Basic gait pattern and impact of fall risk factors on gait among older adults in India

Muscle dynamics analysis by clustered categories during jogging in patients with anterior cruciate ligament deficiency

BACKGROUND

Patients with anterior cruciate ligament (ACL) deficiency (ACLD) tend to have altered lower extremity dynamics. Little is known about the changes in dynamic function and activation during jogging in patients with ACLD.

METHODS

Twenty patients with an injured ACL before ACL reconstruction (ACLD group) and nine healthy male volunteers (control group) were recruited. Each volunteer repeated the jogging experiment five times. Based on the experimental data measured, a musculoskeletal multibody dynamics model was employed to simulate the tibiofemoral joint dynamics during jogging. Eighteen muscles were used for analysis. The obtained dynamics data were used for clustering and curve difference analysis.

RESULTS

The 18 muscles studied were divided into 3 categories. All the quadriceps, the soleus, the gastrocnemius, and the popliteus were classified as label 1. All the hamstrings were classified as label 2, and the sartorius muscles were classified as label 3. Among them, the classification of the short head of the biceps femoris was significantly different between the two groups (P < 0.001). The force curves of all 18 muscles and the between-group differences were studied according to clustered categories. Most muscle force in label 1 was approaching zero in the terminal stance phase, which was significantly lower than that in the control group (P < 0.05). The muscle force in label 2 had areas with significant differences in the stance phase. Muscle force in label 3 was significantly lower than that in the control group in the pre-swing phase.

CONCLUSIONS

This study showed that there are various changes of muscle function and activation in patients with ACLD. Through clustering and curve analysis, the joint reactions and changes of different muscle forces in the gait cycle between the ACLD and control groups could be further clarified.

Trip-Related Fall Risk Prediction Based on Gait Pattern in Healthy Older Adults: A Machine-Learning Approach

Trip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall. Therefore, this study aimed to develop trip-related fall risk prediction models from one's regular gait pattern using machine-learning approaches. A total of 298 older adults (≥60 years) who experienced a novel obstacle-induced trip perturbation in the laboratory were included in this study. Their trip outcomes were classified into three classes: no-falls (n = 192), falls with lowering strategy (L-fall, n = 84), and falls with elevating strategy (E-fall, n = 22). A total of 40 gait characteristics, which could potentially affect trip outcomes, were calculated in the regular walking trial before the trip trial. The top 50% of features (n = 20) were selected to train the prediction models using a relief-based feature selection algorithm, and an ensemble classification model was selected and trained with different numbers of features (1-20). A ten-times five-fold stratified method was utilized for cross-validation. Our results suggested that the trained models with different feature numbers showed an overall accuracy between 67% and 89% at the default cutoff and between 70% and 94% at the optimal cutoff. The prediction accuracy roughly increased along with the number of features. Among all the models, the one with 17 features could be considered the best model with the highest AUC of 0.96, and the model with 8 features could be considered the optimal model, which had a comparable AUC of 0.93 and fewer features. This study revealed that gait characteristics in regular walking could accurately predict the trip-related fall risk for healthy older adults, and the developed models could be a helpful assessment tool to identify the individuals at risk of trip-falls.

Gait Slip-Induced Fall-Type Assessment Based on Regular Gait Characteristics in Older Adults

Older adults could experience split falls or feet-forward falls following an unexpected slip in gait due to different neuromuscular vulnerabilities, and different intervention strategies would be required for each type of faller. Thus, this study aimed to investigate the key factors affecting the fall types based on regular gait pattern. A total of 105 healthy older adults who experienced a laboratory-induced slip and fall were included. Their natural walking trial immediately prior to the novel slip trial was analyzed. To identify the factors related to fall type, gait characteristics and demographic factors were determined using univariate logistic regression, and then stepwise logistic regression was conducted to assess the slip-induced fall type based on these factors. The best fall-type prediction model involves gait speed and recovery foot angular velocity, which could predict 70.5% of feet-forward falls and 86.9% of split falls. Body mass index was also a crucial fall-type prediction with an overall prediction accuracy of 70.5%. Along with gait parameters, 84.1% of feet-forward falls and 78.7% of split falls could be predicted. The findings in this study revealed the determinators related to fall types, which enhances our knowledge of the mechanism associated to slip-induced fall and would be helpful for the development of tailored interventions for slip-induced fall prevention.