Baseline characteristics did not identify people with low back pain who respond best to a Movement System Impairment-Based classification treatment

Abstract

Study design: Secondary analysis of data from a randomized controlled trial. Background: Treatment based on the Movement System Impairment-Based classification for chronic low back pain results in the same benefit when compared to other forms of exercise. It is possible that participant's characteristics measured at baseline can identify people with chronic low back pain who would respond best to a treatment based on the Movement System Impairment model. Objectives: To assess if specific characteristics of people with chronic low back pain measured at baseline can modify the effects of a treatment based on the Movement System Impairment model on pain and disability. Methods: Four variables assessed at baseline that could potentially modify the treatment effects of the treatment based on the Movement System Impairment model were selected (age, educational status, physical activity status and STarT back tool classification). Separate univariate models were used to investigate a possible modifier treatment effect of baseline participant's characteristics on pain and disability after the treatment. Findings of interaction values above 1 point for the outcome mean pain intensity or above 3 points for disability (Roland Morris questionnaire) were considered clinically relevant. Results: Linear regression analyses for the outcomes of pain and disability did not show interaction values considered clinically relevant for age, educational status, physical activity status and STarT back tool classification.

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

“…For prognosis, STarT Back classification for improving pain or disability is shown in Supplementary Table 4 . Of these, 17 studies assessed pain and disability prognosis with univariate models 70 , 74 , 77 , 80 , 81 , 84 – 86 , 89 , 94 , 96 , 97 , 104 – 108 . Of the univariate analyses, eight showed significant prognostic benefits for pain intensity 74 , 83 , 85 , 89 , 93 , 97 , 106 , 107 , 13 showed significant prognostic benefits for disability 74 , 83 – 86 , 89 , 93 , 94 , 96 , 97 , 102 , 105 , 108 , while two showed significant prognostic benefits on mixed pain intensity and disability analyses 80 , 81 .…”

“…For prognosis, STarT Back classification for improving pain or disability is shown in Supplementary Table 4. Of these, 17 studies assessed pain and disability prognosis with univariate models 70,74,77,80,81,[84][85][86]89,94,96,97,[104][105][106][107][108] . Of the univariate analyses, eight showed significant prognostic benefits for pain intensity 74,83,85,89,93,97,106,107 , 13 showed significant prognostic benefits Azimi et al 22 1 1 1 1 0 0 0 1 0 5 / 9 Barons et al 52 1 1…”

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

“…Thus, baseline characteristics of individuals would be useful to predict the differential response to intervention strategies. Previous research has found that baseline clinical and demographic factors influence treatment response, but these characteristics have not achieved the accuracy required for prediction ( Underwood et al, 2007 ; Azevedo et al, 2019 ; Witt et al, 2019 ). Some studies have focused on quantitative sensory testing (QST) in the prediction of analgesic effects, but with contradictory results ( Grosen et al, 2013 ).…”