Novel sensing technology in fall risk assessment in older adults: a systematic review

Key factor cutoffs and interval reference values for stratified fall risk assessment in community-dwelling older adults: the role of physical fitness, body composition, physical activity, health condition, and environmental hazards

Background

Fall risk assessment and determination of older adults’ individual risk profiles are crucial elements in fall prevention. As such, it is essential to establish cutoffs and reference values for high and low risk according to key risk factor outcomes. This study main objective was to determine the key physical fitness, body composition, physical activity, health condition and environmental hazard risk outcome cutoffs and interval reference values for stratified fall risk assessment in community-dwelling older adults.

Methods

Five-hundred community-dwelling Portuguese older adults (72.2 ± 5.4 years) were assessed for falls, physical fitness, body composition, physical (in) activity, number of health conditions and environmental hazards, and sociodemographic characteristics.

Results

The established key outcomes and respective cutoffs and reference values used for fall risk stratification were multidimensional balance (low risk: score > 33, moderate risk: score 32–33, high risk: score 30–31, and very high: score < 30); lean body mass (low risk: > 44 kg, moderate risk: 42–44 kg, high risk: 39–41 kg, and very high: < 39 kg); fat body mass (low risk: < 37%, moderate risk: 37–38%, high risk: 39–42%, and very high: > 42%); total physical activity (low risk: > 2800 Met-min/wk., moderate risk: 2300–2800 Met-min/wk., high risk: 1900–2300 Met-min/wk., and very high: < 1900 Met-min/wk); rest period weekdays (low risk: < 4 h/day, moderate risk: 4–4.4 h/day, high risk: 4.5–5 h/day, and very high: > 5 h/day); health conditions (low risk: n < 3, moderate risk: n = 3, high risk: n = 4–5, and very high: n > 5); and environmental hazards (low risk: n < 5, moderate risk: n = 5, high risk: n = 6–8, and very high: n > 8).

Conclusions

Assessment of community-dwelling older adults’ fall risk should focus on the above outcomes to establish individual older adults’ fall risk profiles. Moreover, the design of fall prevention interventions should manage a person’s identified risks and take into account the determined cutoffs and respective interval values for fall risk stratification.

Beyond wellness monitoring: Continuous multiparameter remote automated monitoring of patients

The pursuit of more efficient patient-friendly health systems and reductions in tertiary health services use has seen enormous growth in the application and study of remote patient monitoring systems for cardiovascular patient care. While there are many consumer-grade products available to monitor patient wellness, the regulation of these technologies varies considerably, with most products having little to no evaluation data. As the science and practice of virtual care continues to evolve, clinicians and researchers can benefit from an understanding of more comprehensive solutions, capable of monitoring three or more biophysical parameters (e.g., oxygen saturation, heart rate) continuously and simultaneously. These devices, herein referred to as continuous multiparameter remote automated monitoring (CM-RAM) devices, have the potential to revolutionize virtual patient care. Through seamless integration of multiple biophysical signals, CM-RAM technologies can allow for the acquisition of high-volume big data for the development of algorithms to facilitate early detection of negative changes in patient health status and timely clinician response. In this article, we review key principles, architecture, and components of CM-RAM technologies. Work to date in this field and related implications are also presented, including strategic priorities for advancing the science and practice of CM-RAM.

Objective evaluation of the risk of falls in individuals with traumatic brain injury: feasibility and preliminary validation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:4658-4661. [PMID: 34892252 DOI: 10.1109/embc46164.2021.9630020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Falls are a significant health concern for individuals with traumatic brain injury (TBI). For developing effective preemptive strategies to reduce falls, it is essential to get an accurate and objective assessment of fall-risk. The current investigation evaluates the feasibility of a robotic, posturography-based fall-risk assessment to objectively quantify the risk of falls in individuals with TBI. Five individuals with chronic TBI (age: 56.2 ± 4.7 years, time since injury: 13.09±11.95 years) performed the fall-risk assessment on hunova- a commercial robotic platform for assessing and training balance. The unique assessment considers multifaceted fall-driving components, including static and dynamic balance, sit-to-stand, limits of stability, responses to perturbations, gait speed, and history of previous falls and provides a composite score for risk of falls, called silver index (SI), a number between 0 (no risk) and 100 (high risk) based on a machine learning-based predictive model. The SI score for individuals with TBI was 66±32.1 (min: 32, max: 100) - categorized as medium-to-high risk of falls. The construct validity of SI outcome was performed by evaluating its relationship with clinical outcomes of functional balance and mobility (Berg Balance Scale (BBS), Timed-Up and Go (TUG), and gait speed) as well as posturography outcomes (Center of Pressure (CoP) area and velocity). The bivariate Pearson correlation coefficient, although not statistically significant, suggested the presence of linear relationships (0.52 > r > 0.84) between SI and functional and posturography outcomes, supporting the construct validity of SI. A large sample is needed to further prove the validity of the SI outcome before it is used for meaningful interpretations of the risk of falls in individuals with TBI.Clinical Relevance- Clinical assessments of risk of falls are traditionally based on questionnaires that may lack objectivity, consistency, and accuracy. The current work tests the feasibility of using a robotic platform-based assessment to objectively quantify the risk of falls in individuals with TBI.

Systematic review and meta-analysis of clinical trials: In-hospital use of sensors for prevention of falls

BACKGROUND

Intra-hospital falls have become an important public health problem globally. The use of movement sensors with alarms has been studied as elements with predictive capacity for falls at hospital level. However, in spite of their use in some hospitals throughout the world, evidence is lacking about their effectiveness in reducing intra-hospital falls. Therefore, this study aims to develop a systematic review and meta-analysis of existing scientific literature exploring the impact of using sensors for fall prevention in hospitalized adults and the elderly population.

