Predict Fall Risk Research Articles

<b>入院高齢心不全患者の</b><b>6 </b><b>分間歩行距離は</b><b>転倒リスクの有無を推定する</b><b> </b>

<b>入院高齢心不全患者の</b><b>6 </b><b>分間歩行距離は</b><b>転倒リスクの有無を推定する</b><b> </b>

In Community Dwelling Females Age 65 or Older, Gait Speed Declines as Kinesthetic Awareness of the Lower Legs Decreases

Purpose: This study investigates the existence of a correlation between the gait speed and kinesthetic sense in lower legs and to conduct further analysis of kinesthetic sense in relation to the risk of fall among community dwelling females aged 65 years or older. Materials and Methods: A non-experimental correlational, descriptive, and cohort study included 38 community dwelling females (average age of 82.5 years). The kinesthetic ruler (K-Ruler) and kinesthetic test protocol were created and used to assess lower extremity kinesthetic awareness. The GaitRite System was used to assess gait speed. Each subject was categorized into four groups: “LL” (Low K-Score and low gait speed), “LH” (Low K-Score and high gait speed), “HL” (High K-Score and low gait speed), and “HH” (High K-Score and high gait speed) according to kinesthetic awareness and gait speed measured. Voluntary fall incidence reporting over the 6-month period was followed by the initial data collection. Results: Pearson product-moment correlation (2-tailed) showed that there is a statistically significant, positive moderate-to-strong correlation between K-score and gait speed (ICC = 0.692, p < 0.05). Additionally there were statistically significant negative moderate to strong correlations between age and K-score, and age and gait speed (ICC = -0.648, and -0.596 respectively, p < 0.05). The 13 subjects reported fall incidences over the 6-month period. No falls were reported among subjects in HL group. Discussion: Gait speed declines as kinesthetic awareness of lower extremity decreases in community dwelling 65 or older female subjects. Additionally, the combination of kinesthetic awareness and gait speed can be served as a predictor of fall risk. The K-ruler can be used to assess lower extremity kinesthetic awareness in older people as a feasible and standard test.

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

Falls play a critical role in older people's life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively. Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

Multiscale and Shannon entropies during gait as fall risk predictors—A prospective study

Although entropy-based measurements of gait dynamics are becoming widely used tools for fall risk assessment, their relationship to fall occurrence is still unclear. The aim of this study was hence to compare fallers and non-fallers in terms of gait dynamics assessed by the multiscale and Shannon entropy. This study included 139 participants, aged 60–80 years, divided into two groups according to fall occurrence during a 6-month prospective observation (38 fallers, 101 non-fallers). The methodology involved the use of the Tinetti balance assessment tool (TBAT) and 5min of overground walking with 3D accelerometers located near the L5 vertebra and shanks. We analyzed 150 strides for gait complexity, an index of complexity (CI), computed from multiscale entropy (MSE) and Shannon entropy (ShE) derived from the recurrence quantification analysis. We found no significant differences between groups in MSE and CI. The TBAT total score was significantly higher in non-fallers (P=0.033), however, both groups showed low risk of falls. ShE in the anterior-posterior direction from trunk and in the medial-lateral direction from the shanks were both significantly higher in fallers (P=0.020; P=0.024). ShE was negatively correlated with CI, the shank ShE in the vertical direction was positively correlated with TBAT. Taken together, our findings suggest that MSE is not able to distinguish between highly functional groups, whereas Shannon entropy seems to be sufficient in fall risk prediction.

