Establishing central sensitization inventory cut-off values in Dutch-speaking patients with chronic low back pain by unsupervised machine learning.

BACKGROUND: Human Assumed Central Sensitization (HACS) is involved in the development and maintenance of chronic low back pain (CLBP). The Central Sensitization Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off value of 40/100. However, various factors including pain conditions (e.g., CLBP), contexts, and gender may influence this cut-off value. Unsupervised clustering approaches can address these complexities by considering diverse factors and exploring possible HACS-related subgroups. Therefore, this study aimed to determine the cut-off values for a Dutch-speaking population with CLBP based on unsupervised machine learning. METHODS: Questionnaire data covering pain, physical, and psychological aspects were collected from patients with CLBP and aged-matched healthy controls (HC). Four clustering approaches were applied to identify HACS-related subgroups based on the questionnaire data and gender. The clustering performance was assessed using internal and external indicators. Subsequently, receiver operating characteristic (ROC) analysis was conducted on the best clustering results to determine the optimal cut-off values. RESULTS: The study included 63 HCs and 88 patients with CLBP. Hierarchical clustering yielded the best results, identifying three clusters: healthy group, CLBP with low HACS level, and CLBP with high HACS level groups. The cut-off value for the overall groups were 35 (sensitivity 0.76, specificity 0.76). CONCLUSION: This study found distinct patient subgroups. An overall CSI cut-off value of 35 was suggested. This study may provide new insights into identifying HACS-related patterns and contributes to establishing accurate cut-off values.

Relationship between physical activity and central sensitization in chronic low back pain: Insights from machine learning.

BACKGROUND AND OBJECTIVES: Chronic low back pain (CLBP) is a leading cause of disability. The management guidelines for the management of CLBP often recommend optimizing physical activity (PA). Among a subsample of patients with CLBP, central sensitization (CS) is present. However, knowledge about the association between PA intensity patterns, CLBP, and CS is limited. The objective PA computed by conventional approaches (e.g. cut-points) may not be sensitive enough to explore this association. This study aimed to investigate PA intensity patterns in patients with CLBP and low or high CS (CLBP-, CLBP+, respectively) by using advanced unsupervised machine learning approach, Hidden semi-Markov model (HSMM). METHODS: Forty-two patients were included (23 CLBP-, 19 CLBP+). CS-related symptoms (e.g. fatigue, sensitivity to light, psychological features) were assessed by a CS Inventory. Patients wore a standard 3D-accelerometer for one week and PA was recorded. The conventional cut-points approach was used to compute the time accumulation and distribution of PA intensity levels in a day. For the two groups, two HSMMs were developed to measure the temporal organization of and transition between hidden states (PA intensity levels), based on the accelerometer vector magnitude. RESULTS: Based on the conventional cut-points approach, no significant differences were found between CLBP- and CLBP+ groups (p = 0.87). In contrast, HSMMs revealed significant differences between the two groups. For the 5 identified hidden states (rest, sedentary, light PA, light locomotion, and moderate-vigorous PA), the CLBP- group had a higher transition probability from rest, light PA, and moderate-vigorous PA states to the sedentary state (p < 0.001). In addition, the CBLP- group had a significantly shorter bout duration of the sedentary state (p < 0.001). The CLBP+ group exhibited longer durations of active (p < 0.001) and inactive states (p = 0.037) and had higher transition probabilities between active states (p < 0.001). CONCLUSIONS: HSMM discloses the temporal organization and transitions of PA intensity levels based on accelerometer data, yielding valuable and detailed clinical information. The results imply that patients with CLBP- and CLBP+ have different PA intensity patterns. CLBP+ patients may adopt the distress-endurance response pattern with a prolonged bout duration of activity engagement.

Association between central sensitization and gait in chronic low back pain: Insights from a machine learning approach.

