenuate behavioral addiction and improve public health35. This dependence manifests in users’ inability to control their smart phone use, despite potential negative consequences. Mobile phone use has been equated to substance abuse and behavioral addiction, suggesting an underlying continuous failure to resist impulses is central to the persistence of the addiction36. Our study explored whether the simple intervention strategy of daily monitoring and recording of mobile phone use could mitigate mobile phone dependency. Our intervention was grounded in the therapeutic principles of self-monitoring and awareness, which have been effective in managing other forms of addictive behavior32,33. By increasing self-awareness regarding mobile device usage, college students may be better positioned to recognize excessive mobile phone use and change their behaviors. The findings from this study contribute to the broader discourse on effective interventions for reducing mobile phone dependence in college populations.

Existing research has documented intervention approaches such as psychotherapy, pharmacotherapy, sports intervention, internet-based intervention, and multicomponent interventions1. These methods typically require structured protocols and professional guidance due to their specialized nature. In contrast, the intervention protocol developed in this study features simplified implementation procedures, making it readily implementable by individuals without specialized training.

Existing research, including student populations, indicates that individuals with addictive behaviors often lack self-control to resist compulsive or impulsive mobile phone, internet and drug use37–41. Our study introduced the simple daily recording intervention to attenuate mobile phone dependence. We found that participants who engaged for two weeks in the daily recoding of their mobile phone use showed significant reductions in their mobile phone dependence scores, total mobile phone use time, and time watching videos and gaming. Our intervention leverages the concept of self-awareness, which can suppress future urges by highlighting the extent of mobile phone use31,42. The simplicity and ease of daily self-recording makes the intervention a viable strategy in educational settings, offering college student support managers, including medical staff, a practical model to assist students in managing their mobile phone use. Our study adds to the existing literature on the potential of daily recording practices as both preventative and remedial measures, contributing to public health strategies aimed at addressing the escalating issue of digital addiction.

Our analysis considered potential confounding variables potentially influencing mobile phone dependence, including sex, age, ethnicity, urban–rural home location, parents’ annual household income, student living expenses, parents’ occupation and education level. Of these, students’ regular physical activity was the only variable that demonstrated a statistically significant association with their college-aged students’ mobile phone dependence. The inclusion of these potential confounders strengthens the validity of our findings by accounting for a range of socioeconomic and demographic factors that could, but did not, influence individual behavior towards mobile phone use.

The significant effect of regular exercise on reducing mobile phone dependence aligns with the existing literature that suggests physical activity may serve as a protective factor against various forms of addictive behaviors Recent research shows that increased mobile phone use among students is associated with prolonged sedentary behavior and reduced physical activity, and that regular exercise mitigates such dependence20–22. Moreover, individuals who engage in regular physical activity typically experience lower levels of social anxiety and improved sleep quality, which may indirectly influence mobile phone use behaviors43,44. For college students, our study supports the implementation of sports interventions as a viable strategy to reduce mobile phone dependence. The existing literature lacks a comprehensive analysis regarding the optimal intensity and duration of exercise necessary to achieve these effects. Future research should aim to establish a standardized classification for exercise duration and rigorously evaluate the impact of varying exercise intensities on mobile phone dependence. Such studies will be crucial in refining intervention strategies and maximizing the therapeutic benefits of physical activity for individuals with mobile phone dependence.

Limitations

In real-world trial, our study shows a causal relationship between simple daily recording of mobile phone and attenuated mobile phone dependency, which was a highly feasible intervention for college students. The intervention duration, limited to only two weeks, restricts our ability to assess the long-term effects of daily mobile phone use recording. Sustained behavior changes and their impacts over an extended period require further studies. Our sample was restricted to health management students, which may limit the generalizability of the results to students from other academic backgrounds and to non-students. Future research should involve larger, more diverse samples that include students from multiple disciplines and non-students. Our study did not account for potential confounders like exam stress, academic workload, or social desirability bias in self-reports, which might affect phone dependency measurements. Future research should improve response objectivity, for example by including third-party evaluations from peers or teachers of subjective student inputs.

Conclusion

We found that the intervention of daily recording mobile phone use and taking regular exercise effectively reduced mobile phone dependence among college students. The simple and user-friendly daily diary intervention helped college students establish an objective awareness of their usage patterns and fostered a self-monitoring mechanism.‌ ‌Second, we also found exercise intervention reduced mobile phone use through physiological mechanisms, such as diverting attention and stimulating dopamine release.‌ Both these approaches ‌provide a baseline for mental health education initiatives in higher education institutions.‌ Future research is recommended ‌to further explore the integrated implementation of digital mobile usage monitoring tools with personalized exercise programs.

Acknowledgements

The authors thank all the participants from Nanjing Medical University in this study.

Author contributions

Lu Li: Conceptualization, Methodology, Formal analysis, Writing—Original Draft, Writing—Review & Editing. Hengte Wang: Methodology, Formal analysis, Writing—Original Draft, Writing—Review & Editing. Rugang Liu: Conceptualization, Methodology, Software, Formal analysis, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Supervision, Project administration, Funding acquisition. Elizabeth Maitland: Conceptualization, Resources, Writing—Review & Editing, Visualization. Stephen Nicholas: Conceptualization, Resources, Writing—Review & Editing, Supervision.

Funding

The Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (Grant Number: 2024SJZD127, Project name: Research on suicide risk factors identification and intervention strategy of standardized training doctors based on 4P theory) and National Natural Science Foundation of China (Grant Number: 71904089) and Creative Health Policy Research Group, Nanjing Medical University, Nanjing, China.

Data availability

The data presented in this study are available on request from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

All methods in this study were carried out in accordance with the principles of the Declaration of Helsinki.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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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 presented in this study are available on request from the corresponding author.

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