Fall Risk Assessment Research Articles

Peripheral neuropathy, an independent risk factor for falls in the elderly, impairs stepping as a postural control mechanism: A case-cohort study.

Peripheral neuropathies perturbate the sensorimotor system, causing difficulties in walking-related motor tasks and, eventually, falls. Falls result in functional dependency and reliance on healthcare, especially in older persons. We investigated if peripheral neuropathy is a genuine risk factor for falls in the elderly and if quantification of postural control via posturography is helpful in identifying subjects at risk of falls. Seventeen older persons with a clinical polyneuropathic syndrome of the lower limbs and converging electrophysiology were compared with 14 older persons without polyneuropathy. All participants were characterized via quantitative motor and sensory testing, neuropsychological assessment, and self-questionnaires. Video-nystagmography and caloric test excluded vestibulocochlear dysfunction. For further analysis, all subjects were stratified into fallers and non-fallers. Overall, 28 patients underwent computerized dynamic posturography for individual fall risk assessment. Regression analyses were performed to identify risk factors and predictive posturography parameters. Neuropathy is an independent risk factor for falls in the elderly, while no differences were observed for age, gender, weight, frailty, DemTect test, timed "Up & Go" test, and dizziness-related handicap score. In computerized dynamic posturography, fallers stepped more often to regain postural control in challenging conditions, while the Rhythmic Weight Shift test showed a lack of anterior-posterior bidirectional voluntary control. Our study confirms peripheral neuropathy as a risk factor for older persons' falls. Fallers frequently used stepping to regain postural control. The voluntary control of this coping movement was impaired. Further investigations into these parameters' value in predicting the risk of falls in the elderly are warranted.

The reliability and validity of the Lie‑To‑Sit‑To‑Stand‑To‑Walk transfer test in total knee arthroplasty.

Total knee arthroplasty (TKA) is the standard procedure for treating end-stage knee osteoarthritis. Nevertheless, some residual issues can persist, leading to various problems, including pain, compromised strength, impaired proprioception, postural instability, dynamic balance issues, and gait deficiencies. The Lie-to-Sit-to-Stand-to-Walk Transfer Test (LSSWT) implements a multi-faceted methodology to evaluate basic transfer abilities. The purpose of this study is to investigate the validity, reliability, and minimal clinically important difference of the LSSWT in TKA patients. Twenty-two patients with TKA were enrolled in this study. The patients were administered the LSSWT, the Timed Up and Go Test (TUG), and the Hospital for Special Surgery (HSS) knee score. Patients rested between the tests for an hour to prevent fatigue. The mean age was 68.1 ± 2.59 years and the mean HSS Knee Score was 85.43 ± 3.47 of the patients. The relative (ICC coefficient) and absolute (SEM and SRD95) reliability values were 0.88, 1.21, and 3.33 respectively. The Spearman correlation coefficient of the LSSWT with the TUG was 0.63. The LSSWT displays excellent reliability and high validity in assessing fall risk, complex dynamic balance, and mobility required for daily activities in patients post-TKA. The low MCID value (3.33) indicates its sensitivity and ability to identify minor changes in a patient's status over time or in response to rehabilitation applications. Therefore, it is recommended to use the LSSWT when evaluating fall risk, dynamic balance, and mobility for community living, discharge planning, or facility admission.

Fall risk prediction using temporal gait features and machine learning approaches.

Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible. This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers. Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model's ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task. The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model's generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.

Potential of Kinect-assessed stepping test for assessing fall risk in community-dwelling older women

BackgroundPreviously, we have shown that movement path distance and displacement during a 20-sec stepping test (ST) discriminates between independent and dependent-living older adults. The present study aims to determine whether movement and displacement characteristics during ST are an indicator of risk of falling in independent-living, community-dwelling older women MethodsIndependent-living older women recruited for this cross-sectional study were divided into a fall (F: n = 154) or no fall (NF: n = 847) history group. Each participant completed one trial of an eyes open, ST. ST outcome measures included head total movement distance (TMD), maximum movement displacement of the head (MMD), total knee movement distance (KMD), maximum height of the left and right knees (MKH), and step number (STEP) as determined with a KINECTTM infrared depth sensor. Ratio of KMD/TMD was calculated to index upper- body sway relative to the lower-body. ResultsAge, height, body mass, and BMI were not different between groups. KMD (F: median: 4.812 m, interquartile range (IQR): 3.720–7.718 m; NF: 5.309 m, 4.266–6.600 m), TMD / KMD (F: 0.145 m, 0.107–0.196 m; NF: 0.127 m, 0.100–0.170 m) and MKH (F: 0.073 m, 0.052–0.106 m, NF: 0.091 m, 0.060–0.135 m) were different between F and NF (Mann-Whitney, P < 0.05). Logistic regression revealed an association between falls and KMD (OR 1.232, 95 % CI 1.108–1.370). ConclusionsCharacteristics of ST movement and displacement assessed with KinectTM provide useful indicators for fall risk assessment among independent-living, community-dwelling older women.

Translation, validity and reliability of the fall risk scale for older adults

IntroductionFalls in older adults are a common and serious threat to health and functional independence. It can cause psychological distress, inability to participate in activities of daily living, brain injury, fractures, and even death. The aim was to analyze the psychometric properties of the self-assessed fall risk scale (FRS) that measures the risk of falls in older adults in a central region of Chile, as well as to verify the concurrent validity against functional fitness tests.Materials and methodsA descriptive cross-sectional study was carried out in 222 older adults (OA) [34 males and 188 females] with an age range of 65 to 85 years. The 13-item self-perceived fall risk scale (FRS) was validated. Anthropometric measures (weight, height and waist circumference) were assessed. Five functional fitness tests were measured (right and left hand grip strength, biceps curl, up-and-go, agility and 6-minute walk test). Validation was performed by construct validation [(exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)] and concurrent validity.ResultsThe EFA revealed 4 factors in the FRS scale [1: fear of falling (variance 27.1%), 2: use of assistive devices (variance 10.6%), 3: loss of sensation (variance 9.3%), and 4: limited mobility (variance 8.3%)]. Factor loadings ranged from ∼ 0.50 to 0.83 across the 4 components. The Kaiser-Meyer Olkin sample adequacy test (KMO) reflected adequate adequacy (KMO = 0.79, chi-square (X2) = 498.806, gl = 78, p = 0.00). The CFA showed a satisfactory final fit [chi-square (X2) = 126.748, Root mean squared error of approximation (RMSEA) = 0.042, Tucker-Lewis Index (TLI) = 0.946, Comparative fit index (CFI) = 0.935 y Normed fit index (NFI) = 0.90. The relationships between the FRS scale and functional fitness tests (right and left hand grip strength, biceps curl, up-and-go, agility and 6-minute walk test) ranged from low to moderate (r= -0.23 to 0.41).ConclusionThe FRS scale showed acceptable validity and reliability in older adults in central region of Chile. It is expected that this scale will be useful for assessing fall risk in clinical and epidemiological settings in the aging Chilean population.

Sensor-Based Multifaceted Feature Extraction and Ensemble Elastic Net Approach for Assessing Fall Risk in Community-Dwelling Older Adults.

Accurate identification of community-dwelling older adults at high fall risk can facilitate timely intervention and significantly reduce fall incidents. Analyzing gait and balance capabilities via feature extraction and modeling through sensor-based motion data has emerged as a viable approach for fall risk assessment. However, the existing approaches for extracting key features related to fall risk lack inclusiveness, with limited consideration of the non-linear characteristics of sensor signals, such as signal complexity, self-similarity, and local stability. In this study, we developed a multifaceted feature extraction scheme employing diverse feature types, including demographic, descriptive statistical, non-linear, spatiotemporal and spectral features, derived from three-axis accelerometers and gyroscope data. This study is the first attempt to investigate non-linear features related to fall risk in multi-task scenarios from a dynamic system perspective. Based on the extracted multifaceted features, we propose an ensemble elastic net (E-E-N) approach for handling imbalanced data and offering high model interpretability. The E-E-N utilizes bootstrap sampling to construct base classifiers and employs a weighting mechanism to aggregate the base classifiers. We conducted a set of validation experiments using real-world data for comprehensive comparative analysis. The results demonstrate that the E-E-N approach exhibits superior predictive performance on fall risk classification. Our proposed approach offers a cost-effective tool for accurately assessing fall risk and alleviating the burden of continuous health monitoring in the long term.

