Association Between Smartphone Addiction and Near Binocular Vision Function Among University Students: A Cross-Sectional Study

Abstract

Purpose

Currently, the use of smartphones, video games, and the widespread use of digital screens throughout the day have led to a new clinical syndrome named “digital eye strain”. Few studies have examined problematic smartphone use, unlike screen-time duration, in relation to near binocular vision parameters. This study aimed to explore the association between near binocular vision and problematic smartphone use, using the smartphone addiction scale.

Patients and Methods

This cross-sectional study involved 134 University students aged between 18 and 25 years. The validated smartphone addiction scale questionnaire was administered together with optometric measurements such as amplitude of accommodation, near phoria, and stereoacuity, near point of convergence, and a questionnaire on convergence insufficiency symptoms.

Results

The analysis showed a significant relationship between the smartphone addiction scale and the near point of convergence (r=−0.176, p< 0.05), amplitude of accommodation (r=0.191, p< 0.05), and symptoms of convergence insufficiency (r=0.238, p< 0.01). Moreover, the study shows a significant gender difference in the smartphone addiction scale, with females reporting lower scores than males; 70.9% of the male participants were considered smartphone addicted, while 55.5% of the female participants were smartphone addicted (Mann–Whitney test, p = 0.05).

Conclusion

Convergence insufficiency symptom burden in young adults is associated with problematic smartphone use. Binocular vision anomalies could potentially be relevant beyond visual comfort.

Introduction

Smartphone use has become widespread across all ages and segments of society. Smartphones are released daily with various hardware and software, offering growing benefits and opportunities due to easier information accessibility, which supports communication, socialization, education, and entertainment. However, people with a tendency to use smartphones excessively without control may be at risk of smartphone addiction.¹

Smartphone addiction has been defined as “a compulsive pattern of smartphone use, marked by a persistent and overwhelming desire to use the device, which individuals struggle to control”.^2,3 The excessive use of smartphones due to their full integration into daily life has led to different issues, such as anxiety, loneliness, poor sleep quality, musculoskeletal problems due to posture, as well as various ocular manifestations.^4–7

A growing body of evidence associates smartphone or digital‑device use duration with measurable changes in ocular surface and binocular and accommodative function. Ocular manifestations resulting from extended smartphone use include decreased blinking time, dry eye syndrome, eye strain, and accommodative and binocular vision anomalies.^8,9 While asthenopia includes blurred vision, eye strain, double vision, and difficulty in refocusing between viewing distances.¹⁰ However, objective extensive use of smartphones does not necessarily mirror problematic use; smartphone addiction.¹¹

The mechanism behind ocular symptoms is unclear; it might be due to effects on the ocular surface, the accommodative/vergence system, or a combination of both.¹² The accommodation and convergence systems work in hand during near work and form two components of the triad response to near work together with pupil constriction. Cortical commands control eye abduction and adduction to either diverge or converge, depending on the distance of the object of interest. Insufficient convergence is associated with blurred vision.¹³ The use of smartphones may differ from reading books at a distance; phones are often used at a closer distance, leading to high accommodative and vergence demand. Consequently, symptoms of asthenopia can be pronounced.⁸ Moreover, evidence confirms that prolonged screen time can exacerbate pre-existing binocular vision anomalies due to continuous strain on convergence and accommodation, leading to symptoms associated with digital eye strain.¹⁴

Despite robust evidence linking smartphone duration to binocular changes, few studies have simultaneously examined problematic smartphone use (using SAS‑SV), eye symptom reporting, and objective binocular vision metrics within the same population. Although SAS‑SV has strong psychometric evidence,¹⁵ its association with binocular function remains understudied. Furthermore, existing device‑use research primarily assesses general duration rather than addiction‑related behavioral patterns.¹⁶ This creates an important gap in understanding how behavioral addiction tendencies relate to convergence, accommodation, and symptom burden.

The current research aims to study the relationship between smartphone addiction and binocular vision function and eye strain among Qassim University students. Moreover, given prior evidence that binocular and accommodative parameters are associated with screen-time exposure, secondary exploratory analysis was performed in a subsample with available screen-time data to determine whether duration further modulates these associations.

