Predict Fall Risk Research Articles

Prediction of fall risk among community-dwelling older adults using a wearable system

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls.

Predicting Fall Risk Through Automatic Wearable Monitoring

Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.

Falls in older adults after hospitalization for acute myocardial infarction.

After hospitalization for acute myocardial infarction (AMI), older adults may be at increased risk for falls due to deconditioning, new medications, and worsening health status. Our primary objective was to identify risk factors for falls after AMI hospitalization among adults over age 75. We used data from the Comprehensive Evaluation of Risk Factors in Older Patients with AMI (SILVER-AMI) study, a prospective cohort study of 3041 adults age 75 and older hospitalized with AMI at 94 community and academic medical centers across the United States. In-person interviews and physical assessments, as well as medical record review, were performed to collect demographic, clinical, functional, and psychosocial data. Falls were self-reported in telephone interviews and medically serious falls (those associated with emergency department use or hospitalization) were determined by medical record adjudication. Backward selection was used to identify predictors of fall risk in logistic regression analysis. A total of 554 (21.6%) participants reported a fall and 191 (6.4%) had a medically serious fall within 6 months of discharge. Factors independently associated with self-reported falls included impaired mobility, prior fall history, longer hospital stay, visual impairment, and weak grip. Factors independently associated with medically serious falls included older age, polypharmacy, impaired functional mobility, prior fall history, and living alone. Among older patients hospitalized for AMI, falls are common in the 6 months following discharge and associated with demographic, functional, and clinical factors that are readily identifiable. Fall risk should be considered in post-AMI clinical decision-making and interventions to prevent falls should be evaluated.

Cortical Engagement Metrics During Reactive Balance Are Associated With Distinct Aspects of Balance Behavior in Older Adults.

Heightened reliance on the cerebral cortex for postural stability with aging is well-known, yet the cortical mechanisms for balance control, particularly in relation to balance function, remain unclear. Here we aimed to investigate motor cortical activity in relation to the level of balance challenge presented during reactive balance recovery and identify circuit-specific interactions between motor cortex and prefrontal or somatosensory regions in relation to metrics of balance function that predict fall risk. Using electroencephalography, we assessed motor cortical beta power, and beta coherence during balance reactions to perturbations in older adults. We found that individuals with greater motor cortical beta power evoked following standing balance perturbations demonstrated lower general clinical balance function. Individual older adults demonstrated a wide range of cortical responses during balance reactions at the same perturbation magnitude, showing no group-level change in prefrontal- or somatosensory-motor coherence in response to perturbations. However, older adults with the highest prefrontal-motor coherence during the post-perturbation, but not pre-perturbation, period showed greater cognitive dual-task interference (DTI) and elicited stepping reactions at lower perturbation magnitudes. Our results support motor cortical beta activity as a potential biomarker for individual level of balance challenge and implicate prefrontal-motor cortical networks in distinct aspects of balance control involving response inhibition of reactive stepping in older adults. Cortical network activity during balance may provide a neural target for precision-medicine efforts aimed at fall prevention with aging.

SY2.5. Balance and gait assessment

Postural instability and walking difficulties are the most disabling symptoms in Parkinson disease (PD), leading to falls, physical injuries, functional decline and early institutionalization. Medical, surgical and rehabilitation are the mainstay of management for balance and gait disorders. Evaluation of balance and gait disorders is crucial to determine the problems of the patients so that targeted treatment can be given. Postural instability manifests as reductions in limits of stability, postural adjustment, and postural responses. Gait disorders comprise reduced gait speed, short stride length, freezing of gait, and dual-task deficits. Postural instability and gait disorders can be evaluated using reliable and validated clinical tests, measurement scales, questionnaires and technological equipment. Objective clinical tests and measurement scales require simple equipment and needs a short time to complete the tests. Questionnaires are useful to reflect patients’ perceived balance and gait ability. Technological equipment can be costly but they can increase the measurement sensitivity. The clinical tests used to evaluate postural instability include retropulsion tests, Functional reach test, and one-leg-stance test, whilst those for gait disorders include single or dual-task walk test, timed-up-and-go-test, and 6-minute walk test. Measurement scales including Berg’s balance scale, mini-Balance evaluation system test and Tinetti mobility test, are used to assess overall balance and gait limitations. Most of the measurement scales have cut-off scores for predicting fall risk. Validated questionnaires such as Activities-specific balance confidence scale and Fall efficacy scale evaluate one’s perceived level of balance confidence. Freezing-of-gait questionnaire subjectively assess the severity of freezing of gait. Most of the technological equipment are bulky, laboratory-based and may require a long time to complete the test and/or to analysis the data. Nowadays, body worn sensors is getting more popular because they allow measurement to be taken anywhere including patients’ home and in the community. Algorithm has been built in some systems to shorten the data analyzing time. There are many choices of outcome measures and clinicians/researchers can choose the evaluation methods that is best for their measurement purposes.

