Cognitive biases and contextual factors explaining variability in nurses’ fall risk judgements: a multi-centre cross-sectional study

The effectiveness of multifactorial fall risk assessment and intervention strategies is well documented. Although identifying feet/footwear-related influences on fall risk is a vital fall risk assessment component, few evidence-based resources or screening tools are available. To address this need, we developed the Screening Tool for Feet/Footwear-Related Influences on Fall Risk. Our tool is designed for older adults who are identified as at risk for falling, based on the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Algorithm for Fall Risk Screening, Assessment, and Intervention. Tool development was informed by results of our systematic review of lower-limb factors associated with balance and falls. Our initial tool was evaluated by an external group of 9 interprofessional content experts. Those experts recommended modification of 8 items and rated the tool’s clarity as 81.2/100, appeal as 79.1/100, and clinical feasibility as 76.1/100. After incorporating recommended changes, we completed a modified Delphi study using 8 new interprofessional experts (average years of experience: 19.3). During Phase 1, Delphi participants recommended we combine items with similar treatment recommendations, add a question about orthoses, and increase the specificity of 9 items. This refinement resulted in a 20-item screening tool, which met approval after two rounds of consensus voting. Approval was defined based on the Item Content Validation Index, percentage of agreement > 80% on each item. The high level of agreement illustrates the tool’s content validity. Using our tool, an older adult’s feet/footwear-related risk factors can be identified and incorporated into an effective multifactorial fall prevention intervention.

BackgroundFalls are a serious and common problem among older adults. Low-tech, inexpensive, community-based fall prevention programs have been shown to be both effective and cost effective, however, these programs are not well-integrated into clinical practice.Research designWe surveyed primary care providers at a convenience sample of two accountable care organizations in Massachusetts to assess their beliefs, attitudes, knowledge, and practices relative to fall risk assessment and intervention for their older patients.ResultsResponse rate was 71%. Providers’ beliefs about the efficacy of fall risk assessment and intervention were mixed. Eighty-seven percent believed that they could be effective in reducing fall risk among their older adult patients. Ninety-six percent believed that all older adults should be assessed for fall risk; and, 85% believed that this assessment would identify fall risk factors that could be modified. Nonetheless, only 52% believed that they had the expertise to conduct fall risk assessment and only 68% believed that assessing older adult patients for fall risk was the prevailing standard of practice among their peer providers. Although most providers believed it likely that an evidence-based program could reduce fall risk among their patients, only 14% were aware of the Centers for Disease Control and Prevention’s fall risk assessment algorithm (STEADI Toolkit), and only 15% were familiar with Matter of Balance, the most widely disseminated community fall risk prevention program in Massachusetts.DiscussionNew strategies that more directly target providers are needed to accelerate integration of fall risk assessment and intervention into primary care practice.

Mastery learning is a form of competency-based education in which learning time varies but outcomes are uniform. Trainees must meet a minimum passing standard (MPS) before completing a mastery learning curriculum. The objective of this study was to establish a curriculum for fall risk and gait assessment for medical students, determine an MPS for a fall risk and gait assessment clinical skills examination (CSE), and apply the MPS to a sample of medical students completing a fall risk and gait assessment CSE. Medical students completed an interactive session about fall risk and gait assessment including the Timed Up and Go (TUG) test and completed deliberate practice with 3 patients. Skills were evaluated using an 18-item skills checklist. A panel of clinical experts set the MPS at 82%. Eighty-seven medical students participated. The average score on the checklist was 14.7 of 18 (81.4%.) Although almost all performed the TUG correctly, only 61% met the MPS for the checklist. Our results suggest that a mastery learning approach may better prepare the 39% of students that did not meet MPS to complete a fall risk and gait assessment.

