Newly Identified Gait Patterns in Patients With Multiple Sclerosis May Be Related to Push-off Quality

Jiska C.E. Kempen; Caroline A.M. Doorenbosch; Dirk L. Knol; Vincent de Groot; Heleen Beckerman

doi:10.2522/ptj.20150508

Figures

Figure 2.

Typical examples of the 3 gait classes. The video stills show for each class (rows) a patient from the position of the left leg. Each column corresponds to a variable of the core set (ie, heel-rise in terminal stance, clearance in initial swing, and ankle angle in mid-swing). IC=initial contact.

Figure 1.

Histogram of Expanded Disability Status Scale (EDSS) scores of the 81 participants.

Tables

Table 4.

Participant Characteristics for Each Gait Class^{^a}

↵^a FS=functional system.

Table 2.

Model Fit Indexes for Latent Class Analyses^{^a}

↵^a AIC=Akaike information criterion, BIC=Bayesian information criterion.

Table 1.

Kinematic, EMG, and Forceplate Variables Included in the Latent Class Analyses^{^a}

↵^a EMG=electromyographic, LR=loading response, ISw=initial swing, MSw=mid-swing.
^b All foot contacts without clear heel or toe contact.
^c Combination of absent heel-rise in terminal stance, no clearance in initial swing, and a lack of properly timed gastrocnemius muscle activity in pre-swing.

Table 3.

Probability of the 9 Gait Variables Defining Gait Patterns in Patients With Multiple Sclerosis

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Newly Identified Gait Patterns in Patients With Multiple Sclerosis May Be Related to Push-off Quality

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