Association between falls in Alzheimer disease and scores on the Balance Evaluation Systems Test (BESTest) and MiniBESTest

Background and Purpose Alzheimer disease (AD) is the most common form of dementia and the sixth leading cause of death in the United States. Falls are associated with AD and can lead to injury. The Balance Evaluation Systems Test (BESTest) is a balance measure used in other neurological conditions to predict fall risk. The purpose of this study is to examine the relationship between MiniBestest, BESTest, and BESTest subsection scores and fall incidence among individuals with a diagnosis of mild AD. Methods The study was a single centre, prospective, observational cohort study. Participants completed baseline questionnaires including a demographic form, a fall history questionnaire and the Barthel Index of Activities of Daily Living (ADLs). Balance and gait were assessed using the MiniBESTest and BESTest. After completing baseline assessment, participants were given monthly fall calendars to track falls for the next 12 months. Results MiniBESTest total raw score for fallers was 13.4 out of 28 (SD = 3.6) and for non-fallers was 18.4 of out 28 (SD = 3.7). MiniBESTest total percentage score for fallers was 47.8% (SD = 12.8%) and for non-fallers was 65.5% (SD = 13.1%). BESTest total percentage scores for fallers was 58.2% (SD = 3.9%) and for non-fallers was 73.9% (SD = 7.9%). Subsections II-IV of the BESTest correlated with faller status. Discussion and Conclusions Among individuals with mild AD, fall status was associated with certain balance deficits on the BESTest including moving body outside base of support (subsection II), changing centre of mass (subsection III), and reacting to external perturbations (subsection IV). Future studies could explore differences between AD and other neurological conditions and how physical therapy could improve these areas to reduce fall risk.

Gait Difficulties and Postural Instability in Adrenoleukodystrophy.

Background: Gait and balance difficulties are among the most common clinical manifestations in adults with X-linked adrenoleukodystrophy, but little is known about the contributions of sensory loss, motor dysfunction, and postural control to gait dysfunction and fall risk.Objective: To quantify gait and balance deficits in both males and females with adrenoleukodystrophy and evaluate how environmental perturbations (moving surfaces and visual surrounds) affect balance and fall risk.Methods: We assessed sensory and motor contributions to gait and postural instability in 44 adult patients with adrenoleukodystrophy and 17 healthy controls using three different functional gait assessments (25 Foot Walk test, Timed Up and Go, and 6 Minute Walk test) and computerized dynamic posturography.Results: The median Expanded Disability Status Scale score for the patient cohort was 3.0 (range 0.0–6.5). Both males and females with adrenoleukodystrophy showed impairments on all three functional gait assessments relative to controls (P < 0.001). Performance on walking tests and Expanded Disability Status Scale scores correlated with incidence of falls on computerized dynamic posturography, with the 25 Foot Walk being a moderately reliable predictor of fall risk (area under the ROC curve = 0.7675, P = 0.0038).Conclusion: We demonstrate that gait difficulties and postural control deficits occur in patients with adrenoleukodystrophy, albeit at an older age in females. Postural deficits were aggravated by eyes closed and dynamic conditions that rely on vestibular input, revealing challenges to the interplay of motor, sensory and vestibular circuitry in adrenoleukodystrophy.

Deep neural networks for human’s fall-risk prediction using force-plate time series signal

Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks’ capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantified human balance from a wide demographic of human participants (163 females and males aged 18–86) for varied standing conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, eyes-closed foam surface) was used. Classification was based on one of the several indicators of fall-risk tied to the fear of falling: the clinically-used Falls Efficacy Scale (FES) assessment. For human fall-risk prediction, the deep learning architecture implemented comprised of: Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), One Dimensional Convolutional Neural Network (1D-CNN), and a proposed One-One-One Deep Neural Network. Results showed that our One-One-One Deep Neural Networks algorithm outperformed the other aforementioned algorithms and state-of-the-art models on the same dataset. With an accuracy, precision, and sensitivity of 99.9%, 100%, 100%, respectively at the 12th epoch, we found that our proposed One-One-One Deep Neural Network model is the most efficient neural network in predicting human’s fall-risk (based on the FES measure) using the force-plate time series signal. This is a novel methodology for an accurate prediction of human risk of fall.

