OBJECTIVES:The objective of this paper was to evaluate the implementation feasibility of the Johns Hopkins Activity and Mobility Promotion (JH-AMP) framework across geographically diverse hospitals and to examine the association between daily mobility goal achievement and discharge home. DESIGN:This was a prospective, multicenter, type 3 hybrid implementation study of the hospital mobility promotion program JH-AMP built from the translating research into practice model. The intervention included implementation manuals, e-learning modules, and virtual mentoring. SETTING AND PARTICIPANTS:Participants included 5 US academic and community hospitals involving 15,107 unique patient admissions on participating medical and surgical units. METHODS:Primary outcomes were implementation fidelity, measured via documentation compliance (daily Activity Measure for Post-Acute Care Inpatient Mobility 6-clicks Short Form and Johns Hopkins Highest Level of Mobility scores) and mobility goal achievement (observed mobility meeting individualized targets). Multivariable logistic regression estimated the association between consistent goal attainment and discharge home for 7119 analyzable patients. RESULTS:Documentation compliance ranged from 30% to 98% for Activity Measure for Post-Acute Care Inpatient Mobility 6-clicks Short Form and 53% to 92% for Johns Hopkins Highest Level of Mobility. Daily mobility goal achievement varied by site from 42% to 80%. Each 1 percentage point increase in goal achievement days raised the odds of discharge home by 1% (OR, 1.01; 95% CI, 1.006-1.012; P < .001). Achieving goals on 80% vs 20% of hospital days was associated with a 10.4 percentage point higher probability of home discharge. CONCLUSIONS AND IMPLICATIONS:Implementing a systematic mobility program is feasible across diverse hospital environments without hiring additional staff. Consistent daily goal attainment is a significant predictor of functional recovery and discharge home, particularly for lower-functioning patients. Policy and practice should prioritize embedding standardized mobility measurement into routine nursing workflows to reduce reliance on post-acute care and improve patient-centered outcomes.

Purpose: The science that should inform the practice of physical therapists in the acute hospital is rapidly evolving. To ensure clinicians can deliver the most current and effective treatments they must have access to scientific literature. Access refers to the design of products, services, and information for use by relevant populations, to the greatest extent possible. Clinicians often cannot access scientific literature for various reasons. The purpose of this article was to highlight selected acute care physical therapy literature published in 2024 in an accessible form for all members of the acute care physical therapy community.Methods: In this perspective report, peer-reviewed publications relevant to acute care physical therapy from the year 2024 were identified and reviewed. Articles were selected based on relevance to today's health care environment, hot topics, and the anticipated future of acute care physical therapy.Results: Articles were grouped into (1) clinical practice including differential diagnoses and treatment dosage, (2) patient care related to social determinants of health, (3) physical therapy operations, and (4) entry level Doctor of Physical Therapy didactic and clinical education. Following the summary of each topic's articles, clinical relevance and future directions are discussed.Conclusions: The authors' perspective of acute care physical therapy research from 2024 was that it provided an overview of current, clinically relevant topics that are useful to a variety acute care clinicians and educators. This review was presented at the 2025 Combined Sections Meeting.

Introduction:Physical function (PF) is critical to quality of life and healthcare value, especially for older adults following hospitalization. Monitoring PF supports recovery, reduces adverse events, and improves care transitions. Despite the potential of electronic health records (EHRs) enabling systematic PF tracking, such data are rarely captured consistently. Here we examine the availability of PF-related data in EHRs for patients transitioning from hospital to homecare in a large health system, highlighting challenges and offering recommendations. Methods:We assessed availability of elements previously identified important to PF measurement from a single healthcare system. Working with Johns Hopkins Health System informatics and homecare leaders, we determined which recommended elements were captured in the EHR and which were feasible to extract within our resource constraints. We then requested an extraction of a refined data set for adult patients with a hospital admission between July 2016 and March 2021. After validation, data were securely transferred to University of Utah Health. Results:Data from 21 702 patients were included. Of 27 desired elements, 17 were available and successfully extracted. Individual elements were marked "present" if documented at least once during admission, or "missing" if absent. Administrative data had low missingness, although missingness for assessments of cognition and mobility performance in hospital was over 65%, and assessments of PF capacity in home health were missing in over 80% of patients. However, 81.7% of those receiving home health rehabilitation had the expected mobility measure. Overall, 73% of patients had at least 75% of the extracted data elements. Conclusions:Assembling a comprehensive view of PF across a care transition using EHR data proved highly challenging. Our recommendations address data element identification, generation and storage; data extraction, cleaning, and validation; interoperability across care settings; adequate resources to manage complex data; and prospective infrastructure development.

