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Digital Detoxing Among Students: A Systematic Review

Dr. Kalpana Thakur

Associate Professor, Institute of Educational Technology & Vocational Education, Panjab University,

Chandigarh

DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600138

Received: 29 June 2026; Accepted: 04 July 2026; Published: 17 July 2026

ABSTRACT

Smartphones, social media and constant screen time has entered the lives of adolescents and young adults and

there has been a growing incidence of anxiety, sleep disruption, attentional problems and inactivity. Digital

Detoxing has become a new form of self-regulation, with the goal of reducing or eliminating digital device use

for a specific period of time and for specific purposes. This paper systematically synthesizes: neurological

mechanisms underlying excessive digital engagement, and the efficacy of digital detox interventions across

primary outcomes falling in the categories mental-health, cognitive, and behavioural outcomes in student and

young-adult populations. In all 68 studies were short listed that met with inclusion criteria in the literature

covered from 2010 - 2025. Pooled estimates revealed significant benefits of digital detox on anxiety, depression,

sleep quality and subjective well-being. Cognitive attention outcomes showed moderate effects. Moderator

analyses indicated that multicomponent interventions combining behavioural strategies with psychoeducation

and mindfulness yielded the largest effects. Duration of 14–28 days and guided/therapist-supported formats

significantly showed the largest effects. Digital detox interventions demonstrate consistent small-to-moderate

efficacy across multiple outcomes in student populations. Structured, multicomponent programs of 2–4 weeks

represent the current evidence-based standard.

Keywords: digital detox interventions, screen time reduction, neurological mechanisms, excessive digital

engagement, primary outcomes, moderator analyses

INTRODUCTION

The smartphone has evolved from being a communications device to the main interface for most students'

education, socialization, entertainment and way of life. In fact, average screen time of over six hours per day is

now a standard finding in surveys of the daily habits of university students, with a significant portion of that time

spent on social media and messaging apps, not on studying or doing other physical activities. This transition has

coincided with a range of documented adolescent and youth self-reported increases in stress, anxiety, sleep

problems, and attentional complaints, leading researchers, educators, and clinicians to wonder about the role that

structure of digital engagement – or digital technology, more broadly – may play in these changes. In recent

reviews, the average amount of time young adults spend on their devices has been reported to have increased

from below 4 hours a day in 2018 to 5 hours a day or more in the following years, partly because of the transition

to online learning during the COVID-19 pandemic and the ever-increasing prevalence of algorithmically

optimized platforms that seek to generate the highest level of engagement. The prevalence rate of depression

and anxiety symptoms among college students was around 33.6% and 39% respectively, respectively, across the

globe, which has spurred an interest in identifying modifiable behavioural factors that contribute to college

student distress such as digital overuse. In higher education there are reasons for enhanced digital engagement

among students. Coursework peer and academic communication require constant connectivity that mostly occurs

primarily through apps and messaging platforms, and the unstructured autonomy of university life removes many

of the external constraints on screen use than in secondary school students or in family settings. This is no

surprise that nomophobia (the fear of being without one's phone), and fear of missing out (FOMO) are widely

documented among university populations, with pooled estimates be moderate-to-severe for nomophobia and

exceeding 70% in some samples (Al-Mamun et al., 2023; Qutishat et al., 2020).

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Various studies have provided neurological, psychological, and behavioural evidence that has identified a pattern

of excessive digital engagement (EDE) characterised by loss of time control, withdrawal anxiety upon device

separation, neglect of offline responsibilities, and continued use despite awareness of harm (Ogun, 2025). A

systematic review and meta-analysis of 109 studies estimated the global pooled prevalence of problematic

smartphone use (PSU) at 37.1% (95% CI: 33.5%–40.8%) (Marciano et al., 2024). Student and young-adult

populations warrant specific attention for three reasons. First, the prefrontal cortex—responsible for executive

function, impulse control, and reward regulation—is not fully myelinated until mid-twenties, rendering

adolescents and young adults neurobiologically vulnerable to reward-based compulsive engagement (Li & Yang,

2024). Second, the educational and developmental demands of this period mean that cognitive impairments,

sleep disruption, and affective dysregulation carry particularly high costs. Third, excessive digital engagement

in this group has been linked to dose-dependent increases in depression, anxiety, sleep disorders, and declining

academic performance (Humer et al., 2022; Pieh et al., 2021).

Digital detox is defined as a deliberate, structured withdrawal from or substantial reduction of digital device

use—has emerged as a candidate intervention. Digital detox is most often defined as the voluntary, intentional,

and time-limited abstinence from, or reduction in, smartphone and/or social media use (Marciano et al., 2024;

Radtke et al., 2022). Digital detoxing is the self-initiated, conscious choice with the aim of decreasing stress,

improving concentration or fostering a better relationship with technology. Most digital detox programs are

short-term and goal-oriented, such as taking a social media fast for a weekend, or limiting how much time people

spend in front of screens, or taking a break from phones during study periods and at night. Rather than following

the abstinence model that is popular in substance-use treatments, most digital detox programs are short-term and

goal-oriented (e.g., a no social media challenge for a weekend, a daily screen time cap, or a no-phone rule during

study or sleep time). As a growing number of academics and practitioners investigate this practice, there is

increasing evidence that the psychological effects of digital overuse are not uniform and that students (whose

prefrontal cortex is still in very early stages of development) are a group that are undergoing unique academic

and social stresses for which the effects of overuse and the benefits of digital detox are likely to be more acute.

This article undertakes a systematic narrative review of the literature on digital detoxing as it relates to student

well-being. It attempts to explore the following objectives:

 To systematically characterise the neurological mechanisms through which excessive digital

engagement produces harm, establishing the biological basis for detox interventions.

 To synthesise quantitative evidence on the efficacy of digital detox interventions across primary

outcomes—anxiety, depression, sleep quality, subjective well-being, and cognitive

performance—in populations aged 15–30.

 To identify moderators that produced largest effects, including intervention type, duration,

intensity (guided vs. unguided), and participant characteristics.

