Exploring the Alignment of Perceived and Measured Sleep Quality with Working Memory using Consumer Wearables

TL;DR

This study investigates how consumer wearable sleep data correlates with subjective sleep quality and working memory performance, revealing significant predictors and individual differences in sensitivity to sleep markers.

Contribution

It provides new insights into the relationship between wearable sensor data, subjective sleep assessments, and cognitive performance, highlighting individual variability.

Findings

REM sleep, heart rate, and bedtimes predict sleep self-assessment.

REM sleep and self-assessment predict working memory performance.

Sleep tracker data varies in informativeness across individuals.

Abstract

Wearable devices offer detailed sleep-tracking data. However, whether this information enhances our understanding of sleep or simply quantifies already-known patterns remains unclear. This work explores the relationship between subjective sleep self-assessments and sensor data from an Oura ring over 4--8 weeks in-the-wild. 29 participants rated their sleep quality daily compared to the previous night and completed a working memory task. Our findings reveal that differences in REM sleep, nocturnal heart rate, N-Back scores, and bedtimes highly predict sleep self-assessment in significance and effect size. For N-Back performance, REM sleep duration, prior night's REM sleep, and sleep self-assessment are the strongest predictors. We demonstrate that self-report sensitivity towards sleep markers differs among participants. We identify three groups, highlighting that sleep trackers provide more information gain for some users than others. Additionally, we make all experiment data publicly available.