Fall Risk Assessment Tool Research Articles

Assessment of the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) in inpatient settings.

This study was to assess the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) in inpatient settings. A case-control study. This study was conducted in a tertiary hospital based on 2019 data. With a case-control design in a 1:2 ratio, the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) was determined by ROC curve. The best cut point was identified based on sensitivity, specificity, positive predict value and negative predict value. Conditional logistical regression analysis was conducted to test the predictive ability of each indicator. The study included 309 patients, with 103 in the case group and 206 in the control groups. Generally, the predictive ability was acceptable with the area under ROC curve value at 0.73 (95% CI: 0.67-0.79). Positive predict value and negative predict value performed best at the cut point of 13. Sensitivity at cut point 6 was much higher than that at cut point 13, though specificity was lower. Except for age, all indicators in the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) demonstrated significant predictive ability as to occurrence of fall. The Johns Hopkins Fall Risk Assessment Tool (Chinese Version) is a reliable assessment instrumentin the inpatient settings. This is the first study that evaluated the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese version) in the inpatient settings, and proved that the instrument is reliable for assessing inpatient fall risks. Further studies could be carried out to assess the predict ability of Johns Hopkins Fall Risk Assessment Tool (Chinese version) among specific populations.

Systematic review for the prevention and management of falls and fear of falling in patients with Parkinson's disease.

ObjectiveTo synthesize recent empirical evidence for the prevention and management of falls and fear of falling in patients with Parkinson's disease (PD).Data sourceDatabase from PubMed, Cochrane Library, and EMBASE.Study designSystematic review.Data collectionWe searched the PubMed, Cochrane Library, and EMBASE databases for studies published from inception to February 27, 2021. Inclusion criteria were nonreview articles on prevention and management measures related to falls and fall prevention in Parkinson's disease patients.Principal findingsWe selected 45 articles and conducted in‐depth research and discussion. According to the causes of falls in PD patients, they were divided into five directions, namely physical status, pre‐existing conditions, environment, medical care, and cognition. In the cognitive domain, we focused on the fear of falling. On the above basis, we constructed a fall prevention model, which is a tertiary prevention health care network, based on The Johns Hopkins Fall Risk Assessment Tool to provide ideas for the prevention and management of falling and fear of falling in PD patients in clinical practiceConclusionsFalls and fear of falls in patients with Parkinson's disease can be reduced by effective clinical prevention and management. Future studies are needed to explore the efficacy of treatment and prevention of falls and fear of falls.

Falls prediction using the nursing home minimum dataset.

The purpose of the study was to develop and validate a model to predict the risk of experiencing a fall for nursing home residents utilizing data that are electronically available at the more than 15 000 facilities in the United States. The fall prediction model was built and tested using 2 extracts of data (2011 through 2013 and 2016 through 2018) from the Long-term Care Minimum Dataset (MDS) combined with drug data from 5 skilled nursing facilities. The model was created using a hybrid Classification and Regression Tree (CART)-logistic approach. The combined dataset consisted of 3985 residents with mean age of 77 years and 64% female. The model's area under the ROC curve was 0.668 (95% confidence interval: 0.643-0.693) on the validation subsample of the merged data. Inspection of the model showed that antidepressant medications have a significant protective association where the resident has a fall history prior to admission, requires assistance to balance while walking, and some functional range of motion impairment in the lower body; even if the patient exhibits behavioral issues, unstable behaviors, and/or are exposed to multiple psychotropic drugs. The novel hybrid CART-logit algorithm is an advance over the 22 fall risk assessment tools previously evaluated in the nursing home setting because it has a better performance characteristic for the fall prediction window of ≤90 days and it is the only model designed to use features that are easily obtainable at nearly every facility in the United States.