In this study, we aim to better align fall risk prediction from the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) with clinical expert fall risk perception via a data-driven modelling approach. We conducted a retrospective cohort analysis of 54,209 inpatient admissions from three Johns Hopkins Health System hospitals between March 2022 and October 2023. In the absence of a true fall risk ground truth, we apply proxy labels based on clinician choices for the application of targeted preventative interventions, resulting in a total of 20,208 high-risk encounters and 13,941 low-risk encounters. We employed constrained score optimization (CSO) models to recalibrate the JHFRAT scoring weights, while preserving its additive structure and clinical thresholds. Recalibration refers to adjusting item weights so that the resulting score can order encounters more consistently by the study's risk labels, and without changing the tool's form factor or deployment workflow. The CSO model demonstrated significant improvements over the current JHFRAT in classification alignment with the proxy labels (CSO AUC-ROC = 0.91, JHFRAT AUC-ROC = 0.86). This model performance translates to a weekly average of an additional 35 Johns Hopkins Health System patients who are perceived as high risk (per our proxy labels) being classified by JHFRAT as high risk. The ablation analyses also suggest that the CSO model, though outperformed in prediction metrics by the benchmark black-box XGBoost model, is more robust than XGBoost to variations in risk labeling. Our evidence-based approach provides a robust foundation for understanding risk factor contributions to various indicators of clinician-perceived fall risk. Future research can build upon this foundation to improve risk assessment utility as a decision-support tool in clinical practice.

Objective: To characterize changes in mobility and activities of daily living (ADLs) from acute hospitalization through home health care across diagnostic categories, and to identify demographic and clinical factors associated with these changes. Design: Retrospective cohort study. Setting: Acute hospitalization and home health care. Participants: A total of 2060 adults in the mobility cohort (67.8±13.6 years old; 53.7% female); 614 adults in the ADL cohort (69.6±12.9 years old; 56.2% female). Interventions: Not applicable. Main Outcome Measures: Physical function was measured using the Activity Measure for Post-Acute Care (AM-PAC) “6-Clicks” Basic Mobility (BM) and Daily Activity (DA) short forms in both hospital and home health settings. Results: Linear mixed-effects models indicated that both AM-PAC BM and DA scores improved significantly over time, with BM and ADL increasing 0.13 and 0.08 t score points per day, respectively (BM: β=0.13/d; DA: β=0.08/d; P<.05). After controlling for all other variables (including time), older age and higher body mass index were associated with lower functional scores. Diagnostic category significantly influenced both baseline function and rate of improvement for both BM and DA. For example, individuals in the Metabolic/Cancer diagnosis group improved more slowly in BM when compared with all other diagnosis groups. Conclusions: Physical function improves from hospital admission through home health care, but recovery trajectories through home health care vary by age, body mass index, and diagnostic category. These findings demonstrate trajectories of change across care settings, highlight the importance of standardized functional assessments across care settings, and suggest a need for individualized prognostic models. This study informs clinicians, patients, and caregivers about expected recovery trajectories in mobility and ADLs that could support discharge planning and goal setting. Future research should explore the role of therapy dosage and timing and develop individualized models to better predict functional recovery.

