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A prior study reported that the concentric strength imbalance between hamstrings and quadriceps is associated with falls in older adults. Given that the concentric strength may not be measured as conveniently as the isometric strength, it is meaningful to test whether the isometric hamstring-quadricep strength imbalance is related to falls among older adults. This study sought to explore whether the hamstrings-quadriceps ratio could differentiate fallers from non-fallers in community-dwelling older adults. One hundred and eleven older adults were included in this cross-sectional study. Their isometric knee joint strength capacity (extensors and flexors) was measured. Based on their fall history in the past year, they were classified as fallers (at least one fall) or non-fallers (no fall). The hamstrings-quadriceps ratio was compared between the faller and non-faller groups. The receiver operating characteristic analysis was used to determine the cutoff value of the hamstrings-quadriceps ratio able to best classify fallers and non-fallers. Fallers showed a significantly lower hamstrings-quadriceps ratio than non-fallers (p = 0.008). The receiver operating characteristic analysis identified 0.733 as the best ratio to differentiate fallers from non-fallers with an accuracy of 64.0 %. A 0.1-unit reduction in the hamstrings-quadriceps ratio increases the probability of falling by a factor of 1.30. The hamstrings-quadriceps ratio could be used as an additional fall risk factor when assessing the risk of falls among older adults. A smaller than 0.733 hamstring-quadriceps ratio may indicate a high risk of falls., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Falls are a leading cause of severe injury and death in older adults. Remote screening of fall risk may prevent falls and hence, advance health and wellness of older adults. While remote health care is becoming a common practice, we question if remote evaluation of fall risk is as reliable as face-to-face (FTF)., Objective: To assess the inter-tester reliability of synchronized remote and FTF fall risk assessment., Methods: This inter-format, inter-rater reliability study included 48 home dwelling older adults aged 65 and over. Five valid functional and balance tests were conducted: 30 Second Sit-to-Stand (STS), MiniBESTest, Timed up and go (TUG), 4-Meter Walk (4MWT), and Berg Balance Scale (BBS). Instructions were provided via videoconferencing, and two physiotherapists scored performance simultaneously, one remotely, and one in the room. Inter-rater reliability between remote and FTF scores was analyzed using intraclass correlation coefficient (ICC 2,1 ), standard error of measurement (SEM), minimal detectable change (MDC 95 ) and Bland and Altman analysis., Results: Excellent ICCs were found for STS, MiniBESTest, TUG, and BBS (0.90-0.99), and moderate for 4MWT (0.74). SEM and MDC 95 values were STS (0.37,1.03 repetitions), MiniBESTest (1.43,3.97 scores), TUG (1.22,3.37 seconds), 4MWT (0.17,0.47 m/second), and BBS (1.79,4.95 scores). The Bland and Altman analysis showed excellent agreement between remote and FTF assessments of the STS. All other tests showed low to moderate agreement. Mean difference ± SD and 95%LOA were as follows: STS (-0.11 ± 0.52), (-1.13,0.91) repetitions, MiniBESTest (0.45 ± 1.98), (-3.43,4.32) scores, TUG (-0.35 ± 1.54), (-3.37,2.67) seconds, 4MWT (-0.08 ± 0.22), (-0.35,0.51) meter/second, and BBS (0.04 ± 2.53), (-4.93,5.01) scores., Conclusions: The findings support the responsible integration of remote fall risk assessment in clinical practice, enabling large-scale screenings and referrals for early intervention to promote healthy aging and fall prevention.

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy., Competing Interests: The authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Authors.)

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., (© 2024 The Authors. The Kaohsiung Journal of Medical Sciences published by John Wiley & Sons Australia, Ltd on behalf of Kaohsiung Medical University.)

Introduction: 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., Methods: 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., Results: 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)., Conclusion: 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

This review sought to meta-analyze previous research observing the effects of fat mass distribution on the fall risk among people with obesity. The literature search yielded five qualified studies enrolling 1218 participants (650 with android vs. 568 with gynoid). The outcome variables included the annual fall prevalence (primary outcome) and the center of pressure (COP) movement measurements during a posturography test (secondary) among people with android or gynoid obesity. Meta-analyses were conducted using the inverse variance weighted random-effects model. The odds ratio (OR) and standardized mean difference (SMD) were used as the effect size for the primary and secondary variables, respectively. The results revealed that more people with android obesity fall annually than their gynoid obesity counterparts (OR = 1.78 [1.34, 2.37], p < 0.0001). People with android obesity also exhibited significantly faster overall COP velocity (SMD = 0.49 [0.11, 0.88], p = 0.01) during standing compared to individuals with gynoid obesity. Our results indicated that people with android obesity could have a greater fall risk than those with gynoid obesity. Given the limited number of studies included, more well-designed and quality work is desired to further clarify how fat mass distribution alters the fall risk among people with obesity. A standardized approach to quantify the fat mass distribution (android vs. gynoid) is imperatively needed for people with obesity., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2024 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.)

Falls are a prevalent cause of injury among older people. While some wearable inertial measurement unit (IMU) sensor-based systems have been widely investigated for fall risk assessment, their reliability, validity, and identification ability in community-dwelling older people remain unclear. Therefore, this study evaluated the performance of a commercially available IMU sensor-based fall risk assessment system among 20 community-dwelling older recurrent fallers (with a history of ≥2 falls in the past 12 months) and 20 community-dwelling older non-fallers (no history of falls in the past 12 months), together with applying the clinical scale of the Mini-Balance Evaluation Systems Test (Mini-BESTest). The results show that the IMU sensor-based system exhibited a significant moderate to excellent test-retest reliability (ICC = 0.838, p < 0.001), an acceptable level of internal consistency reliability (Spearman's rho = 0.471, p = 0.002), an acceptable convergent validity (Cronbach's α = 0.712), and an area under the curve (AUC) value of 0.590 for the IMU sensor-based receiver-operating characteristic (ROC) curve. The findings suggest that while the evaluated IMU sensor-based system exhibited good reliability and acceptable validity, it might not be able to fully identify the recurrent fallers and non-fallers in a community-dwelling older population. Further system optimization is still needed.

