Fall Risk Assessment Tool Research Articles

An Electronic Medical Record-Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation.

Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts. The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data. We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve. There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots. Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance.

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

BackgroundFalls 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.MethodsThis 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.ResultsThe 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.ConclusionThe 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.

Discriminative ability of instrumented cognitive-motor assessments to distinguish fallers from non-fallers.

In older populations, sensitive fall risk assessment tools are important to timely intervene and prevent falls. Instrumented assessments have shown to be superior to standardized fall risk assessments such as the TimedUp and Go Test (TUG) and should capture both motor and cognitive functions. Therefore, the aim was to test novel instrumented assessments with and without a cognitive component. One hundred thirty-seven older adults aged 73.1 ± 7.3years, 38 categorized as fallers and 99 as non-fallers, conducted five instrumented assessments on the Dividat Senso, a pressure sensitive stepping platform, and three standardized geriatric assessments (TUG, TUG-dual task, 30-s Sit-to-Stand Test (STS)). T-tests were applied to compare the test performance of fallers versus non-fallers. Furthermore, logistic regression analyses and area under the curve (AUC) analyses were performed. Statistically significant differences between fallers and non-fallers were found in the Go/No-Go test (p = .001, d = .72), the TUG (p = .014, d = .48), and the STS (p = .008, d = .51). Only the Go/No-Go test contributed significantly to all regression models. Significant AUC values were found for the Reaction Time Test (RTT) (AUC = .628, p = .023), Go/No-Go (AUC = .673, p = .002), TUG (AUC = .642, p = .012), and STS (AUC = .690, p = .001). The Go/No-Go test measuring inhibition showed the best discriminative ability suggesting added value of instrumented assessments with a cognitive component for clinical fall risk assessment in relatively healthy older adults. The study should be extended with a frailer population, in which TUG and the other instrumented assessments are possibly good predictors as well.

Development and External Validation of a Machine Learning–based Fall Prediction Model for Nursing Home Residents: A Prospective Cohort Study

ObjectivesTo develop and externally validate a machine learning–based fall prediction model for ambulatory nursing home residents. The focus is on predicting fall occurrences within 6 months after baseline assessment through a binary classification task, aiming to provide staff with an effective and user-friendly fall-risk assessment tool. DesignProspective cohort study. Setting and ParticipantsA total of 864 older residents living in 4 nursing homes between May 2022 and March 2023 in China. MethodsPotential fall-risk predictors were collected through in-person interviews and assessments of anthropometric and physical function. Participants were followed for 6 months, with falls recorded by trained nurses. Seven machine learning algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Decision Tree (DT), were used to develop prediction models. Performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) and Precision-Recall curve (PR-AUC), with calibration assessed via a calibration curve. Feature importance was visualized using SHapley Additive exPlanations (SHAP). ResultsThe 6 selected predictors were balance, grip strength, fatigue, fall history, age, and comorbidity. The ROC-AUC for the models ranged from 0.710 to 0.750, PR-AUC from 0.415 to 0.473, sensitivity from 0.704 to 0.914, and specificity from 0.511 to 0.687 in the validation cohort. The LR model was converted into a nomogram. Conclusions and ImplicationsThe machine learning–based fall-prediction models effectively identified nursing home residents at high risk of falls. The developed nomogram can be integrated into clinical practice to enhance fall risk assessment protocols, ultimately improving patient safety and care in nursing homes.

Root cause analysis and strategies for reducing falls amonginpatients in healthcare facilities: A narrative review.