METHODS

We explored literature based on clinical trials in Spanish, English, and Portuguese, assessing the impact of devices used for hospital fall prevention in adult and elderly populations. The search included databases such as IEEE Xplore, the Cochrane Library, Scopus, PubMed, MEDLINE, and Science Direct databases. The critical appraisal was performed independently by two researchers. Methodological quality was assessed based on the ratings of individual biases. We performed the sum of the results, generating an estimation of the grouped effect (Relative Risk, 95% CI) for the outcome first fall for each patient. We assessed heterogeneity and publication bias. The study followed PRISMA guidelines.

RESULTS

Results were assessed in three randomized controlled clinical trials, including 29,691 patients. A total of 351 (3%) patients fell among 11,769 patients assigned to the intervention group, compared with 426 (2.4%) patients who fell among 17,922 patients assigned to the control group (general estimation RR 1.20, 95% CI 1.04, 1.37, P = .02, I2 = 0%; Moderate GRADE).

CONCLUSION

Our results show an increase of 19% in falls among elderly patients who are users of sensors located in their bed, bed-chair, or chair among their hospitalizations. Other types of sensors such as wearable sensors can be explored as coadjutants for fall prevention care in hospitals.

FallSkip device is a useful tool for fall risk assessment in sarcopenic older community people

PURPOSE

Fall prevention is a major health concern for the ageing population. Sarcopenia is considered a risk factor for falls. Some instruments, such as Time Up and Go (TUG), are used for screening risk. The use of sensors has also been shown to be a viable tool that can provide accurate, cost-effective, and easy to manage assessment of fall risk. One novel sensor for assessing fall risk in older people is the Fallskip device. The present study evaluates the performance of the FallSkip device against the TUG method in fall risk screening and assesses its measurement properties in sarcopenic older people.

METHODS

A cross-sectional study was made in a sample of community-dwelling sarcopenic and non-sarcopenic older people aged 70 years or over.

RESULTS

The study sample consisted of 34 older people with a mean age of 77.03 (6.58) years, of which 79.4% (n = 27) were females, and 41.2% (n = 14) were sarcopenic. The Pearson correlation coefficient between TUG time and FallSkip time was 0.70 (p < 0.001). The sarcopenic individuals took longer in performing both TUG and FallSkip. They also presented poorer reaction time, gait and sit-to-stand - though no statistically significant differences were observed. The results in terms of feasibility, acceptability, reliability and validity in sarcopenic older people with FallSkip were acceptable.

CONCLUSIONS

The FallSkip device has suitable metric properties for the assessment of fall risk in sarcopenic community-dwelling older people. FallSkip analyses more parameters than TUG in assessing fall risk and has greater discriminatory power in evaluating the risk of falls.

App-based strength and balance self-test in older adults: an exploratory study from a user perspective

Objectives

Falls are a common problem, especially in the older population. The number of older adults aged over 65 years is increasing globally, leading to a major challenge in providing effective fall prevention interventions to older adults requiring such interventions. This study aimed to explore the usability of an app-based strength and balance self-tests in a small sample of four older adults. This study is a side product of another project.

Results

The results from this study indicated that self-test of strength and balance by using a smartphone application is a challenge for older adults. Basic test measures, such as start and stop and counts of sit-to-stand, were difficult to self-administer. However, from a user perspective, the possibility of independently performing these measures was considered important and needed to be further developed and evaluated in future studies.

Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.

A novel model to quantify balance alterations in older adults based on the center of pressure (CoP) measurements with a cross-sectional study

Background

The timely detection of fall risk or balance impairment in older adults is transcendental because, based on a reliable diagnosis, clinical actions can be taken to prevent accidents. This study presents a statistical model to estimate the fall risk from the center of pressure (CoP) data.

Methods

This study is a cross-sectional analysis from a cohort of community-dwelling older adults aged 60 and over living in Mexico City. CoP balance assessments were conducted in 414 older adults (72.2% females) with a mean age of 70.23 ± 6.68, using a modified and previously validated Wii Balance Board (MWBB) platform. From this information, 78 CoP indexes were calculated and analyzed. Multiple logistic regression models were fitted in order to estimate the relationship between balance alteration and the CoP indexes and other covariables.

Results

The CoP velocity index in the Antero-Posterior direction with open eyes (MVELAPOE) had the best value of area under the curve (AUC) to identify a balance alteration (0.714), and in the adjusted model, AUC was increased to 0.827. Older adults with their mean velocity higher than 14.24 mm/s had more risk of presenting a balance alteration than those below this value (OR (Odd Ratio) = 2.94, p<0.001, 95% C.I.(Confidence Interval) 1.68–5.15). Individuals with increased age and BMI were more likely to present a balance alteration (OR 1.17, p<0.001, 95% C.I. 1.12–1.23; OR 1.17, p<0.001, 95% C.I. 1.10–1.25). Contrary to what is reported in the literature, sex was not associated with presenting a balance alteration (p = 0.441, 95% C.I. 0.70–2.27).

Significance

The proposed model had a discriminatory capacity higher than those estimated by similar means and resources to this research and was implemented in an embedded standalone system which is low-cost, portable, and easy-to-use, ideal for non-laboratory environments. The authors recommend using this technology to support and complement the clinical tools to attend to the serious public health problem represented by falls in older adults.

Can Older Adult Emergency Department Patients Successfully Use the Apple Watch to Monitor Health?

OBJECTIVE

To determine usability of the Apple Watch in older adult emergency department (ED) patients after a fall.

METHODS

We recruited older adults who fell and visited two urban EDs. They participated in an Apple Watch orientation and interviews on their experiences using the watch to complete varied tasks for 30 days. Interviews were recorded, transcribed, coded, and analyzed using framework analyses.

RESULTS

Eight participants (mean age 77.6 years) enrolled from November 2019 to March 2020. Participants reported being able to apply and charge the watch but struggled with navigating screens, monitoring charging status, and responding with de novo text messages. Many cited difficulties with advanced tasks, such as the study's app-based movement and memory activities. Experience with smartphones and caregiver assistance enhanced users' ability to complete tasks.

CONCLUSIONS

Older adults successfully performed basic Apple Watch functions. Family and community members may be necessary to assist with complex tasks.

WITHDRAWN: Health risk assessment and control of the elderly under the reliability theory

Ahead of Print article withdrawn by publisher.

