Fall risk assessment measures: an analytic review

Effectiveness of various exercise types in reducing fall risk among older adults with diabetic peripheral neuropathy: A systematic review and meta-analysis

Background

Diabetic peripheral neuropathy (DPN) increases fall risk in diabetics. Due to varying variables used to assess fall risk, the impact of exercise on fall prevention remains inconsistent. This study reviews and compares the effects of different exercises on fall risk among older adults with DPN.

Methods

A comprehensive literature search was conducted in PubMed, EBSCO, Web of Science, and Cochrane Library up to February 17th, 2025. Inclusion criteria were: older adults with DPN; exercise intervention only, an inactive or non-exercising control group, and randomized controlled trials with outcome variables: timed up and go (TUG) time, gait speed, Berg Balance Scale (BBS) score, one-legged standing (OLS) time with eye open (EO) and closed (EC). The mean difference (MD) and 95 % confidence interval (CI) were calculated.

Results

A total of 21 articles included five exercise types: balance exercise (BE), multi-component exercise (ME), strength exercise (SE), whole-body vibration (WBV) and foot-ankle functional training (FT). BE reduced TUG time (MD = -1.47, 95 % CI = -1.79 to -1.15) and increased gait speed (0.11, 0.04-0.18), BBS score (0.93, 0.49-1.37), and OLS time (EO: 2.72, 1.86-3.58; EC:1.58, 1.0-2.17). ME reduced TUG time (-1.71, -2.26 to -1.17) and increased BBS score (2.0, 1.28-2.72) and OLS time (EO: 7.07, 4.35-9.79; EC: 2.61, 1.28-3.94); SE reduced TUG time (-1.45, -2.75 to -0.15) and increased gait speed (0.09, 0.06-0.12); WBV increased OLS time (EO: 1.94, 1.32-2.56; EC: 1.86, 0.16-3.56) but did not affect TUG time or gait speed. FT did not affect TUG time or gait speed.

Conclusions

Exercise reduced fall risks among older adults with DPN. BE and ME were effective in reducing fall risks, followed by SE. WBV improved static balance but failed in dynamic balance. FT showed limited effects on fall prevention and was not recommended.

Application of wearable sensors in constructing a fall risk prediction model for community-dwelling older adults: A scoping review

BACKGROUND

Falls are a particularly important public health problem among older people. Early identification of risk factors is crucial for reducing the risk of falls in older adults. Studies have confirmed the effectiveness of sensor-based fall risk prediction models for the older population. This article aims to sort out the current use of wearable sensors in building fall risk models for older adults in the community and explore the suitable use of sensors in model construction and the prospects and possible difficulties of model application.

METHODS

This scoping review was conducted from 26 November 2023 to 9 March 2024. It was searched through Web of Science, PubMed, OVID, EBSCO and CNKI using the terms "wearable sensor" or "inertial sensor" or "inertial motion capture" or "wearable electronic devices" or "IMU" or "MEMS" or "accelerometer" or "gyroscope" or "magnetometer" or "smartphone" and "fall" and "predict" or "prediction" and "older adults" or "older men" or "older women" or "elderly" and "community" or "neighborhood" or "dwelling".

RESULTS

Thirty-one articles were included, and the selection of sensor type, location, and other characteristics and indicators, as well as model types, was summarized.

DISCUSSION AND CONCLUSIONS

Wearable sensors with a frequency of 100 Hz located in a combination of spine/ pelvis/ hip-shank-feet position is recommended. In addition, walking tests and TUG and its variants are appropriate in the community. However, more empirical research is needed to obtain the best model construction combination and apply it effectively to the community.

A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests

Balance is crucial for various athletic tasks, and accurately assessing balance ability among elite athletes using simple and accessible measurement methods is a significant challenge in sports science. A common approach to balance assessment involves recording center of pressure (CoP) displacements using force platforms, with various indicators proposed to distinguish subtle balance differences. However, these indicators have not reached a consensus, and it remains unclear whether these analyses alone can fully explain the complex interactions of postural control. In this study, we investigated four parameters related to balance control-anterior-posterior (AP) displacement, medial-lateral (ML) displacement, length, and tilt angle-in 13 elite athletes and 12 freestyle skiing aerial expert athletes. Data were recorded during 30-second trials on both soft and hard support surfaces, with eyes open and closed. We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. A classic train-test split method was applied, and the performance of different classifiers was evaluated using Receiver Operating Characteristic(ROC) analysis. The ROC results showed that traditional time-domain features were insufficient for accurately distinguishing athletes' balance abilities, whereas CMCI performed the best overall. Among all classifiers, the combination of CMCI and Ranking Forest yielded the best performance, with a sensitivity of 0.95 and specificity of 0.35. This nonlinear, multidimensional approach appears to be highly suitable for assessing the complexity of postural control.

The use and usefulness of the Peninsula Health Falls Risk Assessment Tool (PHFRAT) process in residential aged care: a mixed methods study across 25 aged care facilities

BACKGROUND

Falls remain a persistent problem in residential aged care (RAC) facilities. Fall screening and assessment tools such as the Peninsula Health Falls Risk Assessment Tool (PHFRAT) are widely used to inform falls risk and guide fall prevention interventions. However, it is unclear how it is used in practice and whether clinicians believe it supports resident care. This study aimed to measure the extent of use of PHFRAT to understand clinicians' perceptions of its value and usefulness.

METHODS

This mixed method study involved an analysis of PHFRAT assessment from 25 RAC facilities in New South Wales, Australia, and interviews with seven RAC staff about how PHFRAT information is used in practice. In the quantitative component, descriptive statistics were applied to PHFRAT data to summarise how RAC staff use the PHFRAT including the completeness and content of the three parts. In the qualitative component, thematic analysis techniques were applied to interview data.

RESULTS

The sample included 215 RAC residents with 703 PHFRATs, of which 617 documented fall prevention interventions. Among these 617 PHFRATs, 593 (96.1%) included strategies related to staff assistance and 283 (45.9%) recorded strategies related to device provision. While nearly all residents (96.74%) received at least one PHFRAT assessment over the study period, many PHFRAT assessments were incomplete (part 1: 11.5% of information missing; part 2: 10.8%; part 3: 17.1%). There were few variations in fall interventions prescribed to individual residents by their fall risk level. Interviews with RAC staff indicated that PHFRAT assessments are the responsibility of registered nurses with limited input from other staff or residents. While the structured process was viewed positively in guiding risk assessment and intervention assessment, a lack of input from others prevented strategies from being tailored to residents' specific needs and preferences. A shortage of resources, lack of communication, and limited staff education were identified as the main barriers to PHFRAT guideline implementation.

CONCLUSION

The PHFRAT provides a useful structure for clinicians to assess falls risk factors and plan falls prevention strategies. In the future, increased multidisciplinary input into fall prevention strategy development may improve the comprehensiveness of fall prevention plans.

Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data

Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the data gathered from IMU readings while the patient is performing the TUG test can be used to build a predictive model that would provide an estimate of the probability of suffering a fall in the near future, i.e., assessing prospective fall risk. This study applies deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) to build such predictive models based on features extracted from IMU data acquired during TUG test realizations. Data were obtained from a cohort of 106 older adults wearing wireless IMU sensors with sampling frequencies of 100 Hz while performing the TUG test. The dependent variable is a binary variable that is true if the patient suffered a fall in the six-month follow-up period. This variable was used as the output variable for the supervised training and validations of the deep learning architectures and competing machine learning approaches. A hold-out validation process using 75 subjects for training and 31 subjects for testing was repeated one hundred times to obtain robust estimations of model performances At each repetition, 5-fold cross-validation was carried out to select the best model over the training subset. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

Recreating Fall Risk Appraisal matrix using R to support fall prevention programs

Objective

This study aimed to optimize Fall Risk Appraisal (FRA) graphing for use in intervention programs tailored toward reducing the fall risk of older adults by using computing graphic functions in the R language.

Materials and Methods

We utilized RStudio, a free development environment for the R language, as well as the functions within the "ggplot2" and "grid" packages, to develop a code that would recreate the FRA matrix for use in data visualization and analysis, as well as feedback for older adults.

Results

The developed code successfully recreates the FRA matrix in R and allows researchers and clinicians to graph participant data onto the matrix itself.

Discussion

The use of an R code allows for a streamlined approach to manipulating the FRA matrix for use in data visualization and feedback for older adults, which improves upon the traditional paper-pencil method that has been previously used.

Conclusions

The code presented in this study recreates the FRA matrix instrument in the R language and gives researchers the ability to instantaneously add, remove, or change different aspects of the instrument to improve its readability for researchers and older adults.

Aberrant decision-making as a risk factor for falls in aging

Neuromotor impairments resulting from natural aging and aging-related diseases are often accompanied by a heightened prevalence of falls and fall-related injuries. Conventionally, the study of factors contributing to falls focuses on intrinsic characteristics, such as sensorimotor processing delays and weakness, and extrinsic factors, such as environmental obstacles. However, the impact of these factors only becomes evident in response to people's decisions about how and where they will move in their environment. This decision-making process can be considered a behavioral risk factor, and it influences the extent to which a person engages in activities that place them near the limits of their capacity. While there are readily available tools for assessing intrinsic and extrinsic fall risk, our understanding of how to assess behavioral risk is limited. Measuring behavioral risk requires a systematic assessment of how people make decisions when walking in complex environments and how these decisions relate to their functional capacity. We propose that experimental methods and computational models derived from behavioral economics can stimulate the development of such assessments. Behavioral economics relies on theoretical models and empirical studies to characterize the factors that influence how people make decisions under risky conditions where a given decision can have variable outcomes. Applying a behavioral economic approach to walking can provide insight into how internal assessment of one's fall risk influences the tasks that one is willing to perform. Ultimately, these assessments will allow us to identify people who make choices that increase their likelihood of fall-related injuries.

Enhancing fall risk assessment: instrumenting vision with deep learning during walks

BACKGROUND

Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait.

METHOD

The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset.

RESULTS

VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications.

CONCLUSION

The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

A novel multistep approach to standardize the reported risk factors for in-hospital falls: a proof-of-concept study

Background

Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature.

Objective

(1) To perform a literature review to identify the fall RFs among hospitalized adults; (2) to link the found RFs to the corresponding categories of international health classifications to reduce the heterogeneity of their definitions; (3) to perform a meta-analysis on the risk categories to identify the significant RFs; (4) to refine the final list of significant categories to avoid redundancies.

Methods

Four databases were investigated. We included observational studies assessing patients who had experienced in-hospital falls. Two independent reviewers performed the inclusion and extrapolation process and evaluated the methodological quality of the included studies. RFs were grouped into categories according to three health classifications (ICF, ICD-10, and ATC). Meta-analyses were performed to obtain an overall pooled odds ratio for each RF. Finally, protective RFs or redundant RFs across different classifications were excluded.

Results

Thirty-six articles were included in the meta-analysis. One thousand one hundred and eleven RFs were identified; 616 were linked to ICF classification, 450 to ICD-10, and 260 to ATC. The meta-analyses and subsequent refinement of the categories yielded 53 significant RFs. Overall, the initial number of RFs was reduced by about 21 times.

Conclusion

We identified 53 significant RF categories for in-hospital falls. These results provide proof of concept of the feasibility and validity of the proposed methodology. The list of significant RFs can be used as a template to build more accurate measurement instruments to predict in-hospital falls.

Reliability, Validity, and Identification Ability of a Commercialized Waist-Attached Inertial Measurement Unit (IMU) Sensor-Based System in Fall Risk Assessment of Older People

Falls are a prevalent cause of injury among older people. While some wearable inertial measurement unit (IMU) sensor-based systems have been widely investigated for fall risk assessment, their reliability, validity, and identification ability in community-dwelling older people remain unclear. Therefore, this study evaluated the performance of a commercially available IMU sensor-based fall risk assessment system among 20 community-dwelling older recurrent fallers (with a history of ≥2 falls in the past 12 months) and 20 community-dwelling older non-fallers (no history of falls in the past 12 months), together with applying the clinical scale of the Mini-Balance Evaluation Systems Test (Mini-BESTest). The results show that the IMU sensor-based system exhibited a significant moderate to excellent test-retest reliability (ICC = 0.838, p < 0.001), an acceptable level of internal consistency reliability (Spearman's rho = 0.471, p = 0.002), an acceptable convergent validity (Cronbach's α = 0.712), and an area under the curve (AUC) value of 0.590 for the IMU sensor-based receiver-operating characteristic (ROC) curve. The findings suggest that while the evaluated IMU sensor-based system exhibited good reliability and acceptable validity, it might not be able to fully identify the recurrent fallers and non-fallers in a community-dwelling older population. Further system optimization is still needed.

Targeted optimum care approach for osteoporotic fragility fractures: tailored strategy based on risk stratification to reduce incidents of falls-an initiative by the Egyptian Academy of bone health based on the FLS national register

Since falling is the third cause of chronic disability, a better understanding of the frequency, severity, and risk factors of falls across diagnostic groups is needed to design and implement customized, effective fall prevention, and management programs for these individuals, particularly those at risk of sustaining a fragility fracture.

OBJECTIVE

(1) To assess the incidence of falls among osteoporotic patients with fragility fractures. (2) To evaluate the potential for stratifying the people at risk of falling in bone health setting aiming to provide targeted optimum care for them.

METHODS

This was a multi-center, cross-sectional, observational study. Both men and postmenopausal women, admitted with an osteoporotic fracture (whether major osteoporosis or hip fracture), were consecutively recruited for this work and managed under Fracture Liaison Service. All the patients were assessed for their Fracture risk (FRAX), falls risk (FRAS), and sarcopenia risk (SARC-F) as well as functional disability (HAQ). Blood tests for bone profile as well as DXA scan were offered to all the patients.

RESULTS

Four hundred five patients (121 males, 284 females) were included in this work. Mean age was 70.1 (SD = 9.2) years. The incidence of falls was 64.9%. The prevalence of falls was high (64.8%) in the patients presenting with major osteoporosis fractures and in those with hip fractures (61.8%). The prevalence of fragility fractures was positively correlated with HAQ score and the SARC-F score (p = 0.01 and 0.021 respectively). Falls risk score was positively correlated with FRAX score of major osteoporotic fractures, HAQ score, and SARC-F score (p = 0.01, 0.013, and 0.003 respectively). Seventy percent of the osteopenia patients who sustained fragility fracture had high falls risk and/or SARC-F score.

CONCLUSION

This study highlighted the importance of falls risk stratification in osteoporotic patients presenting with fragility fractures. Identification of the patients at increased risk of falls should be a component of the standard practice.

