Body postural sway analysis in older people with different fall histories

Discriminative ability of instrumented cognitive-motor assessments to distinguish fallers from non-fallers

In older populations, sensitive fall risk assessment tools are important to timely intervene and prevent falls. Instrumented assessments have shown to be superior to standardized fall risk assessments such as the Timed Up and Go Test (TUG) and should capture both motor and cognitive functions. Therefore, the aim was to test novel instrumented assessments with and without a cognitive component. One hundred thirty-seven older adults aged 73.1 ± 7.3 years, 38 categorized as fallers and 99 as non-fallers, conducted five instrumented assessments on the Dividat Senso, a pressure sensitive stepping platform, and three standardized geriatric assessments (TUG, TUG-dual task, 30-s Sit-to-Stand Test (STS)). T-tests were applied to compare the test performance of fallers versus non-fallers. Furthermore, logistic regression analyses and area under the curve (AUC) analyses were performed. Statistically significant differences between fallers and non-fallers were found in the Go/No-Go test (p = .001, d = .72), the TUG (p = .014, d = .48), and the STS (p = .008, d = .51). Only the Go/No-Go test contributed significantly to all regression models. Significant AUC values were found for the Reaction Time Test (RTT) (AUC = .628, p = .023), Go/No-Go (AUC = .673, p = .002), TUG (AUC = .642, p = .012), and STS (AUC = .690, p = .001). The Go/No-Go test measuring inhibition showed the best discriminative ability suggesting added value of instrumented assessments with a cognitive component for clinical fall risk assessment in relatively healthy older adults. The study should be extended with a frailer population, in which TUG and the other instrumented assessments are possibly good predictors as well.

Biomechanical Balance Measures During Timed Up and Go Test Improve Prediction of Prospective Falls in Older Adults

OBJECTIVE

To assess the feasibility of using biomechanical gait balance measures, the frontal and sagittal plane center of mass (COM)-Ankle angles, to prospectively predict recurrent falls in community-dwelling older adults.

DESIGN

A cohort study with a 1-year longitudinal follow-up. Logistic regression was used to test the ability of the COM-Ankle angles to predict prospective falls.

SETTING

General community.

PARTICIPANTS

Sixty older adults over the age of 70 years were recruited using a volunteer sample.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE(S)

Biomechanical balance parameters: the sagittal and frontal plane COM-Ankle angles during the sit-to-walk and turning phases of the timed Up and Go test. The COM-Ankle angles are the inclination angles of the line formed by the COM and lateral ankle (malleolus) marker of the stance foot in the sagittal and frontal planes. We also included the following clinical balance tests in the analysis: Activity-Specific Balance Confidence, Berg Balance Scale, Fullerton Advanced Balance scale, and timed Up and Go test. Their abilities to predict falls served as a reference for the biomechanical balance parameters.

RESULTS

When the biomechanical gait balance measures were added to all the confounders, the explained variance was increased from 25.3% to 50.2%. Older adults who have a smaller sagittal plane COM-Ankle angle at seat-off, a greater frontal plane COM range of motion during STW and a smaller frontal plane angle during turning were more likely to become recurrent fallers.

CONCLUSION(S)

Our results indicated that dynamic biomechanical balance parameters could provide valuable information about a participant's future fall risks beyond what can be explained by demographics, cognition, depression, strength, and past fall history. Among all biomechanical parameters investigated, frontal plane COM motion measures during STW and turning appear to be the most significant predictors for future falls.

Clinical Static Balance Assessment: A Narrative Review of Traditional and IMU-Based Posturography in Older Adults and Individuals with Incomplete Spinal Cord Injury

Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual's quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations.

Toward a Multi-Modal Brain-Body Assessment in Parkinson's Disease: A Systematic Review in fNIRS

Parkinson's disease (PD) causes impairments in cortical structures leading to motor and cognitive symptoms. While common disease management and treatment strategies mainly depend on the subjective assessment of clinical scales and patients' diaries, research in recent years has focused on advances in automatic and objective tools to help with diagnosing PD and determining its severity. Due to the link between brain structure deficits and physical symptoms in PD, objective brain activity and body motion assessment of patients have been studied in the literature. This study aimed to explore the relationship between brain activity and body motion measures of people with PD to look at the feasibility of diagnosis or assessment of PD using these measures. In this study, we summarised the findings of 24 selected papers from the complete literature review using the Scopus database. Selected studies used both brain activity recording using functional near-infrared spectroscopy (fNIRS) and motion assessment using sensors for people with PD in their experiments. Results include 1) the most common study protocol is a combination of single tasks. 2) Prefrontal cortex is mostly studied region of interest in the literature. 3) Oxygenated haemoglobin (HbO 2) concentration is the predominant metric utilised in fNIRS, compared to deoxygenated haemoglobin (HHb). 4) Motion assessment in people with PD is mostly done with inertial measurement units (IMUs) and electronic walkway. 5) The relationship between brain activity and body motion measures is an important factor that has been neglected in the literature.

Ecological Fall Prediction Sensitivity, Specificity, and Accuracy in Patients with Mild Cognitive Impairment at a High Risk of Falls

While falls among patients with mild cognitive impairment (MCI) have been closely associated with an increased postural sway during ecological activities of daily living, there is a dearth of postural sway detection (PSD) research in ecological environments. The present study aimed to investigate the fall sensitivity, specificity, and accuracy of our PSD system. Forty healthy young and older adults with MCI at a high risk of falls underwent the sensitivity, specificity, and accuracy tests for PSD by simultaneously recording the Berg Balance Scale and Timed Up and Go in ecological environments, and the data were analyzed using the receiver operating characteristic curve and area under the curve. The fall prediction sensitivity ranged from 0.82 to 0.99, specificity ranged from 0.69 to 0.90, and accuracy ranged from 0.53 to 0.81. The PSD system's fall prediction sensitivity, specificity, and accuracy data suggest a reasonable discriminative capacity for distinguishing between fallers and non-fallers as well as predicting falls in older adults with MCI in ecological testing environments.

Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Evaluation of the Effect of Reduction Mammoplasty on Body Posture in Patients with Macromastia

BACKGROUND

Breast hypertrophy is a significant health problem with both physiological and psychological impacts on the patients' lives. Patients with macromastia adopt a corrective posture due to the effect of the breast on the center of gravity and possibly in a subconscious effort to conceal their breasts.

OBJECTIVE

This study aimed to evaluate whether the posture of patients with macromastia changed after the reduction of mammoplasty.

MATERIAL AND METHODS

In this prospective study, patients with breast cup sizes C, D, and DD were scheduled for reduction mammoplasty in 3 Shiraz University Hospitals. Age, weight, height, and preoperative cup sizes of the breasts were recorded for every patient, and all patients underwent posture analysis with forceplate before and after reduction mammoplasty. Finally, the preoperative and postoperative data were compared.

RESULTS

Mean age at the time of reduction mammaplasty was 43.57±9.1; the mean pre-operation, such as weight, height, and mean the body mass index (BMI) was 76.57±10 kg, 158.28±6 cm and 30.57±4.1, respectively. The average Anterior-posterior (AP) direction velocity before and after the surgery was 0.85±0.12 cm/s and 0.79±0.098, respectively. These values were 0.83±0.09 and 0.81±0.10 for the mediolateral direction. The Detrended Fluctuation Analysis (DFA) value for the AP direction was 1.63±0.3 and 1.60±0.2 for pre-and post-surgery, respectively, which was not statistically different. The DFA value for maximum likelihood (ML) direction was 1.65±0.2 and 1.48±0.2 in pre-op and post-op, respectively, which was statistically significantly different.

CONCLUSION

Reducing the weight of enlarged breasts can correct disturbed sagittal balance and postural sway.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

The association between fear of falling and occurrence of falls: a one-year cohort study

BACKGROUND

Both multiple fall experiences and fear of falling (FoF) would make people susceptible to another fall; however, the associations are unknown. This study investigates the association of FoF with fall occurrence among older adults according to their fall history.

METHODS

In this study, we adopted a longitudinal observational design. We visited 20 community centers to recruit 1,025 older adults (aged 65 years or older). At baseline, FoF was assessed using a single-item questionnaire. The number of falls in the past year was obtained via a self-questionnaire and participants were classified into three fall history groups (0: non-faller, 1: single faller, 2 or more: multiple faller). After a year of following-up, the number of falls during the year was considered as the main outcome. Poisson regression models clarified the influence of FoF on fall occurrence during the one-year follow-up, according to the participants' fall history.

RESULTS

The final sample comprised 530 individuals (follow-up rate: 530/801, 66.4%). Fall history, FoF, and interaction between multiple fallers and FoF were significant in the adjusted statistical model (rate ratio [95% confidence interval]: single faller = 2.81 [1.06, 6.30], multiple faller = 13.60 [8.00, 23.04], FoF = 3.70 [2.48, 5.67], multiple faller*FoF = 0.37 [0.20, 0.68]).

CONCLUSIONS

We found that FoF was associated with the occurrence of falls in community-dwelling older adults. However, its association was lower in multiple fallers.

Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.

Dynamic Stability and Trunk Control Improvements Following Robotic Balance and Core Stability Training in Chronic Stroke Survivors: A Pilot Study

Stroke survivors show greater postural oscillations and altered muscular activation compared to healthy controls. This results in difficulties in walking and standing, and in an increased risk of falls. A proper control of the trunk is related to a stable walk and to a lower falling risk; to this extent, rehabilitative protocols are currently working on core stability. The main objective of this work was to evaluate the effectiveness of trunk and balance training performed with a new robotic device designed for evaluation and training of balance and core stability, in improving the recovery of chronic stroke patients compared with a traditional physical therapy program. Thirty chronic stroke patients, randomly divided in two groups, either underwent a traditional rehabilitative protocol, or a robot-based program. Each patient was assessed before and after the rehabilitation and at 3-months follow-up with clinical and robot-based evaluation exercises focused on static and dynamic balance and trunk control. Results from clinical scores showed an improvement in both groups in balance and trunk control. Robot-based indices analysis indicated that the experimental group showed greater improvements in proprioceptive control, reactive balance and postural control in unstable conditions, compared to the control group, showing an improved trunk control with reduced compensatory strategies at the end of the training. Moreover, the experimental group had an increased retention of the benefits obtained with training at 3 months follow up. These results support the idea that such robotic device is a promising tool for stroke rehabilitation.

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

BACKGROUND

Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults.

METHODS

Community-dwelling subjects aged ≥ 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters-were identified by means of a cross-validation method.

RESULTS

Of the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall ("fallers"), while 64 (67%) did not ("non-fallers"). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72-0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71-0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55-0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (p<0.001), respectively. The best model that comprised only robotic parameters (the 20 parameters identified in the final model) achieved a better performance than the clinical parameters alone, but worse than the combination of both clinical and robotic variables (ROC AUC: 0.73, 95% CI 0.63-0.83).

CONCLUSION

A multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken.