  Background: Human gait exhibits complex fractal fluctuations among
consecutive strides. The time series of gait parameters are long-range
correlated (statistical persistence). In contrast, when gait is synchronized
with external rhythmic cues, the fluctuation regime is modified to stochastic
oscillations around the target frequency (statistical anti-persistence). To
highlight these two fluctuation modes, the prevalent methodology is the
detrended fluctuation analysis (DFA). The DFA outcome is the scaling exponent,
which lies between 0.5 and 1 if the time series exhibit long-range
correlations, and below 0.5 if the time series is anti-correlated. A
fundamental assumption for applying DFA is that the analyzed time series
results from a time-invariant generating process. However, a gait time series
may be constituted by an ensemble of sub-segments with distinct fluctuation
regimes (e.g., correlated and anti-correlated). Methods: Several proportions of
correlated and anti-correlated time series were mixed together and then
analyzed through DFA. The original (before mixing) time series were generated
via autoregressive fractionally integrated moving average (ARFIMA) modelling or
actual gait data. Results: Results evidenced a nonlinear sensitivity of DFA to
the mix of correlated and anti-correlated series. Notably, adding a small
proportion of correlated segments into an anti-correlated time series had
stronger effects than the reverse. Significance: In case of changes in gait
control during a walking trial, the resulting time series may be a patchy
ensemble of several fluctuation regimes. When applying DFA, the scaling
exponent may be misinterpreted. Cued walking studies may be most at risk of
suffering this issue in cases of sporadic synchronization with external cues.
