Cognitive behavioral intervention on digital addiction behavior among nursing students in Saudi Arabia

Abstract

Aim

To assess the effectiveness of a brief cognitive behavioral intervention (CBI) on digital dependence among nursing students in Saudi Arabia and to examine demographic and usage predictors of post-intervention outcomes.

Methods

A pretest–posttest quasi-experimental study was conducted with 163 students (aged 18–23 years) at K’ University. Participants completed the Digital Addiction Scale (DAS) before and after a three-session group CBI. Paired t-tests and correlations explored inter-domain relationships, and linear regressions examined predictors of post-intervention scores.

Results

Mean DAS scores improved significantly for overuse (mean difference 0.40, p < .001), non-restraint (0.22, p = .010), and dependence (0.39, p < .001). Emotional state increased but not significantly (p = .135) and inhibiting the flow of life was unchanged (p = .742). Post-intervention overuse was predicted by daily hours of device use (β = 0.94 for 3–4 h; β = 1.04 for ≥7 h; all p < .05), while other demographic factors were non-significant.

Conclusion

A brief CBI improved behavioral aspects of digital dependence but had limited effect on emotional dimensions. Integrating culturally adapted CBIs and digital-wellness modules into nursing curricula could reduce digital distraction and enhance self-regulation. Further controlled studies are needed to validate and expand upon these results.

Keywords

digital dependence cognitive behavioral intervention students smartphone use Saudi Arabia healthcare setting

1. Introduction

Digital technology dependence has increasingly emerged as a critical behavioral health concern among university students worldwide. The widespread integration of smartphones, social media, and internet platforms into academic and personal life has generated profound changes in how university students learn, communicate, and interact.¹ In healthcare education, these technologies are also central to health informatics, enabling students to access clinical guidelines, electronic health records, decision-support tools, and digital learning platforms that support evidence-based practice. While digital technologies enhance learning and professional competencies, their excessive or uncontrolled use may contribute to digital dependence, characterized by compulsive device use that disrupts daily functioning and wellbeing.^2,3

Health informatics is increasingly integrated into nursing education as healthcare systems rely on digital technologies for clinical decision-making, communication, and patient care.⁴ Nursing students must develop competencies in using electronic health records (EHRs), mobile health applications, clinical databases, and digital learning platforms to support evidence-based practice and safe patient care.⁵ These technologies allow students to access real-time clinical information, enhance learning, and improve patient safety during training.⁶ However, excessive or uncontrolled use of digital devices may negatively affect concentration, academic engagement, and professional developments.

In Saudi Arabia, research has demonstrated high prevalence rates of internet and smartphone addiction among university and medical students, with notable consequences for mental health, sleep quality, and academic performance.^7–9 Among higher education populations, nursing students are particularly vulnerable. Their dual responsibilities of academic study and clinical training require sustained concentration, professional discipline, and interpersonal engagement.^10,11 Yet, research indicates that misuse of health informatics and internet digital dependence among nursing students is linked to distraction in clinical environments, increased stress, reduced academic performance, and diminished sleep quality.^12,13 Similarly, in Saudi Arabia, excessive smartphone use among medical students has been associated with reduced productivity, social withdrawal, and negative effects on mental health.¹⁴

Nomophobia has been associated with higher levels of anxiety, depressive symptoms, and maladaptive coping strategies.^15,16 Furthermore, social media dependence, such as Facebook or WhatsApp addiction, has been linked to poor self-esteem, emotional dysregulation, and impaired social functioning in student populations.^17,18

Given these risks, interventions are needed to mitigate the negative consequences of digital dependence.

Cognitive Behavioral Intervention (CBI) has proven effective for behavioral addictions, including problematic internet use and smartphone overuse. CBI helps individuals identify maladaptive cognitions; restructure thought patterns and develop healthier behavioral strategies.^19,20 Randomized trials demonstrate that CBI significantly reduces symptoms of internet addiction and improves emotional well-being and quality of life among young adults.^21,22

Within the context of health informatics, systematic monitoring of digital behavior is therefore essential to ensure that technology supports rather than disrupts healthcare training and practice. One validated tool for assessing problematic digital use is the Digital Addiction Scale (DAS), which measures multiple dimensions of digital dependence, including overuse, non-restraint, inhibiting the flow of daily life, emotional state, and dependence,^22,23 In this study, we use a version of the DAS, to measure overuse, non-restraint, emotional dependence, etc., before and after a brief CBI. Global evidence has documented rising prevalence of problematic smartphone use among university and health profession students, reporting associations with psychological stress, depression, poor academic outcomes, and reduced professional attentiveness.^24–26 Integrating DAS-based assessment with CBI programs offers a practical health informatics approach for monitoring digital behaviors and implementing targeted interventions to promote responsible technology use among healthcare students in Saudi Arabia.

