Physiotherapists’ beliefs of the working mechanisms of manual therapeutic techniques for spinal pain relief: a quantitative content analysis
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Manual therapeutic techniques (MTTs), including high velocity thrust manipulation (HVT) and spinal mobilization (MOB), are used to reduce spinal pain. Physiotherapists' beliefs on their working mechanisms to relieve pain are unknown. OBJECTIVES: To explore the prevalence of physiotherapists' beliefs regarding the working mechanisms of MTTs on pain relief and their associated factors. DESIGN: Quantitative content analysis. METHOD: The results of the verbal and online survey were analyzed based on a theory-driven coding framework (categories and depth). Ordinal, linear, and nominal regression analyses were used to analyze the secondary aim. RESULTS: A total of 541 physiotherapists were included (survey, n = 383, 70.8 %; verbal surveys, n = 158, 29.2 %), resulting in 759 HVT and 713 MOB responses. Six categories were present: biomechanical (39.3 % HVT, 50.9 % MOB), neurophysiological (39.4 % HVT, 31.4 % MOB), immunological (2.0 % HVT, 1.1 % MOB), non-specific (13.4 % HVT, 14.2 % MOB), unknown (4.3 % HVT, 1.3 % MOB), and not categorizable (1.6 % HVT, 1.1 % MOB). Levels of depth were low (65.1 % HVT, 64.1 % MOB), moderate (17.3 % HVT, 19.0 % MOB), and high (17.4 % HVT, 16.8 % MOB). Having a Master degree, network participation and work experience were associated with the category of working mechanism, level of depth and number of working mechanisms. CONCLUSION: The beliefs of physiotherapists in the Netherlands regarding the working mechanisms of MTTs for spinal-related pain reduction are mainly biomechanical and neurophysiological. The working mechanisms were dominantly explained in an unifactorial manner and with a low level of depth. Having a Master of Science degree was strongly associated with more evidence-consistent beliefs and deeper understanding.
Authors: J.P. Hendriks, R.R. Reezigt, M.F. Reneman
Language: English
Subfield: Pharmacology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W4412813342
