scholarly journals Descriptive evaluation and accuracy of a mobile app to assess fall risk in seniors: Retrospective Case Control Study (Preprint)

2019 ◽  
Author(s):  
Sophie Rabe ◽  
Arash Azhand ◽  
Wolfgang Pommer ◽  
Swantje Müller ◽  
Anika Steinert
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Logistic Regression ◽  
Random Forest ◽  
Fall Risk ◽  
Performance Metrics ◽  
Gradient Boosting ◽  
Support Vector ◽  
Discriminative Ability ◽  
Fall Risk Assessment

BACKGROUND Fall risk assessment is complex. Based on current scientific evidence, a multifactorial approach including the analysis of physical performance, gait parameters and both extrinsic and intrinsic risk factors is highly recommended. Using these determinants, a smartphone-based application was designed to assess the individual risk of falling with a score that combines multiple fall risk factors into one comprehensive metric. OBJECTIVE This study provides a descriptive evaluation of the designed fall risk score as well as an analysis of its discriminative ability based on real-world data. METHODS Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall risk assessment app. First, we provide a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification and Random Forest Regression) are trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall risk score’s ability to discriminate fallers from non-fallers. For the sake of completeness, specificity, precision and overall accuracy were provided for each model as well. RESULTS Out of 242 participants with a mean age of 84.6 ± 6.7 years, 139 (57.4%) reported no previous falls (non-faller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 ± 12.4 points. The performance metrics for the Logistic Regression Model were AUC = 0.9; Sensitivity = 100%; Specificity = 52%; Accuracy = 73%. The performance metrics for the Gaussian Naive Bayes Model were AUC = 0.9; Sensitivity = 100%; Specificity = 52%; Accuracy = 73%. The performance metrics for the Gradient Boosting Model were AUC = 0.85; Sensitivity = 88%; Specificity = 62%; Accuracy = 73%. The performance metrics for the Support Vector Classification Model are AUC = 0.84; Sensitivity = 88%; Specificity = 67%; Accuracy = 76%. The performance metrics for the Random Forest Model were AUC = 0.84; Sensitivity = 88%; Specificity = 57%; Accuracy = 70%. CONCLUSIONS Descriptive statistics for the dataset were provided as comparison and reference values. The fall risk score exhibited a high discriminative ability to distinguish fallers from non-fallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93% and an average specificity of 58%. Average overall accuracy was 73%. Hence, the fall risk app has the potential to support caretakers in easily conducting a valid fall risk assessment. The fall risk score’s prospective accuracy will be further validated in a prospective trial.

scholarly journals Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study

JMIR Aging ◽  
10.2196/16131 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e16131 ◽  
Author(s):  
Sophie Rabe ◽  
Arash Azhand ◽  
Wolfgang Pommer ◽  
Swantje Müller ◽  
Anika Steinert
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Logistic Regression ◽  
Random Forest ◽  
Fall Risk ◽  
Performance Metrics ◽  
Gradient Boosting ◽  
Support Vector ◽  
Discriminative Ability ◽  
Fall Risk Assessment

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

Fall Risk Assessment and Risk Factors of Inpatients in Armed Forces Hospital

2016 ◽  
Vol 34 (1) ◽  
pp. 42-53
Author(s):  
Kyung-Wan Seo ◽  
Jeong-Ok Lee ◽  
Sun-Young Choi ◽  
Min-Jung Park
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fall Risk ◽  
Armed Forces ◽  
Fall Risk Assessment

scholarly journals Comparison of Fall Risk Assessment Tools for Older Indonesian Adults in the Elderly Home and the Community

2019 ◽  
Vol 8 (3) ◽  
pp. 241
Author(s):  
Indri Hapsari Susilowati ◽  
Susiana Nugraha ◽  
Sabarinah Sabarinah ◽  
Bonardo Prayogo Hasiholan ◽  
Supa Pengpid ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fall Risk ◽  
Assessment Tool ◽  
The Elderly ◽  
Risk Assessment Tool ◽  
Fall Risk Assessment ◽  
Elderly Home ◽  
Balance Confidence ◽  
Confidence Scale