Accordingly, this study aimed to evaluate the effectiveness of a brief CBI in reducing digital dependence among undergraduate nursing students, as measured by changes in overall DAS scores and its five domains (overuse, non-restraint, inhibiting the flow of life, emotional state, and dependence) from pretest to posttest, and to examine sociodemographic and digital-use factors associated with post-intervention outcomes.

2. Materials and methods

2.1. Design and setting

This study adopted a one-group pretest–posttest quasi-experimental design to evaluate the effectiveness of a brief CBI on digital dependence among undergraduate nursing students at K University in Saudi Arabia. The study was conducted between 5 April 2024 and 30 September 2024.

The study protocol was approved by the Institutional Review Board of the affiliated university (ECM#2024-805). Written informed consent was obtained from all participants prior to enrollment. Participation was voluntary, and students could withdraw at any time without academic penalty. All data were de-identified and stored on secure, password-protected servers accessible only to the research team. We also adopted the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) statement, and the provided TREND checklist (Supplementary Table 1).

2.2. Participants

Study participants were recruited using convenience sampling from all undergraduate Bachelor of Nursing students from 1 to 4 years. Recruitment was conducted through classroom announcements and information sessions during scheduled lectures. All the students aged 18–23 years and who owned a smartphone and consented to participate throughout the intervention period. Students were excluded if they were currently receiving psychiatric treatment specifically for addiction or severe mental health conditions, were unable to attend at least two of the three intervention sessions, or declined or subsequently withdrew consent at any stage of the study. No random allocation to groups was performed because the study used a single-group pre–post design.

Sample-size determination was performed using an a-priori power analysis in G*Power 3.1. Assuming a medium effect size (Cohen’s f = 0.25), α = .05, and power (1–β) = .80 for a repeated-measures (within-subjects) ANOVA with two time points (pre and post), the required total sample was N = 128. Our actual sample of N = 161 exceeded this minimum, ensuring adequate statistical power to detect the expected effects on the primary outcome.

2.3. Measures

2.3.1. Sociodemographic questionnaire

A brief sociodemographic questionnaire was used to collect information on participants’ age, year of study (I–IV), number of digital devices owned, average daily hours of device use, and monthly family income.

2.3.2. Digital Addiction Scale (DAS)

Digital dependence was assessed using the Digital Addiction Scale (DAS) it also used as a digital health informatics assessment tool to monitor problematic technology use and evaluate behavioral interventions among healthcare students. It consists of 19-item instruments rated on a five-point Likert scale and comprising five sub-dimensions: Overuse, Non-restraint, Inhibiting the Flow of Life, Emotional State, and Dependence. Respondents indicate their agreement with each statement (5 = strongly agree to 1 = strongly disagree). Item scores are summed and divided by the total number of items to generate a mean score, with possible scores ranging from 1 (lowest) to 5 (highest); higher scores indicate greater levels of digital addiction (Supplementary Table 2).

2.3.2.1. Validity and reliability

The validity and reliability of the instruments used in this study were carefully established. Digital dependence was measured using DAS, a validated instrument developed for university students. The DAS demonstrates strong internal consistency, with a reported Cronbach’s alpha of 0.874, indicating high reliability. Construct validity has been confirmed through confirmatory factor analysis, with acceptable model fit indices (χ²/df = 2.33; RMSEA = 0.05; CFI = 0.94), supporting its five-factor structure. Test–retest reliability over a three-week interval has also been shown to be satisfactory (r = 0.779, p < .001). Criterion-related validity was established through significant correlations with related measures of internet and smartphone use reported in prior studies. In the present study, the DAS demonstrated high internal consistency across the total scale and subdomains, confirming its suitability for assessing digital addiction among nursing students (Supplementary Table 3).