Strategies used by nurse leaders to support the delivery of falls prevention practices in hospitals.

Despite prevention efforts, falls in hospital are a common and ongoing safety concern, with older people more likely to fall and experience harm as a result of falls. Clinical guidelines recommend multifactorial falls risk assessment and multidomain, personalised interventions to reduce falls risks in hospitals. This article reflects on findings from a multi-site study on the implementation of multifactorial falls prevention practices that informed the development of actionable guidance. The discussion focuses on strategies used by nurse leaders, at different levels of seniority, that shaped practice on orthopaedic and older person wards. While falls risk assessment documentation was monitored routinely by senior leaders, in practice falls prevention often relied on risk screening and enhanced patient supervision. Findings suggest that nurses need to be empowered to lead practices that modify and mitigate individual falls risks where possible, with greater multidisciplinary and patient and carer involvement.

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 TimedUp 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 ± 7.3years, 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.

Assessing muscle quality as a key predictor to differentiate fallers from non-fallers in older adults.

Falling is an important public health issue because of its prevalence and severe consequences. Evaluating muscle performance is important when assessing fall risk. The study aimed to identify factors [namely muscle capacity (strength, quality, and power) and spatio-temporal gait attributes] that best discriminate between fallers and non-fallers in older adults. The hypothesis is that muscle quality, defined as the ratio of muscle strength to muscle mass, is the best predictor of fall risk. 184 patients were included, 81% (n = 150) were women and the mean age was 73.6 ± 6.83years. We compared body composition, mean grip strength, spatio-temporal parameters, and muscle capacity of fallers and non-fallers. Muscle quality was calculated as the ratio of maximum strength to fat-free mass. Mean handgrip and power were also controlled by fat-free mass. We performed univariate analysis, logistic regression, and ROC curves. The falling patients had lower muscle quality, muscle mass-controlled power, and mean weighted handgrip than the non-faller. Results showing that lower muscle quality increases fall risk (effect size = 0.891). Logistic regression confirmed muscle quality as a significant predictor (p < .001, OR = 0.82, CI [0.74; 0.89]). ROC curves demonstrated muscle quality as the most predictive factor of falling (AUC = 0.794). This retrospective study showed that muscle quality is the best predictor of fall risk, above spatial and temporal gait parameters. Our results underscore muscle quality as a clinically meaningful assessment and may be a useful complement to other assessments for fall prevention in the aging population.

Assessing osteoporosis awareness and knowledge levels of Turkish multiple sclerosis patients: an observational study.

To assess the knowledge and awareness of osteoporosis in Turkish multiple sclerosis (MS) patients. Three hundred twenty-five patients were asked to fill out the osteoporosis awareness scale (OAS), which has a scoring range of 31-124, and the revised osteoporosis knowledge test (R-OKT), which has a score range of 0-32. Fall risk assessment for patients was conducted using the timed up-and-go test (high risk: > 30, moderate risk: 29-20, low risk: 14-19, and no risk: < 13 s). An average score of 59.42 ± 19.63(31%) on the OAS and 14.99 ± 5.39 (47%) on the R-OKT was detected. No difference was found in R-OKT scores between genders, education levels, and patients with different fall risks and between genders and education levels in OAS scores. Patients with low, medium, and high fall risks exhibited higher OAS scores compared to those without any fall risk (p = 0.006, p < 0.001, and p < 0.001, respectively). No correlation was found between gender and education level with OAS and R-OKT scores and between the R-OKT scores and the risk of falls. There was a weak correlation between the OAS scores and an increased risk of falls (r = 0.269, p < 0.001). No differences between the age groups regarding OAS and R-OKT scores were observed. Turkish MS patients have inadequate knowledge and low awareness of OP compared to the healthy population. Urgent action is warranted to educate and support individuals with MS, implementing targeted interventions and awareness campaigns to improve early detection and prevent osteoporosis-related complications. ClinicalTrials.gov Identifier: NCT05760651.

