scholarly journals Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Barry R. Greene ◽  
Killian McManus ◽  
Stephen J. Redmond ◽  
Brian Caulfield ◽  
Charlene C. Quinn
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Falls Prevention ◽  
Sensor Data ◽  
Emergency Department Utilization ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Assessment Protocol ◽  
Risk Of Falls

AbstractFalls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the “first fall”, delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.

scholarly journals SENSOR-BASED ASSESSMENT OF FALLS RISK OF THE TIMED UP AND GO IN REAL-WORLD SETTINGS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S10-S10
Author(s):  
Charlene C Quinn ◽  
Barry R Greene ◽  
Killian McManus ◽  
Stephen J Redmond ◽  
Brian Caulfield
Keyword(s):  
Fall Risk ◽  
Population Based ◽  
Falls Prevention ◽  
Sensor Data ◽  
Gait Velocity ◽  
Community Settings ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Slow Walking Speed

Abstract Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings. Second objective is to generate individual targeted falls prevention programs. 14,611 QTUG records from 8,521 participants (63% female) (72.7±10.7 years) available for analysis. QTUG time was 13.9±7.4 s; gait velocity was 101.9±32.5 cm/s. 25.8% of patients reported falling in previous 12 months; 26.2% of patients were at high fall risk. 21.5% not reporting a fall, were high fall risk. Participants had slow walking speed (29.8%); high gait variability (19.8%); problems with transfers (17.5%). Easily captured and interpreted sensor data is useful in a population-based approach to quantify falls risk stratification.

scholarly journals Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time–Frequency Representations of the Timed Up and Go Test

2021 ◽  
Vol 12 ◽  
Author(s):  
Shih-Hai Chen ◽  
Chia-Hsuan Lee ◽  
Bernard C. Jiang ◽  
Tien-Lung Sun
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Learning Algorithm ◽  
The Elderly ◽  
Developed Countries ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Fall Risk Assessment ◽  
Time Frequency ◽  
Stacked Autoencoder

Fall risk assessment is very important for the graying societies of developed countries. A major contributor to the fall risk of the elderly is mobility impairment. Timely detection of the fall risk can facilitate early intervention to avoid preventable falls. However, continuous fall risk monitoring requires extensive healthcare and clinical resources. Our objective is to develop a method suitable for remote and long-term health monitoring of the elderly for mobility impairment and fall risk without the need for an expert. We employed time–frequency analysis (TFA) and a stacked autoencoder (SAE), which is a deep neural network (DNN)-based learning algorithm, to assess the mobility and fall risk of the elderly according to the criteria of the timed up and go test (TUG). The time series signal of the triaxial accelerometer can be transformed by TFA to obtain richer image information. On the basis of the TUG criteria, the semi-supervised SAE model was able to achieve high predictive accuracies of 89.1, 93.4, and 94.1% for the vertical, mediolateral and anteroposterior axes, respectively. We believe that deep learning can be used to analyze triaxial acceleration data, and our work demonstrates its applicability to assessing the mobility and fall risk of the elderly.

scholarly journals Pelaksanaan Asesmen Risiko Jatuh di Rumah Sakit

2017 ◽  
Vol 5 (2) ◽  
pp. 123
Author(s):  
Hirza Ainin Nur ◽  
Edi Dharmana ◽  
Agus Santoso
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Secondary Data ◽  
Sampling Technique ◽  
Primary Data ◽  
Analysis Model ◽  
Fall Risk Assessment ◽  
Falls Risk ◽  
Assessment Protocol ◽  
Inpatient Ward

<em>Falls are the most worrying incidence of patients in the hospital and that has an impact on injury and even death. The incidence was second ranks of adverse event after medication errors. Hospitals are already making efforts to reduce the fall incident but in reality, the incidence of falls still occurs. Data obtained from March to September 2016 have 6 cases of incident patients falling from a total of 43 patient safety incidents. The results of the observations show that most of the fall prevention programs that have not been done are falling risk assessments. Fall risk assessment is the first step to prevent the occurrence of falls in the patient, if not done then the incident will occur. This study aims to explore the implementation of falls risk assessment conducted by nurses in the hospital wards. The research method using qualitative research with phenomenology approach. The population used is inpatient ward nurses as many as 304 nurses.The sample used by purposive sampling technique with 10 informants. Data collection using primary and secondary data. Primary data obtained by in-depth interview with </em>semi-structured<em> interview to all informants. Secondary data was used document review of SOP prevention of fall risk, assessment protocol, and patient's medical record status. Data analysis used Miles and Huberman analysis model by reducing data, making display data, and drawing conclusions. The results of the research are two themes that are the existence of internal training and socialization affects the nurse's understanding of the implementation of falls risk assessment both initial of falls risk assessment and re-assessment of falls risk and implementation of falls risk assessment influenced by the inhibiting and supporting factors, both of these factors affect compliance of falls risk assessment conducted by the nurse. This study suggests that the understanding of fall risk assessment does not guarantee the nurse to always adhere to the implementation of fall risk assessment. It is expected that there will be cooperation between hospital management, working group prevention of falls risk, and head of ward to always supervise and monitor evaluation related to implementation of falls risk assessment along with giving reward and punishment.</em>

scholarly journals Technologies for fall risk assessment and conceptual design in personal health record system