This systematic review aimed to explore the recommended fall risk assessment practices in audiology, identify audiologists' reported practices in fall risk assessment, and recognize the barriers and facilitators affecting fall risk assessment in clinical practice. This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. CINAHL, PubMed, and gray literature yielded 262 articles. A total of 27 full-text articles were included in this review article. An additional 16 scope of practice and guideline documents were also reviewed. Pertinent data and findings from the review were tabulated and analyzed using a qualitative, inductive approach. Results revealed that despite fall risk assessment measures and protocols being mentioned, discussed, and reportedly implemented clinically in audiology literature, many audiologists are not conducting fall risk assessments clinically. The main challenges presented appear to be due to limited guidance within audiology documentation and inadequate training and knowledge of audiologists on fall risk factors and measures. This review article highlighted that all audiologists have an important role to play in reducing the global crisis of falls in older adults. However, without further research to aid in the development of standardization of documentation and training programs, we may continue to see a lack of awareness and education on fall risk and on the audiologist's role in the screening and early detection hereof.

Falls are caused by complex interaction between multiple risk factors which may be modified by age, disease and environment. A variety of methods and tools for fall risk assessment have been proposed, but none of which is universally accepted. Existing tools are generally not capable of providing a quantitative predictive assessment of fall risk. The need for objective, cost-effective and clinically applicable methods would enable quantitative assessment of fall risk on a subject-specific basis. Tracking objectively falls risk could provide timely feedback about the effectiveness of administered interventions enabling intervention strategies to be modified or changed if found to be ineffective. Moreover, some of the fundamental factors leading to falls and what actually happens during a fall remain unclear. Objectively documented and measured falls are needed to improve knowledge of fall in order to develop more effective prevention strategies and prolong independent living. In the last decade, several research groups have developed sensor-based automatic or semi-automatic fall risk assessment tools using wearable inertial sensors. This approach may also serve to detect falls. At the moment, i) several fall-risk assessment studies based on inertial sensors, even if promising, lack of a biomechanical model-based approach which could provide accurate and more detailed measurements of interests (e.g., joint moments, forces) and ii) the number of published real-world fall data of older people in a real-world environment is minimal since most authors have used simulations with healthy volunteers as a surrogate for real-world falls. With these limitations in mind, this thesis aims i) to suggest a novel method for the kinematics and dynamics evaluation of functional motor tasks, often used in clinics for the fall-risk evaluation, through a body sensor network and a biomechanical approach and ii) to define the guidelines for a fall detection algorithm based on a real-world fall database availability.

To assess the fall of risk among the elderly in selected urban area of Thrissur. Objectives a) To assess the fall risk among the elderly people in selected urban area of Kachery, Thrissur. b) To associate fall risk score with selected demographic variables. c) To correlate fall risk assessment questionnaire score and modified fall prevention checklist for personal risk factors score among elderly individuals. d) To teach the elderly people about fall prevention exercise. Methodology: Non-experimental descriptive research design is adopted in this study. We selected 60 samples through random sampling technique. Standardized fall risk assessment questionnaire built by national aging research institute and modified fall prevention checklist for personal risk factors built by Hamilton county was used to collect the data. Tool was administered by interview method for assessing fall risk. Fall preventive intervention module developed, validated and administered following data collection. The data collected were analyzed by using descriptive and inferential statistics. Result: The demographic profile of elderly people shows that 46.66% belongs to age group between 60-70 years and most of them 58.33% were females. The majority 73.33% of elderly person are at low risk for fall and 26.66% are at high risk for fall in questionnaire and 81.67% of elderly are low risk for fall, 18.33% are risk and there is no elderly person high risk for fall in checklist. The overall fall risk is high among elderly individual alone in home and low in elderly in nuclear families. The risk for fall among elderly based on previous history of fall shows that elderly with visual impairment those who don’t have previous knowledge about fall prevention and elderly age between 91-100 is high risk for fall. The risk for fall based on ability to perform ADL in elderly shows those who living alone in the home high risk for fall. There is significant association between score with selected variables like age, previous knowledge about fall prevention, elderly residing in their home alone. We found that there is perfect positive correlation r=1 between fall risk assessment, questionnaires and checklist score. Discussion: At the end of the study the investigator found that the risk for fall based on the assessment of fall history among elderly people shows that, there is significant association between age (p=0.0273) that is, age group between 91-100 years are high risk for fall. Previous knowledge about fall prevention shows that, elderly without previous knowledge about fall prevention is at high risk for fall (p= 0.03074). In sensory impairment that is, elderly having visual impairment (p=0.998) having risk for fall. The risk for fall among elderly people based on their ability to perform activities of daily living shows that, elderly residing in their home alone shows more risk for fall.

