Smartphone use health outcomes in children and adolescents: A systematic review of behavioral, developmental, and environmental risk pathways

Table 1 PICO framework for the systematic review

Component	Description	Operational definition in this review
Population (P)	Children and adolescents	Individuals aged 0-18 years. Studies including mixed populations were eligible only if pediatric data were reported separately. Subgroup analyses planned for early childhood (0-5 years), school-age children (6-12 years), and adolescents (13-18 years)
Exposure (I/E)	Mobile phone use and exposure	Use of smartphones or cellular phones, including: (1) Duration and frequency of use; (2) Voice calls and messaging; (3) Internet browsing; (4) Social media engagement; (5) Mobile gaming; (6) Night-time use; and (7) RF-EMF exposure attributable to mobile phones
Comparator (C)	Lower or no exposure	(1) Non-users vs users; (2) Low vs high usage groups; (3) Short vs prolonged exposure; (4) Baseline vs follow-up (longitudinal studies); and (5) Different exposure intensities (e.g., hours/day, nighttime use)
Primary outcomes (O1)	Core health outcomes	(1) Sleep disturbances (duration, quality, latency); (2) Mental health outcomes (anxiety, depression, emotional symptoms); (3) Attention and executive function; and (4) Neurodevelopmental outcomes
Secondary outcomes (O2)	Additional health and behavioral outcomes	(1) Visual complaints (digital eye strain, myopia); (2) Musculoskeletal symptoms; (3) Physical activity levels and obesity; (4) Problematic smartphone use/addiction behaviors; (5) Academic performance; and (6) Biological or health effects related to RF-EMF exposure
Study designs	Eligible study types	Randomized controlled trials, cohort studies, case-control studies, and cross-sectional studies
Setting	Geographic scope	No geographic restriction; studies from all regions included

RF-EMF: Radiofrequency electromagnetic field.

Table 2 Prevalence and patterns of smartphone use in children and adolescents before and during the coronavirus disease 2019 pandemic

Ref.	Country	Study design	Sample size (n)	Age range	Period	Prevalence/usage findings	Key observations	Key strengths	Potential bias/limitations	Overall risk category
Kabali et al[13], 2015	United States	Cross-sectional (community-based)	350	6 months to 4 years	Pre-COVID (2014)	96.6% had used a mobile device	Most initiated < 1 year; approximately 75% owned device by age 4; daily use common by age 2	Clear inclusion criteria; utilized adapted validated survey from Common Sense Media	Specific to urban, low-income, minority population; limited generalizability to high-SES groups	Low
Shah and Phadke[14], 2023	India	Cross-sectional (hospital-based)	90	6 months to 4 years	Pre-COVID	73.3% prevalence of use	19% (3-4 years) ≥ 3 hours/day; parental reluctance high despite use	Validated questionnaire used; clear ethical and consent protocols	Small sample size (n = 90) from a single tertiary hospital	Moderate
Kopecký et al[9], 2021	Czech Republic	Cross-sectional (school survey)	27177	7-17 years	Pre-COVID (2014-2018)	Near-universal exposure1	High engagement in social media, YouTube, gaming; school policy influenced usage	Exceptionally large sample size (n = 27177); objective comparison of school policies	Reliance on self-reported behaviors rather than objective logs	Low
Kayiran et al[15], 2010	Turkey	Cross-sectional	724	6 months to 15 years	Pre-COVID	Not mobile-specific	Increasing device access and bedroom ownership with age	Large sample size (n = 724) for the specific SES demographic	Convenience sampling in a private hospital; potential for social desirability bias in parent reporting	Moderate
Lee et al[17], 2025	South Korea	National survey secondary analysis	54948	Middle and high school	During COVID	25.5% PSU; mean use 2828 minutes (weekday), 393.4 minutes (weekend)	Higher PSU among females and high school students; alcohol and smoking increased risk	Huge national dataset (n = 54948); complex sample statistical weighting for accuracy	Secondary data analysis limits control over initial measurement tools; self-reported	Low
Chun et al[18], 2023	South Korea	Cross-sectional	360	15-18 years	During COVID	Increased addiction among those with increased usage time	Associated with depressive symptoms, low self-control, cyberbullying; socioeconomic factors relevant	Uses a social-ecological model to categorize factors (individual, family, school)	Sample restricted to Korean adolescents aged 15-18; limited age range	Low
Serra et al[19], 2021	Italy	Cohort (self-report, pre-post comparison)	184	6-18 years	During COVID	Significant increase in frequency and duration of use vs pre-epidemic	Increased overuse/addiction; sleep, ocular, and musculoskeletal complaints	Evaluates specific health outcomes (ocular, musculoskeletal) alongside addiction	Anonymous questionnaire limits follow-up; significant gender imbalance in respondents (more females)	Low
Ferrara et al[20], 2023	Italy	Pre-post survey	130	6-18 years	During COVID	Significant increase in screen time (P < 0.02); higher addiction index (P < 0.001)	Increased early-morning headaches; reduced physical activity	Direct pre- vs post-lockdown comparison	Retrospective recall bias (asking about pre-lockdown habits during lockdown)	Moderate

¹Exact prevalence not extractable due to variation in outcome definitions. Risk of Bias tool: Joanna Briggs Institute (analytical).

PSU: Problematic smartphone use; COVID: Coronavirus disease; SES: Socioeconomic status.

