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

10.2196/23663 ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. e23663
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 mHealth falls risk screening app (FallSA©) among Malaysian Community Dwelling Older Persons (Preprint)

2020 ◽  
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 ◽  
Community Dwelling ◽  
Discriminative Ability ◽  
Validity Test ◽  
Falls Risk

BACKGROUND Recent falls prevention guidelines recommend early routine falls risk assessment among older persons. OBJECTIVE The purpose of current study was to develop a Falls Screening Mobile Application (FallSA©), determine its acceptance, concurrent validity, test-retest reliability, discriminative ability and predictive validity as a self-screening tool to identify falls risk among Malaysian older persons. METHODS FallSA© acceptance was tested among 15 participants (mean age: 65.93±7.42 years); its validity and reliability among 91 participants (mean age: 67.34±5.97); discriminative ability and predictive validity among 610 participants (mean age: 71.78±4.70). Acceptance of FallSA© was assessed using a questionnaire and it was validated against a comprehensive falls risk assessment tool, Physiological Profile Assessments (PPA). Participants used FallSA© to test their falls risk repeatedly twice between an hour. Its discriminative ability and predictive validity were determined by comparing participants fall risk scores between fallers and non-fallers and prospectively through a 6 months follow-up respectively RESULTS The findings of our study showed that FallSA© had a high acceptance level with 80% older persons agreeing on its suitability as a falls self-screening tool. Concurrent validity test demonstrated a significant moderate correlation (rs= 0.518, P<0.001) and agreement (K= 0.516, P<0.001) with acceptable sensitivity (80.4%) and specificity (71.1%). FallSA© also had good reliability (ICC: 0.948, CI: 0.921-0.966) and an internal consistency (α= 0.948, P<0.001). FallSA© score demonstrated a moderate to strong discriminative ability in classifying fallers and non-fallers. FallSA© had a predictive validity of falls with positive likelihood ratio of 2.27, pooled sensitivity of 82% and specificity of 64%, and AUC 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. CLINICALTRIAL NA

scholarly journals Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly

Medicine ◽  
2019 ◽  
Vol 98 (39) ◽  
pp. e17105 ◽  
Author(s):  
Greta Castellini ◽  
Silvia Gianola ◽  
Elena Stucovitz ◽  
Irene Tramacere ◽  
Giuseppe Banfi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Diagnostic Test ◽  
Fall Risk ◽  
Screening Tool ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Fall Risk Assessment

scholarly journals Modified CAGE as a Screening Tool for Mechanical Fall Risk Assessment: A Pilot Survey

2013 ◽  
Vol 62 (4) ◽  
pp. S107-S108 ◽  
Author(s):  
M.R. Greenberg ◽  
M.C. Nguyen ◽  
B.G. Porter ◽  
R.D. Barracco ◽  
B. Stello ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Screening Tool ◽  
Fall Risk Assessment ◽  
Pilot Survey

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.

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 Development of a Screening Tool for Feet/Footwear- Related Influences on Fall Risk

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 790-790
Author(s):  
Mariana Wingood ◽  
Elizabeth Peterson ◽  
Christopher Neville ◽  
Jennifer Vincenzo
Keyword(s):  
Risk Assessment ◽  
Delphi Study ◽  
Fall Risk ◽  
Screening Tool ◽  
Phase 1 ◽  
Screening Tools ◽  
Similar Treatment ◽  
Fall Risk Assessment ◽  
Modified Delphi ◽  
High Level

Abstract The effectiveness of multifactorial fall risk assessment and intervention strategies is well documented. Although identifying feet/footwear-related influences on fall risk is a vital fall risk assessment component, few evidence-based resources or screening tools are available. To address this need, we developed the Screening Tool for Feet/Footwear-Related Influences on Fall Risk. Our tool is designed for older adults who are identified as at risk for falling, based on the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Algorithm for Fall Risk Screening, Assessment, and Intervention. Tool development was informed by results of our systematic review of lower-limb factors associated with balance and falls. Our initial tool was evaluated by an external group of 9 interprofessional content experts. Those experts recommended modification of 8 items and rated the tool’s clarity as 81.2/100, appeal as 79.1/100, and clinical feasibility as 76.1/100. After incorporating recommended changes, we completed a modified Delphi study using 8 new interprofessional experts (average years of experience: 19.3). During Phase 1, Delphi participants recommended we combine items with similar treatment recommendations, add a question about orthoses, and increase the specificity of 9 items. This refinement resulted in a 20-item screening tool, which met approval after two rounds of consensus voting. Approval was defined based on the Item Content Validation Index, percentage of agreement &gt; 80% on each item. The high level of agreement illustrates the tool’s content validity. Using our tool, an older adult’s feet/footwear-related risk factors can be identified and incorporated into an effective multifactorial fall prevention intervention.

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
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