Materials and Methods

Study Design, Participants and Sample Size

A cross-sectional observational study design was conducted to comply with the objective of the study. The study was approved by the ethical committee of Qassim University (IRB no. 08–04-25), and it conformed to the Declaration of Helsinki.¹⁷ All participants signed written informed consent forms after receiving detailed information about the nature and objectives of the study in plain and clear language. Healthy University students aged between 18 and 25 years were enrolled in the study, The invitation for participation was sent to students and advertised in the faculty’s main areas at the main University campus. Subjects with manifest strabismus, previous squint surgery, ocular pathologies, or those undergoing ophthalmic treatment were excluded from the study.

To determine the required sample size, power analyses were conducted using G*Power (version 3.1). For correlation analysis, a significance level of α = 0.05, a statistical power of 95% (1-β = 0.95), and a medium effect size of |ρ| = 0.30, a minimum sample size of 134 participants was required (df = 132, critical t = 1.978, actual power = 0.951). For the linear multiple regression analysis, an effect size of f² = 0.15, and identical α and power parameters, a minimum of 89 participants was required (df = 84, critical t = 1.989, actual power = 0.951).

The larger estimate of 134 was adopted as the target sample size. A total of 147 subjects were enrolled, of whom 12 were excluded due to incomplete data, specifically due to incomplete questionnaire answers, resulting in a final sample of 134 participants.

All optometric tests were carried out by optometrists in a single setting to avoid illumination variations and following the same order: NPC, amplitude of accommodation, stereoacuity, near phoria. After completing the tests, participants were asked to fill out the CISS and SAS-SV questionnaires.

Smartphone Addiction Scale- Short Version

A smartphone addiction scale—short version (SAS-SV) will determine the degree of student smartphone addiction. A Likert scale with 1 denoting “strongly disagree” and 6 denoting “strongly agree” was used to score the ten questions. The threshold score for assessing smartphone addiction in the literature was 33/60 for females and 31/60 for males.³

Convergence Insufficiency Symptom Survey (CISS)

The Convergence Insufficiency Symptom Survey (CISS) is a valid and reliable questionnaire that consists of 15 items that quantify near-work-related symptoms characterized by difficulty maintaining close visual focus. A score of 21 or more is considered symptomatic when used with adults.¹⁸ A previously cross-culturally adapted and independently validated Arabic translation of the CISS was used in the current study.¹⁹

Amplitude of Accommodation

The amplitude of accommodation was measured by using push-up method to measure the amplitude of accommodation was used by using near optotype and pushing towards the patient’s eye till it becomes blurred. Then, the distance in cm was measured and converted into accommodative power.²⁰ Expected amplitude of accommodation was referenced against Hofstetter’s age-amplitude formula (minimum expected amplitude = 15 − 0.25 × age in years), with measured values below this age-adjusted threshold considered reduced.

Near Point of Convergence (NPC)

Near point of convergence (NPC) was measured by a RAF ruler using a vertical line target. The participant was asked to follow the target, then the examiner pushed the target towards the patient eyes, facing the bridge of the nose. The break of convergence was determined subjectively when the patient reported diplopia, or objectively when the examiner observed divergence in one or both eyes.²¹ A break point of 5 cm or more from the nasal bridge was used as the clinical cut-off for a receded/abnormal result.

Stereoacuity

Stereoacuity was measured by Titmus fly test using polaroid glasses. Normal binocular function should be able to achieve 20 seconds of stereopsis with the contour stimuli and appreciate stereopsis with the gross random dot targets. The test was conducted at 40cm with two eyes open, while the patient wore polaroid glasses.²²

Measurement of Near Phoria

The near phoria was measured by Maddox Wing. It consists of two eye pieces and a septum dissociated between the eyes. It is used to measure horizontal (esophoria and exophoria), vertical (hyperphoria and hypophoria), and cyclo-deviation (incyclophoria and excyclophoria). Clinically, the patient was asked to hold the Maddox wing and look through the eyepieces. The right eye sees arrows, and the left eye sees scales. The participant was asked to read the degree of phoria from the scale marked with arrows.

