Nurses as Agents of Safety: Identifying unique contributors to falls in hospitalized Adult Hematology Patients: A Retrospective Study

Falls continue to be a major safety concern in acute care settings and are the second cause of unintentional injury deaths globally [1].The World Health Organization defines a fall as an event that results in a person coming to a rest inadvertently on the ground or floor or other lower level. Prevention of a fall is a safety measure, which is significantly affected by nursing care according to the National Database of Nursing Quality Indicators (NDNQI) [2].This retrospective review of one hundred medical records was conducted to assess if there are unique contributors to falls in hospitalized adult hematology patients. The study sample was drawn from the parent study that examined the impact of video-based educational intervention on the occurrence of falls among hematology patients hospitalized for the management of cancer treatment and complications. Patients with cancer are at an increased risk of sustaining a fall related injury due to impaired functional status, low blood counts, treatment related fatigue, frailty, and poor nutritional status [3,4]. Simple logistic regression between continuous variables and dependent variable, and cross-tabulation between categorical variables and the dependent variable was used to analyze study results.The study revealed that there was a significant relationship between fall incident and fall risk assessment scores on admission; X2 (1) = 6.153, p = .013, Cramer’s V = .256.

The Impact of COVID Restrictions Among Rates of Falling in Assisted Living Residents

As a result of the COVID-19 pandemic, assisted living group activities and congregate dining stopped and residents were confined to their rooms. While this may have kept residents safer from contracting the virus, it also reduced their physical activity levels. The aim of this study was to investigate if rates of falls in one assisted living community varied as a result of COVID-19 restrictions. We analyzed fall incident reports from n=155 residents from October 2019 to October 2020. Results showed a total of n=802 falls in the year-long period (range of 1-30 falls per resident; mean = 5.17; SD=5.6 in the 12 month period). The majority (65%) of falls occurred in resident rooms. 55% of falls occurred between 6am and 6pm. The primary cause of these falls was loss of balance (30%). Comparing falls that occurred 5 months pre-restriction (Oct 2019-Feb 2020) with 5 months post-restriction (April 2020-August 2021) showed non-significant differences between time periods (p=.59). However, analyzing rates of falls by month showed a range of 46 - 88 falls by month with the lowest number occurring in winter months and the peak number of falls occurring in both Oct 2019 and 2020. Despite the majority of falls occurring in resident rooms, Covid restrictions of room confinement did not appear to impact the prevalence of falls in this sample. However, the seasonal variation warrants further research and those in assisted living should consider seasonal variations and proactively implement policies to prevent falls during these times.

The prevalence of delirium in belgian nursing homes: a cross-sectional evaluation

Background Delirium is a common geriatric syndrome, but only few studies have been done in nursing home residents. Therefore, the aim of this study was to investigate (point) prevalence of and risk factors for delirium in nursing homes in Belgium. Methods A multisite, cross-sectional study was conducted in six nursing homes in Belgium. Residents of six nursing homes were screened for delirium. Exclusion criteria were coma,‘end-of-life’ status and residing in a dementia ward. Delirium was assessed using the Delirium Observation Screening Scale. Results 338 of the 448 eligible residents were included in this study. Of the 338 residents who were evaluated, 14.2 % (95 %CI:3.94–4.81) screened positive for delirium with the Delirium Observation Screening Scale. The mean age was 84.7 years and 67.5 % were female. Taking antipsychotics (p = 0.009), having dementia (p = 0.005), pneumonia (p = 0.047) or Parkinson’s disease (p = 0.03) were more present in residents with delirium. The residents were more frequently physically restrained (p = 0.001), participated less in activities (p = 0.04), had had more often a fall incident (p = 0.007), had lower levels of cognition (p < 0.001; MoCA ≥ 26, p = 0.04; MoCA ≥ 25, p = 0.008) and a higher “Activities of Daily Living” score (p = 0.001). In multivariable binary logistic regression analysis, a fall incident (2.76; 95 %CI: 1.24–6.14) and cognitive impairment (OR: 0.69; 95 %CI: 0.63–0.77) were significantly associated with delirium. Conclusions Delirium is an important clinical problem affecting almost 15 % of the nursing home residents at a given moment. Screening of nursing home residents for risk factors and presence of delirium is important to prevent delirium if possible and to treat underlying causes when present.

Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults

The aging population has resulted in interest in remote monitoring of elderly individuals’ health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual’s pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual’s typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual’s observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject’s health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver.

Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

ObjectivesTo develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.DesignObservational, retrospective case–control study.SettingNursing homes.ParticipantsA total of 1668 (824 in part I, 844 in part II) NH-residents from the Netherlands were included. Data of participants from part I were excluded in part II.Primary and secondary outcome measuresDevelopment and validation of a fall risk CR in NH-residents. Logistic regression analysis was conducted to identify the fall risk-variables in part I. With these, three CRs were developed (ie, at the day of the fall incident and 3 days and 5 days prior to the fall incident). The overall prediction quality of the CRs were assessed using the area under the receiver operating characteristics (AUROC), and a cut-off value was determined for the predicted risk ensuring a sensitivity ≥0.85. Finally, one CR was chosen and validated in part II using a new retrospective data set.ResultsEleven fall risk-variables were identified in part I. The AUROCs of the three CRs form part I were similar: the AUROC for models I, II and III were 0.714 (95% CI: 0.679 to 0.748), 0.715 (95% CI: 0.680 to 0.750) and 0.709 (95% CI: 0.674 to 0.744), respectively. Model III (ie, 5 days prior to the fall incident) was chosen for validation in part II. The validated AUROC of the CR, obtained in part II, was 0.603 (95% CI: 0.565 to 0.641) with a sensitivity of 83.41% (95% CI: 79.44% to 86.76%) and a specificity of 27.25% (95% CI 23.11% to 31.81%).ConclusionMedication data and resident characteristics alone are not sufficient enough to develop a successful CR with a high sensitivity and specificity to predict fall risk in NH-residents.Trial registration numberNot available.

A Case Study Demonstrating the Utility of Surveillance Video in Assessing Gait Dynamics and Behavior During a Slip and Fall

As the use of surveillance video at commercial properties becomes more prevalent, it is more likely an incident involving a personal injury will be captured on film. This provides a unique opportunity for Human Factors practitioners involved in forensic investigations to analyze the behavior of the individual prior to, during, and after the event in question. It also provides an opportunity to gather unique and objective data. The present work describes a case study of a slip and fall where surveillance video and onsite measurements were combined and analyzed to quantify a plaintiff’s gait pattern. Using this type of analysis, we were able to determine that the plaintiff was likely aware that the floor was slippery and adjusted her gait and behavior prior to the slip and fall incident.

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

BALANCE EXERCISE UNTUK MENCEGAH KEJADIAN JATUH PADA LANSIA

Background. Fall incident is one of the problems that often occurs in the elderly. The falling event occurs because of decreased strength in the lower extremity muscle. The elderly need balance exercises to increase muscle strength and improve body balance. The Aim of The Study. To Analyze the effect of balance exercise on falling events in the elderly. Subject and Method. The research method used is quasi experimental. The population of 60 elderly with 52 samples was divided into 26 elderly in the intervention group and 26 elderly in the control group. The sampling technique is purposive sampling. The measuring instrument used is the fall event questionnaire and the Breg Balance Scale observation sheet, nominal data scales using McNemar and Chi-Square data analysis techniques. Result. McNemar and Chi-Square statistical test results show the value of p = 0,000 with a significance value α <0.05. Conclusion. There is an effect of balance exercise on the incidence of falls in the elderly.Keywords: balance exercise, elderly, fall eventsKorespondensi: Iswati. Program Studi D3 Keperawatan STIKES Adi Husada, Jln. Kapasari 95 Kota Surabaya, Jawa Timur. Email: [email protected]