2.4. Cognitive behavior intervention

The pretest survey was administered prior to the CBI in April 2024. After providing informed consent, participants completed baseline questionnaires including demographic information and the DAS. The three-session CBI program was delivered weekly (90 minutes per session) in small groups of 15–20 students by trained faculty between May and June 2024.

The intervention integrated cognitive-behavioral strategies with digital self-monitoring tools consistent with health informatics approaches to digital behavior management. Session 1 focused on psychoeducation and awareness of digital dependence, during which students interpreted their DAS scores and reviewed smartphone screen-time analytics to identify personal usage patterns. Session 2 emphasized trigger identification and cognitive restructuring; students used digital tracking features to monitor device use and reflect on contextual triggers and maladaptive beliefs related to excessive digital engagement. Session 3 focused on behavioral change and relapse prevention, introducing stimulus control, time-management strategies, graded goal setting, and relapse-prevention planning. Students developed individualized digital-use action plans using time-management planners, behavioral contracts, and group feedback. They were also guided to adopt health-monitoring and tracking applications, manage notifications, and configure device-based usage limits to support sustained self-regulation across all DAS domains.

Learning activities included short lectures, group discussions, worksheets, role-playing, and digital self-monitoring exercises. The intervention was adapted from established CBI protocols for problematic internet and smartphone use. Post-test DAS assessment was conducted in July 2024, one week after the intervention (Supplementary Table 4).

3.5. Statistical analysis

Data analyses were conducted using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA). Descriptive statistics, including means, standard deviations, frequencies, and percentages, were used to summarize participants’ sociodemographic characteristics and baseline Digital Addiction Scale (DAS) scores. The primary effectiveness of the cognitive behavioral intervention was evaluated using paired-samples t-tests to compare pretest and posttest mean scores for the overall DAS and each of its five domains, as this approach is appropriate for within-subject comparisons in a single-group pre–post design. Effect sizes (Cohen’s d) were calculated to quantify the magnitude of change. Pearson correlation coefficients were computed to examine relationships among DAS domains at baseline and post-intervention. Multiple linear regression analyses were performed to explore predictors of post-intervention digital addiction outcomes, with DAS domain scores and covariates; additional subgroup analyses included corresponding pretest scores as covariates to adjust for baseline severity. All statistical tests were two-tailed, with significance set at p<0.05. Assumptions (normality, homoscedasticity, multicollinearity) were evaluated and met.

3. Results

3.1. Sociodemographic and digital use characteristics

Table 1 presents the sociodemographic and digital use characteristics of the nursing students. Most participants were aged 18–19 years (64.8%), followed by 20–21 years (19.8%) and 22–23 years (15.4%). With respect to academic level, most were in their second year of study (37.4%), while 35.6% were in the first year and 27.0% in the fourth year. In terms of digital device ownership, the majority reported using two devices (67.5%), whereas 19.6% used three devices, and smaller proportions used only one device (6.1%) or four or more devices (6.7%). Daily digital usage was high, with 38.0% of students spending seven hours or more on devices and 30.7% reporting 5–6 hours of use; comparatively fewer reported 3–4 hours (27.6%) or 0–2 hours (3.7%). Monthly family income varied, with the highest proportion belonging to households earning 6000–10,000 SAR (29.6%), followed by 0–5000 SAR (25.3%), 16,000 SAR and above (23.5%), and 11,000–15,000 SAR (21.6%).

Table 1.

Sociodemographic and digital use characteristics of students (N = 163).

Variables	Categories	Frequency (n)	Percentage (%)
Age (years)	18–19	105	64.8
	20–21	33	19.8
	22–23	25	15.4
Year of Study	I year	58	35.6
	II year	61	37.4
	IV year	44	27.0
No. of Digital Devices Used	1	10	6.1
	2	110	67.5
	3	33	19.6
	4 and more	11	6.7
Daily Hours of Use	0–2 hours	7	3.7
	3–4 hours	45	27.6
	5–6 hours	50	30.7
	7 and above	62	38.0
Monthly Family Income (SAR)	0–5000	41	25.3
	6000–10,000	48	29.6
	11,000–15,000	36	21.6
	16,000 and above	38	23.5

SAR, Saudi Arabian Riyal.