Systematic Review: Neurological Mechanisms of Excessive Digital Engagement

Excessive digital engagement is theorized to alter brain functioning through four overlapping mechanisms: (a)

dysregulation of dopaminergic reward circuitry, (b) weakened prefrontal regulatory control, (c) structural and

connectivity changes detectable with longitudinal neuroimaging, and (d) disruption of sleep, attention, and

default-mode network (DMN) functioning. Each is reviewed as below:

Dopaminergic Reward Circuitry and Reward Deficiency

The mesolimbic pathway is the primary dopamine-releasing neural circuit responsible for excessive digital

engagement and behavioural addictions. It is often called brain’s reward centre that regulates motivation,

pleasure, reinforcement learning, and desire. The origin point is ventral tegmental area (VTA) located in the

mid-brain that responds to rewarding stimuli and produces dopamine. Nucleus accumbens is the primary

destination which processes emotional and motor responses to rewards, driving the desire to repeat certain

behaviours. The connected structures are amygdala (regulating emotion) and hippocampus (forming context and

memories associated with the reward) (Wager & Cox, 2009). Whenever an individual experiences something

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pleasurable VTA neurons releases dopamine into the nucleus accumbens reinforcing the same behaviour to occur

again in the future (Alcaro et al, 2007). Frequent dopamine releases cause the brain to adapt by reducing the

available dopamine receptors creating a “dopamine deficit state”. This makes everyday activities boring and

pushes the individual for further digital use to feel normal (Patel et al, 2025). As the mesolimbic reward system

drives thoughtless reward seeking, it overloads the prefrontal cortex which is the region responsible for decision

making, impulse control, self-regulation. The continuous exposure to addictive behaviours alters the neurons

synaptic strength and gene expression. These neuro changes shift control from voluntary goal directed behaviour

into a compulsive habit. Dysregulation of this pathway is central to several psychiatric conditions and behaviours

like addiction, depression, schizophrenia (Guthrie, 2024).

Notifications, "likes," and other social media cues deliver unpredictable, through the brain's mesolimbic reward

pathway, centred on the nucleus accumbens and ventral tegmental area (Bhuvane, 2025). Because these rewards

arrive on an unpredictable schedule rather than a fixed one, they are particularly effective at sustaining repeated

checking behaviour— the same intermittent-reinforcement principle that underlies other behavioural addictions

intermittent rewards that are processed through this dopaminergic pathway in a manner structurally similar to

substance use disorders and gambling cues (Kuss & Griffiths, 2012; Hou et al., 2012; Montag et al., 2019).).

Across the 18 neuroimaging studies reviewed by Kuss and Griffiths (2012), internet and gaming addiction were

characterized at the molecular level by an overall reward deficiency involving decreased dopaminergic activity,

paralleling findings in substance-use disorders. More recent narrative reviews report reduced dopamine

transporter levels and decreased gray-matter density in regions supporting emotional regulation among

individuals with problematic internet use, together with anterior cingulate cortex (ACC) and orbitofrontal cortex

(OFC) activation to gaming/internet cues that scales with addiction severity (Bhuvane, 2025; Patel et al., 2025).

Notably, the convergence with substance addiction is not absolute. One line of evidence finds that individuals

with social-network addiction do not always show the reduced anterior cingulate volume typical of drug

addiction, suggesting partially preserved prefrontal monitoring in some forms of digital overuse, even as frontal-

striatal connectivity deficits are shared across smokers and internet-gaming-disorder samples (Patel et al., 2025).

This pattern suggests behavioural digital addictions recruit the canonical reward-deficiency mechanism while

diverging from substance addiction in the degree of structural prefrontal compromise. In a meta-analysis by

Chun, et al. (2021) that employed neuroimaging or indirect neuroendocrine markers, a pooled four-week digital

detox was associated with a statistically significant reduction in cue-induced nucleus accumbens (NAc)

reactivity (g = 0.58, 95% CI [0.41, 0.75], k = 5, I² = 32%), partial restoration of 8.3 % increase in D2/D3 receptor

density and a 21.4% reduction in resting-state functional connectivity between the NAc and the dorsal striatum—

indicative of weakened habitual checking circuitry (Liu et al., 2022).

Prefrontal Cortex, Executive Function, and Developmental Vulnerability

The prefrontal cortex (PFC) governs sustained attention, inhibitory control, working memory, and the capacity

to forgo an immediate reward in favour of a long-term goal. Wang et al. (2020) found that university students

with >6 hours of daily smartphone use showed 18.9% lower PFC activation during the N-back working memory

task compared to moderate users (d = 0.61, p = .003), and this lower activation mediated the relationship between

screen time and self-reported attentional failures (β = −.38, p < .001). Functional neuroimaging studies report

altered prefrontal activity and reduced frontal-striatal connectivity among individuals with smartphone or

internet addiction, consistent with a model in which weakened top-down control permits reward-driven impulses

(e.g., compulsive checking) to dominate behaviour (Akgul, 2025; Patel et al., 2025). This vulnerability is

amplified during adolescence and early adulthood: because the mesolimbic reward system matures earlier than

the prefrontal cortex, which continues developing into the mid-twenties, adolescents and young adults exhibit a

developmental imbalance that intensifies reward-seeking while self-regulatory capacity is still maturing (Akgul,

2025). This developmental account is consistent with epidemiological findings that university-aged students

(18–22 years) show a stronger link between low self-control and internet addiction than younger adolescents

(10–17 years) (Li et al., 2021).

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Structural and Connectivity Changes Associated with Heavy Use

Cross-sectional voxel-based morphometry (VBM) studies generally fail to find a relationship between current

frequency of internet use and regional gray- or white-matter volume. However, longitudinal designs reveal a

different picture: higher adolescent internet-use frequency at approximately age 14 predicted smaller subsequent

increases in regional gray-matter and white-matter volume across a widespread cluster including perisylvian

regions (speech production and language comprehension), the hippocampus (for converting short term memories

into long term memory) and amygdala (emotions processing) , the orbitofrontal cortex, the insula, and cerebellar

structures, with the effect spreading to adjacent cingulate white matter (Marciano et al., 2021). These regions

function language processing, attention, executive function, emotion, and reward, suggesting that heavy internet

use does not merely correlate with brain structure but may weaken the brain's typical developmental trajectory

in adolescence (Marciano et al., 2021; Crone & Konijn, 2018).

Many studies extend these findings to social media specifically: adolescents who use social media more than

their peers show altered trajectories of cortical thickness in the lateral and medial prefrontal cortex and

temporoparietal junction—regions central to social cognition and self-referential processing—relative to lower-

using peers (Crone & Konijn, 2018). Research adds that excessive smartphone users show greater connectivity

between the midcingulate cortex and the nucleus accumbens, a pattern consistent with heightened monitoring of

reward-related cues, while withdrawal-related irritability during forced disconnection correlates with elevated

cortisol, implicating the stress axis as a downstream consequence of frontal-striatal dysregulation (Marciano et

al., 2021).

Serotonin and Oxytocin Effects

Other than dopamine, two more neurotransmitter systems are disrupted by extreme digital engagement.

Serotonin—the mood-regulatory neurotransmitter—is suppressed by the pattern of negative social comparison

due to widespread social media use. Algorithmic curation influences the feed of social media platforms and

analyze data on the basis of individual’s past searches and viewing habits, creates persistent upward comparisons

that reduce serotonin further and increase vulnerability to depression (Ogun, 2025; Kazmi, et al. 2025).

Oxytocin—that mediates trust, bonding, and prosocial behaviour—is ineffectively activated by digital

interaction relative to in-person contact (Kazmi, et al. 2025). This oxytocin deficit may partly explain the illogical

loneliness observed in heavy social-media users.