Root Cause Analysis (RCA) is a systematic process which can be applied to analyze fall incidences in reactive manner to identify contributing factors and propose actions for preventing future falls. To better understand cause of falls and effective interventions for their reduction we conducted a narrative review of RCA and Strategies for Reducing Falls among Inpatients in Healthcare Facilities. In this narrative review, databases including Scopus, ISI Web of Science, Cochrane, and PubMed were searched to obtain the related literature published. Databases were searched from January 2005 until the end of March 2023. The Joanna Briggs Institute (JBI) tool was used for quality assessment of articles. To analyze the data, a five-stage framework analysis method was utilized. Seven articles that fulfilled the inclusion criteria were identified for this study. All of the selected studies were interventional in nature and employed the RCAmethod to ascertain the underlying causes of inpatient falls. The root causes discovered for falls involved patient-related factors (37.5%), environmental factors (25%), organizational and process factors (19.6%), staff and communication factors (17.9%). Strategies to reduce falls involved environmental measures and physical protection (29.4%), identifying, and displaying the causes of risk (23.5%), education and culturalization (21.6%), standard fall risk assessment tool (13.7%), and supervision and monitoring (11.8%). the findings identify the root causes of falls in inpatient units and provide guidance for successful action plan execution. Additionally, it emphasizes the importance of considering the unique characteristics of healthcare organizations and adapting interventions accordingly for effectiveness in different settings.

To assess fall risk in the elderly OPD patients using conventional fall risk tool and Kinesis QTUG device: A comparative study

BackgroundFalls are common among the elderly population and are usually multifactorial. Most of the falls are preventable if the risk factors are identified early and addressed. The primary objective was to assess the agreement between the fall-risk assessment as measured by the digital sensor–based Kinesis Quantitative Timed Up and Go (QTUG) device and conventional Fall Risk Assessment Tool (FRAT) in elderly Indian population. MethodsThe study was conducted in a tertiary care hospital of Western India. Conventional fall-risk assessment was done using the FRAT score and digital sensor–based fall risk was assessed using the Kinesis QTUG device. Agreement between both the fall-risk assessments was done using unweighted kappa. ResultsThe mean age was 68.54 (±2.62) years with females constituting 53.3%. The mean timed-up-and-go test score was 20.33(±7.7) seconds. A total of 253 falls were noted for 147 elderly individuals. Combined fall risk using the Kinesis QTUG device showed that 59.56% (218/366) elderly patients had a high fall risk, whereas the FRAT score showed high fall risk in only 1.36% (5/366) elderly patients. There was a poor agreement between the two tools with unweighted kappa of 0.634. ConclusionsPrevalence of falls in elderly in the last year was 40% in our study. There was a poor agreement in fall-risk estimate using the Kinesis QTUG device and FRAT score with an unweighted kappa value of 0.634.

Predictive Validity of the Johns Hopkins Fall Risk Assessment Tool for Older Patients in Stroke Rehabilitation.

The aim of this retrospective, cross-sectional, observational study was to assess the frequency of falls and evaluate the predictive validity of the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) among patients aged ≥65 years, transferred to the rehabilitation ward of a university hospital. The predictive ability was assessed using receiver operating characteristic curve analysis, and the optimal threshold was established using the Youden index. We analyzed the overall cohort (N = 175) with subacute stroke and the subgroup with a low unaffected handgrip strength (HGS; men: <28 kg, women: <18 kg). Overall, 135/175 patients (77.1%) had a low HGS. The fall rate was 6.9% overall and 5.9% for patients with a low HGS. The JHFRAT predictive value was higher for patients with a low HGS than that for the overall cohort, but acceptable in both. The optimal cutoff score for the overall cohort was 11 (sensitivity, 67%; specificity, 68%), whereas that for the subgroup was 12 (sensitivity, 75%; specificity: 72%). These results are expected to aid nurses working in rehabilitation wards in more effectively utilizing JHFRAT outcomes for post-stroke older patients with a low HGS and contribute to the development of more appropriate fall prevention strategies for high-risk patients in the future.

Exploring the relationship between AM-PAC scores and mobility components in falls and pressure injury risk assessment tools: A pathway to improve nursing clinical efficiency.