Environmental Hazards and Falls among Elderly with Low Back Pain

This study aimed to identify the existing environmental hazards and their association with falls among the elderly with LBP. A cross-sectional study involving 284 participants was conducted using Fall Risk Assessment Tools (FRAT), and Home Falls and Accident Screening Tools (HOME FAST). There were 234 respondents included for analysis. Most hazards reported no slip-resistant mats in the bathroom (73.5%). There was no association between environmental hazards and falls among the elderly (p > 0.05). Environmental factors are the indecisive cause of most falls in the institutions, but their interaction with the physical abilities of the elderly could contribute to falls. Keywords: Environmental hazards; Fall; Elderly; Low back pain eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i20.3497

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review.

Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling.

Mobile Technology for Falls Prevention in OlderAdults.

Falls are the leading cause of accidental death in older adults that result from a complex interplay of risk factors. Recently, the need for person-centered approach utilizing personalization, prediction, prevention, and participation, known as the P4 model, in fall prevention has been highlighted. Features of mobile technology make it a suitable technological infrastructure to employ such an approach. This narrative review aims to review the evidence for using mobile technology for personalized fall risk assessment and prevention since 2017 in older adults. We aim to identify lessons learned and future directions for using mobile technology as a fall risk assessment and prevention tool. Articles were searched in PubMed and Web of Science with search terms related to older adults, mobile technology, and falls prevention. Atotal of 23 articles were included. Articles were identified as those examining aspects of the P4 model including prediction (measurement of fall risk), personalization (usability), prevention, and participation. Mobile technology appears to be comparable to gold-standard technology in measuring well-known fall risk factors including static and dynamic balance. Seven applications were developed to measure different fall risk factors and tested for personalization, and/or participation aspects, and 4 were integrated into a falls prevention program. Mobile health technology offers an innovative solution to provide tailored fall risk screening, prediction, and participation. Future studies should incorporate multiple, objective fall risk measures and implement them in community settings to determine if mobile technology can offer tailored and scalable interventions.

The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data

BackgroundThe 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.MethodsA 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.ResultsA 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%).ConclusionsAlthough 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.

The Fall Risk Screening Scale Is Suitable for Evaluating Adult Patient Fall.

(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.

Risk Factors according to Fall Risk Level in General Hospital Inpatients

Purpose: This study was conducted to identify the predictors of falls according to the fall risk level in general hospital inpatients.Methods: Data were extracted from electronic medical records from January 2017 to December 2019. This study included 170 fallers and 340 non-fallers, who were matched for gender, age, clinical department, and length of stay before falls among inpatients aged 18 years and over. The collected data were analyzed with the x2 test, Fisher exact test, independent t-test, and logistic regression analysis using SPSS for Windows version 24.0.Results: In the low-risk group, defecation disorder (odds ratio [OR]=12.49, 95% confidence interval [CI]=1.16~134.33, p=.037) and laxative use (OR=9.62, 95% CI=2.14~43.22, p=.003) were statistically significant risk factors for falls. In the medium-risk group, disorientation (OR=2.40, 95% CI=1.11~5.19, p=.026) was a statistically significant risk factor for falls. In the high-risk group, disorientation (OR=12.54, 95% CI=1.39~113.26, p=.024) and hypoglycemic agent use (OR=5.39, 95% CI=1.58~18.44, p=.007) were statistically significant risk factors for falls.Conclusion: The results of this study show that risk factors for falls differed among the fall risk groups. It is necessary to use fall prevention strategies tailored to fall risk groups and fall risk assessment tools that have been revised accordingly.

Psychometric Properties of the European Portuguese Version of the Memorial Emergency Department Fall Risk Assessment Tool.

Falls are a public health problem that cause serious damage to people’s health and health systems. This study aims to estimate the validity and reliability of the Memorial Emergency Department Fall Risk Assessment Tool for the European Portuguese population. The sample included 186 adults from an emergency department of a District Hospital in Portugal. Reliability and precision (inter-rater reliability) are assessed by two independent raters. The relationship between MEDFRAT and the Morse Fall Risk Scale is evaluated. All items presented a high Kappa index. The MEDFRAT showed a high and significant correlation with the Morse Fall Risk Scale. The influence of sociodemographic and clinical data was also checked. The MEDFRAT is adequate, valid and reliable for the European Portuguese population to assess the risk of falling of emergency department patients.

