Predict Fall Risk Research Articles

Difference scores between single-task and dual-task gait measures are better than clinical measures for detection of fall-risk in community-dwelling older adults

BackgroundAs the proportion of older adults in the population increases, so does the associated prevalence of falls, making falls the leading cause of fatal and nonfatal injuries among adults aged ≥65 years. In response, researchers and clinicians seek to develop a clinical tool that accurately predicts fall risk. These Investigations have included measures of clinical mobility and balance tests, strength, physiologically based tests, postural sway, and mean and variability of gait measures. To date, no study has concurrently explored all these measures to determine which measures, alone or in combination, emerge as the most predictive of fall risk. While there is evidence that dual-task gait conditions are sensitive indicators of fall risk, difference scores between dual-task and single-task gait conditions (DS) have not been explored. Research questionThis study included outcome measures representing diverse domains (clinical mobility and balance, strength, physiological, postural sway, and mean and variability of difference scores between dual- and single-task gait conditions) to determine the combination of measures that were the most sensitive for retrospectively classifying fallers from non-fallers. MethodsForty-two (mean: 75.8 yrs ± 3.3) community-dwelling older adults completed a comprehensive battery of 76 measures and classified into two groups based on self-report of having one or more falls in the previous year. ResultsResults suggest that 11 measures captured the salient characteristics of the total cohort (fallers (N = 27) and non-fallers (N = 15) and that five gait measures were sufficient for correctly classifying fallers and non-fallers with 92.3% sensitivity and 66.7% specificity with a total model classification of 82.9%. SignificanceThe five variables comprise mean DS of stride timing, stride width, and stride length and DS in variability for stride width and stride velocity demonstrating that difference in performance between dual-task and single-task gait trials was essential for discriminating fallers and superior to other measures.

Maximum Lyapunov exponent revisited: Long-term attractor divergence of gait dynamics is highly sensitive to the noise structure of stride intervals

BackgroundThe local dynamic stability method (maximum Lyapunov exponent) can assess gait stability. Two variants of the method exist: the short-term divergence exponent (DE), and the long-term DE. Only the short-term DE can predict fall risk. However, the significance of long-term DE has been unclear so far. Some studies have suggested that the complex, fractal-like structure of fluctuations among consecutive strides correlates with long-term DE. The aim, therefore, was to assess whether the long-term DE is a gait complexity index. MethodsThe study reanalyzed a dataset of trunk accelerations from 100 healthy adults walking at preferred speed on a treadmill for 10 min. By interpolation, the stride intervals were modified within the acceleration signals for the purpose of conserving the original shape of the signal, while imposing a known stride-to-stride fluctuation structure. Four types of hybrid signals with different noise structures were built: constant, anti-correlated, random, and correlated (fractal). Short- and long-term DEs were then computed. ResultsThe results show that long-term DEs, but not short-term DEs, are sensitive to the noise structure of stride intervals. For example, it was that observed that random hybrid signals exhibited significantly lower long-term DEs than hybrid correlated signals did (0.100 vs 0.144, i.e. a 44% difference). Long-term DEs from constant hybrid signals were close to zero (0.006). Conversely, short-term DEs of anti-correlated, random, and correlated hybrid signals were closely grouped (2.49, 2.50, and 2.51). ConclusionsThe short-term DE and the long-term DE, although they are both computed from divergence curves, should not be interpreted in a similar way. The long-term DE is very likely an index of gait complexity, which may be associated with gait automaticity or cautiousness. Consequently, to better differentiate between short- and long-term DEs, the use of the term attractor complexity index (ACI) is proposed for the latter.

A dynamic risk model for inpatient falls.

