Factors included in adult fall risk assessment tools (FRATs): a systematic review

2020 ◽  
pp. 1-25
Author(s):  
Hendrika de Clercq ◽  
Alida Naudé ◽  
Juan Bornman
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Risk Assessment ◽  
Fall Risk ◽  
The Body ◽  
Assessment Tools ◽  
Personal Factors ◽  
Fall Risk Assessment ◽  
Environmental Consequences ◽  
Preventative Measures

Abstract Falls often have severe financial and environmental consequences, not only for those who fall, but also for their families and society at large. Identifying fall risk in older adults can be of great use in preventing or reducing falls and fall risk, and preventative measures that are then introduced can help reduce the incidence and severity of falls in older adults. The overall aim of our systematic review was to provide an analysis of existing mechanisms and measures for evaluating fall risk in older adults. The 43 included FRATs produced a total of 493 FRAT items which, when linked to the ICF, resulted in a total of 952 ICF codes. The ICF domain with the most used codes was body function, with 381 of the 952 codes used (40%), followed by activities and participation with 273 codes (28%), body structure with 238 codes (25%) and, lastly, environmental and personal factors with only 60 codes (7%). This review highlights the fact that current FRATs focus on the body, neglecting environmental and personal factors and, to a lesser extent, activities and participation. This over-reliance on the body as the point of failure in fall risk assessment clearly highlights the need for gathering qualitative data, such as from focus group discussions with older adults, to capture the perspectives and views of the older adults themselves about the factors that increase their risk of falling and comparing these perspectives to the data gathered from published FRATs as described in this review.

scholarly journals Validated Fall Risk Assessment Tools for Use with Older Adults: A Systematic Review

2018 ◽  
Vol 36 (4) ◽  
pp. 331-353 ◽  
Author(s):  
Marcello Ruggieri ◽  
Biagio Palmisano ◽  
Giancarlo Fratocchi ◽  
Valter Santilli ◽  
Roberta Mollica ◽  
...  
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Risk Assessment ◽  
Fall Risk ◽  
Assessment Tools ◽  
Fall Risk Assessment ◽  
Risk Assessment Tools

scholarly journals Proposals for continued research to determine older adults’ falls risk

2020 ◽  
Vol 05 (04) ◽  
pp. 89-91
Author(s):  
Beatrice Pettersson ◽  
Ellinor Nordin ◽  
Anna Ramnemark ◽  
Lillemor Lundin-Olsson
Keyword(s):  
Older Adults ◽  
Risk Assessment ◽  
Fall Risk ◽  
Assessment Tools ◽  
Risk Of Falling ◽  
Self Assessment ◽  
Preventative Measures ◽  
Increased Risk ◽  
Risk Assessment Tools ◽  
Falls Prediction

Early detection of older adults with an increased risk of falling could enable early onset of preventative measures. Currently used fall risk assessment tools have not proven sufficiently effective in differentiating between high and low fall risk in community-living older adults. There are a number of tests and measures available, but many timed and observation-based tools are performed on a flat floor without interaction with the surrounding. To improve falls prediction, measurements in other areas that challenge mobility in dynamic conditions and that take a persons’ own perception of steadiness into account should be further developed and evaluated as single or combined measures. The tools should be easy to apply in clinical practice or used as a self-assessment by the older adults themselves.

scholarly journals A Systematic Review of the Guidelines and Delphi Study for the Multifactorial Fall Risk Assessment of Community-Dwelling Elderly

2020 ◽  
Vol 17 (17) ◽  
pp. 6097
Author(s):  
Jieun Kim ◽  
Worlsook Lee ◽  
Seon Heui Lee
Keyword(s):  
Systematic Review ◽  
Risk Assessment ◽  
Delphi Study ◽  
Fall Risk ◽  
Assessment Tool ◽  
The Elderly ◽  
Community Dwelling ◽  
Assessment Tools ◽  
Risk Assessment Tool ◽  
Fall Risk Assessment

As falls are among the most common causes of injury for the elderly, the prevention and early intervention are necessary. Fall assessment tools that include a variety of factors are recommended for preventing falls, but there is a lack of such tools. This study developed a multifactorial fall risk assessment tool based on current guidelines and validated it from the perspective of professionals. We followed the Meta-Analysis of Observational Studies in Epidemiology’s guidelines in this systematic review. We used eight international and five Korean databases to search for appropriate guidelines. Based on the review results, we conducted the Delphi survey in three rounds; one open round and two scoring rounds. About nine experts in five professional areas participated in the Delphi study. We included nine guidelines. After conducting the Delphi study, the final version of the “Multifactorial Fall Risk Assessment tool for Community-Dwelling Older People” (MFA-C) has 36 items in six factors; general characteristics, behavior factors, disease history, medication history, physical function, and environmental factors. The validity of the MFA-C tool was largely supported by various academic fields. It is expected to be beneficial to the elderly in the community when it comes to tailored interventions to prevent falls.

scholarly journals Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jelena Bezold ◽  
Janina Krell-Roesch ◽  
Tobias Eckert ◽  
Darko Jekauc ◽  
Alexander Woll
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Risk Assessment ◽  
Cognitive Impairment ◽  
Fall Risk ◽  
Wearable Sensors ◽  
Sensor Location ◽  
Risk Of Falling ◽  
Fall Risk Assessment ◽  
Fall Risk Factors

Abstract Background Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design. Results Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%). Conclusion Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment. Trial registration This systematic review is registered in PROSPERO (CRD42020171118).

scholarly journals Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data (Preprint)

2018 ◽  
Author(s):  
Insook Cho ◽  
Eun-Hee Boo ◽  
Eunja Chung ◽  
David W. Bates ◽  
Patricia Dykes
Keyword(s):  
Risk Assessment ◽  
Error Rate ◽  
Fall Risk ◽  
Assessment Tool ◽  
Assessment Tools ◽  
Fall Risk Assessment ◽  
C Statistic ◽  
Risk Assessment Tools ◽  
Hospital Days ◽  
Cross Site

BACKGROUND Electronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research. OBJECTIVE In this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools. METHODS We used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. The modeling cohort included 14,307 admissions (122,179 hospital days), and the validation cohort comprised 21,172 admissions (175,592 hospital days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and medications and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross-validation. RESULTS The initial model showed an error rate of 11.7% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients. CONCLUSIONS A risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients likely to fall.

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