This was a cross-sectional study with descriptive and analytical objectives. It was conducted in the city of Maradi, the economic capital of Niger, over a six-month period from April 1 to September 30, 2025. The study setting included various public and professional venues (universities, government offices, markets) in order to reach a diverse population.

The target population consisted of individuals aged 15 and older who regularly used at least one type of digital screen (cell phone, computer, tablet, etc.) in their daily, professional, or leisure activities. We used convenience sampling. Participants were approached at the selected sites and invited to participate after a clear explanation of the study’s objectives. The total number of participants in this study was 210; we did not include those who refused to participate after we had clearly explained the study to them.

2.2.1. Inclusion Criteria

The study included participants who:

• Were 15 years of age or older;

• Used one or more digital screens for at least one hour per day;

• Who had given their free and informed consent to participate in the study (or the consent of a parent/guardian for minors aged 15 to 17).

2.2.2. Exclusion Criteria

The following subjects were excluded from the study:

• Those with a known, pre-existing severe eye condition (e.g., advanced glaucoma, AMD, proliferative diabetic retinopathy) that could constitute a major confounding factor;

• Who had undergone recent eye surgery (within the past 3 months);

• Who were unable to understand and respond coherently to the questionnaire.

A total of 210 individuals meeting the criteria were included in the study.

2.3. Data Collection

The data collection tool was a structured questionnaire that was pre-tested and administered in person by trained interviewers. The questionnaire was designed to collect information on several variables:

• Sociodemographic variables: age, gender, occupation, educational level.

• Screen usage habits: type (s) of screen (s) used, average daily usage time (in hours), usual eye-to-screen distance (estimated by the participant: <30 cm, 30 - 50 cm, >50 cm), taking regular breaks, use of visual protection measures (blue light filters, artificial tears, brightness adjustment, etc.).

• Visual symptoms (eye strain): presence, type, and frequency of symptoms experienced during or after screen use. The symptoms examined were: eye strain, blurred vision, dry eyes, headaches, and sleep disturbances. Frequency was classified as: daily, frequent, occasional, rare.

• Management of symptoms: approach taken toward symptoms (no treatment, self-medication, consultation with an eye care professional, consultation with a traditional practitioner).

• Knowledge and opinions: awareness of the “20-20-20” rule and personal opinion on preventive measures.

2.4. Statistical Analysis

The collected data were entered and analyzed using Epi Info version 7.2. Quantitative variables (such as age) were described using their means and standard deviations, as well as their extremes. Qualitative variables were described in terms of counts and percentages. The chi-square (χ²) test was used to investigate associations between qualitative variables, particularly between the presence of symptoms and potential risk factors (duration of exposure, lack of breaks, etc.). The threshold for statistical significance was set at p < 0.05.

The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Approval was obtained from the relevant authorities. Written informed consent was obtained from each participant prior to enrollment. The anonymity and confidentiality of the data were ensured throughout the process. Participants with severe symptoms were advised to consult an ophthalmologist.

Age: In our study, the 26 - 35 age group was the most represented, accounting for 33.33% (n = 70). The mean age was 34.64 years ± 11.12, with ages ranging from 15 to 63 years.

Figure 1.Breakdown of patients by age group.

The mean age of the participants was 34.64 years, with a standard deviation of 11.12. Ages ranged from 15 to 63 years. The most represented age group was 25 - 34 years.

Our sample was predominantly male, with 154 men (73.33%) and 56 women (26.67%), resulting in a male-to-female ratio of 2.68, as shown in Figure 1.

Table 1. Breakdown of participants by occupation.

In terms of occupation, students constituted the largest group, accounting for 36.67% of the sample, followed by civil servants (26.19%) and teachers (12.86%). Details of the occupational breakdown are presented in Table 1.

Figure 2 shows the distribution of participants by educational level.

The majority of participants had a higher education background (university level), reflecting the composition of our sample, which includes a high proportion of students and civil servants.

Levelofeducation

Inourstudy,themajorityofparticipantshadahighereducationlevel,accountingfor87.62%(n=184).

Figure 2.Distribution of patients by educational level.

Table 2. Type of screen used.

The cell phone is the most ubiquitous device. It is used on its own by 40.48% of participants. The combined use of multiple devices (cell phone, computer, television) is also very common, as detailed in Table 2.

Averagedailyscreentime

Inourstudy,morethanhalf(116,or55.24%)hadascreentimeof≤6hoursperday,asshowninFigure 3.