Table 3 Effects of smartphone use on sleep outcomes by age group and study design

Ref.	Country	Age group	Sample size (n)	Study design	Exposure definition	Sleep outcomes	Key findings	Appraisal tool	Primary bias considerations	Overall risk
Kim et al[21], 2020	South Korea	5-8 years	Cohort (K-CURE)	Prospective cohort	Smartphone overuse (> 1 hour/day)	Total sleep time; Children’s Sleep Habits Questionnaire score; nocturnal awakening	Overuse group had shorter total sleep time (P < 0.05) and higher sleep problem scores, including more night awakenings	NOS	Strengths: Longitudinal design; part of a large prospective cohort (K-CURE). Limitations: Reliance on parental questionnaires for sleep assessment, which may introduce recall bias	Moderate
Lee et al[22], 2022	South Korea	4-7 years	314	4-year longitudinal study	Frequency of smartphone use	Sleep problems; bedtime resistance; sleep duration; daytime sleepiness	Smartphone use significantly predicted sleep problems (β = 0.328, P < 0.001); associated with shorter sleep and greater bedtime resistance	NOS	Strengths: Robust 4-year follow-up (2017-2020); utilized mixed-model analysis. Limitations: Sleep data were caregiver-reported via self-administered questionnaires	Moderate
Pickard et al[23], 2024	United Kingdom	16-30 months	105	Randomized clinical trial	Removal of screen use in hour before bedtime	Actigraphy-measured sleep efficiency; night awakenings	Screen removal improved sleep efficiency and reduced awakenings (small-moderate effect sizes)	RoB 2	Strengths: Assessor-blinded; objective sleep measurement via actigraphy; high retention (99%) and adherence (94%)	Low
Abid et al[24], 2024	Tunisia	Mean 9 years	13	Experimental (acute and repeated exposure)	90-minute nocturnal smartphone exposure	Total sleep time; waking after sleep onset; cognitive performance	Acute and repeated exposure reduced sleep time (-29 minutes to -47 minutes, P < 0.01); repeated exposure produced greater sleep disruption	RoB 2	Strengths: Objective measures (actigraphy, salivary cortisol, cognitive tests). Limitations: Extremely small sample size (n = 13), which limits statistical power and generalizability	Moderate to high
Foerster et al[27], 2019	Switzerland	7th-9th grade	843	Prospective cohort	Nocturnal awakenings due to phone; high screen time	Restless sleep; difficulty falling asleep	Nocturnal awakenings associated with new-onset restless sleep (OR = 5.66); high screen time linked to sleep onset problems	NOS	Strengths: Large sample (n = 843); controlled for relevant confounders using logistic regression. Limitations: Subjective reporting of nocturnal phone-related awakenings	Low to moderate
Nagata et al[25], 2023	United States	10-14 years	10280	Cross-sectional (ABCD study)	Bedtime screen behaviors; device in bedroom	Trouble falling/staying asleep; sleep disturbance	Device in bedroom increased sleep disturbance risk (RR = 1.27); bedtime streaming, texting, gaming associated with sleep problems	JBI	Strengths: National dataset with very large sample size (n = 10280); extensive control for socioeconomic and demographic variables. Limitations: Cross-sectional nature precludes causal inference	Low
Yoon et al[26], 2021	South Korea	Grade 4 and 7 (approximately 10-13 years)	4940	Cross-sectional panel analysis	Smartphone addiction subscales	Sleep duration	Addiction (tolerance subscale) associated with shorter sleep; gender moderated effect	JBI	Strengths: High sample size (n = 4940) from a national panel survey; used validated smartphone addiction sub-factors	Low
Rafique et al[28], 2020	Saudi Arabia	17-23 years	1925	Cross-sectional	≥ 8 hours/day use; ≥ 30 minutes after lights-off; phone near pillow	PSQI sleep quality; sleep latency; daytime sleepiness	High use and bedtime exposure associated with poor sleep quality and longer sleep latency	JBI	Strengths: Large sample size (n = 1925); used the validated PSQI. Limitations: Potential for social desirability bias in reporting screen time	Low to moderate
Tu et al[29], 2023	China	University students	152	Randomized longitudinal intervention	Restricting while-in-bed smartphone use	PSQI sleep quality; cognitive arousal	Restriction improved sleep quality; effect mediated by reduced pre-sleep cognitive arousal	RoB 2	Strengths: Longitudinal mediation analysis with a 4-week follow-up; significant focus on physiological mechanisms like cognitive arousal	Low

The following Risk of Bias assessment has been conducted: The Newcastle-Ottawa Scale for longitudinal/cohort studies, the Joanna Briggs Institute checklist for cross-sectional studies, and the Cochrane Risk of Bias framework for randomized trials and experimental designs. PSQI: Pittsburgh Sleep Quality Index; OR: Odds ratio; RR: Risk ratio; NOS: Newcastle-Ottawa Scale; JBI: Joanna Briggs Institute; RoB 2: Cochrane Risk of Bias.

Table 4 Association between smartphone use and mental health outcomes by age group and study design