While We Waited: Incidence and Predictors of Falls in Older Adults With Cataract

Strong evidence indicates an increased fall risk associated with cataract. Although cataract surgery can restore sight, lengthy wait times are common for public patients in many high-income countries. This study reports incidence and predictors of falls in older people with cataract during their surgical wait. Data from a prospective study of falls in adults aged ≥65 years who were awaiting cataract surgery in public hospitals in Australia were analyzed. Participants underwent assessment of vision, health status, and physical function, and recalled falls in the previous 12 months. Falls were self-reported prospectively during the surgical wait. Of 329 participants, mean age was 75.7 years; 55.2% were female. A total of 267 falls were reported by 101 (30.7%) participants during the surgical wait (median observation time, 176 days): an incidence of 1.2 falls per person-year (95% confidence interval [CI] 1.0-1.3). Greater walking activity (incidence rate ratio [IRR] 1.06, 95% CI 1.01-1.10; P = 0.02, per additional hour/week), poorer health-related quality of life (IRR 1.12, 95% CI 1.05-1.20; P < 0.001, per 5-unit decrease), and a fall in the prior 12 months (IRR 2.48, 95% CI 1.57-3.93; P < 0.001) were associated with incident falls. No visual measure independently predicted fall risk. More than one-half (51.7%) of falls were injurious. We found a substantial rate of falls and fall injury in older adults with cataract who were awaiting surgery. Within this relatively homogenous cohort, measures of visual function alone inadequately predicted fall risk. Assessment of exposure to falls through physical activity frequency may prove valuable in identifying those more likely to fall during the surgical wait.

Entropy-based measures for fall risk prediction

Entropy-based measures for fall risk prediction

Senior SWAY: A Mobile Application to Predict Fall Risk in Older Adults

Abstract Date Presented 4/8/2016 This study evaluated a new mobile application’s ability to predict fall risk in older adults. Senior SWAY was significantly associated with patient-reported previous falls and the Timed Up and Go. This application could potentially ease identification of older adults at risk of falling. Primary Author and Speaker: Mackenzi Pergolotti Additional Author and Speaker: Ashley Bryant Contributing Authors: A. Bennett, G. R. Williams

Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls.

BackgroundFalls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest.MethodsThis study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model.ResultsWe found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults.ConclusionThe interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.

Fall-risk prediction in older adults with cancer: an unmet need.

Falls in older adults with cancer are more common than in noncancer controls, yet no fall-risk screening tool has been validated in this population. We undertook a cross-sectional pilot study of the Falls Risk Questionnaire (FRQ) in 21 adults aged ≥65 receiving systemic cancer therapy. Participants completed the FRQ, geriatric assessment measures, and a measure of fear-of-falling. The recruitment rate was 87.5%, with 95.2% completion of the FRQ and additional geriatric assessment and quality of life measures. The FRQ correlated significantly with the Timed Up and Go test (Pearson r 0.479, p = 0.028). In addition, the FRQ score correlated directly with fear-of-falling and inversely with QOL, particularly physical health and neurotoxicity subscales. In conclusion, the FRQ was feasible in older adults receiving cancer therapy and correlates with measures of physical performance, functional status, and fear-of-falling. The FRQ may prove to be a valuable fall-risk screening tool to implement fall-prevention interventions in this vulnerable population of older adults with cancer.

A Validation of the Fullerton Advanced Balance Scale in High Functioning Older Adults

The Fullerton Advanced Balance Scale (FAB) is a commonly used assessment for predicting fall risk in high functioning older adults. While it has been shown to be a reliable and valid tool in clinical and therapy settings, a comparison with laboratory measures of balance capable of detecting subtle differences in balance ability has not been conducted. This shortcoming with the current FAB scale allows the possibility of a ceiling effect in the capability to identify balance deficits in high functioning older adults. PURPOSE: This study investigates whether a ceiling effect exists within the FAB, and specifically investigates the validity of the FAB assessment as a method for detecting subtle balance deficits in highly functioning older adults. METHODS: 33 community living older adults (age: 62.97 ± 7.14 years) were assessed using the short from of the FAB. While performing the single leg balance test participants were scored 1 through 4 based on observed errors. Simultaneously, center of pressure (COP) movement was recorded using a plantar pressure pad. COP sway, velocity, and variability were quantified using the pressure data. A one way ANOVA was used to examine differences in COP metrics between FAB groups. A k-means cluster analysis was used to separate participants into 4 groups based on COP sway. Ability of the FAB to detect subtle differences in balance was quantified by examining the percent agreement between grouping based on COP sway and grouping based on FAB scores. RESULTS: Average COP sway was significantly different between FAB groups, with participants who score a 4 demonstrating less sway (6.21 ± 3.08 cm) than those who scored a 1 (13.30 ±4.56 cm, p = 0.001) or a 2 (13.19 ± 2.95 cm, p = 0.001). However, there were no differences between FAB groups 1 and 2. The kappa statistic indicated poor agreement between the groups based on COP sway and groups based on FAB scores (Kappa= .210). CONCLUSIONS: Results suggest that the FAB scale lacks the sensitivity to detect subtle balance changes in high functioning older adults. Further investigations are necessary to construct a sensitive balance scale which is both clinically useful but does not produce a ceiling effect when assessing high functioning older adults.