Objective: To evaluate the effectiveness of an early, targeted, individualized, intensive rehabilitation program called Rehab2Home, designed to transition surgical patients directly from acute care to home. Design: The Rehab2Home program was implemented using a quality improvement (QI) approach between March 2023 and June 2023. The outcomes of the program were compared with a historical cohort of similar patients. Setting: Academic medical center. Participants: Postsurgical patients (n=74) included were aged 18 years or older, recommended for subacute rehabilitation by physical therapy or occupational therapy, had some level of support at home, mild to no cognitive impairments, and moderate mobility impairments. Interventions: Patients received an enhanced rehabilitation therapy program from physical therapy, occupational therapy, speech-language pathology, and consultations with a physiatrist emphasizing readiness for discharge home. The team also conducted weekday interdisciplinary huddles. Main Outcome Measure(s): The primary outcome for the evaluation of the program was discharge location from the hospital. Secondary outcomes included the length of hospital stay and emergency department visits and potentially avoidable utilization (PAU) within 30 days of hospital discharge. Results: Seventy-four patients were included in the Rehab2Home program, with 66% discharging home compared to 47% in the historical controls. The program resulted in a 1.4 (95% CI, 1.1-1.6) times greater likelihood of discharging home and decreased the proportion of patients with potentially avoidable health care utilization by 63% (Risk Ratio: 0.37, 95% CI, 0.1-0.7), without a significant increase in length of stay (-0.6 days, 95% CI,-2.2 to 1.9). Conclusions: The Rehab2Home program for postsurgical patients successfully facilitated home discharges and reduced postdischarge utilization. This model of rehabilitation shows promise for improving transitions of care from the hospital in this population. Archives of Physical Medicine and Rehabilitation 2025;106:910-6 (c) 2024 Published by Elsevier Inc. on behalf of the American Congress of Rehabilitation Medicine

Health care value, quantified as outcome per unit cost, requires knowing which outcomes are influenced by which intervention at what cost. The value of rehabilitation is still largely unknown. Much of the reason for this limited evidence is historically poor standardization and collection of rehabilitation interventions, and objectively measured outcomes across care settings, care providers, and health care systems. The purposeful standardization and aggregation of rehabilitation-relevant data about interventions, cost, and outcomes from routine clinical practices offers potential to understand and improve the value of rehabilitation. This perspective details the critical need for rehabilitation-relevant data that are aggregated across settings, providers, and systems and proposes 3 options to meet this need, including (1) integrating rehabilitation-relevant data into existing research registry databases that are condition specific, (2) adding rehabilitation-relevant data to federally funded research networks, and (3) creating a novel rehabilitation registry database. There must be continued pursuit of discovering which rehabilitation interventions achieve which specific outcomes, in which settings, for which patients, and at what costs. Successfully aggregating rehabilitation-relevant data is critical for generating evidence that answers these key questions about the value of rehabilitation.

Objectives: This study aimed to identify psychological factors and characteristics associated with fear of falling (FOF) and fear of falling avoidance behavior (FFAB) among older adults. MethodsThis cross-sectional study used data from the National Health and Aging Trends Study (Wave 9, n = 4,977). Results: We found that increased fall history, more frequent depression and anxiety, and poorer perceived overall health were significantly higher among older adults with FFAB compared to FOF (ps < .001). Perceived overall health, depression, and anxiety explained a significant amount of variance in FOF and FFAB. Lastly, demographic characteristics differ between older adults reporting no FOF/FFAB, FOF, and FFAB. Conclusions: FOF and FFAB are prevalent among older adults. Older adults experiencing FFAB had poorer health perceptions, more falls, and more frequent depression and anxiety than those experiencing FOF. The association of psychological factors and demographic characteristics with FOF and FFAB may indicate potential treatment targets. Clinical Implications: Addressing psychological variables, such as health perception, anxiety, and depression among older adults, may mitigate the impact of FOF and the development of FFAB; however, further research is needed.