Background: Globally in healthcare delivery, inpatient falls and fall-related injuries contribute to unsafe patient care environments. On two inpatient oncology units, the frequency of patient falls had increased, despite the use of a fall screening tool., Objectives: This project aimed to determine whether implementing the Agency for Healthcare Research and Quality (AHRQ) 3B Scheduled Rounding Protocol would reduce the average daily fall rate and number of fall-related injuries on two adult inpatient oncology units., Methods: This quantitative, quasi-experimental quality improvement project evaluated the implementation of the AHRQ protocol to reduce the average fall rate and number of fall-related injuries for adult patients with cancer., Findings: The average daily fall rate decreased following implementation of the AHRQ protocol, indicating clinical significance. This project's results suggest that implementing a standard fall prevention protocol can reduce the rate of patient falls.

Fall risk increases with age, and one-third of adults over 65 years old experience a fall annually. Due to the aging population, the number of falls and related medical costs will progressively increase. Correct prediction of who will fall in the future is necessary to timely intervene in order to prevent falls. Therefore, the aim of this scoping review is to determine the predictive value of fall risk assessments in community-dwelling older adults using prospective studies. A total of 37 studies were included that evaluated clinical assessments (questionnaires, physical assessments, or a combination), sensor-based clinical assessments, or sensor- based daily life assessments using prospective study designs. The posttest probability of falling or not falling was calculated. In general, fallers were better classified than non-fallers. Questionnaires had a lower predictive capability compared to the other assessment types. Contrary to conclusions drawn in reviews that include retrospective studies, the predictive value of physical tests evaluated in prospective studies varies largely, with only smaller-sampled studies showing good predictive capabilities. Sensor-based fall risk assessments are promising and improve with task complexity, although they have only been evaluated in relatively small samples. In conclusion, fall risk prediction using sensor data seems to outperform conventional tests, but the method's validity needs to be confirmed by large prospective studies.

Background Falls among the adult population are a major global health concern with severe repercussions for individuals and healthcare systems. The purpose of this study was to investigate the prevalence and associated risk factors of falls in hospitalized patients in order to improve hospital care for elderly adults. Materials and methods The research was conducted at two institutions of tertiary care in Abbottabad, Pakistan. After extensive screening and obtaining informed consent, a total of 210 participants aged 50 and older were enrolled in the study. Mental status, history of falls, ambulation/elimination status, vision, gait/balance, systolic blood pressure, medication use, and predisposing diseases were evaluated using the Long Term Care Fall Risk Assessment Form. Additionally, the Dynamic Gait Index was utilized to evaluate various aspects of gait. Results 58.6% of participants reported a history of falls in the previous year, according to the findings. BMI, imbalance, vertigo, and fear of falling were significantly associated with an increased risk of falls in older individuals. The Long-Term Care Fall Risk Assessment, the Montreal Cognitive Assessment (MoCA), the Dynamic Gait Index (DGI), and the Mini-BESTest scores revealed that patients with a history of falls had inferior functional and cognitive outcomes. Falls were more common among individuals with a robust BMI, especially men. Conclusions The study results highlight the multifactorial nature of falls in the adult population and the need for targeted interventions to address modifiable risk factors. To enhance hospital care for high-risk patients, proactive fall prevention strategies, including regular risk assessments and individualized interventions, should be implemented. This study provides important insights into the prevalence and causes of accidents among hospitalized patients, particularly in developing nations such as Pakistan. ​​​​​​., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Khawaja et al.)

Objective measures of balance and gait have the potential to improve prediction of future fallers because balance and gait impairments are common precursors. We used the Instrumented Stand and Walk Test (ISAW) with wearable, inertial sensors to maximize the domains of balance and gait evaluated in a short test. We hypothesized that ISAW objective measures across a variety of gait and balance domains would improve fall prediction beyond history of falls and better than gait speed or dual-task cost on gait-speed. We recruited 214 high-functioning older men (mean 82 years), of whom 91 participants (42.5%) had one or more falls in the 12 months following the ISAW test. The ISAW test involved 30 s of stance followed by a 7-m walk, turn, and return. We examined regression models for falling using 17 ISAW metrics, with and without age and fall history, and characterize top-performing models by AUC and metrics included. The ISAW test improved distinguishing between future fallers and non-fallers compared to age and history of falls, alone (AUC improved from 0.69 to 0.75). Models with 1 ISAW metric usually included a postural sway measure, models with 2 ISAW measures included a turning measure, models with 3 ISAW measures included a gait variability measure, and models with 4 or 5 measures added a gait initiation measure. Gait speed and dual-task cost did not distinguish between fallers and non-fallers in this high-functioning cohort. The best fall-prediction models support the notion that older people may fall due to a variety of balance and gait impairments., (© 2022. The Author(s).)

Objectives: This study examined the associations of discrepancies between perceived and physiological fall risks with repeated falls., Methods: We analyzed the 2016 Medicare Current Beneficiary Survey of 2,487 Medicare beneficiaries aged ≥ 65 years with ≥ 1 fall. The outcome variable was repeated falls (≥ 2 falls), the key independent variable was a categorical variable of discrepancies between perceived (fear of falling) and physiological fall risks (physiological limitations), assessed using multivariate logistic regression., Results: Among Medicare beneficiaries with ≥ 1 fall, 25.1% had low fear of falling but high physiological fall risk (Low Fear-High Physiological), 9.4% had high fear of falling but low physiological fall risk (High Fear-Low Physiological), 23.5% had low fear of falling and low physiological fall risks (Low Fear-Low Physiological), and 42.0% had high fear of falling and high physiological fall risks (High Fear-High Physiological). Having High Fear-High Physiological was associated with repeated falls (OR = 2.14; p < .001) compared to Low Fear-Low Physiological. Having Low Fear-High Physiological and High Fear-LowPhysiological were not associated with repeated falls., Conclusions: Given that High Fear-High Physiological was associated with repeated falls and that many at-risk Medicare beneficiaries had High Fear-High Physiological, prevention efforts may consider targeting those most at-risk including Medicare beneficiaries with High Fear-High Physiological., Clinical Implications: Assessing both perceived and physiological fall risks is clinically relevant, given it may inform targeted interventions for different at-risk Medicare beneficiaries among clinicians and other stakeholders.

