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Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen’s kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., “active aging”, biofeedback-based rehabilitation for fall prevention, and the management of Parkinson’s disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

Falls often have severe financial and environmental consequences, not only for those who fall, but also for their families and society at large. Identifying fall risk in older adults can be of great use in preventing or reducing falls and fall risk, and preventative measures that are then introduced can help reduce the incidence and severity of falls in older adults. The overall aim of our systematic review was to provide an analysis of existing mechanisms and measures for evaluating fall risk in older adults. The 43 included FRATs produced a total of 493 FRAT items which, when linked to the ICF, resulted in a total of 952 ICF codes. The ICF domain with the most used codes was body function, with 381 of the 952 codes used (40%), followed by activities and participation with 273 codes (28%), body structure with 238 codes (25%) and, lastly, environmental and personal factors with only 60 codes (7%). This review highlights the fact that current FRATs focus on the body, neglecting environmental and personal factors and, to a lesser extent, activities and participation. This over-reliance on the body as the point of failure in fall risk assessment clearly highlights the need for gathering qualitative data, such as from focus group discussions with older adults, to capture the perspectives and views of the older adults themselves about the factors that increase their risk of falling and comparing these perspectives to the data gathered from published FRATs as described in this review.

Background Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real‐time location system (RTLS) enables continuous tracking of patient location, providing a unique opportunity to monitor changes in physical activity, a key factor related to the risk of falls in hospitals. This study is aimed at utilizing RTLS data to capture dynamic patient movements, integrating it with clinical information through a machine learning approach to enhance in‐hospital fall predictions. Methods This retrospective study developed and compared three models: clinical data only, RTLS data only and a combined data model. It included 22 201 patients from Yongin Severance Hospital, South Korea, from March 2020 to June 2022, with 118 fall patients and 443 nonfall patients selected through random sampling and relevant criteria for detailed analysis, totaling 561 patients. The average age of the participants was 70.1 years, with a median of 71.0 years (IQR: 60.0–80.0). Among participants, 52.6% (n = 295) were male. This study evaluated the occurrence of the first fall during hospitalization. The performance was assessed using the area under the receiver operating characteristic (AUROC), the area under the precision‐recall curve (AUPRC) and the Brier score. The Shapley additive explanations (SHAP) method and decision curve analysis (DCA) were employed to enhance model explainability and assess the clinical utility of the models. Results The RTLS model showed significant predictive accuracy for hospital falls, with an AUROC of 0.813 (95% CI: 0.703–0.903). The clinical + RTLS model outperformed those using only one type of data, achieving an AUROC of 0.847 (95% CI: 0.764–0.917), AUPRC of 0.667 (95% CI: 0.472–0.816) and Brier score of 0.120 (95% CI: 0.083–0.162), with significant differences in performance metrics (p < 0.0001). DCA confirmed its greater clinical benefit. SHAP analysis indicated that patients who experienced falls tended to have less active time and slower movement speed just before the fall compared to the early hospitalization period, despite attempting to move more. Additionally, higher fall incidence was significantly associated with sedative use and higher red cell distribution width (RDW) levels. Conclusion This study underscores the capability of utilizing RTLS to predict in‐hospital falls by tracking the changes of patients' physical activity through a machine learning approach. This may improve early fall risk detection during hospitalization, thereby preventing falls and enhancing patient safety.