Purpose. This study examined the reliability and validity of a Falls Risk Assessment (FRA) tool developed to identify risk factors associated with falls in homebound older adults. Design and Methods. FRA scores of 307 Medicare-eligible adults over 65 admitted sequentially to a home health agency (HHA) were analyzed retrospectively using a case-control design. A total of 18 subjects participated prospectively in assessment of criterion-related validity and rater reliability. Results. Mean FRA scores of fallers were significantly higher than non-fallers (10.13, 7.2, respectively; p < 0.05). The FRA and Performance-Oriented Mobility Assessment (POMA) demonstrated a strong negative correlation (r = −0.74), utilizing a Pearson correlation. Using percent agreement, FRA scores of nine raters yielded 94.74% agreement. Kuder-Richardson (KR)-20 analysis yielded internal consistency of 0.98, 0.97, and 0.98 within subjects scored, indicating high consistency among raters. An intraclass correlation coefficient (Model 3, 1) of 0.83 supports intra-rater reliability. History of recurrent falls was the only significant predictor in logistic regression (p = 0.027, odds ratio: 2.83). Implications. Data support use of the FRA in screening for fall-related risk factors in home-bound older adults.

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Just as there are many causes for falls among older adults, there also are many prevention strategies. The key to finding the right ones for each patient is to perform a thorough fall risk assessment, according to experts speaking in a Web-based conference sponsored by ECRI, a nonprofit health services research agency. Effective fall prevention can make a big difference in quality of life for seniors and give residents and caregivers a greater sense of peace. And, as with all safety measures, effective fall prevention also can mean savings for long-term care facilities. The majority of falls (60%) occur in the home environment, and 30% of falls occur in an institutional setting. It's estimated that 30% of falls in acute care facilities and 20% of falls in long-term care facilities result in “serious injury,” said Karen Holloway, a risk management analyst at ECRI and primary author of the organization's new guide “Falls Prevention Strategies in Healthcare Settings.” Of those who sustain hip fractures, 50% never return to their normal level of functioning. The cost of treating the 20% of falls resulting in serious injury in long-term care facilities in 2005 is estimated at $4.9 billion, excluding physician fees, according to the Center for Injury Prevention and Control and Treatment Costs data. Defensive barriers form the core of fall prevention strategies, according to Patricia Quigley, Ph.D, deputy director of the Veterans Health Administration's Patient Safety Center of Inquiry in Tampa. Fall prevention with defensive barriers may best be described through the “Swiss cheese theory,” she said. Defensive barriers to falls can be thought of as holes in a block of Swiss cheese. For a fall to occur, all of the holes have to line up—in other words, all of the barriers have to fail. Fall prevention assessments can be invaluable in pulling together the most effective defensive barriers for each patient. One effective model has been established at several VA centers, where fall risk assessments are performed for all patients at these clinics. The assessment team spans several disciplines, including a geriatrician, an advanced registered nurse practitioner, a physical therapist (who specializes in balance problems), and a pharmacist. The 2-hour fall risk assessment attempts to identify potential causes for falls and includes evaluations of vital signs, visual acuity and depth perception, and deep tendon reflexes, lower extremity sensory testing, a neurologic assessment, laboratory tests, and imaging. In an ongoing analysis of the reasons patients were referred to her falls clinic, accident and environmental factors accounted for 37%, followed by weakness and balance and gait disorders (12%), dizziness and vertigo (8%), and orthostatic hypotension (5%). Other reasons (acute illness, confusion, poor eyesight, and drugs) together accounted for 18%. Certain medications also contribute to fall risk, especially psychotropics, diuretics, antiarrhythmics, and digoxin. When assessing the home environment, an assessment team looks for uneven ground, and scrutinizes steps or ramps into the house and entry stairs. It also looks for clutter, throw rugs, problem floor surfaces, quality of lighting, and bathroom issues such as nonslip shower mats and grab bars. The team also tries to determine whether assistive devices are being used or are appropriate. Once the assessment is completed, the team evaluates the findings to develop an individualized fall prevention plan. In cases where a fall has occurred, the mnemonic SPLATT (Symptoms, Previous Falls, Location, Activity, Time, and Trauma) can provide a framework for doing a postfall evaluation to identify risk factors for additional falls. The algorithm could help narrow in on interventions to reduce fall risks, Dr. Quigley said. The ECRI guide is available for purchase by visiting www.ecri.org/Products_and_Services/Products.