How Can We Develop an Efficient eHealth Service for Provision of Care for Elderly People with Balance Disorders and Risk of Falling? A Mixed Methods Study

This study aimed to identify relevant topics for the development of an efficient eHealth service for elderly people with balance disorders and risk of falling, based on input from physicians providing healthcare to this patient group. In the quantitative part of the study, an open multiple-choice questionnaire was made available on the website of the Portuguese General Medical Council to assess the satisfaction with electronic medical records regarding clinical data available, the time needed to retrieve data and the usefulness of the data. Of the 118 participants, 55% were dissatisfied/very dissatisfied with data availability and 61% with the time spent to access and update data related to the focused patient group. Despite this negative experience, 76% considered future e-Health solutions as pertinent/very pertinent. Subsequently, these findings were further explored with eight semi-structured interviews. The physicians confirmed the reported dissatisfactions and pointed out the lack of comprehensive data and system interoperability as serious problems, causing inefficient health services with an overlap of emergency visits and uncoordinated diagnostics and treatment. In addition, they discussed the importance of camera and audio monitoring to add significant value. Our results indicate considerable potential for e-Health solutions, but substantial improvements are crucial to achieving such future solutions.

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

BACKGROUND

Higher age and cognitive impairment are associated with a higher risk of falling. Wearable sensor technology may be useful in objectively assessing motor fall risk factors to improve physical exercise interventions for fall prevention. This systematic review aims at providing an updated overview of the current research on wearable sensors for fall risk assessment in older adults with or without cognitive impairment. Therefore, we addressed two specific research questions: 1) Can wearable sensors provide accurate data on motor performance that may be used to assess risk of falling, e.g., by distinguishing between faller and non-faller in a sample of older adults with or without cognitive impairment?; and 2) Which practical recommendations can be given for the application of sensor-based fall risk assessment in individuals with CI? A systematic literature search (July 2019, update July 2020) was conducted using PubMed, Scopus and Web of Science databases. Community-based studies or studies conducted in a geriatric setting that examine fall risk factors in older adults (aged ≥60 years) with or without cognitive impairment were included. Predefined inclusion criteria yielded 16 cross-sectional, 10 prospective and 2 studies with a mixed design.

RESULTS

Overall, sensor-based data was mainly collected during walking tests in a lab setting. The main sensor location was the lower back to provide wearing comfort and avoid disturbance of participants. The most accurate fall risk classification model included data from sit-to-walk and walk-to-sit transitions collected over three days of daily life (mean accuracy = 88.0%). Nine out of 28 included studies revealed information about sensor use in older adults with possible cognitive impairment, but classification models performed slightly worse than those for older adults without cognitive impairment (mean accuracy = 79.0%).

CONCLUSION

Fall risk assessment using wearable sensors is feasible in older adults regardless of their cognitive status. Accuracy may vary depending on sensor location, sensor attachment and type of assessment chosen for the recording of sensor data. More research on the use of sensors for objective fall risk assessment in older adults is needed, particularly in older adults with cognitive impairment.

TRIAL REGISTRATION

This systematic review is registered in PROSPERO ( CRD42020171118 ).

Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units

Loss-of-balance (LOB) events, such as trips and slips, are frequent among community-dwelling older adults and are an indicator of increased fall risk. In a preliminary study, eight community-dwelling older adults with a history of falls were asked to perform everyday tasks in the real world while donning a set of three inertial measurement sensors (IMUs) and report LOB events via a voice-recording device. Over 290 h of real-world kinematic data were collected and used to build and evaluate classification models to detect the occurrence of LOB events. Spatiotemporal gait metrics were calculated, and time stamps for when LOB events occurred were identified. Using these data and machine learning approaches, we built classifiers to detect LOB events. Through a leave-one-participant-out validation scheme, performance was assessed in terms of the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR). The best model achieved an AUROC ≥0.87 for every held-out participant and an AUPR 4-20 times the incidence rate of LOB events. Such models could be used to filter large datasets prior to manual classification by a trained healthcare provider. In this context, the models filtered out at least 65.7% of the data, while detecting ≥87.0% of events on average. Based on the demonstrated discriminative ability to separate LOBs and normal walking segments, such models could be applied retrospectively to track the occurrence of LOBs over an extended period of time.

Inertial sensing of step kinematics in ambulatory patients with ALS and related motor neuron diseases

Amyotrophic lateral sclerosis (ALS) is a progressive disorder which impairs gait and elevates the risk for falls. Current methods of assessing gait in these patients are infrequent and subjective. The goal of this study was to evaluate wearable-based methods for assessing gait to facilitating better monitoring of ambulatory health and ultimately lessen fall risk. Thirty ambulatory patients seen in ALS clinic were guided by a physical therapist on a short walk, during which inertial sensors recorded their movement. Two methods, utilising sensors at the waist or foot, were used independently to estimate gait parameters. Decreased stride length, increased stride duration and decreased walking speed were associated with lower functional walking scores, and the presence of a cane or walker. Overall, there was no group-wide mean walking speed differences between methods, though the waist method overestimated stride length and walking speed in those with more significant gait dysfunction compared to the foot method. Reconstruction of movement using the foot-based sensor resulted in route segments that were 94 ± 1% standard error of the mean (SEM) the length of a centre-to-centre hallway reference vector, with an angular error of 0.66 ± 0.28° SEM.

eHealth for Addressing Balance Disorders in the Elderly: Systematic Review

Background

The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied.

Objective

The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease.

Methods

A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies.

Results

Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments.

Conclusions

eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed.