OBJECTIVE:To create a crosswalk linking Activity Measure for Postacute Care (AM-PAC) Inpatient Basic Mobility ‟6-Clicks" Short Form with a sum score from 15 mobility-related section GG standardized data elements (GG Items), aiding longitudinal measurement of physical function. DESIGN:Correlational analyses of paired standardized AM-PAC and GG Items scores, recorded within 48 hours of each other, were analyzed. The equipercentile method established bidirectional concordance links. Performance evaluated using R2, intraclass correlation coefficient (2,1), and bootstrapped standard errors. SETTING:An acute rehabilitation hospital. PARTICIPANTS:There were 923 patients (N=923). Their mean age was 58 years, and 90% had neurologic diagnoses. INTERVENTIONS:Not applicable. MAIN OUTCOME MEASURES:The primary outcome was the bidirectional concordance (crosswalk) between AM-PAC and GG Items. RESULTS:The mean AM-PAC T score was 48 (SD=7.1), and the mean GG Items score was 72 (SD=16). Pearson correlation between the measures was 0.84. Linking accuracy was good; R2 was 0.70 for GG Items (derived from AM-PAC) and 0.74 for AM-PAC T score (derived from GG Items). Respective intraclass correlation coefficients between linked and observed scores were 0.87 and 0.85. SEs were low. Case examples demonstrated consistent crosswalked scores between hospital discharge (AM-PAC) and inpatient rehabilitation admission (GG Items). CONCLUSIONS:A reliable crosswalk between AM-PAC and GG Items scores was successfully developed, enabling score conversion. This tool offers clinicians and researchers an improved ability to longitudinally track patient function across different care settings, potentially enhancing care continuity, supporting research, and informing resource allocation. Future validation in other populations is recommended.

Objectives: This study had 2 objectives: (1) translate to Spanish and cross-culturally adapt the Activity Measure for Post-Acute Care (AM-PAC) Inpatient Mobility Short Form, the AM-PAC Inpatient Activity Short Form, and the Johns Hopkins Highest Level of Mobility (JH-HLM) tools; and (2) assess the reliability, validity, and responsiveness of the translated and cross-culturally adapted tools. Design: After an expert committee approved a forward-backward translation of the tools, we evaluated the interrater reliability, concurrent validity, and responsiveness of the translated tools. Reliability was assessed by a team of 2 physical therapists and 1 nurse who independently scored 59 patients at admission and discharge. Concurrent validity was evaluated via the assessment of convergent validity with other measures of function. Responsiveness was assessed by quantifying the minimally important difference with 1 anchor-based and 2 distribution-based methods. Results: The interrater reliability (intraclass correlation coefficient) was larger than 0.95 at admission and discharge. Evidence for concurrent validity (correlations) ranged from 0.64 to 0.95. AM-PAC minimally important difference ranged between +3.13 and −6.75 for improvement and decline, respectively, and JH-HLM minimally important difference ranged between +0.73 and −1.07. Conclusions: The Spanish AM-PAC and JH-HLM tools have excellent interrater reliability, measure constructs of mobility and function, and detect clinically meaningful changes in patients’ mobility.

