Front. PsychiatryFrontiers in PsychiatryFront. Psychiatry1664-0640Frontiers Media S.A.10.3389/fpsyt.2025.1629251PsychiatrySystematic ReviewThe influence of different intervention measures on improving mobile phone addiction among teenagers or young adults: a systematic review and network meta-analysisLiYing1†ZhangZhiyu2†ZhangJianhua3ZhangQiuyan4*LuoZhaojun5TaoDan11College of Sports Science, Jishou University, Jishou, Hunan, China2Faculty of Health Sciences and Sports, Macao Polytechnic University, Macao, Macao SAR, China3School of Physical Education and Arts, Hunan University of Medicine, Huaihua, Hunan, China4Physical Education Department, Beijing Wuzi University, Beijing, China5Football College, Wuhan Sports University, Wuhan, China
Edited by: Aviv M. Weinstein, Ariel University, Israel
Reviewed by: André Luiz Monezi Andrade, Pontifical Catholic University of Campinas, Brazil
Riza Hayati Ifroh, Northeast Normal University, China
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Objective
This study employed network meta-analysis to evaluate the impact of several exercise interventions on mobile phone addiction. The aim is to determine the most effective exercise intervention measures and provide a reference for future intervention measures to improve mobile phone addiction.
Method
Systematically search for relevant literatures in domestic and foreign databases such as Web of Science, PubMed, Embase, Cochrane Library, China Knowledge, Wanfang, etc. We evaluated the risk of bias according to the revised Cochrane Randomized Trial Risk Tool and conducted traditional and web-based meta-analyses using Review Manager 5.3 and Stata 14.0.
Result
Traditional Meta-results showed that all interventions were superior to the control group in improving mobile phone addiction (SMD= -1.38, 95%CI=-1.75, -1.01). Network meta-analysis shows that Badminton and Mindfulness-Based Therapy (MBT) have better improvement effects on mobile phone addiction among teenagers than other forms of exercise.
Conclusion
All kinds of interventions have a significant impact on reducing mobile phone addiction. Badminton and MBT have more advantages in improving mobile phone addiction. However, due to the influence of the sample size and the quality of the included literature, it is recommended to further verify the results in the future.
mobile phone addictionteenagersyoung adultnetwork analysismetasection-in-acceptanceAddictive DisordersIntroduction
Smartphones have become an inseparable part of daily life. Their use makes human life easier because they have many convenient functions and software applications (1). While mobile phones bring convenience, they also bring potential risks, leading to new behavioral problems, among which is mobile phone addiction (2). Mobile phone addiction is the theoretical criterion for defining behavioral addiction, including psychological (craving, cognitive prominence, loss of control, emotional correction), physical dependence (tolerance and withdrawal symptoms), significance, impulsiveness, spotlight behavior, and relapse (3–5). The core signs and symptoms of mobile phone addiction include obsessive thoughts about the phone (craving), spending extra time on the smartphone (tolerance), and experiencing anxiety when the smartphone is unavailable (withdrawal) (6, 7). Mobile phone addiction reduces activity levels and leads to an increase in fat and a decrease in muscle mass (8). It also lowers the sleep quality of teenagers, causes damage to the lens, and leads to immune system dysfunction (9). In addition, teenagers’ skulls are thinner, and their brain tissue is more electrically conductive. They are more likely to absorb mobile phone radiation than adults and have a higher risk of developing brain tumours than adults (10). In addition, mobile phone addiction can also cause anxiety in various aspects, such as self-existence, social interaction, and academic studies. The cognitive dissonance and negative automated thinking it triggers can further exacerbate depression, leading to more suicidal thoughts among teenagers. Furthermore, mobile phone addiction is negatively correlated with the academic performance of adolescents (11). Excessive use of mobile phones can weaken students’ inhibitory control, working memory and attention, affect teaching coherence and hinder establishing a supportive, cooperative learning environment, and those at high risk also find it more challenging to improve school adaptability through self-regulation (12). Mobile phone addiction has seriously affected the physical fitness and intelligence level of teenagers. Relevant reports indicate that mobile phone addiction has become a label for teenagers. Research shows that the incidence of mobile phone addiction among teenagers is 70% (13–15), and 21.3% of college students in China are addicted to smartphones (16). In Italy, 30% of teenagers are addicted to mobile phones (17). Therefore, it is extremely urgent to solve the problem of teenagers’ addiction to mobile phones.