Aims and Objectives: To evaluate the measured fall risk score that more accurately reflects the changeable conditions in acute care settings, and to efficiently evaluate the association between falls and fall risk score., Background: The Morse Fall Scale (MFS) is a well-known easy-to-use tool, while the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) consists of items with high specificity. Evaluating suitable fall-risk assessment tools to measure these changeable conditions may contribute to preventing falls in acute care settings., Design: Retrospective case-control study using the STROBE checklist., Methods: In an acute care setting (708-bedded university hospital with a regional emergency medical centre), the non-fall group was adjusted to fall group using propensity score matching. According to the fall rate of 3-5%, non-fall groups for each tool were selected (1386 and 1947) from the before adjusted data, and the fall groups included 42 and 59. The applied covariates were individual characteristics that ordinarily changed such as age, gender, diagnostic department and hospitalisation period. The adjusted data were analysed using generalised estimating equations and mixed effect model., Results: After adjustment, the fall group measured using the JHFRAT had a significantly higher difference between the initial and re-measured total score than the non-fall group. The JHFRAT, especially with the re-measured score, had a higher AUC value for predicting falls than the MFS. MFS's sensitivity was 85.7%, and specificity was 58.8% at 50 points; for JHFRAT, these were 67.8% and 80.2% at 14 points, respectively. These cut-off points were used to evaluate validity during tool development and are commonly used as reference scores., Conclusions: JHFRAT more accurately reflects acute changeable conditions related to fall risk measurements after admission., Relevance to Clinical Practice: JHFRAT may be useful for effective fall prevention activities in acute care settings., (© 2021 John Wiley & Sons Ltd.)

Background: Osteoporosis is characterized by a low bone mass of bone tissue. If osteoporosis is not treated properly, it will increase the high risk of fracture. The common causes of fracture on osteoporosis condition due to falls. This study aims to find the correlation between the risk of osteoporosis with fall risk (ONTARIO) based on osteoporosis fracture risk (FRAX)., Methods: This study is an analytic study with a cross-sectional method. We collected the sample using random cluster sampling in the six primary health care in Malang on different times service since August-September 2021. Total patient 139, however only 132 patients were included in this study. After collecting data is complete, we analyze using Chi-square tests., Results: The mean age of participants was 63.9 ± 7.14. with the age group was dominated by the range of 60-64. It was found that the result of the FRAX SCORE had a low-risk category for major fracture osteoporosis and risk hip fracture. In contrast, from the OSTA score in this study, more than 68 participants (50.8%) were found medium and high-risk scores. Then, in ONTARIO score of the risk fall assessment, and high score in 57 participants (43.2%). If compared between OSTA and ONTARIO, there was a significant relationship between OSTA score and ONTARIO score ( p  < 0.000) with high-risk OSTA have a significant relationship with a high risk of falling and vice versa., Conclusion: In this study, there was a relationship between the risk of high osteoporosis and the risk of falling., Competing Interests: Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

This study investigated the diagnostic accuracy of different clusters of performance based outcome measures (PBOMs) recommended by two consensus-based guidelines: Stopping Elderly Accidents, Deaths, and Injuries (STEADI), and those recommended by a systematic review completed by the American Physical Therapy Association and Academy of Geriatric Physical Therapy (APTA-SR, APTA-SR3). 33 community-dwelling older adults (25 females, 8 males) aged mean 79.45 ± 7.64 years participated in this study. Participants completed a fall history questionnaire and were evaluated via a battery of PBOMs for comparative analysis. The diagnostic accuracy of each PBOM cluster was analyzed retrospectively (previous 1 year fall history) and prospectively (6 month follow up). Retrospective analysis revealed the APTA-SR3 had the highest clinical utility and diagnostic accuracy: Sp 88.24% (63.56-98.54), Sn 62.5% (35.43-84.8), LR+ 2.35 (1.22-4.53), LR- 0.19 (0.05-0.73), accuracy 70.22% (51.83-84.81). Prospective analysis revealed the cluster of the APTA-SR and APTA-SR3 had identical diagnostic accuracy: Sn 100% (39.76-100), Sp 43.75% (19.75-70.12), LR+ 1.78 (1.15-2.74), LR- 0 (0), accuracy 60.62% (36.63-81.36). The APTA-SR 3 cluster demonstrated the highest diagnostic accuracy and in this study was the most effective and efficient group of PBOMs to identify fall risk in community dwelling older adults., Competing Interests: The authors have no declaration of interest to report., (© 2022 The Authors.)

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.