A Novel Environment-Adaptive Timed Up and Go Test System for Fall Risk Assessment With Wearable Inertial Sensors

Objective: Falls are often accompanied by huge social costs, and fall risk assessment is essential to protect the elderly from serious injuries and reduce financial burdens. The standard timed up and go (TUG) balance assessment test focuses on the total walking time and scenarios without environmental changes, which is flawed in providing rich information related to falls and evaluating the gait adaptability in response to environmental changes. Therefore, a fall risk assessment system that relies on a variable environment is actually needed. Methods: We have constructed an environment-adaptive TUG (EATUG) test system with three terrain surfaces (levels/obstacles/stairs). One hundred and three elderly from Shenzhen Luohu Hospital is recruited to participate in the experiment. The wearable inertial sensors attached to the two shanks are used to acquire data, and the gait parameters that may be related to falls are extracted and quantified. Results: Most of the parameters have significant differences between the high-risk group and the low-risk group (e.g., peak power, maximum radius, double support, etc., p <; 0.001). In addition, the average sensitivity and specificity of fall risk prediction are 85.7% and 92.9%, while the average accuracy is 9.52% higher than the standard TUG test. Conclusion: The EATUG test system can provide richer gait characteristics and fall-related information, which is a good improvement on the drawbacks of the standard TUG test. Significance: The proposed test system is expected to replace the standard TUG test and be used for fall screening of high-risk elderly in the community to reduce the occurrence of falls.

Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics.

Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician’s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects’ locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults’ fall-risk status with relatively high sensitivity to geriatrician’s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants’ gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations.

Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

ObjectivesTo develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.DesignObservational, retrospective case–control study.SettingNursing...

Empirical Mode Decomposition-Derived Entropy Features Are Beneficial to Distinguish Elderly People with a Falling History on a Force Plate Signal.

Fall risk prediction is an important issue for the elderly. A center of pressure signal, derived from a force plate, is useful for the estimation of body calibration. However, it is still difficult to distinguish elderly people’s fall history by using a force plate signal. In this study, older adults with and without a history of falls were recruited to stand still for 60 s on a force plate. Forces in the x, y and z directions (Fx, Fy, and Fz) and center of pressure in the anteroposterior (COPx) and mediolateral directions (COPy) were derived. There were 49 subjects in the non-fall group, with an average age of 71.67 (standard derivation: 6.56). There were also 27 subjects in the fall group, with an average age of 70.66 (standard derivation: 6.38). Five signal series—forces in x, y, z (Fx, Fy, Fz), COPX, and COPy directions—were used. These five signals were further decomposed with empirical mode decomposition (EMD) with seven intrinsic mode functions. Time domain features (mean, standard derivation and coefficient of variations) and entropy features (approximate entropy and sample entropy) of the original signals and EMD-derived signals were extracted. Results showed that features extracted from the raw COP data did not differ significantly between the fall and non-fall groups. There were 10 features extracted using EMD, with significant differences observed among fall and non-fall groups. These included four features from COPx and two features from COPy, Fx and Fz.

Hearing loss and increased gait variability among older adults.

The influence of age-related hearing loss on slow gait has been suggested; however, whether it is associated with increased gait variability, an important predictor of fall risk, remains unclear. Is poor auditory acuity associated with increased gait variability, and does this gait change relate to accidental falls among older adults with hearing loss? We studied 107 older adults (mean age, 76.5 years; 80.5 % women). Auditory acuity was measured using a pure tone average (PTA) of hearing thresholds for 0.5-4 kHz tones in the better-hearing ear. Hearing loss was defined as a PTA of >25 dB. Gait speed and spatiotemporal variability (i.e., stride length and time variabilities) were assessed using a 5-m electronic walkway. We also assessed the occurrence of multiple falls within the previous year. Fifty-two participants (48.6 %) experienced hearing loss. Multiple regression analysis adjusted for potential covariates showed that poor PTA was associated with slower gait speed and stride length variability, but not stride time variability. Among older adults with hearing loss, fall occurrence was associated with an increased stride length variability and not a slow gait or increased stride time variability. The association between hearing loss and increased gait variability observed in the present study suggests that age-related hearing loss can jeopardize gait control during daily activities. This leads to increased gait variability and increased risk of accidental falls. Our results provide additional information on how age-related hearing loss increases the risk of falls.