2019 ◽  
Vol 11 (1-2) ◽  
pp. 53-67
Author(s):  
Milla Sinikka Immonen ◽  
Heidi Similä ◽  
Mikko Lindholm ◽  
Raija Korpelainen ◽  
Timo Jämsä
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Personal Health Record ◽  
Public Health Problem ◽  
Falls Prevention ◽  
Screening Tools ◽  
Sensor Data ◽  
Personal Health ◽  
Health Record ◽  
Fall Risk Assessment

Falls among older people are a major economic and public health problem. Due to the demographic change and aging of populations, there is an urgent need for accurate screening tools to identify those at risk to target effective falls prevention strategies. Clinical fall risk assessments are costly and time-consuming and thus cannot be performed frequently. Technologies provide means for assessing fall risk during daily living, making self-evaluations and fast methods for fall risk assessment for professional use. This study collects and evaluates existing technological solutions for fall risk assessment including various different sensor technologies. The study also presents one easy to use solution for assessing fall risk and suggests a concept-design for integrating sensor-based solutions into the Finnish national Kanta Personal Health Record. The optimal solution for technological fall risk assessment is still unclear. A wide implementation still requires extensive validation studies, adoption to health care processes and novel IoT -solutions for collecting large amounts of sensor data. Thorough methods should be utilised in designing the privacy and security aspects of fall risk assessment solutions, as well as different user profiles, to allow suitable interfaces and visualisations to users. It should always be clear what kind of data are collected from users and how the data are utilised. The consent of the users should also always be collected.

scholarly journals Sensory-Motor Mechanisms Increasing Falls Risk in Diabetic Peripheral Neuropathy

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 457
Author(s):  
Neil D. Reeves ◽  
Giorgio Orlando ◽  
Steven J. Brown
Keyword(s):  
Peripheral Neuropathy ◽  
Diabetic Peripheral Neuropathy ◽  
Motor Nerve ◽  
Step Length ◽  
Falls Prevention ◽  
Motor Deficits ◽  
Falls Risk ◽  
Walking Gait ◽  
Risk Of Falls ◽  
Diabetes Patients

Diabetic peripheral neuropathy (DPN) is associated with peripheral sensory and motor nerve damage that affects up to half of diabetes patients and is an independent risk factor for falls. Clinical implications of DPN-related falls include injury, psychological distress and physical activity curtailment. This review describes how the sensory and motor deficits associated with DPN underpin biomechanical alterations to the pattern of walking (gait), which contribute to balance impairments underpinning falls. Changes to gait with diabetes occur even before the onset of measurable DPN, but changes become much more marked with DPN. Gait impairments with diabetes and DPN include alterations to walking speed, step length, step width and joint ranges of motion. These alterations also impact the rotational forces around joints known as joint moments, which are reduced as part of a natural strategy to lower the muscular demands of gait to compensate for lower strength capacities due to diabetes and DPN. Muscle weakness and atrophy are most striking in patients with DPN, but also present in non-neuropathic diabetes patients, affecting not only distal muscles of the foot and ankle, but also proximal thigh muscles. Insensate feet with DPN cause a delayed neuromuscular response immediately following foot–ground contact during gait and this is a major factor contributing to increased falls risk. Pronounced balance impairments measured in the gait laboratory are only seen in DPN patients and not non-neuropathic diabetes patients. Self-perception of unsteadiness matches gait laboratory measures and can distinguish between patients with and without DPN. Diabetic foot ulcers and their associated risk factors including insensate feet with DPN and offloading devices further increase falls risk. Falls prevention strategies based on sensory and motor mechanisms should target those most at risk of falls with DPN, with further research needed to optimise interventions.

Is Fall Risk Systematically Evaluated in Memory Clinics? A National Survey of Practice in France

2021 ◽  
pp. 1-9
Author(s):  
Victoire Leroy ◽  
Yaohua Chen ◽  
Naiara Demnitz ◽  
Florence Pasquier ◽  
Pierre Krolak-Salmon ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Standardized Tests ◽  
Fall Risk ◽  
Routine Practice ◽  
Cross Sectional Survey ◽  
Online Questionnaire ◽  
Timed Up And Go ◽  
Major Health Problem ◽  
Fall Risk Assessment ◽  
Memory Clinics