Nurses routinely perform multiple risk assessments related to patient mobility in the hospital. Use of a single mobility assessment for multiple risk assessment tools could improve clinical documentation efficiency, accuracy and lay the groundwork for automated risk evaluation tools. We tested how accurately Activity Measure for Post-Acute Care (AM-PAC) mobility scores predicted the mobility components of various fall and pressure injury risk assessment tools. AM-PAC scores along with mobility and physical activity components on risk assessments (Braden Scale, Get Up and Go used within the Hendrich II Fall Risk Model®, Johns Hopkins Fall Risk Assessment Tool (JHFRAT) and Morse Fall Scale) were collected on a cohort of hospitalised patients. We predicted scores of risk assessments based on AM-PAC scores by fitting of ordinal logistic regressions between AM-PAC scores and risk assessments. STROBE checklist was used to report the present study. AM-PAC scores predicted the observed mobility components of Braden, Get Up and Go and JHFRAT with high accuracy (≥85%), but with lower accuracy for the Morse Fall Scale (40%). These findings suggest that a single mobility assessment has the potential to be a good solution for the mobility components of several fall and pressure injury risk assessments.

Combining user-centered design and behavioral theory to enhance health technologies: A personas-based approach for a primary-care based multifactorial falls risk assessment tool

IntroductionMultifactorial falls risk assessment tools (FRATs) can be an effective falls prevention method for older adults, but are often underutilized by health care professionals (HCPs). This study aims to enhance the use and implementation of multifactorial FRATs by combining behavioral theory with the user-centered design (UCD) method of personas construction. Specifically, the study aimed to (1) construct personas that are based on external (i.e., needs, preferences) and intrinsic user characteristics (i.e., behavioral determinants); and (2) use these insights to inform requirements for optimizing an existing Dutch multifactorial FRAT (i.e., the ‘Valanalyse’). MethodsSurvey data from HCPs (n = 31) was used to construct personas of the ‘Valanalyse.’ To examine differences between clusters on 68 clustering variables, a multivariate cluster analysis technique with non-parametric analyses and computational methods was used. The aggregated external and intrinsic user characteristics of personas were used to inform key design and implementation requirements for the ‘Valanalyse,’ respectively, whereby intrinsic user characteristics were matched with appropriate behavior change techniques to guide implementation. ResultsSignificant differences between clusters were observed in 20 clustering variables (e.g., behavioral beliefs, situations for use). These variables were used to construct six personas representing users of each cluster. Together, the six personas helped operationalize four key design requirements (e.g., guide treatment-related decision making) and 14 implementation strategies (e.g., planning coping responses) for optimizing the ‘Valanalyse’ in Dutch geriatric, primary care settings. ConclusionThe findings suggest that theory- and evidence-based personas that encompass both external and intrinsic user characteristics are a useful method for understanding how the use and implementation of multifactorial FRATs can be optimized with and for HCPs, providing important implications for developers and eHealth interventions with regards to encouraging technology adoption.

Practices of falls risk assessment and prevention in acute hospital settings: a realist investigation.