Ref.	Country	Age group	Sample (n)	Study design	Exposure type	Mental health outcomes	Key effect estimates	Appraisal tool	Primary bias considerations	Overall risk
Meskini et al[30], 2024	Morocco	Middle school adolescents	341	Cross-sectional	Smartphone overuse (SAS)	Depression (HADS); anxiety (HADS)	Depression: r = 0.403 (P < 0.001); anxiety: r = 0.244 (P = 0.013)	JBI	Strengths: Used validated assessment scales (SAS, HADS). Limitations: Geographically restricted to one city (Kenitra); cross-sectional design prevents causal claims	Moderate
Carter et al[31], 2024	United Kingdom	16-18 years	657	Cross-sectional (multi-school)	Problematic smartphone use (SAS); screen time	Moderate depression (PHQ-9); anxiety (GAD-7); insomnia	PSU associated with depression (aOR = 2.96); anxiety (aOR = 2.03); insomnia (aOR = 1.64). Screen time not significant	JBI	Strengths: Multi-school sample (n = 657); adjusted for confounders via multi-level logistic regression	Low to moderate
Mayerhofer et al[32], 2024	Austria	14-20 years	913	Cross-sectional	PSU (SAS-SV); screen time	Depression; anxiety; disordered eating; loneliness	PSU associated with depression (aOR = 1.46); anxiety (aOR = 1.86). Screen time associated with loneliness	JBI	Strengths: Large sample size (n = 913). Limitations: Online survey format may lead to self-selection bias	Moderate
Liu et al[34], 2025	United States	16-18 years	137	Cross-sectional	Smartphone attachment (MPIQ)	Anxiety; depression	Anxiety (adjusted β = 0.26); depression (adjusted β = 0.15)	JBI	Strengths: Used PROMIS pediatric short forms. Limitations: Small sample size (n = 137) and lack of ethnic diversity (79.6% White)	Moderate
Zablotsky et al[33], 2025	United States	12-17 years	Nationally representative	Cross-sectional (NHIS-Teen)	≥ 4 hours/day non-school screen time	Depression; anxiety; low social support	High screen use associated with depression and anxiety symptoms	JBI	Strengths: Large-scale, nationally representative dataset (NHIS-Teen); includes parent-reported covariates	Low
Poulain et al[40], 2025	Germany	10-17 years	1113 (2576 observations)	Repeated cross-sectional (2018-2024)	PSU; > 3 hours/day use	Quality of life	PSU and long duration associated with lower QoL; stronger post-COVID; greater effect in girls	JBI	Strengths: Seven-year time trend analysis (2018-2024) within a dedicated cohort	Low
Selak et al[42], 2025	Europe	10-15 years	284	4-wave longitudinal	Parental smartphone use during interaction	Anger; sadness; subjective well-being	Parental use predicted child anger/sadness → lower well-being (mediation model)	NOS	Strengths: Longitudinal design with four time points; unique predictor (parental phubbing)	Low to moderate
Gath et al[41], 2026	New Zealand	2-8 years (prospective)	6281	Longitudinal cohort	> 1.5-2.5 hours/day early screen exposure	Peer problems; social functioning	> 2.5 hours/day at age 2 associated with increased peer problems at age 8	NOS	Strengths: Very large sample (n = 6281); long-term prospective data from age 2 years to 8 years	Low
El-Sayed Desouky and Abu-Zaid[35], 2020	Saudi Arabia	University students	1513	Cross-sectional	Smartphone addiction (PUMP)	Depression; trait anxiety	Significant positive correlations between addiction and depression/anxiety	JBI	Strengths: Large university sample (n = 1513); used multiple validated tools (PUMP, Taylor, Beck)	Low to moderate
Nikolic et al[36], 2023	Serbia	Medical students	761	Cross-sectional	Smartphone addiction (SAS-SV)	Depression; anxiety; stress	Depression (OR = 2.51); anxiety (OR = 2.04); stress (OR = 1.75)	JBI	Strengths: Multi-city selection; comprehensive analysis (multivariate regression) of independent factors	Low
Daniyal et al[39], 2022	Pakistan	University students	400	Cross-sectional	High vs low cell phone use	Depression; mood disorder; loneliness	Depression (r = 0.430); mood disorder (r = 0.608)	JBI	Strengths: Correlated smartphone use with both physical symptoms (neck/back pain) and mental health	Moderate
Zhu et al[37], 2025	China	Undergraduates	322	Cross-sectional mediation	Smartphone addiction (MPAI)	Depression; anxiety; life satisfaction	Addiction → negative emotions (r = 0.332); depression mediated ↓ life satisfaction	JBI	Strengths: Provides a specific mediation model for life satisfaction	Moderate
Pieh et al[38], 2025	Austria	University students	111	Randomized controlled trial	Screen reduction ≤ 2 hours/day (3 weeks)	Depression (PHQ-9); stress; well-being	Significant reduction in depression (η2 = 0.109); stress (η2 = 0.085); improved well-being	RoB 2	Strengths: RCT design; includes follow-up; intention-to-treat analysis. Limitations: Study was non-blinded	Low to moderate

To evaluate the association between smartphone use and mental health outcomes, the following Risk of Bias assessment has been conducted: Assessments follow the Joanna Briggs Institute checklist for cross-sectional studies, the Newcastle-Ottawa Scale for longitudinal designs, and the Cochrane Risk of Bias for randomized trials. SAS: Zung’s Self-rating Anxiety Scale; HADS: Hospital Anxiety and Depression Scale; JBI: Joanna Briggs Institute; PHQ-9: Patient Health Questionnaire 9; GAD-7: General Anxiety Disorder 7; aOR: Adjusted odds ratio; PSU: Problematic smartphone use; MPIQ: Mobile Phone Involvement Questionnaire; QoL: Quality of life; COVID: Coronavirus disease; NOS: Newcastle-Ottawa Scale; PUMP: Problematic Use of Mobile Phones; OR: Odds ratio; MPAI: Mobile Phone Addiction Index; RoB 2: Cochrane Risk of Bias; RCT: Randomized controlled trial; SAS-SV: Smartphone Addiction Scale-Short Version.

Table 5 Executive function outcomes associated with smartphone use by age group and study design