At present, taking scientific and effective measures to reduce teenagers or young adults mobile phone dependence has become the focus of multidisciplinary attention. The existing intervention measures for mobile phone addiction mainly include group counseling intervention, cognitive behavioral intervention and exercise intervention, etc. At present, there are also many clinical randomized controlled trials (RCTS) to verify the intervention effects of various measures (18, 19), and some scholars have conducted research through traditional meta-analysis (20). However, there is a lack of direct comparisons among the intervention effects of different measures. Network meta-analysis (NMA) is an extension of traditional pairwise meta-analysis, with the advantage of simultaneously comparing the effectiveness of multiple interventions for a specific outcome. Even when direct comparisons between two interventions are not available within the network structure, NMA can still calculate indirect comparisons (21). By integrating both direct and indirect evidence, NMA facilitates the ranking of various interventions’ effectiveness (22). However, research on the efficacy of different interventions for mobile phone addiction remains limited. Therefore, we studied and explored the influence of varying intervention methods on mobile phone addiction, and classified the interventions in detail to determine the best intervention methods for improving mobile phone addiction, guiding teenagers or young adults with mobile phone addiction to choose the best intervention methods, and reducing mobile phone addiction among teenagers or young adults.
Methods
This study was reported per the PRISMA NMA guidelines (23). The review protocol was registered with the International Prospective Register of Systematic Review (PROSPERO CRD420251127958).
Search strategy
The computer searched PubMed, Web of Science, Embase, Cochrane Library, CNKI, and other databases, and the search period was established until April 28, 2025. The search takes the way of combining subject words and free words. We conducted a search using Pubmed as an example. For the search strategy, please refer to the Supplementary Materials (Appendix 1).
Study selection
The inclusion criteria for study selection were based on the PICOS methodology (Participants, interventions, comparators, outcomes, and study design) (23), shown in Table 1.
Inclusion and exclusion criteria.
Category
Inclusion criteria
Exclusion criteria
Population
Teenagers or Young adults who have reached the level of mobile phone addiction after assessment
Using Smartphone Addiction Scale Shortened Version (SAS-SV), The mobile phone addiction index (MPAI). SAS-SV or MPAI has good reliability and validity (24, 25).
Study
Randomized controlled trial; published in English or Chinese
duplicate publications; conference papers and review articles.
The types of interventions were based on the exercise interventions actually reported in the included studies.
Data extraction
Following database screening, all identified records were imported into Note Express software for duplicate removal. Two independent investigators systematically applied our pre-defined PICOS criteria through title/abstract screening. Potentially eligible studies were then transferred to Zotero software (v6.0.30) for full-text evaluation and data extraction. Discrepancies were resolved through consultation with a third reviewer. Standardized extraction forms captured (1): basic characteristics (first author, publication year, region) (2); participant demographics (sample size, age range) (3); intervention protocols (modality, weekly frequency, duration); and (4) outcome measures with corresponding assessment methods.
Risk of bias assessment
The risk of bias was assessed independently by two reviewers and by a third reviewer using the tools provided by the Cochrane Collaboration (26), including sequence generation, hidden assignment, blinking, incomplete outcome data, non-selective reporting of results, and other sources of bias. Each criterion was judged to have a low, unclear, or high risk of bias.
Data analysis
Meta-analysis was performed using Rev Man 5.3 software. The standardized mean difference (SMD) with 95% confidence intervals was selected as the effect measure for continuous outcomes, and a random-effects model was applied to pool effect sizes across studies. A 95% confidence interval (95% CI) represented each effect size. I2 was used to determine the heterogeneity of effect indicators among different studies quantitatively. An I2 >50% or ap value of 0.10 or less for the Q test was interpreted as indicating substantial heterogeneity (26). When the heterogeneity was significant, the random effects model was used; otherwise, the fixed effects model was used. Subgroup analysis was based on movement characteristics and population characteristics. The source of heterogeneity was explored through sensitivity analysis for the studies with significant heterogeneity. Sensitivity analysis was used to test whether the source of heterogeneity was due to one of the original studies.