Falls are the most common safety incident reported by acute hospitals. The National Institute of Health and Care Excellence recommends multifactorial falls risk assessment and tailored interventions, but implementation is variable. To determine how and in what contexts multifactorial falls risk assessment and tailored interventions are used in acute National Health Service hospitals in England. Realist review and multisite case study. (1) Systematic searches to identify stakeholders' theories, tested using empirical data from primary studies. Review of falls prevention policies of acute Trusts. (2) Theory testing and refinement through observation, staff interviews (n = 50), patient and carer interviews (n = 31) and record review (n = 60). Three Trusts, one orthopaedic and one older person ward in each. Seventy-eight studies were used for theory construction and 50 for theory testing. Four theories were explored. (1) Leadership: wards had falls link practitioners but authority to allocate resources for falls prevention resided with senior nurses. (2) Shared responsibility: a key falls prevention strategy was patient supervision. This fell to nursing staff, constraining the extent to which responsibility for falls prevention could be shared. (3) Facilitation: assessments were consistently documented but workload pressures could reduce this to a tick-box exercise. Assessment items varied. While individual patient risk factors were identified, patients were categorised as high or low risk to determine who should receive supervision. (4) Patient participation: nursing staff lacked time to explain to patients their falls risks or how to prevent themselves from falling, although other staff could do so. Sensitive communication could prevent patients taking actions that increase their risk of falling. Within the realist review, we completed synthesis for only two theories. We could not access patient records before observations, preventing assessment of whether care plans were enacted. (1) Leadership: There should be a clear distinction between senior nurses' roles and falls link practitioners in relation to falls prevention; (2) shared responsibility: Trusts should consider how processes and systems, including the electronic health record, can be revised to better support a multidisciplinary approach, and alternatives to patient supervision should be considered; (3) facilitation: Trusts should consider how to reduce documentation burden and avoid tick-box responses, and ensure items included in the falls risk assessment tools align with guidance. Falls risk assessment tools and falls care plans should be presented as tools to support practice, rather than something to be audited; (4) patient participation: Trusts should consider how they can ensure patients receive individualised information about risks and preventing falls and provide staff with guidance on brief but sensitive ways to talk with patients to reduce the likelihood of actions that increase their risk of falling. (1) Development and evaluation of interventions to support multidisciplinary teams to undertake, and involve patients in, multifactorial falls risk assessment and selection and delivery of tailored interventions; (2) mixed method and economic evaluations of patient supervision; (3) evaluation of engagement support workers, volunteers and/or carers to support falls prevention. Research should include those with cognitive impairment and patients who do not speak English. This study is registered as PROSPERO CRD42020184458. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR129488) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 5. See the NIHR Funding and Awards website for further award information.

Prevalence and risk factors for falls among the community dwelling older adults of Thrissur: A pilot study.

According to World Health Organization (WHO) statistics, every year 28-35% of people over 65 years and 32-42% of people over 70 years experience falls. Given that many falls are preventable, can occur in any population, and can result in significant morbidity and mortality, falls are receiving more attention as a major global issue. The objectives of this study were as follows: 1. To measure the prevalence of falls among the elderly living in the Thrissur Taluk Health Centre. 2. To identify the risk factors associated with falls in the elderly using the Centre for Disease Control and Prevention, Stopping Elderly Accidents, Deaths and Injuries (CDCs STEADI) 2019 scale and the Timed Up and Go (TUG) scale. 3. To find the association between the risk factors and the prevalence of fall among older people. A cross-sectional study was conducted to find the prevalence of fall among the elderly in Thrissur Taluk Health Centers. CDCs STEADI 2019 fall risk assessment tool was used to assess the risk factors associated with the elderly. TUG test was used to determine gait, balance, strength, and posture. In our study, we discovered a prevalence of 41 percent of falls among the community-dwelling older adults of Thrissur. This study has shown that the risk of fall was higher among the female elderly population. 88% are found to be at high risk of fall, and 65% of the population were worried about fall. This study found out a high prevalence of falls among community dwelling older adults.A future study with a larger sample size would be more helpful to confirm the impact of different variables in relation to the risk of fall among the elderly.

Risk of falls is associated with 30-day mortality among older adults in the emergency department

ObjectiveFalls in older adults correlate with heightened morbidity and mortality. Assessing fall risk in the emergency department (ED) not only aids in identifying candidates for prevention interventions but may also offer insights into overall mortality risk. We sought to examine the link between fall risk and 30-day mortality in older ED adults. MethodsObservational cohort study of adults aged ≥ 75years who presented to an academic ED and who were assessed for fall risk using the Memorial Emergency Department Fall Risk Assessment Tool (MEDFRAT), a validated, ED-specific screening tool. The fall risk was classified as low (0–2 points), moderate (3–4 points), or high (≥5) risk. The primary outcome was 30-day mortality. Hazard ratios (HR) with 95% confidence intervals (CIs) were calculated. ResultsA total of 941 patients whose fall risk was assessed in the ED were included in the study. Median age was 83.7 years; 45.6% were male, 75.6% lived in private residences, and 62.7% were admitted. Mortality at 30 days among the high fall risk group was four times that of the low fall risk group (11.8% vs 3.1%; HR 4.00, 95% CI 2.18 to 7.34, p < 0.001). Moderate fall risk individuals had nearly double the mortality rate of the low-risk group (6.0% vs 3.1%), but the difference was not statistically significant (HR 1.98, 95% CI 0.91 to 4.32, p = 0.087). ConclusionED fall risk assessments are linked to 30-day mortality. Screening may facilitate the stratification of older adults at risk for health deterioration.

