A clinical trial of cognitive behavioral interventions for social media addiction, psychological problems, quality of family relationships and academic performance among university students

Abstract

Background

Social media use is habitual among students, and its availability and usefulness contribute to its prevalence. Numerous benefits are associated with social networking sites. Conversely, social media use can become addictive, and excessive use can lead to various psychological problems. It influences students’ overall lifestyle and education. Therefore, it is essential to manage social media use to mitigate potential harms. Interventions based on cognitive behavioural therapy can help balance social media use with other crucial aspects of life.

Methods

This RCT was conducted by the Department of Applied Psychology at Government College University Faisalabad across various public and private universities in Faisalabad. Initially, 190 students were recruited for the assessment. After initial screening, 164 participants meeting the criteria were randomly assigned to the experimental group (n = 82) and the wait-list control group (n = 82). The initial screening tool used was the Social Media Addiction Scale (SNAS), along with other tools, including the Adolescent Sleep Hygiene Scale, the Copenhagen Burnout Inventory—Student Version, the Meezan Meta-Memory and Meta-Concentration Scale, and the Satisfaction with Family Life Scale, as well as the student’s GPA. Data were analyzed using SPSS version 2.0.

Results

The findings indicate a significant difference in the level of social media addiction (i.e., F (1, 126) = 159.36, p < 0.001, ηp² = 0.58) following the cognitive behavioural interventions. There was a notable increase in sleep hygiene (i.e., F (1, 126) = 150.48, p < 0.001, ηp² = 0.54). Additionally, cognitive behavioral interventions led to a significant decrease in burnout levels (i.e., F (1, 126) = 58.74, p < 0.001, ηp² = 0.47). They also resulted in a significant increase in memory and concentration (i.e., F (1, 126) = 46.40, p < 0.001, ηp² = 0.26). Furthermore, students’ academic performance significantly improved (i.e., F (1, 126) = 106.73, p < 0.001, ηp² = 0.45), playing an important role in the student’s success.

Conclusion

Cognitive Behavioural Interventions effectively manage social media addiction and psychological issues. Sessions utilizing cognitive behavioural interventions have enhanced family relationships and academic performance in the experimental group compared to the control group on the waitlist. Students have reported reductions in social media addiction, lower levels of academic burnout, better sleeping conditions, improved memory and concentration, and strengthened family relationships.

Trial Registration Thai Clinical Trial Registry (i.e. TCTR: TCTR20220924002).

Supplementary Information

The online version contains supplementary material available at 10.1007/s44192-026-00430-7.

Introduction

Social media addiction (SMA) is a behavioral condition in which an individual experiences an uncontrollable urge to engage with social media platforms, leading to excessive use that interferes with daily life functioning [1]. SMA typically refers to symptoms of preoccupation (to think about the social media, when not using and to plan the next usage) [2], tolerance (need to spend more time and remain engage) [3], withdrawal (negative emotions when unable to access social media) [4], loss of control (unsuccessful efforts to cut down or stop use) [5], mood modification (using social media to feel better or escape negative feelings) [6, 7], conflict (with personal relationships, and responsibilities) [8]. It typically affects mood [9], sleep quality [10], social [11], and occupational functioning [12, 13] which develops academic problems [14] and psychiatric conditions [15, 16].

Currently, social media channels provide all kinds of information and different flavors of entertainment, which is the main attraction for users [17]. Negative use of social media is rising among students, which has a significant and direct negative impact on physical and mental health [18] as well as on academic performance [19]. It is reducing physical activities [20], games [21] and social gatherings [22], and promoting problematic social media use [15] and internet gaming addiction among students [23]. University students often engage with social media due to its widespread availability in educational settings, leading to excessive consumption [24]. According to an estimate, 5.17 billion individuals actively use social media, 4–5% (or 210 million) suffering social media addiction, and it is projected to 6 billion by 2027 [25]. In the United States, 90% of individuals aged 18 to 29, 82% aged 30 to 49, 69% aged 50 to 64, and 40% of those over 65 are active social media users [26]. In Pakistan, 45 million people access the internet, with 92% of them being young adults actively engaged on platforms like Facebook [27]. In Asia, the internet usage rate stands at 49.7%, with many countries in the region facing a high risk of internet addiction [28]. According to an estimate of 51 studies globally, reported 18.4% prevalence of social media addiction among university students [29].

Social media use activates the brain’s mesolimbic dopamine pathway which is similar to substance addictions and overuse leads to dependence [30]. Social media addiction falls under the category of behavioral addiction and involves maladaptive cognitions [31]. It causes psychological issues as it is linked to cognitive, emotional, and behavioral processes and serves as a source of many psychological problems [32]. The influence of social media addiction is evident in all significant life areas, including personal relationships, social settings, professional environments, and academics issues [33]. Internet addiction shares characteristics such as salience, mood modification, conflict, withdrawal tolerance, and relapse, similar to other addictions [34]. Excessive internet use is associated with factors negatively affecting students’ academic performance. Reports indicate that students’ academic motivation and achievement decline due to excessive social media use, which also leads to psychological and physical issues [35].

