A mastery learning approach to education about fall risk and gait assessment

Simulation-Based Mastery Learning With Virtual Coaching: Experience in Training Standardized Upper Endoscopy to Novice Endoscopists

Category: Other; Midfoot/Forefoot Introduction/Purpose: Charcot-Marie-Tooth (CMT) disease is an inherited peripheral neuropathy known to cause progressive sensory and motor impairment. Patients often develop foot structure abnormalities, including pes cavus, due to intrinsic muscle weakness. These deformities, paired with the disease’s typical sensory and motor symptoms, can increase the risk of falls. Ankle foot orthoses (AFOs) are commonly used with the intent of reducing fall risk and restoring a more normal gait. The Timed Up and Go (TUG) test is widely used as a clinical screening tool to identify patients at risk of falling. No previous study has applied the TUG test in the setting of CMT fall risk assessment. The purpose of this study was to assess the overall predictive value of TUG on fall risk in CMT. Methods: Existing data from patients seen at the at the University of Iowa Hospitals and Clinics, as part of research with the Inherited Neuropathy Consortium (INC), were used for this investigation. Patients in the INC return to clinic on an annual basis and data from each patient’s most recent evaluation were included for analysis. All CMT sub-types were included. Clinical outcome measures were taken from the CMT Functional Outcome Measure (CMT-FOM) and the CMT Health Index (CMT-HI). Timed up and go (TUG) and patient reported ankle-foot orthoses (AFO) use was recorded as part of the CMT-FOM. The CMT-HI provides patient perspective on the impact of various health related topics, including difficulty getting up from the floor or ground, falling, and difficulty rising from a seated position. Kruskal-Wallis and Wilcoxon Rank Sum tests were utilized for statistical analysis and pair-wise group comparisons. Results: In total, 126 patients of 4,971 available records were included for analysis. The remainder were excluded due to missing data, unconfirmed CMT diagnosis, or statistical outliers. TUG scores ranged from 3.4s to 26.0s (mean 9.70s). Responses to the CMT-HI Falls question included no effect, little effect, moderate effect, and severely effecting, and poorer responses were correlated with a slower average TUG (p< 0.0001). Participants who reported AFO use had a average TUG score of 11.29 seconds while non-users averaged 10.0 seconds (p=0.0015). 117/126 (93%) patients reported foot deformity: 106 pes cavus, 6 pes planus, 5 “other”, 7 reported none, and 2 unreported. Of the 117 with reported deformities, those with pes planus averaged a TUG of 9.76 seconds compared to 9.45 seconds with pes cavus. Conclusion: This is the first study to identify a significant correlation between patient-reported fall risk and average TUG in CMT patients. This observation of slower TUG times in individuals with greater fall risk is consistent with findings available for other at-risk falls populations, such as stroke patients. The most predominant deformity was pes cavus. However, neither the presence nor absence of foot deformities showed a substantial difference in TUG results. Patients who wore Ankle-foot orthoses had, on average, slower times in the TUG test than non-users. This is likely due to greater a dependency on AFOs with increased disease progression.

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.

BackgroundEarly detection of falls risk among older adults using simple tools may assist in fall prevention strategies. The aim of this study was to identify the best parameters associated with previous falls, either the timed up and go (TUG) test combined with sociodemographic factors and a self-rated multifactorial questionnaire (SRMQ) on falls risk or the TUG on its own. Falls risk was determined based on parameters associated with previous falls.DesignThis was a retrospective cohort study.SettingThe study was conducted in a community setting.ParticipantsThe participants were 1,086 community-dwelling older adults, with mean age of 69.6±5.6 years. Participants were categorized into fallers and nonfallers based on their history of falls in the past 12 months.MethodParticipants’ sociodemographic data was taken, and SRMQ consisting of five falls-related questions was administered. Participants performed the TUG test twice, and the mean was taken as the result.ResultsA total of 161 participants were categorized as fallers (14.8%). Multivariate logistic regression analysis showed that the model (χ2(6)=61.0, p<0.001, Nagelkerke R2=0.10) consisting of the TUG test, sociodemographic factors (gender, cataract/glaucoma and joint pain), as well as the SRMQ items “previous falls history” (Q1) and “worried of falls” (Q5), was more robust in terms of falls risk association compared to that with TUG on its own (χ2(1)=10.3, p<0.001, Nagelkerke R2=0.02).ConclusionCombination of sociodemographic factors and SRMQ with TUG is more favorable as an initial falls risk screening tool among community-dwelling older adults. Subsequently, further comprehensive falls risk assessment may be performed in clinical settings to identify the specific impairments for effective management.

