Beyond self-report data, we lack reliable and non-intrusive methods for identifying flow. However, taking a step back and acknowledging that flow occurs during periods of focus gives us the opportunity to make progress towards measuring flow by isolating focused work. Here, we take a mixed-methods approach to design a logs-based metric that leverages machine learning and a comprehensive collection of logs data to identify periods of related actions (indicating focus), and validate this metric against self-reported time in focus or flow using diary data and quarterly survey data. Our results indicate that we can determine when software engineers at a large technology company experience focused work which includes instances of flow. This metric speaks to engineering work, but can be leveraged in other domains to non-disruptively measure when people experience focus. Future research can build upon this work to identify signals associated with other facets of flow.

翻译：除了自我报告数据外，我们缺乏可靠且非侵入性的方法来识别心流状态。然而，退一步承认心流发生在专注期间，这为我们提供了通过隔离专注工作来推进心流测量的机会。本文采用混合方法设计一种基于日志的指标，该指标利用机器学习和全面的日志数据集合来识别连续相关动作（表示专注），并通过日记数据和季度调查数据，对照自我报告的专注或心流时长对该指标进行验证。结果表明，我们能够确定一家大型科技公司的软件工程师何时经历包含心流实例的专注工作。该指标适用于工程工作，但也可在其他领域中用于非干扰性地测量人们何时进入专注状态。未来研究可在此基础上，识别与心流其他方面相关的信号。