Technology in Physiotherapy
Articles published in this theme: 230 (scroll down to load remaining articles)
2026
Quantitative Postural Evaluation in Orchestral Musicians (the OPErA Project): Protocol for a Cross-Sectional Study
JMIR Res Protoc 2026 (Jun 22); 15:e81885
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Development of Data-Driven Models for Just-in-Time Digital Self-Management Advice to Improve Physical Functioning in Hip and Knee Osteoarthritis: Protocol for the e-cOAch Cross-Over Study
JMIR Res Protoc 2026 (Jun 22); 15:e94709
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Comparing 2-Hz and 100-Hz Electroacupuncture for Postoperative Musculoskeletal Pain: Protocol for a 2 × 2 Prospective Randomized Crossover Trial
JMIR Res Protoc 2026 (Jun 16); 15:e91706
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Health Care Providers’ Perspectives on a Hybrid Outpatient Stroke Telerehabilitation Program: Qualitative Implementation Study
JMIR Rehabil Assist Technol 2026 (Jun 15); 13:e83081
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Practice Areas, Skills, and Core Competencies of Advanced Practice Physiotherapists Working in Tertiary Care in Germany: Protocol for a 3-Round Delphi Study to Establish Expert Consensus
JMIR Res Protoc 2026 (Jun 11); 15:e90704
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Effects of a Home-Based Exercise Program Incorporating Mindfulness and Yoga on Balance and Mobility in People With Parkinson Disease: Protocol for a Randomized Controlled Trial
JMIR Res Protoc 2026 (Jun 03); 15:e97781
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Wearable Sensor–Derived Gait Parameters Across Self-Reported Physical Activity Levels in Individuals With Knee Osteoarthritis and Healthy Controls: Pilot Cross-Sectional Validation Study
JMIR Form Res 2026 (Jun 03); 10:e80728
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Asynchronous Broadcasting of Audiovisual Content as a Telerehabilitation Strategy for Patients in Rural Areas: Development and Usability Study
JMIR Rehabil Assist Technol 2026 (May 29); 13:e66343
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Accuracy in the Estimation of Self-Reported Knee Brace Wear Time in Young Adults With a Symptomatic Knee Following ACL Reconstruction: Secondary Analysis of a Pilot Randomized Controlled Trial
JMIR Rehabil Assist Technol 2026 (May 27); 13:e79725
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Validity and Reliability of an Immersive Virtual Reality System for Multidimensional Assessment of Cervical Sensorimotor Control: Cross-Sectional Study
JMIR Rehabil Assist Technol 2026 (May 21); 13:e88498
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mHealth-Supported Exercise Rehabilitation to Reverse Frailty After Autologous Transplantation in Multiple Myeloma: Randomized Controlled Trial
JMIR Mhealth Uhealth 2026 (May 21); 14:e87628
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Multigesture Electromyographic Control Complexity in Upper Limb Prostheses Actuated via Single Sensor Input Contraction Magnitude: Qualitative Study for Evaluating Performance and Cognitive Load
JMIR Rehabil Assist Technol 2026 (May 20); 13:e88528
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Neuromuscular Reactivation Through Guided Visualization Combining Mindfulness and Motor Imagery via the Yuzit App: Protocol for a Randomized Controlled Trial
JMIR Res Protoc 2026 (May 11); 15:e87895
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Effectiveness of Digital Health Interventions in Older Adults With Frailty and Sarcopenia: Systematic Review and Meta‐Analysis of Randomized Controlled Trials
J Med Internet Res 2026 (May 11); 28:e88374
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Validity and Reliability of the Track-UL Algorithm Compared With Kinovea Software for Measuring Upper-Limb Functional Range of Motion in People After Stroke: Cross-Sectional Observational Study
JMIR Rehabil Assist Technol 2026 (May 11); 13:e87128
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Effects of Remotely Supervised Home-Based High-Speed Bodyweight Resistance Training on Bradykinesia in Individuals With Parkinson Disease: Protocol for a Randomized Controlled Trial
JMIR Res Protoc 2026 (May 04); 15:e84689
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Emotional Training via Telerehabilitation After Surgical Treatment for Facial Palsy: Prospective, Assessor-Blinded, 2-Arm Pilot Cohort Study
JMIR Rehabil Assist Technol 2026 (Apr 27); 13:e79520
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Effects of an Exercise-Assisting Mobile App (Osteoarthritis-Rehabilitation Assistant [O-RA]) on Rehabilitation Outcomes in Older Adults: Randomized Controlled Parallel Clinical Trial
JMIR Rehabil Assist Technol 2026 (Apr 15); 13:e80971
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Progressive Achilles Loading via Clinician e-Support (PACE): Protocol for a Dual-Site Randomized Controlled Trial
JMIR Res Protoc 2026 (Apr 07); 15:e90955
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Smartphone-Based Interpretable Machine Learning for Classifying Single-Leg Squat Performance Using Trunk, Pelvic, and Knee Kinematics: Cross-Sectional Study
JMIR Mhealth Uhealth 2026 (Mar 12); 14:e85126
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