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The share of the aged population over and above 65 years increased from 6% in 1990 to 9% in 2019. The percentage of the aged population might increase further to 16% by 2050. Due to socio-demographic changes and nuclear family setups, elder people live alone and encounter many issues at home, especially falls, while doing their daily activities. The probability of a fall inside the bathroom is higher than a fall inside the living place due to specific hazards. Various research works have been proposed to monitor people from falls (fall detection systems -FADE) inside the home environment. However, those systems do not concentrate much on fall detection inside the bathrooms (FADEB). Alternatively, we witness certain smart gadgets for the safety of the elders inside the bathroom, which prevents them from a fall by assisting. However, these gadgets are not designed in a way to report the fall. To overview the FADEB systems, electronic databases such as PubMed, Web of Science (WoS), Scopus, Google Scholar, and DBLP were used to fetch the research works published from 2007 to 2023. The FADEB papers are filtered out and critically reviewed in terms of implementation details concerning data collection, feature extraction, and classification. The specific challenges related to FADEB are quoted that need to be addressed in the near future to monitor the falls inside the bathroom. [ABSTRACT FROM AUTHOR]

BackgroundObjectiveMethodsResultsConclusionsFalls 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).To assess the inter-tester reliability of synchronized remote and FTF fall risk assessment.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 (ICC2,1), standard error of measurement (SEM), minimal detectable change (MDC95) and Bland and Altman analysis.Excellent ICCs were found for STS, MiniBESTest, TUG, and BBS (0.90–0.99), and moderate for 4MWT (0.74). SEM and MDC95 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.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. [ABSTRACT FROM AUTHOR]

This work describes a system to help in the remote assessment of fall risk in elderly people. A portable hardware system equipped with an RGB-D sensor is used for motion capture. A set of anonymous frames, representing the process of skeleton tracking, and a collection of sequences of interesting features, obtained from body landmark evaluations through time, are stored in the Cloud for each patient. A WEB dashboard allows for tailored tests to be designed, which include the typical items within well-known fall risk evaluation tests in the literature. Such a dashboard helps therapists to evaluate each item from the analysis and observation of the sequences and the 3D representation of the body through time, and to compare the results of tests carried out in different moments, checking on the evolution of the fall risk. The software architecture that implements the system allows the information to be stored in a safe manner and preserves patients' privacy. The paper shows the obtained results after testing an early prototype of the system, a discussion about its advantages, and the current limitations from the Human–Computer Interaction point of view, and a plan to deploy and evaluate the system from the usability perspective in the near future. [ABSTRACT FROM AUTHOR]

