Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A highly replicable decline in mood during rest and simple tasks

Nature Human Behaviour (2023)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

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.

References

  1. Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. Statistical Parametric Mapping: The Analysis of Functional Brain Images (Elsevier Science, 2011).

  2. Keren, H. et al. The temporal representation of experience in subjective mood. eLife 10, 1–24 (2021).

    Article  Google Scholar 

  3. Rutledge, R. B., Skandali, N., Dayan, P. & Dolan, R. J. A computational and neural model of momentary subjective well-being. Proc. Natl Acad. Sci. USA 111, 12252–12257 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Frijda, N., Mesquita, B., Sonnemans, J. & Goozen, S. The duration of affective phenomena or emotions, sentiments and passions. Int. Rev. Stud. Emotion 1, 187–225 (1991).

    Google Scholar 

  5. Scherer, K. R. & Wallbott, H. G. Evidence for universality and cultural variation of differential emotion response patterning. J. Pers. Soc. Psychol. 66, 310–328 (1994).

    Article  CAS  PubMed  Google Scholar 

  6. Davidson, R. J. Affective style and affective disorders: perspectives from affective neuroscience. Cogn. Emot. 12, 307–330 (1998).

    Article  Google Scholar 

  7. Davidson, R. J. Comment: affective chronometry has come of age. Emot. Rev. 7, 368–370 (2015).

    Article  Google Scholar 

  8. Gilboa, E. & Revelle, W. Personality and the Structure of Affective Responses (Psychology Press, 1994).

  9. Hemenover, S. H. Individual differences in rate of affect change: studies in affective chronometry. J. Pers. Soc. Psychol. 85, 121 (2003).

    Article  PubMed  Google Scholar 

  10. Kring, A. M. & Barch, D. M. The motivation and pleasure dimension of negative symptoms: neural substrates and behavioral outputs. Eur. Neuropsychopharmacol. 24, 725–736 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sonuga Barke, E. J. S., Taylor, E., Sembi, S. & Smith, J. Hyperactivity and delay aversion-I. The effect of delay on choice. J. Child Psychol. Psychiatry 33, 387–398 (1992).

    Article  CAS  PubMed  Google Scholar 

  12. Solanto, M. V. et al. The ecological validity of delay aversion and response inhibition as measures of impulsivity in AD/HD: a supplement to the NIMH multimodal treatment study of AD/HD. J. Abnorm. Child Psychol. 29, 215–228 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Sonuga Barke, E. J. S., Cortese, S., Fairchild, G. & Stringaris, A. Annual research review: transdiagnostic neuroscience of child and adolescent mental disorders-differentiating decision making in attention deficit/hyperactivity disorder, conduct disorder, depression, and anxiety. J. Child Psychol. Psychiatry 57, 321–349 (2016).

    Article  PubMed  Google Scholar 

  14. McRae, T. W. Opportunity and incremental cost: an attempt to define in systems terms. Account. Rev. 45, 315–321 (1970).

    Google Scholar 

  15. Hoskin, R. E. Opportunity cost and behavior. J. Account. Res. 21, 78–95 (1983).

    Article  Google Scholar 

  16. Palmer, S. & Raftery, J. Opportunity cost. BMJ 318, 1551–1552 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cohen, J. D., McClure, S. M. & Yu, A. J. Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philos. Trans. R. Soc. B 362, 933–942 (2007).

    Article  Google Scholar 

  18. Constantino, S. M. & Daw, N. D. Learning the opportunity cost of time in a patch-foraging task. Cogn. Affect. Behav. Neurosci. 15, 837–853 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Addicott, M. A., Pearson, J. M., Sweitzer, M. M., Barack, D. L. & Platt, M. L. A primer on foraging and the explore/exploit trade-off for psychiatry research. Neuropsychopharmacology 42, 1931–1939 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Geana, A., Wilson, R., Daw, N. D. & Cohen, J. D. Boredom, Information-Seeking and Exploration. Proc. 38th Annual Conference of the Cognitive Science Society (2016).

  21. Agrawal, M., Mattar, M. G., Cohen, J. D. & Daw, N. D. The temporal dynamics of opportunity costs: a normative account of cognitive fatigue and boredom. Psychol. Rev. 129, 564–585 (2022).

    Article  PubMed  Google Scholar 

  22. Eastwood, J. D., Frischen, A., Fenske, M. J. & Smilek, D. The unengaged mind: defining boredom in terms of attention. Perspect. Psychol. Sci. 7, 482–495 (2012).

    Article  PubMed  Google Scholar 

  23. Robison, M. K., Miller, A. L. & Unsworth, N. A multi-faceted approach to understanding individual differences in mind-wandering. Cognition 198, 104078 (2020).

