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.

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.

Simulating the effects of a clinical guidelines screening algorithm for fall risk in community dwelling older adults

Background

The current guidelines for fall prevention in community-dwelling older adults issued by the American Geriatrics Society and British Geriatrics Society (AGS/BGS) indicate an algorithm for identifying who is at increased risk of falling. The predictive accuracy of this algorithm has never been assessed, nor have the consequences that its introduction in clinical practice would bring about.

Aims

To evaluate this risk screening algorithm, estimating its predictive accuracy and its potential impact.

Methods

The analyses are based on 438 community-dwelling older adults, participating in the InCHIANTI study. We analysed different tests for gait and balance assessment. We compared the AGS/BGS algorithm with alternative strategies for fall prevention not based on fall risk evaluation.

Results

The AGS/BGS screening algorithm (using TUG, cut-off 13.5 s) has a sensitivity for single falls of 35.8% (95% confidence interval 23.2%–52.7%) and a specificity of 84.0% (79.3%–88.4%). It marks 18.0% (13.7%–22.4%) of the older population as at high risk. A policy of targeting people with preventive intervention regardless of their individual risk could be as effective as the policy based on risk screening but at the price of intervening on 17.3% (4.1%–34.0%) more people of the older population.

Discussion

This study is the first that validates and estimates the impact of the screening algorithm of these guidelines. Main limitations are related to some modelling assumptions.

Conclusions

The AGS/BGS screening algorithm has low sensitivity. Nevertheless, its adoption would bring benefits with respect to policies of preventive interventions that act regardless of individual risk assessment.

Electronic supplementary material

The online version of this article (10.1007/s40520-018-1051-5) contains supplementary material, which is available to authorized users.

Accelerometer-based predictive models of fall risk in older women: a pilot study

Current clinical methods of screening older adults for fall risk have difficulties. We analyzed data on 67 women (mean age = 77.5 years) who participated in the Objective Physical Activity and Cardiovascular Health (OPACH) study within the Women’s Health Initiative and in an accelerometer calibration substudy. Participants completed the short physical performance battery (SPPB), questions about falls in the past year, and a timed 400-m walk while wearing a hip triaxial accelerometer (30 Hz). Women with SPPB ≤ 9 and 1+reported falls (n = 19) were grouped as high fall risk; women with SPPB = 10–12 and 0 reported falls (n = 48) were grouped as low fall risk. Random Forests were trained to classify women into these groups, based upon traditional measures of gait and/or signal-based features extracted from accelerometer data. Eleven models investigated combined feature effects on classification accuracy, using 10-fold cross-validation. The models had an average 73.7% accuracy, 81.1% precision, and 0.706 AUC. The best performing model including triaxial data, cross-correlations, and traditional measures of gait had 78.9% accuracy, 84.4% precision, and 0.846 AUC. Mediolateral signal-based measures—coefficient of variance, cross-correlation with anteroposterior accelerations, and mean acceleration—ranked as the top 3 features. The classification accuracy is promising, given research on probabilistic models of falls indicates accuracies ≥80% are challenging to achieve. The results suggest accelerometer-based measures captured during walking are potentially useful in screening older women for fall risk. We are applying algorithms developed in this paper on an OPACH dataset of 5000 women with a 1-year prospective falls log and week-long, free-living accelerometer data.

A hip-worn device that measures walking motion can help identify which older women are at heightened risk for falling. Andrew Hua, from the University of Illinois at Urbana-Champaign, USA, and colleagues put 67 elderly women through a series of tests to assess their lower extremity function. They also asked the study participants about fall histories in the past year and strapped a triaxial accelerometer to the women’s hips while they completed a 400-meter walking test. Analyses showed that the accelerometry data, when fed into a machine-learning algorithm, were predictive of physical ability and fall risk. Based on these results, the researchers are validating the algorithm in a larger study of 5000 women who wore hip accelerometers for a full week and reported falls prospectively for one-year.

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

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

Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model

Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial–lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior–posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly.

Predictive Performance of a Fall Risk Assessment Tool for Community-Dwelling Older People (FRAT-up) in 4 European Cohorts

BACKGROUND AND OBJECTIVE

The fall risk assessment tool (FRAT-up) is a tool for predicting falls in community-dwelling older people based on a meta-analysis of fall risk factors. Based on the fall risk factor profile, this tool calculates the individual risk of falling over the next year. The objective of this study is to evaluate the performance of FRAT-up in predicting future falls in multiple cohorts.

METHODS

Information about fall risk factors in 4 European cohorts of older people [Activity and Function in the Elderly (ActiFE), Germany; English Longitudinal Study of Aging (ELSA), England; Invecchiare nel Chianti (InCHIANTI), Italy; Irish Longitudinal Study on Aging (TILDA), Ireland] was used to calculate the FRAT-up risk score in individual participants. Information about falls that occurred after the assessment of the risk factors was collected from subsequent longitudinal follow-ups. We compared the performance of FRAT-up against those of other prediction models specifically fitted in each cohort by calculation of the area under the receiver operating characteristic curve (AUC).