Introduction: One of the causes of disability among elderly is falling. The ability to predict the risk of falls among this group is important so that the appropriate treatment can be provided to reduce the risk. The objective of this study was to compare the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) Initiative from the Centers for Disease Control and Prevention (CDC) and The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) from the Johns Hopkins University. Methods: This study used the STEADI tool, JHFRAT, Activities-Specific Balance Confidence Scale (ABC), and The Geriatric Depression Scale (GDS). The study areas were in community and elderly home in both public and private sectors and the samples were 427 after cleaning. Results: The results for the STEADI and JHFRAT tools were similar where the respondents at highest risk of falling among women (STEADI: 49%; JHFRAT: 3.4%), in Bandung area (63.5%; 5.4%), in private homes (63.3%; 4.4%), non-schools (54.6%; 6.2%), aged 80 or older (64.8%; 6.7%) and not working (48.9%;3.3%). The regression analysis indicated that there was a significant relationship between the risk factors for falls in the elderly determined by the JHFRAT and STEADI tools: namely, region, type of home, age, disease history, total GDS and ABC averages. Conclusion: Despite the similarity in the risk factors obtained through these assessments, there was a significant difference between the results for the STEADI tool and the JHFRAT. The test strength was 43%. However, STEADI is more sensitive to detect fall risk smong elderly than JHFRATKeywords: Activities-Specific Balance Confidence scale, elderly, fall risk,The Johns Hopkins Fall Risk Assessment Tool, the Stopping Elderly Accidents, Deaths, & Injuries

scholarly journals Machine Learning Framework to Predict Chronic Kidney Disease using Ensemble Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 1-6
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Performance Metrics ◽  
Weighted Average ◽  
Gradient Boosting ◽  
Support Vector ◽  
The Individual

Chronic Kidney Disease (CKD) is a worldwide concern that influences roughly 10% of the grown-up population on the world. For most of the people the early diagnosis of CKD is often not possible. Therefore, the utilization of present-day Computer aided supported strategies is important to help the conventional CKD finding framework to be progressively effective and precise. In this project, six modern machine learning techniques namely Multilayer Perceptron Neural Network, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Decision Tree, Logistic regression were used and then to enhance the performance of the model Ensemble Algorithms such as ADABoost, Gradient Boosting, Random Forest, Majority Voting, Bagging and Weighted Average were used on the Chronic Kidney Disease dataset from the UCI Repository. The model was tuned finely to get the best hyper parameters to train the model. The performance metrics used to evaluate the model was measured using Accuracy, Precision, Recall, F1-score, Mathew`s Correlation Coefficient and ROC-AUC curve. The experiment was first performed on the individual classifiers and then on the Ensemble classifiers. The ensemble classifier like Random Forest and ADABoost performed better with 100% Accuracy, Precision and Recall when compared to the individual classifiers with 99.16% accuracy, 98.8% Precision and 100% Recall obtained from Decision Tree Algorithm

scholarly journals Case-Based Study of Metrics Derived from Instrumented Fall Risk Assessment Tests

2016 ◽  
Author(s):  
Anabela Martins ◽  
Joana Silva ◽  
António Santos ◽  
João Madureira ◽  
Carlos Alcobia ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fall Risk ◽  
Inertial Sensors ◽  
Risk Of Falling ◽  
Fall Risk Assessment ◽  
Subjective Analysis ◽  
Additional Information ◽  
Study Results ◽  
Case Based

Purpose: National Institute for Health and Care Excellence (NICE) has recently published quality standards for assessment of fall risk and preventing further falls. According to the standards, multifactorial fall risk assessments should include: identification of falls history; analysis of gait, balance, mobility and muscle strength, among other factors. Despite being based on subjective analysis or simple timing and not being multifactorial, physiotherapists and physicians quite often use these tests as reference scales to differentiate between lower and higher risk of falling. Instrumented TUG has been recently reported to provide important additional information to the overall score. Objective: To explore a case-based approach of fall risk assessment to identify the most relevant and informative risk factors that in combination could better define a person risk profile. Materials and Methods: A multifactorial assessment of fall risk through questionnaires, standard functional tests, tests instrumented with inertial sensors, and force platforms has been studied within a group aged 55-80 years old. Different fall risk factors and fall risk assessment methods were analyzed in a case-based descriptive study. Results & Discussion: Subjects at higher risk of falling were identified based on their detailed profiles. A set of features were obtained from the instrumented standard tests differing significantly between subjects presenting higher or lower fall risk. Therefore, instrumenting conventional tests with wearables containing inertial sensors and force platforms gives more detailed and quantitative insights. This information can be used to better define and tailor fall prevention exercises and to improve the follow-up of the evolution of the subject.

Using Electronic Health Records to Predict Short-Term Falls: Machine learning Applications in Senior Care Facilities (Preprint)

2021 ◽  
Author(s):  
Sepideh Shokouhi ◽  
Rahul Thapa ◽  
Anurag Garikipati ◽  
Myrna Hurtado ◽  
Gina Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Risk Assessment ◽  
Electronic Health Records ◽  
Fall Risk ◽  
Vital Signs ◽  
Short Term ◽  
Health Records ◽  
Fall Risk Assessment ◽  
Care Facilities