Age group	Ref.	Study design	Country	Sample size (n)	EF domains assessed	Main findings	Direction of association	Appraisal tool	Primary bias considerations	Overall risk
Preschool (2-6 years)	Lakicevic et al[43], 2025	Cross-sectional	Russia	1016	Cognitive flexibility, verbal working memory, and inhibition	Screen time weakly negatively correlated with flexibility and verbal WM; very weak negative correlation with inhibition	Negative (small effect)	JBI	Strengths: Large, representative sample (n = 1016). Limitations: Reliance on parent-reported screen time; cross-sectional design limits causal interpretation of EF deficits	Low
	Horowitz-Kraus et al[44], 2024	EEG cross-sectional	Israel	4-year-old (n ≈ 80)	Behavioral EF, N200, P300	Longer screen exposure is associated with poorer EF performance and altered executive-related EEG markers	Negative	JBI	Strengths: Use of objective neurophysiological markers (N200, P300) alongside behavioral tasks. Limitations: Small sample size (n ≈ 80); specific to 4-year-old	Moderate
	Oflu et al[45], 2021	Cross-sectional	Turkey	240	Emotional regulation (behavioral EF)	≥ 4 hours/day associated with higher emotional lability and negativity	Negative (dose-related)	JBI	Strengths: Identifies a clear dose-response relationship (at ≥ 4 hours). Limitations: Potential for social desirability bias in parent reports	Moderate
School-age (7-12 years)	Al-Amri et al[46], 2023	Cross-sectional	Saudi Arabia	186	Selective attention accuracy, reaction time	Smartphone addiction associated with reduced attention accuracy; no difference in reaction time	Negative	JBI	Strengths: Assessed specific attentional accuracy rather than general cognition. Limitations: Small sample size (n = 186)	Moderate
	Shekhawat et al[47], 2024	Cross-sectional	India	50	Parent-reported cognitive flexibility	> 4 hours/day associated with cognitive impairment in 66.7%	Negative	JBI	Strengths: Focuses on both cognitive and physical (posture) outcomes. Limitations: Very small sample (n = 50) and use of a self-constructed, unvalidated e-questionnaire	High
Adolescents (13-18 years)	Tauste-Garcia et al[48], 2025	Cross-sectional	Spain	269	Parent-rated EF, performance-based EF	PSU associated with greater parent-reported EF deficits; no difference in lab tasks	Negative (behavioral > performance)	JBI	Strengths: High-quality comparison between subjective (parent-rated) and objective (lab-based) EF tasks	Low to moderate
	Yum et al[49], 2025	RCT protocol	Hong Kong	Planned n = 240	Attention, inhibitory control, and EEG	Trial targeting smartphone overuse in ADHD adolescents; outcomes pending	Not yet available	RoB 2	Strengths: Planned randomization and use of objective EEG markers in a high-risk (ADHD) population. Note: As a protocol, risk assessment is based on design intent	Low (for design)
Young adults (comparative experimental evidence)	Ward et al[50], 2017	Laboratory experimental	United States	> 800	Working memory (OSpan), fluid intelligence (Raven), sustained attention	Smartphone presence (desk vs other room) reduced working memory and fluid intelligence; sustained attention was unaffected	Negative (causal evidence)	RoB 2	Strengths: Large sample size (n > 800); randomized experimental conditions allow for causal claims regarding “brain drain”	Low

The table illustrates that across developmental stages, smartphone exposure is consistently associated with executive function difficulties. In preschool children, studies show small yet consistent negative associations between screen time and core executive function domains, accompanied by emerging neurophysiological correlations. Among school-age children, excessive smartphone use is associated with reduced attentional accuracy and increased parent-reported cognitive impairment. In adolescents, behavioral executive dysfunction is observed more frequently than deficits on laboratory-based tasks, suggesting a greater impact on everyday self-regulation than on isolated cognitive performance. Experimental evidence further reinforces these findings, demonstrating strong causal effects whereby the mere presence of a smartphone can deplete attentional resources and diminish working memory capacity. Assessments of bias of included studies utilized the Joanna Briggs Institute checklist for cross-sectional studies and the Cochrane Risk of Bias framework for experimental designs and randomized controlled trial protocols. EF: Executive function; WM: Working memory; JBI: Joanna Briggs Institute; EEG: Electroencephalography; PSU: Problematic smartphone use; RCT: Randomized controlled trial; ADHD: Attention-deficit hyperactivity disorder; RoB 2: Cochrane Risk of Bias.

Table 6 Neurodevelopmental outcomes associated with smartphone/screen exposure by age group and developmental domain

Age group	Ref.	Sample (n)	Developmental domain	Assessment tool	Exposure measure	Main findings	Direction	Appraisal tool	Primary bias considerations	Overall risk
Infants (approximately 18 months)	Gastaud et al[51], 2023 (Brazil)	470	Global cognitive development	BSID-III	≥ 2 hours/day screen time	≥ 2 hours/day associated with significantly lower cognitive scores (β = -3.6 to -0.5); 58.8% ≥ 1 hour/day	Negative (dose-related)	JBI (analytical)	Strengths: Population-based sample (n = 470); utilized standardized BSID-III for objective cognitive assessment. Limitations: Screen time based on caregiver-reported questionnaires (recall bias)	Low
Preschool (3-5 years)	Chaibal and Chaiyakul[52], 2022 (Thailand)	85	Gross motor	Denver Developmental Screening Test II	Mean 82.8 ± 62.8 minutes/day smartphone/tablet use	Significant correlation between usage duration and gross motor delay	Negative	JBI (analytical)	Strengths: Used the Denver II screening tool for multi-domain developmental assessment. Limitations: Small sample size (n = 85); 7-day retrospective recording of usage may involve estimation errors	Moderate
			Fine motor-adaptive	Denver II	Same	32.9% suspected delay; higher use correlated with poorer outcomes	Negative
			Language	Denver II	Same	9.4% suspected delay; weaker association	Negative (small)
			Personal-social	Denver II	Same	11.8% suspected delay; limited statistical strength	Inconclusive
Children with NDs	Butti et al[53], 2026 (Italy)	407 children (352 families)	Functional regulation, attention, behavioral control	Survey-based parental report	Device type & daily screen exposure	Older children more likely high-use; ADHD associated with difficulty disengaging; parental screen use predicted child exposure	Vulnerability-enhancing	JBI (analytical)	Strengths: Focused on a high-risk clinical population (children with NDs); relatively large sample (n = 407). Limitations: Self-selection bias from online survey distribution; parent-reported data for both child and own usage	Moderate
Educational/therapeutic context (professional training)	Mostowfi et al[54], 2022	30 OT students	Knowledge of NDT	Knowledge questionnaire; QUIS usability scale	Structured educational app use (2 weeks)	Significant improvement in knowledge acquisition; high usability	Positive (structured use)	JBI (quasi-experiment)	Strengths: Includes a control group; pre- and post-intervention knowledge testing. Limitations: Very small sample (n = 30); unblinded intervention in an educational setting	Moderate to high

To evaluate the neurodevelopmental outcomes associated with smartphone and screen exposure, the following Risk of Bias assessment has been performed utilizing the Joanna Briggs Institute checklists for cross-sectional and quasi-experimental studies, as outlined in your methods section. NDs: Neurodevelopmental disorders; OT: Occupational therapy; NDT: Neurodevelopmental treatment; BSID-III: Bayley Scales of Infant Development-III; QUIS: Questionnaire for User Interaction Satisfaction; ADHD: Attention-deficit hyperactivity disorder; JBI: Joanna Briggs Institute.