Construction and validation of a fall risk prediction model specific to inpatients receiving fall risk-increasing drugs (FRIDs) are described. In a retrospective cohort study of 75,036 admissions to 2 hospitals over a designated 22-month period that involved FRID exposure during the first 5 hospital days, factors influencing fall risk were investigated via logistic regression. The resultant risk prediction model was internally validated and its performance compared with that of a model based on Morse Fall Scale (MFS) scores. A total of 220,904 patient-days of FRID exposure were evaluated. The three most commonly administered FRIDs were oxycodone (given on 79,697 patient-days, 36.08%), morphine (52,427, 23.73%) and hydromorphone (42,063, 19.04%). Within the 90th percentile of modeled risk scores, 144 of the 466 documented falls (30.9%) were captured by the developed risk prediction model (unbiased C statistic, 0.69), as compared with 94 falls (20.2%) captured using the MFS model (unbiased C statistic, 0.62). Strong predictors of inpatient falls included a history of falling (odds ratio [OR], 1.99; 95% confidence interval (CI), 1.42-2.80); overestimation of ability to ambulate (OR, 1.53; 95% CI, 1.12-2.09); and "comorbidity predisposition," a composite measure encompassing a history of falling and 11 past diagnoses (OR, 1.60; 95% CI, 1.30-1.97). The proposed risk model for inpatient falls achieved superior predictive performance when compared with the MFS model. All risk factors were operationalized from discrete electronic health record fields, allowing full automation of real-time identification of high-risk patients.

The diagnostic accuracy of hip abductor strength in the fall risk of older persons

Persons aged over 65 years have an increased risk of falling and falls often trigger serious consequences like independency loss and increased mortality. An early detection of persons at risk of falling and a well-targeted fall prevention is thus of great interest. Previous studies showed that hip abductor strength is the muscle groups among all hip muscles, which is particularly related to the fall risk of older persons. Therefore we aimed to investigate the diagnostic accuracy of hip abductor strength by using history of falls as the external criterion. Hip abductor maximum voluntary isometric strength (ABD MVIS) and rate of force generation (ABD RFG) were assessed with a dynamometer fixed to a custom made frame in 60 persons aged over 65 years. The diagnostic accuracy [sensitivity (sens), specificity (spec), positive predictive value (PPV), negative predictive value (NPV), positive and negative likelihood ratios (LR + , LR − )] was assessed at a clinically important 90% sensitivity level. Cutoff values were calculated. Hip ABD MVIS (Sens. 90.6%, Spec. 57.1%, PPV 70.7%, NPV 84.2%, LR+ 2.11, LR− 0.16, cutoff value ≤ 1.06 N/kg) shows a slightly higher diagnostic accuracy than hip ABD RFG (Sens. 90.6%, Spec. 46.4%, PPV 65.9%, NPV 81.3%, LR+ 1.69, LR− 0.20, cutoff ≤ 8.47 N/kg/s). The results indicate that hip abductor strength shows a comparable diagnostic accuracy as other fall risk or mobility assessment tools (e.g. timed up and go test and short physical performance battery). Hip abductor strength shows a good diagnostic accuracy to discriminate between older fallers and non-fallers and therefore might be an interesting parameter for fall risk prediction and a targeted intervention program. This very promising parameter should further be investigated in prospective studies.

The effect of nurses’ training on sensitivity of fall risk prediction (Kyt: Kiken Yochi training)

The number of incidents of falls is high in the hospital or rehabilitation department. Kiken yochi training (KYT) stands for training to predict risk, and it is one of the widespread methods for increasing the sensitivity of risk prediction in medical practices and preventing medical accidents in Japan. However, it is not sufficiently clarified that KYT increases the sensitivity of risk prediction. The purpose of this study was to clarify the effect of KYT in improving the sensitivity of fall risk prediction. In 2015, we evaluated 78 nurses among the first-year newcomers employed at our hospital. Each nurse examined a slide on the risk of falling before KYT, and then had the items of risk prediction enumerated. Furthermore, using the same procedure, the number of items related to fall risk immediately and one year after KYT was compared with that before intervention. The numbers of items related to fall risk before and after KYT were analyzed using one-way analysis of variance and multiple comparisons. The numbers of items related to of fall risk immediately and one year after KYT were 4.4 ± 1.21 and 3.9 ± 1.1, respectively. The number of items after KYT was significantly higher than 3.2 ± 1.1 before intervention ( P < 0.01). Conversely, the number of items was significantly lower immediately after KYT than one year after the intervention ( P < 0.05). We believe that KYT increased the sensitivity of nurses to predict patients’ risk of falling. KYT also demonstrated a sustained effect on risk prediction after one year. Therefore, KYT is considered to be one of effective methods for enhancing risk sensitivity. Further studies are needed to verify whether KYT increases the sensitivity and sustainability of fall risk predictions to prevent medical accidents.

Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection.

