  Dynamic balance in human locomotion can be assessed through the local dynamic
stability (LDS) method. Whereas gait LDS has been used successfully in many
settings and applications, little is known about its sensitivity to individual
characteristics of healthy adults. Therefore, we reanalyzed a large dataset of
accelerometric data measured for 100 healthy adults from 20 to 70 years of age
performing 10 min. treadmill walking. We sought to assess the extent to which
the variations of age, body mass and height, sex, and preferred walking speed
(PWS) could influence gait LDS. The random forest (RF) and multiple adaptive
regression splines (MARS) algorithms were selected for their good bias-variance
tradeoff and their capabilities to handle nonlinear associations. First,
through variable importance measure (VIM), we used RF to evaluate which
individual characteristics had the highest influence on gait LDS. Second, we
used MARS to detect potential interactions among individual characteristics
that may influence LDS. The VIM and MARS results indicated that PWS and age
correlated with LDS, whereas no associations were found for sex, body height,
and body mass. Further, the MARS model detected an age by PWS interaction: on
one hand, at high PWS, gait stability is constant across age while, on the
other hand, at low PWS, gait instability increases substantially with age. We
conclude that it is advisable to consider the participants' age as well as
their PWS to avoid potential biases in evaluating dynamic balance through LDS.