Table 7 Summary of visual outcomes associated with smartphone use in children and adolescents

Ref.	Country	Age group	Design	Sample (n)	Exposure definition	Visual domain	Key findings	Appraisal tool	Primary bias considerations	Overall risk
Nasir et al[55], 2024	Pakistan	13-18 years	Cross-sectional	200	< 2 hours/day vs > 2 hours/day; continuous ≥ 20 minutes	Myopia + CVS	Excessive users had significantly higher myopia (54 cases vs 5 cases). Headache (72%) and eye strain (70%) were common in continuous users (P ≤ 0.001)	JBI	Strengths: Utilized objective visual acuity and refraction tests alongside habit questionnaires. Limitations: Geographically specific to one region	Low to moderate
Li[57], 2025	China	6-14 years	Prospective cohort (2 years)	523	App-monitored usage (mean 5.1 hours/day vs 3.4 hours/day)	Myopia progression	Smartphone use independently predicted myopic progression (P < 0.001). Outdoor time protective; shorter viewing distance increased risk	NOS	Strengths: Used an objective mobile monitoring app to trace usage patterns rather than relying solely on recall; longitudinal design with multiple exam points	Low
Qasim et al[56], 2021	Pakistan	18-25 years	Cross-sectional	200	> 4-6 hours/day	Refractive errors	27.5% myopia; prolonged use associated with higher refractive error prevalence. Non-neutral postures reported	JBI	Strengths: Equal gender representation. Limitations: Utilized convenient sampling; reliance on self-reported history via proforma	Moderate
Moon et al[60], 2016	South Korea	7-12 years	Case-control	916	Daily smartphone duration	Pediatric DED	Smartphone use strongly associated with DED (OR = 13.07). Outdoor activity protective (OR = 0.33). Symptoms improved after 4-week cessation	JBI	Strengths: Large sample size (n = 916); utilized standardized International Dry Eye Workshop guidelines for diagnosis	Low to moderate
Chu et al[58], 2023	Hong Kong	8-14 years	1-year prospective	1508 (1298 follow-up)	≥ 181-241+ minutes/day	DES	Higher baseline use predicted higher DES scores at baseline and 1-year follow-up (P < 0.001). Eye fatigue most common symptom (53%)	NOS	Strengths: Large sample size (n = 1508) and high retention rate at 1-year follow-up (86%); controlled for demographic confounders in analysis	Low
Chidi-Egboka et al[61], 2023	Australia	6-15 years	Experimental (1-hour gaming)	36	1-hour continuous gaming	Blink + dry eye symptoms	Blink rate reduced from 20.8 blinks/minute to 8.9 blinks/minute (P < 0.001). Symptoms worsened; tear film unchanged acutely	JBI	Strengths: Objective tracking of blink rates using eye-tracking headsets. Limitations: Small sample size (n = 36) and short observation window (1 hour)	Moderate
Akib et al[62], 2021	Indonesia	12-16 years	Cross-sectional	143	> 3 hours/day vs ≤ 3 hours/day	Dry eye disease	Significant association between prolonged use and abnormal TBUT, TMH, Schirmer, and OSDI (P < 0.01)	JBI	Strengths: Used a comprehensive battery of objective tests (TBUT, TMH, Schirmer) to confirm dry eye incidence	Moderate
Issa et al[59], 2021	Saudi Arabia	University students	Cross-sectional	546	4-6 hours/day	Ocular symptoms	66% reported ≥ 1 ocular complaint; 39.7% reported ocular pain/dryness after prolonged use	JBI	Strengths: Employed multistage random sampling to select participants from medical and pharmacy colleges	Low to moderate

Risk of Bias assessment has been performed using the Joanna Briggs Institute checklist for cross-sectional and case-control studies, and the Newcastle-Ottawa Scale for prospective cohort designs. CVS: Computer vision syndrome; JBI: Joanna Briggs Institute; NOS: Newcastle-Ottawa Scale; DED: Dry eye disease; OR: Odds ratio; DES: Digital eye strain; TBUT: Tear break-up time; TMH: Tear meniscus height; OSDI: Ocular surface disease index.

Table 8 Summary of musculoskeletal outcomes associated with smartphone use in children and adolescents (by age and domain)