Sensitivity of Fall Risk Outcome Measures by Age

Many balance assessments have been created to identify causes and outcomes of older adult falls, and in turn, help health care providers better assess fall risk. The Activities-Specific Balance Confidence Scale (ABC), Timed Up-and-Go (TUG), Berg Balance Scale (BBS), and Fullerton Advanced Balance Scale (FAB) have been developed to assess physical function and fear of falling. Few studies have considered age as a moderator of the sensitivity of these four tests. PURPOSE: The aim was to identify if the sensitivity of the BBS, FAB, TUG, and ABC, changed as a function of age group (60-79 vs. 80-100) for retrospective falls. METHODS: Forty-six community-dwelling older adults (mean age 79.1±10.2 years) were recruited from multiple facilities and asked how many times they had fallen in the past six months. Next, the ABC, TUG, BBS, and FAB were administered in random order to assess the participants’ fear of falling, mobility, and balance. Logistic regression was used to determine the discriminative power of these tests related to falls. Logistic regression models had a binary outcome of self-reported history of at least one fall versus no falls. Receiver operating characteristics (ROC) curves were calculated, as well as the corresponding area under the curve (AUC). Specificity and sensitivity were calculated by age group (60-79 and 80-100). RESULTS: While the ABC, TUG, BBS, and FAB were significantly correlated with each other, their ability to discriminate fallers from non-fallers was poor (AUC = 0.366, 0.554, 0.426, and 0.437, respectively). Most models were better suited to determine non-fallers as opposed to fallers, indicated by a larger specificity than sensitivity. For all outcome measures, the sensitivity was 25.0% for 60-79 years and 37.7% for 80-100 years. The specificity was 100.0% for all outcome measures for 60-79 years, and was ≤68.8% for 80-100 years. CONCLUSIONS: Sensitivity, while poor in both age groups, was greater in the older group. Based on the present data we suggest pairing these tools with other measures to reliably assess and provide a total fall risk prediction.

The Role of Cognitive Factors in Predicting Balance and Fall Risk in a Neuro-Rehabilitation Setting.

IntroductionThere is a consistent body of evidence supporting the role of cognitive functions, particularly executive function, in the elderly and in neurological conditions which become more frequent with ageing. The aim of our study was to assess the role of different domains of cognitive functions to predict balance and fall risk in a sample of adults with various neurological conditions in a rehabilitation setting.MethodsThis was a prospective, cohort study conducted in a single centre in the UK. 114 participants consecutively admitted to a Neuro-Rehabilitation Unit were prospectively assessed for fall accidents. Baseline assessment included a measure of balance (Berg Balance Scale) and a battery of standard cognitive tests measuring executive function, speed of information processing, verbal and visual memory, visual perception and intellectual function. The outcomes of interest were the risk of becoming a faller, balance and fall rate.ResultsTwo tests of executive function were significantly associated with fall risk, the Stroop Colour Word Test (IRR 1.01, 95% CI 1.00–1.03) and the number of errors on part B of the Trail Making Test (IRR 1.23, 95% CI 1.03–1.49). Composite scores of executive function, speed of information processing and visual memory domains resulted in 2 to 3 times increased likelihood of having better balance (OR 2.74 95% CI 1.08 to 6.94, OR 2.72 95% CI 1.16 to 6.36 and OR 2.44 95% CI 1.11 to 5.35 respectively).ConclusionsOur results show that specific subcomponents of executive functions are able to predict fall risk, while a more global cognitive dysfunction is associated with poorer balance.

