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Updated: May 27, 2026

Tea Aroma Analysis Based on Solvent-Assisted Flavor Evaporation Enrichment
Published on: May 26, 2023
Relationships between tea and other beverage consumption to work performance and mood
Janet Bryan1, Michelle Tuckey, Suzanne J L Einöther
1School of Psychology, Social Work and Social Policy, Magill Campus, University of South Australia, GPO Box 2471, Adelaide, South Australia 5001, Australia. janet.bryan@unisa.edu.au
Drinking tea may boost work performance and reduce tiredness, especially without milk or sugar. Non-caffeinated drinks aid relaxation and recovery, but caffeinated beverages can negatively impact mindfulness.
Area of Science:
- Occupational Health Psychology
- Behavioral Science
Background:
- Workplace settings often involve high stress and cognitive demands.
- Beverage consumption is a common behavior in work environments.
- Understanding the impact of beverages on work performance and mood is crucial.
Purpose of the Study:
- To investigate the relationship between tea, coffee, and other beverage consumption.
- To examine associations with work performance and mood in demanding workplaces.
- To explore how beverage intake influences psychological states during work.
Main Methods:
- Naturalistic, cross-sectional study design.
- 95 professional and academic staff participated.
- Multilevel modeling analyzed daily self-reports of beverage intake, work performance, mood, strain, and recovery over 10 working days.
Main Results:
- Tea consumption correlated with improved perceived work performance and reduced tiredness, particularly without milk/sugar.
- Non-caffeinated beverages were linked to enhanced relaxation and work recovery.
- Caffeinated beverages amplified negative effects of evening recovery and morning mood on daytime mindfulness.
Conclusions:
- Beverage intake, specifically tea and non-caffeinated options, can influence work-related psychological states.
- Strategic beverage choices may play a role in optimizing work performance and well-being.
- Further research could explore causal links and personalized recommendations.
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