Neue Publikation zu Rückenschmerz mit Dr. Christian Schneider
Jüngst erschienen in „Archives of Physical Medicine and Rehabilitation“: Finding predictive factors of exercise adherence in randomized controlled trials on low back pain: an individual data re-analysis using machine learning techniques.
Abstract
Objective
To identify predictors of adherence in supervised and self-administered exercise interventions for individuals with low back pain.
Design
Cohort study.
Setting
Rehabilitation.
Participants
This pre-planned re-analysis within the MiSpEx Network included 1,511 participants with low back pain (57% female, mean age 40.9 years, SD ±14 years).
Interventions
Participants underwent an initial 3-week supervised phase of sensorimotor exercises, followed by a 9-week self-administered phase.
Main Outcome Measures
Biological, psychological, and social factors potentially impacting training adherence were evaluated. During the supervised phase, adherence was tracked through a standardized training log. During the self-administered phase, adherence was monitored via a diary, with adherence calculated as the percentage of scheduled versus completed sessions. Adherence was analyzed both as an absolute percentage and as a dichotomized variable (adherent vs. non-adherent, with a 70% adherence cut-off). Predictors for adherence were identified using Gradient Boosting Machines and Random Forests (R-package caret). Seventy percent of the observations were used for training, while 30% were retained as a hold-out test set.
Results
The average overall adherence was 64% (±31%), with 81% (±28%) adherence during the supervised phase and 58% (±39%) in the self-administered phase. The root mean square error for the test-set ranged from 36.2 (R²=0.18, self-administered phase) to 19.3 (R²=0.47, supervised phase); prediction accuracy for dichotomized models was between 64% and 83%. Predictors of low to intermediate adherence included poorer baseline postural control, decline in exercise levels, and fluctuations in pain intensity (both increases and decreases).
Conclusion
Identified predictors could aid in recognizing individuals at higher risk for non-adherence in low back pain exercise therapy settings.
Ann-Christin Pfeifer PhD , Paul Pfeifer-Schroder PhD , Marcus Schiltenwolf MD, Lutz Vogt PhD , Christian Schneider MD , Petra Platen MD ,
Heidrun Beck MD , Pia-Maria Wippert PhD , Tilman Engel PhD , Monique Wochatz PhD, Frank Mayer MD , Daniel Niederer PhD , Finding predictive factors of exercise adherence in randomized controlled trials on low back pain: an individual data re-analysis using machine learning techniques, Archives of Physical Medicine and Rehabilitation (2025), doi: https://doi.org/10.1016/j.apmr.2024.12.015.