Falls are critical events for human health due to the associated risk of physical and psychological injuries. Several fall-related systems have been developed in order to reduce injuries. Among them, fall-risk prediction systems are one of the most promising approaches, as they strive to predict a fall before its occurrence. A category of fall-risk prediction systems evaluates balance and muscle strength through some clinical functional assessment tests, while other prediction systems investigate the recognition of abnormal gait patterns to predict a fall in real time. The main contribution of this paper is a nonlinear model of user gait in combination with a threshold-based classification in order to recognize abnormal gait patterns with low complexity and high accuracy. In addition, a dataset with realistic parameters is prepared to simulate abnormal walks and to evaluate fall prediction methods. The accelerometer and gyroscope sensors available in a smartphone have been exploited to create the dataset. The proposed approach has been implemented and compared with the state-of-the-art approaches showing that it is able to predict an abnormal walk with a higher accuracy (93.5%) and a higher efficiency (up to 3.5 faster) than other feasible approaches.

A Reactive Balance Rating Method That Correlates With Kinematics After Trip-like Perturbations on a Treadmill and Fall Risk Among Residents of Older Adult Congregate Housing.

A growing number of studies are using modified treadmills to train reactive balance after trip-like perturbations that require multiple steps to recover balance. The goal of this study was thus to develop and validate a low-tech reactive balance rating method in the context of trip-like treadmill perturbations to facilitate the implementation of this training outside the research setting. Thirty-five residents of five senior congregate housing facilities participated in the study. Participants completed a series of reactive balance tests on a modified treadmill from which the reactive balance rating was determined, along with a battery of standard clinical balance and mobility tests that predict fall risk. We investigated the strength of correlation between the reactive balance rating and reactive balance kinematics. We compared the strength of correlation between the reactive balance rating and clinical tests predictive of fall risk with the strength of correlation between reactive balance kinematics and the same clinical tests. We also compared the reactive balance rating between participants predicted to be at a high or low risk of falling. The reactive balance rating was correlated with reactive balance kinematics (Spearman's rho squared = .04-.30), exhibited stronger correlations with clinical tests than most kinematic measures (Spearman's rho squared = .00-.23), and was 42%-60% lower among participants predicted to be at a high risk for falling. The reactive balance rating method may provide a low-tech, valid measure of reactive balance kinematics, and an indicator of fall risk, after trip-like postural perturbations.

Prospective elderly fall prediction by older-adult fall-risk modeling with feature selection

Abstract This study implemented feature-selection methods for prospective elderly fall-risk prediction and compared modeling outcomes to retrospective fall occurrence classification. Seventy-five elderly adults without prior falls (75.2 (6.6) years; 47 non-fallers, 28 fallers based on 6 month prospective falls) walked 7.62 m while wearing F-Scan insoles and tri-axial accelerometers at the left and right shanks, pelvis, and head. Feature sets were reduced using Relief-F, correlation-based feature selection, and fast correlation based filter algorithms. Naive Bayesian, multi-layer perceptron neural network, and support vector machine (SVM) classifiers were used for faller prediction. The top twenty feature selection-based models and top ten all-variable-based models determined by single 75:25 train:test stratified holdouts were further examined by training 10,000 models with randomized 75:25 train:test stratified holdouts. Model evaluation parameters were averaged across all 10,000 models and then ranked. Feature selection increased predictive accuracy by 9%, comparing models with and without feature selection. The increase in accuracy was greater than for retrospective faller classification using feature selection. Based on the 10,000 randomized holdouts, the highest ranked model had 65% accuracy and 59% sensitivity, achieved by a pressure-sensing-insole and left-shank-accelerometer based feature subset. The best performance without feature selection was 56% accuracy and 42% sensitivity. Feature selection improved model performance and should therefore be included as a model development step for elderly fall-risk prediction. Some model performance results, such as Relief-F providing the best feature selection and high single-sensor performance using the lower-back accelerometer, were consistent for prospective faller prediction and retrospective classification. However, other contributions to high model performance, such as dual-task assessment for single-sensor models and use of SVM, were specific to older adult fall-risk prediction based on prospective fall occurrence.