    Article  PubMed  Google Scholar 

  24. Killingsworth, M. A. & Gilbert, D. T. A wandering mind is an unhappy mind. Science 330, 932 (2010).

    Article  CAS  PubMed  Google Scholar 

  25. Fox, K. C., Thompson, E., Andrews-Hanna, J. R. & Christoff, K. Is thinking really aversive? A commentary on Wilson et al.’s “Just think: the challenges of the disengaged mind". Front. Psychol. 5(DEC), 10–13 (2014).

    Google Scholar 

  26. Fox, K. C. et al. Affective neuroscience of self-generated thought. Ann. N. Y. Acad. Sci. 1426, 25–51 (2018).

    Article  Google Scholar 

  27. van Hooff, M. L. & van Hooft, E. A. Boredom at work: proximal and distal consequences of affective work-related boredom. J. Occup. Health Psychol. 19, 348–359 (2014).

    Article  PubMed  Google Scholar 

  28. Miner, A. G. & Glomb, T. M. State mood, task performance, and behavior at work: a within-persons approach. Organ. Behav. Hum. Dec. Process. 112, 43–57 (2010).

    Article  Google Scholar 

  29. Camille, N. et al. The involvement of the orbitofrontal cortex in the experience of regret. Science 304, 1167–1170 (2004).

    Article  CAS  PubMed  Google Scholar 

  30. Eldar, E., Rutledge, R. B., Dolan, R. J. & Niv, Y. Mood as representation of momentum. Trends Cogn. Sci. 20, 15–24 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Vinckier, F., Rigoux, L., Oudiette, D. & Pessiglione, M. Neuro-computational account of how mood fluctuations arise and affect decision making. Nat. Commun. 9, 1708 (2018).

  32. Liuzzi, L. et al. Magnetoencephalographic correlates of mood and reward dynamics in human adolescents. Cerebr. Cortex 32, 3318–3330 (2022).

    Article  Google Scholar 

  33. Bedder, R. L., Vaghi, M. M., Dolan, R. J. & Rutledge, R. B. Risk taking for potential losses but not gains increases with time of day. PsyArXiv https://doi.org/10.31234/osf.io/3qdnx (2020).

  34. Grilli, L. & Rampichini, C. Specification of random effects in multilevel models: a review. Qual. Quant. 49, 967–976 (2015).

    Article  Google Scholar 

  35. Schielzeth, H. et al. Robustness of linear mixed effects models to violations of distributional assumptions. Methods Ecol. Evol. 11, 1141–1152 (2020).

    Article  Google Scholar 

  36. Feingold, A. Confidence interval estimation for standardized effect sizes in multilevel and latent growth modeling. J. Consult. Clin. Psychol. 83, 157 (2015).

    Article  PubMed  Google Scholar 

  37. Pizzagalli, D. A., Iosifescu, D., Hallett, L. A., Ratner, K. G. & Fava, M. Reduced hedonic capacity in major depressive disorder: evidence from a probabilistic reward task. J. Psychiatr. Res. 43, 76–87 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Halahakoon, D. C. et al. Reward-processing behavior in depressed participants relative to healthy volunteers: a systematic review and meta-analysis. JAMA Psychiatr. 77, 1286–1295 (2020).

  39. Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Routledge, 2013).

  40. Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D. & Mermelstein, R. J. A practical guide to calculating Cohen’s ff 22, a measure of local effect size, from PROC MIXED. Front. Psychol. 3, 111 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Isen, A. M. & Patrick, R. The effect of positive feelings on risk taking: when the chips are down. Organ. Behav. Hum. Perform. 31, 194–202 (1983).

    Article  Google Scholar 

  42. Arkes, H. R., Herren, L. T. & Isen, A. M. The role of potential loss in the influence of affect on risk-taking behavior. Organ. Behav. Hum. Dec. Process. 42, 181–193 (1988).

    Article  Google Scholar 

  43. Schulreich, S. et al. Music-evoked incidental happiness modulates probability weighting during risky lottery choices. Front. Psychol. 4, 981 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hunter, J. A., Dyer, K. J., Cribbie, R. A. & Eastwood, J. D. Exploring the utility of the Multidimensional State Boredom Scale. Eur. J. Psychol. Assess. 32, 241–250 (2016).

    Article  Google Scholar 

  45. Struk, A. A., Carriere, J. S. A., Cheyne, J. A. & Danckert, J. A short boredom proneness scale: development and psychometric properties. Assessment 24, 346–359 (2017).

    Article  PubMed  Google Scholar 

  46. Seli, P. et al. Mind-wandering as a natural kind: a family-resemblances view. Trends Cogn. Sci. 22, 479–490 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Christoff, K. et al. Mind-wandering as a scientific concept: cutting through the definitional haze. Trends Cogn. Sci. 22, 957–959 (2018).