Fall-risk assessment of aged workers using wearable inertial measurement units based on machine learning

Background. Peripheral neuropathy is regarded as one of the leading causes of fatal and nonfatal falls. Wearable sensors, due to their increasing availability and flexibility of setting and space, are used widely to obtain wearer’s kinematic data to analyze one’s balance capacities for the evaluation of risk of fall. There is yet to have a review study focusing on the application of wearable sensors in the scope of fall risk for patients with peripheral neuropathy. Objective. To investigate the methods by which researchers adopt to assess risk of fall in peripheral neuropathy patients and potentially shed light on future researches. Methods. A systematic review design was used to identify articles on fall risk assessment and balance training using wearable sensors in patients with peripheral neuropathy. The study is aimed at extracting the following information: the type of sensors, the type of signal and data processing employed, the scales and tests used in the study, and the type of application. Results. We identified 351 studies, from which 8 were included. An average sample size of 35.6 patients enrolled the studies. The accelerometer was the most common wearable sensor used. 10‐meter walk test was the preferable procedure for assessing risk of fall. Conclusion. This review examined several key components in studies on assessing and improving the risk of fall using wearable sensors. We identified the preferred functional test (10‐meter walk test), sensor technology (accelerometer), locations (torso and lower legs), and fall risk improvement methods (prostheses). However, due to the limited number of articles specializing in this field of research, a consensus on patient sample size and procedures is not reached. We would recommend future researches to examine more parameters and adopt a fusion sensor setup.

Falls are the most common preventable adverse events in hospitals and are strongly linked to movement-related disorders. Conducting fall risk assessments and implementing personalized interventions for older adults in sports rehabilitation settings can significantly reduce fall incidence. Sensor-based techniques and machine learning models offer new opportunities for measuring gait and balance in a more sophisticated way to enhance fall risk assessments. This study aims to develop an integrated sensor method to provide continuous and effective fall risk assessments for older adults in rehabilitation settings. A joint feature extraction scheme based on integrated sensors was proposed, including temporal features from inertial measurement unit (IMU) signals and spatial features from depth camera data during 3-m timed up and go (TUG) test. A set of classifiers, including support vector machine (SVM), logistic regression (LR), k-nearest neighbors (KNNs), random forest (RF), and eXtreme gradient boosting (XGBoost), were used in conjunction with a feature selection strategy to facilitate developing the predictive models for fall risk assessment. We conducted validation experiments using real-world data for comprehensive comparative analysis. The results demonstrate that our integrated approach achieves superior classification performance (AUC: 0.8633–0.9586). These findings suggest that the complementary features from sensors have advantages in bridging information gaps, reducing missed diagnoses, and assisting clinicians in early fall risk identification. The proposed method shows significant potential to deliver comprehensive fall risk assessments for older adults in rehabilitation settings.