A comparison study of local dynamic stability measures of daily life walking in older adult community-dwelling fallers and non-fallers

In the present study we compared the performance of three different estimations of local dynamic stability λ to distinguish between the dynamics of the daily-life walking of elderly fallers and non-fallers. The study re-analyses inertial sensor data of 3-days daily-life activity originally described by Weiss et al. (2013). The data set contains inertial sensor data from 39 older persons who reported less than 2 falls and 31 older persons who reported two or more falls the previous year. 3D-acceleration and 3D-velocity signals from walking epochs of 50s were used to reconstruct a state space using three different methods. Local dynamic stability was estimated with the algorithms proposed by Rosenstein et al. (1993), Kantz (1994), and Ihlen et al. (2012a). Median λs assessed by Ihlen׳s and Kantz׳ algorithms discriminated better between elderly fallers and non-fallers (highest AUC=0.75 and 0.73) than Rosenstein׳s algorithm (highest AUC=0.59). The present results suggest that the ability of λ to distinguish between fallers and non-fallers is dependent on the parameter setting of the chosen algorithm. Further replication in larger samples of community-dwelling older persons and different patient groups is necessary before including the suggested parameter settings in fall risk assessment and prediction models.

A multidisciplinary assessment instrument to predict fall risk in hospitalized patients: A prospective matched pair case study

Objective: To identify fall risk for hospitalized patients utilizing a multidisciplinary assessment tool based on patients’ physical, mental, pharmacological, and metabolic data. Methods: A prospective case-control design comparing 48 patients who fell (incidence group) and 48 patients who did not experience fall (control group), based on patients’ age, gender, and hospital unit location. The study was conducted over an 8-month period at a large academic hospital. Setting: The Methodist Hospital, tertiary care academic referral center with 824 operating beds in Houston, TX. Participants: One hundred and twenty patients, sixty patients who fell, and sixty control subjects. Main Outcome Measures: The sensitivity and specificity of variables identified in logistic regression are able to distinguish patients who fell from patients who did not fall. Results: Logistic regression results identified six variables (2 summary variables and 4 individual variables) that correctly classified patients with 90% sensitivity (patients who fell) and 90% specificity (patients who did not fall). The first variable was an 11-item summary variable that included history, weakness or balance problem, altered mental status or confusion, visual impairment, dizziness or vertigo, urinary tract infection or abnormal urinary analysis (UA), diuretics/IV drips, continence, acute renal failure (ARF), antihypertensives and narcotics. The second variable represented the combination of 3 medication classes: neuroleptic, anticonvulsant and antidepressant. The third variable that had a negative impact on fall risk was the presence of a therapeutic anticoagulant. The other 3 significant variables were hypoglycemia, vital sign abnormality, and low hemoglobin. Conclusions: A multidisciplinary fall-risk assessment tool that screens combinations of physiological, pharmacological and metabolic patient factors improves the probability of correctly distinguishing patients who were more likely to fall from those patients who were less likely to fall.

Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.

To construct and evaluate an easy-to-use fall risk prediction model based on the daily condition of inpatients from secondary use electronic medical record system data. The present authors scrutinized electronic medical record system data and created a dataset for analysis by including inpatient fall report data and Intensity of Nursing Care Needs data. The authors divided the analysis dataset into training data and testing data, then constructed the fall risk prediction model FiND from the training data, and tested the model using the testing data. The dataset for analysis contained 1,230,604 records from 46,241 patients. The sensitivity of the model constructed from the training data was 71.3% and the specificity was 66.0%. The verification result from the testing dataset was almost equivalent to the theoretical value. Although the model's accuracy did not surpass that of models developed in previous research, the authors believe FiND will be useful in medical institutions all over Japan because it is composed of few variables (only age, sex, and the Intensity of Nursing Care Needs items), and the accuracy for unknown data was clear.

