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

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.