The effect of time and day of the week on burnout-related experiences: an experience sampling study

ABSTRACT

Burnout has traditionally been characterized as a relatively stable construct, leaving the question of whether and how burnout-related experiences fluctuate within and between days unaddressed. In the current study, we assess the effect of time of day (expressed as external time, internal time, or time awake) and day of the week on momentary experiences of the two core components of burnout, i.e., exhaustion and disengagement. We employed a 7-day experience sampling method in the field among 65 working employees, with seven momentary assessments per day. Results indicated that a large proportion of variance in burnout-related experiences occurred between moments (46%-68%), with only minor variance occurring between days within participants (2%-6%). Notably, experiences related to the disengagement component showed no clear pattern over the day, while exhaustion remained relatively stable throughout the morning and then increased moderately towards the end of the day. We conclude that burnout-related experiences typically fluctuate between moments, supporting the view of burnout as a dynamic rather than a purely static state. Furthermore, much of the variance in momentary burnout-related experiences remains to be explained in absence of a structural temporal pattern.

KEYWORDS:

• Burnout
• disengagement
• experience sampling
• exhaustion
• momentary states

Disclosure statement

No potential conflict of interest was reported by the author(s).

Disclaimer

The views expressed in the submitted article are the authors own and not an official position of the institution they work for.

Notes

1. Items were not centred at the person-mean. Momentary correlations are based on complete dataset.

2. Given the convergence and power issues when testing random slopes for the non-linear patterns, we did not explore moderations for the higher-order terms for time.

3. It should be noted that Arend & Schäfer’s (2019) guidelines were developed for two-level models, whereas this study employed three-level models, making this is a rough estimate of the effect sizes this study was able to detect.