NMA was also conducted to perform a random-effects multivariate NMA for pooled estimates within the frequentist framework (27). We conduct NMA based on the frequency-based framework through Stata16.0 (network and mvmeta packages). The standardized mean difference (SMD) and its 95% confidence interval were used as combined statistics, and the direct and indirect comparison evidence was integrated through the multivariate random-effects model. The geometry of the network is summarized into a networkplot, in which the lines connecting the nodes represent direct head-to-head comparisons between interventions, and the size of each node and the thickness of each line connecting the nodes are directly proportional to the number of studies. Draw the network contribution graph (netweight package) and calculate the contributions of each direct comparison. The local-global inconsistency test (network meta inconsistency/consistency) is adopted, supplemented by the node splitting method to identify the inconsistency of specific loops. The surface under the cumulative ranking curve (SUCRA) was used to rank and compare the effects of the different interventions. SUCRA values range from 0 to 100, where 100 indicates the best treatment with no uncertainty, and 0 indicates the worst treatment without uncertainty (28). Moreover, a network funnel plot was generated to check for publication bias (netfunnel).
ResultsStudy selection
Our research obtained a total of 2,784 records. After deleting 436 duplicate records, the titles and abstracts of 2,348 studies were screened. Subsequently, read the remaining 67 articles in full. After reading the full text, we eliminated 35 articles that did not conform to the research. Ultimately, 32 studies were included in the meta-analysis. The research flow chart is shown in Figure 1.
PRISM flow diagram.
Flowchart illustrating the systematic review process, showing four main stages: Identification, Screening, Eligibility, and Included. Initially, 2784 records were identified and 436 duplicates removed. After screening 2348 titles and abstracts, 67 full-text articles were assessed. 2281 articles were excluded for reasons like no randomized controlled trials (RCTs) and no relevant interventions. During eligibility assessment, 35 more articles were excluded for issues like inconsistent intervention measures. Ultimately, 32 studies were included in the meta-analysis.Basic information included in the study
As shown in Table 2, a total of 2,891 teenagers or young adults addicted to mobile phones were involved in the 32 included studies. In terms of result measurement, 15 studies used MAPI to assess the severity of mobile phone addiction, 3 studies used SAS-SV for assessment, and 14 studies used MPATS for assessment. 15 studies were published before 2020, and 17 studies were published after 2020.
The methodological quality of the included 32 articles was evaluated. The summary of the risk of bias assessment is shown in Figure 2.
Summary of risk of bias.
Bar graph showing risk of bias in different areas: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other bias. Colors represent low (green), unclear (yellow), and high risk (red). Most areas indicate low risk, except blinding of participants and personnel, and blinding of outcome assessment, which show high risk.Meta-analysis
The effect of the intervention measures was compared with that of the control group. A meta-analysis was conducted on 32 studies. The overall result is shown in Figure 3. Compared with the blank control group, exercise intervention had a significant effect on improving mobile phone addiction in adolescents [SMD= -1.38, 95%CI (-1.75, -1.01), p < 0.001], and I2 showed significant heterogeneity (I2 = 94%, p < 0.001).
Impact of interventions on Mobile phone addiction.
Forest plot comparing experimental and control groups across multiple studies. It displays mean differences, standard deviations, total sample sizes, weights, and confidence intervals for each study. Most studies favor the experimental group, with an overall standardized mean difference of -1.38. The summary diamond indicates significant overall effect favoring the experimental group, with substantial heterogeneity (I² = 94%).Subgroup analysis
We conducted subgroup analyses based on sample size, Intervention mode, outcome measurement, Intervention duration, Year of publication, and intervention frequency. There was no statistically significant difference between the two groups in terms of publication year and Intervention frequency, sample size and Intervention duration (P > 0.05). In terms of the outcome measurement, and Intervention mode, the differences between subgroups were statistically significant (p < 0.05), as shown in Table 3.
Subgroup analysis to assess the effect of interventions on adolescents’ intervention addiction.