Risk stratification for early and late falls in acute care settings.

Falling generally injures patients, lengthens hospital stays and leads to the wastage of financial and medical resources. Although falls can occur at any stage after hospital admission, there are no studies that characterise falls with length of hospital stay in acute care settings. This study aims to clarify risk stratification of early and late falls in acute care settings. A retrospective cohort study was conducted for participants who were admitted to a teaching hospital in Japan. Patients' falls were divided into two groups based on the median of the fall date (day 10). Considering a 70/30split, the logistic regression model was used to extract independent predictors for early and late falls for nine risk variables based on exploratory analysis among 26 items selected from the modified Japanese Nursing Association Fall Risk Assessment Tool, and risk models were validated. This study was conducted according to the STROBE guideline. Of the 10,975 patients admitted, 87 and 90 with early and late falls, respectively, were identified. The five significant risk factors extracted for early falls were fall history, muscle weakness, impaired understanding, use of psychotropics and the personality trait of 'doing everything on one's own'; risk factors identified for late falls were being older than 65years, impaired extremities and unstable gait, in addition to muscle weakness. Using these variables for early and late falls in the validation cohort, the concordance indices of the risk models were both over 0.80. By separately extracting risk factors for early and late falls in an acute care hospital setting, this study shed light on the characteristics of the respective types of falls. As the risk factors of falls vary according to the length of hospitalisation, specific preventive care can be implemented to avoid fall incidents.

Designing and Implementing a Zero Harm Falls Prevention Program: A Quality Improvement Study.

Inpatient falls with harm have severe implications on patients and the health care system. We implemented a zero harm approach to falls prevention, which aimed to reduce falls with injury by 25% within 1 year. We implemented a multifaceted and multidisciplinary quality improvement falls prevention strategy that included facilitating organization-wide education, adopting the Morse Fall Risk Assessment tool, displaying real-time unit-specific falls rates, and implementing a transparent root-cause analysis process after falls. Our outcome measure was falls with injury per 1000 patient-days. We observed a decrease in the rate of patient falls with injury from 2.03 (baseline period) to 1.12 (1 year later) per 1000 patient-days. We also observed increases in awareness around falls prevention and patient safety incident reporting. Our zero harm approach reduced falls with injury while improving our patient safety culture.

Correlation between fall risk increasing drugs (FRIDs) and fall events at a rehabilitation hospital.

Background and aim:Falls and fall-related injuries are a major public health issue which needs global attention due to its clinical and socioeconomic impact. Important risk factors for falls are polypharmacy and the assumption of so-calledFall Risk Increasing Drugs(FRIDs). Aims of our study were to investigate the associations between falls and the use of medications among inpatients by conducting a retrospective case-control study in a rehabilitation hospital in Northern Italy in 2018.Methods:A Conditional Logistic Regression was performed to analyze the impact that 13 types of FRIDs individually and the number of administrated FRIDs had on the risk of falling. A second regression model was obtained adjusting the case-control matching for CIRS, Morse and Barthel scores.Results:We identified 148 cases and 444 controls. 3 types of FRIDs were significantly correlated (p < 0,05) with an increased risk of falling: Antipsychotics, Antidepressants, Diuretics. Antidepressants were the only type of FRID significantly correlated (p=0,008) even in the model adjusted for CIRS, Morse and Barthel scores. The unadjusted model showed that the addition of one type of FRID to therapy was significantly associated with the fall event (p<0.05).Conclusion:Assumption of drugs, in particular antidepressant and polypharmacy, can play a role in hospital falling. The fall risk assessment tools available, suffer from low specificity and sensitivity and do not assess these risk factors. A holistic approach with a multidimensional evaluation of the patient through screening tools, functional assessment tools and a full medical evaluation should be pursued to improve prediction. (www.actabiomedica.it)

Comparison of Two Different Models to Predict Fall Risk in Hospitalized Patients.