Ref.	Country	Age group	Study design	Exposure definition	Musculoskeletal domain	Key findings	Statistical association	Primary bias considerations	Overall risk
Mongkonkansai et al[63], 2022	Thailand	Primary school (6-9 years)	Cross-sectional	Continuous use > 60 minutes; posture type	General musculoskeletal pain	53% used phone lying down; prone posture strongly associated with pain	Prone posture OR = 7.37	Strengths: Explicitly analyzed specific postures (prone, sitting, lying) in primary school children. Limitations: Reliance on parental reports for child posture habits	Moderate
Aziz and Bakir[64], 2022	Iraq	Children and adolescents	Cross-sectional	> 3 hours/day use	Neck pain (“text neck”)	69% prevalence of text neck syndrome	Higher disability scores with > 3 hours/day	Strengths: Large clinical sample from primary health centers. Limitations: Diagnosis of “text neck” was based on a combination of self-reported symptoms and duration rather than imaging or clinical exam	Moderate
Yang et al[65], 2017	Taiwan	Adolescents	Cross-sectional	Talking > 3 hours/day	Neck, shoulder, upper back pain	Nearly 50% reported discomfort	Upper back discomfort OR = 4.23	Strengths: Focused on phone-call duration specifically. Limitations: Does not account for posture during calls; potential recall bias in duration estimation	Moderate
Parra-Fernandez et al[66], 2025	Colombia	Adolescents (10-18 years)	Cross-sectional	Mobile phone dependence score	Neck and upper back pain	56.3% reported pain; upper back most common (30.4%)	MPD score significantly higher in pain group (P < 0.001)	Strengths: Robust sample size (n = 622); utilized a validated MPD scale to categorize exposure	Low to moderate
Tokgöz[67], 2023	Turkey	Adolescents	Cross-sectional	Smartphone addiction scale	Postural curvature (head and shoulder)	Moderate correlation between addiction and forward posture	Significant positive correlation (P < 0.05)	Strengths: Utilized objective photogrammetric measurements and ImageJ software to analyze craniofacial symmetry and FHP	Low to moderate
Namwongsa et al[73], 2018	Thailand	Adolescents/young adults	Ergonomic assessment	Observed smartphone posture	Neck and trunk posture risk	80%-90% had RULA grand score = 6 (action level 3)	Neck/trunk posture correlated with neck MSD (P < 0.01)	Strengths: Employed the RULA, a validated objective tool for identifying postural risks in musculoskeletal disorders	Low
Mokhtarinia et al[71], 2022	Iran	University students	Cross-sectional	Addiction; mean use 685 hours/day	Neck, shoulder, wrist, upper back pain	53.3% addiction prevalence; higher pain with addiction	Significant correlation (P < 0.001)	Strengths: Comprehensive assessment across multiple domains (neck, shoulder, wrist, back). Limitations: Potential self-selection bias in university-based survey	Moderate
Al’Saani et al[69], 2023	Saudi Arabia	University students	Cross-sectional	> 4 hours/day; 1-hour continuous use	Upper limb disability (QuickDASH)	Higher disability with longer use and short viewing distance	P < 0.05	Strengths: Used the QuickDASH scale for upper limb disability. Limitations: Online distribution may limit sample representativeness	Moderate
Mersal et al[68], 2024	Egypt	University students	Cross-sectional	> 5 hours/day use	Neck and shoulder pain	38.8% neck pain; 20.3% shoulder pain	Higher prevalence among females (P < 0.001)	Strengths: Specific focus on gender differences in symptom prevalence. Limitations: Convenient sampling of nursing students may limit generalizability to all adolescents	Moderate
Alghadir et al[72], 2025	Saudi Arabia	Young adults	Cross-sectional + biomarkers	≥ 5 hours/day	Neck and hand pain; oxidative stress	Higher pain scores; reduced TIMP-1/2; increased MDA	Significant biochemical differences (P < 0.05)	Strengths: Exceptionally robust due to the inclusion of biochemical markers (MDA, TIMP-1/2) to correlate physical pain with oxidative stress	Low
Czępińska and Wiśniewska[70], 2024	Poland	Young adults	Cross-sectional	Early phone ownership	Chronic neck pain	Earlier ownership associated with neck pain	P < 0.05	Strengths: Investigated the longitudinal impact of “early ownership” on chronic pain. Limitations: Small, specialized sample of physiotherapy students	Moderate

Risk of Bias assessment has been performed using the Joanna Briggs Institute checklist for analytical cross-sectional studies and ergonomic assessments. OR: Odds ratio; RULA: Rapid Upper Limb Assessment; MDA: Malondialdehyde; TIMP-1: Tissue inhibitor of metalloproteinase-1; MSD: Musculoskeletal disorder; MPD: Mobile phone dependence; FHP: Forward head posture.

Table 9 Summary of studies examining smartphone use, physical activity, and childhood obesity (by age and study design)

Ref.	Country	Age group	Design	Sample size	Exposure definition	Physical activity outcome	Obesity outcome	Primary bias considerations	Risk of bias (JBI)
Byun et al[74], 2024	Korea	4th and 7th grade (approximately 10-13 years)	Cross-sectional	5180	Smartphone ≥ 180 minutes/day vs < 60 minutes/day	Isotemporal substitution: Replacing 1 hour screen with PA reduced obesity odds (OR = 0.75)	OR = 2.75 (95%CI: 2.06-3.68) for ≥ 180 minutes/day	Large sample size; however, height, weight, and screen time were all self-reported by adolescents	Moderate
Li et al[75], 2025	Korea	Adolescents (approximately 12-18 years)	Cross-sectional (national survey)	50407	≥ 6 hours weekend smartphone use	Sedentary time ≥ 6 hours ↑ obesity risk; MSE protective (OR = 0.45 males)	Females: OR = 1.57 for ≥ 6 hours weekend smartphone use	Robust national dataset with high power; adjusted for multiple socioeconomic confounders	Low
Ma et al[76], 2021	China (Shanghai)	Primary-high school	Cross-sectional	8419	Problematic smartphone use (entertainment)	Not primary focus	OR ≈ 1.03 per unit PSU score; stronger in females	Large multi-school sample; obesity status derived from objective school health records	Low to moderate
Yaakoubi et al[77], 2026	Tunisia	14-16 years	Cross-sectional	960	Problematic smartphone use (SAS-SV)	Vigorous PA markedly reduced in PSU group	Obesity 12.6% vs 0.7% in non-PSU	High accuracy in exposure via device tracking features; moderate RoB due to cross-sectional nature	Low to moderate
Raustorp et al[78], 2015	Sweden	8-9 years and 11-12 years	Repeated cross-sectional (2000-2013)	397 total cohorts	Era comparison (smartphone uptake period)	24% decline in steps/day (11-12 years boys)	BMI stable over time	Used objective pedometers; however, utilized a convenience sample with smaller cohort sizes in later years	Moderate

Risk of Bias assessment has been performed using the Joanna Briggs Institute checklist for analytical cross-sectional studies and ergonomic assessments. JBI: Joanna Briggs Institute; PA: Physical activity; OR: Odds ratio; CI: Confidence interval; PSU: Problematic smartphone use; SAS-SV: Smartphone Addiction Scale-Short Version; RoB: Risk of Bias; BMI: Body mass index.