Variable
Number of trials
Sample size
Meta-analysis
Heterogeneity
Experimental
Control
SMD
CI
Pa
I2
Chi2
Pb
All
32
1437
1454
-1.38
-1.75, -1.01
—
94%
488.95
<0.001
Year of publication
Before 2020
15
835
808
-1.58
-2.17, -1.00
0.38
94%
229.76
<0.001
After 2020
17
602
646
-1.25
-1.73, -0.76
93%
214.75
<0.001
Sample size
≤50
15
198
229
-1.41
-2.17, -0.66
0.87
90%
133.88
<0.001
>50
17
1239
1225
-1.48
-1.93, -1.04
95%
339.17
<0.001
Outcome measurement
MPAI
15
853
846
-1.97
-2.58, -1.37
<0.001
94%
236.24
<0.001
SAS-SV
3
189
188
-4.43
-6.29, -2.58
0
0.04
0.56
MPATS
14
395
420
-0.91
-1.34, -0.53
84%
79.33
<0.001
Intervention duration
4–8 Weeks
21
952
962
-1.60
-2.08, -1.11
0.14
93%
289.33
<0.001
12–16 Weeks
11
485
492
-1.05
-1.60, -0.50
83%
138.83
<0.001
Intervention mode
Exercise Intervention
17
749
748
-1.04
-1.53, -0.55
0.03
94%
272.90
<0.001
Psychological Intervention
15
688
706
-1.91
-2.54, -1.28
93%
194.85
<0.001
Intervention frequency
Once a week
15
409
422
-1.91
-2.62, -1.19
0.14
94%
216.48
<0.001
2–3 times a week
14
716
730
-1.06
-1.50, -0.63
90%
126.40
<0.001
Other
3
312
302
-1.36
-3.06, 0.35
98%
126.09
<0.001
Sensitivity analysis
Sensitivity analysis of the included literature showed that no single study changed the overall outcome.
Publication bias
The funnel plot showed potential publication bias (Figure 4). We further conducted the test through Begg, and the result of the Egger’s test was P = 0.450 (P > 0.05), indicating that the probability of surface bias in the included studies was relatively low.
Funnel plot for the publication bias of adolescents’ Mobile phone addiction.
Funnel plot with open circles representing data points on a graph. The x-axis is labeled MD and the y-axis is labeled SE(MD). Two symmetrical dashed blue lines form a funnel shape, with points distributed mainly within the funnel.Network meta-analysis
To examine the differences in effects among the different interventions, network meta-analyses were further performed.
Network diagram
As shown in Figure 5, the dots in the figure represent the number of subjects in each group; the larger the dots are, the larger the sample size of the subjects. The lines connecting the dots represent the number of original studies directly compared in pairs; the thicker the lines are, the more original studies there are.
Network diagram of mobile phone addiction. Aerobic Aerobics (AA), Tai Chi (TC), Table tennis (TT), Jump rope (JR), Control group (CG), Combined Intervention (CI), Mindfulness-Based Therapy (MBT), Cognitive Therapy (CT), Group Counseling (GC).
A network diagram illustrates the connections between different nodes labeled CG, CI, CT, GC, MBT, Biofeedback, Basketball, Baduanjin, Badminton, AA, Yoga, Volleyball, Sanda, TC, and TT. The nodes are represented as blue circles of varying sizes, and the lines indicate the strength of connections, with thicker lines depicting stronger associations.Inconsistency of the network
The inconsistent model was adopted for verification. The result (P=0.991) indicated that the model inconsistency was not significant. The inconsistency and local inconsistency tests were conducted again. We did not find significant inconsistencies in all the results.
Results of network meta-analysis
Network meta-analysis shows that badminton is significantly superior to CG (SMD -3.86, 95% -6.34 to -1.38) and volleyball (SMD -4.71, 95% -9.07 to -0.34). MBI was significantly superior to CG (SMD -2.37, -3.76 to -0.97), as shown in Figure 6. The forest plots of the comparisons that meet the conditions are shown in Supplementary Materials -Appendix 2.
Results of network meta-analysis.
A colored table displaying numerical data across various activities and methods, such as badminton, Baduanjin, basketball, and yoga. Each cell contains a number with a confidence interval in parentheses, formatted as “(lower bound, upper bound)”. The table uses alternating colors for sections: green for headings, blue for certain data rows, and peach for others. Some numbers are bolded for emphasis, such as those in the lower right and upper areas, suggesting focus or significance in those sections.Intervention effect ranking
The SUCRA probability of each intervention in the network is shown in Figure 7. The SUCRA value (Table 4) is the probability that each intervention is among the best of those in the network, with larger values representing higher-ranking probabilities. The SUCRA probability of each intervention in the network is shown in Figure 8. The SUCRA value (Table 4) is the probability that each intervention is among the best of those in the network, with larger values representing higher-ranking probabilities. Badminton (SUCRA=93.8)> MBT(SUCRA=77.3)> Baduanjin (SUCRA=74.3)> GC (SUCRA = 69.6) > Aerobic Aerobics (SUCRA = 62.1) > Basketball (SUCRA = 56.8) > CI (SUCRA = 46.2) > CT (SUCRA = 45.5)> TC (SUCRA = 41.6) >Biofeedback (SUCRA = 39.1) > Yoga (SUCRA = 37.7)> Sanda (SUCRA = 36.3)> TT (SUCRA = 25.7) > CG (SUCRA = 24.0) > Volleyball (SUCRA = 20.0).