Objectives To determine the effect of physical therapy (PT) treatment frequency on discharge disposition among hospitalized adults. Design An emulated trial design comparing the effect of PT intensity on the risk of patient discharge to home versus postacute care (PAC). The risk of discharge to home under each PT intensity was estimated using a parametric g-computation approach that modeled the patient's hospital stay including whether the patient remained in the hospital or was discharged on each day, the daily AM-PAC score, receipt of PT, daily JH-HLM score, and discharge location (models adjusted for baseline and prior days information). Using these models, we simulated 50 realizations of each patient's hospitalization under the 3 physical therapy intensities; the estimated risk of discharge home is the proportion of patients across the 50 realizations that discharged home. Setting Acute hospitals in the Johns Hopkins Health System. Participants Adult hospital patients with an AM-PAC raw score of 13-21 within 48 hours of admission and whom received at least one PT session. Interventions PT visit frequency defined as low (<3/wk), medium (3-5/wk), and high (6-7/wk). Main Outcome Measures Discharge disposition (Home vs PAC). Results The study included 25,205 patients who were on average (standard deviation) 65 (16) years of age and majority female (52%) and White (58%). They were admitted to a medicine (50%), surgical science (21%), neuroscience (18%), oncology (5%), surgery (4%), or orthopedic (2%) unit. On average, patients received their first PT visit by day 4 (4) of hospitalization and spent 10 (5) days hospitalized. Sixty-four percent were discharged home. We estimated that if all patients had received low or medium PT intensity, 64% would be discharged home; whereas 71% would be discharged home if all were to receive high-intensity PT. Conclusions We estimate an almost 7% increase in the proportion of people being discharged home if PT were to be provided 6 or 7 days a week for hospitalized adults. Therapy provided 3 days per week costs about $265, therefore 6 days a week would be an additional $265. The median cost for a PAC stay is between $9034 and $11,073. If 7% fewer patients needed inpatient PAC, we could effectively reduce the net cost by between $367 and $510 per patient ([$9034 × 0.07]–$265 or [$11,073 × 0.07]–$265). Disclosures none.

Patient mobility during hospitalization is essential for high-quality healthcare as mobility is linked to physical function and quality of life. The Johns Hopkins Highest Level of Mobility (JH-HLM) scale is a validated method to assess mobility in hospitalized patients. Although the JH-HLM is widely utilized, it has limitations including ceiling effects, unobserved mobility events going unrecorded, and the staff time needed to observe and document.We explored the feasibility of using a consumer-grade activity monitor (Fitbit) to predict JH-HLM scores and address these limitations.JH-HLM scores and step counts were recorded simultaneously using behavioral mapping and analyzed over 1-hour periods among inpatients. We predicted JH-HLM scores based on step counts by fitting ordinal logistic regressions, according to three categorizations of JH-HLM scores reflecting increasing mobility-granularity.We collected data for 189 patient-hours in a cohort of 20 participants. Step counts increased with higher JH-HLM mobility scores. When predicting JH-HLM scores from step counts, there was a trade-off between accuracy and mobility granularity: overall accuracy was 75% when categorizing patient-hours as immobility (JH-HLM of 1 to 5) or mobility (JH-HLM of 6 to 8); accuracy was 68% when categorizing immobility, shorter walking behavior (JH-HLM of 6 to 7), and longer walking behavior (JH-HLM of 8); accuracy was 61% when categorizing immobility and three progressively higher volumes of walking (JH-HLM of 6, 7 and 8).Step counts from the activity monitor could be used to predict whether a patient was immobile or mobile but may lack the sensitivity to accurately predict specific mobility levels.

AIMS AND OBJECTIVES:To explore the association between different aspects of patient functional mobility, specifically, mobility capability (i.e., what the patients could do) versus mobility performance (i.e., what the patients actually did) and hospital falls. BACKGROUND:Fall risk assessments are important strategies to mitigate inpatient falls, and mobility is a crucial factor in determining a patient's risk. However, different fall assessment tools vary in how they attribute risk based on mobility difficulties. Understanding how various aspects of mobility uniquely influence fall risk is essential for accurately capturing and assessing a patient's true fall risk. DESIGN:A retrospective analysis was conducted using routine electronic medical record data at three hospitals, encompassing 498 patients who experienced falls and 53,708 patients who did not fall. METHODS:We examined patient mobility in three distinct ways and their relationship with in-hospital falls. Mobility was assessed within the first 48 h of admission using the mobility questions in the Johns Hopkins Fall Risk Assessment Tool (JHFRAT). Additionally, we evaluated other aspects of mobility using the AM-PAC scale, which measures mobility capability, and the JH-HLM scale, which assesses mobility performance. RESULTS:A negative linear/stepwise relationship was observed between both AM-PAC scores and JHFRAT mobility scores with fall incidence, indicating that lower mobility capability is consistently linked to a higher risk of falls. In contrast, the relationship between JH-HLM scores and falls followed an inverse U-shaped curve, with a lower fall incidence in patients with the lowest mobility performance. CONCLUSIONS:This exploratory study highlights that a one-size-fits-all approach to assessing mobility may not accurately capture a patient's true fall risk, emphasising the importance of evaluating different aspects of patient mobility for a more precise assessment. By considering both functional mobility capacity and actual mobility performance, we can better understand and address the unique ways in which mobility impacts fall risk.