Abstract 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

Falls among older adults (OAs) living in retirement homes (RHs) in Canada are a major public health concern due to high morbidity and mortality as well as significant healthcare expenditures. This quality improvement (QI) initiative, conducted for the author's Doctor of Nursing Practice (DNP) project, aimed to decrease fall rates and ED transfers related to falls among OAs in six RHs across the Greater Toronto Area in Ontario, Canada through a multipart intervention with two primary goals. First, the project aimed to facilitate RH NPs' implementation of a comprehensive fall risk assessment and fall prevention strategy in their practice by incorporating the Stopping Elderly Accidents, Deaths & Injuries (STEADI) toolkit into their armamentarium. Second, it sought to enhance the knowledge of the RHs' registered practical nurses (RPNs), personal support workers (PSWs), and unregulated care providers (UCPs) in assessing fall risk and incorporating fall prevention strategies in their daily practice. By improving NP, RPN, PSW, and UCP knowledge and increasing (by 20%) RPN, PSW, and UCP use of fall prevention strategies, this QI initiative successfully reduced fall rates in the RHs by 40.4%, with no falls requiring transfer to the ED, in the postintervention period. The results of this project highlight the need for an interdisciplinary approach to fall risk reduction in RHs that includes implementation of multifactorial intervention strategies as well as effective organizational policies and procedures for maximum impact. Falls among older adults (OAs) living in retirement homes (RHs) in Canada are a major public health concern due to high morbidity and mortality as well as significant healthcare expenditures. This quality improvement initiative aimed to decrease fall rates and ED transfers related to falls among OAs in six RHs across the Greater Toronto Area through a multipart intervention. Results of the intervention highlight the need for an interdisciplinary approach to fall risk reduction that includes implementation of multifactorial interventions as well as effective organizational policies and procedures for maximum impact. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective To investigate the effect of hierarchical nursing based on fall risk assessment for prevention of falls in elderly patients with Parkinson's disease (PD). Methods Totally 200 PD patients were randomly divided into the control group and observation group, with 100 cases in each group. The control group received routine fall prevention and management, while the observation group received hierarchical nursing based on fall risk assessment. The self-made Knowledge-Attitude-Practice (KAP) questionnaire was used to assess the patients’ fall prevention awareness. The Client Satisfaction Questionnaire(CSQ) was used to assess patients’ satisfaction with nursing service. The incidence of falls in a one-year follow-up was observed. Results Total score of KAP and dimension scores of knowledge, attitude and practice were increased in both two groups after interventions, and total score of KAP in the observation group was higher than that in the control group (P＜0. 01). The overall satisfaction rate of the observation group was higher than that of the control group (P＜0. 05). The incidence of falls of the observation group was lower than that of the control group after a one-year follow-up (P＜0. 05). Conclusion The hierarchical nursing based on fall risk assessment for prevention of falls is effective to enhance the knowledge, attitude and practice skills of PD patients, improve the quality of falls prevention and management, and reduce the incidence of falls. (目的 探讨基于跌倒风险评估的分层护理干预对老年帕金森病(PD)患者知信行水平的影响及预防跌倒的效果。方法 选取医院神经内科2018年3月—2020年3月收治的200例老年PD患者作研究对象, 随机分成对照组和观察组, 各100例。对照组实施常规预防跌倒护理干预, 观察组在对照组基础上实施基于跌倒风险评估的分层护理干预。运用自制知信行量表(KAP)问卷评估患者知信行水平, 运用顾客满意度量表(CSQ)评价患者对护理服务的满意度, 记录随访1年期间跌倒发生情况。结果 实施护理干预后, 两组知识、信念、行为维度得分和KAP问卷总分均较干预前提高, 且观察组知识、信念、行为维度得分和KAP问卷总分高于对照组, 差异有统计学意义(P＜0. 01)。观察组总满意度高于对照组(P＜0. 05)。随访1年时观察组总跌倒发生率低于对照组(P＜0. 01)。结论 基于跌倒风险评估的分层护理干预能提升PD患者的护理效果, 提高患者知信行水平, 降低跌倒发生率。)

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. [ABSTRACT FROM AUTHOR]

This study examined the associations of discrepancies between perceived and physiological fall risks with repeated falls. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient generalization ability can’t perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain. However, these methods focus on the distribution of the source and the target domain, but ignore the potential correlation among multiple source subjects, which deteriorates domain adaptation performance. In this paper, we proposed a novel method named domain adaptation with subject fusion (SFDA) for fall risk assessment, greatly improving the cross-subject assessment ability. Specifically, SFDA synchronously carries out source target adaptation and multiple source subject fusion by domain adversarial module to reduce source-target gap and distribution distance within source subjects of same class. Consequently, target samples can learn more task-specific features from source subjects to improve the generalization ability. Experiment results show that SFDA achieved mean accuracy of 79.17 % and 73.66 % based on two backbones in a cross-subject classification manner, outperforming the state-of-the-art methods on continuous plantar pressure dataset. This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment.

Background: Falls are the most prevalent adverse event among hospitalized patients. Multilevel risk factors are associated with falls, yet falls continue. Purpose: To evaluate the relationship between the Johns Hopkins Fall Risk instrument, patient characteristics, and perception of fall risk. Methods: The Johns Hopkins Fall Risk score, patient perception of fall risk, and patient characteristics were analyzed among inpatient adults (n = 201) from 5 acute care units in a large southern California medical center. Results: Bivariate analyses revealed that fall risk was inversely associated with participants' confidence in their ability to perform high fall risk behaviors without help and without falling (P =.018). Conclusions: Perception of fall risk is a promising new indicator in preventing falls. Patient perception of fall risk may elicit a behavior change to help prevent falls. Increased health care provider awareness of patient perception of fall risk may improve fall risk interventions and prevention programs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Patient falls remain a major cause of adverse events in the medical setting. Many patients receiving Mohs micrographic surgery are at high risk, both for falling and resultant injuries. Although the incidence of patient falls in dermatologic surgery is low, falls can have significant consequences for both patient and provider. Therefore, effective interventions to improve organizational safety are critical. Though there is a considerable amount of research pertaining to fall prevention strategies, the majority of studies have been confined to the inpatient setting and long-term care facilities. Implementation of fall prevention initiatives in the outpatient setting has rarely been evaluated and no studies have focused on the Mohs patient population to date. Methods: We reviewed the literature pertaining to fall risk and prevention guidelines in the inpatient and outpatient settings as it applies to the dermatologic surgery environment. Results: Herein we will discuss patient risk factors for falling relevant to the Mohs setting and review existing validated fall risk assessment tools and strategies for fall prevention. Conclusion: Identifying fall risk factors can improve patient safety and reduce falls in the dermatologic surgery clinic.

