The Use of Static Posturography Cut-Off Scores to Identify the Risk of Falling in Older Adults

Age-related decline in GABAergic intracortical inhibition can be counteracted by long-term learning of balance skills

Ageing induces a decline in GABAergic intracortical inhibition, which seems to be associated not only with decremental changes in well-being, sleep quality, cognition and pain management but also with impaired motor control. So far, little is known regarding whether targeted interventions can prevent the decline of intracortical inhibition in the primary motor cortex in the elderly. Therefore, the present study investigated whether age-related cortical dis-inhibition could be reversed after 6 months of balance learning and whether improvements in postural control correlated with the extent of reversed dis-inhibition. The results demonstrated that intracortical inhibition can be upregulated in elderly subjects after long-term balance learning and revealed a correlation between changes in balance performance and intracortical inhibition. This is the first study to show physical activity-related upregulation of GABAergic inhibition in a population with chronic dis-inhibition and may therefore be seminal for many pathologies in which the equilibrium between inhibitory and excitatory neurotransmitters is disturbed. KEY POINTS: Ageing induces a decline in GABAergic intracortical inhibition. So far, little is known regarding whether targeted interventions can prevent the decline of intracortical inhibition in the primary motor cortex in the elderly. After 6 months of balance learning, intracortical inhibition can be upregulated in elderly subjects. The results of this study also revealed a correlation between changes in balance performance and intracortical inhibition. This is the first study to show physical activity-related upregulation of GABAergic inhibition in a population with chronic dis-inhibition.

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

BACKGROUND

Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability.

METHODS

A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model.

RESULTS

The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model.

CONCLUSION

Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model's performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable.

Body Balance of Children and Youths with Visual Impairment (Pilot Study)

AIM

The study was designed to assess the effects of surface instability in the response of the balance control system in children and youths with visual impairment (BL) and in normally sighted controls (NE).

MATERIALS AND METHODS

The empirical research study involved 80 individuals, aged from 6 to 20 years, with a mean age of 14.37 (±4.68), including 40 blind individuals and a randomly selected control group 40 normally sighted. Stabilometric measurements were performed with the use of the Platform CQ Stab 2P, with eyes open (EO) and closed (EC) on the solid surface, and then, the same procedure was performed on the platform covered with 1-centimetre-thick foam.

RESULTS

Statistical analyses (Wilcoxon matched-pairs test, Mann-Whitney U test) of the results identified during the trials reveal the following findings in the BL group in the EO and EC tests. The results of the foam surface test were higher and the differences were statistically significant in the BL group (sway path EO p = 0.009, EC p = 0.006; mean amplitude EC p = 0.030; mean velocity EO p = 0.009, EC p = 0.006; sway area EO p = 0.017, EC p = 0.009; and number of COP deflections along the sagittal plane EO p = 0.004). No similar correlations were observed in the NE group, except for the mean amplitude EO p = 0.033 and sway area EO p = 0.030. There was one difference between the BL and the NE group for the mean amplitude parameter, p = 0.018, in a solid surface test with open eyes. The results were higher in the BL group.

CONCLUSIONS

The present study showed no worse balance in the BL group than in the NE group but worse performance on the foam than without it. It indicates the need to develop body balance skills in blind people by improving their proprioceptive sensitivity. In everyday life and training, blind people should experience exteroceptive stimuli, different textures, and unstable surfaces as much as possible.