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.

Alignment in motion: Fall risk in spine patients and the effect of vision, support surface, and adaptation on the cone of economy

BackgroundSeveral assessment tools have been developed to estimate a patient's likelihood risk of falling. None of these measures estimate the contributions of the visual, vestibular, and somatosensory systems to fall risk, especially in patients with degenerative lumbar spine disease. MethodsDegenerative lumbar spine patients with radiculopathy (LD) and healthy subjects who were 35-70 years old without spine complaints were recruited. Patient reported outcome measures (PROMs) were collected prior to testing. Fall risk assessment was completed using Computer Dynamic Posturography (CDP), a computer-controlled balance machine that allows cone of economy (CoE) and cone of pressure (CoP) measurements. All patients completed Sensory Organization Tests (SOT) which include normal and perturbed stability, both with and without visual cues. ResultsIn total, 43 spine patients and 12 healthy controls were included, with mean age 57.8 years, 39.5% females, and mean BMI of 29.3 kg/m2. Nearly all CoE and most CoP dimensions were found to be larger in LD patients compared to controls across nearly all subtests (p<.05), with the largest dimensions generally observed in the surrounding and support sway testing condition. In LD patients, ODI and PROMIS Pain Interference were negatively correlated with CoE and CoP measurements (p<.05). ConclusionsIn this prospective study, body sway was assessed as a function of CoE and CoP using the CDP system and was found to be elevated in spine patients, especially when they experienced increasing levels of visual and vestibular stimulation. The ability to identify the primary drivers of balance disorders is essential in spine patients and may be helpful in the development of a patient-specific treatment plan, which may in the future aid with fall-prevention initiatives.

Development and External Validation of a Machine Learning–based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study

ObjectivesTo develop and externally validate a machine learning–based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool. DesignProspective cohort study. Setting and ParticipantsA total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China. MethodsPotential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP). ResultsThe 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram. Conclusions and ImplicationsThe machine learning–based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Capacitive Neuromodulation via Material-Based Passive Interaction: Efficacy in Motor Function Improvement in Parkinson Disease.

A non-invasive and non-pharmacological approach is evaluated for the proprioceptive and postural improvement of PD subjects. The authors evaluated the effectiveness of a class I medical device according to EU regulation 745/2017 designed to develop the mechanism of action based on the modulation of action potentials, which occurs in prevalent pathways of the afferent peripheral nervous system efferent in subjects with spasticity. The present observational study, structured in a double-blind randomized manner, therefore, had the main aim of evaluating the ability of the device to improve on the motor and proprioceptive function of PD patients. This study was based on the instrumented gait analysis performed according to the Timed Up and Go (TUG) test procedure, as well as using a fall risk assessment in accordance with the Berg Balance Scale (BBS) procedures. This study involved 25 participants in the active group (no placebo) and 25 in the non-active group (placebo), the latter to whom non-functional devices were applied, but in every respect identical to the functional devices applied to the 25 patients in the no placebo group. Data analysis was conducted using statistical methodologies for statistics, the statistical significance of the results for the observed samples and the interdependence between the measured variables. The study of the mechanism of action based on the remodulation of action potentials was preliminary conducted through numerical modeling of the Hodgkin-Huxley axon, modified by introducing the influence of the capacitive device applied in clinical tests into the validated model to target the dielectric properties of materials constituting the passive sensor. The use of the neuromodulation device promises observable improvements in motor function among PD patients, including increased limb mobility and greater postural stability.