Falls in older adults represent the most common cause of injuries and a major cause of mortality in this vulnerable population. The morbidity and mortality rate of falls among older people makes balance analysis in older adults very important. Therefore, this study aims to explore different metrics that can potentially be used to identify early indications of balance loss and fall risk. To that end, the motion strategies and chest and pelvis coordination of a group of younger, a group of older non-faller and a group of older faller participants while conducting the functional reach test were investigated. To analyse the motion strategies of the different participant groups, four metrics of maximum angular rotation of chest, maximum angular rotation of pelvis, time warped chest and pelvis angular rotation difference, and the mean continuous relative phase of the chest and pelvis were assessed. In this study younger participants are found to have larger maximum chest rotation, maximum pelvis rotation, and time warped chest and pelvis angular rotation difference compared to older participants. However, these metrics were not significantly different in older non-fallers compared to older fallers. Meanwhile, the mean continuous relative phase of the chest and pelvis was the only metric found to be significantly different among all three participant groups. This metric is indicative of the chest and pelvis coordination which is associated with the ability to construct proper coordination and maintain balance. The mean continuous relative phase yielded the sensitivity of 92.3% and specificity of 73.7% in recognizing older fallers from older non-fallers. The results suggest that this metric might be useful in identifying the risk of falling in older population, thus, it should be further studied in a prospective study., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Although fall prevention in patients after stroke is crucial, the clinical validity of fall risk assessment tools is underresearched in this population. The study aim was to determine the cut-off scores and clinical validity of the Sensory Organization Test (SOT), the Berg Balance Scale (BBS), and the Fall Efficacy Scale-International (FES-I) in patients after stroke., Methods: In this prospective cross-sectional study, we analyzed data for patients admitted to a rehabilitation unit after stroke from 2018 through 2021. Participants underwent SOT, BBS, and FES-I pre-discharge, and the fall incidence was recorded for 6 months. We used an area under the receiver operating characteristic curve (AUC) to calculate predictive values., Results: Of 84 included patients (median age 68.5 (interquartile range 67-71) years), 32 (38.1%) suffered a fall. All three tests were significantly predictive of fall risk. Optimal cut-off scores were 60 points for SOT (AUC 0.686), 35 and 42 points for BBS (AUC 0.661 and 0.618, respectively), and 27 and 29 points for FES-I (AUC 0.685 and 0.677, respectively)., Conclusions: Optimal cut-off scores for SOT, BBS, and FES-I were determined for patients at risk for falls after a stroke, which all three tools classified with a good discriminatory ability.

Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Subramaniam, Faisal and Deen.)

Introduction: Previous studies suggested that physical and cognitive function can be indicators to assess the risk of falls in the elderly. Various tests are widely used in geriatric clinical studies as assessment tools of physical and cognitive function. However, large sample studies comparing the fall predictive value of these tests are still sparse. This study was conducted to investigate the value of cognitive and physical function tests in predicting the risk of subsequent falls in the elderly, with the overarching goal of providing more evidence on fall-risk assessment., Methods: The current study was based on the data of respondents aged 60 and above from the China Health and Retirement Longitudinal Study (CHARLS). Data from the 2015 CHARLS national survey were used as the baseline data, and the fall data in 2018 were used as the follow-up data. Physical function tests included balance, walking speed, the five times sit-to-stand test (FTSST), and grip strength. The value of cognitive and physical function tests in predicting falls was evaluated by logistic regression analysis and receiver operating characteristic (ROC) curves., Results: The incidence of falls among the 4,857 subjects included in this study was 20.86%. Results showed that cognition (OR = 0.83, 95% CI: 0.70-0.98), the FTSST (OR = 3.51, 95% CI: 1.66-7.46), and grip strength (OR = 1.02, 95% CI: 1.01-1.03) were independent predictors of falls in the full sample after adjusting for various confounders. Notably, the above tests showed better predictive value for falls for the oldest-old (≥ 80 years) subjects., Conclusion: Overall, results showed that grip strength, the FTSST, and cognition tests are simple and practicable tools for identifying individuals at higher risk of falls in the community. Moreover, the fall predictive performance of physical and cognitive function tests was age-dependent, with a higher predictive value in older adults aged 80 and above., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhou, Li and Chen.)

Background: The Peninsula Health Falls Risk Assessment Tool (PH-FRAT) is a validated and widely applied tool in residential aged care facilities (RACFs) in Australia. However, research regarding its use and predictive performance is limited. This study aimed to determine the use and performance of PH-FRAT in predicting falls in RACF residents., Methods: A retrospective cohort study using routinely-collected data from 25 RACFs in metropolitan Sydney, Australia from Jul 2014-Dec 2019. A total of 5888 residents aged ≥65 years who were assessed at least once using the PH-FRAT were included in the study. The PH-FRAT risk score ranges from 5 to 20 with a score > 14 indicating fallers and ≤ 14 non-fallers. The predictive performance of PH-FRAT was determined using metrics including area under receiver operating characteristics curve (AUROC), sensitivity, specificity, sensitivity Event Rate(ER) and specificity ER ., Results: A total of 27,696 falls were reported over 3,689,561 resident days (a crude incident rate of 7.5 falls /1000 resident days). A total of 38,931 PH-FRAT assessments were conducted with a median of 4 assessments per resident, a median of 43.8 days between assessments, and an overall median fall risk score of 14. Residents with multiple assessments had increased risk scores over time. The baseline PH-FRAT demonstrated a low AUROC of 0.57, sensitivity of 26.0% (sensitivity ER 33.6%) and specificity of 88.8% (specificity ER 82.0%). The follow-up PH-FRAT assessments increased sensitivity ER values although the specificity ER decreased. The performance of PH-FRAT improved using a lower risk score cut-off of 10 with AUROC of 0.61, sensitivity of 67.5% (sensitivity ER 74.4%) and specificity of 55.2% (specificity ER 45.6%)., Conclusions: Although PH-FRAT is frequently used in RACFs, it demonstrated poor predictive performance raising concerns about its value. Introducing a lower PH-FRAT cut-off score of 10 marginally enhanced its predictive performance. Future research should focus on understanding the feasibility and accuracy of dynamic fall risk predictive tools, which may serve to better identify residents at risk of falls., (© 2022. The Author(s).)