Validating the accuracy of the Hendrich II Fall Risk Model for hospitalized patients using the ROC curve analysis.

This retrospective study was conducted at a medical center in southern Taiwan to assess the accuracy of the Hendrich II Fall Risk Model (HIIFRM) in predicting falls. Sensitivity, specificity, accuracy, and optimal cutoff points were analyzed using receiver operating characteristic (ROC) curves. Data analysis was conducted using information from the electronic medical record and patient safety reporting systems, capturing 303 fall events and 47,146 non-fall events. Results revealed that at the standard threshold of HIIFRM score ≥5, the median score in the fall group was significantly higher than in the non-fall group. The top three units with HIIFRM scores exceeding 5 were the internal medicine (50.6%), surgical (26.5%), and oncology wards (14.1%), indicating a higher risk of falls in these areas. ROC analysis showed an HIIFRM sensitivity of 29.5% and specificity of 86.3%. The area under the curve (AUC) was 0.57, indicating limited discriminative ability in predicting falls. At a lower cutoff score (≥2), the AUC was 0.75 (95% confidence interval: 0.666-0.706; p < 0.0001), suggesting acceptable discriminative ability in predicting falls, with an additional identification of 101 fall events. This study emphasizes the importance of selecting an appropriate cutoff score when using the HIIFRM as a fall risk assessment tool. The findings have implications for fall prevention strategies and patient care in clinical settings, potentially leading to improved outcomes and patient safety.

Evaluating Wilson sims tool: Quality improvement on fall risk in UAE psychiatric patients

BACKGROUND Falls among patients in psychiatric inpatient healthcare settings present a significant global challenge, despite ongoing efforts to reduce the risks. Falls negatively impact patient safety, post-treatment recovery, and medical costs. AIM This study aimed to evaluate the predictive validity of the Wilson Sims Fall Risk Assessment Tool (WSFRAT), investigate predictors of falls, and determine the optimal cut-off point for the WSFRAT. Additionally, we aimed to assess the predictive validity of clinical judgement in identifying individuals at high risk of falls. METHODS We conducted a psychometric study at a specialized psychiatry hospital in Dubai, United Arab Emirates, using data collected from hospital-wide quality projects between April 16, 2019, and March 31, 2021. Our sample comprised 492 patients. RESULTS Contrary to the recommended cut-off point of 7 in the literature, this study results indicate that the optimal cut-off point for the WSFRAT was 5+. This yielded an accuracy of 87%, a diagnostic odds ratio (DOR) of 0.728, a kappa value of 0.208, a sensitivity of 83%, and a specificity of 87%. Furthermore, the regression analyses identified significant predictors of fall risk, including age, gender, assistive device, WSFRAT, and evaluation duration. Notably, clinical judgement did not significantly predict fall risk (p=0.331). CONCLUSION In conclusion, the present research demonstrates that the WSFRAT is a reliable tool with high sensitivity and specificity for predicting falls in psychiatric inpatient settings. The findings emphasize the importance of employing evidence-based tools and a comprehensive assessment approach to prevent falls. Furthermore, our findings challenge the recommended cut-off point of 7 and highlight the need for further research to confirm the optimal cut-off point. Finally, this investigation revealed that clinical judgement alone is not an effective method for predicting falls in this population.

Perceived balance and self-reported falls: A retrospective cross-sectional study using the National Health and Aging Trend Study.