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

Pediatric Fall Risk Assessment Tool Comparison and Validation Study

PurposeCompare two pediatric fall risk assessment tools (I'M SAFE and Humpty Dumpty) used at the same organization to determine if one is better able to predict which patients fall. Design and MethodsRetrospective data was obtained from patients admitted in 2014. Each patient who experienced a fall during hospitalization was matched with two non-fallers based on age and diagnosis. Logistic regression was performed to identify which tool more accurately determines fall risk and reliability testing was completed for the I'M SAFE tool. ResultsOver 22,000 patient files were extracted for this study. One hundred seventy-seven falls were identified, seventy-one of them were intrinsic. Of those patients who fell, the majority were assessed to be at high risk for falls. There were too few falls during the study period using the Humpty Dumpty tool to assess and make formal conclusions. The results for the I'M SAFE tool were opposite of what was expected and showed an increased risk for falls for patients who scored low risk using this tool. ConclusionsAt completion of this study the data reflected that the I'M SAFE tool was not adequately predicting patients at greatest risk for intrinsic falls for this particular population. Practice ImplicationsFurther research on these tools is needed in other populations or across multiple sites. Additional work to adapt the tools may be necessary to better predict fall risk without over identifying high risk patients.

Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis.

Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks, and feature categories. We also conducted a meta-analysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed [Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014]; the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the timed up and go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p = 0.01) during walking.

Frailty Versus Stopping Elderly Accidents, Deaths and Injuries Initiative Fall Risk Score: Ability to Predict Future Falls

To compare the ability of frailty status to predict fall risk with that of community fall risk screening tools. Analysis of cross-sectional and longitudinal data from NHATS. National Health and Aging Trend Study (NHATS) 2011-2015. Individuals aged 65 and older (N = 7,392). Fall risk was defined according to the Stopping Elderly Accidents, Deaths and Injuries (STEADI) initiative. Frailty was defined as exhaustion, weight loss, low activity, slow gait speed, and weak grip strength. Robust was defined as meeting 0 criteria, prefrailty as 1 or 2 criteria, and frailty as 3 or more criteria. Falls were self-reported and ascertained using NHATS subsequent rounds (2012-2015). We compared the ability of frailty to predict future falls with that of STEADI score, adjusting for age, race, sex, education, comorbidities, hearing and vision impairment, and disability. Of the 7,392 participants (58.5% female), there 3,545 (48.0%) were classified as being at low risk of falling, 2,966 (40.1%) as being at moderate risk, and 881 (11.9%) as being at high risk. The adjusted risk of falling over the 4 subsequent years was 2.5 times as great for the moderate-risk group (hazard ratio (HR) = 2.50, 95% confidence interval (CI) = 2.16-2.89) and almost 4 times as great (HR = 3.79, 95% CI = 2.76-5.21) for the high-risk group as for the low-risk group. Risk of falling was greater for those who were prefrail (HR = 1.34, 95% CI 1.16-1.55) and frail (HR = 1.20, 95% CI = 0.94-1.54) than for those who were robust. STEADI score is a strong predictor of future falls. Addition of frailty status does not improve the ability of the STEADI measure to predict future falls.

Fractal Dynamics, Variability, and Coordination in Human Locomotion

In human locomotion, the magnitude of gait variability is a strong predictor of fall risk and frailty due to aging and disease. Beyond variability magnitude, the past two decades have provided emerging alternative methodologies for studying biological variability. Specifically, coordination variability has been found to be critically important within a healthy, adaptive system. While many activities aim to minimize end-point variability, greater coordination variability indicates a more flexible system, and is greater in experts compared to novices, or healthy compared to diseased individuals. Finally, variability structure (i.e., fractal dynamics) may describe the overall adaptive capacity of the locomotor system. We provide empirical support that fractal dynamics are associated with step length symmetry during challenging split-belt treadmill walking. Individuals whose fractal scaling approached 1/f fractal scaling during constrained walking also exhibited the best gait adaptability performance. Importantly, this relation between fractality and gait adaptability was not observed in unperturbed preferred speed walking.

The reactive leg drop: a simple and novel sensory-motor assessment to predict fall risk in older individuals.

