The COVID-19 pandemic led to restrictions in the implementation of therapeutic offers worldwide. Telemedicine approaches can not only help in this case to close the gaps in supply that have arisen but can also supplement routine sensorimotor therapies.
The aim of this prospective study is to examine the effectiveness of telerehabilitation in relation to existing standards and the dependence of the effect on other training and personal variables.
In a controlled, repeated measurement design for a total of 12 weeks, patients with distal and proximal hemipareses of the upper extremities of different etiologies are tested. Four measurement times are compared:
- at the beginning of the experiment (baseline),
- after four weeks without training (spontaneous recovery),
- after a further four weeks of individualized training for the patient with the Raccoon.Recovery platform in their own home (acquisition phase),
- after another four weeks without training (retention phase).
The exercises are gradually adapted in the course of the training and their correct execution is continuously monitored. The training frequency and kinematic data are recorded and combined with factors such as the degree of paresis, the body part being trained (e.g. finger vs. wrist vs. elbow), the type of movements being trained (e.g. wrist flexion / extension vs. rotation), the disorder aetiology, the age of the patient and previous experience with computer games were examined as predictors of training success. Various clinical scales serve as primary indicators of effectiveness.
The first pilot and individual investigations show promising results with regard to the applicability of the approach and training success, but also possible obstacles to conducting the study, especially in very severely affected patients. The gamification approach was mostly rated as positive, but it represents an initial challenge, especially for older patients and patients with cognitive disorders.
The study was presented in December 2020 at the 8th joint annual meeting of the DGNR and DGNKN.
Read the full abstract here.