FLOW: A Feedback-Driven Synthetic Longitudinal Dataset of Work and Wellbeing

TL;DR

FLOW addresses the scarcity of publicly accessible longitudinal data on work-life balance and wellbeing by providing a rule-based, feedback-driven synthetic dataset with daily resolution for 1,000 individuals over two years. It encodes interpretable interactions among workload, lifestyle behaviors, and wellbeing, enabling exploratory analysis, methodological development, and benchmarking without compromising privacy. The contributions include a publicly released dataset, a transparent generation process, and a configurable generator for scenario analysis under explicit assumptions. FLOW serves as a controlled experimental environment that supports robust, reproducible research in domains where real-world data are inaccessible.

Abstract

Access to longitudinal, individual-level data on work-life balance and wellbeing is limited by privacy, ethical, and logistical constraints. This poses challenges for reproducible research, methodological benchmarking, and education in domains such as stress modeling, behavioral analysis, and machine learning. We introduce FLOW, a synthetic longitudinal dataset designed to model daily interactions between workload, lifestyle behaviors, and wellbeing. FLOW is generated using a rule-based, feedback-driven simulation that produces coherent temporal dynamics across variables such as stress, sleep, mood, physical activity, and body weight. The dataset simulates 1{,}000 individuals over a two-year period with daily resolution and is released as a publicly available resource. In addition to the static dataset, we describe a configurable data generation tool that enables reproducible experimentation under adjustable behavioral and contextual assumptions. FLOW is intended as a controlled experimental environment rather than a proxy for observed human populations, supporting exploratory analysis, methodological development, and benchmarking where real-world data are inaccessible.