An electronic medical record-based fall risk assessment tool for pediatric inpatients in South Korea: Improved sensitivity and specificity.

PurposeTo develop a fall risk assessment tool to predict fall risk in pediatric inpatients.MethodsThree tools widely used in clinical practice (Humpty Dumpty Fall Scale, General Risk Assessment for Pediatric Inpatient Falls, and Seoul National University Hospital Pediatric Fall Risk Scale) were examined, and assessment items were extracted. Employing a case-control design, 29 children who experienced falls during hospital stays were selected as the "fall" group, and a control group (93 children) was selected based on age and sex matching. The sensitivity, specificity, and positive and negative predictive values of the newly developed tool (Newfs-PI) were analyzed.ResultsThe Newfs-PI consisted of seven items: age, activity, history of falls, length of hospital stay, and medication. The total score ranged from 0 to 15. Its sensitivity and specificity were 62.07% and 74.19%, respectively.ConclusionThe Newfs-PI has high specificity and sensitivity, which are essential for a fall risk assessment tool that complements existing tools. These values are high relative to those of existing assessment tools and satisfy both sensitivity and specificity criteria. As the EMR enables monitoring of the components of the Newfs-PI, the tool can be used as a fall risk assessment and prevention scale for pediatric inpatients.

Fall risk prediction model for older men and women based on ambulatory physical activity level – A cross-sectional population-based study from the Oporto Region

Introduction. Falls among the elderly are an important community health problem due to its high incidence, functional and social repercussion. Dissimilar results arose in recent studies concerning fall risk and physical activity levels. This study measures the association between physical activity (PA) levels, and fall risk (FR), investigates which levels of PA are influential in FR and presents a fall risk prediction models for the elderly. Material and Methods. One hundred and seventy elderly adults (72.34 ± 6.70 years old, 124 female), completed Performance-Orientated-Mobility-Assessment; PA was assessed by accelerometry. Pearson’s correlation verified the association between FR, Age, and PA. Multiple linear regression (MLR) was used to investigate the influence of variables on FR. Results. PA, age are predictors of FR, with PA (moderate, negative) age (moderate, positive). MLR analysis showed FR variability explained by PA (42.0%) and by age (37.0%), and by gender, female FR explained by light PA (47.0%), while in male, FR explained by sedentary behaviour (44.1%) and age (22.7%) independently. Conclusion. Individuals with higher physical activity have lower fall risk. Older are prone to fall. Older women with light physical activity are less likely to fall. Older men with more sedentary behaviour are prone to fall.

Slower gait speed increases risk of falling in older adults with depression and cognitive complaints

ABSTRACT Slowed gait is one of the strongest predictors of fall risk in older adults. The present study investigated whether gait speed mediated the relationship between depression and fall history in 147 older adults presenting to a memory clinic for cognitive complaints. Depression, cognitive status, gait speed, and number of falls within the last year were the primary measures. Results revealed fallers, relative to non-fallers, had slower gait speed and higher depression scores. As hypothesized, analyses using the PROCESS macro found that gait mediated the relationship between depression and fall history. Additionally, the combination of depression and mild cognitive impairments (MCI) associated with a significantly greater likelihood of falling. Our findings indicate that combined depression and MCI have additive effects on fall risk, likely through the destabilizing effect of slowed gait on balance. Better understanding the underlying pathophysiology involved in MCI and depression-related gait disturbances may lead to improved intervention targets for fall risk prevention.

Associations between worse executive control and greater fall risk in Geriatric Major Depressive Disorder