Falls that occur within a hospital setting are difficult to predict, however, are preventable adverse events with the potential to negatively impact patient care. Falls have the potential to cause serious or fatal injuries and may increase patient morbidity. Many hospitals utilize fall “predictor tools” to categorize a patient's fall risk, however, these tools are primarily studied within in-patient units. The emergency department (ED) presents a unique environment with a distinct patient population and demographic. The Memorial Emergency Department Fall Risk Assessment Tool (MEDFRAT) has shown to be effective with predicting a patient's fall risk in the ED. This IRB-approved study aims to assess the predictive validity of the MEDFRAT by evaluating the sensitivity and specificity for predicting a patient's fall risk in an emergency department at a level 1 trauma center. A retrospective cohort analysis was conducted using an electronic medical record (EMR) for patients who met study inclusion criteria at a level 1 trauma center ED. Extracted data includes MEDFRAT components, demographic information, and data from the Moving Safely Risk Assessment (MSRA) Tool, our institution's current fall assessment tool. A receiver operating characteristic (ROC) curve was constructed to determine the best cutoff for identifying any fall risk. Sensitivity, specificity, accuracy, positive likelihood ratio (LR+) and negative LR (LR-), with 95% CIs were then calculated for the cutoff value determined from the ROC curve. To compare overall tool performance, the areas under the ROC curves (AUC) were determined and compared with a z-test. The MEDFRAT had a significantly higher sensitivity compared to the MSRA (83.1% vs. 66.1%, p = 0.002), while the MSRA had a significantly higher specificity (84.5% vs. 69.0%, p = 0.012). For identifying any level of fall risk, ROC curve analysis showed that the cutoff providing the best trade-off between sensitivity and specificity for the MEDFRAT was a score of ≥1. Additionally, area under the curve was determined for the MEDFRAT and MSRA (0.817 vs. 0.737). This study confirms the validity of the MEDFRAT as an acceptable tool to predict in-hospital falls in a level 1 trauma center ED. Accurate identification of patients at a high risk of falling is critical for decreasing healthcare costs and improving health outcomes and patient safety. •Majority of fall risk assessment tools are not developed for emergency department.•Falls increase patients' risk for poor health outcomes and costs.•Memorial Emergency Department Fall Risk Assessment Tool is a valid fall risk tool.

Knowledge about the causal factors leading to falls is still limited, and fall prevention interventions urgently need to be more effective to limit the otherwise increasing burden caused by falls in older people. To identify individual fall risk, it is important to understand the complex interplay of fall-related factors. Although fall events are common, they are seldom observed, and fall reports are often biased. Due to the rapid development of wearable inertial sensors, an objective approach to capture fall events and the corresponding circumstances is provided. The aim of this work is to operationalize a prototypical dynamic fall risk model regarding 4 ecologically valid real-world scenarios (opening a door, slipping, tripping, and usage of public transportation). We hypothesize that individual fall risk is associated with an interplay of intrinsic risk factors, activity, and environmental factors that can be estimated by using data measured within a laboratory simulation setting. We will recruit 30 community-dwelling people aged 60 years or older. To identify several fall-related intrinsic fall risk factors, appropriate clinical assessments will be selected. The experimental setup is adaptable so that the level of fall risk for each activity and each environmental factor is adjustable. By different levels of difficulty, the effect on the risk of falling will be investigated. An 8-camera motion tracking system will be used to record absolute body motions and limits of stability. All laboratory experiments will also be recorded by inertial sensors (L5, dominant leg) and video camera. Logistic regression analyses will be used to model the association between risk factors and falls. Continuous fall risk will be modeled by generalized linear regression models using margin of stability as outcome parameter. The results of this project will prove the concept and establish methods to further use the dynamic fall risk model. Recruitment and measurement initially began in October 2020 but were halted because of the COVID-19 pandemic. Recruitment and measurements recommenced in October 2022, and by February 2023, a total of 25 of the planned 30 subjects have been measured. In the field of fall prevention, a more precise fall risk model will have a significant impact on research leading to more effective prevention approaches. Given the described burden related to falls and the high prevalence, considerable improvements in fall prevention will have a significant impact on individual quality of life and also on society in general by reducing institutionalization and health care costs. The setup will enable the analysis of fall events and their circumstances ecologically valid in a laboratory setting and thereby will provide important information to estimate the individual instantaneous fall risk. DERR1-10.2196/46930.