Exploratory Secondary Analysis: Screen-Time Exposure

Screen time is a known exposure variable associated with binocular dysfunctions. Therefore, it was collected in a subsample of participants (n=66). Analyses involving screen time were treated as exploratory and conducted only in this subgroup.

Statistical Analysis

Statistical analysis was done using GraphPad Prism 10 for macOS (GraphPad Software, Boston, Massachusetts, USA). Quantitative data were presented as mean ± SD or mean ± SEM and mentioned in the text. Data normality was tested by the Shapiro–Wilk test, and non-parametric analysis was used where data were not normally distributed. And p < 0.05 was considered statistically significant.

Missing data were handled by complete exclusion: of 147 participants originally enrolled, 12 with incomplete data were excluded prior to analysis, leaving the analyzed sample of 134. Multicollinearity in the regression model was assessed via variance inflation factors only (all values below 1.3; see Results).

Results

A total of 134 young adults were included in the current study, of whom 60% were females with a mean age of 21.53±1.83 (range 18–25 years).

The description of the optometric measurements including: near phoria, NPC, amplitude of accommodation, stereoacuity, CISS, together with the smartphone addiction scale, is summarized in Table 1.

Table 1.

Descriptive Analysis of the Overall Sample

Parameter	Range	Mean ± SD
Near phoria (∆)	0 - 22	3.77 ± 4.56
Near point of convergence (cm)	5 - 24	7.49 ± 2.33
Amplitude of Accommodation OU (D)	4 - 20	11.87 ± 3.71
Stereo-acuity (Sec of arc)	0 - 3000	124.50 ±312.90
CISS score	0 - 55	21.00 ± 12.85
Smartphone scale	10 - 57	35.28 ± 11.11

Based on the smartphone addiction scale cut-off values for each gender, 70.9% of the male participants were considered smartphone addicted, and 55.5% of the female participants recorded values above the cut-off point. Average scores in male subjects were 37.63 ± 11.33 and 33.72 ± 10.75 in females (Mann–Whitney test, p = 0.05).

Binocular Vision Parameters and Smartphone Dependence: Correlation Analysis

Spearman correlation analysis was calculated between seven variables: age, amplitude of accommodation, near point of convergence (NPC), stereoacuity, near phoria, CISS score, and smartphone scale score.

A significant negative correlation was observed between the amplitude of accommodation and NPC (ρ = −0.466, p < 0.001), indicating that a greater amplitude of accommodation was associated with a closer near point of convergence. Age was positively correlated with NPC (ρ= 0.267, p= 0.001), suggesting a tendency for the near point to recede with increasing age, and was negatively correlated with OU amplitude of accommodation (ρ= −0.243, p= 0.003), consistent with the established age-related decline in accommodative function. While stereoacuity showed positive correlation with both CISS score (ρ = 0.252, p = 0.003), and NPC (ρ = 0.218, p = 0.008).

Smartphone addiction scale scores revealed statistically significant, though weak in magnitude (ρ = 0.18–0.24), correlations with the amplitude of accommodation, NPC, and CISS score; results are shown in Table 2.

Table 2.

Correlations Between the Smartphone Addiction Scale and Binocular Vision

Variable	Spearman’s ρ	p-value
Near phoria	0.077	0.381
Near point of convergence	−0.176	0.043*
Amplitude of Accommodation	0.191	0.027*
Stereoacuity	0.168	0.052
CISS score	0.238	0.006**

Notes: Spearman correlations between the smartphone addiction scale and binocular vision parameters (n = 134), *p < 0.05, ** p < 0.01.

Multiple Linear Regression: Predictors of Smartphone Addiction

A multiple linear regression analysis was performed to examine whether the amplitude of accommodation, NPC, stereoacuity, near phoria, and CISS score are predictors of smartphone scale scores.

