Fall prediction in a quiet standing balance test via machine learning: Is it possible?

The elderly population is growing rapidly in the world and falls are becoming a big problem for society. Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. They are considered the gold standard to indicate optimal mobility and performance individually, but their sensitivity and specificity are not good enough to predict who is at higher risk of falling. An innovative approach for fall prediction is the machine learning. Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms in classify participants by age, number of falls and falls frequency based on features extracted from a public database of stabilometric assessments. 163 participants (116 women and 47 men) between 18 and 85 years old, 44.0 to 75.9 kg mass, 140.0 to 189.8 cm tall, and 17.2 to 31.9 kg/m2 body mass index. Six different machine learning algorithms were tested for this classification, which included Logistic Regression, Linear Discriminant Analysis, K Nearest-neighbours, Decision Tree Classifier, Gaussian Naive Bayes and C-Support Vector Classification. The machine learning algorithms were applied in this database which has sociocultural, demographic, and health status information about participants. All algorithm models were able to classify the participants into young or old, but our main goal was not achieved, no model identified participants at high risk of falling. Our conclusion corroborates other works in the biomechanics field, arguing the static posturography, probably due to the low daily living activities specificity, does not have the desired effects in predicting the risk of falling. Further studies should focus on dynamic posturography to assess the risk of falls.

Systematic review of candidate prognostic factors for falling in older adults identified from motion analysis of challenging walking tasks.

The objective of this systematic review is to identify motion analysis parameters measured during challenging walking tasks which can predict fall risk in the older population. Numerous studies have attempted to predict fall risk from the motion analysis of standing balance or steady walking. However, most falls do not occur during steady gait but occur due to challenging centre of mass displacements or environmental hazards resulting in slipping, tripping or falls on stairs. We conducted a systematic review of motion analysis parameters during stair climbing, perturbed walking and obstacle crossing, predictive of fall risk in healthy older adults. We searched the databases of Pubmed, Scopus and IEEEexplore.A total of 78 articles were included, of which 62 simply compared a group of younger to a group of older adults. Importantly, the differences found between younger and older adults did not match those found between older adults at higher and lower risk of falls. Two prospective and six retrospective fall history studies were included. The other eight studies compared two groups of older adults with higher or lower risk based on mental or physical performance, functional decline, unsteadiness complaints or task performance. A wide range of parameters were reported, including outcomes related to success, timing, foot and step, centre of mass, force plates, dynamic stability, joints and segments. Due to the large variety in parameter assessment methods, a meta-analysis was not possible. Despite the range of parameters assessed, only a few candidate prognostic factors could be identified: older adults with a retrospective fall history demonstrated a significant larger step length variability, larger step time variability, and prolonged anticipatory postural adjustments in obstacle crossing compared to older adults without a fall history. Older adults who fell during a tripping perturbation had a larger angular momentum than those who did not fall. Lastly, in an obstacle course, reduced gait flexibility (i.e., change in stepping pattern relative to unobstructed walking) was a prognostic factor for falling in daily life. We provided recommendations for future fall risk assessment in terms of study design.In conclusion, studies comparing older to younger adults cannot be used to explore relationships between fall risk and motion analysis parameters. Even when comparing two older adult populations, it is necessary to measure fall history to identify fall risk prognostic factors.

Strength Training to Prevent Falls in Older Adults: A Systematic Review with Meta-Analysis of Randomized Controlled Trials.

We performed a systematic review with meta-analysis of randomized controlled trials (RCTs) to assess the effects of strength training (ST), as compared to alternative multimodal or unimodal exercise programs, on the number of falls in older adults (≥60 years). Ten databases were consulted (CINAHL, Cochrane Library, EBSCO, EMBASE, PEDro, PubMed, Scielo, Scopus, SPORTDiscus and Web of Science), without limitations on language or publication date. Eligibility criteria were as follows: RCTs with humans ≥60 years of age of any gender with one group performing supervised ST and a group performing another type of exercise training, reporting data pertaining falls. Certainty of evidence was assessed with Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Meta-analysis used a random effects model to calculate the risk ratio (RR) for number of falls. Five RCTs with six trials were included (n = 543, 76% women). There was no difference between ST and alternative exercise interventions for falls (RR = 1.00, 95% CI 0.77-1.30, p = 0.99). The certainty of evidence was very low. No dose-response relationship could be established. In sum, ST showed comparable RR based on number of falls in older adults when compared to other multimodal or unimodal exercise modalities, but evidence is scarce and heterogeneous, and additional research is required for more robust conclusions. Registration: PROSPERO CRD42020222908.