With regard to screen time, a significant proportion of the study population spends many hours in front of screens each day. More than half of the participants reported using screens for more than 4 hours a day.

Figure 3. Distribution of patients by average daily exposure time.

The eye-to-screen working distance is a crucial ergonomic factor. Most participants estimate that they work at a distance of between 30 and 50 cm, which is considered adequate for most tasks. as shown in Figure 4.

Distanceusedbythemaximum

Anaverageeye-to-screendistancewasthemostcommon,accountingfor62.38%.

Figure 4.Distribution of patients based on the maximum distance used.

Of the 210 participants, 173 (82.38%) reported at least one visual symptom related to screen use, while 37 (17.62%) reported none. The overall prevalence of asthenopic symptoms in our sample is therefore 82.38%, shown in Table 3.

The most frequently cited symptoms, either alone or in combination, were eye strain and blurred vision, each reported by 29 people (13.81%) as their primary or sole symptom. Headaches are also very common, often associated with eye strain.

Table 3. details the distribution of symptoms and their combinations.

Table 4. Distribution of patients by symptom frequency.

Regarding the frequency of these symptoms, they are described as “occasional” by 42.38% of participants and “rare” by 40.95%. However, 16.67% of the sample (35 people) report frequent or daily symptoms, indicating a significant impact on their quality of life. as shown in Table 4.

Table 5.Breakdown of patients by adherence to ergonomic posture.

In response to these symptoms, the majority of participants (66.67%) took no action to seek treatment. As shown in Table 5 only 48 individuals (22.86%) consulted an eye care professional. Self-medication was reported by 10% of participants.

Table 6. Breakdown of participants by protective measures.

With regard to protective measures, adjusting screen brightness is the most commonly adopted measure (23.81%), followed closely by the use of blue light filters (glasses or apps) (24.76%). However, more than a quarter of participants (26.67%) do not take any visual protection measures, as shown in Table 6.

Multivariate analysis

Sociodemographic variables and visual impairmentsageettroublesvisuels.

Table 7. Relationship between age group and visual impairments.

P: 0.30.

Table 7 of our study shows that, no statistically significant association was found between age group and the occurrence of visual impairments (P = 0.30).

Gender and Visual Impairments.

Table 8.Association between gender and visual impairments.

P: 0.07.

Table 8 of our study shows that, no significant association was observed between gender and the occurrence of visual disturbances (P = 0.07).

Profession et troubles visuels

Table 9. Association between occupation and visual impairments.

P: 0.17.

Table 9 of our study shows that, no significant association was found between occupation and the occurrence of visual impairments (P = 0.17).

Educational level and visual impairments

Table 10. Association between educational level and visual impairments.

P: 0.48.

Table 10 of our study shows statistical analysis did not reveal a significant association between educational level and visual problems (P = 0.48).

Screen exposure and visual problems

Screen type and visual problems

Table 11 of our study shows statistical analysis revealed a significant association between the onset of visual disturbances and the use of certain screens, particularly computers (P = 0.04) and televisions (P = 0.03).

Table 11.Association between screen type and visual problems.

Duration of exposure and visual disturbances

Eye-Screen Distance and Visual Problems

Table 12. Association between eye-screen distance and visual problems.

P: 0.84

In Table 12 of our study, no significant association was observed between eye-screen distance and the occurrence of visual disturbances (P = 0.84).

Clinical signs of visual disturbances

Types of signs and visual disturbances

Table 13. Association between types of signs and visual disturbances.

In Table 13 of our study, statistical analysis revealed a significant association between the occurrence of visual disturbances and most of the clinical signs examined (P = 0.00 for eye strain, blurred vision, headaches, and sleep disturbances), with dry eyes also showing a significant association, though with a P-value of 0.02.

Prevalence of visual symptoms and disorders

Table 14. Association between the prevalence of visual symptoms and visual disorders.

P: 0.00.

In Table 14 of our study, the frequency of symptoms was significantly associated with the onset of visual disturbances (P = 0.001).

Preventive Measures for Vision Problems

Table 15. Relationship between preventive measures and vision problems.

In Table 15 of our study, no significant association was observed between the various preventive measures examined and the onset of visual impairments.

When asked about the best prevention strategies, the majority of participants suggested behavioral measures. The most common responses included reducing screen time, wearing protective eyewear (anti-glare/blue light filters), taking regular breaks, and adjusting screen brightness. Some also mentioned the importance of awareness and regular check-ups. These opinions, though subjective, reflect a general awareness of the risks, even if their implementation remains limited.