Association of knee and hip osteoarthritis with the risk of falls and fractures: a systematic review and meta-analysis

OBJECTIVE

Studies evaluating the association of knee and hip osteoarthritis (OA) with falls and fractures have inconsistent findings. We aimed to investigate associations of symptomatic and radiographic knee and hip OA with risk of falls, recurrent falls, and fractures.

METHODS

We conducted an electronic search of databases from inception to February 2023. Two authors independently screened studies, extracted data, and assessed the risk of bias using the Newcastle-Ottawa Scale tool in eligible studies. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using random-effects models.

RESULTS

Of 17 studies included (n = 862849), 2 had a high risk of bias. Among studies that evaluated falls or fractures as outcomes, 7/8 (87.5%) and 5/11 (45.5%) were self-reported, respectively. Both symptomatic knee and hip OA were associated with increased risk of recurrent falls (knee: OR = 1.55, 95% CI 1.10 to 2.18; hip: OR = 1.50, 95% CI 1.28 to 1.75) but not falls or fractures. Radiographic knee OA increased risk of falls (OR = 1.28, 95% CI 1.03 to 1.59) and did not significantly increase risk of recurrent falls (OR = 1.39, 95% CI 0.97 to 1.97) or fractures (OR = 1.22, 95% CI 0.99 to 1.52). Radiographic hip OA decreased the risk of recurrent falls (OR = 0.70, 95% CI 0.51 to 0.96) but had no statistically significant association with fractures (OR = 1.16, 95% CI 0.79 to 1.71).

CONCLUSION

Symptomatic knee and hip OA were both associated with an increased risk of recurrent falls, and radiographic knee OA was associated with an increased risk of falls. No statistically significant associations of radiographic and symptomatic knee or hip OA with fractures were found.

Value of the short physical performance battery (SPPB) in predicting fall and fall-induced injury among old Chinese adults

BACKGROUND

The short physical performance battery (SPPB) is an easy-to-use tool for fall risk prediction, but its predictive value for falls and fall-induced injuries among community dwellers has not been examined through a large-sample longitudinal study.

METHODS

We analyzed five-round follow-up data (2, 3, 4, 5, 7 years) of the China Health and Retirement Longitudinal Study (CHARLS) (2011-2018). Data concerning falls and fall-induced injuries during multi-round follow-ups were collected through participant self-report. The Cochran-Armitage trend test examined trends in fall incidence rate across SPPB performance levels. Multivariable logistic regression and negative binomial regression models examined associations between SPPB performance and subsequent fall and fall-induced injury. The goodness-of-fit and area under the receiver operating curve (AUC) were used together to quantify the value of the SPPB in predicting fall and fall-induced injury among community-dwelling older adults.

RESULTS

The CHARLS study included 9279, 6153, 4142, 4148, and 3583 eligible adults aged 60 years and older in the five included follow-up time periods. SPPB performance was associated with fall and fall-induced injury in two and three of the five follow-up time periods, respectively (P < 0.05). The goodness-of-fit for all predictive models was poor, with both Cox-Snell R2 and Nagelkerke R2 under 0.10 and AUCs of 0.53-0.57 when using only SPPB as a predictor and with both Cox-Snell R2 and Nagelkerke R2 lower than 0.12 and AUCs of 0.61-0.67 when using SPPB, demographic variables, and self-reported health conditions as predictors together. Sex and age-specific analyses displayed highly similar results.

CONCLUSIONS

Neither use of SPPB alone nor SPPB together with demographic variables and self-reported health conditions appears to offer good predictive performance for falls or fall-induced injuries among community-dwelling older Chinese adults.

Development and evaluation of a simple predictive model for falls in acute care setting

AIMS AND OBJECTIVES

To develop a simple and reliable assessment tool for predicting falls in acute care settings.

BACKGROUND

Falling injures patients, lengthens hospital stay and leads to the wastage of financial and medical resources. Although there are many potential predictors for falls, a simple and reliable assessment tool is practically necessary in acute care settings.

DESIGN

A retrospective cohort study.

METHODS

The current study was conducted for participants who were admitted to a teaching hospital in Japan. Fall risk was assessed by the modified Japanese Nursing Association Fall Risk Assessment Tool consisting of 50 variables. To create a more convenient model, variables were first limited to 26 variables and then selected by stepwise logistic regression analysis. Models were derived and validated by dividing the whole dataset into a 7:3 ratio. Sensitivity, specificity, and area under the curve for the receiver-operating characteristic curve were evaluated. This study was conducted according to the STROBE guideline.

RESULTS

Six variables including age > 65 years, impaired extremities, muscle weakness, requiring mobility assistance, unstable gait and psychotropics were chosen in a stepwise selection. A model using these six variables with a cut-off point of 2 with one point for each item, was developed. Sensitivity and specificity >70% and area under the curve >.78 were observed in the validation dataset.

CONCLUSIONS

We developed a simple and reliable six-item model to predict patients at high risk of falling in acute care settings.

RELEVANCE TO CLINICAL PRACTICE

The model has also been verified to perform well with non-random partitioning by time and future research is expected to make it useful in acute care settings and clinical practice.

PATIENT OR PUBLIC CONTRIBUTION

Patients participated in the study on an opt-out basis, contributing to the development of a simple predictive model for fall prevention during hospitalisation that can be shared with medical staff and patients in the future.

Novel interventions significantly reduce falls with fractures: A meta-analysis and systematic review

INTRODUCTION

Worldwide, falls lead to possible complications such as prolonged hospitalization, prolonged bed rest, pressure injuries, fractures and mortality.

AIMS

The study aimed to evaluate the effectiveness of novel fall prevention strategies that utilized technology in preventing falls.

METHODS

The meta-analysis and systematic review was guided by the Cochrane guidelines for systematic reviews of interventions. The authors searched databases for specified keywords.

RESULTS

A total of 22 studies were included. Interventions included multi-modal fall prevention interventions, camera surveillance, motion sensors and bed/chair exit alarms. Video monitoring was equivocal in reducing fall rates. Exit alarms statistically significantly reduce falls between groups but not within groups. The interventions were not statistically significant in reducing falls with minor injuries but they were statistically significant in reducing falls with serious injuries including fractures.

CONCLUSION

A comprehensive fall prevention care plan, rather than one specific intervention, is necessary to prevent falls effectively.

Differences in fall-related characteristics across cognitive disorders

Approximately 40-60% of falls in the elderly lead to injuries, resulting in disability and loss of independence. Despite the higher prevalence of falls and morbidity rates in cognitively impaired individuals, most fall risk assessments fail to account for mental status. In addition, successful fall prevention programmes in cognitively normal adults have generally failed in patients with cognitive impairment. Identifying the role of pathological aging on fall characteristics can improve the sensitivity and specificity of fall prevention approaches. This literature review provides a thorough investigation into fall prevalence and fall risk factors, the accuracy of fall risk assessments, and the efficacy of fall prevention strategies in individuals with diverse cognitive profiles. We show that fall-related characteristics differ between cognitive disorders and fall risk assessment tools as well as fall prevention strategies should critically consider each patient's cognitive status to facilitate the identification of fallers at an earlier stage and support clinical decision-making.

CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls

Objective

Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk.

Methods

In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve.

Results

Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657.

Conclusion

Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity.

Advances in knowledge

There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.

