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A highly replicable decline in mood during rest and simple tasks

Nature Human Behaviour volume 7, pages 596–610 (2023)Cite this article

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Abstract

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants’ mood, an effect we call ‘Mood Drift Over Time’. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (−13.8% after 7.3 min of rest, Cohen’s d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time’s effects when studying mood and behaviour.

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Fig. 1: One cycle (mood rating + task).
Fig. 2: The timecourse of mood drift is consistently present across many cohorts and task modulations.
Fig. 3: Individual subject LME slope parameters for online participants (blue) and mobile app participants (orange).
Fig. 4: Individual differences in sensitivity to the passage of time relate to other individual differences in the mobile app cohort.
Fig. 5: Rest periods decreased the likelihood of choosing to gamble in the first four trials after rest ended.

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Data availability

All data used in the manuscript have been made publicly available. Online participants’ data can be found on the Open Science Framework at https://osf.io/km69z/. Mobile app participants’ data can be found on Dryad at https://doi.org/10.5061/dryad.prr4xgxkk (ref. 101).

Code availability

The code for the task and survey is available on GitLab at https://gitlab.pavlovia.org/mooddrift. Our data analysis software, as well as the means to create a Python environment that automatically installs it on a user’s machine, has been made available online at https://github.com/djangraw/MoodDrift.

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Acknowledgements

This research was supported in part by the Intramural Research Program of the National Institute of Mental Health, part of the NIH (grant nos. ZIAMH002957 (to A.S.), ZICMH002968 (to F.P.), ZIAMH002871 (to D.S.P.), ZIAMH002872 (to D.S.P.) and ZICMH002960 (to A.G.T.)). This work used the computational resources of the NIH high-performance computing (HPC) Biowulf cluster (http://hpc.nih.gov). Data collection for the mobile app dataset was supported by the Wellcome Trust (grant no. 101252/Z/13/Z). The online adolescent sample in this study was collected under NIH IRB protocol number 18-M-0037, registered on clinicaltrials.gov as NCT03388606. The online adult sample was collected under NIH Office of Human Subjects Research Protection protocol P194594. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The views expressed in this article do not necessarily represent the views of the NIH, the Department of Health and Human Services or the United States Government.

Author information

Author notes

  1. These authors contributed equally: Dylan M. Nielson, Argyris Stringaris.

Authors and Affiliations

  1. National Institute of Mental Health, Bethesda, MD, USA

    David C. Jangraw, Francisco Pereira, Adam G. Thomas, Daniel S. Pine, Charles Zheng & Dylan M. Nielson

  2. Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, USA

    David C. Jangraw & Haorui Sun

  3. Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel

    Hanna Keren

  4. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK

    Rachel L. Bedder & Robb B. Rutledge

  5. Wellcome Centre for Human Neuroimaging, University College London, London, UK

    Rachel L. Bedder & Robb B. Rutledge

  6. Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA

    Robb B. Rutledge

  7. Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece

    Argyris Stringaris

  8. Faculty of Brain Sciences, Division of Psychiatry, University College London, London, UK

    Argyris Stringaris

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Contributions

D.C.J., H.K., D.M.N. and A.S. devised the task. D.C.J. wrote the online experiments. D.C.J. and H.S. collected the online data. R.L.B. and R.B.R. provided data and information from the mobile app experiments. C.Z. and F.P. devised the computational model. D.C.J., C.Z. and D.M.N. wrote analysis code. D.C.J. and D.M.N. ran the analyses. D.C.J., D.M.N. and A.S. wrote the manuscript. All authors provided revisions and finalized the text.

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Correspondence to David C. Jangraw.

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Nature Human Behaviour thanks Aaron Heller, Caitlin Mills and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Mood rating frequency does not affect mood drift slope.

Mean ± SE mood rating at each time in the 4 cohorts with 60 s, 30 s, 15 s, and 7.5 s of rest between mood ratings (cohorts 60sRestBetween, 30sRestBetween, 15sRestBetween, and 7.5sRestBetween, respectively). The magnitude of mood drift did not vary with the frequency of mood ratings.

Extended Data Fig. 2 Mood slope parameter distributions vary with analysis choice.