Facebook addiction is directly linked to burnout in university students and is causing stress among them; the dropout rate continues to rise [35, 36]. Social media brings people closer, yet it undermines life satisfaction and contributes to feelings of loneliness [37]. Increasing hours spent on social media disrupt family relationships, reducing the time available for family communication, making it difficult for children to interact with their family members and siblings. This creates conflict, resulting in fewer opportunities for affection and expression, and leading to a communication gap within the family [38]. The rise in internet use is impacting daily functioning [39], with prominent issues in students’ lives such as social conflicts [40], relationship problems [33], communication issues with family [41], and a decline in mental health [42], particularly low self-esteem and academic performance [43]. Excessive use of social media applications negatively impacts attention and can deplete one’s energy, decreasing productivity, particularly among students [44]. The fleeting relief these platforms provide soon dissipates, leaving underlying issues unresolved [45]. This can lead to exhaustion, with burnout correlating with reduced academic performance [46].

Cognitive behavioural interventions have proven effective in treating social media and internet-related addictions [47]. Cognitive behavioural therapy is a short-term, skill-based, and problem-focused treatment that addresses a range of psychological issues and mental disorders [48]. It primarily focuses on cognition by helping individuals develop a rational perspective of the world and their problems [49]. Research has demonstrated the efficacy of cognitive behavioural therapy in managing internet addiction [50]. It addresses the harmful patterns of internet use and the thoughts behind them through cognitive restructuring [51]. Factors contributing to social media addiction, such as narcissistic tendencies, low self-esteem, and loneliness, can be effectively addressed [52]. Maladaptive cognitions form the foundation of social media addiction, and CBT has been shown to be effective in managing these distorted beliefs [53].

The prevalence of social media addiction is increasing rapidly, but there is no evidence-based intervention to address this problem, which is the main gap in the research in Pakistan. To address this problem, we designed a tailored cognitive behavioural intervention to address mental health problems among students facing social media addiction.

Materials and methods

Study setting

This clinical trial was conducted at various public and private universities in the Faisalabad division. The enrolment and eligibility assessment process was completed after obtaining permission from the authorities and informed consent from the participants. We organized all the groups by calculating comparable group characteristics. We accommodated all the participants within the university’s counselling unit. We managed the timings according to their availability, ensuring that participants’ academic activities would not be disrupted during this process.

Study design

In this RCT, we used a pre- and post-research design to investigate the effectiveness of cognitive behavioural interventions for social media addiction. We designed the experimental and waitlist control conditions to evaluate the efficacy of the cognitive behavioural interventions. The experimental group received the intended treatment, and the control group received no treatment. Before starting the study, we received ethical approval from the Institutional Review Board (IRB), Government College University, Faisalabad (i.e. Ref.No.GCUF/ERC/104, dated 7/06/2022). Then the protocol was approved by the Thai Clinical Trial Registry with the protocol (i.e. TCTR: TCTR20220924002, with URL: https://www.thaiclinicaltrials.org/show/TCTR20220924002, dated 23/09/2022 and first enrollment dated 1/10/2022, respectively. All methods and procedures were followed by the Institutional Review Board (IRB).

Participants

In this prospective clinical trial, a sample was collected from the public and private universities of the division of Faisalabad from January 2021 to October 2022. The study sample size was calculated using G-Power software with effect size (f) = 0.20, α = 0.05, power (1-β error prob.) = 0.95, and actual power = 0.96, assuming a 1:1 ratio between intervention and control conditions. It structured 55 participants per treatment condition, assuming a 20–25% dropout rate. We targeted 350 participants, 190 were selected for eligibility assessment, and 164 met the inclusion criteria and were allocated to experimental (n = 82) and waitlist control (n = 82) conditions (see Fig. 1). Both male and female participants were taken in equal numbers and with equal characteristics. The age ranges of participants were 17 to 22 years (M = 19.01; SD = 0.971).

Fig. 1.

CONSORT flow chart

Inclusion and exclusion criteria

In this prospective RCT, all the participants were university students. We set the following inclusion and exclusion criteria for the study. Such as, (a) participants who have their smart cell phone (e.g., iPhone/Android), have internet access all the time, and use social media accounts (e.g., Facebook, Instagram, WhatsApp, TikTok, X(Twitter), etc.) for at least the last 1 year regularly, (b) participants use cell phone at least 4 h daily [54], (c) only day scholars living with parents and siblings in a nuclear family system, (d) participants scored > 84 (above average) on social media networking scale (SMNS), (e) students scored between 50–74 (moderate degree of burnout) on CBI-SV, (f) with disturbed quality of sleep below average score (< 75) on ASHS, and (g) participants living in boarding, living with extended families, having no smart cell phones, with physical disability, and history of psychiatric and medical conditions were excluded.

Assessment and screening

Although, use of social media addiction is common among students, however, it was difficult to identify the relevant cases from the university. For this purpose, we initially started interviews with university students to explore potential candidates. In this brief interview, we asked several questions. We targeted the participants who responded “Yes” on the questions, (1) availability of personal smart cell phone, (2) internet connection 24/7, active user on Facebook, Instagram, WhatsApp, TikTok, and X(Twitter), at least 4 h daily/30 h per week on use of social media apps, difficulty managing academic tasks, and disturbed sleeping patterns. These potential participants were enrolled for baseline assessment. Participants who scored high on the Social Networking Addiction Scale (SNAS), and the Copenhagen Burnout Inventory-Student Version (CBI-SV), and low scores on the Adolescent Sleep Hygiene Scale (ASHS) were found eligible for the study.

Randomization and allocation

Eligible participants were equally divided in a 1:1 ratio into two groups: the experimental group (n = 82) and the waitlist control condition (n = 82). Before randomization, participants were stratified based on critical baseline characteristics such as age, gender, and education to ensure balance across these potentially confounding variables. Within each stratum, participants were randomized using a random sequence generated by a computer, which an independent statistician prepared. Block randomization with block sizes of 4 and 6 was utilized to keep allocation balanced during the enrollment period.