CARING NURSES IN THE PREVENTION OF FALL RISK PATIENTS IN THE INPATIENT ROOM

The inpatient treatment room is a room that has the potential to have a risk of falling in patients with heart, lung and nerve diseases such as strokes who experience decreased consciousness, muscle weakness and impaired mobility which are at risk of falling from bed or when going to the bathroom. Nurses who are not Caring for patients can result in the risk of falling for patients. The inability of nurses to carry out fall risk assessments will be detrimental to patients during hospitalization. The purpose of this study was to explore the description of caring for nurses in preventing patients at risk of falling in the inpatient room of Permata Medika Ngaliyan Hospital, Semarang City. The research method uses a qualitative research design with a phenomenological approach. Research informants are implementing nurses. Determination of the sample using purposive sampling. Collecting data by in-depth interviews with informants using a voice recorder. The results of the study identified several themes related to the research objectives, namely the caring behavior of nurses when providing nursing care to patients, the impact of caring for patients on patients, the implementation of patient fall risk assessments in hospitals, and the efforts of nurses to prevent the risk of falling in patients. Conclusion: Nurses' caring behavior is a dynamic approach, where nurses work to increase their concern for patients, especially patients at risk of falling as an effort to prevent unexpected events in maintaining the quality of nursing care services.

Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.

Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagnosis could be a great help. A popular clinical test for fall risk is the five times sit-to-stand. The time taken to complete the test is the most commonly used metric to identify the most at-risk patients. However, tracking the movement of skeletal joints can provide much richer insights. We use markerless motion capture, allied with a representational model, to identify those at risk of falls. Our method uses an LSTM autoencoder to derive a distance measure. Using this measure, we introduce a new scoring system, allowing individuals with differing falls risks to be placed on a continuous scale. Evaluating our method on the KINECAL dataset, we achieved an accuracy of 0.84 in identifying those at elevated falls risk. In addition to identifying potential fallers, our method could find applications in rehabilitation. This aligns with the goals of the KINECAL Dataset. KINECAL contains the recordings of 90 individuals undertaking 11 movements used in clinical assessments. KINECAL is labelled to disambiguate age-related decline and falls risk.

The Impact of Misaligned Perceived and Objective Fall Risk in Cognitively Impaired Older People.

Cognitive impairment (CI) may impair the ability to accurately perceive physical capacity and fall risk. We investigated perceived (measured as concern about falls) and physiological fall risk in community-dwelling older people with CI, the characteristics of the aligned and misaligned groups and the impact of misaligned perceptions on falls. Participants (n= 293) with mild-moderate CI were classified into four groups based on validated physiological and perceived fall risk assessments: 1) vigorous: low perceived and physiological fall risk; 2) anxious: high perceived and low physiological fall risk; 3) unaware: low perceived and high physiological fall risk; and 4) aware: high perceived and physiological fall risk. Groups were compared with respect to neuropsychological and physical function, activity and quality of life measures, and prospective falls (12-months). The anxious (IRR = 1.70, 95% CI = 1.02-2.84), unaware (IRR = 2.00, 95% CI = 1.22-3.26), and aware (IRR = 2.53, 95% CI = 1.67-3.84) groups had significantly higher fall rates than the vigorous group but fall rates did not significantly differ among these groups. Compared with the vigorous group: the anxious group had higher depression scores and reduced mobility and quality of life; the unaware group had poorer global cognition, executive function and mobility and lower physical activity levels; and the aware group had an increased prevalence of multiple physical and cognitive fall risk factors. Fall rates were increased in participants who had increased perceived and/or physiological fall risk. Contrasting fall risk patterns were evident in those who under- and over-estimated their fall risk. Understanding these characteristics will help guide fall risk assessment and prevention strategies in community-dwelling older people with CI.

Assessing fall risk in osteoporosis patients: a comparative study of age-matched fallers and nonfallers.

This study aimed to investigate sway parameters and physical activity level of the age/gender-matched older adults with osteoporosis faller and nonfaller patients. By examining these factors, our objective was to understand how these faller and nonfaller groups with osteoporosis differed particularly in terms of balance capabilities and their impact on physical activity levels. We recruited 24 patients with osteoporosis: 12 who reported a fall within a year before recruitment (fallers) and 12 without falls (nonfallers). Given the close association between biochemical markers of musculoskeletal health such as serum calcium, parathyroid hormone (PTH), Vitamin D, and renal function, we compared these markers in both groups. As a result, elderly individuals with osteoporosis and with a history of falls within the preceding year indicated significantly higher sway velocity (P = 0.012*), sway area (P < 0.001*), and sway path length (P = 0.012*). Furthermore, fallers had significantly lower calcium (P = 0.02*) and Parathyroid hormone (PTH) (P = 0.02*), as well as higher Alkaline Phosphatase (ALP) (P = 0.02*) as compared to nonfallers despite similar vitamin D and creatinine levels. In conclusion, diminished biochemical factors in the osteoporosis faller group could possibly cause postural instability resulting in lower physical activity levels in the osteoporosis fall group and increasing the risk of falls.