Histogram of the LME mood slope parameters for the online cohort (blue) and the confirmatory mobile app cohort (orange), along with the computational model time sensitivity parameter for the confirmatory mobile app cohort (green). Mobile app participants with outlier task completion times were excluded from the LME analysis (see Methods). Note that the use of LME modeling to analyze the mobile app data significantly lowered the distribution of slopes compared to when the computational model was used (median = -0.752 vs. -0.0408, IQR= 2.10 vs. 0.764 %mood min−1, 2-sided Wilcoxon rank-sum test, W42771 = -54.2, p<0.001), but the LME slopes from the mobile app were still significantly greater than those of the online cohort (median = -1.53 vs. -0.752, IQR = 2.34 vs. 2.1 %mood min−1, 2-sided Wilcoxon rank-sum test, W21761 = 14.5, p<0.001). Vertical lines represent group medians. Stars indicate p<0.05. P values were not corrected for multiple comparisons.

Extended Data Fig. 3 Sample fits of the computational model.

Sample fits of the computational model for three random subjects in the confirmatory mobile app cohort. SSE = sum squared error, a measure of goodness of fit to the training data. In the top plots, the red bars are in units of the left-hand y axis, and the blue bars are in units of the right-hand y axis.

Extended Data Fig. 4 Histogram of computational model parameters.

Histogram of computational model parameters across the 21,896 confirmatory mobile app subjects.

Extended Data Fig. 5 Mood drift stability over blocks, days, and weeks.

Stability of LME coefficients estimating the initial mood (top) and slope of mood over time (bottom) for each participant across rest periods one block apart (left), 1 day apart (middle), and 2 weeks apart (right). ICC denotes the intra class correlation coefficient for each comparison. P values shown are one-sided (since ICC values are expected to be positive) with no correction for multiple comparisons.

Extended Data Fig. 6 Relationship between mood drift and depression risk.

Relationship between mood drift and depression risk. (a) Mood ratings over time of online participants at risk of depression (defined as MFQ>12 or CES-D>16) vs. those not at risk for the 768 participants with at least 6 minutes of resting mood data (error bars are SEM). The dotted line represents the mean initial rating (mean of cohort means). (b) We fitted simple regressions of time versus mood within each individual and determined significance of the time term with Benjamini-Hochberg false-discovery rate correction (2-sided α = 0.5, p<0.05) to better understand the relationship between depression risk and the change in mood over time. Depression risk is operationalised as score on the CES-D or MFQ divided by the threshold for depression risk on each measure (16 and 12 respectively). The line is a linear best fit, and the patch shows the 95% confidence interval of this fit. (c) Proportion of individuals with or without risk of depression (that is, depression risk >1 or <1) with positive (significantly greater than zero), non-significant (no evidence of a significant difference from zero), and negative (significantly less than 0) slopes of mood over time. 13 more individuals at risk of depression have a positive slope than the 35 expected based on the rates in individuals not at risk of depression, χ2(1,N=886)=14.57, p<0.001 (2-sided Pearson’s chi-squared statistic with no correction for multiple comparisons).

Extended Data Fig. 7 Mood drift’s relation to other computational model parameters.

Time sensitivity parameter βT vs. other parameters in the confirmatory mobile app cohort. Each dot is a participant (n=21,896). Each line is a linear best fit, and patches show the 95% confidence interval of this fit. rs denotes Spearman correlation coefficient. P values shown are 2-sided with no correction for multiple comparisons.

Extended Data Fig. 8 Initial mood parameter’s relation to life happiness.

Initial mood parameter vs. life happiness rating in the online cohort (left) and the confirmatory mobile app cohort (right). Life happiness ratings were always multiples of 0.1; small positive random values were added during plotting to reduce overlap between data points. Each dot is a participant (left: n=886, right: n=21,896). rs denotes Spearman correlation coefficient. P values shown are 2-sided with no correction for multiple comparisons.

Extended Data Table 1 List and description of cohorts collected
Full size table
Extended Data Table 2 Linear mixed effects (LME) model results
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Jangraw, D.C., Keren, H., Sun, H. et al. A highly replicable decline in mood during rest and simple tasks. Nat Hum Behav 7, 596–610 (2023). https://doi.org/10.1038/s41562-023-01519-7

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