(1) Background: This study aimed to test the feasibility of utilizing the screening tool for fall risk assessment in adult inpatient and verify its accuracy in a medical center in Taiwan. (2) Methods: This study retrospectively collected all adult fall cases among inpatients occurring in the general wards of a medical center between 1 January 2013 and 31 December 2015. This inpatient fall risk screening scale was measured by the sensitivity, specificity, and accuracy. (3) Results: There were 1331 (0.4%) falls among a total of 357,395 inpatients during this period. Factors predictive of falling risk included: age, consciousness, body shift assistance, use of fall risk medications, fall history, dizziness or weakness, toileting, and impaired mobility. Using the eight-factor assessment, two was the best cutoff point for identifying the fall risk group, with area under Receiver Operating Characteristic (ROC) curve (AUC) = 0.817, sensitivity = 80.93%, specificity = 73.0%, accuracy = 73.03%, and likelihood ratio = 11.48. (4) Conclusions: The accuracy of the eight-item fall risk assessment tool created for this study was validated. These results can serve as a reference for institutions to develop more effective fall risk assessment scale for inpatients, enabling clinical nurses to identify and more comprehensively assess the groups at highest risk for falling during their hospital stay.

Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.

Purpose: The purpose of this study was to evaluate the validity and reliability of the Peninsula Health Fall Risk Assessment Tool (PHFRAT) in acute care in various medical specialties. The assessment has not been previously studied in acute care., Methods: The cross-sectional study was conducted in a large acute care hospital system. The retrospective medical data were used to explore the validity of the PHFRAT. The data consisted of all adult inpatients (≥ 18 age) evaluated by the PHFRAT during 2014-2016 (n = 22,700). The Poisson regression, logistic regression, sensitivity, specificity, and the area under the ROC curve were evaluated. The data for the reliability study were collected in 2016 in twelve units by evaluating the patients (n = 359) twice using the PHFRAT. The prospective data were analyzed using Fleiss' Kappa, and the content validity index was also counted., Results: In the somatic data, the change in the risk level from low risk to high risk increases the probability of falls by a factor of 2.8 (p<0.01). When the cut-off point was 9, sensitivity and specificity were 72% and 59%, respectively, and the area under the ROC curve was 0.67 (p<0.01). Validity varied by medical specialties. In the validity analysis, it was not possible to calculate the statistical significance from the psychiatry data. The inter-rater reliability was 0.68 (p<0.01)., Conclusion: This study shows that the PHFRAT proved to be moderately suitable for detecting the risk of falling for adult patients admitted to somatic units in acute care. The reliability of the PHFRAT was moderate. The results indicate the need to study the PHFRAT more broadly in psychiatric care as well as some specialties in somatic care., Competing Interests: Mrs Anniina Heikkilä reports grants from Finnish Governmental Research Grant for patient safety studies during the conduct of the study. The authors declare that they have no competing interests., (© 2021 Heikkilä et al.)

Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has recently made great progress, attracting the growing attention of researchers from medicine and engineering. However, there is a lack of studies on this topic which elaborate the state of the art. This paper presents a comprehensive survey to discuss the development and current status of various aspects of sensor-based fall risk assessment. Firstly, we present the principles of fall risk assessment. Secondly, we show knowledge of fall risk monitoring techniques, including wearable sensor based and non-wearable sensor based. After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment. We also discuss some challenges and promising directions in this field at last.

Objective: Foot tactile sensitivity loss, commonly assessed by monofilaments, is a fall risk factor among older adults. The broadly used threshold of the monofilament for fall risk assessment in older adults is 5.07. However, this threshold originates from assessing foot ulceration risk in people with peripheral neuropathy. The primary purpose of this study was to identify the optimal filament size and its cutoff number of sensitive sites that can be used to best identify a high risk of falls in terms of the foot tactile sensitivity for community-dwelling older adults., Methods: In this cross-sectional study, the foot tactile sensitivity was assessed by a 6-piece Semmes-Weinstein monofilament kit at 9 sites per foot among 94 older adults, including 38 fallers and 56 nonfallers. The number of sensitive sites was determined for each monofilament size as the cutoff. Logistical regression analyses were used to determine the monofilament size and number of sensitive sites best able to differentiate fallers from nonfallers., Results: Fallers showed overall worse foot tactile sensory measurements than nonfallers. Logistical regression analyses identified 4.31 as the best monofilament size and 7 as the number of sensitive sites to differentiate fallers from nonfallers with an accuracy of 71.3%., Conclusion: The 4.31 monofilament could be the best filament to detect the risk of falls among older adults in terms of tactile sensory loss. Inability to feel the pressure from the 4.31 filament at more than 7 sites could indicate a high risk of falls., Impact: These findings could help physical therapists and other rehabilitation professionals improve decision making in detecting older adults with a high risk of falls, thus facilitating the effort of fall prevention in older adults., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Physical Therapy Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall., Background: While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk., Methods: We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction., Results: Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features., Conclusions: This innovative approach to room design evaluation and resulting estimation of patient fall risk show promise as a proactive evidence-based tool to evaluate the relationship of potential fall risk and room design. The development of the model highlights the challenge of heterogeneity in factors and reporting found in the studies of patient falls, which hinder our understanding of the role of the built environment in mitigating risk. A more comprehensive investigation comparing the model with actual patient falls data is needed to further refine model development.

This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject's body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test.

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Introduction: Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices., Methods: In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automatedalgorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists., Results: There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests., Conclusions: The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed., Competing Interests: Declaration of conflicting interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MAM has received an unconditional grant from Next Step Dynamics AB (advisory and drafting). Fraunhofer IGD (MH, WC, FH, GB, MA) was contracted for expert consultancy., (© The Author(s) 2020.)

