Cited by 98Virtual Reality Applications and ImpactsCognitive Functions and MemoryCognitive Abilities and Testing

Exergaming executive functions: an immersive virtual reality-based cognitive training for adults aged 50 and older

Huang, Kuo-Ting·2020·Cyberpsychology Behavior and Social Networking

Abstract

Prior research suggests that both exergaming and virtual reality (VR)-based training programs could improve executive functions in older adults. However, few studies investigated whether combining exergaming with VR would be more effective. This study seeks to (a) investigate whether playing exergames in an immersive virtual environment (IVE) would yield differential outcomes in selected executive functions, including inhibition, task switching, and working, and (b) examine the role of feeling of presence as a potential mediator between immersive exergaming and cognitive improvement in specific domains. Thirty-three participants over 50 years of age (mean age = 62) participated in a 4-week training program and were randomly assigned into an IVE and non-IVE to play an exergame (Fruit Ninja) for eight sessions within 4 weeks. The results revealed a significant effect of the IVE on the Stroop Test and Trail Making Test after the 4-week training. Furthermore, the impacts of the IVE exergaming on these two tasks were mediated by the feeling of presence. These findings suggested that the immersive experience of exergaming would elicit the feeling of presence, which later contributes to improved cognitive performances in inhibition and task switching. For the theoretical implications, this study extends previous research by showing that (a) feeling of presence could contribute to older adults' cognitive improvement, and (b) the impacts of immersive exergame training on executive functions vary across individual domains. Additionally, this study provides practical implications such that the design of exergames could emphasize the game features requiring mental simulation, which can serve as a novel strategy for preventing cognitive decline in midlife and old age.

Integrity signals

Registry

Crossref · 10.1089/cyber.2019.0269

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Huang, K.-T.
Affiliations
Ball State University (US)
Corresponding author
Huang, Kuo-Ting
Venue
Cyberpsychology Behavior and Social Networking
Published
2020-03-01
Volume
23
Issue
3
Pages
143-149
ISSN
  • 2152-2715 (ISSN-L)
  • 2152-2723
OpenAlex
W2993967054
Cited by
98
References
38
Citations by year
2026: 13 · 2025: 18 · 2024: 15 · 2023: 17 · 2022: 18 · 2021: 9 · 2020: 8
FWCI
8.27
Type
journal article
Language
en
Open access
closed
Locations
  • Cyberpsychology Behavior and Social Networking (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
Keywords
exergames, virtual reality, executive function, older adults, cognitive training, immersion, presence, inhibition, task switching, working memory, cognitive decline prevention, Stroop task, Trail Making Test
MeSH terms
Virtual Reality, Aged, Female, Humans, Male, Middle Aged, Random Allocation, Video Games, Executive Function

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review