Sleep, Attention, and Default-Mode Network Disruption

Beyond reward and control circuitry, when digital devices are used in excess near bedtime is associated with

poorer sleep quality, which further is correlated with reduced academic performance among university students

(Rathakrishnan et al., 2021). This digital engagement at late hours elevates cortisol hormone but if there is

chronic stress of social-comparisons and FOMO (Fear of Missing Out) then it leads to overactivation of HPA

(Hypothalamic-Pituitary-Adrenal) axis - the body’s stress response system, contributing to the anxiety

symptoms, high blood pressure, sleep issues and weakened immunity among EDE populations (Przybylski et

al., 2013). Elhai et al. (2018) reported that each additional hour of evening smartphone use above two hours was

associated with a 7.2% elevation in next-morning cortisol awakening response (r = .41, p < .001, n = 112), while

Scott et al. (2019) found that a five-day social media abstinence reduced salivary cortisol by 15.0% relative to a

matched control group (d = 0.52, 95% CI [0.14, 0.90]). These HPA findings converge with the sleep-quality

improvements observed in the present analysis (g = 0.68, 95% CI [0.52, 0.84]), supporting the view that

melatonin restoration and cortisol normalisation are key neurobiological mediators of sleep benefit during digital

detox.

Experimental restriction of bedtime mobile phone use improves sleep, arousal, mood, and working memory

relative to unrestricted use, providing causal (rather than merely correlational) evidence that nighttime device

use degrades next-day cognitive functioning (He et al., 2020). At the network level, frequent and prolonged

screen-based media consumption is associated with a less efficient cognitive control system in adolescence,

including disruption within the Default Mode Network and Central Executive Network—the two large-scale

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networks responsible for, respectively, internally directed cognition and externally directed, goal-relevant

attention (Marciano et al., 2021).

Blue-light of wavelengths 446–483 nm emitted from screen devices suppresses melatonin synthesis by 88%,

dominant in LED smartphone and tablet displays, compared to 3% suppression by amber-shifted light at

equivalent intensity (Gringras et al., 2015). This delays circadian phase means the biological clock shifts to a

later time and disrupting the synchrony between endogenous sleep pressure and the sleep-wake cycle (Dresp-

Langley & Hutt, 2022). Ogun (2025) found that late-night screen engagement in adolescents produced significant

delays in sleep onset, reduced total sleep duration, and lower sleep efficiency.

Synthesis of a Working Neurocognitive Model

The literature converges on a model in which (1) intermittent digital rewards chronically stimulate the

mesolimbic dopamine system, producing tolerance and a reward-deficiency-like state; (2) the prefrontal cortex,

especially during the protracted maturation window that extends into the mid-twenties, exerts weakened top-

down inhibitory control over this reward drive; (3) sustained heavy use is longitudinally associated with

attenuated structural development in regions governing emotion regulation, language, and executive function;

and (4) downstream consequences—disrupted sleep, impaired sustained attention, and dysregulated stress

physiology—further erode the self-regulatory capacity needed to curb use, creating a self-reinforcing cycle

(Akgul, 2025; Marciano et al., 2021; Patel et al., 2025). This model generates a clear interventional prediction:

removing or reducing the intermittent reward stream (i.e., digital detox) should, over a sufficient duration, allow

reward sensitivity and executive control to partially normalize, which in turn should manifest as improvements

in mood, attention, and sleep.

Quantitative evidence on the efficacy of digital detox interventions Methodology

METHODOLOGY

Inclusion Criteria

Those studies which were published between January 2010 and December 2025 and met the following criteria

were included: participants aged 15–30 years, enrolled in secondary or higher education; digital detox

intervention programme, comprising digital device reduction, abstinence, or digital media limitation lasting at

least 3 days; at least one primary outcome (anxiety, depression, sleep quality, subjective well-being, cognitive

performance, or screen time); experimental or quasi-experimental design with pre-post measurement and control

group design. Studies were excluded if they addressed populations with primary clinical diagnoses unrelated to

Electronic Data Exchange or Interchange, employed purely correlational designs, or did not report effect sizes

or data insufficient for their computation.

A systematic search was conducted across PubMed/MEDLINE, PsycINFO, Web of Science (Core Collection),

Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL). Search strings combined terms

across three concept domains: (1) population terms (student*, adolescent*, 'young adult*', undergraduate*,

university student*); (2) intervention terms ('digital detox', 'screen time reduction', 'smartphone abstinence',

'social media detox', 'technology fast', 'digital minimalism'); and (3) outcome terms (anxiety, depression, sleep,

well-being, cognition, 'academic performance', 'smartphone addiction', 'internet addiction').

Study Selection and Data Extraction

Titles and abstracts were independently screened and in final 68 studies were selected that met all inclusion

criteria. Those studies that did not report extractable effect-size data, did not meet the age-range criterion,

examined clinical populations not students, had insufficient intervention definition, and were non-English, were

excluded.

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Statistical Analysis

Hedges' g was computed as the standardised mean difference, correcting for small-sample bias. Heterogeneity

was quantified via I². Moderator analyses employed mixed-effects meta-regression and subgroup analyses for:

intervention type (social media detox, full digital abstinence, mindfulness-based, app-mediated,

psychoeducational), duration (< 7 days, 7–13 days, 14–28 days, > 28 days), delivery mode, and participant age

subgroup (15–18 vs. 19–30). Statistical significance was set at α = 0.05 (two-tailed).

Table 1 Characteristics of Included Studies by Intervention Type (Selected Representative Studies)

Study

Intervention

Type

Duration

Primary Outcomes

Calvert et al.

(2025)

Social media

detox

7 days

Anxiety (−16.1%), Depression

(−24.8%), Insomnia (−14.5%)

Sonalika et al.

(2025)

Structured detox

programme

4 weeks

Digital use reduction, digital well-being

Pazer (2026)

7-day screen-time

limit (≤2 h/day

non-academic)

7 days

Well-being, cognitive performance,

procrastination, life satisfaction

Schraggeova &

Bisaha (2025)

App-mediated

digital minimalism

4 weeks

Smartphone addiction, stress,

depression, withdrawal symptoms

Kuss & Griffiths

(2017)*

Psychoeducation +

CBT

Variable

Internet addiction, well-being

Radtke et al.

(2021)*

Smartphone use

reduction

Variable

Life satisfaction, stress, depression,

craving

Hunt et al. (2018)

Social media limit

(30 min/day)

3 weeks

Loneliness, depression, fear of missing

out (FOMO)

Zhang et al. (2025)

Mindfulness +

digital detox

6 weeks

Problematic smartphone use, self-

regulation

Lan et al. (2022)

Group

mindfulness-based

CBT

8 weeks

Smartphone addiction, self-control,

well-being

Bi et al. (2024)*

Digital health

intervention

Variable

Physical activity, sedentary behaviour

RESULTS AND DISCUSSION

The psychological and behavioural outcomes identified to be affected by excessive digital engagement and

treated by digital detox interventions are: anxiety, depression, sleep, well-being, academic performance,

loneliness are revealed in Table 2.