The prevalence of 82.38% observed in our study is comparable to in similar populations. For example, a study conducted among university students in Malaysia found a prevalence of 89.9% [13], while another in Egypt reported 87.2% [15]. Among office workers, high rates have also been documented, such as 73.3% in Ethiopia [12] and 75% in India [11]. This convergence of results, despite geographical and cultural differences, suggests that the pathophysiological mechanisms underlying VSI are universal and directly linked to eye-screen interaction. The slight variation may be attributed to methodological differences (case definition, data collection tools), characteristics of the studied populations (age, occupation), and the average duration of screen exposure. The most common symptoms in our study—namely eye strain (13.81%) and blurred vision (13.81%), followed by headaches—are also the most frequently reported in the global literature [5][16]. Eye strain, or asthenopia, results from overuse of the ciliary muscles responsible for accommodation (focusing) and the extraocular muscles responsible for convergence [17]. Blurred vision may result from this accommodative fatigue or from tear film instability due to dry eye [9]. Headaches, on the other hand, are often tension-type headaches, linked to both visual strain and poor posture [18].

Our analysis revealed a significant association between daily screen time and symptom frequency. Participants who used screens for more than 4 hours a day were more likely to report frequent or daily symptoms. This causal link is well established. Rosenfield (2011) demonstrated that continuous computer work lasting more than two hours is sufficient to induce significant symptoms [4]. Similarly, the study by Loh and Redd (2008) confirmed that each additional hour spent in front of a screen increases the risk of developing VSI [19].

The lack of regular breaks is another major risk factor identified in our study, corroborating the findings of numerous other studies [20][21]. The “20-20-20” rule (every 20 minutes, look at something 20 feet, or 6 meters, away for 20 seconds) is a simple ergonomic recommendation aimed at periodically relaxing accommodation and promoting blinking [22]. The low level of awareness and application of this rule in our sample suggests a lack of information and awareness.

The type of screen used also plays a role. The ubiquity of smartphones (used by over 90% of our sample, either alone or in combination) is an aggravating factor. Due to their small size, smartphones are held closer to the eyes, which increases the effort required for accommodation and convergence [23]. Furthermore, small font sizes and variable screen quality can contribute to eye strain. A Japanese study specifically linked increased time spent on smartphones to a higher prevalence of dry eye among office workers [24].

One of the most concerning findings of our study is the low rate of seeking care. Two-thirds of symptomatic participants (66.67%) did not seek any form of treatment, and only 22.86% consulted an eye care professional. This finding can be explained by several factors: the trivialization of symptoms, viewed as a “normal” consequence of computer work; economic or geographic barriers to accessing eye care; or a lack of awareness regarding available solutions [25]. This situation is particularly concerning because persistent symptoms may mask an uncorrected refractive error, convergence insufficiency, or chronic dry eye, all of which require specific treatment [10].

Self-medication, reported by 10% of participants, also poses a risk. The use of over-the-counter vasoconstrictive eye drops to relieve redness can, in the long term, cause a rebound effect and mask signs of severe dry eye [26].

These findings highlight an urgent need for public health awareness and education campaigns in Niger. These campaigns should aim to inform the public about the reality of ODS, its causes, and, above all, its preventive and curative solutions. Promoting visual ergonomics in the workplace and in schools is essential. This includes recommendations on posture, screen distance, ambient lighting, and the importance of regular breaks [27].

Furthermore, it is crucial to encourage regular eye screenings. Proper vision correction is the first step toward reducing visual strain. Glasses specifically designed for computer work, with an anti-glare coating and a blue light filter, can provide additional comfort, although the benefit of the latter is still the subject of scientific debate [28][29].

Our study has several limitations that should be acknowledged. First, convenience sampling may introduce selection bias, and the results therefore cannot be generalized to the entire population of Maradi or Niger. Our sample was predominantly male and comprised a high proportion of students and civil servants, which may not reflect the structure of the general population. Second, data on exposure duration and work distance were self-reported and subject to recall and estimation bias. Objective measurements (e.g., via screen time tracking apps) could have provided more accurate data. Third, the study did not include an ophthalmological examination, which prevented us from correlating reported symptoms with objective clinical signs (e.g., tear film break-up time measurement, refractive status assessment). Finally, the cross-sectional nature of the study allows for the identification of associations but not definitive causal links.

Despite these limitations, our study provides valuable baseline data on a health issue that has been little explored in our context to date and paves the way for more in-depth future research, including prospective studies or clinical trials to evaluate the effectiveness of preventive interventions.