This meta-analysis aims to analyse how the activities of rail passengers have changed in Japan as a result of rapid technological developments. To be eligible for inclusion in this analysis, source studies must have reported the number of passengers performing specific activities, and the number must have been directly counted by surveyors who actually ride on trains. Databases searched included CiNii, J-STAGE, Web of Science, and Google Scholar. References in selected studies were trialled using a snowballing method. In addition, past onboard activities were retrospectively identified by content analysis of YouTube videos in which the surveyors hypothetically travelled on a train and observed the passengers. The use of YouTube videos for meta-analysis of rail passengers’ activities is a novel contribution of this study. The search for the YouTube video was entirely manual. In total, 23 independent studies with 332,355 passengers were included in the analysis. Data were collected from 1983 to 2019. The effect sizes were the proportion of each of the following activities: ‘(a) mobile phones’, ‘(b) sleeping’, ‘(c) reading’, ‘(d) music’, and ‘(e) other’. Meta-regressions were performed with the year of data collection as a moderator. Demonstrating historical changes in activities through statistical analysis is another novel contribution: ‘(a) mobile phones’ and ‘(d) music’ had a significantly increasing trend, ‘(c) reading’ had a significantly decreasing trend, and ‘(b) sleeping’ and ‘(e) other’ did not change. Studies with and without YouTube videos did not affect the conclusions, which supports the use of YouTube videos for the purposes of this study. Ideas are presented for research methods that use directly observed data to explain the possible social factors behind longitudinal variation in travel-based multitasking.

这项荟萃分析旨在分析日本铁路乘客的活动因技术的快速发展而发生的变化。为了有资格纳入此分析，来源研究必须报告进行特定活动的乘客数量，并且该数量必须由实际乘坐火车的调查员直接计算。检索的数据库包括 CiNii、J-STAGE、Web of Science 和 Google Scholar。使用滚雪球方法对选定研究中的参考文献进行了试验。此外，通过对 YouTube 视频的内​​容分析来回顾性地确定了过去的车上活动，在这些视频中，调查员假设乘坐火车并观察乘客。使用 YouTube 视频对铁路乘客的活动进行元分析是本研究的一个新颖贡献。 YouTube 视频的搜索完全是手动的。分析中总共纳入了 23 项独立研究，涉及 332,355 名乘客。数据收集于 1983 年至 2019 年。效应大小是以下各项活动的比例：“(a) 手机”、“(b) 睡觉”、“(c) 阅读”、“(d) 音乐”、和“(e)其他”。以数据收集年份作为调节因子进行元回归。通过统计分析展示活动的历史变化是另一个新颖的贡献：“(a)手机”和“(d)音乐”有显着增加的趋势，“(c)阅读”有显着减少的趋势，“(b) “睡觉”和“(e)其他”没有改变。有或没有 YouTube 视频的研究并不影响结论，这支持在本研究中使用 YouTube 视频。 提出了一些研究方法的想法，这些方法使用直接观察的数据来解释基于旅行的多任务处理的纵向变化背后可能的社会因素。