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.

IntroductionTemporary transvenous cardiac pacing (TVP) is a critical intervention that emergency physicians perform infrequently in clinical practice. Prior simulation studies revealed that emergency medicine (EM) residents and board-certified emergency physicians perform TVP poorly during checklist-based assessments. Our objective in this report was to describe the design and implementation of a simulation-based mastery learning (SBML) curriculum and evaluate its impact on EM residents’ ability to perform TVP.MethodsAn expert panel of emergency physicians and cardiologists set a minimum passing standard (MPS) for a previously developed 30-item TVP checklist using the Mastery Angoff approach. Emergency medicine residents were assessed using this checklist and a high-fidelity TVP task trainer. Residents who did not meet the MPS during baseline testing viewed a procedure video and completed a 30-minute individual deliberate practice session before retesting. Residents who did not meet the MPS during initial post-testing completed additional deliberate practice and assessment until meeting or exceeding the MPS.ResultsThe expert panel set an MPS of correctly performing 28 (93.3%) checklist items. Fifty-seven EM residents participated. Mean checklist scores improved from 13.4 (95% CI 11.8–15.0) during baseline testing to 27.5 (95% CI 26.9–28.1) during initial post-testing (P < 0.01). No residents met the MPS at baseline testing. The 21 (36.8%) residents who did not meet the MPS during initial post-testing all met or exceeded the MPS after completing one additional 30-minute deliberate practice session.ConclusionEmergency medicine residents demonstrated significantly improved TVP performance with reduced variability in checklist scores after completing a simulation-based mastery learning curriculum.

BackgroundThe study aim was to elucidate the relationship between change in cognition and fall risk in persons with Alzheimer’s disease (AD) stratified for severity of illness and following a 10‐week exercise program. The authors hypothesize that a linear relationship exists between the severity of AD and decrease fall risk and severity of AD and improvement in cognition.MethodParticipants underwent a 10‐week exercise program designed to improve cognition and decrease fall risk. Cognition (Montreal Cognitive Assessment (MoCA)) and fall risk (Timed Up and Go (TUG)) were assessed prior to and after the exercise program. Normality was established for change in MoCA and change in TUG and for age of participants. RM‐MANOVA statistics were used to evaluate changes in the outcome measures prior to and after the exercise program. A post hoc Bonferroni evaluated differences between groups. The severity of AD was compared with change in cognition and fall risk outcome measures to determine if disease severity influenced cognition and fall risk with a 10‐week exercise program.ResultsThe TUG analysis yielded two significant mean differences (p&lt;0.05) between groups ‐ the mild/severe (p&lt;0.001) and moderate/severe (p = 0.03) comparisons. However, there was no significant difference between the mild/moderate severity groups (p = 0.085). MoCA group differences comparison produced all statistically significant (p&lt;0.001) mean differences, the largest occurring between the mild and severe groups. These data indicate a statistically significant relationship between severity of AD and change in TUG and MoCA scores following a ten‐week exercise program.ConclusionPersons with AD demonstrate impairments in cognition and fall risk leading to increased stress and costs. Exercise is an effective intervention for improving cognition and reducing fall risk in persons with AD. The relationship found between improvement of cognition and fall risk with exercise appears to be linear. The lesser severity of AD, the greater the cognitive and fall risk improvement. These data suggest that a 10‐week exercise program may improve cognition and decrease fall risk in persons with AD. The results from this study suggest that healthcare practitioners should consider early referral of persons with AD to group exercise programs to reduce the likelihood of falls and improve cognition.

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.

Strategies for fall risk assessment are currently not multifactorial neither implemented as a regular assessment of health status in clinics or hospital. The reason could be related with a lack of an easy to implement, complete and objective test to assess elderly's fall risk level. More recently, inertial wearable sensors have been used in combination with standard tests to evaluate the performance of the person during each phase of the test in an objective way. This paper proposes a methodology for collecting and analyzing the Timed-Up and Go (TUG) test instrumented with wearable inertial sensors. An automatic algorithm to segment the TUG test into three components was implemented prior to feature extraction. Overall, features from the walking and first turning phases of the tests could provide meaningful information to differentiate groups of high and low fall risk.

