The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data: a systematic review

Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach

BACKGROUND

Falls are a prevalent and serious health condition among older people in residential aged care facilities, causing significant health and economic burdens. However, the likelihood of future falls can be predicted, and thus, falls can be prevented if appropriate prevention programs are implemented. Current fall prevention programs in residential aged care facilities rely on risk screening tools with suboptimal predictive performance, leading to significant concerns regarding resident safety.

OBJECTIVE

This study aimed to develop a predictive, dynamic dashboard to identify residents at risk of falls with associated decision support. This paper provides an overview of the technical process, including the challenges faced and the strategies used to overcome them during the development of the dashboard.

METHODS

A predictive dashboard was co-designed with a major residential aged care partner in New South Wales, Australia. Data from resident profiles, daily medications, fall incidents, and fall risk assessments were used. A dynamic fall risk prediction model and personalized rule-based fall prevention recommendations were embedded in the dashboard. The data ingestion process into the dashboard was designed to mitigate the impact of underlying data system changes. This approach aims to ensure resilience against alterations in the data systems.

RESULTS

The dashboard was developed using Microsoft Power BI and advanced R programming by linking data silos. It includes dashboard views for those managing facilities and for those caring for residents. Data drill-through functionality was used to navigate through different dashboard views. Resident-level change in daily risk of falling and risk factors and timely evidence-based recommendations were output to prevent falls and enhance prescriptive decision support.

CONCLUSIONS

This study emphasizes the significance of a sustainable dashboard architecture and how to overcome the challenges faced when developing a dashboard amid underlying data system changes. The development process used an iterative dashboard co-design process, ensuring the successful implementation of knowledge into practice. Future research will focus on the implementation and evaluation of the dashboard's impact on health processes and economic outcomes.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-048657.

Application of machine learning for detecting high fall risk in middle-aged workers using video-based analysis of the first 3 steps

OBJECTIVES

Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first 3 steps in middle-aged workers.

METHODS

Participants to provide training data (n = 190, mean [SD] age = 54.5 [7.7] years, 48.9% male) and validation data (n = 28, age = 52.3 [6.0] years, 53.6% male) were enrolled in this study. Pose estimation was performed using a marker-free deep pose estimation method called MediaPipe Pose. The first 3 steps, including the movements of the arms, legs, trunk, and pelvis, were recorded using an RGB camera, and the gait features were identified. Using these gait features and fall histories, a stratified k-fold cross-validation method was used to ensure balanced training and test data, and the area under the curve (AUC) and 95% CI were calculated.

RESULTS

Of 77 gait features in the first 3 steps, we found 3 gait features in men with an AUC of 0.909 (95% CI, 0.879-0.939) for fall risk, indicating an "excellent" (0.9-1.0) classification, whereas we determined 5 gait features in women with an AUC of 0.670 (95% CI, 0.621-0.719), indicating a "sufficient" (0.6-0.7) classification.

CONCLUSIONS

These findings suggest that fall risk prediction can be developed based on ML and the first 3 steps in men; however, the accuracy was only "sufficient" in women. Further development of the formula for women is required to improve its accuracy in the middle-aged working population.

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data

BACKGROUND

Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.

METHODS

Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively.

RESULTS

We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination.

CONCLUSIONS

Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Fall Prevalence and Associated Risk Factors Among the Elderly Population in Tabuk City, Saudi Arabia: A Cross-Sectional Study 2023

Background Falls are common among older adults, and they constitute a major public health issue with high morbidity and mortality. Aim This study aimed to estimate the prevalence of falls and investigate the contributing risk factors among the elderly population in Tabuk City, Saudi Arabia. Methods This cross-sectional study recruited a random representative sample of the elderly aged ≥ 60 years. We collected data by interviewing the participants using a structured, Arabic-language questionnaire. It included personal information, a history of falls in the past three and 12 months, comorbidities, and environmental factors. The main outcome was a history of falls in the preceding year. Multivariable logistic regression was used to evaluate the association between potential risk factors and falls. Results The study included 296 participants. Most participants were female (66.9%), aged 60-69 years (68.2%), and married (68.9%). The self-reported prevalence of falls over the preceding 12 months was 25.3% (95% confidence interval (CI): 20.6-30.5). Older people with depressive symptoms had significantly increased vulnerability to falls (adjusted odds ratio (AOR): 0.452, 95% CI: 0.239-0.854). Environmental factors were associated with a 1.799 times (95% CI: 1.041-3.109) increased likelihood of fall, and gait impairment was the strongest risk factor (AOR: 2.775, 95% CI: 1.558-4.942). Conclusions Falls are common among the elderly population in Tabuk City, Saudi Arabia. Gait impairment, the presence of depressive symptoms, and environmental hazards were substantially associated with falls, suggesting that most falls are preventable.

Falls and associated risk factors in a sample of old age population in Egyptian community

Introduction

Falling is a major health problem among old age persons and are the sixth cause of mortality and morbidity among them. Assessing the prevalence of falls among elderly in an Egyptian community and investigating its associated risk factors using the Arabic translation of the SHARE-Questionnaire.

Subjects and methods

This cross-sectional analytic study was a part of the pilot for AL-SEHA project. It included 289 old age people (50+ years age) residing in the study areas. The main project data were collected using the Arabic translation of the SHARE (Survey of Health, Aging, and Retirement in Europe) questionnaire. The original project data were collected by investigators from five universities, then uploaded to the internet server domain of the American University in Cairo (AUC) Social Research Center.

Results

The prevalence of falls was 11.07% (95% CI: 7.95-15.21). Falls were significantly more among 70 years or older (p < 0.001), unemployed or housewives (p = 0.026), have a family caregiver (p = 0.022), and home facilities for disability (p = 0.015). They had significantly higher rates of ischemic heart disease, hypertension, dyslipidemia, stroke, and diabetes mellitus. The most frequently reported problems were the fear of fall and dizziness (62.5%). The multivariate analysis identified the history of stroke and diabetes mellitus, the fear of fall and dizziness, and the total number of health problems and the score of difficulty in performing physical activities as significant independent predictors of fall occurrence. The history of stroke was the strongest risk factor (OR 33.49, CI: 3.45-325.40).

Discussion and recommendations

The prevalence of falls among old age persons in the studied community is not alarmingly high. It is highest among stroke patients. Community interventions and rehabilitation programs are recommended to train and educate old age people, especially those at risk such as stroke and diabetic patients, and those with dizziness to improve their physical fitness and reduce the fear of fall among them.