Measures

Demographic form A demographic form for social media addiction participants collects key personal and behavioural data such as age, gender, education, and occupation. It also includes details on social media use patterns (platforms used, time spent, and purpose), access to technology (devices, internet), and optionally, mental health status. This information helps ensure group comparability and supports matching in the trial.

Social networking addiction scale (SNAS) The SNAS original version comprises six dimensions designed to assess social media addiction among university students [55]. Each dimension includes the following items; for example, salience comprised of items 1 to 4, items 5, 6, and 7 pertain to mood modification; tolerance contains items 8, 9, and 10; items 11 to 14 relate to the withdrawal dimension; items 15, 16, and 17 belong to the conflict dimension; and the relapse dimension consists of items 18 to 21. It employs a 7-point Likert rating scale. The score range is between 21 and 147, and a score > 84 predicts social media addiction in the participant. The SNAS sub-scales exhibit good reliability estimation (i.e., 0.81, 0.71, 0.78, 0.86, 0.75 & 0.89), which reflects that SNAS is a reliable and valid measurement.

Adolescent sleep hygiene scale (ASHS) The ASHS English version is designed to assess the quality of sleep among university students and consists of 32 items, 4 of which are qualitative, and 28 are quantitative, comprising eight subscales [56, 57]. Subscales are physiological (5 items), cognitive (6 items), emotional (3 items), sleep environment (4 items), daytime sleep (1 item), substances (2 items), sleep stability (4 items), bedtime routine (1 item), and bed sharing (2 items). Each item is scored on the 6-point ordinal rating scale, range from 1 (never) to 6 (always). High scores on the ASHS reflect high-quality sleep hygiene, and low scores show poor sleep hygiene quality. The overall scale internal consistency reliability is α = 0.84, with subscales as follows: physiological: α = 0.60; behavioral arousal: α = 0.62; cognitive/emotional: α = 0.81; sleep environment: α = 0.61; sleep stability: α = 0.68; daytime sleep: α = 0.78; substance factors: α = 0.50 and bedtime: α = 0.70 respectively. This shows that ASHS has sound psychometric properties.

Copenhagen burnout inventory-student version (CBI-SV) The CBI English version was originally proposed by Kristensen et al. [58], revised by Campos et al. [59], and further validated by Bolatov et al. [60]. CBI-SV is found more reliable measure to assess the degree of burnout among university students [61]. This scale comprises four domains: personal burnout, study-related burnout, classmate-related burnout, and instructor-related burnout. Each statement is scored on a 5-point rating scale. Scores 50–74 are considered moderate, 75–99 high, and scores above 100 show severe burnout [58]. The internal consistency reliability for the four dimensions is 0.901, 0.875, 0.910, and 0.931, respectively, and the total scale internal consistency reliability is α = 0.975 [59]. It shows that CBI-SV is a reliable and valid assessment measure for estimating the degree of burnout among students.

Mezaan meta memory and meta concentration scale (MMMCS) The MMMCS English version was developed by Manzar et al. [62] and it assesses the level of memory and concentration among university students. This scale has nine items with two subscales i.e., the meta-memory scale and the meta-concentration scale. The scale items are scored on the 5-point rating scale. The internal reliability and Cronbach’s alpha values for the meta-memory and meta-concentration subscales are 0.84 and 0.80, respectively. This scale demonstrates good psychometric properties for assessing meta-memory and meta-concentration among university students [62].

Satisfaction with family life scale (SFLS) The SFLS original version was used to assess the relationship satisfaction with family among adolescents and adults [63]. The SFLS is comprised of 5 items as a single factor. Each item is scored on a 7-point Likert scale. Scores are determined by summing all items, resulting in a family life satisfaction score ranging from 5 to 35. High scores determine high satisfaction and strong bonding with family members, and low scores represent poor bonding and satisfaction with family relationships. The scale Cronbach’s alpha 0.97 indicates high internal consistency reliability.

Academic performance (AP) Students’ CGPA is a valid criterion to assess students’ academic performance over semesters [64]. In this study, students’ academic performance was estimated by calculating the percentage of two exams before and after the enrollment protocol completion. Participants’ recent exam percentage was taken initially, and then they were engaged in the study. After completing the survey, the students completed the current semester exam and received their exam percentage. The two exam percentages were compared, and we investigated the impact of the intervention on the students’ academic performance.

Interventions

Cognitive Behavioural Interventions (CBI) have significantly benefited students wrestling with social media addiction [22], aiding in the management of psychological challenges [65], improving family dynamics [6], and enhancing academic success [14]. This evidence-based method focuses on specific problems and is short-term in nature [47]. Studies have demonstrated the effectiveness of treatments for internet-related addictions [65]. CBT assists individuals in managing stress and addressing symptoms of internet addiction [66], thereby enhancing overall functioning and promoting better mental health [67]. This approach encompasses techniques such as cognitive restructuring [50, 51], relaxation methods, time management [68], scheduling activities [69], problem-solving skills [70], life skills training [71], journaling, and homework exercises [72], all of which support a steady pace of improvement. It focuses on a structured treatment agenda [73] and practical and attainable goal-setting [74]. Furthermore, CBT significantly impacts sleep issues by enhancing sleep hygiene and mental health [75]. Cognitive behavioural interventions can address stress and burnout [14, 76].