Occupational Fall Risk Assessment Tool for older workers

BACKGROUND

No easy-to-use fall risk assessment tools have been devised to assess occupational fall risk in older workers.

AIMS

To develop an Occupational Fall Risk Assessment Tool (OFRAT) and report its predictive validity and reliability in older workers.

METHODS

The baseline fall risk assessment was completed by 1113 participants aged ≥60 years who worked ≥4 days/month in Saitama, Japan. Participants were followed up for falls during occupational activities for 1 year, and 30 participants were assessed twice for test-retest reliability. The following assessment measures were summed to form the OFRAT risk score: older age, male sex, history of falls, physical work participation, diabetes, use of medications increasing fall risk, reduced vision, poor hearing, executive dysfunction and slow stepping. The scores were then classified into four grades (0-2 points: very low, 3 points: low, 4 points: moderate and ≥5 points: high).

RESULTS

During follow-up, 112 participants fell 214 times during work. The negative binomial regression model showed that participants with higher grades had a higher incidence rate ratio [95% confidence interval] for falls than those with very low grades (low: 1.64 [1.08-2.47], moderate: 4.23 [2.82-6.34] and high: 6.12 [3.83-9.76]). The intraclass correlation coefficient for risk score was 0.86 [0.72-0.93], and the weighted kappa coefficient for grade assessment was 0.74 [0.52-0.95].

CONCLUSIONS

The OFRAT is a valid and reliable tool for estimating the occupational fall risk in older workers. It may assist occupational physicians implement strategies to prevent falls in this group.

Walking Stability and Risk of Falls

Walking stability is considered a necessary physical performance for preserving independence and preventing falls. The current study investigated the correlation between walking stability and two clinical markers for falling risk. Principal component analysis (PCA) was applied to extract the three-dimensional (3D) lower-limb kinematic data of 43 healthy older adults (69.8 ± 8.5 years, 36 females) into a set of principal movements (PMs), showing different movement components/synergies working together to accomplish the walking task goal. Then, the largest Lyapunov exponent (LyE) was applied to the first five PMs as a measure of stability, with the interpretation that the higher the LyE, the lower the stability of individual movement components. Next, the fall risk was determined using two functional motor tests-a Short Physical Performance Battery (SPPB) and a Gait Subscale of Performance-Oriented Mobility Assessment (POMA-G)-of which the higher the test score, the better the performance. The main results show that SPPB and POMA-G scores negatively correlate with the LyE seen in specific PMs (p ≤ 0.009), indicating that increasing walking instability increases the fall risk. The current findings suggest that inherent walking instability should be considered when assessing and training the lower limbs to reduce the risk of falling.

Comparison of the Ability of Static and Dynamic Balance Tests to Determine the Risk of Falls among Older Community-Dwelling Individuals

This study aimed to examine the validity of balance tests and compare their diagnostic accuracy to determine the risk of falls among older community-dwelling individuals. Eighty-five older participants were assessed based on their demographics and fall data. They were then assessed for the ability to perform balance measures, including five times sit-to-stand tests (FTSSTs), timed up and go tests (TUGs), three times stand and walk tests (TTSWs), functional reach tests (FRTs), and single-leg stance tests (SLSs). The correlation between fall data and balance measures was found to be significant for all parameters (p < 0.05). The TTSW showed the highest level of ability to indicate the risk of falls among older community-dwelling adults with sensitivity = 92.68%, specificity = 84.09%, and AUC = 0.931 (95%CI = 0.860 to 1.000). These findings confirm the benefits of the practical functional balance measures to determine the risk of falls among older community-dwelling individuals.

Development of the home environment risk rating scale and investigation of the psychometric properties in the elderly and adult individuals

Aim

To reduce home accidents, which is one of the critical public health problems, it is necessary to evaluate the home environment first. The aim of this study was to develop the Home Environment Risk Rating Scale (HERRS) and examine its psychometric properties in elderly and adult individuals.

Subject and methods

This study was conducted on 220 elderly and adult individuals (63.68±10.31 years old, 68.2% female, 31.8% male) living in their homes. The participants completed the Sociodemographic Information Form, Home Environment Conditions Evaluation Form for Falls, and Home and Environment Risk Rating Scale. In addition, psychometric measurement results for horizontal and vertical measurements were analyzed by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) methods.

Results

It was found that the Kaiser-Mayer-Olkin (KMO) value for horizontal and vertical measurements were 0.613 and 0.704, respectively. Results of EFA for horizontal and vertical measurements revealed that five factors together explain 72.033% and three factors together explain 68.368% of the total variance, respectively. The result of CFA for horizontal and vertical measurements demonstrate that the 5-sub-dimension horizontal scale structure and the 3-sub-dimension vertical measurement structure generally fit acceptable in this scale. Cronbach's alpha was satisfactory in all of the measurements (0.73 and 0.80 respectively); the ICCs were good/excellent in all of the measurements (0.99 and 0.90, respectively).

Conclusion

The results show that HERRS has the potential to examine the risks of the home environment adequately for the home structure of Turkish society in detail and is a valid and reliable test that health professionals can use.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01885-6.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

Risk stratification for early and late falls in acute care settings

BACKGROUND AND AIMS

Falling generally injures patients, lengthens hospital stays and leads to the wastage of financial and medical resources. Although falls can occur at any stage after hospital admission, there are no studies that characterise falls with length of hospital stay in acute care settings. This study aims to clarify risk stratification of early and late falls in acute care settings.

METHODS

A retrospective cohort study was conducted for participants who were admitted to a teaching hospital in Japan. Patients' falls were divided into two groups based on the median of the fall date (day 10). Considering a 70/30 split, the logistic regression model was used to extract independent predictors for early and late falls for nine risk variables based on exploratory analysis among 26 items selected from the modified Japanese Nursing Association Fall Risk Assessment Tool, and risk models were validated. This study was conducted according to the STROBE guideline.

RESULTS

Of the 10,975 patients admitted, 87 and 90 with early and late falls, respectively, were identified. The five significant risk factors extracted for early falls were fall history, muscle weakness, impaired understanding, use of psychotropics and the personality trait of 'doing everything on one's own'; risk factors identified for late falls were being older than 65 years, impaired extremities and unstable gait, in addition to muscle weakness. Using these variables for early and late falls in the validation cohort, the concordance indices of the risk models were both over 0.80.

CONCLUSIONS

By separately extracting risk factors for early and late falls in an acute care hospital setting, this study shed light on the characteristics of the respective types of falls.

RELEVANT TO CLINICAL PRACTICE

As the risk factors of falls vary according to the length of hospitalisation, specific preventive care can be implemented to avoid fall incidents.

