Predicting different dimensions of fatigue from speech data: a longitudinal study in shift workers

Abstract

ABSTRACT Background Shift work, or working outside of normal circadian cycles, is associated with both experience of fatigue and poorer long-term physical and mental health. However, current methods for assessing fatigue present several barriers to building understanding of how these risks develop over time within individuals. Objective Here, we explore the potential of momentary speech activity-based fatigue measurement in a large, multi-lingual cohort of shift workers, using an intensive longitudinal study design (twice-daily measurement over two weeks in N =1,197 individuals from six different countries). Methods Paralinguistic speech features were used to predict different aspects of acute and chronic fatigue at each study time-point, with performance assessed in unseen (held-out) data. Results are reported both across the dataset as a whole, and for user-specific prediction models. Findings In the cross-sectional analysis, good (close to or exceeding current state-of-the-art) performance was achieved for both current sleep deprivation and self-reported sleepiness levels. The within-user analysis revealed robust increases in performance, yielding the ability to detect more subjective aspects of fatigue such as pervasive physical and mental exhaustion. Conclusions These findings offer preliminary support for the utility of using brief momentary speech samples as a low-burden, acceptable, and reliable measure of different aspects of fatigue in high-risk populations such as shift workers. Clinical Implications Developing brief, accessible measures of different dimensions of fatigue is an important step towards building understanding of how risks for poorer health outcomes develop over time within individuals exposed to significant circadian disruption. Summary What is already known on this topic Previous studies have indicated that speech data may be a promising source of information about fatigue: however to date these have primarily been carried out in small, unrepresentative samples and in non-naturalistic settings, making results hard to generalize. What this study adds Here, we present evidence from a large international study where participants both provided momentary speech activity data and reported levels of different aspects of fatigue, as they went about their usual working lives. Importantly, we use a modelling framework that explicitly takes into account potential influences of factors such as age, sex, and language on speech features and target fatigue measures, as well as assessing potential biases and reliability of model output. How this study might affect research, practice or policy If individuals who are at heightened risk of fatigue-related health problems are able to monitor their fatigue levels regularly and in real-time, this may yield the opportunity to intervene prior to transitioning into less tractable states of poor physical and mental health.

The authors' abstract, as published at the source. npj Digital Medicine, 2026 · DOI ↗