Remote programming versus standard in-person programming following deep brain stimulation in patients with Parkinson's disease: a randomised controlled trial.

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BackgroundRemote programming (RP) offers a convenient alternative for patients with Parkinson's disease (PD) who have undergone deep brain stimulation (DBS). However, its clinical efficacy remains underexplored. We aimed to compare the treatment effects of RP and in-person standard programming (SP) in this population.MethodsWe conducted an open-label, non-inferiority randomised controlled trial at a single centre in China between October 2023 and February 2025. This study included PD patients who underwent bilateral subthalamic nucleus DBS surgery. Participants were randomly assigned (1:1) to either the RP or SP group. The primary outcome was the improvement rate in motor symptoms from baseline to 6-month follow-up, assessed using the Unified Parkinson's Disease Rating Scale (UPDRS). Secondary outcomes included changes in non-motor symptoms, quality of life, and patients' subjective experiences of the programming sessions. This study is registered at ClinicalTrials.gov (NCT06078397).FindingsA total of 50 patients were enrolled; 20 in the SP group and 21 in the RP group completed the 6-month follow-up. With a non-inferiority margin of 10%, the improvement rate in the total UPDRS-III score in the RP group was found to be non-inferior to that in the SP group, as the one-sided lower bound of the 95% confidence interval (-0.033) exceeded -0.100 (-10%). No significant differences were observed between the two groups in non-motor symptoms, quality of life, or subjective programming experiences (all p > 0.05).InterpretationRP offers short-term motor improvements that are non-inferior to in-person SP in PD patients following DBS surgery, which also yields comparable outcomes in non-motor symptoms and quality of life.FundingDianyou Li was funded by Shanghai Municipal Health Commission (Grant No. 202140181), Shanghai Science and Technology Commission (Grant No. 22Y11903900), Guangci Innovative Technology Launch Program (Grant No. GCQH202205), and the National Natural Science Foundation of China (82571678 to D.L.); Xiaonan Wan was funded by King's College London-China Scholarship Council Scholarship.

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