Fall prevention is a patient safety and economic priority for health care organizations. An automated model within the electronic medical record (EMR) that accurately predicts risk for falling would be valuable for mitigation of inpatient falls. The aim of this study was to validate the reliability of an EMR-based computerized predictive model (ROF Model) for inpatient falls. The hypothesis was that the ROF Model would be similar to the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in predicting fall events in the inpatient setting at a large academic medical center. This observational study compared the falls predicted by each model against actual falls over an eight-month period in a single institution. Descriptive statistics were used to compare the distribution of scores and accuracy of fall risk categorization for each model immediately preceding a fall. For 35,709 inpatient encounters, the total fall rate was 0.92%. Of the 329 patients who fell, 60.8% were high risk by ROF Model (fall rate 1.82%), and 75.4% were high risk by JHFRAT (fall rate 1.39%). The ROF Model had a better specificity than the JHFRAT (69.7% vs. 49.2%) but a similar C-statistic (0.717 vs. 0.702) and a lower sensitivity (60.8% vs. 79.3%). The performance of the ROF Model was similar to that of the JHFRAT in predicting inpatient falls. This comparison provides evidence to support a transition to a more automated process. Future studies will determine prospectively if implementation of the ROF Model will reduce falls in the inpatient setting.

Development of a Fall Risk Assessment Tool for Patients with Neurological Disorders

Development of a Fall Risk Assessment Tool for Patients with Neurological Disorders

Evidence-Based Nursing Case for Optimal Pediatric Fall Risk Assessment tools

Evidence-Based Nursing Case for Optimal Pediatric Fall Risk Assessment tools

Validity of the Morse Fall Scale and the Johns Hopkins Fall Risk Assessment Tool for fall risk assessment in an acute care setting.

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. 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. Retrospective case-control study using the STROBE checklist. 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. 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. JHFRAT more accurately reflects acute changeable conditions related to fall risk measurements after admission. JHFRAT may be useful for effective fall prevention activities in acute care settings.

Comparison of the psychometric properties of three commonly used fall risk assessment tools: a prospective observational study for stroke patients

ABSTRACT Background A reliable tool recommendation is needed to identify the risk of falling in hospitalized stroke patients. Objectives The aim of this study was to identify the most reliable fall risk assessment tool among the Morse Fall Scale (MFS), Itaki Fall Risk Scale (Itaki FRS) and Hendrich II Fall Risk Model (HIIFRM) for stroke patients. Methods The study was planned as an observational prospective study. It was carried out over the period July-December 2018 with 125 stroke patients. The Functional Independence Measure (FIM), Itaki FRS, HIIFRM, and MFS were used for the study data. The fall risk and incidents of falling were monitored on a daily basis over the course of the patients’ stay at the hospital. The differentiation between the fall risk tools was assessed with sensitivity-specificity analysis and the ROC curve. Results The mean age of the research participants was 71.47 ± 11.16 years. It was determined that 9.6% of the patients fell at least once during the follow-up period, which was 8.66 ± 1.80 days on average. The sensitivity and specificity rates of the fall risk assessment tools were respectively 75.0% and 63.7% for the Itaki FRS, 83.3% and 50.4% for the HIIFRM, and 91.7% and 73.5% for the MFS. The cutoff points on the tools were 14 for the Itaki FRS, 4.5 for the HIIFRM, and 66.2 for the MFS. Conclusion MFS is a more reliable tool than Itaki FRS or HIIFRM in determining fall risk in hospitalized stroke patients.

