How Game Balancing Affects Play: Player Adaptation in an Exergame for Children with Cerebral Palsy
Susan Hwang, Adrian L. Jessup Schneider, Daniel Clarke, Alexander MacIntosh, Lauren Switzer, Darcy Fehlings, and T.C. Nicholas Graham
Player balancing helps people with different levels of physical ability and experience play together by providing customized assistance. Player balancing is particularly important in exergames, where differences in physical ability can have a large impact on game outcomes, and in making games accessible to people with motor disabilities. To date, there has been little research into how balancing affects people’s gameplay behaviour over time. This paper reports on a six-day study with eight youths with cerebral palsy. Two games incorporated algorithms to balance differences in pedaling ability and aiming ability. Balancing positively impacted motivation versus non-balanced conditions. Even in “blowout” games where one player won by a large margin, perceived fun and fairness were higher for both players when a player balancing algorithm was present. These results held up over six days, demonstrating that the results of balancing continued even after players had the opportunity to understand and adapt to the balancing algorithms.
Game balancing; exergame; active video game; player balancing; video game design.
Balancing for Gross Motor Ability in Exergaming Between Youth with Cerebral Palsy at Gross Motor Function Classification System Levels II and III
Alexander MacIntosh, Lauren Switzer, Hamilton Hernandez, Susan Hwang, Adrian L. Jessup Schneider, Daniel Moran, T.C. Nicholas Graham, and Darcy L. Fehlings
Objective: To test how three custom-built balancing algorithms minimize differences in game success, time
above 40% heart rate reserve (HRR), and enjoyment between youth with cerebral palsy (CP) who have different
gross motor function capabilities. Youth at Gross Motor Function Classification System (GMFCS) level II
(unassisted walking) and level III (mobility aids needed for walking) competed in a cycling-based exercise
video game that tested three balancing algorithms.
Materials and Methods: Three algorithms: a control (generic-balancing [GB]), a constant non-person specific
(One-Speed-For-All [OSFA]), and a person-specific (Target-Cadence [TC]) algorithms were built. In this
prospective repeated measures intervention trial with randomized and blinded algorithm assignment, 10 youth
with CP aged 10–16 years (X – standard deviation = 12.4 – 1.8 years; GMFCS level II n = 4, III n = 6) played six
exergaming sessions using each of the three algorithms. Outcomes included game success as measured by a
normalized game score, time above 40% HRR, and enjoyment.
Results: The TC algorithm balanced game success between GMFCS levels similarly to GB (P = 0.11) and
OSFA (P = 0.41). TC showed poorer balancing in time above 40% HRR compared to GB (P = 0.02) and OSFA
(P = 0.02). Enjoyment ratings were high (6.4 – 0.7/7) and consistent between all algorithms (TC vs. GB:
P = 0.80 and TC vs. OSFA: P = 0.19).
Conclusion: TC shows promise in balancing game success and enjoyment but improvements are needed to
balance between GMFCS levels for cardiovascular exercise.
Exergames; fitness; game mechanisms; clinical training; game therapy
Ability-Based Balancing Using the Gross Motor Function Measure in Exergaming for Youth with Cerebral Palsy
Alexander MacIntosh, Lauren Switzer, Susan Hwang, Adrian L. Jessup Schneider, Daniel Clarke, T.C. Nicholas Graham, and Darcy L. Fehlings
Objective: To test if the gross motor function measure (GMFM) could be used to improve game balancing
allowing youth with cerebral palsy (CP) with different physical abilities to play a cycling-based exercise
videogame together. Our secondary objective determined if exergaming with the GMFM Ability-Based algorithm
Materials and Methods: Eight youth with CP, 8–14 years of age, GMFM scores between 25.2% and 87.4%
(evenly distributed between Gross Motor Function Classification System levels II and III), competed against
each other in head-to-head races, totaling 28 unique race dyads. Dyads raced three times, each with a different
method of minimizing the distance between participants (three balancing algorithms). This was a prospective
repeated measures intervention trial with randomized and blinded algorithm assignment. The GMFM Ability-
Based algorithm was developed using a least squares linear regression between the players’ GMFM score and
cycling cadence. Our primary outcome was dyad spread or average distance between players. The GMFM
Ability-based algorithm was compared with a control algorithm (No-Balancing), and an idealized algorithm
(one-speed-for-all [OSFA]). After each race, participants were asked ‘‘Was that game fun?’’ and ‘‘Was that
game fair?’’ using a five-point Likert scale.
Results: Participants pedaled quickly enough to elevate their heart rate to an average of 120 – 8 beats per minute
while playing. Dyad spread was lower when using GMFM Ability-Based balancing (4.6 – 4.2) compared with
No-Balancing (11.9 – 6.8) (P < 0.001). When using OSFA balancing, dyad spread was (1.6 – 0.9), lower than
both GMFM Ability-Based (P = 0.006) and No-Balancing (P < 0.001). Cycling cadence positively correlated to
GMFM, equal to 0.58 (GMFM) +33.29 (R2 adj = 0.662, P = 0.004). Participants rated the games a median score
4/5 for both questions: ‘‘was that game fun?’’ and ‘‘was that game fair?.’’
Conclusion: The GMFM Ability-Based balancing decreased dyad spread while requiring participants to pedal
quickly, facilitating interaction and physical activity.
Exergames; fitness; game therapy; youth fitness; game mechanisms