BACKGROUND Evidence for the best choice of fall risk assessment in long-term care facilities is limited. Short-term fall predictions may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. This can be achieved through the use of electronic health records (EHRs), which contain routinely collected information regarding the majority of known fall risk factors. OBJECTIVE We implemented machine learning algorithms that use EHR data to predict a three-month fall risk in residents from a variety of senior care facilities providing different levels of care. METHODS This retrospective study obtained EHR data (2007-2021) from Juniper communities’ proprietary database of 2,785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performances of three machine learning (ML)-based fall predictions models and the Juniper Communities fall risk assessment across these different facilities. The ML input features included vital signs and several known risk factors, such as history of fall, comorbidities, and medications. These features were identified within the EHR system based on relevant International Classification of Diseases codes, string searches, or keyword queries. Additional analyses were conducted to examine how the changes in the input features, training datasets, and prediction window affected the performance of these models. RESULTS The extreme gradient boosting (XGB) model exhibited the highest performance with an area under the receiver operating characteristic curve (AUROC) of 0.846, specificity of 0.848, and sensitivity of 0.706 while achieving the best tradeoff in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident's number of active diseases, and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features reached a higher prediction accuracy than using either group of features alone. When reducing the prediction window to two months, the XGB model continued to exhibit the highest performance (AUROC = 0.753) in comparison to logistic regression (AUROC = 0.690), multi-layered perceptron (AUROC = 0.678) and Juniper's fall risk assessment (AUROC = 0.582). CONCLUSIONS This study provides novel insights into EHR-based features for predicting short-term fall risk in different types of care facilities. The integration of EHR data into fall prediction models, and particularly vital signs, yields a cost-effective and automated fall risk surveillance. Our XGB model uncovered the impact of a wide range of clinical and pathophysiological fall predictors across heterogenous cohorts while outperforming traditional fall risk assessments and standard ML techniques that are less compatible with EHR data. CLINICALTRIAL N/A

scholarly journals 21 Fall Risk Assessment Outcomes in Community Dwelling Elderly Patients on Warfarin (FRAIL-W)

2019 ◽  
Vol 48 (Supplement_4) ◽  
pp. iv6-iv8
Author(s):  
Syarifah Nurul Ain ◽  
Liew Houng Bang ◽  
Premala Subramaniam ◽  
Ho Hee Kheen
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Elderly Patients ◽  
Fall Risk ◽  
Warfarin Therapy ◽  
Community Dwelling ◽  
Bleeding Complications ◽  
Fall Risk Assessment ◽  
Falls Risk ◽  
Severity Of Injury

Abstract Background Elderly patients on warfarin are prone to experience severe bleeding complications when they fall. In warfarin clinic, they are not routinely screened for falls risk before starting on warfarin therapy. The purpose of our study was to determine the incidence of fall and its associated factors, severity of injury following fall and grading of falls risk among community dwelling elderly patients on warfarin in two tertiary hospitals in Sabah. Methods This is a cross-sectional study conducted in warfarin outpatient clinic, Hospital Queen Elizabeth and Hospital Queen Elizabeth II for 10 weeks (Mac-May 2019). Inclusion; patients aged ≥60 years old, on lifelong warfarin therapy. Exclusion; dementia, psychosis, severe cognitive impairment, institutionalised, inability to stand. Face-to-face interviews were done using Falls Risk for Older People – Community (FROP-Com) and Timed Up and Go (TUG) test. Results Out of 162 patients, majority were males (65.4%), Chinese (50.6%), married (93.2%), stays with family (96.9%) and had secondary education (42.6%). Mean age was 70 years old. 82.1% of them had atrial fibrillation; 63.2% had low CHA2DS2VASC score (less than 4) and 91.7% had low HAS-BLED score (less than 3). 22 patients (13.6%) experienced actual fall in past 12 months; only 1 patient experienced major injury. FROP-Com showed majority 133 patients (82.1%) were at low risk of fall. Risk factors of fall include polypharmacy and comorbidity affecting balance and mobility. Mean TUG test score was high; 13.7 seconds. Conclusion Fall incidence among patients on warfarin is substantial. Risk factors include polypharmacy and comorbidity affecting balance and mobility. This interim analysis showed majority patients had low fall risk (82.1% on FROP-Com, 58.0% on TUG test). Among fallers, FROP-Com risk score was moderate-high in 10 patients (45.5%). Further analysis could reveal potential value of these tests in refining fall risk assessment in this group of patients.

scholarly journals Comparison of Fall Risk Assessment Tools for Older Indonesian Adults in the Elderly Home and the Community

2019 ◽  
Vol 8 (3) ◽  
pp. 241
Author(s):  
Indri Hapsari Susilowati ◽  
Susiana Nugraha ◽  
Sabarinah Sabarinah ◽  
Bonardo Prayogo Hasiholan ◽  
Supa Pengpid ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fall Risk ◽  
Assessment Tool ◽  
The Elderly ◽  
Risk Assessment Tool ◽  
Fall Risk Assessment ◽  
Elderly Home ◽  
Balance Confidence ◽  
Confidence Scale