This article presents a five-part dataset for human gait analysis in a healthy older adult population (37 women and seven men; age 69.98 ± 8.57 years, body mass index 27.71 ± 4.57). Part 1 compromises demographic data for the 44 participants, along with the results of the Short Physical Performance Battery (SPPB) motor function test and the Mini-Mental State Examination (MMSE). We used the MMSE to ensure the participants could understand the experimental protocol instructions. Part 2 presents the anthropometric measurements of the participants. Part 3 shows the results for each item of the Gait component of the Performance Oriented Mobility Assessment (POMA-G). Part 4 contains five files per participant, storing motion-capture data for the lower limb in the Coordinate 3D (C3D) format. These files were generated using a Vicon motion analysis system, consisting of 24 reflective markers and seven cameras (Vantage V5) sampled at 100 Hz. Part 5 contains 26 gait parameters for each C3D file obtained using Nexus 2.9.3. The dataset is available in a Mendeley repository (Reserved DOI: 10.17632/xgw6bg3g8h.1)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Author(s).)

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.

Falls are a well-known danger for older adults. With the worldwide population aging, there has been an increasing interest in assessing the risk of falling. This work presents a novel algorithm for continuous fall risk assessment, relying on a linear regression model whose inputs consist of both measured and self-reported risk factors. Two models were conceived and compared, following two distinct approaches, a theoretical and an empirical one. The system is pervasive and was tested in free-living unsupervised conditions. The results of our fall risk scoring system unveiled a strong correlation with the output of the clinical functional tests POMA and TUG (90% and 89%, respectively), which was deemed a promising outcome concerning the feasibility of pervasive monitoring for fall risk assessment in daily living.

Aim: This study aimed to define the concept of fall risk appraisal (FRA) and its measures by integrating the older adults' perspectives with an integrative literature review., Background: Appraised fall risk is based mainly on either subjective or objective measures, which may result in inaccurate predictions of fall risk., Design: This study was developed based on three phases of the hybrid concept analysis including a theoretical phase, a fieldwork phase, and an analytic phase. Eleven articles were selected to review in the theoretical phase. Qualitative data from in-depth interviews and focus groups in the fieldwork phase were used to compare and integrate into the analytic phase., Results: FRA is a two-dimensional assessment of fall risk in older adults. Two independent dimensions were defined as (a) physiological fall risk dimension reflecting physical or body function, which may decline with age, and (b) perceived fall risk dimension reflecting the older adults' perception of their likelihood to fall. FRA consists of two categories including adaptive fall risk appraisal and maladaptive fall risk appraisal., Conclusion: A practical definition of FRA and its measures could be extremely valuable for supporting the need for fall risk assessments and would be integral to nursing practice., (© 2019 Wiley Periodicals, Inc.)

Background: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants., Objective: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app's discriminative ability based on real-world data., Methods: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score's ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model., Results: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%., Conclusions: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score's prospective accuracy will be further validated in a prospective trial., (©Sophie Rabe, Arash Azhand, Wolfgang Pommer, Swantje Müller, Anika Steinert. Originally published in JMIR Aging (http://aging.jmir.org), 14.02.2020.)

Purpose: To identify characteristics of paediatric falls within a healthcare setting., Design and Methods: A retrospective analysis of falls occurring within inpatient, outpatient, emergency and community healthcare settings of children aged 0-<18 years was conducted using data from the Children's Health Queensland Hospital and Health Service (CHQ-HHS) Clinical Incident Database and Electronic Medical Record between January 1st 2015 and December 31st 2017. One-sample and two-sample Chi-squared tests with post-hoc tests were performed to assess relationships between categorical variables., Results: The final dataset contained 385 fall events. Children 0-2 years fell most frequently (46.75%) and falls were higher in males (55.58%). Falls from bed were the most common mechanism (30.65%). The incidence rate of inpatient falls was 0.53 falls per 1000 bed days in the tertiary hospital setting and 1.2% of presentations to inpatient community health facilities. Falls from bed were most common in the tertiary hospital inpatient setting (39.84%, p < .001) and the emergency department (72.13%, p < .001). Falls from furniture/equipment constituted 26.04% of outpatient falls. Most falls occurred in the presence of parents/caregivers (79.48%) and 4.66% of fallers sustained multiple falls., Conclusions: This study provides a comprehensive review of the characteristics of fall events in CHQ-HHS over a three-year period and summarises the existing literature in paediatric fall prevention., Practice Implications: Risk assessment and management plans should focus on education, particularly surrounding bed safety. Our findings have informed the development of an integrated evidence-based paediatric-specific fall risk assessment tool and management plan to prevent falls in hospital and community healthcare settings., Competing Interests: Declaration of competing interest No known conflicts of interest associated with this study., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background and Objective: Falls are the leading cause of fatal and non-fatal injuries among seniors worldwide. While laboratory evidence supports the view that impaired ability to execute compensatory balance responses (CBRs) is linked to an increased risk of falling, existing unsupervised fall risk assessment methods are mainly focused on detecting changes in spatio-temporal gait parameters over time rather than naturally-occurring CBR events. To address the gap in available methods, this paper compares the capability of machine learning-based models trained on the kinematic data from inertial measurement units (IMU) and surface electromyography (sEMG) features to detect lateral CBRs, to ultimately address detection of CBRs in free-living conditions. Moreover, we propose a novel "Hybrid" feature set, which considers cross-correlation and temporal similarities between the normalized kinematic and sEMG signals., Methods: Focusing on frontal plane perturbations, a classifier to automatically: 1) detect lateral CBRs during normal gait, and 2) identify type (i.e., crossover, sidestep) using data from three wearable IMUs and 4 sEMG signals from the thigh (i.e., biceps femoris, rectus femoris) and lower leg muscles (i.e., gastrocnemious, tibialis anterior) was developed. In total, 600 trials (including 358 lateral CBRs) from 7 young, healthy adults were analyzed. The effects of feature type (IMU, sEMG, Hybrid) and sensor placement on the random forest-based classifier performance were investigated., Results: CBR detection (i.e., CBR vs normal gait) accuracies (leave-one-subject-out cross validation) were 83.95% and 99.21% using sEMG-based and IMU-based features, respectively, which dropped to 72.17% and 84.83% for the multiclass identification (i.e., side-step vs cross-over vs normal gait) problem. Findings yielded shank as the best overall location for the multiclass problem, and chest as the most accurate for CBR detection. In general, adding sEMG and Hybrid features to IMUs yielded incremental improvements in CBR detection and type identification (87.03% leave-one-subject-out cross-validation for type identification)., Conclusion: The findings of this study demonstrate that IMU-based features are favourable over sEMG and Hybrid features for the task of CBR detection, with incremental value for type identification. Evidence presented suggests that Hybrid features may increase performance for other wearable sensor applications (e.g. activity recognition systems)., (Copyright © 2019. Published by Elsevier B.V.)