To examine how perceived balance problems are associated with self-reported falls in the past month after controlling for known correlates of falls among older adults. Approximately 30% of adults age 65 and older fall each year. Most accidental falls are preventable, and older adults' engagement in fall prevention is imperative. Limited research suggest that older adults do not use the term 'fall risk' to describe their risk for falls. Instead, they commonly use the term 'balance problems'. Yet, commonly used fall risk assessment tools in both primary and acute care do not assess older adults' perceived balance. The Health Belief Model and the concept of perceived susceptibility served as the theoretical framework. A retrospective, cross-sectional secondary analysis using data from the National Health and Aging Trends Study from year 2015 was conducted. The outcome variable was self-reported falls in the last month. A subsample of independently living participants (N = 7499) was selected, and 10.3% of the sample reported a fall. Multiple logistic regression analysis revealed that the odds of reporting a fall in the past month was 3.4 times (p < .001) greater for participants who self-reported having a balance problem compared to those who did not. In contrast, fear of falling and perceived memory problems were not uniquely associated with falls. Using a mobility device, reporting pain, poor self-rated health status, depression and anxiety scores were also associated with falling. Older adults' perceived balance problem is strongly associated with their fall risk. Perceived balance may be important to discuss with older adults to increase identification of fall risk. Older adults' perceived balance should be included in nursing fall risk assessments and fall prevention interventions. A focus on balance may increase older adults' engagement in fall prevention.

Are Your Patients Safe to Mobilize Alone in Their Room? The Johns Hopkins Hospital In-Room Independence Scale.

Given the stigma of falls while hospitalized, inpatient rehabilitation facilities often restrict the independent room mobility of their patients. We assessed the psychometric properties of a new tool, the Johns Hopkins In-Room Independence Scale, to evaluate its effectiveness at identifying independence for in-room mobility. Construct validity was assessed by comparing the Johns Hopkins In-Room Independence Scale to its "gold standard" counterparts, The Johns Hopkins Falls Risk Assessment Tool, the Activity Measure for Post-Acute Care Mobility and Activity, and the Centers for Medicare Quality Indicators. Reliability was assessed via Cronbach α. One hundred ninety-seven episodes were included. In construct validity, the Johns Hopkins In-Room Independence Scale had significantly negative low correlation to the Johns Hopkins Falls Risk Assessment Tool (Spearman ρ = -0.281), and a significant moderate correlation to the Activity Measure for Post-Acute Care Activity and Activity Measure for Post-Acute Care Mobility (Spearman ρ =0.678 and 0.530, respectively). The Johns Hopkins In-Room Independence Scale was moderately positively correlated to the quality indicators for mobility and activity (Spearman ρ = 0.577 and 0.643, respectively). All two-tailed, α levels were 0.01. The Johns Hopkins In-Room Independence Scale reliability was strong (Cronbach α = 0.822, 15 items). Sensitivity and specificity were both 100%. Given the moderate validity, strong reliability, and high sensitivity/specificity, our study suggests that the Johns Hopkins In-Room Independence Scale be used to identify patients who have the capacity to mobilize independently in their room.

The effect of COVID-19 on the risk of falling in old age adult patients: a cross-sectional study.

To improve care for the elderly and avoid falls and associated injuries, examining fall risk factors and defining their components is crucial, especially for those who are infected with COVID-19. The purpose of the study was to investigate the effect of COVID-19 on the risk of falling in the elderly in the Kingdom of Saudi Arabia. Eighty-six subjects (42 COVID-19 patients and 44 non-infected participants). The participants were selected from the central, western, and eastern provinces of Saudi Arabia. Their ages ranged from 60-75 years. Two groups of participants were formed: group A, the group infected with COVID-19, and group B, which included subjects not infected with COVID-19. The Missouri Alliance Fall Risk Assessment Tool for Home Care (MAHC-10) questionnaire was used to detect fall risk among the participants. This study showed that 47.6% of the participants who suffered from COVID-19 had a high falling risk, while only 18.2% of the non-affected participants had a high falling risk. So, there was a significant association between the risk of falling and COVID-19. COVID-19 has a negative impact on the risk of falls in elderly people. So, the introduction of balance exercises to these subjects is of high importance to improve their balance and proprioception skills and decrease the fall risk.