<h3>Introduction</h3> Psychomotor disturbances (e.g. psychomotor retardation/agitation) are a core feature of major depressive disorder (MDD) and when present, predict poor treatment outcomes. Psychomotor impairment in late life MDD (LLD) reflect deficits in motor and sensorimotor function, which impair balance control in older adults (OAs). When compared to healthy OAs, those with LLD are ∼35% more likely to fall, develop a fear of falling, and avoid physical activity, all of which predict early mortality. OAs who fall lose their independence and have a worse quality of life, and the rate of deaths from falls is rising. The association between LLD and fall-risk is complicated by pharmacologic treatments that intensify fall-risk; anti-depressants decrease walking speed and increase fall-risk equivalent to antipsychotic and benzodiazepine medications. A poor understanding of <i>when, why, and how</i>falls occur in LLD has led to a knowledge gap regarding relationships between psychomotor disturbance, LLD and fall-risk. Complicating matters, roughly 40% of patients with LLD suffer from cognitive difficulty called cognitive control deficits (CCD) that predispose them to poor clinical outcomes like disability, SSRI treatment resistance, relapse, and suicide. Converging evidence suggests a common neurobiological etiology underlie LLD, CCD and fall-risk. OAs with CCD are three times more likely to fall than their healthy peers and are more susceptible to recurrent falls. OAs with CCD are more likely to exhibit worse health outcomes and mobility deficits suggesting greater fall-risk. The co-occurrence and clinical relevance of these phenomena provides a unique opportunity for investigation. The purpose of this study was to quantify associations between CCD and fall-risk in treatment resistant LLD. Our hypothesis was LLD patients who fail to respond to treatment with SSRI/SNRI would be more likely to exhibit CCD, which would be negatively associated with fall history. <h3>Methods</h3> Participants were aged ≥ 65 years and met DSM-IV-TR/Research Diagnostic criteria for unipolar MDD (Hamilton Depression Rating Scale (HDRS)>19) in an interview with a licensed clinician (SM) despite treatment with SSRI/SNRI antidepressants for ≥8 weeks. Data collection was part of screening for a larger intervention study at Weill Cornell Medical College. Exclusion criteria included: No diagnosis or evidence of dementing disorder (Mini-Mental State Examination (MMSE) score <25), reporting psychotic features/disorders or acute suicidal ideation, severe medical illness, or neurological disorders. Fall history was determined with a brief survey. Participants completed a neuropsychological battery including the Trail Making Test; a test of cognitive flexibility that is associated with fall risk in OA. Participants who reported a fall one or more times in the preceding year were classified as a ‘faller', those that had not fallen were ‘non-fallers'. Group differences (fallers, non-fallers) in CCD were compared with independent t-tests. Logistic regression predicted fall status with CCD performance outcomes, while controlling for gender. <h3>Results</h3> A total of 30 OA participants with MDD were included in the analysis. Approximately 1/3 of our OA participants with MDD reported falling within the last year (<i>n</i> = 9). Fallers exhibited significantly poorer cognitive control as determined by TMT B and D TMT (both <i>p <</i> 0.001). Logistic regression including gender as a covariate showed D TMT significantly predicted fall status, Gender ß = -0.014, <i>p</i> = 0.918, and Faller status: ß = 0.746, <i>p</i> < 0.001. <h3>Conclusions</h3> The purpose of this study was to estimate the risk of falling in treatment resistant LLD and examine the relationship between CCD and fall risk in depressed OA. Our results demonstrate that in treatment resistant LLD, patients on adequate dosages of SSRI/SNRI antidepressants, roughly one third of patients have fallen within the last year, and that CCD may have a strong independent influence on rates of falling. This rate of falls is consistent with existing reports of fall-risk in community dwelling OAs and OAs with MDD. Our preliminary results suggest that CCD is an important factor that catalyzes fall-risk in LLD, and highlight yet another vulnerability of treatment resistant LLD patients. Though this is a preliminary retrospective study, our results suggest that clinicians may improve prediction of fall risk in LLD patients by assessing CCD, with a short, paper-pencil test that can be administered bedside. Given the new NIMH mandate to quantify features of sensorimotor function across psychiatric conditions, we propose that future investigations of LLD include objective, dimensional, large-scale assessment of motor behavior that can be integrated with other objective assessments (e.g. cognitive, functional, affective). If the associations between CCD and falls prove robust in LLD patients, CCD could serve as an intervention target to reduce fall-risk, depression, and other related negative clinical sequalae. <h3>Funding</h3> Morimoto: K 23 MH 095830, UL1TR000457 Alexopolous: R01 MH065653, P50 MH113838

Performance evaluation of acceleration and jerk in unstable walking detection

Accelerometers have been widely used for human activity recognition as an early prediction of fall risk. However, acceleration data do not consider the force of gravity. Recent studies found that jerk, the derivative of acceleration, can describe the changes of body accelerations without considering the sensor orientation. This might overcome the issues caused by the displacement of the sensor, especially if a smartphone-based accelerometer is used as the sensor. This study aimed to compare the performance of acceleration and jerk in detecting postural stability using the postural stability index (PSI). Slightly different daily activity living such as walking on a flat surface, walking upstairs, and walking downstairs were chosen to compare the sensitiveness of acceleration and jerk in detecting the slight postural sway in healthy subjects. The collected data were pre-processed using the 8-modes of ensemble empirical mode decomposition (EEMD). Then, the multiscale entropy (MSE) of each intrinsic mode function (IMF) was calculated, and in the end, the PSI values were obtained. The paired t-test calculation using acceleration data showed that walking on a flat surface and walking downstairs are significantly different (p = 0.039). Whereas, the jerk dataset could not distinguish walking on a flat surface and walking downstairs (p = 0.228). From this result, it is evident that acceleration is better in recognizing human activities than jerk.

