Mechanisms underlying blood pressure changes in obstructive sleep apnoea (OSA) are incompletely understood. Increased respiratory effort (RE) is one of the main features of OSA and is associated with sympathetic overactivity, leading to increased vascular wall stiffness and remodelling. This study investigated associations between a new measure of RE (percentage of sleep time spent with increased RE based on measurement of mandibular jaw movements [MJM]; REMOV, %TST) and prevalent hypertension in adults referred for evaluation of suspected OSA. A machine learning model was built to predict hypertension from clinical data, conventional polysomnography (PSG) indices, and MJM-derived parameters (including REMOV, %TST). The model was evaluated in a training subset and a test subset. The analysis included 1127 patients, 901 (80%) in the training subset and 226 (20%) in the test subset. The prevalence of hypertension was 31% and 30%, respectively, in the training and test subsets. A risk stratification model based on eighteen input features including REMOV had good accuracy for predicting prevalent hypertension (sensitivity 0.75, specificity 0.83). Using the Shapley additive explanation (SHAP) method, REMOV was the best predictor of hypertension after clinical risk factors (age, sex, body mass index, neck circumference) and time with oxygen saturation <90%, ahead òf standard PSG metrics (including the apnoea-hypopnoea index and oxygen desaturation index). The proportion of sleep time spent with increased RE automatically derived from MJM was identified as a potential new reliable metric to predict prevalent hypertension in patients with OSA.