Risk assessment tools in healthcare commonly employ point-based scoring systems that map patients to ordinal risk categories via thresholds. While electronic health record (EHR) data presents opportunities for data-driven optimization of these tools, two fundamental challenges impede standard supervised learning: (1) labels are often available only for extreme risk categories due to intervention-censored outcomes, and (2) misclassification cost is asymmetric and increases with ordinal distance. We propose a mixed-integer programming (MIP) framework that jointly optimizes scoring weights and category thresholds in the face of these challenges. Our approach prevents label-scarce category collapse via threshold constraints, and utilizes an asymmetric, distance-aware objective. The MIP framework supports governance constraints, including sign restrictions, sparsity, and minimal modifications to incumbent tools, ensuring practical deployability in clinical workflows. We further develop a continuous relaxation of the MIP problem to provide warm-start solutions for more efficient MIP optimization. We apply the proposed score optimization framework to a case study of inpatient falls risk assessment using the Johns Hopkins Fall Risk Assessment Tool.

BACKGROUND:Promoting safe patient mobility for providers and patients is a safety priority in the hospital setting. Safe patient handling equipment aids safe mobility but can also deter active movement by the patient if used inappropriately. Nurses need guidance to choose equipment that ensures their safety and that of the patients while promoting active mobility and preventing workplace-related injury. METHODS:Using a modified Delphi approach with a diverse group of experts, we created the Johns Hopkins Safe Patient Handling Mobility (JH-SPHM) Guide. This diverse group of 10 experts consisted of nurses, nurse leaders, physical and occupational therapists, safe patient handling committee representatives, and a fall prevention committee leader. The application of the tool was then tested in the hospital environment by two physical therapists. FINDINGS:Consensus was reached for safe patient handling (SPH) equipment recommendations at each level of the Johns Hopkins Mobility Goal Calculator (JH-Mobility Goal Calculator). Expert SPH equipment recommendations were then added to JH-Mobility Goal Calculator levels to create the JH-Safe Patient Handling Mobility Guide. JH-Safe Patient Handling Mobility Guide equipment suggestions were compared with equipment recommendations from physical therapists revealing strong agreement (n = 125, 88%). CONCLUSION:The newly created JH-Safe Patient Handling Mobility Guide provides appropriate safe patient-handling equipment recommendations to help accomplish patients' daily mobility goals. APPLICATIONS TO PRACTICE:The Johns Hopkins Safe Patient Handling Mobility Guide simultaneously facilitates patient mobility and optimizes safety for nursing staff through recommendations for safe patient handling equipment for use with hospitalized patients.

Objective: Although patient cognition can have an impact on health services needs once discharged from the hospital, it is typically not evaluated as part of routine care. We aimed to investigate how routinely collected Activity Measure for Post-Acute Care Applied Cognitive Inpatient Short Form (AM-PAC ACISF) scores, a measure of applied cognition, are associated with discharge disposition. Methods: A retrospective analysis was conducted on 5,236 electronic medical records of adult patients admitted in Johns Hopkins Hospital (JHH) between July 1, 2020 to November 2, 2021. Data was evaluated on whether patients who have been admitted across hospital services required post-acute care based on their AM-PAC ACISF scores. A cut-off raw score of 21 or less was considered as having cognitive impairment. Results: The applied cognitive t-scale scores assessed near time of admission were 9.3 points lower in patients discharged to PAC compared to discharge home. Adjusted regression models showed the odds of PAC needs for patients with AM-PAC ACISF t-scale scores in the lowest tertile were 3.4 times greater than patients in the highest tertile (95% CI 2.8,4.0; p <0.001). Patients with scores in the middle tertile have 1.9 times greater odds for PAC needs than those in the highest tertile (95% CI 1.6,2.2; p<0.001). Bivariate and multivariate logistic regression models showed AM-PAC ACISF, living alone, male gender, prior residence outside of home, admission due to general surgery, neurology, or orthopedics services versus medicine service all increased the odds of discharge to PAC (OR 1.2–4.4, p=<0.001). Conclusions: This study provides empirical evidence that a routinely collected cognitive assessment could be a care coordination strategy to help identify patients who are more likely to require PAC after discharge.