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. [ABSTRACT FROM AUTHOR]

Abstract 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, sensitivityEvent Rate(ER) and specificityER. 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% (sensitivityER 33.6%) and specificity of 88.8% (specificityER 82.0%). The follow-up PH-FRAT assessments increased sensitivityER values although the specificityER 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% (sensitivityER 74.4%) and specificity of 55.2% (specificityER 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.

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. [ABSTRACT FROM AUTHOR]

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.

The high fall rate of the elderly brings enormous challenges to families and the medical system; therefore, early risk assessment and intervention are quite necessary. Compared to other sensor-based technologies, in-shoe plantar pressure sensors, effectiveness and low obtrusiveness are widely used for long-term fall risk assessments because of their portability. While frequently-used bipedal center-of-pressure (COP) features are derived from a pressure sensing platform, they are not suitable for the shoe system or pressure insole owing to the lack of relative position information. Therefore, in this study, a definition of “weak foot” was proposed to solve the sensitivity problem of single foot features and facilitate the extraction of temporal consistency related features. Forty-four multi-dimensional weak foot features based on single foot COP were correspondingly extracted; notably, the relationship between the fall risk and temporal inconsistency in the weak foot were discussed in this study, and probability distribution method was used to analyze the symmetry and temporal consistency of gait lines. Though experiments, foot pressure data were collected from 48 subjects with 24 high risk (HR) and 24 low risk (LR) ones obtained by the smart footwear system. The final models with 87.5% accuracy and 100% sensitivity on test data outperformed the base line models using bipedal COP. The results and feature space shown the novel features of wearable plantar pressure could comprehensively evaluate the difference between HR and LR groups. Our fall risk assessment models based on these features had good generalization performance, and showed practicability and reliability in real-life monitoring situations.

Objective: Older adults’ falls are a critical public health problem. The majority of free-living fall risk assessment methods have investigated fall predictive power of step-related digital biomarkers extracted from wearable inertial measurement unit (IMU) data. Alternatively, the examination of characteristics and frequency of naturally-occurring compensatory balance reactions (CBRs) may provide valuable information on older adults’ propensity for falls. To address this, models to automatically detect naturally-occurring CBRs are needed. However, compared to steps, CBRs are rare events. Therefore, prolonged collection of criterion standard data (along with IMU data) is required to validate model’s performance in free-living conditions. Methods: By investigating 11 fallers’ and older non-fallers’ free-living criterion standard data, 8 naturally-occurring CBRs, i.e., 7 trips (self-reported using a wrist-mounted voice-recorder) and 1 hit/bump (verified using egocentric vision data) were localized in the corresponding trunk-mounted IMU data. Random forest models were trained on independent/unseen datasets curated from multiple sources, including in-lab data captured using a perturbation treadmill. Subsequently, the models’ translation/generalization to older adults’ out-of-lab data were assessed. Results: A subset of models differentiated between naturally-occurring CBRs and free-living activities with high sensitivity (100%) and specificity (≥99%). Conclusions: The findings suggest that accurate detection of naturally-occurring CBRs is feasible. Clinical/Translational Impact- As a multi-institutional validation study to detect older adults’ naturally-occurring CBRs, suitability for larger-scale free-living studies to investigate falls etiology, and/or assess the effectiveness of perturbation training programs is discussed.

Anniina Heikkilä,1 Lasse Lehtonen,2 Jari Haukka,3 Satu Havulinna,4 Kristiina Junttila5 1HUS Nursing Administrative Group, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; 2HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; 3Clinicum/Department of Public Health, University of Helsinki, Helsinki, Finland; 4Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; 5Nursing Research Center, Helsinki University Hospital and University of Helsinki, Helsinki, FinlandCorrespondence: Anniina Heikkil&#x00E4HUS Nursing Administrative Group, Helsinki University Hospital and University of Helsinki, P.O. Box 705, Helsinki, FI-00029, FinlandTel +358415437870Email anniina.heikkila@hus.fiPurpose: 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.Keywords: falls, fall risk assessment, PHFRAT, acute care

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.