Assessment of Selected Spatio-Temporal Gait Parameters on Subjects with Pronated Foot Posture on the Basis of Measurements Using OptoGait. A Case-Control Study

Walking is part of daily life and in asymptomatic subjects it is relatively easy. The physiology of walking is complex and when this complex control system fails, the risk of falls increases. As a result, gait disorders have a major impact on the older adult population and have increased in frequency as a result of population aging. Therefore, the OptoGait sensor is intended to identify gait imbalances in pronating feet to try to prevent falling and injury by compensating for it with treatments that normalize such alteration. This study is intended to assess whether spatiotemporal alterations occur in the gait cycle in a young pronating population (cases) compared to a control group (non-pronating patients) analyzed with OptoGait. Method: a total of n = 142 participants consisting of n = 70 cases (pronators) and n = 72 healthy controls were studied by means of a 30 s treadmill program with a system of 96 OptoGait LED sensors. Results: Significant differences were found between the two groups and both feet in stride length and stride time, gait cycle duration and gait cadence (in all cases p < 0.05). Conclusions: pronating foot posture alters normal gait patterns measured by OptoGait; this finding presents imbalance in gait as an underlying factor. Prevention of this alteration could be considered in relation to its relationship to the risk of falling in future investigations.

Technology for Healthy Independent Living: Creating a Tailored In-Home Sensor System for Older Adults and Family Caregivers

Sensing technologies hold enormous potential for early detection of health changes that can dramatically affect the aging experience. In previous work, we developed a health alert system that captures and analyzes in-home sensor data. The purpose of this research was to collect input from older adults and family members on how the health information generated can best be adapted, such that older adults and family members can better self-manage their health. Five 90-minute focus groups were conducted with 23 older adults (mean age = 80 years; 87% female) and five family members (mean age = 64; 100% female). Participants were asked open-ended questions about the sensor technology and methods for interacting with their health information. Participants provided feedback regarding tailoring the technology, such as delegating access to family and health care providers, receiving health messages and alerts, interpreting health messages, and graphic display options. Participants also noted concerns and future likelihood of technology adoption. [Journal of Gerontological Nursing, 46(7), 35-40.].

Smart homes for the older population: particularly important during the COVID-19 outbreak

Osteoporosis, one of the leading causes of disability in older adults, significantly reduces the quality of life and leads to loss of independence. Dynamic development of “smart” solutions based on artificial intelligence more and more commonly applied in older people’s houses may be an answer to the above issues.

The aim of this study is to present selected “smart home” solutions for the diagnosis and prevention of falls in the older population through a literature review. The conducted meta-analysis based on a review of the scientific literature available in English and Polish in the Medline/PubMed, Embase, Scopus, and GBL databases was undertaken from 01.01.2015 to 01.10.2020 with the string search method using key words.

According to the authors of this study, the development of new technology based on artificial intelligence allows older people to live independently, which contributes to a higher level of life satisfaction and quality.

Comparison of Retrospective and Prospective Falls Reporting Among Community-Dwelling Older People: Findings From Two Cohort Studies

Background: While prospective recording is considered as the gold standard, retrospective recall is widely utilized for falls outcomes due to its convenience. This brings about the concern on the validity of falls reporting in Southeast Asian countries, as the reliability of falls recall has not previously been studied. This study aimed to evaluate the reliability of retrospective falls recall compared to prospective falls recording.

Methods: A secondary analysis of data from two prospective recording methods, falls diary and falls calendar, from two different research projects were obtained and analyzed. Retrospective falls recall was collected either through phone interview or follow-up clinic by asking the participants if they had fallen in the past 12 months.

Results: Two-hundred-sixty-eight and 280 elderly participated in the diary and calendar groups, respectively. Moderate (46%) and poor (11%) return rates were found on completed diary and calendar recording. Under-(32%) and overreporting (24%) of falls were found in diary compared to only 4% of overreporting for the calendar. Retrospective recall method achieved 57% response rate for the diary group (followed up at clinic) and 89% for the calendar group (followed up via telephone interview). Agreement between retrospective and prospective reporting was moderate for the diary (kappa =0.44; p < 0.001) and strong for the calendar (kappa = 0.89; p < 0.001).

Conclusion: Retrospective recall is reliable and acceptable in an observation study within healthy community older adults, while the combination of retrospective and prospective falls recording is the best for an intervention study with frailer older population. Telephone interview is convenient, low cost, and yielded a high response rate.

Fully Automatic Fall Risk Assessment Based on a Fast 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.

Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study

BACKGROUND

Wearable technology for fall alerts among older adult care recipients is one of the more frequently studied areas of technology, given the concerning consequences of falls among this population. Falls are quite prevalent in later life. While there is a growing amount of literature on older adults' acceptance of technology, less is known about how caregivers' attitudes toward technology can impact care recipients' use of such technology.

OBJECTIVE

The objective of our study was to examine associations between caregivers' attitudes toward technology for caregiving and care recipients' use of fall alert wearables.

METHODS

This study examined data collected with an online survey from 626 caregivers for adults 50 years and older. Adapted from the technology acceptance model, a structural equation model tested the following prespecified hypotheses: (1) higher perceived usefulness of technologies for caregiving would predict higher perceived value of and greater interest in technologies for caregiving; (2) higher perceived value of technologies for caregiving would predict greater interest in technologies for caregiving; and (3) greater interest in technologies for caregiving would predict greater use of fall alert wearables among care recipients. Additionally, we included demographic factors (eg, caregivers' and care recipients' ages) and caregiving context (eg, caregiver type and caregiving situation) as important predictors of care recipients' use of fall alert wearables.

RESULTS

Of 626 total respondents, 548 (87.5%) with all valid responses were included in this study. Among care recipients, 28% used fall alert wearables. The final model had a good to fair model fit: a confirmatory factor index of 0.93, a standardized root mean square residual of 0.049, and root mean square error of approximation of 0.066. Caregivers' perceived usefulness of technology was positively associated with their attitudes toward using technology in caregiving (b=.70, P<.001) and interest in using technology for caregiving (b=.22, P=.003). Greater perceived value of using technology in caregiving predicted greater interest in using technology for caregiving (b=.65, P<.001). Greater interest in using technology for caregiving was associated with greater likelihood of care recipients using fall alert wearables (b=.27, P<.001). The caregiver type had the strongest inverse relationship with care recipients' use of fall alert wearables (unpaid vs paid caregiver) (b=-.33, P<.001).

