六年儿童智能设备使用趋势和决定因素:使用增长混合模型的潜轨道课程
Saebom Jeon1, Sangha Lee2, Yunmi Shin3
1Department of Marketing BigData, Mokwon University, Daejeon, Korea.
Journal of Korean medical science
|November 11, 2025
概括
这项研究在6年内确定了儿童屏幕时间的三个组:低风险,中风险和高风险. 儿童年龄和家长教育等行为和社会经济因素影响了这些模式,表明需要量身定制的干预措施.
科学领域:
- 儿童发展 儿童发展
- 数字媒体研究数字媒体研究
- 公共卫生 公共卫生
背景情况:
- 研究了儿童长期智能设备使用模式.
- 利用潜伏轨迹类分析和生长混合模型 (GMM).
- 根据6年的屏幕时间轨迹确定了风险组.
研究的目的:
- 为了分析儿童智能设备使用的六年模式.
- 根据屏幕时间轨迹识别不同的风险组.
- 评估人口和行为因素对群体成员身份的影响.
主要方法:
- 来自儿童队列的数据,以了解互联网成的早期儿童风险因素 (2018-2023年).
- 增长混合模型 (GMM) 应用于六波调查数据 (n=313).
- 多项逻辑回归用于评估群体成员的预测因素 (性别,年龄,父母教育,收入,CBCL分数).
主要成果:
- 确定了三个风险组:低风险 (69.86%),中等风险 (21.92%) 和高风险 (8.22%).
- 高风险组表现出持续高且不断增加的屏幕时间.
- 儿童年龄较大,儿童行为检查清单 (CBCL) 评分较高,母亲教育程度较低,收入较低与更高的风险有关.
结论:
- 儿童使用智能设备存在显著差异,受行为和社会经济因素的影响.
- 这些发现强调了针对不同屏幕时间轨迹的有针对性的干预措施的必要性.
- 有效的策略需要考虑个人和家庭因素,以促进健康的数字习惯.
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