3.2. Comparison of pre and posttest digital use characteristics of the students

Table 2 shows the paired samples t-test results comparing pre- and post-test scores across the digital dependence subscales. Significant improvements were observed for overuse (mean difference = 0.396, t = 4.379, p < .001), non-restraint (mean difference = 0.221, t = 2.606, p = .010), and dependence (mean difference = 0.394, t = 3.759, p < .001), indicating that the cognitive behavioral intervention produced meaningful changes in these areas. In contrast, no statistically significant differences were found for inhibiting the flow of life (mean difference = 0.035, p = .742) and emotional state (mean difference = 0.102, p = .269), suggesting that these domains were less responsive to the intervention over the study period.

Table 2.

Pre- and post-test means, standard deviations, and paired t-test results for digital dependence subscales (N = 163).

Variables (Pre–Post)	Pretest mean (SD)	Post test mean (SD)	Mean difference	SE mean	t	df	P (2-tailed)
Overuse	2.86 (0.85)	3.26 (0.81)	0.396	0.090	4.379	162	.000***
Non-restraint	2.99 (0.81)	3.21 (0.76)	0.221	0.085	2.606	162	.010**
Inhibiting the flow of life	3.00 (0.83)	3.10 (0.82)	0.035	0.105	0.330	162	.742
Emotional state	3.00 (0.83)	3.10 (0.82)	0.102	0.092	1.109	162	.269
Dependence	3.20 (1.00)	3.59 (0.90)	0.394	0.105	3.759	162	.000***

SD, Standard deviation.

3.3. Correlation between the digital dependence subscales pre- and post-test

Table 3 shows that at baseline all subscales were strongly interrelated. Overuse correlated highly with non-restraint (r = 0.557), inhibiting the flow of life (r = 0.606), and emotional state (r = 0.632); emotional state was also strongly linked to non-restraint (r = 0.562) and inhibiting the flow of life (r = 0.628); and dependence showed moderate correlations with overuse (r = 0.425), emotional state (r = 0.424), and non-restraint (r = 0.319) (all p < .001), indicating a generalized pattern of problematic digital use before the intervention. After the intervention, strong correlations remained mainly within the post-test subscales, with non-restraint post significantly associated with inhibiting the flow of life (r = 0.527), emotional state post (r = 0.548), and dependence (r = 0.454); inhibiting the flow of life with emotional state (r = 0.629) and dependence (r = 0.407); and emotional state with dependence (r = 0.504) (all p < .001).

Table 3.

Pearson correlations among digital dependence subscales pre- and post-test (N = 163).

Variables	Overuse	Non-restraint	Inhibiting the flow of life	Emotional state	Dependence
Pre-test
Overuse	1	.557**	.606**	.632**	.425**
Non-restraint		1	.524**	.562**	.319**
Inhibiting Flow			1	.628**	.205**
Emotional State				1	.424**
Dependence					1
Post-test
Overuse	1	.532**	.415**	.555**	.547**
Non-restraint		1	.527**	.548**	.454**
Inhibiting Flow			1	.629**	.407**
Emotional State				1	.504**
Dependence					1

**p<0.001.

3.4. Linear regression analysis association of post-intervention of five domains of DAS

Table 4 shows that the regression model explained about 12% of the variance in the outcome (R = 0.346, R² = 0.121, adjusted R² = 0.042, RMSE = 0.716; F = 1.54, p = .011). Students aged 20–21 years scored significantly lower than those aged 18–19 (β = –0.44, p = .027), while no significant difference was found for 22–23 years (β = –0.23, p = .529). Years of study, number of devices, daily hours of use, and most income categories were not significant predictors; however, students from households earning ≥16,000 SAR showed a small but significant positive effect (β = 0.02, p = .041).

Table 4.

Model coefficients for predicting non-restraint scores (N = 163).

