Abstract
BACKGROUND:
A major issue for interventions for patients with chronic low back pain (CLBP) is the heterogeneity of the patient population. Many studies have shown that gait patterns among patients with CLBP are inconsistent. One of the explanations for the inconsistent finding could be the presence of central sensitization (CS). This study aimed to explore whether patients with CLBP with low or high CS levels (a CS Inventory score lower than 40 (CLBP-), or 40-100 (CLBP+)) have different gait performance, by using machine learning classification methods based on accelerometry obtained from the daily-life environment.
METHODS:
Forty-three patients with CLBP were included (24 CLBP- and 19 CLBP+). Patients wore a 3D accelerometer for about one week. From each patient, 4 days of accelerometer data were selected randomly. For each day data, continuous gait cycles were extracted to compute 36 gait outcomes which represent the pace, regularity, synchronization, smoothness, stability, and predictability of gait. A Random Forest classifier was trained to classify CLBP- and CLBP+ groups based on gait outcomes and SHapley Additive exPlanations (SHAP) method was used to explain the differences between groups in gait outcomes.
RESULTS:
The low and high CS groups were classified by a Random Forest method with the F1-score of 82.6%, an accuracy of 84.4%. The top ten gait outcomes indicated by SHAP were: index of harmonicity-vertical and harmonic ratio-mediolateral (smoothness), stride frequency variability- mediolateral/anteroposterior, stride length variability (variability), stride regularity-mediolateral (regularity), maximal Lyapunov exponent-vertical/mediolateral and maximal Lyapunov exponent per stride-vertical (stability), and sample entropy-anteroposterior (predictability). CONCLUSION:
Differences in gait performance could be classified with high accuracy. The results suggest that CLBP- and CLBP+ presented different motor control strategies. CLBP- presented a more “loose” control, including higher gait smoothness and stability. CLBP+ presented a more “tight” control, including a more regular, less variable, and more predictable gait pattern.
A major issue for interventions for patients with chronic low back pain (CLBP) is the heterogeneity of the patient population. Many studies have shown that gait patterns among patients with CLBP are inconsistent. One of the explanations for the inconsistent finding could be the presence of central sensitization (CS). This study aimed to explore whether patients with CLBP with low or high CS levels (a CS Inventory score lower than 40 (CLBP-), or 40-100 (CLBP+)) have different gait performance, by using machine learning classification methods based on accelerometry obtained from the daily-life environment.
METHODS:
Forty-three patients with CLBP were included (24 CLBP- and 19 CLBP+). Patients wore a 3D accelerometer for about one week. From each patient, 4 days of accelerometer data were selected randomly. For each day data, continuous gait cycles were extracted to compute 36 gait outcomes which represent the pace, regularity, synchronization, smoothness, stability, and predictability of gait. A Random Forest classifier was trained to classify CLBP- and CLBP+ groups based on gait outcomes and SHapley Additive exPlanations (SHAP) method was used to explain the differences between groups in gait outcomes.
RESULTS:
The low and high CS groups were classified by a Random Forest method with the F1-score of 82.6%, an accuracy of 84.4%. The top ten gait outcomes indicated by SHAP were: index of harmonicity-vertical and harmonic ratio-mediolateral (smoothness), stride frequency variability- mediolateral/anteroposterior, stride length variability (variability), stride regularity-mediolateral (regularity), maximal Lyapunov exponent-vertical/mediolateral and maximal Lyapunov exponent per stride-vertical (stability), and sample entropy-anteroposterior (predictability). CONCLUSION:
Differences in gait performance could be classified with high accuracy. The results suggest that CLBP- and CLBP+ presented different motor control strategies. CLBP- presented a more “loose” control, including higher gait smoothness and stability. CLBP+ presented a more “tight” control, including a more regular, less variable, and more predictable gait pattern.
| Original language | English |
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| Publication status | Accepted/In press - 29-Oct-2021 |
| Event | PA!N Congres 2021 - online Duration: 29-Oct-2021 → 29-Oct-2021 |
Conference
| Conference | PA!N Congres 2021 |
|---|---|
| Period | 29/10/2021 → 29/10/2021 |