目的：探索一种无创的、智能可穿戴设备监测学龄儿童量化的用眼行为，并定量分析近视发生的相关因素。方法：招募佛山市禅城区石湾第二小学三年级及狮城中学小学部五年级的年龄为7~11岁部分学生共171例。所有受试者均按照非睫状肌麻痹主觉验光结果分为近视组108例和非近视组63例，所有受试者均佩戴智能可穿戴设备“云夹”，进行为期10 d(2022年9月21日—2022年10月2日)的用眼行为数据(近距离用眼距离、近距离用眼时间、近距离环境光照、有效户外时间)采集。采用t检验比较近视组与非近视组儿童在用眼行为数据之间的差异，并应用Logistic回归分析用眼行为与近视发生的相关性。绘制受试者操作特征(receiver operating characteristic curve,ROC)曲线，并计算曲线下面积(area undercurve,AUC)分析用眼行为习惯对近视发生的预测价值。结果：学龄期儿童近视患病率为63.2%。近视组与非近视组在每天用眼时间、单次用眼时间、用眼距离、白天用眼光照、晚上用眼光照、每天户外活动时间及每天有效户外活动暴露次数比较差异均有统计学意义(均P＜0.05)。Logistic回归分析显示，单次用眼时间、每天用眼时间是近视发生的危险因素。Spearman相关性分析显示，单次用眼时间及每天用眼时间均与近视发生呈正相关(均P＜0.05)。单次用眼时间预测近视发生的ROC曲线下面积为0.939。结论：可穿戴设备“云夹”可量化学龄期儿童用眼行为；学龄期儿童近视发生可能与近距离用眼时间有一定相关性；预测模型可结合儿童屈光发育档案，量化近视发生风险，对儿童实现分类管理，及时采取个性化干预。

Objective: To investigate a non-invasive,smart device capable of monitoring the quantitative visual behavior of school age children, and to analyze quantitatively the relationship between visual behavior and the occurrence of myopia. Methods: This study recruited 171 subjects aged between 7 and 11 years from the third grade of Shiwan SecondPrimary School and the fifth grade of Shicheng Middle School in Chancheng District, Foshan City. Participants werecategorized into a myopia group (108 subjects) and a non-myopia group (63 subjects) based on results from non-ciliary muscle paralysis optometry. All subjects wore "clips" to track their near-work distance, near-work duration, lighting conditions during near-work, and time spent on outdoor activities between September 21, 2020, and October 10, 2020. Differences in these habits between the myopia and non-myopia groups were compared, and logistic regression analysis was conducted to assess the impact of habitual eye use on myopia. Results: The prevalence of myopia was found to be 63.2%. Statistically significant differences (all P<0.05) were observed between the myopic and non-myopic groups regarding average daily near-work time, average single near-work session duration, average near-work distance, average daytime and nighttime near-work lighting conditions, average daily outdoor activity time, and average daily effective outdoor activity exposure. Logistic regression analysis indicated that longer average single near-work sessions and increased average daily near-work time were risk factors for myopia. Spearman correlation analysis further supported these findings, showing a positive correlation between average single near-work session duration and average daily  near-work time with the occurrence of myopia (all P<0.05). The predictive accuracy of a model combining average single near-work session duration and average daily near-work time for myopia occurrence was high, with an area under the curve of 0.939. Conclusions: The wearable device "Cloud clip" effectively monitors the visual behavior of school-age children. The occurrence of myopia in this age group may be associated with increased near-work activities. A predictive model incorporating refractive development in myopic children can assess the quantitative risk of myopia, enabling the classification and management of school-age children. Personalized interventions may serve as protective factors against myopia.