停止还是不停止：探索智能手机使用中的意图-行为差距
To Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage
AI总结：
本研究提出将问题性智能手机使用操作化为意图-行为差距（IBG），通过37名参与者两周数据构建机器学习模型实时预测IBG，发现性别、时间、应用和输入交互等因素影响IBG，为干预工具设计提供依据。
AI中文摘要：
随着智能手机成为日常生活中不可或缺的一部分，研究人员一直试图识别何时使用会变得有问题。以往的研究从意图或行为的角度来操作化问题性智能手机使用（PSU）。这两种角度都存在提供用户不欢迎的干预措施的风险。我们提出了一种新的方法，将PSU操作化为意图-行为差距（IBG）。我们收集了37名参与者为期两周的自我报告停止使用手机的意图数据以及使用行为数据。我们计算了IBG，考察了人口统计学和情境变量的影响，并开发了机器学习模型以实时预测IBG。我们发现IBG可由性别、时间、应用和输入交互等因素解释。仅使用个人数据时，意图的预测最为准确，而行为和IBG在使用个人和全局数据时预测最为准确。我们的发现可以为未来针对时机和自适应强度进行优化的干预工具设计提供信息。
英文摘要：
As smartphones become integral to daily life, researchers have sought to identify when the use becomes problematic. Previous studies have operationalized problematic smartphone usage (PSU) from either an intention or a behavior perspective. Both risk delivering interventions not welcomed by users. We propose a novel approach to operationalizing PSU as the intention-behavior gap (IBG). We collected self-reported data on intentions to stop phone usage, alongside usage behavior data, from 37 participants over two weeks. We calculated IBG, examined effects of demographic and contextual variables, and developed machine learning models to predict IBG in real time. We found that IBG was explained by gender, time, app, and input interactions, among other factors. Intention was predicted most accurately with only personal data, whereas behavior and IBG were predicted most accurately with both personal and global data. Our findings can inform the design of future intervention tools optimized for timing and adaptive intensity.