Introduction: To examine the effectiveness of a multifactorial intervention to reduce falls for elderly hospice patients who live at home and in skilled nursing facilities. Materials and Methods: This pre--post intervention study was conducted in a local agency of a healthcare organization for hospice patients in California. The study included 51 hospice patients who did not object to the intervention at the local agency. The multifactorial intervention consisted of four components: (1) fall risk assessment, (2) post-fall huddle session, (3) universal fall precautions education, and (4) the 5 Ps to reduce falls for elderly hospice patients. The primary outcome was the fall rates (falls per 1,000 occupied bed days). Other outcomes included patient fall risk scores, post-fall huddle compliance, and compliance of universal fall precautions and the 5 Ps. Results: The multifactorial intervention reduced the fall rates for hospice patients from 6.9 in 2017 (baseline period) to 1.7 in 2019 (implementation period) per 1,000 occupied bed days. There was 100% compliance with fall risk assessment, post-fall huddle administration, universal fall prevention documentation, 5 Ps education, and care plan initiation and modification. Conclusion: The finding of this study indicated a significantly positive effect of a nurse-led multifactorial intervention on fall prevention of hospice patients cared for at home or at skilled nursing facilities. The intervention was easy to implement, cost effective, and took a very short time to complete, which would allow nursing leadership to initiate such interventions to prioritize fall prevention in every hospice healthcare organization.

Depression and falls are highly prevalent, interrelated concerns for nursing home (NH) residents. Relationships between depression and falls should guide nurses towards developing evidence-based practices for assessing these conditions together. This study aimed to ascertain NH nurses' clinical practices and perceptions regarding the assessment of depression and fall risk. This study was an exploratory descriptive study on the reported practices and perceptions from NH nurses in the canton of Vaud, Western Switzerland. Statistical analyses included descriptive statistics, nonparametric tests and a content analysis of responses to open-ended questions. The mean age of our 116 responding nurses was 44.6 years old (SD = 11.3), 99 were women and their mean work experience in NHs was 13.1 years (SD = 9.2). The reporting showed that 88.8% of nurses relied on mood observation for assessing depression and 88.8% relied on the history of falls to identify fall risk. Only 75.9% and 61.2% of nurses used validated scales to detect depression and fall risk, respectively. Additionally, 56.9% of participants considered depression to be a significant factor in fall risk. Validated tools to assess depression and fall risk in NHs should be used more widely. Health policies must support and enhance NH nurses' training and skills.

The objective of this study is to ascertain the suitability of haemoglobin as a screening factor for falls among obstetrics and gynaecology inpatients and to formulate a stratified scheme for assessing fall risk based on haemoglobin. A retrospective analysis and Delphi surveys were employed for this investigation. Initially, a retrospective survey analysed falls among obstetrics and gynaecology inpatients in two hospitals from January 1, 2020, to July 10, 2022. Descriptive statistics, receiver operating characteristic (ROC) curve analysis, Youden index, sensitivity and specificity were utilised for data examination. The conclusions drawn were subsequently validated by Delphi surveys, featuring 21 experts participating in five rounds of consultation. The Kappa value and the coefficient of variation (CV) were employed to assess expert advice. The area under the Receiver Operating Characteristic curve (AUC) of haemoglobin was 0.762 ± 0.030, 95% CI (0.703, 0.821). The highest Youden index was 0.425, with sensitivity at 0.705 and specificity at 0.720 when haemoglobin was 107.5 g/L. Two consensuses were reached by experts: anaemia was important in causing falls in obstetrics and gynaecology wards, and haemoglobin should be employed as a screening factor for falls. The stratification of anaemia was developed as follows: ≥ 110; 90-109; 60-89; and < 60 g/L. Approval for the final results was unanimous among all experts. The Kappa value (K*) was 1, and the CV of expert advice ranged from 0.092 to 0.219. Haemoglobin could potentially be used as a predictor of fall risk in Gynaecological and Obstetric Wards. The recommended stratified scheme for anaemia in fall risk assessment is as follows: ≥ 110; 90-109; 60-89; and < 60 g/L. What problem did the study address? The study revealed a relationship between falls and haemoglobin in obstetrics and gynaecology inpatients. It also proposed a stratification scheme for assessing fall risk based on haemoglobin levels. What were the main findings? Haemoglobin has a good performance on fall risk prediction in Gynaecological and Obstetric Wards. The stratified scheme of anaemia for fall risk assessment was suggested as follows: ≥ 110; 90-109; 60-89; and < 60 g/L. Where and on whom will the research have an impact? Nurses and inpatients in obstetrics and gynaecology wards will be affected by the results of this study, and it provided a reference for fall prevention. This study has adhered to relevant EQUATOR guidelines and named the reporting method. No Patient or Public Contribution.