CONCLUSIONS

This study underscores the importance of caregivers' attitudes in care recipients' technology use for falls management. Raising awareness and improving perception about technologies for caregiving may help caregivers and care recipients adopt and better utilize technologies that can promote independence and enhance safety.

Technology Utilization in Fall Prevention

ABSTRACT

Falls, defined as unplanned descents to the floor with or without injury to an individual, remain to be one of the most challenging health conditions. Fall rate is a key quality metric of acute care hospitals, rehabilitation settings, and long-term care facilities. Fall prevention policies with proper implementation have been the focus of surveys by regulatory bodies, including The Joint Commission and the Centers for Medicare and Medicaid Services, for all healthcare settings. Since October 2008, the Centers for Medicare and Medicaid Services has stopped reimbursing hospitals for the costs related to patient falls, shifting the accountability for fall prevention to the healthcare providers. Research shows that almost one-third of falls can be prevented and extensive fall prevention interventions exist. Recently, technology-based applications have been introduced in healthcare to obtain superior patient care outcomes and experience via efficiency, access, and reliability. Several areas in fall prevention deploy technology, including predictive and prescriptive analytics using big data, video monitoring and alarm technology, wearable sensors, exergame and virtual reality, robotics in home environment assessment, and personal coaching. This review discusses an overview of these technology-based applications in various settings, focusing on the outcomes of fall reductions, cost, and other benefits.

Predicting functional performance via classification of lower extremity strength in older adults with exergame-collected data

Objective

The goal of this article is to present and to evaluate a sensor-based functional performance monitoring system. The system consists of an array of Wii Balance Boards (WBB) and an exergame that estimates whether the player can maintain physical independence, comparing the results with the 30 s Chair-Stand Test (30CST).

Methods

Sixteen participants recruited at a nursing home performed the 30CST and then played the exergame described here as often as desired during a period of 2 weeks. For each session, features related to walking and standing on the WBBs while playing the exergame were collected. Different classifier algorithms were used to predict the result of the 30CST on a binary basis as able or unable to maintain physical independence.

Results

By using a Logistic Model Tree, we achieved a maximum accuracy of 91% when estimating whether player’s 30CST scores were over or under a threshold of 12 points, our findings suggest that predicting age- and sex-adjusted cutoff scores is feasible.

Conclusion

An array of WBBs seems to be a viable solution to estimate lower extremity strength and thereby functional performance in a non-invasive and continuous manner. This study provides proof of concept supporting the use of exergames to identify and monitor elderly subjects at risk of losing physical independence.

Foreseeing future falls with accelerometer features in active community-dwelling older persons with no recent history of falls

BACKGROUND

Acceleration sensors are a viable option for monitoring gait patterns and its application on monitoring falls and risk of falling. However the literature still lacks prospective studies to investigate such risk before the occurrence of falls.

OBJECTIVE

To investigate features extracted from accelerometer signals with the purpose of predicting future falls in individuals with no recent history of falls.

METHODS

In this study we investigate the risk of fall in active and healthy community-dwelling living older persons with no recent history of falls, using a single accelerometer and variants of the Timed Up and Go (TUG) test. A prospective study was conducted with 74 healthy non-fallers older persons. After collecting acceleration data from the participants at the baseline, the occurrence of falls (outcome) was monitored quarterly during one year. A set of frequency features were extracted from the signal and their ability to predict falls was evaluated.

RESULTS

The best individual feature result shows an accuracy of 0.75, sensitivity of 0.71 and specificity of 0.76. A fusion of the three best features increases the sensitivity to 0.86. On the other hand, the cut-off points of the TUG seconds, often used to assess fall risk, did not demonstrate adequate sensitivity.

CONCLUSION

The results confirms previous evidence that accelerometer features can better estimate fall risk, and support potential applications that try to infer falls risk in less restricted scenarios, even in a sample stratified by age and gender composed of active and healthy community-dwelling living older persons.

Estimating Postural Stability Using Improved Permutation Entropy via TUG Accelerometer Data for Community-Dwelling Elderly People

To develop an effective fall prevention program, clinicians must first identify the elderly people at risk of falling and then take the most appropriate interventions to reduce or eliminate preventable falls. Employing feature selection to establish effective decision making can thus assist in the identification of a patient's fall risk from limited data. This work therefore aims to supplement professional timed up and go assessment methods using sensor technology, entropy analysis, and statistical analysis. The results showed the different approach of applying logistic regression analysis to the inertial data on a fall-risk scale to allow medical practitioners to predict for high-risk patients. Logistic regression was also used to automatically select feature values and clinical judgment methods to explore the differences in decision making. We also calculate the area under the receiver-operating characteristic curve (AUC). Results indicated that permutation entropy and statistical features provided the best AUC values (all above 0.9), and false positives were avoided. Additionally, the weighted-permutation entropy/statistical features test has a relatively good agreement rate with the short-form Berg balance scale when classifying patients as being at risk. Therefore, the proposed methodology can provide decision-makers with a more accurate way to classify fall risk in elderly people.

Falls in People with Multiple Sclerosis: Risk Identification, Intervention, and Future Directions

Falls are highly prevalent in people with multiple sclerosis (MS) and result in a range of negative consequences, such as injury, activity curtailment, reduced quality of life, and increased need for care and time off work. This narrative review aims to summarize key literature and to discuss future work needed in the area of fall prevention for people with MS. The incidence of falls in people with MS is estimated to be more than 50%, similar to that in adults older than 80 years. The consequences of falls are considerable because rate of injury is high, and fear of falling and low self-efficacy are significant problems that lead to activity curtailment. A wide range of physiological, personal, and environmental factors have been highlighted as potential risk factors and predictors of falls. Falls are individual and multifactorial, and, hence, approaches to interventions will likely need to adopt a multifactorial approach. However, the literature to date has largely focused on exercise-based interventions, with newer, more comprehensive interventions that use both education and exercise showing promising results. Several gaps in knowledge of falls in MS remain, in particular the lack of standardized definitions and outcome measures, to enable data pooling and comparison. Moving forward, the involvement of people with MS in the design and evaluation of programs is essential, as are approaches to intervention development that consider implementation from the outset.