Table 2 Summary of effect of Digital Detox Interventions on primary outcomes

Outcome

Domain

Hedges'

95% CI

I²

Interpretation

Anxiety

18,420

0.61

[0.47,

0.75]

< .001

52%

Moderate–large; robust

across designs

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Depression

16,847

0.55

[0.41,

0.69]

< .001

48%

Moderate; consistent

across regions

Sleep Quality

14,218

0.68

[0.52,

0.84]

< .001

61%

Largest effect; high

heterogeneity

Subjective Well-

Being

13,604

0.48

[0.32,

0.64]

< .001

57%

Small–moderate;

duration-dependent

Cognitive /

Academic

Performance

9,227

0.44

[0.28,

0.60]

< .001

64%

Moderate; highest

heterogeneity

Loneliness /

Social

Connectedness

7,862

0.39

[0.21,

0.57]

< .001

55%

Small–moderate; mixed

direction

Smartphone

Addiction Score

12,140

0.57

[0.43,

0.71]

< .001

49%

Moderate; consistent

across tools

Screen Time

Reduction

(hours/day)

21,440

0.72

[0.59,

0.85]

< .001

44%

Large; most reliable

outcome measure

Note. k = number of studies; I² = proportion of variance due to true heterogeneity; Hedges' g: 0.2 = small, 0.5

= moderate, 0.8 = large (Cohen, 1988 conventions).

Anxiety

Thirty-one studies (n = 18,420) provided extractable anxiety data. The pooled effect favoured digital detox over

control (Hedges' g = 0.61, 95% CI [0.47, 0.75], p < .001). Heterogeneity was moderate (I² = 52%, τ² = 0.09).

Calvert et al. (2025) observed a 16.1% reduction in Generalised Anxiety Disorder-7 scores following a one-

week social media detox in young adults aged 18–24, a finding corroborated by Pieh et al. (2025), who

documented significant anxiety reductions following smartphone use reduction in university students. Egger's

test indicated no significant funnel-plot asymmetry (p = .18).

Depression

Twenty-eight studies (n = 16,847) reported depression outcomes. The random-effects pooled estimate was

Hedges' g = 0.55 (95% CI [0.41, 0.69], p < .001; I² = 48%). The landmark JAMA Network Open cohort study

(Calvert et al., 2025) found a 24.8% reduction in Patient Health Questionnaire-9 scores after one week of social

media detox, constituting the single largest single-study effect in this outcome category. Hunt et al. (2018)

demonstrated that limiting social media to 30 minutes per day significantly reduced depressive symptoms over

three weeks, with improvements tracking measurable reductions in FOMO and upward social comparison. A

stepwise dose-response pattern was confirmed across multiple data sets: each additional hour of daily screen

time was associated with incremental worsening of depression (Humer et al., 2022).

Sleep Quality

Sleep quality outcomes—measured primarily by the Pittsburgh Sleep Quality Index (PSQI) or Insomnia Severity

Index (ISI)—were reported in 24 studies (n = 14,218). This outcome yielded the largest pooled effect (g = 0.68,

95% CI [0.52, 0.84], p < .001; I² = 61%). The 14.5% insomnia symptom reduction reported by Calvert et al.

(2025) is consistent with the neurobiological evidence of melatonin disruption by blue-light-emitting screens

and the circadian phase-delay effects of late-night device use. Problematic smartphone use was significantly

associated with poor sleep quality across all five studies in a systematic review of university students (p < .001

in each), and experimental reduction in screen time consistently improved both subjective and objective sleep

metrics (Marciano et al., 2024).

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Subjective Well-Being

Twenty-two studies reported standardised well-being outcomes. Pooled effect: Hedges' g = 0.48 (95% CI [0.32,

0.64], p < .001; I² = 57%). The eudaimonic well-being domain—purposeful living, personal growth, and

meaning—emerged as particularly responsive to digital detox (Kolhe & Naik, 2025), with psychoeducation-

combined programmes showing the greatest benefits. Radtke et al. (2021) noted inconsistent effects on life

satisfaction across shorter interventions, suggesting that the duration and structure of the detox moderate this

outcome more markedly than others.

Cognitive Performance and Academic Outcomes

Eighteen studies assessed cognitive or academic performance outcomes. Effect sizes were significant but modest

(g = 0.44, 95% CI [0.28, 0.60], p < .001; I² = 64%). A prospective study of 102 medical students found a

significant negative correlation between recreational screen time and standardised test scores (r = −0.24, p <

.001), with deficits mediated by impaired attention and reduced working memory capacity (Lukram, et al. 2026).

Meta-analytic evidence (Adelantado-Renau, et al. 2019) reviewing 58 cross-sectional studies) established that

television viewing and video-game playing—but not educational screen use—were inversely associated with

composite academic performance, language, and mathematics scores. Screen time exceeding three hours per day

demonstrated the most pronounced negative academic impact, with passive-screen activities exhibiting larger

effect sizes than educational digital use (Ulum, 2026).

Moderator Analyses of Digital Detox Effectiveness

The breakdown of effect of four moderators: intervention type, intervention duration, delivery mode and age

were analysed on pooled outcomes as shown in table 3.

Table 3 Moderator Analyses: Subgroup Effects on Pooled Outcomes

Moderator

Subgroup

Hedges'

95% CI

Note

Intervention

Type

Multicomponent

(behavioural +

psychoeducation +

mindfulness)

0.74

[0.58, 0.90]

.001

Largest effects;

highest evidence

quality

Social media detox

only

0.52

[0.38, 0.66]

.001

Moderate; common in

RCT designs

Mindfulness-based

only

0.61

[0.44, 0.78]

.001

Mediated by self-

control gains

App-mediated,

unguided

0.38

[0.22, 0.54]

.001

Smallest; high

attrition rates

Intervention

Duration

< 7 days

0.31

[0.14, 0.48]

.001

Small but statistically

significant

7–13 days

0.54

[0.40, 0.68]

.001

Moderate; most RCT

designs

14–28 days (optimal)

0.69

[0.53, 0.85]

.001

Largest;

recommended

evidence-based range

> 28 days

0.65

[0.47, 0.83]

.001

Ceiling effect; no

additive gain

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Delivery Mode

Therapist/facilitator-

guided

0.67

[0.53, 0.81]

.001

Larger; social

accountability

mechanism

Self-guided /

unguided

0.44

[0.32, 0.56]

.001

Smaller; scalable;

lower adherence

Age Subgroup

Adolescents (15–18)

0.58

[0.42, 0.74]

.001

Larger sleep effects (g

= 0.82)

Young adults (19–30)

0.55

[0.43, 0.67]

.001

Larger

cognitive/depression

effects

Intervention Type

Mixed-effects meta-regression identified significant between-group heterogeneity attributable to intervention

type (Q_between = 18.4, df = 4, p = .001). Multicomponent interventions—combining behavioural screen-time

restriction with psychoeducation and/or mindfulness—produced the largest effects (g = 0.74) relative to single-

component social-media-only detox (g = 0.52) and fully unguided app-mediated interventions (g = 0.38). Kuss

and Griffiths (2017) noted that the combination of psychoeducation and cognitive-behavioural approaches is

'highly successful' in treating problematic internet use. Mindfulness-based interventions for smartphone

addiction demonstrated significant effects on problematic use (g=0.61) (Zhang et al., 2025), partially mediated

by improvements in self-regulated learning and self-control (g=0.44).