The risk of fall-related hospitalisations at entry into permanent residential aged care

BackgroundEntering permanent residential aged care (PRAC) is a vulnerable time for individuals. While falls risk assessment tools exist, these have not leveraged routinely collected and integrated information from the Australian aged and health care sectors. Our study examined individual, system, medication, and health care related factors at PRAC entry that are predictors of fall-related hospitalisations and developed a risk assessment tool using integrated aged and health care data.MethodsA retrospective cohort study was conducted on N = 32,316 individuals ≥65 years old who entered a PRAC facility (01/01/2009-31/12/2016). Fall-related hospitalisations within 90 or 365 days were the outcomes of interest. Individual, system, medication, and health care-related factors were examined as predictors. Risk prediction models were developed using elastic nets penalised regression and Fine and Gray models. Area under the receiver operating characteristics curve (AUC) assessed model discrimination.Results64.2% (N = 20,757) of the cohort were women and the median age was 85 years old (interquartile range 80-89). After PRAC entry, 3.7% (N = 1209) had a fall-related hospitalisation within 90 days and 9.8% (N = 3156) within 365 days. Twenty variables contributed to fall-related hospitalisation prediction within 90 days and the strongest predictors included fracture history (sub-distribution hazard ratio (sHR) = 1.87, 95% confidence interval (CI) 1.63-2.15), falls history (sHR = 1.41, 95%CI 1.21-2.15), and dementia (sHR = 1.39, 95%CI 1.22-1.57). Twenty-seven predictors of fall-related hospitalisation within 365 days were identified, the strongest predictors included dementia (sHR = 1.36, 95%CI 1.24-1.50), history of falls (sHR = 1.30, 95%CI 1.20-1.42) and fractures (sHR = 1.28, 95%CI 1.15-1.41). The risk prediction models had an AUC of 0.71 (95%CI 0.68-0.74) for fall-related hospitalisations within 90 days and 0.64 (95%CI 0.62-0.67) for within 365 days.ConclusionRoutinely collected aged and health care data, when integrated at a clear point of action such as entry into PRAC, can identify residents at risk of fall-related hospitalisations, providing an opportunity for better targeting risk mitigation strategies.

Development and validation of a continuous fall risk score in community-dwelling older people: an ecological approach

BackgroundFall risk assessment in older people is of major importance for providing adequate preventive measures. Current predictive models are mainly focused on intrinsic risk factors and do not adjust for contextual exposure. The validity and utility of continuous risk scores have already been demonstrated in clinical practice in several diseases. In this study, we aimed to develop and validate an intrinsic-exposure continuous fall risk score (cFRs) for community-dwelling older people through standardized residuals.MethodsSelf-reported falls in the last year were recorded from 504 older persons (391 women: age 73.1 ± 6.5 years; 113 men: age 74.0 ± 6.1 years). Participants were categorized as occasional fallers (falls ≤1) or recurrent fallers (≥ 2 falls). The cFRs was derived for each participant by summing the standardized residuals (Z-scores) of the intrinsic fall risk factors and exposure factors. Receiver operating characteristic (ROC) analysis was used to determine the accuracy of the cFRs for identifying recurrent fallers.ResultsThe cFRs varied according to the number of reported falls; it was lowest in the group with no falls (− 1.66 ± 2.59), higher in the group with one fall (0.05 ± 3.13, p < 0.001), and highest in the group with recurrent fallers (2.82 ± 3.94, p < 0.001). The cFRs cutoff level yielding the maximal sensitivity and specificity for identifying recurrent fallers was 1.14, with an area under the ROC curve of 0.790 (95% confidence interval: 0.746–0.833; p < 0.001).ConclusionsThe cFRs was shown to be a valid dynamic multifactorial fall risk assessment tool for epidemiological analyses and clinical practice. Moreover, the potential for the cFRs to become a widely used approach regarding fall prevention in community-dwelling older people was demonstrated, since it involves a holistic intrinsic-exposure approach to the phenomena. Further investigation is required to validate the cFRs with other samples since it is a sample-specific tool.