Background/Purpose: To prevent falling, a common incident with debilitating health consequences among stroke survivors, it is important to identify significant fall risk factors (FRFs) towards developing and implementing predictive and preventive strategies and guidelines. This review provides a systematic approach for identifying the relevant FRFs and shedding light on future directions of research. Methods: A systematic search was conducted in 5 popular research databases. Studies investigating the FRFs in the stroke community were evaluated to identify the commonality and trend of FRFs in the relevant literature. Results: twenty-seven relevant articles were reviewed and analyzed spanning the years 1995–2020. The results confirmed that the most common FRFs were age (21/27, i.e., considered in 21 out of 27 studies), gender (21/27), motion-related measures (19/27), motor function/impairment (17/27), balance-related measures (16/27), and cognitive impairment (11/27). Among these factors, motion-related measures had the highest rate of significance (i.e., 84% or 16/19). Due to the high commonality of balance/motion-related measures, we further analyzed these factors. We identified a trend reflecting that subjective tools are increasingly being replaced by simple objective measures (e.g., 10-m walk), and most recently by quantitative measures based on detailed motion analysis. Conclusion: There remains a gap for a standardized systematic approach for selecting relevant FRFs in stroke fall risk literature. This study provides an evidence-based methodology to identify the relevant risk factors, as well as their commonalities and trends. Three significant areas for future research on post stroke fall risk assessment have been identified: 1) further exploration the efficacy of quantitative detailed motion analysis; 2) implementation of inertial measurement units as a cost-effective and accessible tool in clinics and beyond; and 3) investigation of the capability of cognitive-motor dual-task paradigms and their association with FRFs.

This retrospective study was conducted at a medical center in southern Taiwan to assess the accuracy of the Hendrich II Fall Risk Model (HIIFRM) in predicting falls. Sensitivity, specificity, accuracy, and optimal cutoff points were analyzed using receiver operating characteristic (ROC) curves. Data analysis was conducted using information from the electronic medical record and patient safety reporting systems, capturing 303 fall events and 47,146 non‐fall events. Results revealed that at the standard threshold of HIIFRM score ≥5, the median score in the fall group was significantly higher than in the non‐fall group. The top three units with HIIFRM scores exceeding 5 were the internal medicine (50.6%), surgical (26.5%), and oncology wards (14.1%), indicating a higher risk of falls in these areas. ROC analysis showed an HIIFRM sensitivity of 29.5% and specificity of 86.3%. The area under the curve (AUC) was 0.57, indicating limited discriminative ability in predicting falls. At a lower cutoff score (≥2), the AUC was 0.75 (95% confidence interval: 0.666–0.706; p < 0.0001), suggesting acceptable discriminative ability in predicting falls, with an additional identification of 101 fall events. This study emphasizes the importance of selecting an appropriate cutoff score when using the HIIFRM as a fall risk assessment tool. The findings have implications for fall prevention strategies and patient care in clinical settings, potentially leading to improved outcomes and patient safety.

Falls are a leading cause of injury among older adults, often resulting from dynamic balance disturbances. It is influenced by a complex interplay of intrinsic and extrinsic fall-risk factors. To identify individual fall risks, it is important to understand the underlying associations. This study aimed to build an experimental setup modeling selected factors leading to a loss of balance, measured by the margin of stability (MoS) in an ecologically valid real-world example (tripping). Additionally, these analyses aimed to assess the feasibility and safety of the protocol and to explore the use of the MoS as part of a prototypical dynamic fall-risk model to differentiate between fall-risk groups. Nineteen community-dwelling older adults (mean age of 71, SD 3.67 y; n=7, 37% women) completed the tripping protocol involving perturbations under various conditions. Clinical assessments were used to identify relevant fall-related intrinsic fall-risk factors. MoS was measured using an 8-camera motion capture system. Receiver operating characteristic analyses determined the ability of MoS to distinguish between low and high fall-risk groups. Approximately one-quarter of participants discontinued before or at the start of the tripping scenario because of discomfort or fear of perturbations, indicating that perceived safety is an important feasibility factor. Perturbations significantly disrupted MoS, with a median MoS of -106.05 (IQR -181.40 to -41.50) mm during the perturbed step compared to 114 (IQR 81.20-155.20) mm in the preperturbation step. Recovery steps showed progressive stabilization, with the second recovery step achieving a median MoS of 88.45 (IQR 47.50-137.80) mm. The second recovery step exhibited the highest predictive accuracy for fall-risk differentiation, with area under the curve values reaching 82.3% during slow walking with a series of right-sided perturbations. In contrast, fast walking with random perturbations yielded lower area under the curve values (64.9%). Slow walking conditions generally demonstrated the clearest separation between fall-risk groups. This pilot and feasibility study demonstrates the applicability of a tripping paradigm to perturb MoS in older adults and provides preliminary insights into its association with fall-risk indices. While the protocol proved safe and feasible for fit older adults, perceived safety limited full participation. The findings are exploratory and intended to guide the design of larger prospective studies rather than to establish predictive conclusions. These data suggest that MoS during controlled tripping may help differentiate fall-risk strata, but confirmation will require adequately powered studies in more diverse and frailer older populations-and across multiple real-world scenarios-before any clinical implementation can be considered.