Table 10 Use of smartphones to reduce risk of obesity (digital health approaches)

Ref.	Country	Age group	Design	Sample	Digital intervention	Physical activity outcome	BMI outcome	Primary bias considerations	Risk of Bias (JBI)
Vajravelu et al[80], 2022	United States	12-18 years (prediabetes/T2D)	Qualitative formative study	20	SMS reminders + incentives	High acceptability for PA promotion	Not evaluated	High bias regarding clinical outcomes, as they were not evaluated; excellent end-user feedback	N/A (formative)
Ruiz-Hermosa et al[81], 2024 (EUMOVE)	Europe	Primary and secondary school	Program implementation	Multi-country	School-based app + active resources	Promotes active commuting & PA	Preventive framework	Descriptive study of project phases; effectiveness not yet reported in this publication	N/A (descriptive)
Umano et al[82], 2024	Italy	Pediatric obesity	RCT	75	Lifestyle counseling app	Improved engagement; lower dropout	No significant BMI Z-score difference	Randomized controlled design; objective BMI measurements at 6 months and 12 months	Low
Kassari et al[84], 2026 (BigO)	Europe	Mean 12.6 years	Prospective cohort	1727	Smartphone + smartwatch objective monitoring	Behavioral tracking & lifestyle intervention	Decreased obesity proportion	Large prospective sample; used objective sensors (GPS/inertial) for behavioral monitoring	Low
Vaidya et al[83], 2026	India	8-15 years	Pilot clinical trial	26	App-based counseling + yoga	Increased PA duration	Significant BMI reduction (P < 0.001)	Small pilot sample size; lacks a formal control group for the pre-post comparison	Moderate to high

Risk of Bias assessment has been performed using the Joanna Briggs Institute checklist for analytical cross-sectional studies and ergonomic assessments. PA: Physical activity; BMI: Body mass index; JBI: Joanna Briggs Institute; T2D: Type 2 diabetes; RCT: Randomized controlled trial; N/A: Not applicable.

Table 11 Summary of studies examining problematic smartphone use by age, design, predictors, and outcomes

Ref.	Country	Age group	Design	Sample size	Key predictors	Main outcomes	Primary bias considerations	Overall risk
Donati et al[96], 2025	Italy	Adolescents (mean age 163 years)	Randomized controlled classroom intervention	93	Metacognitive beliefs; cognitive-behavioral training	↓ Daily screen time; ↓ risky smartphone behaviors; improved metacognitive regulation (large effects)	Strengths: Randomized design with a control group. Limitations: Preliminary study with a relatively small sample (n = 93) and short duration (5 weeks)	Low to moderate
Park et al[93], 2022	South Korea	Early adolescents (mean age 129 years)	Cross-sectional	209	Emotional overeating; food addiction symptoms	PSU correlated with food addiction; high-risk PSU group had 2.3 × higher food addiction scores	Strengths: School-based community sample; adjusted for BMI and SES. Limitations: Small sample size (n = 209); reliance on self-reported data	Moderate
Grund and Luciana[88], 2025	United States (ABCD)	Baseline 9-10 years → 12-15 years follow-up	Prospective longitudinal	4754	Urgency (impulsivity); reward sensitivity; externalizing; punishment sensitivity	Urgency and punishment sensitivity predicted PSU; cognitive ability not predictive; PSU distinct from screen time	Strengths: Large-scale prospective cohort (n = 4754) from the ABCD study. Controlled for family nesting and site	Low
Meng et al[92], 2020	China (national sample)	Young adolescents (mean age 129 years)	Cross-sectional mediation	8261	Hedonic, instrumental, self-expression motivations; SUT	Hedonic motivation → ↑ PSU via entertainment use; instrumental motivation → ↓ PSU via learning use	Strengths: Large national representative sample (n = 8261). Limitations: Cross-sectional mediation can overlook temporal precedence	Low to moderate
Yoon et al[91], 2025	South Korea	Children (Grade 4) + siblings	4-year longitudinal panel	1978	Sibling smartphone addiction (initial level and slope)	Higher sibling addiction predicted higher child PSU trajectories	Strengths: Long-term follow-up (4 years) using established panel data (KCYPS). Limitations: Nested sibling data requires complex modeling	Low
Lee et al[17], 2025	South Korea	Middle and high school	National cross-sectional survey	54948	Female sex; high school grade; alcohol; smoking	PSU prevalence 255%; alcohol (OR ≈ 1.10) and smoking (OR ≈ 1.30) increased PSU risk	Strengths: Massive, high-powered national survey (n = 54948). Limitations: Entirely self-reported via web survey	Low to moderate
Carter et al[31], 2024	United Kingdom	Adolescents 16-18 years	Multi-school cross-sectional	657	PSU (SAS); not screen time	PSU associated with anxiety (aOR = 2.03), depression (aOR = 2.96), insomnia (aOR = 1.64); screen time not associated	Strengths: Multi-school enrollment; used validated clinical tools (GAD-7, PHQ-9). Limitations: Cross-sectional design	Moderate
Carter et al[94], 2024	United Kingdom	Adolescents 13-16 years	Prospective mixed-method cohort	69	PSU severity	↑ PSU predicted worsening anxiety (β = 0.18); qualitative academic and relational strain	Strengths: Captures longitudinal changes in mood. Limitations: Very small sample (n = 69) and short follow-up period	Moderate to high
Huang et al[95], 2020	Taiwan	Children 9-12 years	Validation study	319	ADHD status	Reliable PSU scale (α = 0.93); ADHD group showed higher PSU proneness	Strengths: Evaluated reliability (α = 0.93) of the SAPS scale in a specific population (ADHD)	Moderate
Bae and Nam[90], 2023	South Korea	Early adolescents	Secondary panel mediation	KCYPS dataset	Maternal PSU; time spent with child; self-esteem	Maternal PSU → ↓ interaction time → ↑ adolescent PSU (sequential mediation)	Strengths: Uses high-quality national panel data (KCYPS) to track maternal-child dynamics over time	Low
Xiao et al[87], 2025	Canada	Adolescents (Grade 8-12)	4-year longitudinal (growth mixture)	2549	FoMo; depression; self-regulation	3 PSU trajectories; FoMo and depression predicted high-stable PSU; self-regulation protective	Strengths: Long-term tracking of trajectories (n = 2549) with sophisticated growth mixture modeling	Low
Lee et al[86], 2024	South Korea	Early childhood (mean 4.5 years)	4-year cohort	313	Parental lack of control; parental PSU	Parental factors predicted higher child smartphone addiction tendency	Strengths: Longitudinal design from early childhood. Limitations: Smaller cohort size (n = 313) compared to national datasets	Low to moderate
Huang et al[89], 2021	China	Children and adolescents (mean 12.3 years)	Network analysis	3248	Self-control; peer attitudes; parent-child relationship; FoMo	Central nodes: Loss of control, peer attitudes, self-control, parent-child relationship	Strengths: Large sample (n = 3248) providing detailed interaction mappings of risk factors	Moderate
Ladani et al[85], 2025	India	Adolescents 15-19 years	Mixed-method cross-sectional	560	Urban residence; parental education; gaming/social media use	Addiction prevalence 64%; gaming and social media associated with PSU	Strengths: Incorporates qualitative insights. Limitations: Cross-sectional nature limits causal interpretation	Moderate