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. [ABSTRACT FROM AUTHOR]

Fall is a major threat to stroke survivors with the problems of gait and balance disorders in the rehabilitation phase following severe consequences on quality of life and a heavy burden to their families. Many solutions have been proposed to assess fall risk for elders based on inertial sensor-based signals, however, there still exists a great challenge of transferring them from elderly populations to the stroke-survivors populations as gait disorder patterns are significant difference between elders and stroke survivors. In this study, we conduct a pilot study to collect inertial sensor-based signals from stroke survivors when they performed the timed up and go test, and build an automatic fall risk assessment model with the architecture of Siamese network, with a merit of mitigating the problem of small sample size. Specifically, the proposed automatic fall risk assessment model consists of two parallel convolutional neural networks, each of which is composed of three convolutional layers, two max-pooling layers, and three fully connected layers. To utilize the space relation among accelerator-based and gyroscope-based signals, two-dimensional discrete wavelet transform extracts image-like features, wavelet coefficients, from inertial sensor-based signals as the input. Experimental results show that the proposed fall risk assessment model has achieved a promising results, which outperform cutting-edge methods with a big margin. The proposed fall risk assessment model with low computational complexity and limited memory consuming can be deployed on an embedded system to provide fall risk assessment service for stroke survivors in point-of-care environments or community settings. [ABSTRACT FROM AUTHOR]

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.

Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement. This paper describes two studies carried out to advance the use of IMU-based balance assessments in older adults. Study 1 (N = 39) presents the development of two new IMU-derived balance measures. Study 2 (N = 248) reports a reliability analysis of IMU postural stability measures and validates the novel balance measures through comparison with clinical scales. We also report a statistical fall risk estimation algorithm based on IMU data captured during static balance assessments alongside a method of improving this fall risk estimate by incorporating standard clinical fall risk factor data. Results suggest that both new balance measures are sensitive to balance deficits captured by the Berg Balance Scale (BBS) and Timed Up and Go test. Results obtained from the fall risk classifier models suggest they are more accurate (67.9%) at estimating fall risk status than a model based on BBS (59.2%). While the accuracies of the reported models are lower than others reported in the literature, the simplicity of the assessment makes it a potentially useful screening tool for balance impairments and falls risk. The algorithms presented in this paper may be suitable for implementation on a smartphone and could facilitate unsupervised assessment in the home. [ABSTRACT FROM AUTHOR]

IntroductionPrevious 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.MethodsThe 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.ResultsThe 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.ConclusionOverall, 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.

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.

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, sensitivityEvent Rate(ER) and specificityER.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% (sensitivityER 33.6%) and specificity of 88.8% (specificityER 82.0%). The follow-up PH-FRAT assessments increased sensitivityER values although the specificityER 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% (sensitivityER 74.4%) and specificity of 55.2% (specificityER 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. [ABSTRACT FROM AUTHOR]

(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. [ABSTRACT FROM AUTHOR]

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.)

Injurious falls remain among the most common, dangerous, and costly adverse events in hospitals, despite the widespread implementation of fall prevention programs. Many current health care system policies and nursing practices oversimplify fall prevention by focusing on limiting the person's mobility and making the environment safer, or simply documenting a fall risk score. But most falls are caused by factors intrinsic to that individual; merely limiting their mobility can increase preventable hospital complications and readmissions, and still leaves them at risk for falls. This article proposes a new approach to reducing injurious falls in older adults--one grounded in evidence-based protocols known to positively impact the health of older adults. The approach, called by the acronym ERA--Electronic health record integration, Risk factors that matter, Assessment and care plans--allows nurses to use a validated fall risk assessment tool to reframe fall risk factors as part of the comprehensive care plan, and to map modifiable risk factors to interventions that address the underlying causes of falls and promote safer mobility. The ERA approach can help nurses use their time more effectively by focusing on targeted actions that improve patient outcomes, working in coordination with an interprofessional, cross-continuum care team. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Background: To ensure successful integration and implementation of evidence into practice, validation of measures and interventions should be performed in the population and setting in which they will be used. Purpose: This article provides a method for evaluating the predictive performance of a risk tool using the Hester Davis fall risk tool as an example. Methods: A retrospective matched-pairs sample of fallers and nonfallers was created. Psychometric properties were calculated using 2 x 2 contingency tables and compared to data in the original report. Results: In this study sample, the risk tool showed minimal ability to distinguish patients at risk for falling from those not at risk. Conclusions: Organizations are urged to assess the performance of risk tools in their own patient population. This article provides a practical approach for the validation of evidence into the practice setting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]