Variables		Estimate	SE	t	p
Intercept		3.855	0.357	10.796	< .001**
Age	18–19	Ref
	20–21	-0.442	0.257	-1.722	0.027
	22–23	-0.2286	0.362	-0.631	0.529
Year of Study	I year	Ref
	II year	0.1197	0.166	0.722	0.471
	IV year	0.4987	0.336	1.486	0.139
Number of Digital Devices	1	Ref
	2 devices	-0.0784	0.298	-0.263	0.793
	3 devices	-0.3731	0.336	-1.111	0.269
	4 or more devices	-0.3732	0.405	-0.921	0.358
Daily Hours of Use	0–2 hours	Ref
	3–4 hours	0.2289	0.386	0.593	0.554
	5–6 hours	0.2052	0.393	0.522	0.602
	7 and above	0.3939	0.395	0.998	0.32
Monthly Family Income	0–5000 SAR	Ref
	6,000–10,000 SAR	-0.0951	0.195	-0.487	0.627
	11,000–15,000 SAR	-0.1202	0.202	-0.596	0.552
	≥16,000 SAR	0.0202	0.181	0.111	0.041
Model fit	R=0.346; R²=0.121; Adjusted R² =0.0417; RMSE=0.716 F=1.54; p=0.011

Table 5 shows that the model predicting post-intervention “inhibiting the flow of life” scores was not significant (R = 0.277, R² = 0.077, adjusted R² = −0.005, RMSE = 0.888; F = 0.94, p = .517, N = 161), with most predictors unrelated to the outcome (p > .05). The only near-significant trend was lower scores among students aged 20–21 years compared with 18–19 (β = −0.56, p = .055).

Table 5.

Linear regression results predicting post-intervention overuse and inhibiting the flow of life scores (N = 163).

Variables		Overuse				Inhibiting flow
Variables		Estimate	SE	t	p	Estimate	SE	t	p
Intercept		2.639	0.392	6.738	<.001	2.595	0.443	5.854	<.001
Age	18–19	Ref
	20–21	–0.191	0.258	–0.741	.460	–0.564	0.292	–1.933	.055
	22–23	0.322	0.364	0.884	.378	–0.702	0.412	–1.703	.091
Year of Study	I year	Ref
	II year	–0.056	0.167	–0.333	.739	0.141	0.188	0.746	.457
	IV year	0.252	0.337	0.747	.456	0.405	0.381	1.062	.290
No. of Devices	1	Ref
	2 devices	–0.490	0.299	–1.640	.103	0.024	0.338	0.070	.945
	3 devices	–0.495	0.337	–1.467	.144	–0.051	0.382	–0.133	.894
	4 or more devices	–0.752	0.407	–1.848	.067	–0.109	0.460	–0.238	.813
Daily use (Hours)	0–2 hours	Ref
	3–4 hours	0.944	0.388	2.434	.016	0.262	0.439	0.596	.552
	5–6 hours	0.811	0.394	2.056	.042	0.516	0.446	1.156	.250
	7 and above	1.037	0.396	2.617	.010	0.284	0.449	0.632	.529
Monthly Income (SAR)	0–5000	Ref
	6,000–10,000	–0.273	0.196	–1.394	.165	–0.343	0.222	–1.543	.125
	11,000–15,000	–0.483	0.203	–2.383	.018	0.114	0.229	0.495	.621
	≥16,000	–0.282	0.182	–1.552	.123	–0.028	0.206	–0.138	.891
Model fit		R = 0.383, R² = 0.147, aR² = 0.071, RMSE = 0.785, F = 1.95, p = .029				R = 0.277, R² = 0.077, a R² = –0.005, RMSE = 0.888, F = 0.937, p = .517

For post-intervention overuse scores, the model was significant (R = 0.383, R² = 0.147, adjusted R² = 0.071, RMSE = 0.785; F = 1.95, p = .029, N = 161), explaining about 15% of variance. Age, year of study, and number of devices were not significant predictors (p > .05), but daily hours of use were: compared with 0–2 hours, students using 3–4 hours (β = 0.94, p = .016), 5–6 hours (β = 0.81, p = .042), and ≥7 hours (β = 1.04, p = .010) had higher overuse scores. Students from households earning >16,000 SAR had significantly lower overuse scores than those from 0–5,000 SAR (β = –0.48, p = .018).

