Is Balance Training Using Biofeedback Effective in the Prophylaxis of Falls in Women over the Age of 65?

Abstract

The paper aims to investigate the usefulness of training in improving mobility and reducing the risk of falls of patients with osteoarthritis by using a force plate and virtual reality as rehabilitation tools. The study involved 60 women randomly divided into two equal groups: the force plate training group, which underwent virtual balance training with visual motor feedback, and the gym training group, which received conventional balance training only. The functional balance assessment was performed before and after the rehabilitation by computerized posturography in a relaxed upright position with open and closed eyes, with visual motor feedback. In the FPT group in the feedback measurement, the mean radius of sways was 30% smaller after rehabilitation (p < 0.00002); the feedback coordination coefficient was more than 10% bigger after rehabilitation (p < 0.001) and reached 92%, which is excellent for elderly people. Total stagnation and stumbling reported by patients decreased after rehabilitation compared to the first examination. Both tested forms of training can contribute to reducing the risk of falls. However, a more significant improvement was obtained in the force plate training group perhaps because the physical effort on a force plate trains the precise movements needed to reposition the centre of gravity without generating excessive inertia forces responsible for loss of balance and falls. Perhaps the most desirable method of intervention is to train a person’s ability to perform slow but definite body movements.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

“…The FreeMed Maxi consists of a plate equipped with sensors that measure the distribution of pressure force and changes in the projection of the center of pressure (COP) on the support surface. The body sways can be translated into COP values in the medial-lateral and anterior-posterior directions, and the trajectory (stabilogram/posturogram) represents the displacement of COP in these two directions, projection onto the X and Y axis, respectively, during the posturographic exam ( Sokołowska et al, 2018 ; Sadura-Sieklucka et al, 2023 ). The following COP trajectory (posturogram) parameters were recorded and calculated: total COP path length ( TL ), average COP velocity ( V ), COP ellipse area (predictive ellipse area with 95% COP values) ( EA ), and two lengths of directional components of sways in left-to-right frontal and forward-backward plane movements ( LRL and FBL , respectively).…”

“…We should select the feature set, out of all available features, which offers the minimum value of the error rate for the optimum k- NN rule. A similar analytical approach was used in our research using a posturography platform, e.g., in Sokołowska et al (2015 , 2018) , Sadura-Sieklucka et al (2023) , among others. Figures 4 , 5 illustrate the operation of the k -nearest neighbors rule and the classification model approach adopted in our research.…”

“…Moreover, in the context of the presented significant and already global problem of aging and the consequences of falls among seniors, methods for assessing balance and developing effective methods of maintaining it in various situations are important ( Silva et al, 2017 ; Lo et al, 2019 ; Roshdibenam et al, 2021 ; Albites-Sanabria et al, 2024 ; Song et al, 2024 ). Posturographic techniques are often used for this purpose ( Phu et al, 2019 ; Błaszczyk and Beck, 2023 ; Liang et al, 2024 ; Pennone et al, 2024 ), also in our research relies on posturography ( Sokołowska et al, 2015 , 2018 ; Sadura-Sieklucka et al, 2023 ).…”