Risk of Bias assessment has been performed using the Joanna Briggs Institute checklist for analytical cross-sectional studies and ergonomic assessments. FoMo: Fear of missing out; SAPS: Smartphone Addiction Proneness Scale; SUT: Smartphone use time; PSU: Problematic smartphone use; SAS: Zung’s Self-rating Anxiety Scale; ADHD: Attention-deficit hyperactivity disorder; OR: Odds ratio; aOR: Adjusted odds ratio; GAD-7: General Anxiety Disorder 7; PHQ-9: Patient Health Questionnaire 9; BMI: Body mass index; SES: Socioeconomic status.

Table 12 Summary of studies evaluating radiofrequency electromagnetic field exposure and health outcomes in children and young people

Ref.	Design	Population/age	Exposure metric	Outcome (s)	Key findings	Primary bias considerations	Risk of bias (JBI)
Moon[97], 2020	Narrative review	Children (various ages)	Environmental RF-EMF (mobile phones, WiFi, base stations)	Carcinogenicity, neurodevelopmental, and biological effects	RF-EMF is classified by IARC as Group 2B (possible carcinogen). No confirmed causal pediatric harm; precautionary approach recommended due to biological uncertainty	As a narrative review, it is prone to selection bias compared to systematic reviews; however, it accurately synthesizes IARC and WHO positions	Moderate to high
Castaño-Vinyals et al[98], 2022 (MOBI-Kids)	Multinational case-control (14 countries)	899 cases, 1910 controls; age 10-24 years	Cumulative call time, number of calls, years since first use, modeled cumulative RF energy at tumor site	Neuroepithelial brain tumors (mainly glioma)	No increased risk with higher exposure. ORs did not increase with cumulative use; some inverse trends likely due to recall bias. Small risk increase cannot be entirely excluded	Robust multinational design with sophisticated RF-energy modeling. Recall bias is a noted limitation common to retrospective case-controls	Low
Elliott et al[99], 2010	National case-control (registry-linked)	1397 cancer cases (0-4 years), 5588 controls	Distance to base stations, modeled RF power density at birth address	All cancers, CNS tumors, leukemia, NHL	No association between prenatal base-station RF exposure and early childhood cancer. ORs = 1.0 across exposure categories	High-quality registry-linked study; used objective distance to base stations, eliminating recall bias for exposure	Low
Durusoy et al[105], 2017	Cross-sectional survey	2150 high school students	Mobile phone use characteristics (calls/day, duration, texts/day, nighttime proximity); measured school EMF levels	Headache, fatigue, sleep disturbance, concentration difficulties	Mobile phone use associated with headache (OR = 1.90), fatigue (OR = 1.78), sleep disturbance (OR = 1.53); dose-response observed. No association with measured school EMF levels	Large sample size; however, symptoms (headache, fatigue) and exposure characteristics were entirely self-reported	Moderate
Edelstyn and Oldershaw[100], 2002	Randomized experimental	38 adults (young)	30-minute exposure to 900 MHz mobile phone vs sham	Attention, processing speed	No deficits observed; transient facilitation in select attentional tasks	Controlled and blinded; however, the sample size is small and the exposure duration was limited to 30 minutes	Low to moderate
Mortazavi et al[101], 2012	Randomized experimental	160 university students (18-31 years)	10-minute real vs sham exposure (high SAR handset)	Visual reaction time	Significant reduction (faster reaction time) after real exposure; no impairment demonstrated	Well-controlled randomized trial with a larger sample for an experimental neurophysiology study	Low
Sauter et al[102], 2011	Randomized crossover	30 young male adults (mean 25 years)	7 hours 15 minutes exposure to GSM 900, WCDMA, or sham	Attention, working memory	No consistent cognitive effects after correction for multiple testing; time-of-day effects significant	Robust crossover design (participants served as their own controls); accounted for multiple testing corrections	Low
Terao et al[103], 2006	Double-blind crossover	16 adults	30-minute pulsed EMF vs sham	Visuo-motor reaction time, movement time	No significant short-term effects on visuo-motor processing	High internal validity due to double-blinding and counterbalanced crossover design	Low
Inomata-Terada et al[104], 2007	Experimental neurophysiology	10 adults (+ 2 MS patients)	30-minute mobile phone EMF exposure	Motor evoked potentials (TMS), intracortical inhibition	No detectable short-term effects on motor cortical excitability or GABAergic inhibition	Small sample size (n = 10) limits power to detect subtle effects, though the neurophysiological methods (TMS) are highly precise	Moderate

Risk of Bias assessment has been performed using the Joanna Briggs Institute checklist for analytical cross-sectional studies and ergonomic assessments. JBI: Joanna Briggs Institute; IARC: International Agency for Research on Cancer; WHO: World Health Organization; TMS: Transcranial magnetic stimulation; MS: Multiple sclerosis; RF-EMF: Radiofrequency electromagnetic field; RE: Radiofrequency; EMF: Electromagnetic field; OR: Odds ratio; CNS: Central nervous system.