Background: Falls are a major health problem in older persons but are still under-diagnosed and challenging to prevent. Current guidelines do not target high-risk populations, especially people living with dementia. In France, people with neurocognitive disorders are mainly referred to memory clinics (MCs). Objective: We aimed to survey the routine practice of physicians working in MCs regarding fall risk assessment. Methods: We conducted a cross-sectional survey in France from January to May 2019 among physicians working in MCs, through an anonymous online questionnaire: twenty-seven questions about the physician’s background and their practice of fall risk assessment, especially use of clinical and paraclinical tools. We compared the results according to the age and the specialty of the physician. Results: We obtained 171 responses with a majority of women (60%) and geriatricians (78%). All age classes and all French regions were represented. Most of respondents (98.8%) stated that they address gait and/or falls in outpatient clinic and 95.9%in day hospitals. When asked about how they assess fall risk, fall history (83%) and gait examination (68.4%) were the most widely used, while orthostatic hypotension (24%) and clinical standardized tests (25.7%) were less common. Among standardized tests, One-leg Balance, Timed Up and Go Test, and gait speed measurements were the most used. Geriatricians had more complete fall risk assessment than neurologists (e.g., 56%versus 13%for use of standardized tests, p <  0.0001). Conclusion: Almost all physicians addressed the question of fall in MC, but practices are widely heterogeneous. Further investigations are needed to standardize fall risk assessment in MCs.

scholarly journals Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1338
Author(s):  
Wojciech Tylman ◽  
Rafał Kotas ◽  
Marek Kamiński ◽  
Paweł Marciniak ◽  
Sebastian Woźniak ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Low Cost ◽  
Body Movement ◽  
Sole Source ◽  
Timed Up And Go ◽  
True Negative ◽  
Fall Risk Assessment ◽  
Positive Ratio ◽  
Mobility Test

This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject’s body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test.

scholarly journals Automated fall risk assessment of elderly using wearable devices

2020 ◽  
Vol 7 ◽  
pp. 205566832094620
Author(s):  
Marian Haescher ◽  
Wencke Chodan ◽  
Florian Höpfner ◽  
Gerald Bieber ◽  
Mario Aehnelt ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Human Error ◽  
Visual Analysis ◽  
Ground Truth ◽  
Wearable Devices ◽  
Healthcare Sector ◽  
Observational Assessment ◽  
Timed Up And Go ◽  
Fall Risk Assessment

Introduction Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. Methods In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automated algorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists. Results There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests. Conclusions The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.

scholarly journals Development and validation of a mHealth falls risk screening app (FallSA©) among Malaysian Community Dwelling Older Persons (Preprint)

2020 ◽  
Author(s):  
Devinder Kaur Ajit Singh ◽  
Jing Wen Goh ◽  
Muhammad Iqbal Shaharudin ◽  
Suzana Shahar
Keyword(s):  
Risk Assessment ◽  
Predictive Validity ◽  
Fall Risk ◽  
Concurrent Validity ◽  
Older Persons ◽  
Screening Tool ◽  
Community Dwelling ◽  
Discriminative Ability ◽  
Validity Test ◽  
Falls Risk

BACKGROUND Recent falls prevention guidelines recommend early routine falls risk assessment among older persons. OBJECTIVE The purpose of current study was to develop a Falls Screening Mobile Application (FallSA©), determine its acceptance, concurrent validity, test-retest reliability, discriminative ability and predictive validity as a self-screening tool to identify falls risk among Malaysian older persons. METHODS FallSA© acceptance was tested among 15 participants (mean age: 65.93±7.42 years); its validity and reliability among 91 participants (mean age: 67.34±5.97); discriminative ability and predictive validity among 610 participants (mean age: 71.78±4.70). Acceptance of FallSA© was assessed using a questionnaire and it was validated against a comprehensive falls risk assessment tool, Physiological Profile Assessments (PPA). Participants used FallSA© to test their falls risk repeatedly twice between an hour. Its discriminative ability and predictive validity were determined by comparing participants fall risk scores between fallers and non-fallers and prospectively through a 6 months follow-up respectively RESULTS The findings of our study showed that FallSA© had a high acceptance level with 80% older persons agreeing on its suitability as a falls self-screening tool. Concurrent validity test demonstrated a significant moderate correlation (rs= 0.518, P<0.001) and agreement (K= 0.516, P<0.001) with acceptable sensitivity (80.4%) and specificity (71.1%). FallSA© also had good reliability (ICC: 0.948, CI: 0.921-0.966) and an internal consistency (α= 0.948, P<0.001). FallSA© score demonstrated a moderate to strong discriminative ability in classifying fallers and non-fallers. FallSA© had a predictive validity of falls with positive likelihood ratio of 2.27, pooled sensitivity of 82% and specificity of 64%, and AUC of 0.802. CONCLUSIONS These results suggest that FallSA© is a valid and reliable fall risk self-screening tool. Further studies are required to empower and engage older persons or care givers in the use of FallSA© to self-screen for falls and thereafter to seek early prevention intervention. CLINICALTRIAL NA

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