Table 6 shows that the model predicting post-intervention emotional state scores was not significant (R = 0.274, R² = 0.075, adjusted R² = –0.006; F = 0.92, p = .533), with most demographic and usage variables unrelated to outcomes. Only age was significant, with students aged 20–21 scoring lower than those aged 18–19 (β = –0.61, p = .021); the effect for 22–23 years was non-significant (β = –0.54, p = .146) and a borderline effect appeared for fourth-year students (β = 0.66, p = .057). By contrast, the model predicting post-intervention dependence scores was significant (R = 0.424, R² = 0.180, adjusted R² = 0.108; F = 2.48, p = .004), explaining about 18% of the variance in dependence behavior after the intervention.

Table 6.

Linear regression results predicting post-intervention emotional state and dependence scores (N = 163).

Variables		Emotional state				Dependence
Variables		Estimate	SE	t	p	Estimate	SE	t	p
Intercept		2.936	0.400	7.346	<.001	2.737	0.451	6.062	<.001
Age	18–19	Ref
	20–21	–0.614	0.263	–2.336	.021	–0.250	0.297	–0.843	.401
	22–23	–0.543	0.371	–1.461	.146	0.553	0.420	1.319	.189
Year of Study	I year	Ref
	II year	0.060	0.170	0.350	.727	0.095	0.192	0.494	.622
	IV year	0.659	0.344	1.918	.057	0.543	0.388	1.399	.164
No. of Devices	1
	2 devices	–0.413	0.305	–1.353	.178	0.291	0.345	0.844	.400
	3 devices	–0.504	0.344	–1.465	.145	0.386	0.389	0.991	.323
	4 or more devices	–0.288	0.415	–0.694	.489	0.227	0.469	0.485	.629
Daily use (Hours)	0–2 hours	Ref
	3–4 hours	0.496	0.396	1.255	.212	0.314	0.447	0.701	.484
	5–6 hours	0.371	0.402	0.920	.359	0.077	0.455	0.169	.866
	7 and above	0.505	0.405	1.249	.214	0.089	0.457	0.196	.845
Monthly Income (SAR)	0–5000	Ref
	6,000–10,000	0.108	0.185	0.583	.560	–0.114	0.209	–0.546	.586
	11,000–15,000	0.035	0.200	0.175	.861	–0.363	0.226	–1.604	.111
	≥16,000	–0.074	0.207	–0.360	.720	–0.291	0.234	–1.246	.215
Model fit		R = 0.274, R² = 0.075, aR² = –0.006, RMSE = 0.801, F= 0.921, p = .533				R = 0.424, R² = 0.180, aR² = 0.108, RMSE = 0.905, F = 2.48, p = .004

3.5. Subgroup analysis

The forest plot illustrates subgroup the estimation of beta coefficients and 95% confidence intervals for the effect of daily hours of digital device use on each of the five Digital Addiction Scale (DAS) domains at post-test (Figure 1). The vertical dashed red line indicates the null value (β = 0). As shown, Overuse had the strongest positive association with daily hours (β ≈ 1.03, 95% CI 0.25–1.82), indicating that students using devices for longer periods had markedly higher overuse scores after the intervention. Non-restraint showed a smaller positive but still significant effect (β ≈ 0.22, 95% CI 0.05–0.39). By contrast, Inhibiting the Flow of Life, Emotional State, and Dependence displayed weaker and statistically non-significant associations with daily hours of use (CIs crossing zero), suggesting that device time was not a meaningful predictor for these domains.

Figure 1.

Forest plot for beta coefficients effect of daily (hours) of digital device use on each five digital addiction scale (DAS) domains at post-test.

4. Discussion

Digital dependence among nursing students is an emerging concern because of its impact on academic performance, clinical practice, and well-being. This study evaluated CBI targeting multiple dimensions of digital use and examined demographic and behavioral predictors of post-intervention outcomes.

Our findings showed that most participants were 18–19 years old, predominantly first- and second-year students, using two digital devices and spending more than five hours online daily. The high proportion of students using multiple digital devices for health monitoring and spending five or more hours per day on screens aligns with global and regional evidence documenting intensive digital engagement among nursing and healthcare students,^27,28 but our higher proportion of students using ≥7 hours suggest a more intense engagement. In Saudi Arabia, rapid digitalization and widespread smartphone penetration may further amplify this pattern, making excessive digital exposure a normative behavior rather than an exception.^25,27,29,30 Nursing schools could incorporate digital wellness modules in first-year orientation to mitigate heavy use patterns before they become entrenched.

