CE: Original Research Predicting Injurious Falls in the Hospital Setting Implications for Practice

Impact of Wii Fit Training (WFT) and Reactive Balance Training (RBT) for in Elder Age Population: A Comparative Analysis

Objective

This study aims to assess the comparative efficacy of two distinct balancing training intervention strategies, namely, Wii Fit Balance Training (WFT) and Reactive Balance Training (RBT), in reducing older individuals' fear of falling, as well as enhancing their balance and functional mobility.

Materials and Methods

One of the two groups was randomly assigned a total of 45 individuals. The first group received Wii Fit training, whereas the second group engaged in reactive balancing challenges. The efficiency of the techniques was evaluated using three outcome measures: the Fullerton Advanced Balance (FAB), Scale Time up and Go Test (TUG), and Berg Balance Scale (BBS). To facilitate the comparison between the groups, an independent student t-test was employed.

Results

Both experimental groups showed significant improvements compared to their respective control groups in the research study (BBS: P = 0.023; P = 0.036; FAB: P = 0.027; P = 0.044; and TUG: P = 0.017; P = 0.025).

Conclusion

It can be inferred that both reactive balance training and Wii Fit training have the potential to serve as efficacious treatment interventions aimed at mitigating balance deterioration among older individuals.

Interventions to reduce falls in hospitals: a systematic review and meta-analysis

BACKGROUND

Falls remain a common and debilitating problem in hospitals worldwide. The aim of this study was to investigate the effects of falls prevention interventions on falls rates and the risk of falling in hospital.

DESIGN

Systematic review and meta-analysis.

PARTICIPANTS

Hospitalised adults.

INTERVENTION

Prevention methods included staff and patient education, environmental modifications, assistive devices, policies and systems, rehabilitation, medication management and management of cognitive impairment. We evaluated single and multi-factorial approaches.

OUTCOME MEASURES

Falls rate ratios (rate ratio: RaR) and falls risk, as defined by the odds of being a faller in the intervention compared to control group (odds ratio: OR).

RESULTS

There were 43 studies that satisfied the systematic review criteria and 23 were included in meta-analyses. There was marked heterogeneity in intervention methods and study designs. The only intervention that yielded a significant result in the meta-analysis was education, with a reduction in falls rates (RaR = 0.70 [0.51-0.96], P = 0.03) and the odds of falling (OR = 0.62 [0.47-0.83], P = 0.001). The patient and staff education studies in the meta-analysis were of high quality on the GRADE tool. Individual trials in the systematic review showed evidence for clinician education, some multi-factorial interventions, select rehabilitation therapies, and systems, with low to moderate risk of bias.

CONCLUSION

Patient and staff education can reduce hospital falls. Multi-factorial interventions had a tendency towards producing a positive impact. Chair alarms, bed alarms, wearable sensors and use of scored risk assessment tools were not associated with significant fall reductions.

Analysis of Inpatient Hospital Falls with Serious Injury

Fall-related serious injuries pose risks to patients and healthcare organizations. This retrospective, single-hospital study used a 38 variable instrument to understand characteristics of those who sustained a fall with serious injury. Analyses included descriptive statistics, frequency, and Chi-square tests of associations between key variables and outcomes of moderate versus major injury. Age range 25-91 years, predominantly 60-69 years (23.3%), and mostly male (50.9%). Highest percentage occurred between 0:00 and 06:59 (39.6%), and on Oncology service (28.3%). Fallers were in the room, (81.1%), sustained major injury (73.6%), fractured a major bone (43.4%), had altered mobility prior to the fall (67.9%), and had received at least one narcotic dose within 24 hours before the fall (43.2%). The associations between injury severity and age, gender, altered mobility, fall risk assessment pre-fall, and unit service line are not statistically significant, however have small-to-moderate clinical significance. This study adds to the literature in identifying characteristics of patients who sustain a fall-related serious injury.

Developing a Scoring Model to Predict the Risk of Injurious Falls in Elderly Patients: A Retrospective Case-Control Study in Multicenter Acute Hospitals

PURPOSE

Injurious falls seriously threaten the safety of elderly patients. Identifying risk factors for predicting the probability of injurious falls is an important issue that still needs to be solved urgently. We aimed to identify predictors and develop a nomogram for distinguishing populations at high risk of injurious falls from older adults in acute settings.

PATIENTS AND METHODS

A retrospective case-control study was conducted at three hospitals in Shanghai, China. Elderly patients with injurious falls from January 2014 to December 2018 were taken as cases, and control patients who did not have falls were randomly matched based on the admission date and the department. The data were collected through a medical record review and adverse events system. The original data set was randomly divided into a training set and a validation set at a 7:3 ratio. A nomogram was established based on the results of the univariate analysis and multivariate logistic regression analysis, and its discrimination and calibration were verified to confirm the accuracy of the prediction. The cut-off value of risk stratification was determined to help medical staff identify the high-risk groups.

RESULTS

A total of 115 elderly patients with injurious falls and 230 controls were identified. History of fractures, orthostatic hypotension, functional status, sedative-hypnotics and level of serum albumin were independent risk factors for injurious falls in elderly patients. The C-indexes of the training and validation sets were 0.874 (95% CI: 0.784-0.964) and 0.847 (95% CI: 0.771-0.924), respectively. Calibration curves were drawn and showed acceptable predictive performance. The cut-off values of the training and validation sets were 146.3 points (sensitivity: 73.7%; specificity: 87.5%) and 157.2 points (sensitivity: 69.2%; specificity: 85.5%), respectively.

CONCLUSION

The established nomogram facilitates the identification of high-risk populations among elderly patients, providing a new assessment tool to forecast the individual risk of injurious falls.