Perception of impact is affected by stimulus intensity.

Perception of external loads may be a central topic to understand adjustments to the mechanical demands during movement. Nevertheless, the association between the perceived and the real load received is still controversial. This study aimed to correlate vertical ground reaction force (vGRF) to the perception of impact in different regimens of stimulus application. Ten physically active men performed drop jumps from four different heights (0.20, 0.40, 0.60 and 0.80 m). A force plate measured the vGRF, while perception of impact was evaluated through Borg's Ratings of Perceived Exertion. Higher values of maximum vGRF (Fy_max) and impulse of the first 50 ms (I_50), and reduced time to reach Fy_max indicate increased external forces as drop jump height raised. Perception of impact increased gradually with increasing jump height for I_50. Fy_max and I_50 showed moderate to strong correlations to perceived load for 70% and 90% of participants, respectively. Higher and different intensity of stimulus facilitated the perception of impact, presenting moderate to strong correlations to kinetic parameters related to external load during landing from drop jump. Perception of higher impacts could be used as a surrogate to monitor 'real' impacts and possibly also for managing impact-related injury risk.

Dynamic Parameters Variability: Time Interval Interference on Ground Reaction Force During Running.

The aim of this study was to determine the effect of the time between measures on ground reaction force running variability; 15 healthy men (age = 23.8 ± 3.7 years; weight = 72.8 ± 7.7 kg; height 174.3 ± 8.4 cm) performed two trials of running 45 minutes at 9 km/hr at intervals of seven days. The ground reaction forces were recorded every 5 minutes. The coefficients of variation of indicative parameters of the ground reaction forces for each condition were compared. The coefficients of variations of the ground reaction forces curve analyzed between intervals and sessions were 21.9% and 21.48%, respectively. There was no significant difference for the ground reaction forces parameters Fy1, tFy1, TC1, Imp50, Fy2, and tFy2 between intervals and sessions. Although the ground reaction forces variables present a natural variability, this variability in intervals and in sessions remained consistent, ensuring a high reliability in repeated measures designs.

Influence of exercise order on electromyographic activity during upper body resistance training.

The purpose of this study was to investigate the effects of exercise order on electromyographic activity in different muscle groups among youth men with experience in strength training. Three sets of 8 RM were performed of each exercise in two sequences order: (a) sequence A: bench press, chest fly, shoulder press, shoulder abduction, close grip bench press and lying triceps extension; (b) sequence B: the opposite order. The electromyographic activity was analyzed in the sternocostal head of the pectoralis major, anterior deltoid, and long head triceps brachii, normalized for maximal voluntary isometric contraction. The muscles activity of the sternocostal head of the pectoralis major, anterior deltoid, and long head triceps brachii showed significant interaction between sequence and exercise. The sternocostal head of the pectoralis major showed considerably higher activity in sequence A (100.13 ± 13.56%) than sequence B (81.47 ± 13.09%) for the chest fly. The anterior deltoid showed significantly higher electromyographic activity in sequence B (86.81 ± 40.43%) than sequence A (66.15 ± 22.02%) for the chest fly, whereas for the lying triceps extension, the electromyographic activity was significantly higher in sequence A (53.89 ± 27.09%) than sequence B (34.32 ± 23.70%). For the long head triceps brachii, only the shoulder press showed differences between sequences (A = 52.43 ± 14.64 vs. B = 38.53 ± 16.26). The present study showed that the exercise order could modify the training results even though there was no alteration in volume and intensity of the exercise. These changes may result in different training adaptations.