OBJECTIVES:Clinical criteria for Traumatic Encephalopathy Syndrome (ccTES) were developed for research purposes to reflect the clinical symptoms of Chronic Traumatic Encephalopathy (CTE). The aims of this study were to 1) determine whether there was an association between the research diagnosis of TES and impaired postural balance among retired professional fighters, and 2) determine repetitive head impacts (RHI) exposure thresholds among both TES positive and TES negative groups in retired professional fighters when evaluating for balance impairment. METHODS:This was a pilot study evaluating postural balance among participants of the Professional Athletes Brain Health Study (PABHS). Among the cohort, 57 retired professional fighters met the criteria for inclusion in this study. A generalized linear model with generalized estimating equations was used to compare various balance measures longitudinally between fighters with and without TES. RESULTS:A significant association was observed between a TES diagnosis and worsening performance on double-leg balance assessments when stratifying by RHI exposure thresholds. Additionally, elevated exposure to RHI was significantly associated with increased odds of developing TES; The odds for TES diagnosis were 563% (95% CI = 113, 1963; p-value = 0.0011) greater among athletes with 32 or more professional fights compared to athletes with less than 32 fights when stratifying by balance measures. Likewise, the odds for TES diagnosis were 43% (95% CI = 10, 102; p-value = 0.0439) greater with worsening double leg stance balance in athletes exposed to 32 or more fights. CONCLUSION:This pilot study provides preliminary evidence of a relationship between declining postural balance and a TES diagnosis among retired professional fighters with elevated RHI exposure. Further research exploring more complex assessments such as the Functional Gait Assessment may be of benefit to improve clinical understanding of the relationship between TES, RHI, and balance.

BackgroundVenous thromboembolism risk increases in hospitals due to reduced patient mobility. However, initial mobility evaluations for thromboembolism risk are often subjective and lack standardization, potentially leading to inaccurate risk assessments and insufficient prevention.MethodsA retrospective study at a quaternary academic hospital analyzed patients using the Padua risk tool, which includes a mobility question, and the Johns Hopkins-Highest Level of Mobility (JH-HLM) scores to objectively measure mobility. Reduced mobility was defined as JH-HLM scores ≤3 over ≥3 consecutive days. The study evaluated the association between reduced mobility and hospital-acquired venous thromboembolism using multivariable logistic regression, comparing admitting health care professional assessments with JH-HLM scores. Symptomatic, hospital-acquired thromboembolisms were diagnosed radiographically by treating providers.ResultsOf 1715 patients, 33 (1.9%) developed venous thromboembolisms. Reduced mobility, as determined by the JH-HLM scores, showed a significant association with thromboembolic events (adjusted OR: 2.53, 95%CI:1.23-5.22, P=0.012). In contrast, the initial Padua assessment of expected reduced mobility at admission did not. The JH-HLM identified 19.1% of patients as having reduced mobility versus 6.5% by admitting health care professional, suggesting 37 high-risk patients were misclassified as low risk and were not prescribed thrombosis prophylaxis; 4 patients developed thromboembolic events. JH-HLM detected reduced mobility in 36% of thromboembolic cases, compared to 9% by admitting health care professionals.ConclusionInitial mobility evaluations by admitting health care professionals during venous thromboembolism risk assessment may not reflect patient mobility over their hospital stay. This highlights the need for objective measures like JH-HLM in risk assessments to improve accuracy and potentially reduce thromboembolism incidents.