The model was significant (F(5,127) = 3.57, p = 0.005) and explained 12.3% of the variance in smartphone scale scores (R² = 0.123), indicating modest explanatory power. Multicollinearity was not evident with all variance inflation factors below 1.3. Both CISS score (β =0.223, 95% CI 0.080–0.366, p = 0.003) and amplitude of accommodation (β =0.634, 95% CI 0.106–1.163, p=0.019) were significant independent predictors of smartphone scale scores, indicating that greater convergence insufficiency symptoms and amplitude of accommodation was associated with higher smartphone addiction scores. In contrast, NPC, stereoacuity, and near phoria did not contribute significantly to the model (Table 3). The normality of residuals was tested, and results were non-significant; hence, they passed normality.

Table 3.

Multiple Linear Regression Parameter Estimates for Smartphone Scale Score

Predictor	β	95% CI	p-value
Amplitude of accommodation	0.634	0.106 to 1.163	0.019*
NPC	−0.170	−1.019 to 0.678	0.692
Stereoacuity	−0.001	−0.006 to 0.005	0.821
Near phoria	0.225	−0.176 to 0.626	0.269
CISS score	0.223	0.080 to 0.366	0.003**

Notes: Multiple linear regression parameter estimates for smartphone scale score (n = 134). Bold rows indicate significant predictors. * p < 0.05, ** p < 0.01.

Exploratory Secondary Analysis: Screen-Time Exposure

The primary analysis examined the relationship between smartphone addiction scale scores and near binocular vision parameters (n= 134). To determine whether higher addiction scores corresponded to greater daily screen-time exposure, an exploratory subgroup analysis was performed among participants with available screen-time data (n = 66).

Analyses involving screen time were performed in the subset of participants with available screen-time data. No significant differences were observed between this subgroup and the full cohort with respect to demographics or clinical measures (all p > 0.05), reducing the likelihood of selection bias in the secondary analysis.

Correlation analysis in this subsample (n=66) showed that the smartphone addiction scale is not associated with daily screen time (ρ= 0.092, p= 0.465). In the other hand, near phoria was positively correlated with screen time (ρ= 0.297, p= 0.016), suggesting that greater deviation was associated with longer screen use. The amplitude of accommodation showed a significant negative correlation with screen time (ρ= −0.241, p= 0.049), indicating that higher accommodative amplitude was associated with lower screen time. Lastly, no significant correlation was observed between daily screen time and the rest of the optometric parameters.

Discussion

The current observational cross-sectional study assessed the association between smartphone addiction scale and near binocular vision in young adults with an average age of 21.5 ± 1.8 years. The analysis showed a significant relationship between scores on a validated smartphone addiction scale questionnaire and the amplitude of accommodation, near point of convergence, and symptoms of convergence insufficiency; the latter was measured using a validated questionnaire (CISS). Moreover, the study shows a significant gender difference in the smartphone addiction scale, with females reporting lower scores than males, although the mean age of the previous evidence was younger than the current study, with a mean age of 14.5 years.³ This tendency was different from the results from different countries; studies done in China showed that females are at a higher risk of smartphone addiction than males.^23,24 Several cultural factors could explain the opposite pattern of smartphone addiction between the two samples, for instance, only children with no siblings showed significantly higher scores of smartphone addiction in China,²⁴ an aspect that was not measured in the current study due to its lower probability in the Saudi population. Different countries, such as India, showed no gender differences in smartphone addiction, supporting the cultural role in the pattern observed.²⁵ Beyond only-child status, broader cultural and family-structure factors, such as household composition, social expectations around smartphone use by gender, and differing patterns of parental monitoring, may contribute to the gender pattern observed in the present sample, although these specific factors were not measured in the current study.²⁶

Biologically, these findings are consistent with compulsive near-device engagement sustaining prolonged accommodative and vergence demand, which would be expected to manifest first as self-reported symptom burden, captured here by the CISS, rather than as large shifts in objectively measured convergence or accommodative capacity.²⁷

