Investigating the Causal Mechanisms of Symptom Recovery in Chronic Whiplash-associated Disorders Using Bayesian Networks

• Objectives: The present study's objective was to understand the causal mechanisms underpinning the recovery of individuals with whiplash-associated disorders (WAD). We applied Bayesian Networks (BN) to answer 2 study aims: (1) to identify the causal mechanism(s) of recovery underpinning neck-specific exercise (NSE), and (2) quantify if the cyclical pathway of the fear-avoidance model (FAM) is supported by the present data.Materials and Methods: We analyzed a prospective cohort data set of 216 individuals with chronic WAD. Fifteen variables were used to build a BN model: treatment group (NSE with or without a behavioral approach, or general physical activity), muscle endurance, range of motion, hand strength, neck proprioception, pain catastrophizing, fear, anxiety, depression, self-efficacy, perceived work ability, disability, pain intensity, sex, and follow-up time.Results: The BN model showed that neck pain reduction rate was greater after NSE compared with physical activity prescription (beta=0.59 points per month [P<0.001]) only in the presence of 2 mediators: global neck muscle endurance and perceived work ability. We also found the following pathway of variables that constituted the FAM: anxiety, followed by depressive symptoms, fear, catastrophizing, self-efficacy, and consequently pain.Conclusions: We uncovered 2 mediators that explained the mechanisms of effect behind NSE, and proposed an alternative FAM pathway. The present study is the first to apply BN modelling to understand the causal mechanisms of recovery in WAD. In doing so, it is anticipated that such analytical methods could increase the precision of treatment of individuals with chronic WAD.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Sjukgymnastik (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Physiotherapy (hsv//eng)

Nyckelord

whiplash

pain

mediation analysis

Bayesian Networks

fear avoidance model

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