Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform
4 affiliations
- 1Department of Psychology, University of Arizona College of Science, Tucson, Arizona, United States of America
- 2Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, United States of America
- 3Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- 4Department of Psychiatry, University of Arizona College of Medicine, Tucson, Arizona, United States of America
Abstract
Mood follows a well-documented circadian rhythm. Sleep timing and duration are similarly shaped by biological and social factors. However, large-scale, real-world data on how these patterns vary across demographic groups remain limited. Prior studies often rely on laboratory data or social media linguistic analysis, which lack direct self-reports. This study examines hourly, weekly, and demographic trends in mood and sleep duration reports using one of the largest online datasets of directly self-reported behavioral health: 468,092 Lumosity users (aged 20–79) who reported daily mood (−2 to +2 scale) and sleep duration (≤5–9 + hours). We assessed variation by time of day, day of week, and interactions of age, gender, and race/ethnicity using ANOVA and linear regression. Mood was higher among users reporting in the afternoon and evening, and lower from late night through morning. Mood showed a U-shaped pattern with age, consistent throughout the week, and longer sleep duration was linked to better mood reports throughout the day and week. Sleep duration varied by time of report and by day of week, with longer durations in the afternoon/evening and on weekends. Women reported longer sleep overall, Black users reported shorter sleep, and sleep duration declined from young adulthood through midlife before rising slightly among older users. Higher mood was associated with longer sleep, and the weekday-weekend mood difference was larger among users reporting longer sleep. These findings demonstrate the value of large-scale digital data for public health insight, highlighting demographic disparities in sleep and the importance of midlife as a key intervention period. Because the sample is likely skewed toward higher educational attainment and health engagement, disparity estimates may understate true population-level differences. The late-night peak in negative affect aligns with literature on nocturnal suicide risk, though this nonclinical sample cannot directly speak to clinical risk or crisis-service need. Author summary: In this study, we used a very large real-world dataset from an online cognitive-training platform (Lumosity) to examine how people’s self-reported mood and sleep vary across the day and across the week. We found clear time-based patterns: mood was generally lower among people logging in late at night and in the morning, and higher among those logging in later in the day, while sleep duration also differed in predictable ways between weekdays and weekends, with longer sleep typically reported on weekends. We also found that mood and sleep were closely linked, with longer sleep generally associated with better mood. At the same time, these patterns were not the same for everyone: they varied by age, gender, and race/ethnicity, and some groups consistently reported shorter sleep, pointing to broader social influences on sleep health. We see this work as part of a broader effort to use large digital datasets to better understand everyday mental health and sleep in natural settings. These findings may help inform more targeted support for insomnia, mood-related difficulties, and overnight crisis care. Because our sample reflects users of a commercial app rather than the general population, these disparities may understate, rather than overstate, true differences across the broader population.
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BibTeX
@article{mendoza2026largescale,
title = {{Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform}},
author = {Reina A. Mendoza and Andrew S. Tubbs and Michael L. Perlis and Fabian-Xosé Fernandez and Michael A. Grandner},
journal = {PLOS Digital Health},
year = {2026},
month = sep,
doi = {10.1371/journal.pdig.0001754},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001754},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform
AU - Reina A. Mendoza
AU - Andrew S. Tubbs
AU - Michael L. Perlis
AU - Fabian-Xosé Fernandez
AU - Michael A. Grandner
PY - 2026
DA - 2026/09/24
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001754
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001754
AB - Mood follows a well-documented circadian rhythm. Sleep timing and duration are similarly shaped by biological and social factors. However, large-scale, real-world data on how these patterns vary across demographic groups remain limited. Prior studies often rely on laboratory data or social media linguistic analysis, which lack direct self-reports. This study examines hourly, weekly, and demographic trends in mood and sleep duration reports using one of the largest online datasets of directly self-reported behavioral health: 468,092 Lumosity users (aged 20–79) who reported daily mood (−2 to +2 scale) and sleep duration (≤5–9 + hours). We assessed variation by time of day, day of week, and interactions of age, gender, and race/ethnicity using ANOVA and linear regression. Mood was higher among users reporting in the afternoon and evening, and lower from late night through morning. Mood showed a U-shaped pattern with age, consistent throughout the week, and longer sleep duration was linked to better mood reports throughout the day and week. Sleep duration varied by time of report and by day of week, with longer durations in the afternoon/evening and on weekends. Women reported longer sleep overall, Black users reported shorter sleep, and sleep duration declined from young adulthood through midlife before rising slightly among older users. Higher mood was associated with longer sleep, and the weekday-weekend mood difference was larger among users reporting longer sleep. These findings demonstrate the value of large-scale digital data for public health insight, highlighting demographic disparities in sleep and the importance of midlife as a key intervention period. Because the sample is likely skewed toward higher educational attainment and health engagement, disparity estimates may understate true population-level differences. The late-night peak in negative affect aligns with literature on nocturnal suicide risk, though this nonclinical sample cannot directly speak to clinical risk or crisis-service need. Author summary: In this study, we used a very large real-world dataset from an online cognitive-training platform (Lumosity) to examine how people’s self-reported mood and sleep vary across the day and across the week. We found clear time-based patterns: mood was generally lower among people logging in late at night and in the morning, and higher among those logging in later in the day, while sleep duration also differed in predictable ways between weekdays and weekends, with longer sleep typically reported on weekends. We also found that mood and sleep were closely linked, with longer sleep generally associated with better mood. At the same time, these patterns were not the same for everyone: they varied by age, gender, and race/ethnicity, and some groups consistently reported shorter sleep, pointing to broader social influences on sleep health. We see this work as part of a broader effort to use large digital datasets to better understand everyday mental health and sleep in natural settings. These findings may help inform more targeted support for insomnia, mood-related difficulties, and overnight crisis care. Because our sample reflects users of a commercial app rather than the general population, these disparities may understate, rather than overstate, true differences across the broader population.
SN - 2767-3170
ER -