A Prospective Evaluation of an Outpatient Assessment of Postural Instability to Predict Risk of Falls in Patients with Parkinson's Disease Presenting for Deep Brain Stimulation.

Postural instability (PI) and falls, major causes of morbidity in patients with PD, are often overlooked. DBS is a mainstay therapy for Parkinson's disease (PD) and has been purported to both worsen and improve PI. An effective PI evaluation that can predict fall risk in patients with PD presenting for DBS is needed. Forty-nine consecutive patients with PD were enrolled. Self-reported falls were the gold standard. Tests evaluated were the Berg Balance Scale (BBS), Timed-Up-and-Go (TUG), Pull Test, and Biodex Balance System Sway Index on firm (SI-FIRM) and soft (SI-SOFT) surfaces. The best single tests for fall risk were the BBS and SI-FIRM, each with sensitivities of 79% and specificities of 60% and 65%, respectively. When the evaluation was combined into a composite measure requiring four positive tests out of five, the sensitivity was 72% and specificity was 80%. A simple, efficient outpatient physical therapy assessment is effective in diagnosing fall risk in patients with PD.

A New Scale for Predicting Fall Risk in Acute Inpatient Rehabilitation Facilities

A New Scale for Predicting Fall Risk in Acute Inpatient Rehabilitation Facilities

Multifactoral measures of fall risk in the visually impaired population: A pilot study

To determine the feasibility of taking multiple measures of visual and physical function in adults with visual impairment. A second objective was to obtain preliminary data on risk for falls in this population. Cohort feasibility study. University ambulatory patient care center and research center. Convenience sample of community-dwelling men and women over age 18 with visual impairment (n=12). Thirteen subjects were enrolled in the study; one was subsequently excluded due to self-reported cognitive decline at time of testing. Subjects were grouped by prospective fall incidence. Verbal education. Subjective measures of function; objective measures of visual and physical function. Visually impaired adults can safely complete a battery of physical functions to predict fall risk. Recent onset of visual impairment was correlated with higher fall risk [-0.53±0.22, p=0.04]. It is feasible for an interdisciplinary team to measure risk for falls in adults with a visual impairment. Further investigation is needed to identify predictors of falls in adults of all ages with visual impairment.

Fall Risk and Laterality in the Elderly

Elderly people often show impairment in several motor skills, such as balance and coordination, and their laterality may also be affected. This study sought to analyse the relationship between fall risk and laterality in an elderly population. Thirty-three elders who were participating in exercise programs were studied using two instruments: the Tinetti Mobility Test (TMT), as a clinical test that predicts fall risk in the elderly, and the Dynamic-LATMO, a tool for observing the use of laterality in motor skills execution. Results showed a significant association between the standing balance score on the TMT and the task of standing on one leg in the laterality test.

Improving prediction of fall risk among nursing home residents using electronic medical records.

Falls are physically and financially costly, but may be preventable with targeted intervention. The Minimum Data Set (MDS) is one potential source of information on fall risk factors among nursing home residents, but its limited breadth and relatively infrequent updates may limit its practical utility. Richer, more frequently updated data from electronic medical records (EMRs) may improve ability to identify individuals at highest risk for falls. The authors applied a repeated events survival model to analyze MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain that uses a centralized EMR system from a leading vendor. Estimated regression parameters were used to project resident fall probability. The authors examined the proportion of observed falls within each projected fall risk decile to assess improvements in predictive power from including EMR data. In a model incorporating fall risk factors from the MDS only, 28.6% of observed falls occurred among residents in the highest projected risk decile. In an alternative specification incorporating more frequently updated measures for the same risk factors from the EMR data, 32.3% of observed falls occurred among residents in the highest projected risk decile, a 13% increase over the base MDS-only specification. Incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone. These improvements stem chiefly from the greater frequency with which EMR data are updated, with minimal additional gains from availability of additional risk factor variables.