In recent years, the use of interactive game technology has gained much interest in the research community as a means to measure indicators associated with the risk of falling in the elderly. Input devices used for gaming offer an inexpensive but yet reliable alternative to the costly apparatuses used in clinics and medical centers. In this paper, we explore the feasibility of using virtual reality technology as a tool to assess the risk of falling in the senior community in a more immersive, intuitive and descriptive manner. Our VR-based tool captures stepping performance parameters in order to fulfill the requirements of a well-established clinical test for fall risk assessment. The use of virtual reality allows for an immersive experience where elderly users can fully concentrate on the motor and cognitive functions being assessed rather than the technology being used.

In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.

Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial-lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior-posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly., Competing Interests: The authors declare no conflict of interest.

Background: Falls are a common adverse event in both elderly inpatients and patients admitted to rehabilitation units. The Hendrich Fall Risk Model II (HIIFRM) has been already tested in all hospital wards with high fall rates, with the exception of the rehabilitation setting. This study's aim is to address the feasibility and predictive performances of HIIFRM in a hospital rehabilitation department., Methods: A 6 months prospective study in a Italian rehabilitation department with patients from orthopaedic, pulmonary, and neurological rehabilitation wards. All admitted patients were enrolled and assessed within 24 h of admission by means of the HIIFRM. The occurrence of falls was checked and recorded daily. HIIFRM feasibility was assessed as the percentage of successful administrations at admission. HIIFRM predictive performance was determined in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC), best cutoff, sensitivity, specificity, positive and negative predictive values, along with their asymptotic 95% confidence intervals (95% CI)., Results: One hundred ninety-one patents were admitted. HIIFRM was feasible in 147 cases (77%), 11 of which suffered a fall (7.5%). Failures in administration were mainly due to bedridden patients (e.g. minimally conscious state, vegetative state). AUC was 0.779(0.685-0.873). The original HIIFRM cutoff of 5 led to a sensitivity of 100% with a mere specificity of 49%(40-57%), thus suggesting using higher cutoffs. Moreover, the median score for non-fallers at rehabilitation units was higher than that reported in literature for geriatric non fallers. The best trade-off between sensitivity and specificity was obtained by using a cutoff of 8. This lead to sensitivity = 73%(46-99%), specificity = 72%(65-80%), positive predictive value = 17% and negative predictive value = 97%. These results support the use of the HIIFRM as a predictive tool., Conclusions: The HIIFRM showed satisfactory feasibility and predictive performances in rehabilitation wards. Based on both available literature and these results, the prediction of falls among all hospital wards, with high risk of falling, could be achieved by means of a unique tool and two different cutoffs: a standard cutoff of 5 in geriatric wards and an adjusted higher cutoff in rehabilitation units, with predictive performances similar to those of the best-preforming pathology specific tools for fall-risk assessment.

Falls are the cause for more than half of the injury-related hospitalizations among older people. Accurate assessment of individuals' fall risk could enable targeted interventions to reduce the risk. This paper presents a novel method for using wearable accelerometers to detect early signs of deficits in balance from gait. Gait acceleration data were analyzed from 35 healthy female participants (73.86±5.40 years). The data were collected with waist-mounted accelerometer and the participants performed three supervised balance tests: Berg Balance Scale (BBS), Timed-Up-and-Go (TUG) and 4m walk. The follow-up tests with the same protocol were performed after one year. Altogether 43 features were extracted from the accelerometer signals. Sequential forward floating selection and ten-fold cross-validation were applied to determine models for 1) estimating the outcomes of BBS, TUG and 4m walk tests and 2) predicting decline in balance during one-year follow-up indicated as decline in BBS total score and one leg stance. Normalized root-mean-square errors (RMSE) of the assessment scale result estimates were 0.28 for BBS score, 0.18 for TUG time, and 0.22 for 4m walk test. Area under curve (AUC) was 0.78 for predicting decline in BBS total score and 0.82 for one leg stance, respectively. The results suggest that the gait features can be used to estimate the result of a clinical balance assessment scale and predict decline in balance. A simple walk test with wearable monitoring could be applicable as an initial screening tool to identify people with early signs of balance deficits., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The focus of this paper was on finding wrist sensor-derived features for detecting highly acute fall risk from the sit-to-stand transitions performed in a non-ambulatory environment. Furthermore, the influence of the dominant and non-dominant hand on these features was investigated. A cohort of 174 older subjects was monitored for seven consecutive days in their home setting by using inertial sensors attached at the wrist. Based on the reported falls during a one-month follow-up phase, two groups were defined. Twenty-one time and frequency domain features were implemented for the quantitative assessment of extracted sit-to-stand transitions. The statistical analysis yielded two features that could convincingly distinguish fallers from non-fallers for the dominant hand, and six for the non-dominant hand. A novel feature, energy of the applied support during standing up, showed statistically good performance independently of on which hand the sensor node was worn, as well as for the dominant and non-dominant hand ([Formula: see text], [Formula: see text], and [Formula: see text], respectively). This paper overcomes limitations of clinical tests and shows a reliable application of wrist-worn bands in terms of assessment of highly acute fall risk. In addition, it reveals the sit-to-stand transition as a potential assessment source for the wrist-worn devices in the elderly population. Early assessment of the risk of falling in a widely accepted and non-stigmatized manner has the ability to bring crucial changes in fall prevention strategies, reducing the number of falls and the fall rate.