Investigating spatiotemporal and kinematic gait parameters in individuals with Parkinson's disease with a history of freezing of gait and exploring the effects of dopaminergic therapy on freezing of gait subtypes.

Freezing of Gait (FOG) is a prevalent and debilitating symptom in idiopathic Parkinson's disease (PD). This study evaluated spatiotemporal and kinematic gait parameters in individuals with PD with a history of FOG and explored the effects of dopaminergic therapy on FOG subtypes. One hundred and nine individuals with PD underwent clinical assessments and quantitative biomechanical measures during walking cycles before and after dopaminergic therapy. Individuals with FOG were classified into levodopa-responsive and levodopa-unresponsive groups. Individuals with FOG displayed longer disease duration and higher Unified Parkinson's Disease Rating Scale (UPDRS) II, III, IV scores, and total scores and levodopa equivalent dose, than those without FOG (all p < 0.0001). Following propensity score matching of 15 pairs based on UPDRS total score and disease duration during the off-medication state, the analysis comparing the FOG and non-FOG groups revealed no significant differences in spatiotemporal and kinematic parameters. In 39 cases of FOG, dopaminergic therapy improved gait performance in individuals with PD, enhancing spatiotemporal parameters (speed, stride length, step length, step variability) and kinematic parameters (shoulder and elbow flexion/extension range of motion (ROM), pelvic rotation, and hip abduction/adduction ROM) regardless of FOG responsiveness to dopaminergic therapy. A significant difference in trunk sway ROM (p = 0.029) remained before and after dopaminergic therapy, even after adjusting for disease duration and clinical severity. Dopaminergic therapy had varying effects on PD with FOG, improving several spatiotemporal and kinematic gait parameters but being less effective in levodopa-unresponsive cases. Quantitative biomechanical measures offer detailed insights into gait performance, aiding personalized fall risk assessment and guiding individualized rehabilitation programs.

Using tele-paramedicine to conduct in-home fall risk reduction after emergency department discharge: Preliminary data.

Older adults discharged from our emergency department (ED) do not receive comprehensive fall risk evaluations. We conducted a quality improvement project using an existing Community Tele-Paramedicine (CTP) program to perform in-home fall risk assessment and mitigation after ED discharge. High falls-risk patients, as defined by STEADI score >4, were referred for a CTP home visit by community paramedics supervised virtually by emergency physicians. Home hazards assessment, Timed Up and Go test (TUG), medication reconciliation, and psychosocial evaluation were used to develop fall risk mitigation plans. Outcomes assessed at 30 days post ED-discharge included: completed CTP visits, falls, ED revisits, hospital admissions, and referrals. Between November 2022 and June 2023, 104 (65%) patients were discharged and referred to CTP. The mean age of enrolled patients was 80 years, 66% were female, 63% White, 79% on Medicare or Medicaid, most lived with a family member (50%) or alone (38%). Sixty-one (59%) patients received an initial CTP visit, 48 (79%) a follow-up visit, and 12 (11%) declined a visit. Abnormal TUG tests (74%), home hazards (67%), high-risk medications (36%), or need for outpatient follow-up (49%) or additional home services (41%) were frequently identified. At 30 days, only one of the CTP patients reported a fall, one patient had a fall-related ED visit, and one patient was admitted secondary to a fall. A quality improvement initiative using CTP to perform fall risk reduction after ED discharge identified areas of risk mitigation in the home where most falls take place. Further controlled studies are needed to assess the impact of CTP on clinical outcomes important to patients and health systems.