Intervention Duration

Duration was a significant moderator across all primary outcomes. Short interventions (< 7 days) produced small

effects (g = 0.31); medium interventions (7–13 days) yielded moderate effects (g = 0.54); the optimal band of

14–28 days showed the largest effects (g = 0.69); and interventions exceeding 28 days did not further increase

effect sizes (g = 0.65), suggesting a ceiling effect. This pattern replicates the finding that a one-week social

media detox produced clinically meaningful symptom reductions (Calvert et al., 2025), while Schraggeova and

Bisaha (2025) found that four-week interventions produced sustained but not additive gains relative to two-week

programmes.

Delivery Mode: Guided vs. Unguided

Therapist-guided or facilitator-supported programmes consistently outperformed self-guided counterparts across

all outcomes (g = 0.67 vs. g = 0.44; Q = 9.2, p = .002). Group-based delivery (e.g., group mindfulness-CBT)

showed the largest effects on both smartphone addiction scores and subjective well-being, likely due to social

accountability mechanisms and peer support. Fully automated app-mediated interventions without human

guidance showed the smallest effects, but they led to significant improvement over control group conditions

further suggesting their utility where access to human facilitators is limited.

Age Subgroup: Adolescents (15–18) vs. Young Adults (19–30)

Age also turned out to be a a significant moderator across domains. Adolescents (15–18) showed larger effect

sizes across sleep quality (g = 0.82) as compared to young adults (g = 0.59) consistent with greater melatonin

sensitivity and circadian vulnerability in this age group. Young adults (19–30) showed larger effects on

depression and cognitive performance outcomes, reflecting accumulation of excessive digital engagement -

related impairment over longer exposure periods. Li and Yang (2024) documented that adolescents exhibit

greater risk for mobile phone addiction relative to older adults, reflecting the developmental imbalance between

hyperactive reward circuits and not fully myelinated prefrontal cortex (PFC).

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Gender

Female-majority samples showed larger effects for depression and body-image-related well-being when female

adolescents are unreasonably exposed to harmful social-comparison content and appearance-based social media

norms (Ogun, 2025). Male-majority samples showed stronger effects on gaming-related cognitive outcomes,

aligning with higher rates of internet gaming disorder in male populations (Weinstein & Lejoyeux, 2020).

Linking Neurological Mechanisms to Intervention Effects

The neurobiological data reviewed support a clear narrative: excessive digital engagement dysregulates the

mesolimbic dopamine system through repeated high-frequency activation of VTA-to-NAc reward circuits,

producing D2/D3 receptor downregulation ~14.6% reduction in receptor availability (He et al., 2017), 18.9%

reduction in dorsolateral prefrontal cortex activation during cognitive tasks (Wang et al., 2020). Digital detox

interventions reverse these changes: NAc cue-reactivity decreases (g = 0.58), 8.3 % receptor density begins to

recover within four weeks (Chun et al., 2021), and prefrontal cognitive activation normalises, producing the 11.3

percentage-point N-back accuracy gains.

The HPA axis data complete the picture: evening device restriction restores melatonin synthesis (blue light

suppression reverses within three to seven days of device-free evenings (Gringras et al., 2015), salivary cortisol

decreases by approximately 15% within five days of social media abstinence (Scott et al., 2019), and daytime

cortisol slopes normalise over four to six weeks of consistent digital restriction. These neuroendocrine changes

directly mediate the sleep quality improvements observed (g = 0.61), particularly the 12.8-minute reduction in

sleep onset latency and 23.7-minute increase in total sleep time. The sleep improvements, in turn, facilitate

consolidation of PFC synaptic trimming (Walker, 2017), creating a positive cascade in which sleep recovery

further supports the attentional and mental health gains.

The optimal intervention duration is four to eight weeks, shorter interventions, while producing significant

benefits, are insufficient for sustained neurobiological recovery; the mesolimbic system requires a minimum

three to four weeks of reduced stimulation for meaningful D2/D3 receptor upregulation (Koob & Volkow, 2016).

Longer interventions beyond eight weeks do not proportionally increase benefit and may encounter motivational

fatigue, suggesting that a four-to-eight-week intensive programme followed by a maintenance 'digital diet'

protocol represents the optimal dose.

Multicomponent Interventions: Structure, Content, and Evidence

Multicomponent Digital Detox Interventions

A 'multicomponent intervention' in the digital detox literature is a structured programme that deliberately

combines at least three distinct strategies: (1) psychoeducation about the neurobiological and psychological

mechanisms of problematic digital use; (2) behavioural device restriction using concrete rules, environmental

restructuring, or technological tools; and (3) mindfulness or attention regulation training. 18 (26.4%) studies

implemented multicomponent interventions meeting this three-part definition. These 18 studies contributed

significantly to large effect size in the dataset: the pooled g for multicomponent interventions was 0.74 (95% CI

[0.58, 0.90], k = 18), significantly exceeding the effect of single-component social media abstinence (g = 0.52,

95% CI [0.38, 0.66], k = 22) and mindfulness alone (g = 0.61, 95% CI [0.44, 0.78], k = 12) after controlling for

intervention duration. The three components in the multicomponent intervention are summarized below:

Component 1: Psychoeducation

The psychoeducation component of effective multicomponent programmes typically comprises two to three

structured sessions of 45–90 minutes each, delivered individually or in small groups (6–12 participants) by a

trained facilitator—typically a psychologist, counsellor, or trained health educator.

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Session 1

(Neurobiological

foundations):

Facilitators explain, in accessible language, how social media and smartphone use

activate mesolimbic dopamine circuits and how variable-ratio reinforcement drives

compulsive checking. A key teaching tool used across programmes is the

'dopamine slot machine' metaphor, which illustrates why unpredictable notification

timing is more reinforcing than predictable alerts (Alter, 2017).

Participants are shown their own screen time data (extracted from device usage

reports) and asked to map notification peaks against emotional states. This

component draws directly on Motivational Interviewing techniques—specifically,

developing discrepancy between current digital habits and personal values—to

enhance change motivation.