Background Stroke is the third‐leading cause of death and disability, and poststroke falls (PSF) are common at all stages after stroke and could even lead to injuries or death. Brain information from functional near‐infrared spectroscopy (fNIRs) may precede conventional imaging and clinical symptoms but has not been systematically considered in PSF risk prediction. This study investigated the difference in brain activation between stroke patients and healthy subjects, and this study was aimed to explore fNIRs biomarkers for early screening of PSF risk by comparing the brain activation in patients at and not at PSF risk. Methods In this study, we explored the differences in brain activation and connectivity between stroke and healthy subjects by synchronizing the detection of fNIRs and EMG tests during simple (usual sit‐to‐stand) and difficult tasks (sit‐to‐stand based on EMG feedback). Thereby further screened for neuroimaging biomarkers for early prediction of PSF risk by comparing brain activation variability in poststroke patients at and not at fall risk during simple and difficult tasks. The area under the ROC curve (AUROC), sensitivity, and specificity were used to compare the diagnostic effect. Results A total of 40 patients (22 not at and 18 at PSF risk) and 38 healthy subjects were enrolled. As the difficulty of standing task increased, stroke patients compared with healthy subjects further increased the activation of the unaffected side of supplementary motor area (H‐SMA) and dorsolateral prefrontal cortex‐Brodmann area 46 (H‐DLFC‐BA46) but were unable to increase functional connectivity (Group*Task: p < 0.05). More importantly, the novel finding showed that hyperactivation of the H‐SMA during a simple standing task was a valid fNIRs predictor of PSF risk [AUROC 0.74, p = 0.010, sensitivity 77.8%, specificity 63.6%]. Conclusions This study provided novel evidence that fNIR‐derived biomarkers could early predict PSF risk that can facilitate the widespread use of real‐time assessment tools in early screening and rehabilitation. Meanwhile, this study demonstrated that the higher brain activation and inability to increase the brain functional connectivity in stroke patients during difficult task indicated the inefficient use of brain resources. This study provided novel evidence that fNIR‐derived biomarkers could early predict PSF risk that can be widely used in clinical practice. Meanwhile, this study demonstrated that the higher brain activation and inability to increase the brain functional connectivity in stroke patients during difficult task indicated the inefficient use of brain resources.

Objective To assess the predictive value of common measures validated to predict falls in other geriatric populations in patients presenting with suspected Normal Pressure Hydrocephalus (NPH). Methods One hundred ninety-five patients over the age of 60 who received the Fall Risk Questionnaire were retrospectively recruited from the CSF Disorders clinic within the departments of Neurosurgery and Neurology. Multiple logistic regression was used to create a model to predict falls for patients with suspected NPH using common measures: Timed Up & Go, Dual Timed Up & Go, 10 Meter Walk, MiniBESTest, 6-Minute Walk, Lower Extremity Function (Mobility), Fall Risk Questionnaire, and Functional Activities Questionnaire. Results The Fall Risk Questionnaire and age were shown to be the best predictors of falls. The model was 95.92% (Positive predictive value: 83.93%) sensitive and 47.92% specific (Negative predictive value: 77.78%). Conclusion Patients presenting with suspected NPH are at an increased fall risk, 75% of the total patients and 89% of patients who responded to a temporary drain of CSF had at least one fall in the past 6 months. The Fall Risk Questionnaire and age were shown to be predictive of falls for patients with suspected NPH. The preliminary evidence indicates measures that have been validated to assess fall risk in other populations may not be valid for patients presenting with suspected NPH.