Predicting Short-Term Risk of Falls in a High-Risk Group With Dementia

OBJECTIVES

To develop a prognostic model to predict the probability of a short-term fall (within the next 7 to 30 days) in older adults with dementia.

DESIGN

Prospective observational study.

SETTING AND PARTICIPANTS

Fifty-one individuals with dementia at high risk of falls from a specialized dementia inpatient unit.

METHODS

Clinical and demographic measures were collected and a vision-based markerless motion capture was used to record the natural gait of participants over a 2-week baseline. Falls were tracked throughout the length of stay. Cox proportional hazard regression analysis was used to build a prognostic model to determine fall-free survival probabilities at 7 days and at 30 days. The model's discriminative ability was also internally validated.

RESULTS

Fall history and gait stability (estimated margin of stability) were statistically significant predictors of time to fall and included in the final prognostic model. The model's predicted survival probabilities were close to observed values at both 7 and 30 days. The area under the receiver operating curve was 0.80 at 7 days, and 0.67 at 30 days and the model had a discrimination performance (the Harrel concordance index) of 0.71.

CONCLUSIONS AND IMPLICATIONS

Our short-term falls risk model had fair to good predictive and discrimination ability. Gait stability and recent fall history predicted an imminent fall in our population. This provides some preliminary evidence that the degree of gait instability may be measureable in natural everyday gait to allow dynamic falls risk monitoring. External validation of the model using a separate data set is needed to evaluate model's predictive performance.

Ecological Gait as a Fall Indicator in Older Adults: A Systematic Review

BACKGROUND AND OBJECTIVES

Falls represent a major threat for elders, affecting their life quality and expectancy. Clinical tests and questionnaires showed low diagnostic value with respect to fall risk. Modern sensor technology allows in-home gait assessments, with the possibility to register older adults' ecological mobility and, potentially, to improve accuracy in determining fall risk. Hence, we studied the correlation between standardized assessments and ecological gait measures, comparing their ability to identify fall risk and predict prospective falls.

RESEARCH DESIGN AND METHOD

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines.

RESULTS

From a total of 938 studies screened, nine articles with an observational study design were included. Evidence from selected works was subcategorized in (i) correlations between ecological and clinical measures and comparative statistics of (ii) prospective fall prediction and (iii) fall risk identification. A large number of correlations were observed between single ecological gait assessments and multiple clinical fall risk evaluations. Moreover, the combination of daily-life features and clinical tests outcomes seemed to improve diagnostic accuracy in fall risk identification and fall prediction. However, it was not possible to understand the extent of this enhancement due to the high variability in models' parameters.

DISCUSSION AND IMPLICATIONS

Evidence suggested that sensor-based ecological assessments of gait could boost diagnostic accuracy of fall risk measurement protocols if used in combination with clinical tests. Nevertheless, further studies are needed to understand what ecological features of gait should be considered and to standardize models' definition.

Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis

BACKGROUND

Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications.

RESEARCH QUESTION

In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls?

METHODS

We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test- 6MWT, self-paced walk test - SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared - ω_p²) between patients and controls.

RESULTS

We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination.

SIGNIFICANCE

These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA.

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

BACKGROUND

Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults.

METHODS

Community-dwelling subjects aged ≥ 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters-were identified by means of a cross-validation method.

RESULTS

Of the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall ("fallers"), while 64 (67%) did not ("non-fallers"). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72-0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71-0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55-0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (p<0.001), respectively. The best model that comprised only robotic parameters (the 20 parameters identified in the final model) achieved a better performance than the clinical parameters alone, but worse than the combination of both clinical and robotic variables (ROC AUC: 0.73, 95% CI 0.63-0.83).

CONCLUSION

A multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken.

Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People

Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.

Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study

Background

Recent studies have thoughtfully and convincingly demonstrated the possibility of estimating the circadian rhythms of young adults’ social activity by analyzing their telephone call-detail records (CDRs). In the field of health monitoring, this development may offer new opportunities for supervising a patient’s health status by collecting objective, unobtrusive data about their daily social interactions. However, before considering this future perspective, whether and how similar results could be observed in other populations, including older ones, should be established.

Objective

This study was designed specifically to address the circadian rhythms in the telephone calls of older adults.

Methods

A longitudinal, 12-month dataset combining CDRs and questionnaire data from 26 volunteers aged 65 years or older was used to examine individual differences in the daily rhythms of telephone call activity. The study used outgoing CDRs only and worked with three specific telecommunication parameters: (1) call recipient (alter), (2) time of day, and (3) call duration. As did the studies involving young adults, we analyzed three issues: (1) the existence of circadian rhythms in the telephone call activity of older adults, (2) their persistence over time, and (3) the alter-specificity of calls by calculating relative entropy.

Results

We discovered that older adults had their own specific circadian rhythms of outgoing telephone call activity whose salient features and preferences varied across individuals, from morning until night. We demonstrated that rhythms were consistent, as reflected by their persistence over time. Finally, results suggested that the circadian rhythms of outgoing telephone call activity were partly structured by how older adults allocated their communication time across their social network.

Conclusions

Overall, these results are the first to have demonstrated the existence, persistence, and alter-specificity of the circadian rhythms of the outgoing telephone call activity of older adults. These findings suggest an opportunity to consider modern telephone technologies as potential sensors of daily activity. From a health care perspective, these sensors could be harnessed for unobtrusive monitoring purposes.

Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach

Numerous postural sway metrics have been shown to be sensitive to balance impairment and fall risk in individuals with MS. Yet, there are no guidelines concerning the most appropriate postural sway metrics to monitor impairment. This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment. Participants were further classified into four subgroups based on fall risk: controls, low-risk MS (n = 34), moderate-risk MS (n = 27), high-risk MS (n = 42). Twenty common sway metrics were derived following standard procedures and subsequently used to train a machine learning algorithm (random forest - RF, with 10-fold cross validation) to predict individuals' fall risk grouping. The sway-metric based RF classifier had high accuracy in discriminating controls from MS individuals (>86%). Sway sample entropy was identified as the strongest feature for classification of low-risk MS individuals from healthy controls. Whereas for all other comparisons, mediolateral sway amplitude was identified as the strongest predictor for fall risk groupings.