RESULTS

The AUC attained by FRAT-up is 0.562 [95% confidence interval (CI) 0.530-0.594] for ActiFE, 0.699 (95% CI 0.680-0.718) for ELSA, 0.636 (95% CI 0.594-0.681) for InCHIANTI, and 0.685 (95% CI 0.660-0.709) for TILDA. Mean FRAT-up AUC as estimated from meta-analysis is 0.646 (95% CI 0.584-0.708), with substantial heterogeneity between studies. In each cohort, FRAT-up discriminant ability is surpassed, at most, by the cohort-specific risk model fitted on that same cohort.

CONCLUSIONS

We conclude that FRAT-up is a valid approach to estimate risk of falls in populations of community-dwelling older people. However, further studies should be performed to better understand the reasons for the observed heterogeneity across studies and to refine a tool that performs homogeneously with higher accuracy measures across different populations.

Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data

Falls are the leading global cause of accidental death and disability in older adults and are the most common cause of injury and hospitalization. Accurate, early identification of patients at risk of falling, could lead to timely intervention and a reduction in the incidence of fall-related injury and associated costs. We report a statistical method for fall risk assessment using standard clinical fall risk factors (N = 748). We also report a means of improving this method by automatically combining it, with a fall risk assessment algorithm based on inertial sensor data and the timed-up-and-go test. Furthermore, we provide validation data on the sensor-based fall risk assessment method using a statistically independent dataset. Results obtained using cross-validation on a sample of 292 community dwelling older adults suggest that a combined clinical and sensor-based approach yields a classification accuracy of 76.0%, compared to either 73.6% for sensor-based assessment alone, or 68.8% for clinical risk factors alone. Increasing the cohort size by adding an additional 130 subjects from a separate recruitment wave (N = 422), and applying the same model building and validation method, resulted in a decrease in classification performance (68.5% for combined classifier, 66.8% for sensor data alone, and 58.5% for clinical data alone). This suggests that heterogeneity between cohorts may be a major challenge when attempting to develop fall risk assessment algorithms which generalize well. Independent validation of the sensor-based fall risk assessment algorithm on an independent cohort of 22 community dwelling older adults yielded a classification accuracy of 72.7%. Results suggest that the present method compares well to previously reported sensor-based fall risk assessment methods in assessing falls risk. Implementation of objective fall risk assessment methods on a large scale has the potential to improve quality of care and lead to a reduction in associated hospital costs, due to fewer admissions and reduced injuries due to falling.

Systematic review of risk prediction models for falls after stroke

BACKGROUND

Falls are a significant cause of morbidity after stroke. The aim of this review was to identify, critically appraise and summarise risk prediction models for the occurrence of falling after stroke.

METHODS

A systematic literature search was conducted in December 2014 and repeated in June 2015. Studies that used multivariable analysis to build risk prediction models for falls early after stroke were included. 2 reviewers independently assessed methodological quality. Data relating to model calibration, discrimination (C-statistic) and clinical utility (sensitivity and specificity) were extracted. A narrative review of models was conducted. PROSPERO reference: CRD42014015612.

RESULTS

The 12 included articles presented 18 risk prediction models. 7 studies predicted falls among inpatients only and 5 recorded falls in the community. Methodological quality was variable. A C-statistic was reported for 7 models and values ranged from 0.62 to 0.87. Models for use in the inpatient setting most frequently included measures of hemi-inattention, while those predicting community events included falls (or near-falls) history and balance measures most commonly. Only 2 studies reported any form of validation, and none presented a validated model with acceptable performance.

CONCLUSIONS

A number of falls-risk prediction models have been developed for use in the acute and subacute stages of stroke. Future research should focus on validating and improving existing models, with reference to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines to ensure quality reporting and expedite clinical implementation.

Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

Background

Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs.

Objectives and methods

In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study). We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994), and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015). Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy.

Results

The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC) curve (AUC) for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy–parsimony analysis revealed that tools with a small number of predictors (~1–5) were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20–30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20–30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

The Discriminant Value of Phase-Dependent Local Dynamic Stability of Daily Life Walking in Older Adult Community-Dwelling Fallers and Nonfallers

The present study compares phase-dependent measures of local dynamic stability of daily life walking with 35 conventional gait features in their ability to discriminate between community-dwelling older fallers and nonfallers. The study reanalyzes 3D-acceleration data of 3-day daily life activity from 39 older people who reported less than 2 falls during one year and 31 who reported two or more falls. Phase-dependent local dynamic stability was defined for initial perturbation at 0%, 20%, 40%, 60%, and 80% of the step cycle. A partial least square discriminant analysis (PLS-DA) was used to compare the discriminant abilities of phase-dependent local dynamic stability with the discriminant abilities of 35 conventional gait features. The phase-dependent local dynamic stability λ at 0% and 60% of the step cycle discriminated well between fallers and nonfallers (AUC = 0.83) and was significantly larger (p < 0.01) for the nonfallers. Furthermore, phase-dependent λ discriminated as well between fallers and nonfallers as all other gait features combined. The present result suggests that phase-dependent measures of local dynamic stability of daily life walking might be of importance for further development in early fall risk screening tools.

Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults

The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.