Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy

Abstract

Gait assessments in standardized settings, as part of the clinical follow-up of children with cerebral palsy (CP), may not represent gait in daily life. This study aimed at comparing gait characteristics in laboratory and real life settings on the basis of multiple parameters in children with cp and with typical development (TD). Fifteen children with CP and 14 with TD wore 5 inertial sensors (chest, thighs and shanks) during in-laboratory gait assessments and during 3 days of daily life. Sixteen parameters belonging to 8 distinct domains were computed from the angular velocities and/or accelerations. Each parameter measured in the laboratory was compared to the same parameter measured in daily life for walking bouts defined by a travelled distance similar to the laboratory, using Wilcoxon paired tests and Spearman's correlations. Most gait characteristics differed between both environments in both groups. Numerous high correlations were found between laboratory and daily life gait parameters for the CP group, whereas fewer correlations were found in the TD group. These results demonstrated that children with CP perform better in clinical settings. Such quantitative evidence may enhance clinicians' understanding of the gap between capacity and performance in children with CP and improve their decision-making. Cerebral palsy (CP) describes a group of motor disorders resulting from early damage to the developing brain 1. It is the most frequent motor disability in children, with a prevalence of 1.8 per 1000 live births in Europe 2. Children with CP have heterogeneous clinical profiles and are classified into five levels of severity with the Gross Motor Function Classification System (I: independent walker; II: independent walker with limitations; III: ambulate with walking aids; IV: ambulate with powered mobility; and V: dependent for all mobility) 3,4. In CP, gait disorders are among the leading limitations, with a negative impact on participation and self-perception 5. Current management of gait deviations is largely based on assessments of body structures and body functions of individuals measured in clinical settings 6. 'Clinical gait analysis' (CGA) measures multiple gait parameters in order to identify and understand the main causes of gait deviations 7. Although CGA has become a widely accepted tool in clinical practice, it is not clear whether in-laboratory assessments reflect the usual walking performance of the patients in daily life. Patients are often considered to perform better when walking under clinical supervision to please caregivers 8 , known as the 'Hawthorne effect' 9 , and thanks to improved concentration in the absence of external distractors requiring additional attention 10. Integrating unsupervised assessments of the patients' daily walking into the clinical process could improve clinicians' understanding of their real behavior and overall difficulties, beyond the observation of functional limitations in a purely clinical setting 10. The link between capacity (what an individual can do in ...

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

“…Finally, papers comparing the conditions and results in free-living conditions with those obtained in controlled environments [ 40 , 72 , 74 , 77 , 78 ] and papers dealing with the detection of ancillary parameters (such as wear-time [ 79 , 80 ], walking bouts [ 72 , 75 , 81 ], strides or steps [ 40 , 71 , 81 ], etc.) constitute the fifth and sixth distinct groups of respectively 10 studies and five studies.…”

“…All studies included in this review use accelerometers, gyroscope, IMUs [ 39 , 40 , 82 ], or an association of these three types of sensors. A referencing of the inertial sensors’ types among the 58 studies considered in the review is available in Figure 3 .…”

“…In several articles, protocols include the use of several sensors positioned on different locations on the body [ 40 , 44 , 62 ]. Figure 4 details the distribution of the studies according to the number of inertial sensors associated with them [ 17 , 38 , 62 , 66 , 73 , 78 , 80 , 84 , 85 ].…”

“…In addition, some studies have highlighted the difficulties of transposing high-performance algorithms intended for controlled environments to free-living conditions, in particular because of the greater complexity of movements and their associations [ 33 ]. Nevertheless, these challenges have been, and are still being, met today through studies allowing interesting and reliable quantitative feedback using inertial sensors of the movements carried out in free-living settings [ 37 , 38 , 39 , 40 ]. The increasing use of machine learning and efficient algorithms also contributed to this effort, by allowing the processing of complex data and allowing to transpose protocols from laboratory to natural conditions.…”

Reliability and Repeatability Analysis of Indices to Measure Gait Deterioration in MS Patients during Prolonged Walking

“…Firstly, the scale is questionnaire-based, causing a subjectivity of the answers and, then, no information about the motor aspect is taken into account [ 13 ]. To overcome these limitations, laboratory gait analysis performed by means of motion capture systems still represents the gold standard to fully understand the mechanism of gait deterioration in MS patients [ 14 ]; however, in the last decades, inertial units reached a great popularity thanks to their wearability, low cost, and portability [ 15 , 16 , 17 , 18 ]. As well, the use of virtual reality for gait parameter assessment has also received great popularity [ 19 ].…”