Influence of medications on fall risk assessment in maintenance hemodialysis patients: A cross-sectional study.

Multiple factors influence the fall risk in end-stage kidney disease. This study aims to investigate how medication factors influence the interpretation of fall risk due to age, gender, and years of dialysis treatment among patients undergoing hemodialysis (HD). A cross-sectional study was carried out in 2023 using the Johns Hopkins Fall Risk Assessment tool. Participants were recruited from the HD unit at a tertiary care academic medical center in Ajman, UAE. Data were analyzed between different ages, genders, and years on HD categories with or without medication factors. Data were collected and analyzed for 44 patients. The fall risk of the study population assessed with the Kruskal-Wallis test showed no difference between different age groups (P = 0.43) but did show a significant difference when the score of medication factor was removed from the fall risk estimation (P = 0.002). A pairwise analysis showed fall risk score of the age group 46-60 years was differing from the age cohort >60 (P < 0.001). A positive moderate correlation (Spearman's correlation coefficient 0.514 was found, with a P < 0.001) was seen with an increase in age and fall risk only when the medication factor was removed from the fall risk estimation. Results on gender or duration of dialysis were insignificant. Medication factors being a significant contributor to fall risk among the study population was found to mask the fall risk difference between age groups 46-60 years and >60 years. Such influence was not found for gender or duration of dialysis.

Smartphone Use by Caregivers for Patients With Heart Failure During Hospitalization: An Investigation.

This study investigated the use of smartphones by family caregivers for hospitalized patients with chronic heart failure (CHF). In total, 120 patients and their unpaid family caregivers participated in this study. The caregivers were divided into two groups based on the perceived importance of smartphones in patient care. Both groups completed the General Demographic Information Survey, Problematic Mobile Phone Use Questionnaire, Barthel Index Scale, Modified Early Warning Score (MEWS), Johns Hopkins Fall Risk Assessment Tool (JH-FRAT), and Family Burden Scale of Diseases Survey. Moreover, left ventricular ejection fraction (LVEF) and stroke volume (SV) were measured in all participants. The age of hospitalized patients with CHF was correlated with the Barthel Index Scale, MEWS, and JH-FRAT, whereas LVEF and SV were correlated with MEWS. The important group had a much higher financial burden than the nonimportant group. Linear regression analysis revealed that financial burden and mental health had a remarkable impact on the content of mobile calls about treatment. Furthermore, the economic status of family caregivers determined the importance of smartphone calls in the care of patients with CHF during hospitalization.

Feasibility of Implementing Patient-Reported and Physical Performance Measures to Identify and Manage Fall Risk in Older Adults Within a Secondary Fracture Clinic.

The purpose of this study was to assess the feasibility of implementing both electronic and in-person assessments to assess falls risk in an older adult secondary fracture clinic. Electronic data capture feasibility was defined as the proportion of patients that completed the electronic questionnaires prior to their clinic visit. In-clinic feasibility was defined in two ways: (1) the proportion of patients that consented to participate at their clinic visit; (2) time to complete testing. Patients were contacted electronically through their health system portal for electronic consent. Patients were invited to complete consent, the STopping Elderly Accidents, Deaths, and Injuries (STEADI) falls risk assessment tool, and the visual analog scale (VAS) for pain. The Short Physical Performance Battery (SPPB) was performed at the clinic visit. A total of 310 patients were contacted electronically. No patients (0%) provided consent through their health portal. Of the 310 patients, 200 (65%) consented in person (Ineligible: 67 [21%]; Declined: 43 [14%]), resulting in an 82% response rate. In-person data collection took a median of 38.48 (Range: 12.34-54.30) minutes to complete. It was not feasible to contact and collect older adult patient data electronically prior to clinic; but, was feasible to obtain these patient-reported outcomes and physical performance data in person.