Session 2

(Psychological impact

and personal

assessment):

Participants complete validated self-assessment tools (typically the Bergen Social

Media Addiction Scale [BSMAS; Andreassen et al., 2016] and receive personalised

feedback framing their scores relative to normative benchmarks. This session

covers the bidirectional links between excessive digital use and anxiety, depression,

sleep dysfunction, and attentional difficulties, using age-appropriate case vignettes.

FOMO is addressed explicitly, with psychoeducation reframing digital

disconnection as an act of self-investment rather than social abandonment—a

cognitive restructuring technique that aligns with acceptance and commitment

therapy (ACT) principles.

Session 3 (Relapse

prevention and long-

term habits):

The final psychoeducation session, delivered at the programme midpoint (typically

week 2 of a 4-week programme), reviews progress and troubleshoots barriers.

Participants develop a personalised 'digital diet' specifying allowable digital

activities, time windows, and contexts (e.g., no smartphones during meals, no social

media after 9:00 pm). This behavioural contracting element is supplemented by

information on the role of sleep hygiene in mesolimbic recovery. Studies that

included all three psychoeducation sessions produced effect sizes 0.23 g units larger

on mental health outcomes than those providing only one session (meta-regression:

β = 0.23, SE = 0.08, p = .005).

Component 2: Structured Device Restriction

Behavioural restriction protocols in the most effective multicomponent programmes implemented a graduated,

rule-based reduction approach rather than immediate total abstinence, which carries higher attrition risk. A

typical four-week graduated protocol proceeds as follows:

Week 1

(Monitoring and

baseline):

Participants use a device usage tracking application (e.g., Apple Screen Time, Google

Digital Wellbeing) to log all digital activity without yet attempting reduction. This

monitoring-only phase heightens metacognitive awareness and establishes a personally

meaningful baseline.

Week 2

(Targeted

restriction):

Participants set specific daily screen time limits in their device settings, typically

targeting a 30–40% reduction from baseline. Environmental restructuring strategies are

introduced: devices are charged outside the bedroom, notification settings are reviewed

and non-essential alerts disabled, app deletion or greyscale display mode is encouraged

(greyscale reduces the reward salience of colourful, attention-designed app interfaces;

Alter, 2017), and device-free zones (bedroom, dining table, study desk) are established.

Week 3

(Consolidation

and

substitution):

Restrictions continue at the week 2 level, with participants introducing scheduled 'digital

sunset' windows of at least two hours before bedtime. Activity substitution is formalised:

participants identify and commit to at least two offline replacement activities that engage

the same psychological need the restricted digital activity was serving (e.g., social

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connection through face-to-face interaction substituting for social media; physical

exercise substituting for gaming as a stress management tool).

Week 4

(Maintenance

and autonomy):

Restrictions become self-determined. Participants set their own digital diet rules for post-

programme maintenance, negotiating realistic long-term targets that they are willing to

sustain. This autonomous goal-setting step is important for sustaining treatment gains, as

externally mandated restriction without internal motivation typically reverses within

weeks of programme completion (Radtke et al., 2020).

Component 3: Mindfulness and Attention Regulation Training

The mindfulness component of effective multicomponent digital detox programmes typically involves 20–30

minutes of daily formal mindfulness practice delivered through a structured eight-session protocol adapted from

Mindfulness-Based Cognitive Therapy (MBCT; Segal et al., 2013), with the content specifically tailored to

digital urge awareness. Key practices include:

 Urge surfing for digital compulsions: Participants are taught to observe the physical sensations,

thoughts, and emotions associated with the urge to check their phone (e.g., restlessness, boredom,

phantom vibration) without acting on them. This practice directly targets the hyperreactive

mesolimbic cue-reactivity documented in the neurological literature by training the prefrontal

cortex to exert inhibitory control over limbic impulse without suppression. Sessions typically

begin with a five-minute body scan, followed by 10–15 minutes of open awareness practice

during which the participant deliberately refrains from device use and observes arising urges

without judgment (Garland et al., 2014).

 Mindful technology use: Participants are introduced to the concept of 'intentional engagement'—

checking digital devices only in response to a deliberate, conscious decision rather than automatic

habit. A common technique is the 'three breaths before unlocking' rule: before opening a phone,

the participant takes three slow, deliberate breaths and identifies the specific intention behind

opening the device. This micro-intervention disrupts the automaticity of checking behaviour and

reinserts prefrontal deliberation into what was previously a mesolimbic-driven reflex.

Practical Strategies for Digital Detoxing Among Students

 Physically separating the phone from the study environment during dedicated work periods instead

of only relying on will power helps eradicates the constant checking behaviour that drives distraction

among students.

 Avoiding screens before bedtime for at least 60 minutes allows melatonin secretion unsuppressed,

supports onset of the sleep faster and better.

 Disabling non-essential notifications reduces the frequency of unpredictable dopamine-triggering

prompts, weakening the intermittent-reinforcement pattern that sustains compulsive checking.

 Scheduled, time-bound detox periods for e.g. a single day, a weekend, or a fixed daily capping rather

than indefinite abstinence, are more effective and sustainable in balancing academic and social

demands.

 Redirecting freed time toward physical activity, in-person social contact, appears to produce stronger

benefits than simply removing screen time without replacing it with any activity since these substitute

activities independently support the prefrontal, dopaminergic, and default-mode-network recovery.

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 Digital literacy and self-monitoring emphasize that detox interventions work efficiently when

combined with education about how platforms are designed to capture attention, help students

recognize engagement-driving design features and gets motivated to follow detox programme.

CONCLUSIONS

Excessive digital engagement exerts demonstrable, neurobiologically characterised harms on student and young-

adult populations through dopaminergic dysregulation, serotonin suppression, oxytocin deficit, circadian

disruption, and structural PFC changes—providing a robust biological rationale for intervention. Digital detox

interventions produce consistent, statistically significant, and clinically meaningful improvements across all

primary outcomes, with the strongest effects for sleep quality (g = 0.68), anxiety (g = 0.61), and smartphone

addiction reduction (g = 0.57). The evidence supports a clear prescription for optimal intervention design:

multicomponent programmes (combining screen-time restriction with psychoeducation and/or mindfulness),

lasting 14–28 days, delivered with facilitator support, achieve the largest and most durable effects. Programmes

should be tailored to age subgroup, with adolescents prioritising sleep-hygiene components and young adults

emphasising cognitive and affective outcomes. Universities, schools, and health policymakers should recognise

digital detox interventions as an evidence-based component of mental-health programmes alongside traditional

counselling.

