SearcharxivSearch

arXiv subjects

Fabio Stano

Publications and source records attributed to Fabio Stano.

2 recordsLinked to original sources

Hacking Flow: From Lived Practices to Innovation

In digital knowledge work, flow promises not just productivity; it offers a pathway to well-being. Yet despite decades of flow research in HCI, we know little about how to design digital interventions that support it. In this work, we foreground lived interventions - everyday practices workers already use to foster flow - to uncover overlooked opportunities and chart new directions for digital intervention design. Specifically, we report findings from two studies: (1) a reflexive thematic analysis of open-ended survey responses (n = 160), surfacing 38 lived interventions across four categories: environment, organization, task shaping, and personal readiness; and (2) a quantitative online survey (n = 121) that validates this repertoire, identifies which interventions are broadly endorsed versus polarizing, and elicits visions of technological support. We contribute empirical insights into how digital workers cultivate flow, situate these lived interventions within existing literature, and derive design opportunities for future digital flow interventions.

cs.HC

Exploring Flow in Real-World Knowledge Work Using Discreet cEEGrid Sensors

Flow, a state of deep task engagement, is associated with optimal experience and well-being, making its detection a prolific HCI research focus. While physiological sensors show promise for flow detection, most studies are lab-based. Furthermore, brain sensing during natural work remains unexplored due to the intrusive nature of traditional EEG setups. This study addresses this gap by using wearable, around-the-ear EEG sensors to observe flow during natural knowledge work, measuring EEG throughout an entire day. In a semi-controlled field experiment, participants engaged in academic writing or programming, with their natural flow experiences compared to those from a classic lab paradigm. Our results show that natural work tasks elicit more intense flow than artificial tasks, albeit with smaller experience contrasts. EEG results show a well-known quadratic relationship between theta power and flow across tasks, and a novel quadratic relationship between beta asymmetry and flow during complex, real-world tasks.

cs.HC