Sucra graph of effectiveness among interventions.
Line graph titled “SUCRA for Mobile phone addiction” displaying cumulative probability against rank for various interventions. Different colored and patterned lines represent activities such as AA, Badminton, Basketball, Yoga, and more. Legend identifies each activity.
Funnel plot showing effect sizes centered at a comparison-specific pooled effect on the x-axis and standard error of effect size on the y-axis. Colored dots represent different comparisons, forming a triangular distribution with dashed lines, centered around a red vertical line at zero. A legend identifies comparisons by color.Risk of bias across studies
The publication bias was illustrated by funnel plots (Figure 8). According to the network meta-analysis, the funnel plot showed slight asymmetry.
Discussion
Mobile phone addiction seriously endangers the physical and mental health of teenagers or young adults, reduces academic performance and intelligence levels (10, 27). Therefore, it is of great significance to improve mobile phone addiction among teenagers or young adults. This study includes 32 randomized controlled studies. A traditional meta-analysis was conducted on 32 studies to evaluate the effects of the intervention measures and the control group. Furthermore, a network meta-analysis was conducted on the included studies to analyze the direct and indirect comparisons among different intervention measures.
This meta-analysis is based on 32 randomized controlled trials to evaluate the impact of intervention measures on mobile phone addiction among adolescents or young adults. The results showed that compared with the blank control group, all intervention measures (Combined Intervention (CI), Cognitive Therapy (CT), Group Counseling (GC), Mindfulness-Based Therapy (MBT), Exercise intervention) significantly improved adolescent mobile phone addiction. Total amount effect for [SMD = 8.78, 95% CI (10.64, 6.91), p < 0.001), Similar to the results reported by Wu (61) et al. (2023) [SMD= -2.88, 95%CI: (-3.78 to -1.97)] and Pan et al (20) (2023). [SMD=-3.214, 95%CI (-4.293 to -2.135)]. The differences in the subgroup analysis based on Outcome measurement, Intervention duration, and Intervention mode were statistically significant (P < 0.05). However, the differences were not statistically significant in the subgroup analyses of Year of publication, Sample size, and Intervention frequency (p > 0.05). Due to the significant heterogeneity of the analysis results, further analysis and verification are needed.
The conventional meta-analysis confirmed that various interventions demonstrated significantly greater improvement in smartphone addiction compared to control groups. However, this approach is inherently limited to pairwise comparisons, preventing comprehensive evaluation of relative effectiveness across different interventions (20). As an extension of traditional methods, NMA overcomes this limitation by synthesizing both direct and indirect evidence, enabling simultaneous comparison of multiple interventions and identification of the optimal therapeutic strategy (21). Our NMA results show that badminton is superior to other interventions in improving mobile phone addiction among teenagers or young adults. Apart from walking and jogging, badminton is one of the most participated sports among Chinese mass sports enthusiasts, with a participation rate as high as 42.6%. Regular participation in badminton can not only cultivate the reaction ability of both participants but also develop the ability to cooperate with teammates. The interaction during the sport can meet social needs and reduce teenagers’ reliance on obtaining social satisfaction through mobile phones (62). In addition, badminton is characterized by strong competitiveness, high confrontation, and diverse offensive and defensive variations. Badminton participants devote themselves wholeheartedly and pay less attention to their mobile phones (62). Badminton is a moderate-intensity sport. Compared with low-intensity and high-intensity sports, moderate-intensity sports help strike a balance between the benefits of cardiometabolic metabolism and the promotion of pleasant experiences, achieving the promotion of physical and mental health and the replacement and satisfaction of need (63, 64).