There is need for a functional ability test that appropriately assesses the rapid integration of the sensory and motor systems required for older adults to recover from a slip. The purpose of this study was to assess the efficacy and reliability of a novel test, the reactive leg drop, for assessing sensory-motor function in older adults. Fourteen young (YW; mean age = 20 yr) and 11 older women (OW; mean age = 76 yr) participated in this study. For each drop, the leg was passively moved to full extension and then released. The subjects had to recognize their leg was free-falling and reactively kick up as quickly as possible during varying sensory conditions. To assess the leg drop's reliance on proprioception, other proprioceptive tests (e.g., patellar tendon reflexes and balance) were separately performed. Leg drops performed with the eyes closed ( P = 0.011) and with a blocked view of the leg ( P = 0.033) showed significant differences in drop angle between YW and OW. Significant relationships between leg drop conditions and balance were observed in OW that were not present within YW. When collapsed across groups, reflex latency was correlated with drop angle when the eyes were closed. The reactive leg drop was age sensitive, reliable, and likely reliant on proprioception, as shown by relationships to other sensory-motor assessments, such as balance and the patellar reflex. Although more research is needed, we propose that the reactive leg drop is an effective tool to assess sensory-motor integration in a manner that may mimic fall recovery. NEW & NOTEWORTHY The reactive leg drop was age sensitive and was significantly related to other sensory-motor assessments. The ability to accurately assess sensory-motor integration may aid clinicians, practitioners, and researchers in developing new interventions. The reactive leg drop presented in the current study is a potentially effective tool to assess sensory and motor integration in a manner that may mimic fall recovery.

Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]). Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7). Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

A Screening Tool Using Five Risk Factors Was Developed for Fall-Risk Prediction in Chinese Community-Dwelling Elderly Individuals.

The objective of this study was to determine falls risk profiles to derive a falls risk prediction score and establish a simple and practical clinical screening tool for Chinese community-dwelling elderly individuals. This was a prospective cohort study (n = 619) among adults aged 60 years and older. Falls were ascertained at a 1-year follow-up appointment. Sociodemographic information, medical history, and physical performance data were collected. The mean age was 67.4 years; 57.7% were women. Female sex (odds ratios [ORs] 1.82; 95% confidence interval [95% CI] 1.17-2.82), diabetes (OR 2.13; 95% CI 1.13-3.98), a Timed Up and Go Test (TUGT) ≥10.49 seconds (OR 1.51; 95% CI 1.23-1.94), a history of falls (OR 3.15; 95% CI 1.72-5.79), and depression (Geriatric Depression Scale [GDS] ≥11, OR 2.51; 95% CI 1.36-4.63) were the strongest predictors. These predictors were used to establish a risk score. The area under the curve of the score was 0.748. From a clinical point of view, the most appropriate cutoff value was 7 (97.5% specificity, 70.7% positive predictive value, and 83.6% negative predictive value). For this cutoff, the fraction correctly classified was 82.5%. A cutoff score of 7 derived from a risk assessment tool using four risk factors (gender, falls history, diabetes, and depression) and the TUGT may be used in Chinese community-dwelling elderly individuals as an initial step to screen those at low risk for falls.

Sarcopenia and sarcopenic obesity in Spanish community-dwelling middle-aged and older women: Association with balance confidence, fear of falling and fall risk

ObjectivesTo analyze the association of sarcopenia, obesity, and sarcopenic obesity (SO) with fear of falling (FoF) and balance confidence in a Spanish sample of middle-aged and older community-dwelling women. Study design and outcome measuresA total of 235 women (69.21±7.56 years) participated in this study. Body composition (bioelectrical impedance analysis), hand-grip strength, and physical performance (gait speed) were evaluated for the diagnosis of sarcopenia, obesity, and SO. Anxiety and depression were measured using the Hospital Anxiety and Depression Scale. The Activities-Specific Balance Confidence Scale (ABC) and the Falls Efficacy Scale-International (FES-I) were employed to assess FoF and balance confidence, respectively. Scores of >26 on the FES-I and <67% on the ABC were used to identify women at risk of falling. The independent associations of sarcopenia, obesity and SO with FoF, balance confidence, and fall risk were evaluated by multivariate linear and logistic regressions, adjusting for potential confounding variables. Results27.23% and 18.72% of women presented with sarcopenia and SO, respectively. Gait speed, body mass index (BMI), and fall history were independently associated with ABC score (adjusted-R2=0.152) and fall risk (ABC) (adjusted-R2=0.115). FES-I score was independently associated (adjusted-R2=0.193) with fall history, gait speed, BMI, and depression, which, together with obesity (BMI) and SO, remained independent factors for fall risk measured as FES-I score (adjusted-R2=0.243). ConclusionIn community-dwelling middle-aged and older Spanish women, BMI, gait speed, and fall history were independently associated with FoF, balance confidence, and fall risk. Depression was related only to FoF, and, together with obesity (BMI) and SO, was an independent predictor of fall risk as assessed by the FES-I.