BACKGROUND: Central sensitization (CS) is often present in patients with chronic low back pain (CLBP). Gait impairments due to CLBP have been extensively reported; however, the association between CS and gait is unknown. The present study examined the association between CS and CLBP on gait during activities of daily living. METHOD: Forty-two patients with CLBP were included. CS was assessed through the Central Sensitization Inventory (CSI), and patients were divided in a low and high CS group (23 CLBP- and 19 CLBP+, respectively). Patients wore a tri-axial accelerometer device for one week. From the acceleration signals, gait cycles were extracted and 36 gait outcomes representing quantitative and qualitative characteristics of gait were calculated. A Random Forest was trained to classify CLBP- and CLBP + based on the gait outcomes. The maximum Youden index was computed to measure the diagnostic test's ability and SHapley Additive exPlanations (SHAP) indexed the gait outcomes' importance to the classification model. RESULTS: The Random Forest accurately (84.4%) classified the CLBP- and CLBP+. Youden index was 0.65, and SHAP revealed that the gait outcomes' important to the classification model were related to gait smoothness, stride frequency variability, stride length variability, stride regularity, predictability, and stability. CONCLUSIONS: CLBP- and CLBP + patients had different motor control strategies. Patients in the CLBP- group presented with a more "loose control", with higher gait smoothness and stability, while CLBP + patients presented with a "tight control", with a more regular, less variable, and more predictable gait pattern.

Outcome-dependent effects of walking speed and age on quantitative and qualitative gait measures.

BACKGROUND: Walking speed predicts many clinical outcomes in old age. However, a comprehensive assessment of how walking speed affects accelerometer based quantitative and qualitative gait measures in younger and older adults is lacking. RESEARCH QUESTION: What is the relationship between walking speed and quantitative and qualitative gait outcomes in younger and older adults? METHODS: Younger (n = 27, age: 21.6) and older participants (n = 27, age: 69.5) completed 340 steps on a treadmill at speeds of 0.70 to a maximum of 1.75 m·s-1. We used generalized additive mixed models to determine the relationship between walking speed and quantitative (stride length, stride time, stride frequency and their variability) and qualitative (stride regularity, stability, smoothness, symmetry, synchronization, predictability) gait measures extracted from trunk accelerations. RESULTS: The type of relationship between walking speed and the majority of gait measures (quantitative and qualitative) was characterized as logarithmic, with more prominent speed-effects at speeds below 1.20 m·s-1. Changes in quantitative measures included shorter strides, longer stride times, and a lower stride frequency, with more variability at lower speeds independent of age. For qualitative measures, we found a decrease in gait symmetry, stability and regularity in all directions with decreasing speeds, a decrease in gait predictability (Vertical, V, anterior-posterior, AP) and stronger gait synchronization (AP-mediolateral, ML, AP-V), and direction dependent effects of gait smoothness, which decreased in V direction, but increased in AP and ML directions with decreasing speeds. We found outcome-dependent effects of age on the quantitative and qualitative gait measures, with either no differences between age-groups, age-related differences that existed regardless of speed, and age-related differences in the type of relationship with walking speed. SIGNIFICANCE: The relationship between walking speed and quantitative and qualitative gait measures, and the effects of age on this relationship, depends on the type of gait measure studied.

Multiple gait parameters derived from iPod accelerometry predict age-related gait changes.

INTRODUCTION: Normative data of how natural aging affects gait can serve as a frame of reference for changes in gait dynamics due to pathologies. Therefore, the present study aims (1) to identify gait variables sensitive to age-related changes in gait over the adult life span using the iPod and (2) to assess if these variables accurately distinguish young (aged 18-45) from healthy older (aged 46-75) adults. METHODS: Trunk accelerations were recorded with an iPod Touch in 59 healthy adults during three minutes of overground walking. Gait variables included gait speed and accelerometry-based gait variables (stride, amplitude, frequency, and trajectory-related variables) in the anterior-posterior (AP) and medio-lateral (ML) directions. Multivariate partial least square analysis (PLS) identified variables sensitive to age-related differences in gait. To classify young and old adults, a PLS-discriminant analysis (PLS-DA) was used to test the accuracy of these variables. RESULTS: The PLS model explained 42% of variation in age. Influential variables were: mean stride time, phase variability index, root mean square, stride variability, AP sample entropy and ML maximal Lyaponov exponent. PLS-DA classified 83% of the participants correctly with a sensitivity of 83% and specificity of 71%. DISCUSSION: Contrary to the frequently reported high gait variability observed in old adults with frailty and fall history, the present study showed that younger compared with older healthy adults had a more variable, less predictable and more symmetrical gait pattern. A model based on a combination of variables reflecting gait dynamics, could distinguish healthy younger adults from older adults.