Predicting fall risk using multiple mechanics-based metrics for a planar biped model

For both humans and robots, falls are undesirable, motivating the development of fall prediction models. Many mechanics-based fall risk metrics have been proposed and validated to varying degrees, including the extrapolated center of mass, the foot rotation index, Lyapunov exponents, joint and spatiotemporal variability, and mean spatiotemporal parameters. To obtain a best-case estimate of how well these metrics can predict fall risk both individually and in combination, this work used a planar six-link hip-knee-ankle biped model with curved feet walking at speeds ranging from 0.8 m/s to 1.2 m/s. The true number of steps to fall was determined using the mean first passage times from a Markov chain describing the gaits. In addition, each metric was estimated using the Markov chain of the gait. Because calculating the fall risk metrics from the Markov chain had not been done before, the results were validated using brute force simulations. Except for the short-term Lyapunov exponents, the Markov chains could accurately calculate the metrics. Using the Markov chain data, quadratic fall prediction models were created and evaluated. The models were further evaluated using differing length brute force simulations. None of the 49 tested fall risk metrics could accurately predict the number of steps to fall by themselves. However, when all the fall risk metrics except the Lyapunov exponents were combined into a single model, the accuracy increased substantially. These results suggest that multiple fall risk metrics must be combined to obtain a useful measure of stability. As expected, as the number of steps used to calculate the fall risk metrics increased, the accuracy and precision increased. This led to a corresponding increase in the accuracy and precision of the combined fall risk model. 300 step simulations seemed to provide the best tradeoff between accuracy and using as few steps as possible.

Potentially inappropriate medications are negatively associated with functional recovery in patients with sarcopenia after stroke

BACKGROUND

Evidence is scarce for potentially inappropriate medications (PIMs) in rehabilitation medicine.

AIM

To examine the effect of PIMs on functional recovery in older patients with sarcopenia after stroke.

METHODS

We conducted a retrospective cohort study in a post-acute rehabilitation hospital. All patients diagnosed with sarcopenia aged ≥ 65 years among all post-stroke patients hospitalized from 2015 to 2020 were included. PIMs were defined based on the 2019 Beers criteria. Sarcopenia was diagnosed using handgrip strength (HG) and skeletal muscle mass index (SMI), according to the criteria of the Asian Working Group for Sarcopenia 2019. The outcomes included functional independence measure motor (FIM-motor), HG, and SMI values at discharge. Multiple linear regression analyses were used to determine whether PIMs used at admission were independently associated with outcomes.

RESULTS

Of the eligible patients, 361 were 65 years or older, of whom 196 (mean age 81.0 years, 44.4% male) presented with sarcopenia and were included in the analysis. Of these, 131 (66.8%) were prescribed PIMs at admission. The most frequently prescribed PIMs were proton pump inhibitors, antipsychotics, benzodiazepines, and nonsteroidal anti-inflammatory drugs. The number of PIMs on admission was independently associated with FIM-motor (β = - 0.132, P = 0.001) and HG (β = - 0.091, P = 0.048) at discharge, but not with SMI (β = - 0.055, P = 0.256).

CONCLUSIONS

High admission PIMs prescription numbers are negatively associated with favorable discharge functional status and muscle strength in older patients with sarcopenia after stroke.

Gait evaluation in stroke hemiplegic patients using short physical performance battery

OBJECTIVES

Falls can occur daily in stroke patients and appropriate independence assessments for fall prevention are required. Although previous studies evaluated the short physical performance battery (SPPB) in stroke patients, the relationship between SPPB and fall prediction and walking independence remains unclear. Therefore, we aimed to verify whether SPPB is a predictor of walking independence.

MATERIALS AND METHODS

The present study included 105 hemiplegic stroke patients who were admitted to the rehabilitation ward and gave consent to participate. Cross-sectional physical function and functional independence measure cognitive (FIM-C) evaluation were conducted in hemiplegic stroke patients. Logistic regression analysis using the increasing variable method (likelihood ratio) was performed to extract factors for walking independence. Cutoff values were calculated for the extracted items using the receiver operating-characteristic (ROC) curve.

RESULTS

Among 86 participants included in the final analysis, 36 were independent walkers and 50 were dependent walkers. In the logistic regression analysis, SPPB and FIM-C were extracted as factors for walking independence. The cutoff value was 7 [area under the curve (AUC), 0.94; sensitivity, 0.83; specificity, 0.73)] for SPPB and 32 (AUC, 0.83; sensitivity, 0.69; specificity, 0.57) for FIM-C in ROC analysis CONCLUSIONS: SPPB and FIM-C were extracted as factors for walking independence. Although SPPB alone cannot determine independent walking, combined assessment of SPPB with cognitive function may enable more accurate determination of walking independence.

Effect of home-based exercise on falls in community-dwelling older adults: an umbrella review

Aims

The aim of this review study was to examine the effectiveness of home-based and community-based exercise programmes in the rate of falls and improving physical functioning in community-dwelling older adults.

Methods

All types of home-based and community-based exercise interventions were searched. From 1186 studies identified, 14 studies were selected for the umbrella review. Most studies had high methodological quality. The types of interventions were multi-functional programmes (n = 11 studies) and Otago Exercise Programme (OEP) (n = 3 studies).

Results

The results showed that home-based and community-based exercise interventions can reduce falls by 22-32%. Studies that included meta-analysis showed that the clinical significance of home-based interventions in fall prevention and improving physical function was moderate to high.

Conclusions

In conclusion, home-based and community-based exercise interventions are a safe, effective, and feasible method of fall prevention that could be implemented with minimum supervision by allied health professionals to maximise autonomy, self-efficacy, and adherence in community-dwelling older adults.

Wearable Sensor Systems for Fall Risk Assessment: A Review

Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.

Validation and comparison of fall screening tools for predicting future falls among older adults

BACKGROUND

Falls are the leading cause of injuries among older adults in the United States (US). Falls are preventable and clinicians are advised to screen for fall risk yearly. There are many falls screening tools and not all have been validated for their ability to predict future falls.

METHODS

We enrolled 1905 community-dwelling older adults into a 13-month study using a probability-based representative panel of the US population recruited from NORC at the University of Chicago's National Frame. Respondents completed a baseline survey, 11 monthly fall calendars, and a final survey. The baseline survey included six falls screening tools (the Stay Independent, Three Key Questions (3KQ), a modified American Geriatric/British Geriatric tool, the short Falls Efficacy-1[FES-I]) and two single screening questions ("I have fallen in the past year" and "How many times did you fall in the past 12 months?"). The baseline and final survey collected demographic and health information, including falls. Sensitivity, specificity, positive and negative likelihood ratios, and corresponding 95% confidence intervals were calculated in SAS using weighted proportions.

RESULTS

There were 1563 respondents who completed the final survey (completion rate 82%). Sensitivity estimates ranged from 22.5% for the short FES-I to 68.7% for the 3KQ. Specificity estimates ranged from 57.9% for the 3KQ to 89.4% for the short FES-I.

CONCLUSIONS

Falls screening tools have varying sensitivity and specificity for predicting the occurrence of a fall in the following 12 months.

Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults

Slip-induced falls are a growing health concern for older adults, and near-fall events are associated with an increased risk of falling. To detect older adults at a high risk of slip-related falls, this study aimed to develop models for near-fall event detection based on accelerometry data collected by body-fixed sensors. Thirty-four healthy older adults who experienced 24 laboratory-induced slips were included. The slip outcomes were first identified as loss of balance (LOB) and no LOB (NLOB), and then the kinematic measures were compared between these two outcomes. Next, all the slip trials were split into a training set (90%) and a test set (10%) at sample level. The training set was used to train both machine learning models (n = 2) and deep learning models (n = 2), and the test set was used to evaluate the performance of each model. Our results indicated that the deep learning models showed higher accuracy for both LOB (>64%) and NLOB (>90%) classifications than the machine learning models. Among all the models, the Inception model showed the highest classification accuracy (87.5%) and the largest area under the receiver operating characteristic curve (AUC), indicating that the model is an effective method for near-fall (LOB) detection. Our approach can be helpful in identifying individuals at the risk of slip-related falls before they experience an actual fall.

Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation

BACKGROUND

Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest.

OBJECTIVE

The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults.

METHODS

In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals.

RESULTS

The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN.

CONCLUSIONS

The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.

Sinus-like versus random vibration: Acute effects on elderly people with a high risk of falling

BACKGROUND

Whole-body vibration (WBV) could be an alternative training method for elderly with a high risk of falling.

RESEARCH QUESTION

What acute effects of sinus-like and random WBV intervention occur in this risk group and how do they differ?

METHODS

This prospective pilot study was performed on two days separated by a one-week wash-out phase. Twelve subjects (10 women and 2 men, age 77.7 ± 5.3; 162.3 ± 7.4 cm; 82.4 ± 15.2 kg; BMI 31.2 ± 5.1 kg/m²) undertook sinus-like WBV intervention. One week later random WBV were performed. This consisted of six intervals each lasting 60 s, with heart and respiratory rates monitored. Feasibility outcomes involved safety and compliance. The primary endpoint was postural control, measured by a static posturography before and after the first interval and complete intervention. Secondary outcomes measured before and after the intervention were a Timed-Up-and-Go-Test (TUGT) and an instrumental gait analysis.

RESULTS

Random WBV intervention showed positive acute effect on postural control (Standard deviation (SD) Ellipse area p = 0.007; SD of the Center of force (CoF) independent from direction p = 0.017; SD of the CoF in the antero-posterior direction p = 0.011). There were no significant acute effects on TUGT or gait analysis (comparison between sinus-like and random WBV: Single Task: ΔVelocity p = 0.373, ΔStep time p = 0.077, ΔStep length p = 0.369, ΔStride length p = 0.408, ΔDouble-support-time p = 0.492; Dual task: ΔVelocity p = 0.580, ΔStep-time p = 0.559, ΔStep length p = 0.626, ΔStride length p = 0.584, ΔDouble-support-time p = 0.550). During sinus-like WBV, heart rate increased significantly from rest 69.7 ± 20.9bpm to max.146.0 ± 24.9bpm (p = 0.025). Respiratory rate increased significantly from 10.0 ± 1.0 to max.32.0 ± 6.0 (p = 0.011) during random WBV. No undesirable side effects were observed.

SIGNIFICANCE

Findings demonstrate that random WBV improves acute functional ability of postural control, but not gait for elderly people with a high risk of falling. Intervention with WBV seems safe and well accepted by participants. Monitoring of heart and respiratory rate offers protection for subjects with heart disease.

Changes in activity levels, physical functioning, and fall risk during the COVID-19 pandemic

BACKGROUND

Physical function worsens with older age, particularly for sedentary and socially isolated individuals, and this often leads to injuries. Through reductions in physical activity, the COVID-19 pandemic may have worsened physical function and led to higher fall-related risks.

METHODS

A nationally representative online survey of 2006 U.S. adults aged 50-80 was conducted in January 2021 to assess changes in health behaviors (worsened physical activity and less daily time spent on feet), social isolation (lack of companionship and perceived isolation), physical function (mobility and physical conditioning), and falls (falls and fear of falling) since March 2020. Multivariable logistic regression was used to assess relationships among physical activity, social isolation, physical function, falls, and fear of falling.

RESULTS

Among respondents, 740 (36.9%) reported reduced physical activity levels, 704 (35.1%) reported reduced daily time spent on their feet since March 2020, 712 (37.1%) reported lack of companionship, and 914 (45.9%) social isolation. In multivariable models, decreased physical activity (adjusted risk ratio, ARR: 2.92, 95% CI: 2.38, 3.61), less time spent on one's feet (ARR: 1.95, 95% CI: 1.62, 2.34), and social isolation (ARR: 1.51, 95% CI: 1.30, 1.74) were associated with greater risks of worsened physical conditioning. Decreased physical activity, time spent daily on one's feet, and social isolation were similarly associated with worsened mobility. Worsened mobility was associated with both greater risk of falling (ARR: 1.70, 95% CI: 1.35, 2.15) and worsened fear of falling (ARR: 2.02, 95% CI: 1.30, 3.13). Worsened physical conditioning and social isolation were also associated with greater risk of worsened fear of falling.

CONCLUSION

The COVID-19 pandemic was associated with worsened physical functioning and fall outcomes, with the greatest effect on individuals with reduced physical activity and social isolation. Public health actions to address reduced physical activity and social isolation among older adults are needed.

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.

The Effect of Mixed Reality Technologies for Falls Prevention Among Older Adults: Systematic Review and Meta-analysis

BACKGROUND

Falling is one of the most common and serious age-related issues, and falls can significantly impair the quality of life of older adults. Approximately one-third of people over 65 experience a fall annually. Previous research has shown that physical exercise could help reduce falls among older adults and improve their health. However, older adults often find it challenging to follow and adhere to physical exercise programs. Interventions using mixed reality (MR) technology could help address these issues. MR combines artificial augmented computer-generated elements with the real world. It has frequently been used for training and rehabilitation purposes.

OBJECTIVE

The aim of this systematic literature review and meta-analysis was to investigate the use of the full spectrum of MR technologies for fall prevention intervention and summarize evidence of the effectiveness of this approach.

METHODS

In our qualitative synthesis, we analyzed a number of features of the selected studies, including aim, type of exercise, technology used for intervention, study sample size, participant demographics and history of falls, study design, involvement of health professionals or caregivers, duration and frequency of the intervention, study outcome measures, and results of the study. To systematically assess the results of the selected studies and identify the common effect of MR interventions, a meta-analysis was performed.

RESULTS

Seven databases were searched, and the initial search yielded 5838 results. With the considered inclusion and exclusion criteria, 21 studies were included in the qualitative synthesis and 12 were included in meta-analysis. The majority of studies demonstrated a positive effect of an MR intervention on fall risk factors among older participants. The meta-analysis demonstrated a statistically significant difference in Berg Balance Scale score between the intervention and control groups (ES: 0.564; 95% CI 0.246-0.882; P<.001) with heterogeneity statistics of I2=54.9% and Q=17.74 (P=.02), and a statistical difference in Timed Up and Go test scores between the intervention and control groups (ES: 0.318; 95% CI 0.025-0.662; P<.001) with heterogeneity statistics of I2=77.6% and Q=44.63 (P<.001). The corresponding funnel plot and the Egger test for small-study effects (P=.76 and P=.11 for Berg Balance Scale and Timed Up and Go, respectively) indicate that a minor publication bias in the studies might be present in the Berg Balance Scale results.

CONCLUSIONS

The literature review and meta-analysis demonstrate that the use of MR interventions can have a positive effect on physical functions in the elderly. MR has the potential to help older users perform physical exercises that could improve their health conditions. However, more research on the effect of MR fall prevention interventions should be conducted with special focus given to MR usability issues.

Preoperative fall risk assessment score as a prognostic factor in gastric cancer patients after gastrectomy

OBJECTIVE

Falls are related to frailty, which is known as an unfavorable prognosticator of gastric cancer. In this study, we investigated the influence of the fall risk assessment score on short- and long-term prognoses in patients with gastric cancer after gastrectomy.

METHODS

A total of 430 patients who underwent gastrectomy for gastric cancer were included in this retrospective study. The fall risk assessment score was scored by nursing staffs on admission. We investigated the relationships between the fall risk assessment score and clinicopathological findings, postoperative outcomes and prognoses. We assigned patients with a fall risk assessment score ≥7 to the high-risk group (92 cases, 21.4%) and those with a fall risk assessment score <6 to the low-risk group (338 cases, 78.6%).