A Greater Extent of Insomnia Symptoms and Physician-Recommended Sleep Medication Use Predict Fall Risk in Community-Dwelling Older Adults.

Cross-sectional studies suggest that insomnia symptoms are associated with falls in later life. This longitudinal study examines the independent and interactive effects of the extent of insomnia symptoms (i.e., multiple co-existing insomnia symptoms) and sleep medications on fall risk over a 2-year follow-up among community-dwelling older adults. Using data from the Health and Retirement Study (2006-2014, N = 6882, Mage = 74.5 years ± 6.6 years), we calculated the extent of insomnia symptoms (range = 0-4) participants reported (i.e., trouble falling asleep, waking up during the night, waking up too early, and not feeling rested). At each wave, participants reported recent sleep medications use and falls since the last wave, and were evaluated for balance and walking speed. A greater burden of insomnia symptoms and using physician-recommended sleep medications at baseline independently predicted falling after adjusting for known risk factors of falling. The effects of insomnia symptoms on fall risk differed by sleep medications use. The extent of insomnia symptoms exhibited a positive, dose-response relation with risk of falling among those not using sleep medications. Older adults using physician-recommended sleep medications exhibited a consistently higher fall risk irrespective of the extent of insomnia symptoms. The number of insomnia symptoms predicts 2-year fall risk in older adults. Taking physician-recommended sleep medications increases the risks for falling in older adults, irrespective of the presence of insomnia symptoms. Future efforts should be directed toward treating insomnia symptoms, and managing and selecting sleep medications effectively to decrease the risk of falling in older adults.

Post-fall reporting in aged acute inpatient mental health units: an 18-month observational cohort study.

Despite the high risk of falling for people with severe mental illness, there is limited falls research in mental health settings. Therefore, the objective of this observational cohort study was to conduct a focused post-fall review of fall episodes within aged acute inpatient mental health units at one of Australia's largest publicly funded mental health organizations. A post-fall reporting tool was developed to collect intrinsic and extrinsic fall risk factors among three aged acute mental health inpatient units over an 18-month period. Descriptive and inferential analyses were conducted to describe fall risk factors and predictors of fall risk. There were a total of 115 falls, of which the tool was used for 93 (80.9%) episodes. Falls occurred most often in consumer's bedroom/bathroom and were unwitnessed. Intrinsic risk factors were most often attributed to postural drop and losing balance during walking. However, that was in contrast to consumer's who self-reported feeling dizzy as the reason of the fall. Based on the cohort, future falls could be reduced by targeting those aged above 82 years, or with a diagnosis of dementia. Recurrent falls during admission could be reduced by targeting those with psychotic illness and males with a diagnosis of dementia. A clearer dialogue among consumers and clinical staff reporting about fall episodes may support future remedial interventions and inform programs to reduce fall risk and assist the challenge of describing unwitnessed falls in aged acute inpatient mental health settings.

Imaging gait analysis: An fMRI dual task study

IntroductionIn geriatric clinical diagnostics, gait analysis with cognitive‐motor dual tasking is used to predict fall risk and cognitive decline. To date, the neural correlates of cognitive‐motor dual tasking processes are not fully understood. To investigate these underlying neural mechanisms, we designed an fMRI paradigm to reproduce the gait analysis.MethodsWe tested the fMRI paradigm’s feasibility in a substudy with fifteen young adults and assessed 31 healthy older adults in the main study. First, gait speed and variability were quantified using the GAITRite© electronic walkway. Then, participants lying in the MRI‐scanner were stepping on pedals of an MRI‐compatible stepping device used to imitate gait during functional imaging. In each session, participants performed cognitive and motor single tasks as well as cognitive‐motor dual tasks.ResultsBehavioral results showed that the parameters of both gait analyses, GAITRite© and fMRI, were significantly positively correlated. FMRI results revealed significantly reduced brain activation during dual task compared to single task conditions. Functional ROI analysis showed that activation in the superior parietal lobe (SPL) decreased less from single to dual task condition than activation in primary motor cortex and in supplementary motor areas. Moreover, SPL activation was increased during dual tasks in subjects exhibiting lower stepping speed and lower executive control.ConclusionWe were able to simulate walking during functional imaging with valid results that reproduce those from the GAITRite© gait analysis. On the neural level, SPL seems to play a crucial role in cognitive‐motor dual tasking and to be linked to divided attention processes, particularly when motor activity is involved.