Furthermore, our research found that Mindfulness-Based Therapy (MBT) ranked second only to badminton in improving mobile phone addiction among teenagers or young adults. Mind-based intervention is a long-term and delicate training process. During this process, people with an addiction are expected to gradually change their wrong concepts and establish new and objective resistance to inappropriate mobile phone usage behaviors (65). The current mindfulness training process adopts a group mutual assistance model, that is, group members deepen their understanding of positive beliefs and internal viewpoints during the communication process, thereby improving the efficiency of the training (56). During the discussion process, questions need to be answered and mistakes need to be corrected, which enhances the self-control motivation of people with an addiction. Early studies on event-related potential (ERP) have also confirmed that the attention bias caused by the high sensitivity of Internet addiction patients to specific addiction-related cues may be an essential basis for the generation and maintenance of their addictive behaviors (66). The intervention of MBT on mobile phone addiction can be explained from the aspect of attention bias (67). Through MBT, attention bias was trained to a large extent. Attention bias is one of the strong predictors of the recurrence of addictive behaviors.
Although our research found that badminton and MBT have a better effect on improving mobile phone addiction among adolescents or young adults, due to the relatively few randomized controlled trials involving badminton and MBT in this study, the impact of these intervention measures on adolescent Internet addiction still needs to be further explored. Due to the small number of included studies, the results of this study must be preliminarily considered.
Strengths and limitations
It is crucial to identify and explain some advantages and limitations of this study. Our research results confirmed the effectiveness of intervention measures (exercise intervention, MBT, CT, GC) in improving mobile phone addiction among adolescents, and further explored through network element analysis which intervention measure has the best effect on improving mobile phone addiction. First of all, we conducted a comprehensive and systematic search of the published literature to reduce bias and identify potential related studies. Secondly, in this study, the retrieval method was adopted to search seven databases, and the retrieved literature was analyzed. We conducted a strict literature screening. We included 32 studies, and many of the included studies were published in Chinese. Due to the influence of cultural background, their global application is limited. Caution should be exercised when inferring the results. The studies included in this analysis demonstrated unclear allocation concealment and inadequate blinding procedures, methodological limitations that may have introduced performance bias and detection bias, thereby compromising the overall quality of the evidence. We strongly recommend that future studies adhere strictly to standardized reporting of randomization processes and blinding protocols to enhance methodological rigor. Furthermore, our study has high heterogeneity (I²= 94%), and high heterogeneity can affect the validity of the meta-analysis results and the reliability of the validity interpretation. Finally, the intervention measures were ranked based on the average score of SUCRA. This does not necessarily mean that the intervention measures with higher rankings are statistically significantly superior to those with lower rankings. Therefore, the research results should be interpreted with caution. Our research confirms that badminton and MBT have a very good effect on improving mobile phone addiction. However, the optimal intervention cycle and frequency remain to be further explored. In future research, attention should be paid to the mobile phone addiction behaviors of different groups of people. The impact of various interventions on the addiction problems of other groups in society should be understood from multiple dimensions, and the health and quality of life of the target population should be improved in a targeted manner. Therefore, in the future, more rigorous, comprehensive and high-quality randomized controlled trials with different cultural backgrounds need to be carried out to provide a reliable theoretical basis for the research update in this field. The mechanism of mobile phone dependence behavior is complex. However, the research has only focused on the impact of psychological intervention and exercise intervention on mobile phone addiction. In the future, the physiological mechanism should be further explored from the perspectives of physiology and imaging.
Conclusion
According to our research results, all intervention measures have a significant impact on improving Internet addiction. Based on the results of NMA, badminton and MBT may be the best intervention measures. Mobile phone addiction has brought many adverse effects on the physical and mental health of teenagers. In the future, schools should promptly screen for mobile phone addiction among teenagers or young adults, adopt a combination of physical intervention and psychological strategies, and form a comprehensive and systematic intervention system to prevent and reduce the risk of mobile phone addiction among teenagers. However, our research is also subject to some limitations, such as a majority of studies in China, high heterogeneity, and unclear optimal intervention duration/frequency. Therefore, future research should focus on conducting multi-center, cross-cultural randomized controlled trials to further verify the dose-effect relationship of the optimal intervention plan under different cultural backgrounds.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
Author contributions
YL: Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing. ZZ: Resources, Funding acquisition, Supervision, Writing – review & editing. JZ: Methodology, Software, Writing – original draft. QZ: Data curation, Funding acquisition, Writing – original draft, Writing – review & editing. ZL: Data curation, Formal analysis, Writing – original draft. DT: Formal analysis, Methodology, Writing – original draft.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1629251/full#supplementary-material
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