Optimized scoring tool to quantify the functional performance during the sit-to-stand transition with a magneto-inertial measurement unit

BACKGROUND

Sit-to-stand is used as a qualitative test to evaluate functional performance, especially to detect fall risks and frail individuals. The use of various quantitative criteria would enable a better understanding of musculoskeletal deficits and movement strategy modifications. This quantification was proven possible with a magneto-inertial unit which provides a compatible wearable device for clinical routine motion analysis.

METHODS

Sit-to-stand movements were recorded using a single magneto-inertial measurement unit fixed on the chest for 74 subjects in three groups healthy young, healthy senior and frail. MIMU data was used to compute 15 spatiotemporal, kinematic and energetic parameters. Nonparametric statistical test showed a significant influence of age and frailness. After reducing the number of parameters by a principal component analysis, an AgingScore and a FrailtyScore were computed.

FINDINGS

The fraction of variance explained by the first principal component was 77.48 ± 2.80% for principal component analysis with healthy young and healthy senior groups, and 74.94 ± 2.24% with healthy and frail senior groups. By receiver operating characteristic curve analysis of this score, we were able to refine the analysis to differentiate between healthy young and healthy senior subjects as well as healthy senior and frail subjects. By radar plot of the most discriminate parameters, the motion's strategy could be characterized and be used to detect premature functional deficit or frail subjects.

INTERPRETATION

Sit-to-stand measured by a single magneto-inertial unit and dedicated post processing is able to quantify subject's musculoskeletal performance and will allow longitudinal investigation of aging population.

Fall detection and fall risk assessment in older person using wearable sensors: A systematic review

BACKGROUND

wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening.

OBJECTIVE

To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies.

METHODS

A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application.

RESULTS

We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application.

CONCLUSION

This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.

Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen's kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., "active aging", biofeedback-based rehabilitation for fall prevention, and the management of Parkinson's disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors

This study aimed to investigate the performance of an updated version of our pre-impact detection algorithm parsing out the output of a set of Inertial Measurement Units (IMUs) placed on lower limbs and designed to recognize signs of lack of balance due to tripping. Eight young subjects were asked to manage tripping events while walking on a treadmill. An adaptive threshold-based algorithm, relying on a pool of adaptive oscillators, was tuned to identify abrupt kinematics modifications during tripping. Inputs of the algorithm were the elevation angles of lower limb segments, as estimated by IMUs located on thighs, shanks and feet. The results showed that the proposed algorithm can identify a lack of balance in about 0.37 ± 0.11 s after the onset of the perturbation, with a low percentage of false alarms (<10%), by using only data related to the perturbed shank. The proposed algorithm can hence be considered a multi-purpose tool to identify different perturbations (i.e., slippage and tripping). In this respect, it can be implemented for different wearable applications (e.g., smart garments or wearable robots) and adopted during daily life activities to enable on-demand injury prevention systems prior to fall impacts.

The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment

Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in older adults. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLensTM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older and 12 young adults) participated in this study. An automated functional mobility assessment app was developed, based on the HoloLens platform. The mobility performance was recorded with the headset built-in sensor and reference inertial sensor (Opal, APDM) taped on the headset and lower back. The results indicate that the vertical kinematic measurements by HoloLens were in good agreement with the reference sensor (Normalized RMSE ~ 10%, except for cases where the inertial sensor drift correction was not viable). Additionally, the HoloLens-based test completion time was in perfect agreement with the clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users (without severe mobility deficit) to complete common mobility tests, and this approach has the potential to provide an objective and efficient sensor-based mobility assessment that does not require any direct research/clinical oversight.

Identifying on admission patients likely to develop acute kidney injury in hospital

Background

The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.

Methods

Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation.

Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).

Results

Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64–0.77); FLS II (AUC 0.77, 95% CI: 0.69–0.85) and MLR II (AUC 0.74, 95% CI: 0.65–0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92–0.98).

Conclusions

FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.

Electronic supplementary material

The online version of this article (10.1186/s12882-019-1237-x) contains supplementary material, which is available to authorized users.

Preliminary evaluation of a self-guided fall risk assessment tool for older adults

Falls are a major health problem for older adults with significant physical and psychological consequences. The first step of successful fall prevention is to identify those at risk of falling. Recent technology advancement offers the possibility of objective, lowcost and self-guided fall risk assessment. The present work evaluated the preliminary validity and usability of a Kinect camera-based selfinitiated fall risk assessment system in a hospital setting. A convenience sample of 29 female participants (77.5 ± 7.9 years old) enrolled in this study. This low-cost self-guided system included a Kinect depth-sensing camera, a PC-based computer, and custom-built software. An onscreen Fall Risk Assessment Avatar (FRAAn) utilizing visual and verbal instructions led participants through a fall risk assessment consisting of self-report measures and clinically validated balance and mobility tests. Participants also completed clinical fall risk evaluation (Timed-Up and Go, and Berg Balance Scale) led by a researcher. User experience was evaluated by the System Usability Scale (SUS). Results indicate that FRAAn-based outcome measures (postural sway metrics, and sit-to-stand speed) were highly correlated with clinical fall risk measures, and were able to differentiate individuals with increased fall risk. Additionally, 83% participants reported high usability (SUS > 80), indicating the system is well received among older users. Overall, our results indicate that the FRAAn system has promise for providing a self-guided fall risk assessment, and is well received by older users. This affordable, portable and self-guided system has potential to facilitate objective fall risk assessment in older adults in various settings.

Smartphone technology can measure postural stability and discriminate fall risk in older adults

BACKGROUND

Falls are the leading cause of injury related death in older adults. Impaired postural stability is a predictor of falls but is seldom objectively assessed in clinical or home settings. Embedded accelerometers within smartphones offer potential to objectively measure postural stability. The purpose of this study was to determine if a smartphone embedded accelerometer can measure static postural stability and distinguish older adults at high levels of fall risk.

