Frontiers in medicine · 2026
Association between average screen time and dry eye disease: a secondary analysis of publicly available cross-sectional data.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ling Juan, Zhang ChenJia, Chen LiYing, Ma WenJuan, Zhang Li, Chang Juan
Аннотация
BACKGROUND: The pervasive use of smartphones and other visual display terminals has heightened concerns regarding ocular health, especially the development or exacerbation of dry eye disease (DED) and inflammatory conjunctivitis. Prolonged screen viewing is consistently linked to a reduced spontaneous blink rate, accelerated tear-film evaporation, and a shortened tear-film break-up time (TBUT), factors that synergistically promote tear-film instability and ocular discomfort.
OBJECTIVE: This study aimed to investigate the association between excessive daily screen exposure and the prevalence of DED.
METHODS: This study is a secondary analysis of a publicly available, de-identified dataset from Kaggle (Dry Eye Disease Dataset, n = 20,000 adults aged 18-45 years). We used multivariable logistic regression to examine the association between average screen time and DED, adjusting for demographic, sleep, blood-pressure, body-mass index (BMI), and lifestyle covariates. Generalized additive models (GAMs) with smooth-spline fitting were applied to explore potential non-linear relationships.
RESULTS: Among 20,000 participants, 13,037 (65.2%) were classified as having DED according to the dataset label and 6,963 (34.8%) as not having DED. After full adjustment, each additional hour of average screen time was associated with 2% higher odds of DED [odds ratio [OR] = 1.02, 95% confidence interval [CI] 1.01-1.03, P = 0.0015]. This association remained consistent in Model 2 (OR=1.02, 95% CI 1.01-1.03, P = 0.0017) and in Model 3 (OR=1.02, 95% CI 1.01-1.03, P = 0.0029). Smooth-spline analysis indicated a non-linear dose-response relationship, with a nadir at approximately 3-4 h/day and a sharp increase beyond 9 h/day.
CONCLUSIONS: This cross-sectional analysis demonstrates a consistent, dose-dependent association between daily screen exposure and the prevalence of DED. Each additional hour of screen exposure was associated with higher odds of DED.
OBJECTIVE: This study aimed to investigate the association between excessive daily screen exposure and the prevalence of DED.
METHODS: This study is a secondary analysis of a publicly available, de-identified dataset from Kaggle (Dry Eye Disease Dataset, n = 20,000 adults aged 18-45 years). We used multivariable logistic regression to examine the association between average screen time and DED, adjusting for demographic, sleep, blood-pressure, body-mass index (BMI), and lifestyle covariates. Generalized additive models (GAMs) with smooth-spline fitting were applied to explore potential non-linear relationships.
RESULTS: Among 20,000 participants, 13,037 (65.2%) were classified as having DED according to the dataset label and 6,963 (34.8%) as not having DED. After full adjustment, each additional hour of average screen time was associated with 2% higher odds of DED [odds ratio [OR] = 1.02, 95% confidence interval [CI] 1.01-1.03, P = 0.0015]. This association remained consistent in Model 2 (OR=1.02, 95% CI 1.01-1.03, P = 0.0017) and in Model 3 (OR=1.02, 95% CI 1.01-1.03, P = 0.0029). Smooth-spline analysis indicated a non-linear dose-response relationship, with a nadir at approximately 3-4 h/day and a sharp increase beyond 9 h/day.
CONCLUSIONS: This cross-sectional analysis demonstrates a consistent, dose-dependent association between daily screen exposure and the prevalence of DED. Each additional hour of screen exposure was associated with higher odds of DED.