RESULTS

There were no significant differences between the two groups in pathological stage of gastric cancer and postoperative complications, but the high-risk group had significantly longer postoperative hospital stays than the low-risk group (P < 0.001). The overall and the relapse-free survival rates in the high-risk group were significantly lower than those in the low-risk group. The high-risk group was one of the independent poor prognostic factors for overall survival, with a hazard ratio of 2.91 (P ≤ 0.001) in univariate analysis and a hazard ratio of 2.74 (P = 0.008) in multivariate analysis.

CONCLUSIONS

While the fall risk assessment score is an objective and easy-to-use method to assess fall risk and frailty, it may present a prognostic factor in gastric cancer.

Technology-Based Fall Risk Assessments for Older Adults in Low-Income Settings: Protocol for a Cross-sectional Study

Background

One-third of older adults have maladaptive fall risk appraisal (FRA), a condition in which there is a discrepancy between the level of fear of falling (FOF) and physiological fall risk (balance performance). Older adults who overestimate their physiological fall risk and report a high FOF are less likely to participate in physical activity. Limited data suggest that the association among FOF, body composition, and physical activity intensity differs by fear severity.

Objective

This study aims to examine the associations among FRA, body composition, and physical activity using assistive health technology, including the BTrackS balance system, bioelectrical impedance analysis, and activity monitoring devices. This study also aims to examine the feasibility of recruitment and acceptability of technologies and procedures for use among older adults in low-income settings.

Methods

This cross-sectional study will be conducted in older adults’ homes or apartments in low-income settings in Central Florida, United States. Following consent, participants will be contacted, and our team will visit them twice. The first visit includes questionnaire completion (eg, sociodemographic or FOF) and balance performance test using the BTrackS balance system. The participants will be stratified by the FRA matrix. In addition, they will perform hand grip strength and dynamic balance performance tests. Participants will then be asked to wear the ActiGraph GT9X Link wireless activity monitor on the nondominant wrist for 7 consecutive days. The second visit includes body composition testing and a structured interview about the acceptability of the technologies and procedures.

Results

Ethical approval was obtained from the institutional review board of the University of Central Florida (protocol number 2189; September 10, 2020). As of December 2020, participation enrollment is ongoing and the results are expected to be published in Summer 2022.

Conclusions

Accurate FRA is essential for implementing physical activity programs, especially in older adults with low income. This study will provide data for developing technology-based fall risk assessments to improve participation in physical activity, thus enhancing healthy longevity among older adults in low-income settings.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27381

Risk Factors for Hospital Admission After a Fall: A Prospective Cohort Study of Community-Dwelling Older People

BACKGROUND

Falls in later life that require admission to hospital have well-established consequences for future disability and health. The likelihood and severity of a fall will result from the presence of one or more risk factors. The aim of this study is to examine risk factors identified for their ability to prevent falls and to assess whether they are associated with hospital admission after a fall.

METHODS

Analyses of data from the English Longitudinal Study of Aging (ELSA), a prospective cohort study. In a sample of 3783 men and women older than 60 years old, a range of potential risk factors measured at Wave 4 (demographic, social environment, physical, and mental functioning) were examined as predictors of fall-related hospitalizations, identified using International Classification of Diseases, 10th Revision (ICD-10) code from linked hospital records in the United Kingdom. Subdistribution hazard models were used to account for competing risk of death.

RESULTS

Several risk factors identified by previous work were confirmed. Suffering from urinary incontinence (subdistribution hazard ratio = 1.49; 95% CI: 1.14, 1.95) and osteoporosis (subdistribution hazard ratio = 1.48; 95% CI: 1.05, 2.07), which are not commonly considered at an early stage of screening, were found to be associated with hospital admission after a fall. Both low and moderate levels of physical activity were also found to somewhat increase the risk of hospital admission after a fall.

CONCLUSIONS

Several predictors of having a fall, severe enough to require hospital admission, have been confirmed. In particular, urinary incontinence should be considered at an earlier point in the assessment of risk.

Assessing the predictive value of common gait measure for predicting falls in patients presenting with suspected normal pressure hydrocephalus

Objective

To assess the predictive value of common measures validated to predict falls in other geriatric populations in patients presenting with suspected Normal Pressure Hydrocephalus (NPH).

Methods

One hundred ninety-five patients over the age of 60 who received the Fall Risk Questionnaire were retrospectively recruited from the CSF Disorders clinic within the departments of Neurosurgery and Neurology. Multiple logistic regression was used to create a model to predict falls for patients with suspected NPH using common measures: Timed Up & Go, Dual Timed Up & Go, 10 Meter Walk, MiniBESTest, 6-Minute Walk, Lower Extremity Function (Mobility), Fall Risk Questionnaire, and Functional Activities Questionnaire.

Results

The Fall Risk Questionnaire and age were shown to be the best predictors of falls. The model was 95.92% (Positive predictive value: 83.93%) sensitive and 47.92% specific (Negative predictive value: 77.78%).

Conclusion

Patients presenting with suspected NPH are at an increased fall risk, 75% of the total patients and 89% of patients who responded to a temporary drain of CSF had at least one fall in the past 6 months. The Fall Risk Questionnaire and age were shown to be predictive of falls for patients with suspected NPH. The preliminary evidence indicates measures that have been validated to assess fall risk in other populations may not be valid for patients presenting with suspected NPH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02068-0.

Effects of Enriched Physical Activity Environments on Balance and Fall Prevention in Older Adults: A Scoping Review

The incidence of falling, due to aging, is related to both personal and environmental factors. There is a clear need to understand the nature of the major risk factors and design features of a safe and navigable living environment for potential fallers. The aim of this scoping review was to identify studies that have examined the effectiveness of environments, which promote physical activity and have an impact on falls prevention. Selected studies were identified and categorized into four main topics: built environment, environment modifications, enriched environments, and task constraints. The results of this analysis showed that there are a limited number of studies aiming to enhance dynamic postural stability and fall prevention through designing more functional environments. This scoping review study suggests that the design of interventions and the evaluation of an environment to support fall prevention are topics for future research.

The serum 25(OH)D level and hand grip strength for fall risk assessment among osteoporotic elderly Japanese women

We investigated the relationship between serum 25(OH)D levels, grip strength, and fall score in elderly osteoporotic women for fall risk assessment. Both low serum 25(OH)D and low grip strength were independently associated with increased fall risk. The serum 25(OH)D cutoff specific to increased fall risk was 14 mg/dL (35 nmol/L).

PURPOSE

This study aimed to establish a cutoff value of serum 25-hydroxyvitamin D (25(OH)D) for fall assessment and investigate the relationship between serum 25(OH)D, grip strength, and fall score adjusted for age in osteoporotic elderly Japanese women.

METHODS

This is a cross-sectional study utilizing collected data of osteoporotic elderly (age ≥65 years) female patients. A questionnaire for fall risk assessment was used, in which a score ≥ 6 was determined as increased fall risk. Serum 25(OH)D levels and grip strength were measured, and the cutoff points were calculated by receiver operating curve (ROC) analysis. Logistic regression analysis with age adjustment was conducted for potential risk factors for fall.