Objectives: To investigate the role of a fall risk assessment, using the Downton Fall Risk Index (DFRI), in predicting fall-related injury, fall-related head injury and hip fracture, and death, in a large cohort of older women and men residing in Sweden., Design: Cross sectional observational study., Setting: Sweden., Participants: Older adults (mean age 82.4 ± 7.8) who had a fall risk assessment using the DFRI at baseline (N = 128,596)., Measurements: Information on all fall-related injuries, all fall-related head injuries and hip fractures, and all-cause mortality was collected from the Swedish Patient Register and Cause of Death Register. The predictive role of DFRI was calculated using Poisson regression models with age, sex, height, weight, and comorbidities as covariates, taking time to outcome or end of study into account., Results: During a median follow-up of 253 days (interquartile range 90-402 days) (>80,000 patient-years), 15,299 participants had a fall-related injury, 2,864 a head injury, and 2,557 a hip fracture, and 23,307 died. High fall risk (DFRI ≥3) independently predicted fall-related injury (hazard ratio (HR) = 1.43, 95% confidence interval (CI) = 1.39-1.49), hip fracture (HR = 1.51, 95% CI =1.38-1.66), head injury (HR = 1.12, 95% CI = 1.03-1.22), and all-cause mortality (HR = 1.39, 95% CI = 1.35-1.43). DFRI more strongly predicted head injury (HR = 1.29, 95% CI = 1.21-1.36 vs HR = 1.08, 95% CI = 1.04-1.11) and hip fracture (HR = 1.41, 95% CI = 1.30-1.53 vs HR = 1.08, 95% CI = 1.05-1.11) in 70-year old men than in 90-year old women (P < .001)., Conclusion: Fall risk assessment using DFRI independently predicts fall-related injury, fall-related head injury and hip fracture, and all-cause mortality in older men and women, indicating its clinical usefulness to identify individuals who would benefit from interventions., (© 2016 The Authors. The Journal of the American Geriatrics Society published by Wiley Periodicals, Inc. on behalf of The American Geriatrics Society.)

Each year about a third of elderly aged 65 or older experience a fall. Many of these falls may have been avoided if fall risk assessment and prevention tools where available in a daily living situation. We identify what kind of information is relevant for doing fall risk assessment and prevention using wearable sensors in a daily living environment by investigating current research, distinguishing between prospective and context-aware fall risk assessment and prevention. Based on our findings, we propose a fall risk awareness protocol as a fall prevention tool integrating both wearables and ambient sensing technology into a single platform., (Copyright © 2016. Published by Elsevier Inc.)

Background: Identification of future non-fallers, infrequent and frequent fallers among older people would permit focusing the delivery of prevention programs on selected individuals. Posturographic parameters have been proven to differentiate between non-fallers and frequent fallers, but not between the first group and infrequent fallers., Methods: In this study, postural stability with eyes open and closed on both a firm and a compliant surface and while performing a cognitive task was assessed in a consecutive sample of 130 cognitively able elderly, mean age 77(7)years, categorized as non-fallers (N=67), infrequent fallers (one/two falls, N=45) and frequent fallers (more than two falls, N=18) according to their last year fall history. Principal Component Analysis was used to select the most significant features from a set of 17posturographic parameters. Next, variables derived from principal component analysis were used to test, in each task, group differences between the three groups., Findings: One parameter based on a combination of a set of Centre of Pressure anterior-posterior variables obtained from the eyes-open on a compliant surface task was statistically different among all groups, thus distinguishing infrequent fallers from both non-fallers (P<0.05) and frequent fallers (P<0.05)., Interpretation: For the first time, a method based on posturographic data to retrospectively discriminate infrequent fallers was obtained. The joint use of both the eyes-open on a compliant surface condition and this new parameter could be used, in a future study, to improve the performance of protocols and to verify the ability of this method to identify new-fallers in elderly without cognitive impairment., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

For the prevention of falls, individual fall risk assessment is the necessary first step. Thus, we attempted to identify independent risk factors for falls and develop a prediction model using a scoring system for patients with neurological disorders in acute hospital settings. This study was a secondary analysis of a previous study performed to compare the reliability and validity of three well-known fall assessment tools in patients with neurological disorders. We considered comorbid diseases and potential medications in addition to variables included in the three tools. Multiple logistic regression analysis was used to develop a prediction model for falls. Predictive scores were calculated using the proportional odds ratio (OR) of each predictor. The discriminative power of this model was evaluated by receiver-operating characteristic (ROC) area under the curve (AUC) analysis. A total of 32 falls were noted among 1018 patients. History of falls (OR, 4.01; 95% CI, 1.61-9.98; p=.003), cerebrovascular disease (CVD) (OR, 2.61; 95% CI, 1.11-6.14; p=.028), severe impaired gait (OR, 7.28; 95% CI, 2.45-21.65; p<.001), and overestimate of one's own gait ability (OR, 9.14; 95% CI, 3.89-21.45; p<.001) were identified as meaningful predictors for falling after adjusting for age, diabetes, confusion or disorientation, up-and-go test, altered elimination, and antipsychotics by univariate analysis. The discriminative power of fall risk score calculated by the prediction model was 0.904 of AUC (p<.001). Our results suggest that in addition to fall history and the presence of CVD, neurological assessment for gait and insight into gait ability are imperative to predict falls in patients with neurological disorders., (Copyright © 2015 Elsevier B.V. All rights reserved.)

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms. In this paper, a social- and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task. The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. The combination of several systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors., (Copyright © 2014 Elsevier Ltd. All rights reserved.)