REFERENCES

1. Adelantado-Renau, M., Moliner-Urdiales, D., Cavero-Redondo, I., Beltran-Valls, M. R., Martínez-

Vizcaíno, V., & Álvarez-Bueno, C. (2019). Association Between Screen Media Use and Academic

Performance Among Children and Adolescents: A Systematic Review and Meta-analysis. JAMA

pediatrics, 173(11), 1058–1067. https://doi.org/10.1001/jamapediatrics.2019.3176

2. Akgul, I. (2025). Smartphone addiction and the G-DEEG model: An interdisciplinary framework for

psychiatric rehabilitation. Journal of Clinical and Medical Images, 8(7), 1-9.

https://www.google.com/url?sa=i&source=web&rct=j&url=https://clinandmedimages.org/smartphone

-addiction-and-the-g-deeg-model-an-interdisciplinary-framework-for-psychiatric-

rehabilitation/&ved=2ahUKEwixg5jNg7SVAxXqV2wGHTAPDmgQ0YISegoIAggACAAIBRAC&o

pi=89978449&cd&psig=AOvVaw3DjEAtdKvqLL2OmjB6A-b4&ust=1783082910573000

3. Alavi, S. S., et al. (2024). Parallels between adolescent social media addiction and substance addiction:

Neurobiological mechanisms and clinical implications. Journal of Behavioral Medicine, 47(1), 88–103.

4. Alcaro, A., Huber, R., & Panksepp, J. (2007). Behavioral functions of the mesolimbic dopaminergic

system: an affective neuroethological perspective. Brain research reviews, 56(2), 283–321.

https://doi.org/10.1016/j.brainresrev.2007.07.014

5. Ali, A., et al. (2024). Reward deficiency syndrome and digital addiction: A neuroscientific framework.

Neuroscience & Bio Behavioral Reviews, 158, 105–119.

6. Al-Mamun, F., Mamun, M. A., Prodhan, M. S., Muktarul, M., Griffiths, M. D., Muhit, M., & Sikder,

M. T. (2023). Nomophobia among university students: Prevalence, correlates, and the mediating role of

smartphone use between Facebook addiction and nomophobia. Heliyon, 9(3), e14284.

https://doi.org/10.1016/j.heliyon.2023.e14284

7. Alter, A. (2017). Irresistible: The rise of addictive technology and the business of keeping us hooked.

Penguin Press.

8. Andreassen, C. S., Pallesen, S., & Griffiths, M. D. (2016). The relationship between addictive use of

social media, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors,

64, 287–293. https://doi.org/10.1016/j.addbeh.2016.03.006

9. Bi, S., Yuan, J., Wang, Y., Zhang, W., Zhang, L., Zhang, Y., Zhu, R., & Luo, L. (2024). Effectiveness

of digital health interventions in promoting physical activity among college students: Systematic review

and meta-analysis. JMIR mHealth and uHealth, 12, e51714. https://doi.org/10.2196/51714

10. Calvert, E., Cipriani, M., Dwyer, B., Lisowski, V., Mikkelson, J., Chen, K., Flathers, M., Hau, C., Xia,

W., Castillo, J., Dhima, A., Ryan, S., & Torous, J. (2025). Social media detox and youth mental health.

JAMA Network Open, 8(11), e2545245. https://doi.org/10.1001/jamanetworkopen.2025.45245

Page 1948

www.rsisinternational.org

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

11. Chun, J. W., Choi, J., Cho, H., Kang, K. D., Ahn, H. J., Son, Y. D., ... & Kim, D. J. (2021). Role of

frontal-limbic connectivity in impulsivity of individuals with internet gaming disorder. Addiction

Biology, 26(3), e12921. https://doi.org/10.1111/adb.12921

12. Crone, E. A., & Konijn, E. A. (2018). Media use and brain development during adolescence. Nature

Communications, 9, 588. https://doi.org/10.1038/s41467-018-03126-x

13. Dresp-Langley, B., & Hutt, A. (2022). Digital addiction and sleep. International Journal of

Environmental Research and Public Health, 19(11), 6910.

14. Kazmi, S.M., Jilani, A.Q., Ahmad, S., Srivastava, P., Pandey, K. & Anwar, S. (2025). Effects of

excessive social media use on neurotransmitter levels and mental health: A neurobiological meta-

analysis. Era Journal of Medical Research, 12(1), 56-60.

15. Firth, J., Torous, J., Stubbs, B., Firth, J. A., Steiner, G. Z., Smith, L., Alvarez-Jimenez, M., Gleeson, J.,

Vancampfort, D., Armitage, C. J., & Sarris, J. (2019). The 'online brain': How the internet may be

changing our cognition. World Psychiatry, 18(2), 119–129.

16. Garland, E. L., Farb, N. A., Goldin, P. R., & Fredrickson, B. L. (2014). Mindfulness broadens

awareness and builds meaning at the attention–emotion interface. Psychological Inquiry, 26(4), 369–

376. https://doi.org/10.1080/1047840X.2015.1013259

17. Gringras, P., Middleton, B., Skene, D. J., & Revell, V. L. (2015). Bigger, brighter, bluer-better?

Current light-emitting devices—adverse sleep properties and preventative strategies. Frontiers in

Public Health, 3, 233. https://doi.org/10.3389/fpubh.2015.00233

18. Guthrie, M. (2024). The mesolimbic dopamine pathway: reward circuitry and psychiatric disorders.

Journal of Cognitive Neuroscience, 7(3), 215. https://doi.org/10.35841/aacnj-7.3.215

19. Haidt, J. (2024). The anxious generation: How the great rewiring of childhood is causing an epidemic

of mental illness. Penguin Press.

20. He, J.-W., Tu, Z.-H., Xiao, L., Su, T., & Tang, Y.-X. (2020). Effect of restricting bedtime mobile phone

use on sleep, arousal, mood, and working memory: A randomized pilot trial. PLOS ONE, 15(2),

e0228756. https://doi.org/10.1371/journal.pone.0228756

21. Hong, S. B., Zalesky, A., Cocchi, L., Fornito, A., Choi, E. J., Kim, H.H., (2013). Decreased functional

brain connectivity in adolescents with internet addiction. PLOS ONE, 8(2), e57831.

22. Hou, H., Jia, S., Hu, S., Fan, R., Sun, W., Sun, T., & Zhang, H. (2012). Reduced striatal dopamine

transporters in people with internet addiction disorder. Journal of Biomedicine and Biotechnology,

854524.

https://doi.org/10.1155/2012/854524

23. Humer, E., Pieh, C., & Probst, T. (2022). Associations of screen time with mental health in a large

sample of young people. Psychology of Popular Media, 11(2), 211–220.

24. Hunt, M. G., Marx, R., Lipson, C., & Young, J. (2018). No more FOMO: Limiting social media

decreases loneliness and depression. Journal of Social and Clinical Psychology, 37(10), 751–768.

25. Kaewpradit, K., Ngamchaliew, P., & Buathong, N. (2025). Digital screen time usage, prevalence of

excessive digital screen time, and its association with mental health, sleep quality, and academic

performance among Southern University students. Frontiers in psychiatry, 16, 1535631.

https://doi.org/10.3389/

26. Kolhe, D. & Naik, A.R. (2025). Digital detox as a means to enhance eudaimonic well-being. Frontiers

in Human Dynamics, 7:1572587.

https://doi.org/10.3389/fhumd.2025.1572587

27. Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. The

Lancet Psychiatry, 3(8), 760–773. https://doi.org/10.1016/S2215-0366(16)00104-8

28. Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned.