RESULTS

After applying eligibility criteria, finally, 349 patients were enrolled. The median patient age was 77.0 years, and the mean serum 25(OH)D level was 15.6 ng/mL (36 nmol/L). Based on the ROC analysis, we defined the cutoff values of serum 25(OH)D level and grip strength as 14 ng/mL (35 nmol/L) and 15 kg, respectively. A multivariate analysis adjusted for age was conducted. Low serum 25(OH)D level and grip strength were independent risk factors for ≥6 fall risk scores.

CONCLUSION

Both low serum 25(OH)D level and low grip strength were independently associated with increased fall risk score in osteoporotic elderly women. The appropriate serum 25(OH)D cutoff specific to the increased fall risk group in this population was 14 mg/dL (35 nmol/L). These findings might be used for the identification of patients with high fall risks. These results should be confirmed in other patient groups.

Can Smartphone-Derived Step Data Predict Laboratory-Induced Real-Life Like Fall-Risk in Community- Dwelling Older Adults?

Background: As age progresses, decline in physical function predisposes older adults to high fall-risk, especially on exposure to environmental perturbations such as slips and trips. However, there is limited evidence of association between daily community ambulation, an easily modifiable factor of physical activity (PA), and fall-risk. Smartphones, equipped with accelerometers, can quantify, and display daily ambulation-related PA simplistically in terms of number of steps. If any association between daily steps and fall-risks is established, smartphones due to its convenience and prevalence could provide health professionals with a meaningful outcome measure, in addition to existing clinical measurements, to identify older adults at high fall-risk. Objective: This study aimed to explore whether smartphone-derived step data during older adults' community ambulation alone or together with commonly used clinical fall-risk measurements could predict falls following laboratory-induced real-life like slips and trips. Relationship between step data and PA questionnaire and clinical fall-risk assessments were examined as well. Methods: Forty-nine community-dwelling older adults (age 60-90 years) completed Berg Balance Scale (BBS), Activities-specific Balance Confidence scale (ABC), Timed Up-and-Go (TUG), and Physical Activity Scale for the Elderly (PASE). One-week and 1-month smartphone steps data were retrieved. Participants' 1-year fall history was noted. All participants' fall outcomes to laboratory-induced slip-and-trip perturbations were recorded. Logistic regression was performed to identify a model that best predicts laboratory falls. Pearson correlations examined relationships between study variables. Results: A model including age, TUG, and fall history significantly predicted laboratory falls with a sensitivity of 94.3%, specificity of 58.3%, and an overall accuracy of 85.1%. Neither 1-week nor 1-month steps data could predict laboratory falls. One-month steps data significantly positively correlated with BBS (r = 0.386, p = 0.006) and ABC (r = 0.369, p = 0.012), and negatively correlated with fall history (r _p = -0.293, p = 0.041). Conclusion: Older participants with fall history and higher TUG scores were more likely to fall in the laboratory. No association between smartphone steps data and laboratory fall-risk was established in our study population of healthy community-dwelling older adults which calls for further studies on varied populations. Although modest, results do reveal a relationship between steps data and functional balance deficits and fear of falls.

[Effect of a multidimensional intervention for prevention of falls in the eldery]

OBJECTIVE

To evaluate the effect of a multidimensional intervention on the perception and management of risk factors and frequency of falls in independent elderly people living in the community.

DESIGN

Randomised clinical trial.

SETTING

Family health centre, primary care.

PARTICIPANTS

Independent elderly people living in the community.

INTERVENTION

For intervention group (IG) a multidimensional intervention, consisting of home visits and telephone follow-up was carried out for 5 months (n=77), and those assigned to the control group (CG, n=77) received usual care in the family health centre.

MAIN MEASUREMENTS

Perception of risk of falls, number of risk factors and number of falls in the study period.

RESULTS

In both groups there were increases in the perception of risk factors for falling associated with walking (IG: P<.001 and CG: P<.001). Belonging to the IG was significantly associated with a decrease in the risk factors associated with surfaces (r=0.25) and shoes (r=0.24), as well as an increase in the perception of risk of falls associated with walking (r=0.21) and the presence of objects or furniture (r=0.36). In the IG, 5 participants (7.9%) suffered at least one fall in the 5-month period and 18 (27.7%) patients in the CG (P=.004).

CONCLUSIONS

The multidimensional intervention was effective in reducing the frequency of falls and in the management of extrinsic risk factors associated with surfaces, lighting, and support devices.

Validity and reliability of the self-rated fall risk questionnaire in older adults with osteoporosis

BACKGROUND

Several risk assessments have been developed to evaluate fall risk in older adults, but it has not been conclusively established which of these tools is most effective for assessing fall risk in this vulnerable population. Recently, the U.S. Centers for Disease Control and Prevention (CDC) developed the self-rated Fall Risk Questionnaire (self-rated FRQ), a 12-item questionnaire designed to screen older adults who are at risk of falling and has been widely used in many centers. This study aimed to determine the validity and reliability of the self-rated FRQ in older adults with osteoporosis.

METHODS

This prospective study was conducted at the Department of Orthopedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand from December 2019 to March 2020. Sixty-eight men or postmenopausal women aged > 65 years who were diagnosed with osteoporosis either by bone mineral density T-score or by occurrence of fragility fracture were evaluated with the self-rated FRQ, the Thai falls risk assessment test (Thai-FRAT), the timed get-up-and-go test (TUG test), the Berg Balance Scale (BBS), and the 5 times sit-to-stand test (5TSTS test). Validity of the self-rated FRQ was assessed by evaluating the correlations (r) between the self-rated FRQ score and the scores from the other four assessments. Reliability of the self-rated FRQ was evaluated by measuring test-retest reliability and internal consistency.

RESULTS

The self-rated FRQ was moderately strongly correlated with the BBS, TUG test, and 5TSTS test (r = 0.535 to 0.690; p < 0.001), and fairly correlated with the Thai-FRAT (r = 0.487; p < 0.001). Test-retest reliability of the self-rated FRQ was high, with a Kappa of 1. Internal consistency of the self-rated FRQ was excellent (Cronbach's alpha: 0.936).

CONCLUSIONS

The self-rated FRQ was found to be a valid and reliable tool for evaluating fall risk in older adults with osteoporosis. Since assessment of fall risk requires a multifaceted measurement tool, the self-rated FRQ is an appropriate tool that can be integrated into the fall risk assessment algorithm in older adults with osteoporosis.

Selected Tools for Assessing the Risk of Falls in Older Women

Methods

Fifty-five females were examined (May 2018-June 2019). Stabilographic examinations were performed with eyes open (EO) and closed (EC). An analysis of variance (ANOVA) and Spearman rank correlation were performed to determine the relationships and differences between the above tests.

Results

The results of the TUG correlate with the overall stability index (OSI) EO (r = 0.314), medial-lateral stability index (MLSI) EO (r = 0.297), and fall risk index (FRI6-2; r = 0.435) in stabilographic examinations and the FRT (r = −0.399). The results of the modified Unterberger test correlate with MLSI EO (r = 0.276), OSI EC (r = 0.310), and MLSI EC (r = 0.378). There are statistically significant differences between faller and nonfaller groups in TUG (p = 0.0068), FRT (p = 0.001), and MLSI EO (p = 0.0118).

Conclusions

The modified Unterberger test and TUG can be considered effective in functional FR assessment in older women. Using at least two different functional tests may improve the assessment of FR.