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

TL;DR

FLOW is a synthetic, longitudinal dataset simulating daily interactions between workload, lifestyle, and wellbeing, designed to support research and benchmarking in behavioral and stress modeling without privacy concerns.

Contribution

The paper introduces FLOW, a novel rule-based, feedback-driven synthetic dataset with configurable tools for reproducible research in work-life balance and wellbeing modeling.

Findings

Simulates 1,000 individuals over two years with daily data

Provides a configurable tool for reproducible data generation

Supports benchmarking and exploratory analysis without real-world data

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.