Динаміка функціональної активності осіб похилого віку з хворобою Паркінсона та синдромом старечої астенії з наслідками перелому шийки стегнової кістки під впливом засобів фізичної терапії

The high risk of fall-risk in patients with Parkinson's disease leads to traumatic consequences, in particular, to the femoral neck fractures. Predictors of fall-risk are old age, sarcopenia, and polymorbidity. All of these conditions are independent indications for carrying out rehabilitation measures; but their combination in one patient should introduce specific features into the recovery process aimed at leveling the signs of each disease. The purpose of the study was to assess the dynamics of functional activity in older people with Parkinson's disease and frailty with the consequences of femoral neck fractures under the influence of physical therapy. Material and methods. We examined 21 elderly patients with Parkinson's disease and frailty in the recovery period after the femoral neck fracture. They were divided into two groups: group 1 practiced according to the principles of polyclinic rehabilitation with a predominance of the use of preformed physical factors and general principles of physical therapy and group 2 practiced according to a developed program of physical therapy with an emphasis on functional training aimed at improving physical qualities, movement stereotype, primarily movement and self-service, taking into account the polymorbidity of the pathology. The developed physical therapy program lasted 3 months; corrective sessions were held three times a week. The program was created taking into account the setting and consistent achievement of individual short- and long-term rehabilitation goals. The effectiveness of the program was assessed based on the results of the Barthel Activities of the daily living Index and the Lawton Instrumental activities of the daily living Scale. Results and discussion. In elderly patients with Parkinson's disease and frailty in the recovery period after the femur neck fracture, a low level of basic (according to the Bartel index) and instrumental (according to the Lawton scale) functional activities is diagnosed, which leads to restrictions in everyday activities and self-care. The developed program of physical therapy revealed a statistically significantly better effect on the indices of the basic and instrumental functional activities of the examined persons in comparison with the general polyclinic program: the increase in the general level of the basic functional activity of the Bartel index in group 1 was 15.52%, in group 2 was 35.59% (p &lt;0.05). Improvement in the overall score of instrumental activity on the Lawton scale in group 1 was 18.70%, in group 2 it was 36.11% (p &lt;0.05). Conclusion. Elderly patients with polymorbid pathology with traumatic bone injuries require the preparation of pathogenetically based physical therapy programs, taking into account and correcting the specificity of each disease, which increased the overall effectiveness of rehabilitation and, as a consequence, the functional activity of patients

Center of pressure responses to unpredictable external perturbations indicate low accuracy in predicting fall risk in people with Parkinson's disease.

Falls are associated with impairment in postural control in people with Parkinson's disease (PwPD). We aimed to predict the fall risk through models combining postural responses with clinical and cognitive measures. Also, we compared the center of pressure (CoP) between PwPD fallers and non-fallers after unpredictable external perturbations. We expected that CoP parameters combined with clinical and cognitive measures would predict fall risk. Seventy-five individuals participated in the study. CoP parameters were measured during postural responses through five trials with unpredictable translations of the support-surface in posterior direction. Range and peak of CoP were analyzed in two periods: early and late responses. Time to peak (negative peak) and recovery time were analyzed regardless of the periods. Models included the CoP parameters in early (model 1), late responses (model 2), and temporal parameters (model 3). Clinical and cognitive measures were entered into all models. Twenty-nine participants fell at least once, and 46 PwPD did not fall during 12months following the postural assessment. Range of CoP in late responses was associated with fall risk (p=.046). However, although statistically non-significant, this parameter indicated low accuracy in predicting fall risk (area under the curve=0.58). Fallers presented a higher range of CoP in early responses than non-fallers (p=.033). In conclusion, although an association was observed between fall risk and range of CoP in late responses, this parameter indicated low accuracy in predicting fall risk in PwPD. Also, fallers demonstrate worse postural control during early responses after external perturbations than non-fallers, measured by CoP parameters.