The results demonstrated significant improvements in overuse, non-restraint, and dependence after the CBI, while emotional state and inhibiting the flow of life did not change significantly. These findings are consistent with previous studies demonstrating that cognitive-behavioral approaches are particularly effective in reducing behavioral and control-related aspects of problematic internet and smartphone use^6,9,22 who found that CBI effectively reduced behavioral components of internet addiction but required extended sessions to affect mood. Similarly, Bong et al. reported greater emotional regulation benefits when CBI was combined with music therapy, suggesting adjunctive strategies may be needed to influence emotional outcomes in nursing students.³⁰ Future interventions could combine CBI with mindfulness or emotional-regulation exercises to address the less responsive emotional dimensions.

Moreover, there was a strong positive correlation among all subscales at baseline, particularly between overuse and emotional state that became more domain-specific after the intervention. This echoes the study reported that CBI reduced the interdependence of addiction symptoms in Moroccan nursing students.^18,31 By contrast, Elhai et al. observed persistent cross-domain correlations after psychoeducation in medical students, indicating that program content and intensity may determine how fully maladaptive linkages are dismantled.³²

The paired t-tests and regression models confirmed significant reductions in overuse, non-restraint, and dependence, and showed that post-intervention overuse was predicted primarily by daily hours of device use led impact the academic performance and by family income. This supports research linking screen time directly to behavioral addiction traits and socioeconomic resources to better self-regulation.³³ In addition, the younger generation use smartphones for various activities, such as learning, entertainment, internet access, social networking, and communication. Similarly, studies found that smartphone addiction, largely driven by excessive social media use and procrastination, was significantly associated with poorer academic performance, suggesting that problematic smartphone use may adversely influence both the academic engagement and social behavior of university students.^34–36

Furthermore, results showed that while the overall regression for dependence was significant, no individual predictors reached significance, and emotional state was only weakly predicted by age. This is consistent with study that found CBI reduced problematic smartphone use but not anxiety or depressive symptoms in Korean university students by incorporating mindfulness or emotional regulation modules into CBI may strengthen its impact on these resistant domains and longitudinal follow-up may reveal delayed emotional benefits as seen in digital wellbeing programs.^20,37 The findings of this study highlight the importance of integrating digital wellbeing concepts into digital health education, particularly in nursing programs where students frequently engage with digital technologies for learning and clinical training. Future studies should incorporate m-health and digital health systems to enhance the emotional regulations of the university students’ mental well-being.

The subgroup analysis illustrated by the forest plot further emphasizes that daily hours of device use exert a domain-specific effect, strongly predicting overuse and, to a lesser extent, non-restraint, while having limited influence on emotional state and dependence after the intervention. This finding suggests that time-based behavioral regulation is central to reducing overt digital overuse but insufficient on its own to address deeper emotional reliance on digital devices.³⁸ Overall, these results underscore the need for multi-component interventions that combine behavioral control strategies with emotional regulation and stress-management approaches, particularly for younger nursing students with high baseline screen exposure.

Based on the findings, nursing education programs should integrate brief, structured cognitive-behavioral and digital-health monitoring devices and training into early undergraduate curricula to address high levels of digital dependence, particularly among younger students. Interventions should combine behavioral strategies to regulate screen time with components targeting emotional regulation and stress management, as emotional and functional domains showed limited short-term responsiveness. Routine screening for digital addiction using validated tools may help identify at-risk students, while faculty role-modeling of healthy technology use can reinforce positive behaviors. Longer-term, multi-component interventions and institutional guidelines developed in collaboration with educational and healthcare authorities are recommended to support sustainable and transferable digital-health practices among nursing students.

4.1. Limitations

This study has several limitations that should be considered when interpreting the findings. First, the use of a single-group pre–post design without a control group limits the ability to attribute changes exclusively to the CBI. Second, the relatively small sample size drawn from one geographic region in Saudi Arabia may restrict the generalizability of the results to all nursing students nationally. Third, the reliance on self-reported measures of digital use may introduce recall and social desirability bias. Fourth, the intervention period was relatively short, which may explain why emotional state and “inhibiting the flow of life” scores did not significantly change; longer follow-up may be needed to capture delayed effects. Finally, potentially important psychosocial variables such as perceived stress, coping styles, and academic workload were not assessed, which may have helped explain the variance in post-intervention outcomes.

