Relationship between physical activity and central sensitization in chronic low back pain: Insights from machine learning. (April 2023)

• Record Type: Journal Article
• Authors: Zheng, Xiaoping; Reneman, Michiel F; Preuper, Rita HR Schiphorst; Otten, Egbert; Lamoth, Claudine JC
• Is Part Of: Computer methods and programs in biomedicine. Volume 232(2023)
• Publication Date: 2023-04
• Subjects: Low back pain -- Physical activity -- Central sensitization -- Accelerometer -- Daily life -- Hidden semi-Markov model -- Chronic pain -- Avoidance-endurance model; CLBP chronic low back pain -- PA physical activity -- CS central sensitization -- CLBP+ chronic low back pain with higher central sensitization levels -- CLBP- chronic low back pain with lower central sensitization levels -- HSMM hidden semi-Markov model -- AEM avoidance-endurance model -- CSI central sensitization inventory -- VAS visual analogue scale -- PDI pain disability index -- Rand36-PF physical functioning subscale of the Rand36 -- PCS pain catastrophizing scale -- IEQ injustice experience questionnaire -- BSI brief symptom inventory -- BIC bayesian information criterion -- JSD Jensen–Shannon divergence -- MET metabolic equivalent of Task -- mg milli-gravity -- DE distress-endure; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107432
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Unsupervised machine learning approach, hidden semi-Markov model, can learn physical activity patterns from acceleration. Low and high central sensitization is associated with different physical activity patterns. High central sensitization may associate with distress-endurance response pattern. Abstract: 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.