    Article  PubMed  Google Scholar 

  48. Seli, P. et al. The family-resemblances framework for mind-wandering remains well clad. Trends Cogn. Sci. 22, 959–961 (2018).

    Article  PubMed  Google Scholar 

  49. Turnbull, A. et al. The ebb and flow of attention: between-subject variation in intrinsic connectivity and cognition associated with the dynamics of ongoing experience. NeuroImage 185, 286–299 (2019).

    Article  PubMed  Google Scholar 

  50. Mrazek, M. D., Phillips, D. T., Franklin, M. S., Broadway, J. M. & Schooler, J. W. Young and restless: validation of the Mind-Wandering Questionnaire (MWQ) reveals disruptive impact of mind-wandering for youth. Front. Psychol. 4, 560 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Nunokawa, J. The importance of being bored: the dividends of ennui in "The Picture of Dorian Gray". Studies in the Novel 28, 357–371 (1996).

    Google Scholar 

  52. Shattuck, R. Proust’s Way: A Field Guide to In Search of Lost Time (WW Norton & Company, 2001).

  53. Proust, M. Swann’s Way: In Search of Lost Time Vol. 1 (Yale Univ. Press, 2013).

  54. Ciocan, C. Heidegger and the problem of boredom. J. Br. Soc. Phenomenol. 41, 64–77 (2010).

    Article  Google Scholar 

  55. Ratcliffe, M. in The Cambridge Companion to Heidegger’s Being and Time (ed. Wrathall, M. A.) 157–176 (Cambridge University Press, 2013).

  56. Heidegger, M. The Fundamental Concepts of Metaphysics: World, Finitude, Solitude (Indiana Univ. Press, 1995).

  57. Schopenhaur, A. in Parerga und Paralipomena, Vol. 1 217 (Virtual Library, 1851).

  58. Kierkegaard, S. Either/Or: A Fragment of Life (Penguin Classics, 1992).

  59. Elpidorou, A. The bright side of boredom. Front. Psychol. 5, 1245 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Thompson, P. M. et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Adhikari, B. M. et al. A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol. Brain Imaging Behav. 13, 1453–1467 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Birn, R. M. et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83, 550–558 (2013).

    Article  PubMed  Google Scholar 

  63. Noble, S. et al. Influences on the test–retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cerebr. Cortex 27, 5415–5429 (2017).

    Article  Google Scholar 

  64. Noble, S., Scheinost, D. & Constable, R. T. A decade of test–retest reliability of functional connectivity: a systematic review and meta-analysis. NeuroImage 203, 116157 (2019).

    Article  PubMed  Google Scholar 

  65. Jaspers, K. in Die abnorme Seele in Gesellschaft und Geschichte (Soziologie und Historie der Psychosen und Psychopathien) 594–623 (Springer, 1973).

  66. Schneider, K. Klinische Psychopathologie 14 edn (Georg Thieme Verlag, 1992).

  67. Berrios, G. E. Phenomenology, psychopathology and Jaspers: a conceptual history. Hist. Psychiatry 3, 303–327 (1992).

    Article  CAS  PubMed  Google Scholar 

  68. Westgate, E. C. & Wilson, T. D. Boring thoughts and bored minds: the MAC model of boredom and cognitive engagement. Psychol. Rev. 125, 689 (2018).

    Article  PubMed  Google Scholar 

  69. Barrett, L. F. Feelings or words? Understanding the content in self-report ratings of experienced emotion. J. Pers. Soc. Psychol. 87, 266–281 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Westgate, E. C. & Steidle, B. Lost by definition: why boredom matters for psychology and society. Soc. Pers. Psychol. Compass 14, e12562 (2020).

    Article  Google Scholar 

  71. Frijda, N. H. in The Oxford Companion to Emotion and the Affective Sciences (eds Sander, D. & Scherer, K. R.) 258–259 (Oxford Univ. Press, 2009).

  72. Ekkekakis, P. The Measurement of Affect, Mood, and Emotion: A Guide for Health-Behavioral Research (Cambridge University Press, 2013).

  73. Rottenberg, J. Mood and emotion in major depression. Curr. Dir. Psychol. Sci. 14, 167–170 (2005).

  74. Nowlis, V. & Nowlis, H. H. The description and analysis of mood. Ann. N. Y. Acad. Sci. 65, 345–355 (1956).

    Article  CAS  PubMed  Google Scholar 

  75. Ekman, P. An argument for basic emotions. Cogn. Emot. 6, 169–200 (1992).

    Article  Google Scholar 

  76. Watson, D. Mood and Temperament (Guilford Press, 2000).

  77. Diener, E. Subjective well-being: the science of happiness and a proposal for a national index. Am. Psychol. 55, 34 (2000).

