Does Standing Up Enhance Performance on the Stroop Task in Healthy Young Adults? A Systematic Review and Meta-Analysis

Int J Environ Res Public Health. 2023 Jan 28;20(3):2319. doi: 10.3390/ijerph20032319.

Abstract

Understanding the changes in cognitive processing that accompany changes in posture can expand our understanding of embodied cognition and open new avenues for applications in (neuro)ergonomics. Recent studies have challenged the question of whether standing up alters cognitive performance. An electronic database search for randomized controlled trials was performed using Academic Search Complete, CINAHL Ultimate, MEDLINE, PubMed, and Web of Science following PRISMA guidelines, PICOS framework, and standard quality assessment criteria (SQAC). We pooled data from a total of 603 healthy young adults for incongruent and 578 for congruent stimuli and Stroop effect (mean age = 24 years). Using random-effects results, no difference was found between sitting and standing for the Stroop effect (Hedges' g = 0.13, 95% CI = -0.04 to 0.29, p = 0.134), even when comparing congruent (Hedges' g = 0.10; 95% CI: -0.132 to 0.339; Z = 0.86; p = 0.389) and incongruent (Hedges' g = 0.18; 95% CI: -0.072 to 0.422; Z = 1.39; p = 0.164) stimuli separately. Importantly, these results imply that changing from a seated to a standing posture in healthy young adults is unlikely to have detrimental effects on selective attention and cognitive control. To gain a full understanding of this phenomenon, further research should examine this effect in a population of healthy older adults, as well as in a population with pathology.

Keywords: Stroop task; cognitive-motor interference; dual task; healthy young adults; posture; sit-to-stand workstations.

Publication types

  • Meta-Analysis
  • Systematic Review
  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Cognition
  • Ergonomics*
  • Humans
  • Posture*
  • Sitting Position
  • Stroop Test
  • Young Adult

Grants and funding

Funding for this review was provided through the European Union’s Horizon 2020 research and innovation program under grant agreement No. 952401 (TwinBrain—TWINning the BRAIN with machine learning for neuro-muscular efficiency). The authors also acknowledge financial support from the Slovenian Research Agency (research core funding No. P5-0381).