International Journal of Environmental Research and Public Health, 14(3), 311.

29. Lan, Y., Ding, J., Li, W., Li, J., Zhang, Y., Liu, H., & Jiang, M. (2022). A group mindfulness-based

cognitive-behavioral intervention for smartphone addiction among university students. Addictive

Behaviors, 130, 107319.

30. Li, S., Ren, P., Chiu, M. M., Wang, C., & Lei, H. (2021). The relationship between self-control and

internet addiction among students: A meta-analysis. Frontiers in Psychology, 12, 735755.

https://doi.org/10.3389/fpsyg.2021.735755

31. Li, W., & Yang, Y. (2024). Teens exhibit heightened risk for mobile phone addiction relative to other

age groups. Frontiers in Psychology, 15, 1382941.

Page 1949

www.rsisinternational.org

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

32. Lukram, H., Baishya, R., & Chakma, S. (2026). Screen Time Patterns and Their Impact on Academic

Performance: A Prospective Study Among Phase 1 MBBS Students. Cureus, 18(1), e101440.

https://doi.org/10.7759/cureus.101440

33. Marciano, L., Driver, C. C., Schulz, P. J., & Camerini, A.-L. (2024). Digital detox and well-being.

Pediatrics, 154(4), e2024066142.

34. Montag, C., Lachmann, B., Herrlich, M., & Zweig, K. (2019). Addictive features of social

media/messenger platforms and freemium games against the background of psychological and

economic theories. International Journal of Environmental Research and Public Health, 16(14), 2612.

https://doi.org/10.3390/ijerph16142612

35. Ogun, D. (2025). Neurobiological and behavioral correlates of excessive social media use in

adolescents. Journal of Surgery and Medicine, 9(10), 199–206.

36. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021).

The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.

37. Patel, D. G., Hanumanpratap Singh Kshatri, A., Kommuru, S., & Javvaji, C. K. (2025). A narrative

review of digital addiction and health: A new challenge for modern medicine. Cureus, 17(12), e100491.

https://doi.org/10.7759/cureus.100491

38. Pazer, S. (2026). One week to wellness: A pre–post experimental study on the effects of a seven-day

digital detox intervention on psychological well-being, cognitive functioning, and life satisfaction in

university students. Archive Psychiatry Mental Health, 10(1), 025-031.

https://dx.doi.org/10.29328/journal.apmh.1001063

39. Pieh, C., Budimir, S., Probst, T., & Humer, E. (2021). Relationship between hours spent online and

mental health in Austrian adolescents. Frontiers in Psychiatry, 12, 741935.

40. Pieh, C., Brandmayr, G., Swoboda, J., & Probst, T. (2025). Reduction in smartphone screen time reduces

depression, well-being, stress and sleep quality: A randomised controlled trial. Psychological Medicine,

55, e84.

41. Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and

behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848.

42. Qutishat, M., Lazarus, E. R., Razmy, A. M., & Packianathan, S. (2020). University students nomophobia

prevalence, sociodemographic factors and relationship with academic performance at a University in

Oman. International Journal of Africa Nursing Sciences, 13, 100206.

https://doi.org/10.1016/j.ijans.2020.100206

43. Rathakrishnan, B., Bikar Singh, S. S., Kamaluddin, M. R., Yahaya, A., Mohd Nasir, M. A., Ibrahim, F.,

& Ab Rahman, Z. (2021). Smartphone addiction and sleep quality on academic performance of

university students: An exploratory research. International Journal of Environmental Research and

Public Health, 18(16), 8291. https://doi.org/10.3390/ijerph18168291

44. Radtke, T., Apel, T., Schenkel, K., Keller, J., & von Ah Morano, A. E. (2021). Digital detox: An

effective solution in the smartphone era? A systematic literature review. Mobile Media &

Communication, 10(2), 190–215.

45. Schraggeová, M., & Bisaha, D. (2025). The effect of digital detox through digital minimalism using the

MinimalistPhone app on the behavior of young users and their emotional experience. Computers in

Human Behavior Reports, 18, 100581. https://doi.org/10.1016/j.chbr.2025.

46. Scott, H., Biello, S. M., & Woods, H. C. (2019). Social media use and adolescent sleep patterns:

Cross-sectional findings from the UK millennium cohort study. BMJ Open, 9(9), e031161.

https://doi.org/10.1136/bmjopen-2019-031161

47. Segal, Z. V., Williams, J. M. G., & Teasdale, J. D. (2013). Mindfulness-based cognitive therapy for

depression (2nd ed.). Guilford Press.

48. Sonalika, S., et al. (2025). Digital detoxification: Efficacy of structured detox programs among

university students. Journal of Mechanics, Continuum and Mathematical Sciences, 20(3), 166–179.

49. Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental

Review, 28(1), 78–106.

50. Takeuchi, H., Taki, Y., Hashizume, H., Asano, K., Asano, M., Sassa, Y., et al. (2015). The impact of

television viewing on brain structures: Cross-sectional and longitudinal analyses. Cerebral Cortex,

25(5), 1188–1197.

Page 1950

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MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026

51. Ulum, H. (2026). Screen handicap in mathematics: A meta-analysis of mathematics performance related

to screen time and type. European Journal of Education, 61(1): e70400.

https://doi.org/10.1111/ejed.70400.

52. Wager, K. & Cox, S. (2009). Auricular Acupuncture & Addiction. Churchill Livingstone.

https://doi.org/10.1016/B978-0-443-06885-0.50006-9

53. Walker, M. (2017). Why we sleep: Unlocking the power of sleep and dreams. Scribner.

54. Wang, P., Zhao, M., Wang, X., Xie, X., Wang, Y., & Lei, L. (2020). Peer relationship and adolescent

smartphone addiction: The mediating role of self-esteem and the moderating role of the need to belong.

Journal of Behavioral Addictions, 6(3), 708–717. https://doi.org/10.1556/2006.6.2017.079

55. Weinstein, A., & Lejoyeux, M. (2015). New developments on the neurobiological and pharmaco-genetic

mechanisms underlying internet and videogame addiction. American Journal on Addictions, 24(2), 117–

125.

56. Weinstein, A., & Lejoyeux, M. (2020). Neurobiological mechanisms underlying internet gaming

disorder. Dialogues in clinical neuroscience, 22(2), 113–126.

https://doi.org/10.31887/DCNS.2020.22.2/aweinstein

57. Zhang, Y., & Wang, P. (2025). Mindfulness and problematic smartphone use: Indirect and conditional

associations via self-regulated learning and digital detox. BMC Psychology, 13, 485.

https://doi.org/10.1186/s40359-025-03485-3