Most studies examining the relationship between smartphones and binocular vision have relied on daily screen time as their primary measure of smartphone exposure. This approach has led to meaningful findings; prolonged smartphone use has been associated with measurable deterioration in the convergence system and accommodative function.^16,28 However, screen time as a variable has an important limitation: it quantifies duration without capturing the nature of that engagement. The present study took a different approach by using a validated smartphone addiction scale, which indexes compulsive use, loss of control, and functional impairment in daily life. Shaw, H.et al (2020) demonstrated that effect sizes quadrupled when smartphone behavior was measured with a problematic use scale rather than objective screen time.¹¹ Moreover, a different study showed that the correlation between recorded screen time and addiction scale scores is weak and unstable across populations.²⁹

The present data make this distinction concrete; participants with high SAS-SV scores reported significantly more convergence insufficiency symptoms, unlike smartphone daily screen time in the subset of explanatory data. In fact, the self-reported convergence insufficiency symptoms (CISS) capture three different symptom clusters: somatic sensation, impaired vision, and cognitive performance.³⁰ The somatic sensations factor grouping eye tiredness, ocular discomfort, headache, and concentration loss during near tasks dominates the instrument, accounting for 43.8% of total variance, and reflects the cumulative symptom burden of sustained near work. Using the total CISS score in the present study was therefore adequate, as the somatic sensations factor alone explains the largest share of variance; the total score is heavily weighted toward precisely the symptom profile most relevant to near-device use.^30,31 Compulsive smartphone engagement involves repeated, prolonged fixation at close viewing distances with little opportunity for vergence relaxation. Altogether, high scores are expected due to the somatic and impaired vision clusters. The significant association between smartphone addiction and total CISS scores in the present study is consistent with the clusters covered by the questionnaire.

A weak, yet significant positive association was found between the amplitude of accommodation and smartphone addiction scores, surprisingly. It would be expected to hypothesize that heavy near-device use should strain the accommodative system. The key distinction here is that the amplitude of accommodation measures maximal capacity under optimal conditions and is not a measure of how well accommodation holds up over sustained near work; accommodative facility and endurance are the parameters more sensitive to fatigue-related changes.³² What does appear to change with sustained smartphone engagement is accommodative response and near point function, as demonstrated by studies measuring accommodation before and after device use in comparable populations.⁷ One possible reading of the present finding is that individuals with greater accommodative reserve experience less visual discomfort during near tasks, reducing a natural deterrent to prolonged smartphone engagement. Whether that translates into a higher risk of problematic use patterns is speculative and requires further investigation, and the modest effect size here does not support strong conclusions in either direction.

Several limitations of the present study should be acknowledged. The cross-sectional nature means causality cannot be established; it remains unclear whether convergence insufficiency symptoms could drive compulsive smartphone use, or whether problematic use patterns may worsen visual symptoms over time. The screen time analyses were conducted on a smaller subsample (n = 66), although is in accordance with previous literature, it still limits their statistical power and direct comparability with the primary findings. Nonetheless, the findings point toward a clinically relevant conclusion. Several potentially confounding variables are another limitation of the current study, including refractive error, pre-existing binocular vision anomalies, academic workload and near-task demands, use of other digital devices, and broader environmental and behavioral factors, any of which could influence both smartphone use patterns and binocular vision measures.

Conclusion

Convergence insufficiency symptom burden in young adults is associated with problematic smartphone use independently of how much time they actually spend on their devices. Nonetheless, the observed correlations were weak, and the regression model explained only a modest proportion of variance; hence these findings should be interpreted as associations rather than causal or strongly predictive relationships. For the practicing optometrist, this suggests that patients presenting with convergence-related symptoms may warrant screening for problematic smartphone use patterns, and that managing binocular vision anomalies could potentially be relevant beyond visual comfort alone. However, longitudinal and interventional studies are needed before this is translated into routine clinical practice. Future research should aim to confirm these findings in larger and more diverse populations, incorporate objective, device-logged measures of smartphone use alongside the SAS-SV, and examine these relationships longitudinally.

Funding Statement

The Researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2026).

Disclosure

The authors report no conflicts of interest in this work.