4.2. Implications

Despite these limitations, this study offers several actionable insights for healthcare education and practice. The significant improvements observed in overuse, non-restraint, and dependence subscales after the CBI indicate that behavioral interventions can help nursing and other healthcare students regulate their digital device use. Prior studies have demonstrated that digital mental health interventions and mobile health technologies can effectively support behavioral change and improve psychological wellbeing.^39,40 Future research could explore the development of m-health–based digital wellbeing systems that combine behavioral monitoring, real-time feedback, and cognitive behavioral strategies, thereby creating scalable and technology-supported interventions for managing digital dependence among healthcare students.³⁹ Integrating such systems within nursing curricula may simultaneously address digital addiction while strengthening students’ competencies in digital health and health informatics. For Saudi nursing students, where heavy daily device use appears more prevalent than in comparable international cohorts, culturally adapted CBI in orientation and professional development programs may reduce digital distraction, improve academic and clinical performance, and address underlying socioeconomic risk factors. Partnerships between nursing schools, the Ministry of Health, and professional bodies could ultimately inform national guidelines for healthy technology use among student nurses and other healthcare trainees.

5. Conclusion

This study demonstrates that brief cognitive behavioral intervention can effectively reduce key behavioral aspects of digital dependence overs use, non-restraint, and dependence among nursing students, while emotional and life-flow disruption remains less responsive. Regression analyses highlight that daily device use and socioeconomic factors are important correlations of post-intervention outcomes, suggesting the need for tailored strategies for different subgroups. For Saudi nursing students in particular, where heavy daily device use is highly prevalent, culturally adapted interventions integrated into nursing curricula and orientation programs could support healthier technology habits, improve self-regulation, and enhance academic and clinical performance. Future research should include larger, multi-site samples, control groups, and longer follow-up periods to assess sustained effects and explore additional psychosocial predictors of digital dependence.

Supplemental material

Supplemental material - Cognitive behavioral intervention on digital addiction behavior among nursing students in Saudi Arabia

Supplemental material for Cognitive behavioral intervention on digital addiction behavior among nursing students in Saudi Arabia by Vanitha Innocent Rani, Khalda Ahmed Mohammed MohammedAhmed, Hanem Ahmed Abdelkhalek Ahmed, Shylaja Jeyapaul Nawal Yahya Asiri H, R. A. Chithra, Manal Abdu Albishi B, Hanan Awad Moawad Elmashad, Iman Awad Siddig Mohammed in Health Informatics Journal

Footnotes

Acknowledgement

The authors extend their appreciation to the Deanship of Research and Graduate studies at King Khalid University for funding this work through large Research Project.

ORCID iDs

Vanitha Innocent Rani

Khalda Ahmed Mohammed Ahmed

Hanem Ahmed Abdelkhalek Ahmed

Shylaja Jeyapaul

Nawal Yahya H Asiri

Chithra Radhamany Arackal Thekkathil

Manal Abdu B Albishi

Hanan Awad Moawad Elmashad

Ethical considerations

The study was approved by the institutional review board of King Khalid University (ECM#2024-805).

Consent to participate

Written informed consent was obtained from all participants. Data was de-identified and stored on secure, password-protected servers.

Author contributions

V.I.R. conceived and designed the study and drafted the manuscript. K.A.M.M.A. contributed to data curation, formal analysis, and methodology. H.A.A.A. assisted with investigation, resources, and data interpretation. S.J. performed the literature review, validation, and manuscript editing. N.Y.H.A. oversaw project administration, supervision, and funding acquisition. C.R.A. carried out statistical analysis, visualization, and results writing. M.A.B.A. managed writing—review and editing—and the ethical approval process. H.A.M.E. (handled data collection and quality control. I.A.S.M. revised the draft, approved the final version, and acted as the corresponding author.

Funding

This work was suported by the Deanship of Research and Graduate studies at King Khalid University by large Research Project under grant number RGP2/273/46.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

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