    Article  CAS  PubMed  Google Scholar 

  78. Robinson, M. D. & Clore, G. L. Belief and feeling: evidence for an accessibility model of emotional self-report. Psychol. Bull. 128, 934–960 (2002).

    Article  PubMed  Google Scholar 

  79. Costello, E. J. & Angold, A. Scales to assess child and adolescent depression: checklists, screens, and nets. J. Am. Acad. Child Adolesc. Psychiatry 27, 726–737 (1988).

    Article  CAS  PubMed  Google Scholar 

  80. Pavot, W. & Diener, E. The affective and cognitive context of self-reported measures of subjective well-being. Soc. Indic. Res. 28, 1–20 (1993).

    Article  Google Scholar 

  81. Ebner-Priemer, U. W. & Trull, T. J. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol. Assess. 21, 463 (2009).

  82. Siegel, E. H. et al. Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories. Psychol. Bull. 144, 343 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Gendron, M., Roberson, D. & Barrett, L. F. Cultural variation in emotion perception is real: a response to Sauter, Eisner, Ekman, and Scott (2015). Psychol. Sci. 26, 357–359 (2015).

    Article  PubMed  Google Scholar 

  84. Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M. & Pollak, S. D. Emotional expressions reconsidered: challenges to inferring emotion from human facial movements. Psychol. Sci. Public Interest 20, 1–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E. & Barrett, L. F. The brain basis of emotion: a meta-analytic review. Behav. Brain Sci. 35, 121–143 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Paolacci, G., Chandler, J. & Ipeirotis, P. G. Running experiments on Amazon mechanical turk. Judgm. Dec. Making 5, 411–419 (2010).

    Article  Google Scholar 

  87. Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).

  88. Ho, N. S. P. et al. Facing up to why the wandering mind: patterns of off-task laboratory thought are associated with stronger neural recruitment of right fusiform cortex while processing facial stimuli. NeuroImage 214, 116765 (2020).

    Article  PubMed  Google Scholar 

  89. Adler, N. E., Epel, E. S., Castellazzo, G. & Ickovics, J. R. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol. 19, 586–592 (2000).

    Article  CAS  PubMed  Google Scholar 

  90. Singh-Manoux, A., Marmot, M. G. & Adler, N. E. Does subjective social status predict health and change in health status better than objective status? Psychosom. Med. 67, 855–861 (2005).

    Article  PubMed  Google Scholar 

  91. Radloff, L. S. The CES-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas. 1, 385–401 (1977).

    Article  Google Scholar 

  92. Snaith, R. P. et al. A scale for the assessment of hedonic tone. The Snaith–Hamilton Pleasure Scale. Br. J. Psychiatry 167, 99–103 (1995).

    Article  CAS  PubMed  Google Scholar 

  93. Angold, A., Costello, E. J., Messer, S. C. & Pickles, A. Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. Int. J. Methods Psychiatr. Res. 5, 237–249 (1995).

    Google Scholar 

  94. Birmaher, B. et al. Psychometric properties of the screen for child anxiety related emotional disorders (SCARED): a replication study. J. Am. Acad. Child Adolesc. Psychiatry 38, 1230–1236 (1999).

    Article  CAS  PubMed  Google Scholar 

  95. Jolly, E. Pymer4: connecting R and Python for linear mixed modeling. J. Open Source Softw. 3, 862 (2018).

    Article  Google Scholar 

  96. Snijders, T. A. B. & Bosker, R. J. Modeled variance in two-level models. Sociol. Methods Res. 22, 342–363 (1994).

    Article  Google Scholar 

  97. Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed effects models. Methods Ecol. Evol. 4, 133–142 (2013).

    Article  Google Scholar 

  98. Barton, K. MuMIn: multi-model inference. R Project http://r-forge.r-project.org/projects/mumin/ (2009) .

  99. Paszke, A. et al. Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32, 8026–8037 (2019).

    Google Scholar 

  100. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. arXiv https://doi.org/10.48550/arXiv.1412.6980 (2014).

  101. Rutledge, R. B. Risky decision and happiness task: The Great Brain Experiment smartphone app. Dryad https://doi.org/10.5061/dryad.prr4xgxkk (2021).

Download references

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

Authors
  1. David C. Jangraw
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanna Keren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haorui Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rachel L. Bedder
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robb B. Rutledge
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Francisco Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Adam G. Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daniel S. Pine
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Charles Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dylan M. Nielson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Argyris Stringaris
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to David C. Jangraw.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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
Full size table

Supplementary information

Supplementary Information

Supplementary notes, figures and tables.

Reporting Summary

Peer Review File

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jangraw, D.C., Keren, H., Sun, H. et al. A highly replicable decline in mood during rest and simple tasks. Nat Hum Behav (2023). https://doi.org/10.1038/s41562-023-01519-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41562-023-01519-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing