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PURPOSE: The aim of this study is to explore the feasibility of using machine learning approaches to objectively differentiate the mobilization patterns, measured via accelerometer sensors, of patients pre- and post-intervention. METHODS: The intervention tested the implementation of a Frailty Care Bundle to improve mobilization, nutrition and cognition in older orthopedic patients. The study recruited 120 participants, a sub-group analysis was undertaken on 113 patients with accelerometer data (57 pre-intervention and 56 post-intervention), the median age was 78 years and the majority were female. Physical activity data from an ankle-worn accelerometer (StepWatch 4) was collected for each patient during their hospital stay. These data contained daily aggregated gait variables. Data preprocessing included the standardization of step counts and feature computation. Subsequently, a binary classification model was trained. A systematic hyperparameter optimization approach was applied, and feature selection was performed. Two classifier models, logistic regression and Random Forest, were investigated and Shapley values were used to explain model predictions. RESULTS: The Random Forest classifier demonstrated an average balanced accuracy of 82.3% (± 1.7%) during training and 74.7% (± 8.2%) for the test set. In comparison, the logistic regression classifier achieved a training accuracy of 79.7% (± 1.9%) and a test accuracy of 77.6% (± 5.5%). The logistic regression model demonstrated less overfitting compared to the Random Forest model and better performance on the hold-out test set. Stride length was consistently chosen as a key feature in all iterations for both models, along with features related to stride velocity, gait speed, and Lyapunov exponent, indicating their significance in the classification. CONCLUSION: The best performing classifier was able to distinguish between patients pre- and post-intervention with greater than 75% accuracy. The intervention showed a correlation with higher gait speed and reduced stride length. However, the question of whether these alterations are part of an adaptive process that leads to improved outcomes over time remains.

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Objective: To conduct an economic cost analysis and budget impact assessment (BIA) of implementing the Frailty Care Bundle (FCB) intervention nationally over five-years for hip fracture patients. The FCB was designed to reduce hospital associated decline in older hospitalised patients. Methods: The FCB was delivered in two Irish hospitals on two wards per hospital. A micro level cost analysis, from the Irish health service perspective was undertaken. Direct costs of the FCB were considered (personnel, training, resources), expressed in Euros (2020). For the BIA national population estimates for hip fracture and costs avoided were based on 18% difference in patients returning to their baseline capability in the post compared to pre-intervention group, valued using cost estimates of functional decline. Results: We estimated total intervention costs at €53,619 (89% for personnel) and the average cost per patient was €156.03. The expected costs of implementing the FCB nationally over 12-months was €57,274 per hospital (€72.92 per patient). The BIA for an expected targeted population (16,000 over 5 years), estimated that the cost of implementing the FCB (€1.2m) was less than the expected value of functional decline avoided owing to the intervention (€3.6m), suggesting a positive net effect (€2.4m). Conclusion: Investment in the FCB can be offset with more rapid patient return to baseline functional capability, reducing health care costs. Trial and Protocol Registration (retrospective): BMC ISRCTN 15145850, (https://doi.org/10.1186/ISRCTN15145850).

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Objective: To implement and evaluate a Frailty Care Bundle (FCB) targeting mobilisation, nutrition, and cognition in older trauma patients to reduce hospital associated decline. Methods: We used a two group, pretest-posttest design. The FCB intervention was delivered on two orthopaedic wards and two rehabilitation wards, guided by behaviour change theory (COM-B) to implement changes in ward routines (patient mobility goals, nurse assisted mobilisation, mealtimes, communication). Primary outcomes were patient participants' return to pre-trauma functional capability (modified Barthel Index - mBI) at 6-8 weeks post-hospital discharge and average hospital daily step-count. Statistical analysis compared pre versus post FCB group differences using ordinal regression and log-linear models. Results: We recruited 120 patients (pre n=60 and post n=60), and 74 (pre n=43, post n=36) were retained at follow-up. Median age was 78 years and 83% were female. There was a non-significant trend for higher mBI scores (improved function) in the post compared to pre FCB group (OR 2.29, 95% CI 0.98-5.36), associated with an average 11% increase in step-count. Conclusion: It was feasible, during the Covid-19 pandemic, for multidisciplinary teams to implement elements of the FCB. Clinical facilitation supported teams to prioritise fundamental care above competing demands, but sustainability requires ongoing attention. ISRCTN registry: ISRCTN15145850 (https://doi.org/10.1186/ISRCTN15145850).

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AIMS: To examine the effectiveness of targeted nursing interventions on mobilization, nutrition and cognitive engagement to reduce functional and hospital-associated decline (HAD) in older patients. DESIGN: Systematic review of experimental studies using randomized and quasi-experimental designs. DATA SOURCES: We searched electronic databases CINAHL, MEDLINE, EMBASE, Cochrane library, google scholar and BMJ quality reports from January 2009 to February 2020. REVIEW METHODS: We reviewed intervention studies that targeted ward nursing teams to increase mobilization, nutrition or cognitive engagement of older adults. Inclusion criteria included older patients, acute care (medical, surgical and older adult wards) and reporting patient level outcomes. Quality appraisal included the Joanna Briggs Critical Appraisal Checklist for Quasi-Experimental Studies. RESULTS: From 1729 papers, 18 studies using quasi-experimental and pre-post designs were selected. Study heterogeneity necessitated a narrative synthesis. The quality of evidence was low to moderate. All studies used multicomponent strategies, and 10 studies used evidence translation frameworks to align interventions to local barriers. Overall, 74% (n = 14) of studies reported a significant improvement in the stated primary outcome. Eight studies reported a significant increase in mobilization (e.g., sitting in a chair or walking), and four reported improved functional outcomes. Five studies improved nutrition outcomes (e.g., protein or energy intake), and three studies reported a significant reduction in delirium. CONCLUSION: Acknowledging methodological limitations, the evidence indicates that nursing teams using evidence-translation frameworks can improve mobilization, nutrition and cognitive engagement in acute care settings. Future research requires higher-quality pragmatic trial designs, standardized outcomes, staff co-designed interventions, evidence-translation frameworks and patient engagement to make more confident inference about effectiveness. IMPACT: Nursing teams with the support of hospital management have to address ward and system barriers to prioritize fundamental care to improve patient outcomes. There is sufficient evidence on multicomponent interventions and implementation strategies to inform nurse-led quality improvement.

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AIMS AND OBJECTIVES: To examine mealtime and patient factors associated with meal completion among hospitalised older patients. We also considered contextual factors such as staffing levels and ward communication. BACKGROUND: Sub-optimum nutrition is a modifiable risk factor for hospital associated decline (HAD) in older patients. Yet, the quality of mealtime experiences can be overlooked within ward routinised practice. DESIGN: Cross sectional, descriptive observation study. METHODS: We undertook structured observation of mealtimes examining patient positioning, mealtime set-up and feeding assistance. The outcome was meal completion categorised as 0, 25%, 50%, 75% or 100%. Data were collected on patient characteristics and ward context. We used mixed-effects ordinal regression models to examine patient and mealtime factors associated with higher meal completion producing odds ratios (OR) and 95% confidence intervals (CI). The study was reported as per STROBE guidelines. RESULTS: We included 60 patients with a median age of 82 years (IQR 76-87) and clinical frailty score of 5 IQR (4-6). Of the 279 meals, 51% were eaten completely, 6% three quarters, 15% half, 18% a quarter and 10% were not eaten at all. Mealtime predictors with a weak association with less-meal completion were requiring assistance, special diets, lying in bed, and red tray (indicator of nutrition risk), but were not statistically significant. Significant patient-level factors were higher values for frailty (OR 0.34 [0.11-1.04]) and Malnutrition Universal Screening Tool (OR 0.22 [0.08-0.62]). The average nurse-to-patient ratio was 1:5.5. CONCLUSION: Patient factors were the strongest predictors for meal completion, but mealtime factors had a subtle influence. The nursing teams' capacity to prioritise mealtimes above competing demands is important as part of a comprehensive nutrition strategy. RELEVANCE TO CLINICAL PRACTISE: Nurses are central to optimising nutrition for frail older patients. It requires ward leadership to instil a culture of prioritising assisted mealtimes, improved communication, greater autonomy to tailor nutrition strategies and safe staffing levels.

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