Purpose: The purpose of this study was to evaluate the correlation between fall risk screening tools to determine whether they yield similar results and to compare the accuracy of these tools in predicting fall risk within the geriatric population. An additional objective was to evaluate the concurrent validity of a mobile phone accelerometer screening compared to standard force plate technology during assessments. Method: An experimental validity study was conducted evaluating the Timed Up and Go (TUG) and the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB) within a group of 20 older adults. The m-CTSIB was measured by both an accelerometer and force plate simultaneously. The subjects' history of falls was recorded, which categorized individuals into “faller” and “nonfaller” groups. Results: The results of the m-CTSIB and the TUG were not significantly correlated, suggesting that the two measures most likely assess different factors contributing to an individual's fall risk. Neither screening measure was successful in predicting fall history, which indicates reported fall history may not be accurate representations of an individual's objective balance performance. The accelerometer and force plate demonstrated a significant correlation in limited conditions of the m-CTSIB supporting encouraging validity specific to more challenging balance conditions. Conclusions: This study suggests that the m-CTSIB and the TUG may not be appropriate for interchangeable use and lack substantial clinical utility as standalone assessments or when used in combination. Self-reported fall history may not be a true representation of an individual's history or an accurate marker to compare an individual's performance in objective balance measures. Further research is needed to establish an effective set of balance screening tools that objectively assess fall risk. The additional aim of the study found that the accelerometer may not be suitable for measuring sway across all m-CTSIB conditions; however, it may hold potential for evaluating more complex balance tasks.

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

Falls are a severe problem in older adults, often resulting in severe consequences such as injuries or loss of consciousness. It is crucial to screen fall risk in order to prescribe appropriate therapies that can potentially prevent falls. Identifying individuals who have experienced falls in the past, commonly known as fallers, is used to evaluate fall risk, as a prior fall indicates a higher likelihood of future falls. The methods that have the most support from evidence are Gait Speed (GS) and Time Up and Go (TUG), which use specific cut-off values to evaluate the fall risk. There have been proposals for alternative methods that use wearable sensor technology to improve fall risk assessment. Although these technological alternatives are promising, further research is necessary to validate their use in clinical settings. In this study, we propose a method for identifying fallers based on a Support Vector Machine (SVM) classifier. The inputs for the classifier are the gait parameters obtained from a 30-minute walk recorded using an Inertial Measurement Unit (IMU) placed at the foot of patients. We validated our proposed method using a sample of 157 patients aged over 70 years. Our findings indicate significant differences (p<0.05) in stride speed, clearance, angular velocity, acceleration, and coefficient of variability among steps between fallers and non-fallers. The proposed method demonstrates the its potential to classify fallers with an accuracy of 79.6%, slightly outperforming the GS method which provides an accuracy of 77.0%, and also overcomes its dependency on the cut-off speed to determine fallers. This method could be valuable in detecting fallers during long-term monitoring that does not require periodic evaluations in a clinical setting.

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.

BackgroundTrainee supervision and teaching are distinct skills that both require faculty physician competence to ensure patient safety. No standard approach exists to teach physician supervisory competence, resulting in variable trainee oversight and safety threats. The Objective Structured Teaching Evaluation (OSTE) does not adequately incorporate the specific skills required for effective supervision. To address this continuing medical education gap, the authors aimed to develop and identify validity evidence for an “Objective Structured Supervision Evaluation” (OSSE) for attending physicians, conceptually modeled on the historic OSTE.MethodsAn expert panel used an iterative process to create an OSSE instrument, which was a checklist of key supervision items to be evaluated during a simulated endotracheal intubation scenario. Three trained “standardized residents” scored faculty participants' performance using the instrument. Validity testing modeled a contemporary approach using Kane's framework. Participants underwent simulation‐based mastery learning (SBML) with deliberate practice until meeting a minimum passing standard (MPS).ResultsThe final instrument contained 19 items, including three global rating measures. Testing domains included supervision climate, participant control of patient care, trainee evaluation, instructional skills, case‐specific measures, and overall supervisor rating. Reliability of the assessment tool was excellent (ICC range 0.84–0.89). The assessment tool had good internal consistency (Cronbach's α = 0.813). Out of 24 faculty participants, 17 (70.8%) met the MPS on initial assessment. All met the MPS after SBML and average score increased by 19.5% (95% CI of the difference 10.3%–28.8%, p = 0.002).ConclusionsThe OSSE is a promising tool to assess faculty supervision performance. Further study should evaluate the effect of OSSE training on supervision in the clinical environment, which may have measurable changes in patient‐centered outcomes and resident teaching.

Timed Up and Go test and gastrointestinal disorders among hospitalized older adults with fall risk