Linking Stable and Dynamic Features of Positive Affect to Sleep



Poor sleep contributes to adult morbidity and mortality.


The study examined the extent to which trait positive affect (PA) and PA reactivity, defined as the magnitude of change in daily PA in response to daily events, were linked to sleep outcomes.


Analyses are based on data from 100 respondents selected from the National Survey of Midlife in the United States.


Multilevel analyses indicated that higher levels of trait PA were associated with greater morning rest and better overall sleep quality. In contrast, PA reactivity was associated with diminished sleep efficiency. Finally, interactions between PA reactivity and trait PA emerged on all three sleep measures, such that higher event-related change in daily positive affect was associated with impaired sleep, especially among individuals high in trait PA.


Results suggest that high trait PA, when coupled with high PA reactivity, may contribute to poor sleep.

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  1. 1.

    Random effects for the Level 2 intercept were significant in the full model for sleep efficiency [u 0 = 65.39, χ 2(74) = 1263.19, p < 0.001], morning rest [u 0 = 0.26, χ 2(74) = 260.97, p < 0.001], and overall quality [u 0 = 0.24, χ 2(74) = 231.18, p < 0.001], respectively.

  2. 2.

    To probe the effect of nonevents, we set the slopes of participants who reported no negative events or positive events over the 8-day study period to zero and re-ran all analyses. The pattern of results remained the same. It is also possible that NA reactivity might be driving our results [13]. To explore this possibility, we re-ran all analyses, controlling for NA reactivity effects. NA reactivity was not predictive of sleep efficiency (γ 021 = 0.48, p > 0.10), morning rest (γ 021 = 0.31, p = 0.06), or overall sleep (γ 021 = 0.14, p > 0.10) and, including NA reactivity did not alter the pattern of trait PA and PA reactivity results. From this, we conclude that the main and interactive effects of trait PA and PA reactivity on sleep are not attributable to NA reactivity.


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Author Note

We extend thanks to Gary Evans, Anthony Burrow, and Thomas Fuller-Rowell for their helpful comments on previous versions of this article.

Anthony D. Ong, Cornell University, Department of Human Development, Cornell University. Deinera Exner-Cortens, Department of Human Development and the Bronfenbrenner Center for Translational Research, Cornell University. Catherine Riffin, Department of Human Development and the Bronfenbrenner Center for Translational Research, Cornell University. Andrew Steptoe, Department of Epidemiology and Public Health, University College London. Alex Zautra, Department of Psychology, Arizona State University. David M. Almeida, Department of Human Development and Family Studies, Penn State University.

This research was supported by grants from the National Institute on Aging (P01 AG020166, R01 AG019239) to conduct a longitudinal follow-up of the MIDUS (Midlife in the U.S.) investigation and a National Research Service Award from the National Institute of Mental Health (T32 MH18931). Support was also given to the second author through a Doctoral Foreign Study Award from the Canadian Institutes of Health Research. The original study was supported by the John D. and Catherine T. MacArthur Foundation Research Network on Successful Midlife Development. Correspondence concerning this article should be addressed to: Anthony D. Ong, Department of Human Development, G77 Martha Van Rensselaer Hall, Cornell University, Ithaca, NY 14853–4401.

Conflict of Interest Statement

The authors have no conflict of interest to disclose.

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Correspondence to Anthony D. Ong PhD.



Full Level-1 and Level-2 Models

Level 1: Sleep ij  = β 0j  + β 1j *(Weekend) ij  + β 2j *(Exercise) ij  + β 3j *(Caffeine) ij  + β 4j *(Sleep Medication) ij  + β 5j *(Sleep Time) ij  + r ij

Level 2: β 0j  = γ 00 + γ 01*(Gender) j  + γ 02*(Age) j  + γ 03*(Income) j  + γ 04*(Self-rated Health) j  + γ 05*(Mean Daily PA) j  + γ 06*(Mean Daily NA) j  + γ 07*(Standard Deviation in Daily PA) j  + γ 08*(Standard Deviation in Daily NA) j  + γ 09*(Trait NA) j  + γ 010*(Trait PA) j  + γ 011*(Reactivity to Negative Events) j  + γ 012*(Reactivity to Positive Events) j  + γ 013*(Net Reactivity) j  + γ 014*(Trait PA2) j  + γ 015*(Reactivity to Negative Events2) j  + γ 016*(Reactivity to Positive Events2) j  + γ 017*(Net Reactivity2) j  + γ 018*(Trait PA * Reactivity to Negative Events) j  + γ 019*(Trait PA * Reactivity to Positive Events) j  + γ 020*(Trait PA * Net Reactivity) j  + u 0j

β 1j  = γ 10

β 2j  = γ 20

β 3j  = γ 30

β 4j  = γ 40

β 5j  = γ 50

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Ong, A.D., Exner-Cortens, D., Riffin, C. et al. Linking Stable and Dynamic Features of Positive Affect to Sleep. ann. behav. med. 46, 52–61 (2013).

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  • Trait positive affect
  • Positive affect reactivity
  • Sleep