Sessions were structured around clearly defined goals that addressed the identified issues, with their frequency determined by the nature of these challenges. The agendas for the sessions included psychoeducation, cognitive restructuring, stress management, life skills training, time management, problem-solving, effective communication for fostering relationships, and strategies for preventing lapses and relapses. Each group consisted of 5 to 8 participants. This treatment protocol lasted 12 weeks and included 8–10 agenda-based sessions delivered in a group setting. A trained clinical psychologist delivered these sessions under supervision and details of each session is given in the treatment protocol (see Supplementary file A).

Procedure

The Institutional Review Board (IRB) at Government College University, Faisalabad, approved the research protocol. Participants were randomly assigned to either the experimental group or the waitlist control group, drawn from both public and private universities. All study participants were healthy students. Informed consent was secured from these individuals, detailing confidentiality measures and their right to withdraw from the study. After a pre-assessment, students in the experimental group engaged in sessions based on cognitive behavioural therapy. The pre- and post-assessments were compared following the intervention period. Additionally, eligible students participated in further evaluation using standardized psychometric tools to evaluate four key areas: social media addiction, sleep hygiene, memory and concentration, and family relationships. Students scoring above average in social media addiction but below average in the other three areas were considered for participation. Additional inclusion criteria required that participants be day scholars living with both parents, own smartphones with internet access, and have used social media for at least one year.

Statistical analysis

Descriptive statistics were used to compute the demographic characteristics of the participants. Continuous variables that adhered to a normal distribution are presented as mean ± standard deviation (X ± S), while categorical variables are expressed as n (%). The baseline demographic and clinical characteristics between the experimental and control groups were compared using independent-samples t-tests for continuous data and χ² tests for categorical data. To assess the intervention’s effects over time, a two-way repeated measures analysis of variance (ANOVA) was conducted, considering Group (experimental vs. control) and Time (pre-intervention vs. post-intervention) for each of the four outcome measures: BSMAS, Sleep Hygiene Scale, Memory-Concentration Scale, and Satisfaction with Family Life Scale. The Shapiro–Wilk test was used to assess the normality of the data, and all variables showed non-significant results (p > 0.05), which indicates the assumption of normality is met. Similarly, Levene’s test was conducted to assess the homogeneity of variance between the experimental and waitlist control conditions, which were non-significant, showing that the homogeneity of variance was satisfied. ANOVA effect sizes were reported as partial eta squared (ηp²). Statistical analyses were performed using SPSS version 26.0.

Results

Recruitment and attrition

In this prospective RCT, 300 participants were targeted, 287(95.67%) gave consent for the brief interview, and 190(63.34%) were selected for eligibility assessment. 164(54.67%) participants met the eligibility criteria, and 26(8.67%) were excluded (6 participants declined to participate in the study, and 20 did not meet the study’s inclusion criteria). 128(42.67%) participants completed the study, 55 experimental group and 73 in the control condition (Fig. 1).

Sample characteristics and baselines assessment

The sample characteristics are detailed in Table 1. The chi-square and one-way ANOVA analyses reveal insignificant differences among PTh, CBT, IBIs, and the Waitlist-control concerning gender (X2 = 0.254, p > 0.968), marital status (X2 = 0.830, p > 0.842), education (X2 = 2.059, p > 0.914), referral source (X2 = 2.430, p > 0.488), duration of illness (X2 = 3.570, p > 0.735), family system (X2 = 2.024, p > 0.890), age (F = 0.550, p > 0.650), and monthly income (F = 0.090, p > 0.965). Furthermore, group comparisons were made among the treatment variables using one-way ANOVA. The baseline findings indicate insignificant differences among the four treatment groups and the waitlist-control on PHQ-9 (F = 0.381, p > 0.767); PAS (F = 0.651, p > 0.584), NAS (F = 1.505, p > 0.215), LSS (F = 1.107, p > 0.348), SWB-T (F = 0.328, p < 0.805), DSS (F = 0.063, p > 0.979), SPM (F = 0.635, p > 0.593), SES (F = 0.607, p > 0.612), and DMQ (F = 0.177, p > 0.912).

Table 1.

Baselines sample characteristics of the participants

Variable	Category	N	Groups
Characteristics			Experimental	Control	X2/t	P
N targeted		190
N allocated, n		164	82	82
N final n(%)		128	55	73
Age, M(SD)			18.93(0.99)	19.01(0.94)	− 0.48	0.631
Gender %	Male n(%)	77(46.95%)	38(46.34%)	39(47.56%)
	Female n(%)	87(53.04%)	44(53.65%)	43(53.04%)
Marital status	Single n(%)	164 (100%)	82(50%)	82 (50%)	0.340	1.000
Religion	Islam	164 (100%)	82(50%)	82 (50%)	0.340	1.000
Father’s education	No formal education n (%)	6 (3.65%)	5(6.09%)	1(1.21%)	4.443	0.217
	Metric n (%)	71 (43.29%)	34(41.46%)	37 (45.12%)
	Graduation n (%)	64 (39.02%)	29 (35.36%)	35 (42.68%)
	Post graduation n(%)	23 (14..02%)	14 (17.07%)	9 (10.97%)
Mother’s education	No formal education n(%)	27 (16.46%)	13(15.85%)	14 (17.07%)	1.519	0.678
	Metric n (%)	79 (49.17%)	37 (45.12%)	42 (51.21%)
	Graduation n(%)	45(27.43)	26 (31.70)	19 (2./17%)
	Post graduation n(%)	13(7.92%)	6 (7.31%)	7(8.53%)
Father’s occupation	Unemployed n(%)	10(6.09%)	5 (6.09%)	5 (6.09%)	3.219	0.359
	Govt sector n (%)	35 (21.34%)	20(24.39%)	15(18.29%)
	Private sector n(%)	35 (21.34%)	13(15.85%)	22(26.82%)
	Buisness n(%)	84 (51.21%)	44(53.65%)	40(48.78%)
Mother’s occupation	Housewife n(%)	151(92.07%)	76(56.09%)	76(92.68%)	0.149	0.985
	Govt sector n(%)	7(4.26%)	3(3.65%)	4(4.87%)
	Private n(%)	2(1.21%)	1(1.21%)	1(1.21%)
	Buisness n (%)	4(2.43%)	2(2.43%)	2(2.43%)
District	Faisalabad n (%)	75(45.73%)	37(45.12%)	38(46.34%&	0.025	0.875
	Other n (%)	45(27.43%)	44(53.65%)	89(108.53%)
Family system	Nuclear n(%)	94(57.31%)	50 (60.97%)	44(53.65%)	2.231	0.313
	Joint n(%)	65(39.63%)	31(37.80%)	34(41.46%)
	Extended n(%)	5(3.04%)	1(1.21%)	4(4.87%)
Socioeconomic status	> 20,000 n(%)	23(14.02%)	9 (10.97%)	14(17.07%)	1.34	0.511
	30,000–50,000 n(%)	97(59.14%)	51(62.19%)	46 (56.09%)
	50,000–70,000 n(%)	44(26.82%)	22 (26.82%)	22(26.82%)
Current study program	BSEE n(%)	42(25.60%)	21 (25.60%)	21(25.60%)	0.000	1.000
	BND n(%)	64(39.02%)	32 (39.02%)	32(39.02%)
	Physiology n(%)	58(35.36%)	29(35.36%)	29(35.36%)
Area	Urban n(%)	97(59.14%)	44(53.68%)	53(64.63%)	2.044	0.102
	Rural n(%)	67(40.85%)	38(46.34%)	29(35.36%)
Previous institution type	Government	47(28.65%)	22(26.82%)	25(30.48%)	0.281	0.869
	Private	101(61.58%)	52(63.41%)	49(59.75%)
	Semi Government	16(9.75%)	8(9.75%)	8(9.75%)

Results indicate (see Table 2) statistically significant mean differences between pre- and post-assessment scores on the social media addiction scale (i.e., F(1, 126) = 159.36, p < 0.001, ηp² = 0.58) following the cognitive behavioural interventions. A significant time × group interaction effect within the experimental group is observed, F(1,126) = 215.34, p < 0.000, ηp² = 0.58. Significant effects are noted across the subscales of SAL, MOD, TOL, WIT, CON, and REL, with results as follows: F (1,126) = 70.61, p < 0.001, ηp² = 0.35; F(1, 126) = 70.92, ηp² = 0.36; F(1, 126) = 62.82, p < 0.001, ηp² = 0.33; F(1, 126) = 50.97, p < 0.001, ηp² = 0.28; F(1,126) = 81.87, p < 0.001, ηp² = 0.39; and F(1, 126) = 34.51, p < 0.001, ηp² = 0.21, respectively (see Fig. 2). There is a significant mean difference between pre- and post-assessment on the Adolescent Sleep Hygiene Scale (i.e., F (1, 126) = 150.48, p < 0.001, ηp² = 0.54), highlighting the significant impact of cognitive behavioural interventions. Another significant time × group interaction effect was found, F (1,126) = 252.81, p < 0.000, ηp² = 0.54, within the experimental group. The subscales PH, BAR, CEF, SEF, SSF, DSF, SUB, and BRF exhibited significant effects: F (1,126) = 6.44, p < 0.001, ηp² = 0.04; F(1, 126) = 54.78, ηp² = 0.30; F(1, 126) = 46.84, p < 0.001, ηp² = 0.27; F(1, 126) = 47.13, p < 0.001, ηp² = 0.07; F(1,126) = 10.03, p < 0.001, ηp² = 0.24; F(1, 126) = 39.68, p < 0.001, ηp² = 0.38; F(1, 126) = 79.60, p < 0.001, ηp² = 0.24; and F(1, 126) = 24.34, p < 0.001, ηp² = 0.16, respectively (see Fig. 3). A significant mean difference between pre- and post-assessment was also observed on the Copenhagen Burnout Inventory- Student version (i.e., F (1, 126) = 58.74, p < 0.001, ηp² = 0.47), demonstrating the influence of cognitive behavioural interventions. The interaction effect of time × group is significant, F (1,126) = 113.96, p < 0.000, and ηp² = 0.47, in the experimental group. The subscales PBO, SBO, CBO, and IBO showed significant results: F (1,126) = 35.77, p < 0.001, ηp² = 0.22; F(1, 126) = 44.26, ηp² = 0.26; F(1, 126) = 21.29, p < 0.001, ηp² = 0.14; and F(1, 126) = 13.30, p < 0.001, ηp² = 0.09, respectively (see Fig. 4). There were significant mean differences observed between pre- and post-assessment on the Mezaan Meta Memory and Meta Concentration Scale (i.e., F (1, 126) = 46.40, p < 0.001, ηp² = 0.26), indicating a significant difference due to cognitive behavioural interventions in the experimental group. Furthermore, a significant time × group interaction effect was found, F (1,126) = 145.64, p < 0.000, ηp² = 0.26, within the experimental group (see Fig. 5). The subscales BMS and BMC also demonstrated significant effects: F (1,126) = 17.32, p < 0.001, ηp² = 0.12 and F (1, 126) = 50.56, ηp² = 0.28, respectively. The main effect on the score of the Satisfaction with Family Life Scale (SWFL) was statistically significant (i.e., F (1.126) = 9.168, p < 0.005, ηp² = 0.068). A significant time x group interaction effect was noted (i.e. F(1,126) = 88.53, p < 0.001, ηp² = 0.068). There were significant mean differences between pre- and post-assessment of the grades/percentage of the undergraduate university students in the experimental group (i.e. F (1, 126) = 106.73, p < 0.001, ηp² = 0.45). A significant time × group interaction effect was also observed, F (1,126) = 7.61, p < 0.001, ηp² = 0.45.

Table 2.

Mean (standard deviation) and mixed-design ANOVA of clinical scores during pre- and post-test interventions

	Groups										η p 2
	Experimental group		Wait-list control group						Group x Time
	Pretest	Post-Test	Pretest	Post-Test	Group		Time		Interaction
	M(SD)	M(SD)	M(SD)	M(SD)	F	P	F	P	F	P
SAS	19.16(3.87)	11.56(3.58)	18.23(3.68)	19.53(4.20)	36.59	0.001	70.61	0.001	141.02	0.001	0.35
MMS	15.0(3.34)	9.81(2.86)	14.87(3.14)	14.58(3.52)	23.42	0.001	70.92	0.001	56.78	0.001	0.36
TOS	13.34(3.15)	8.600(2.45)	14.23(2.68)	14.26(3.39)	56.08	0.001	62.82	0.001	64.29	0.001	0.33
WIS	16.10(3.99)	9.12(2.02)	16.02(4.13)	16.65(5.05)	42.20	0.001	50.97	0.001	73.21	0.001	0.28
COS	12.58(2.69)	7.34(2.32)	12.84(3.67)	13.52(3.60)	39.27	0.001	81.87	0.001	137.10	0.001	0.39
RES	16.18(4.40)	10.54(2.63)	16.54(4.30)	16.86(4.15)	37.00	0.001	34.51	0.001	43.17	0.001	0.21
SNAS	92.38(12.36)	57.00(8.99)	92.76(11.04)	95.42(13.33)	142.23	0.001	159.36	0.001	215.34	0.001	0.58
PHF	13.20(3.84)	19.72(3.84)	13.23(4.06)	8.91(2.70)	137.38	0.001	6.44	0.001	154.89	0.001	0.04
BAF	8.72(2.27)	13.10(2.02)	9.57(2.41)	8.83(2.80)	22.96	0.001	54.78	0.001	108.32	0.001	0.30
CEF	18.87(5.36)	25.94(3.96)	17.57(5.68)	16.60(5.66)	40.88	0.001	46.84	0.001	81.48	0.001	0.27
SEF	11.28(3.45)	16.14(5.16)	11.34(3.89)	12.46(4.25)	8.47	0.004	47.13	0.001	18.63	0.001	0.27
SSF	9.89(3.57)	12.61(3.14)	9.97(3.23)	9.45(2.82)	11.78	0.001	10.03	0.001	21.74	0.001	0.07
DSF	5.47(2.27)	9.61(2.48)	6.08(2.83)	5.39(2.50)	24.60	0.001	39.68	0.001	77.32	0.001	0.24
SUF	2.38(1.16)	3.83(2.04)	2.72(1.73)	4.84(2.53)	5.527	0.020	79.60	0.001	2.78	0.098	0.38
BRF	3.21(1.57)	5.43(1.13)	3.17(1.65)	2.84(1.56)	47.49	0.001	24.34	0.001	44.24	0.001	0.16
ASHS	72.98(12.94)	106.43(11.77)	73.68(14.52)	69.36(12.67)	81.33	0.001	150.48	0.001	252.81	0.001	0.54
PBS	18.57(4.04)	13.10(3.52)	17.32(4.13)	18.04(4.68)	8.759	0.004	35.77	0.001	60.71	0.001	0.22
SBS	20.65(4.87)	15.30(3.47)	20.10(4.05)	20.63(3.91)	14.16	0.001	44.26	0.001	65.42	0.001	0.26
CBS	16.50(4.05)	12.21(3.66)	16.79(4.97)	17.46(4.32)	17.18	0.001	21.29	0.001	40.02	0.001	0.14
IBS	15.38(4.09)	10.74(3.14)	16.27(4.54)	17.60(4.50)	42.99	0.001	13.30	0.001	43.26	0.001	0.09
CBI-SV	71.07(12.74)	41.38(10.83)	70.50(14.04)	73.73(13.45)	28.29	0.001	58.74	0.001	113.96	0.001	0.47
MMM	15.81(3.58)	21.23(2.48)	16.28(3.71)	14.01(3.57)	53.01	0.001	17.32	0.001	100.24	0.001	0.12
MMC	13.38(2.97)	19.43(2.45)	13.20(2.99)	12.26(4.12)	64.58	0.001	50.56	0.001	94.89	0.001	0.28
MMMCS	29.20(5.46)	40.76(4.28)	29.49(6.09)	26.27(6.14)	79.17	0.001	46.40	0.001	145.64	0.001	0.26
SFLS	23.37(6.56)	32.96(3.24)	24.97(6.40)	20.05(7.43)	50.021	0.001	9.186	0.003	88.53	0.001	0.068
AP	82.09(6.79)	85.23(5.88)	81.24(4.74)	80.80(4.74)	7.61	0.007	106.73	0.001	184.09	0.001	0.45

* = p < 0.05; *** = p < 0.001; η_p²: Partial Squared Eta; SAL salience subscale, MMS mood modification subscale, TOS tolerance subscale, WIS withdrawal subscale, COS conflict subscale, RES relapse subscale, SNAS social networking addiction scale, PHF physiological factor, BAF behavioral arousal factor, CEF cognitive emotional factor, SEF sleep environment factor, SSF sleep stability factor, DSF daytime sleep factor, SUF substance factor, BRF bedtime routine factor, ASHS adolescent sleep hygiene scale, PBS personal related burnout subscale, SBS study related burnout subscale, CBS classmate related burnout subscale, IBS instructor related burnout subscale, CBI-SV Copenhagen burnout inventory-students version, MMM Mezam meta memory scale, MMC Mezan meta concentration scale, MMMCS Mezan meta memory and meta concentration scale, SFLS satisfaction with family life scale, AP academic performance

Fig. 2.

Scores on SMNS

Fig. 3.

Scores on ASHS

Fig. 4.

Scores on MMMCS

Fig. 5.

Scores on CBI-SV

Discussion

The current study reveals that cognitive-behavioural interventions markedly influence social media addiction, sleep quality, academic burnout, memory and concentration, family dynamics, and academic achievement [14, 21, 77]. The results indicated a significant decrease in social media addiction levels among the experimental group after the intervention. These findings align with previous studies [78]. The research underscores the effectiveness of CBT methods in managing excessive social media use. Cognitive restructuring proved particularly beneficial by addressing distorted beliefs and irrational thought patterns related to social media [79]. Participants gained insights into social media’s addictive features and its impact on essential areas of life. Furthermore, utilizing cost–benefit analyses and promoting reflection on social media usage was advantageous [65]. Students also acquired new coping mechanisms and time management skills, enabling them to more effectively regulate their social media use [80].

Findings indicate that cognitive behavioural interventions have enhanced sleep hygiene, which is closely linked to social media addiction [81]. This study demonstrates that these interventions have effectively improved sleep hygiene. Our results support earlier research conducted by Montgomery & Dennis [65, 82, 83]. Cognitive behavioural interventions positively impacted students’ sleep patterns and overall sleep hygiene. It was found that cognitive behavioural therapy (CBT) is effective in addressing insufficient sleep, leading to better sleep quality and overall well-being among college students [10, 57]. Strategies such as relaxation training and activity scheduling were employed to encourage healthier sleeping habits, contributing to improved sleep duration and quality [77].

One finding indicated that cognitive-behavioural interventions help reduce burnout in undergraduate students. These interventions appear effective in lowering academic burnout levels, consistent with previous research by Ulas and Secer [76]. Furthermore, the study showed that students enhanced their self-confidence, improved time management, and cultivated problem-solving abilities [84]. Their engagement in group discussions and willingness to seek help from professors also increased. Students learned to manage their resources more healthily, and stress management techniques were particularly effective [85]. The study emphasized the significance of resolving conflicts effectively, taking mental breaks, and introducing a work-life balance [33]. Students practicing mindfulness and relaxation exercises experienced greater satisfaction with their daily routines [86].

Furthermore, findings examined the effectiveness of cognitive behavioural interventions on memory and concentration [87]. It is suggested that these interventions could aid students in managing their memory and concentration challenges [88]. The results revealed that students in the experimental group demonstrated improved memory and concentration levels compared to those in the waitlist control group [89]. In this study, students acknowledged the detrimental effects of screen time and multitasking on their attention. They engaged in mental breaks and mindfulness exercises to boost their concentration. Furthermore, students utilized problem-solving strategies and techniques to enhance their understanding and retention of lecture material [9].

Social media addiction has been linked to increased feelings of isolation and a decrease in time dedicated to family interactions, with a noted rise in social media use correlating with weaker family ties [41]. Research also found that cognitive behavioural interventions (CBIs) benefited family relationships [90]. This study illustrated that CBIs significantly improved family dynamics, enhancing communication skills and the overall quality of relationships within the family. Various techniques were employed to boost communication and assertiveness [38]. As a result, students developed practical communication skills and learned how to express their emotions; they began confiding in family members and recognizing their family as a source of support. They embraced self-acceptance and respected different viewpoints. Additionally, students expressed positive sentiments about spending time with their families, which was encouraged by the intervention that included regular family activities [91].

The results of this study clearly show that cognitive-behavioural interventions (CBIs) have improved students’ academic performance. A noticeable difference exists in the grades between the experimental and waitlist control groups. This aligns with previous research by Awadalla et al. [92], which found that cognitive-behavioural interventions reduce academic-related stress and enhance academic success [93]. In this study, students practised time management, setting priorities, establishing goals, cognitive restructuring (challenging incorrect and irrational beliefs about success), boosting academic motivation, acquiring life skills, and adopting positive coping strategies [94]. Learning to unplug from technology, engage in group discussions, successfully tackle individual assignments, and reduce environmental distractions helped students reach their academic goals. Additionally, students received support in developing self-acceptance and resilience in coping with challenges [95].

Conclusion

The findings support the study’s hypothesis, indicating that cognitive behavioral interventions have contributed to reduced social media addiction and alleviated psychological issues. These interventions have successfully mitigated academic burnout among students, enhancing their academic performance and the quality of family relationships. Furthermore, they have improved undergraduate students’ sleep hygiene, memory, and concentration while decreasing academic burnout. Cognitive behavioral interventions have shown a notable impact on scores related to social media addiction, sleep hygiene, academic burnout, memory, family relationships, and academic performance in post-testing compared to a control group. Students gain from participating in cognitive behavioral intervention sessions, which have been beneficial for addressing academic challenges and introducing an element of discipline. In summary, cognitive behavioral interventions have proven effective in reducing social media addiction, addressing psychological difficulties, and enhancing academic success.

Limitations of the study

In Pakistan, intervention programs are pretty uncommon. The significance of students’ mental health and training to handle stress and responsibilities is frequently overlooked. Scheduling treatment sessions around class times poses a challenge due to the limited available time. Additionally, there are inadequate resources, such as seating arrangements. The fast pace of coursework makes it impractical for students to attend sessions and effectively manage their time. Furthermore, students encounter external challenges; those residing in hostels struggle to maintain routines and complete assignments related to the sessions. Additional stressors like family problems, financial difficulties, and varying intellectual levels complicate the evaluation of treatment outcomes. It is also crucial to address the potential for confusion or residual bias and to contemplate longitudinal methodologies or validations on external samples in the future. Although all the participants were university students and they could easily understand the English language, but if the scale in cultural language was used, which would create more impact. This was another limitation of the study.

The implication of the study

There is a lack of research focused on addressing social media addiction; however, numerous studies have illustrated interventions for the addictive use of the internet. In our review, no studies were found that specifically target the increasing prevalence of social media addiction, its associated problems, and their management. Our study seeks to elucidate the mechanisms and pathology underlying social media addiction, aiding students in moderating their use. Rather than advising a complete withdrawal from technology, it offers a framework for the responsible utilization of social media. It provides insights into a balanced approach through self-help strategies pertinent to social media. The current study has heightened awareness about the unrestricted use of social media and underscores that excess in any area can lead to severe consequences. It highlights the necessity of intervention-based programs to enhance students’ mental health and stability. It clarifies that students require motivation and fortitude to navigate psychological, social, and academic challenges. Current students encounter various issues; excelling in their lives necessitates concentrating on academics while developing essential skills such as time management and self-confidence. Students facing different stressors need time to bolster their mental stability and learn how to manage academic-related challenges. Additionally, it provides a framework to prepare students for the practical world and assists them in acting efficiently in their future endeavors.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CBT

Cognitive behavioral therapy

CBI

Cognitive behavioral interventions

SAL

Salience subscale

MMS

Mood modification subscale

TOS

Tolerance subscale

WIS

Withdrawal subscale

COS

Conflict subscale

RES

Relapse subscale

SNAS

Social networking addiction scale

PHF

Physiological factor

BAF

Behavioral arousal factor

CEF

Cognitive emotional factor

SEF

Sleep environment factor

SSF

Sleep stability factor

DSF

Daytime sleep factor

SUF

Substance factor

BRF

Bedtime routine factor

PBS

Personal-related burnout subscale

SBS

Study-related burnout subscale

CBS

Classmate-related burnout subscale

IBS

Instructor-related burnout subscale

CBI

Copenhagen burnout inventory

BMS

Brief meta-memory scale

BMC

Brief meta-concentration scale

MMMS

Mezan meta-memory and meta-concentration scale

SWFL

Satisfaction with family life

Author contributions

Abbas and Iftekhar had full access to all the study data and are responsible for the data integrity and accuracy of the data analysis. Concept and design: Abbas, Iftekhar, Riaz, Maryam, Feda, Aljhani. Acquisition, analysis, or interpretation of data: Abbas, Iftekhar, Riaz, Kareem, Maryam, Tabassum. Drafting of the manuscript: Abbas, Riaz, Iftekhar, Tabbasum, Feda, Aljhani. Critical revision of the manuscript for important intellectual content: Abbas, Iftekhar, Tabbasum, Feda, Aljhani. Statistical analysis: Abbas, Iftekhar, Kareem, Tabbasum, Feda, Aljhani. Administrative, technical, or material support: Abbas, Riaz, Kareem. Supervision: Abbas, Iftekhar, Riaz, Feda, Aljhani.

Funding

No funding or other financial support was received for this study.

Data availability

The present study dataset that we completed and analyzed for this study is not publicly available because we did not get permission from the participants and the hospital administration. The datasets would be available at any time; just send an email to the corresponding author.

Declarations

Ethics approval and consent to participate

Before starting the study, we received ethical approval from the Institutional Review Board (IRB), Government College University, Faisalabad (i.e. Ref.No.GCUF/ERC/104, dated 7/06/2022). Then the protocol was approved by the Thai Clinical Trial Registry with the protocol (i.e. TCTR: TCTR20220924002, with URL: https://www.thaiclinicaltrials.org/show/TCTR20220924002, dated 23/09/2022 and first enrollment dated 1/10/2022, respectively. All the study procedures were accomplished under relevant guidelines, including participant enrollment, screening, data collection, data security, and analysis. In this study, we followed the Consolidated Standards of Reporting Trials CONSORT reporting guidelines. Furthermore, we confirm that all of the procedures and methods of this study were executed by adhering to the requirements of our institutional review board and the global code of conduct for research in resource-poor settings. The Institutional Review Board (IRB) followed all methods and procedures. Written informed consent was obtained from all participants, and then they were enrolled in the study.

Consent to publish

All the authors agreed to publish this article in this journal.

Competing interests

The authors declare no competing interests.