Introduction: One of the causes of disability among elderly is falling. The ability to predict the risk of falls among this group is important so that the appropriate treatment can be provided to reduce the risk. The objective of this study was to compare the Stopping Elderly Accidents, Deaths, & Injuries (STEADI) Initiative from the Centers for Disease Control and Prevention (CDC) and The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) from the Johns Hopkins University. Methods: This study used the STEADI tool, JHFRAT, Activities-Specific Balance Confidence Scale (ABC), and The Geriatric Depression Scale (GDS). The study areas were in community and elderly home in both public and private sectors and the samples were 427 after cleaning. Results: The results for the STEADI and JHFRAT tools were similar where the respondents at highest risk of falling among women (STEADI: 49%; JHFRAT: 3.4%), in Bandung area (63.5%; 5.4%), in private homes (63.3%; 4.4%), non-schools (54.6%; 6.2%), aged 80 or older (64.8%; 6.7%) and not working (48.9%;3.3%). The regression analysis indicated that there was a significant relationship between the risk factors for falls in the elderly determined by the JHFRAT and STEADI tools: namely, region, type of home, age, disease history, total GDS and ABC averages. Conclusion: Despite the similarity in the risk factors obtained through these assessments, there was a significant difference between the results for the STEADI tool and the JHFRAT. The test strength was 43%. However, STEADI is more sensitive to detect fall risk smong elderly than JHFRATKeywords: Activities-Specific Balance Confidence scale, elderly, fall risk,The Johns Hopkins Fall Risk Assessment Tool, the Stopping Elderly Accidents, Deaths, & Injuries

scholarly journals Correction: A Mobile App (FallSA) to Identify Fall Risk Among Malaysian Community-Dwelling Older Persons: Development and Validation Study (Preprint)

2021 ◽  
Author(s):  
Devinder Kaur Ajit Singh ◽  
Jing Wen Goh ◽  
Muhammad Iqbal Shaharudin ◽  
Suzana Shahar
Keyword(s):  
Risk Assessment ◽  
Predictive Validity ◽  
Fall Risk ◽  
Concurrent Validity ◽  
Older Persons ◽  
Screening Tool ◽  
Mobile App ◽  
Discriminative Ability ◽  
Fall Risk Assessment ◽  
Validity Test

BACKGROUND Recent falls prevention guidelines recommend early routine fall risk assessment among older persons. OBJECTIVE The purpose of this study was to develop a Falls Screening Mobile App (FallSA), determine its acceptance, concurrent validity, test-retest reliability, discriminative ability, and predictive validity as a self-screening tool to identify fall risk among Malaysian older persons. METHODS FallSA acceptance was tested among 15 participants (mean age 65.93 [SD 7.42] years); its validity and reliability among 91 participants (mean age 67.34 [SD 5.97] years); discriminative ability and predictive validity among 610 participants (mean age 71.78 [SD 4.70] years). Acceptance of FallSA was assessed using a questionnaire, and it was validated against a comprehensive fall risk assessment tool, the Physiological Profile Assessment (PPA). Participants used FallSA to test their fall risk repeatedly twice within an hour. Its discriminative ability and predictive validity were determined by comparing participant fall risk scores between fallers and nonfallers and prospectively through a 6-month follow-up, respectively. RESULTS The findings of our study showed that FallSA had a high acceptance level with 80% (12/15) of older persons agreeing on its suitability as a falls self-screening tool. Concurrent validity test demonstrated a significant moderate correlation (r=.518, P<.001) and agreement (k=.516, P<.001) with acceptable sensitivity (80.4%) and specificity (71.1%). FallSA also had good reliability (intraclass correlation .948; 95% CI .921-.966) and an internal consistency (α=.948, P<.001). FallSA score demonstrated a moderate to strong discriminative ability in classifying fallers and nonfallers. FallSA had a predictive validity of falls with positive likelihood ratio of 2.27, pooled sensitivity of 82% and specificity of 64%, and area under the curve of 0.802. CONCLUSIONS These results suggest that FallSA is a valid and reliable fall risk self-screening tool. Further studies are required to empower and engage older persons or care givers in the use of FallSA to self-screen for falls and thereafter to seek early prevention intervention.

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

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Jorge Bravo ◽  
Hugo Rosado ◽  
Pablo Tomas-Carus ◽  
Cristina Carrasco ◽  
Nuno Batalha ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Clinical Practice ◽  
Older People ◽  
Risk Score ◽  
Fall Risk ◽  
Community Dwelling ◽  
Fall Risk Assessment ◽  
Standardized Residuals ◽  
Community Dwelling Older People

Abstract Background Fall 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. Methods Self-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. Results The 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). Conclusions The 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.

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