METHODS

Thirty older adults (age: 65.9 ± 8.8) underwent seven balance tests while standing on a force plate and holding a smartphone against their chest in a standardized order. Participants also completed the Physiological Profile Assessment to assess their fall risk. Center of pressure (COP) parameters from the force plate including velocity in the anterioposterior (AP) and mediolateral (ML) directions and 95% confidence ellipse were derived. Maximum acceleration and root mean square (RMS) in ML, AP and vertical axes were derived from the smartphone. Spearman rank-order correlations between force plate and smartphone measures were conducted, and receiver operating characteristic (ROC) and the area under the curves (AUC) were constructed to distinguish between low and high fall risk.

RESULTS

There were moderate to strong significant correlations between measures derived from the force plate and measures derived from the smartphone during challenging balance conditions (ρ = 0.42-0.81; p < 0.01-0.05). The AUC for ROC plots were significant for all COP measures during challenging balance conditions (p < 0.01-0.05). The AUC for ROC plots were significant for RMS vertical and AP during challenging balance conditions (p = 0.01-0.04).

SIGNIFICANCE

This study provides evidence that a smartphone is a valid measure of postural stability and capable of distinguishing fall risk stratification in older adults. There is potential for smartphones to offer objective, fall risk assessments for older adults.

Comparison of Accelerometry-Based Features for Fall Risk Assessment Measured From Two Sensor Locations

Falls are an unfortunate problem for older adults, their relatives and societies. Continuous gait monitoring for fall risk assessment during daily lives would allow early interventions to prevent injurious falls. Continuous gait monitoring is possible using technological solutions such as inertial sensors; for example accelerometers. Current solutions require attaching the sensor to a certain location on the body and many of them to the lower back, which is not convenient for the user. The objective of this study was to find out whether gait variables calculated from the acceleration signal measured during walk from two different locations on waist area differ from each other. Forty two older adult subjects were measured during walk test with a triaxial acceleration sensor worn on an elastic belt at the lower back and frontal hip area. Most of the analyzed gait features from the two locations have a strong correlation, indicating that these features are not sensitive to sensor location around waist level. A subsequent study is needed to confirm other locations for the sensors to allow analyzing gait during everyday lives.

Novel technology for mobility and balance tracking in patients with multiple sclerosis: a systematic review

INTRODUCTION

Mobility and balance impairments in patients with multiple sclerosis (MS) are major factors for decreased quality of life. Novel sensing technologies have great potential to efficiently capture subtle changes in mobility and balance performance, and thus improve current practices by providing an easy-to-implement, objective, and continuous functional tracking in MS population. Areas covered: This review details the collective findings of novel technology utilization in mobility and balance tracking in patients with MS. Thirty-three were systematically identified and included in this review. Pertinent methodological features (participant demographics, sensing technology, study aims, functional assessment protocols, and outcome measures) were extracted from each article. The construct validity, reliability, clinical relevance, and discriminative ability of sensor-based assessment in the MS population were summarized. Expert commentary: Sensor-based balance and mobility assessment are valid in comparison with reference standard techniques and are reliable to measure performance in the MS population. Sensor-based measures are also associated with validated clinical outcomes and are sensitive to functional deficits in individuals with MS. Such technologies may greatly improve the likelihood of detecting mobility and balance dysfunctions in real-world environments, thus allowing healthcare professionals to monitor interventions and manage disease progression precisely and efficiently Abbreviations: PwMS: Patients with Multiple Sclerosis; BBS: Berg Balance Scale; DGI: Dynamic Gait Index; ABC: Activity-specific Balance Confidence; T25FW: Timed 25 Foot Walk; 6MWT: 6 minute walk test; TUG: Timed Up and Go test; EO: Eyes Open; EC: Eyes Closed; ICC: Intraclass Correlation Coefficient; EDSS: Expanded Disability Status Scale; MFIS: Modified Fatigue Impact Scale; MSWS: Multiple Sclerosis Walking Scale; MSIS: Mutliple Sclerosis Impact Scale; PPA: Physiological Profile Assessment; HC: Healthy Control; AP: Anterior-posterior direction; ML: Mediolateral direction.

A predictive model of isolated and recurrent falls in functionally independent community-dwelling older adults

BACKGROUND

Aging is associated with an increased risk of accidental falls. Falls in older people have been widely studied in nursing homes and in the elderly with poor functionality, but there have been few investigations into functionally independent community-dwelling older adults.

OBJECTIVE

To determine the predictive factors for falls in functionally independent community-dwelling older adults.

METHODS

A cohort trial-nested case-control study was carried out. The participants were community-dwelling people aged 70 and over who were treated in primary care centers from December 2012 to May 2014 in la Ribera (Valencia, Spain).

RESULTS

There were a total of 374 participants, with a mean age of 76.1 (SD 3.4) years (63.8% females). The subjects presented high functionality scores: Barthel 96.5 (SD 9.4), Lawton 7.2 (SD1.2), Tinetti 25.6 (SD 3.3). The mean number of prescribed drugs was 4.7 (SD 2.9). The cumulative incidence of falls was 39.2%, and 24.1% of these older adults suffered falls. The number of falls in the previous 12 months (OR=1.3; 95%CI: 1.11-1.53; p<0.001) and alpha-blockers (OR=6.72; 95%CI: 1.62-27.79; p=0.009) were predictors of falls. The presence of previous fractures (OR=9.55; 95%CI: 4.1-22.25; p<0.001), a body mass index of ≥30kg/m² (OR=1.09; 95%CI: 1.01-1.19; p=0.035), and who are using benzodiazepines and beta-blockers (OR=2.77; 95%CI: 1.53-5.02; p<0.001), were predictors of recurrent fallers.

CONCLUSIONS

Older people who use alpha-blockers, benzodiazepines and beta-blockers, had previous fractures, with increased body mass index are more likely to fall.