Stronger together? personality, feedforward, and social play in a VR resistance training exergame

Abstract Resistance training improves muscular strength, bone health, functional capacity, and cardiometabolic health, yet many adults do not meet muscle-strengthening guidelines. Behavioural research further suggests that resistance training has participation patterns and barriers that differ from aerobic activity, so design insights from aerobic exergames cannot be assumed to transfer directly. We developed a VR cable-machine exergame that used feedforward, implemented as replayed visual traces of prior performance, to create social “ghosts.” In a 2 (Mode: cooperation and competition) $$\times $$ 3 (Feedforward Target: self, friend, and stranger) within-subjects study, 30 participants completed all six conditions while we recorded movement quality, repetitions, and self-reported experience. Competition was associated with higher movement quality and more completed repetitions than cooperation. Feedforward Target did not show reliable effects on objective movement outcomes, but friend targets produced higher Interest/Enjoyment than stranger targets. Exploratory analyses suggested selective associations involving Big Five traits, friendship quality, competence, pressure, and flow-related outcomes. These findings distinguish Mode and Feedforward Target as separable design dimensions in VR resistance exergames and suggest cautious opportunities for socially adaptive feedforward designs.

Authors

Institutions

Publication Details

Journal
Virtual Reality
Published
2026-10-13
DOI
https://doi.org/10.1007/s10055-026-01495-z
Primary Topic
Virtual Reality Applications and Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stronger together? personality, feedforward, and social play in a VR resistance training exergame

Burkhard Claus Wünsche, Zixuan Wang, Mark Billinghurst
Virtual Reality
Virtual Reality Applications and Impacts
article

Stronger together? personality, feedforward, and social play in a VR resistance training exergame

Burkhard Claus Wünsche, Zixuan Wang, Mark Billinghurst
article en

Abstract

Abstract Resistance training improves muscular strength, bone health, functional capacity, and cardiometabolic health, yet many adults do not meet muscle-strengthening guidelines. Behavioural research further suggests that resistance training has participation patterns and barriers that differ from aerobic activity, so design insights from aerobic exergames cannot be assumed to transfer directly. We developed a VR cable-machine exergame that used feedforward, implemented as replayed visual traces of prior performance, to create social “ghosts.” In a 2 (Mode: cooperation and competition) $$\times $$ 3 (Feedforward Target: self, friend, and stranger) within-subjects study, 30 participants completed all six conditions while we recorded movement quality, repetitions, and self-reported experience. Competition was associated with higher movement quality and more completed repetitions than cooperation. Feedforward Target did not show reliable effects on objective movement outcomes, but friend targets produced higher Interest/Enjoyment than stranger targets. Exploratory analyses suggested selective associations involving Big Five traits, friendship quality, competence, pressure, and flow-related outcomes. These findings distinguish Mode and Feedforward Target as separable design dimensions in VR resistance exergames and suggest cautious opportunities for socially adaptive feedforward designs.

Virtual Reality
University of Auckland (NZ)
Openalex Percentile: Top 8%
Virtual Reality Applications and Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.