Factors related to the high fall rate in long-term care residents with dementia

Kosse, Nienke M.; Groot, Maartje H. de; Vuillerme, Nicolas; Hortobágyi, Tibor; Lamoth, Claudine J. C.

doi:10.1017/s104161021400249x

Cited by 44 publications

(46 citation statements)

References 27 publications

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“…The proportion of residents prescribed psychiatric medications and urinary incontinence were top factors in the logistic regression, RF, SVM polynomial, SVM radial, and SVM sigmoid. Psychiatric medication was a very strong predictor of falls, consistent with previous research [6,7,9,10,[31][32][33], but inconsistent with a couple of studies [34,35]. The proportion of residents with urinary incontinence was an important factor in falls, supported in previous research [9][10][11][12], but not supported in other studies [7,32].…”

Section: Discussionmentioning

confidence: 45%

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Lee

Ahn

Shin

et al. 2020

Preprint

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Background: To compare machine learning (ML) methods beyond traditional regression analysis using a real dataset on nurse staffing and falls in nursing homes. Methods:We used a total of 6 ML algorithms to compare the superiority of ML over traditional regression methods. We applied three representative ML algorithms-random forest (RF), logistics regression, support vector machine (SVM): linear, polynomial, radial, and sigmoid-to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models.Results: RF was the most accurate model (0.883), followed by the logistic regression model (0.867).Using RF procedures, we identified a total of 6 variables as predictors of falls including hours per resident day of administrative staff, proportion of nursing home residents with psychiatric medications, aggressive behaviors and cognitive dysfunction, urinary incontinence, current number of residents in each nursing home, and the maximum capacity of each nursing home. Conclusions:The appropriate choice of prediction model is quite challenging for nursing researchers.For effective fall management, researchers should consider organizational characteristics as well as personal factors. The examination of related factors on falls is quite meaningful in that preventing falls contributes to improving the quality of life and care of residents and decreases health care costs. BackgroundThe most frequently reported adverse event among nursing home (NH) residents, falls are associated with increased morbidity and mortality, and with reduced functioning [1-3]. The U.S. fall prevalence is 1.7 falls per resident yearly in NHs [1]; about 50% of NH residents in developed countries fall eachyear [4]. Accurate prediction of factors associated with a fall in NHs is important because nurses, health care professionals, researchers in practice, administrative staff, researchers, and politicians address fall issues. These stakeholders develop targeted fall-prevention management and assess residents based on the factors associated with a fall [5]. Prior studies identified a number of risk factors for falls, including age, sex, visual deficits, psychotropic medications, cognitive dysfunction [6][7][8], range of motion, urinary incontinence [9-12], hours per residents day (HPRD) of registered

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Section: Discussionmentioning

confidence: 45%

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Lee

Ahn

Shin

et al. 2020

Preprint

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“…Thus, Hypothesis 3 must be rejected. A clear link between mobility disability and the fall risk, which is often mentioned in the literature [54][55][56] must be critically reflected because of the current study.…”

Section: Hypothesismentioning

confidence: 89%

Prevalence of mobility disabilities in nursing home residents and the association with age, BMI, fall risk and social participation

Fehling¹,

Dassen²

2017

JNEP

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Background and aim: Physical mobility is often described as a basic prerequisite for independence, self-determination, and social participation. Older people, particularly those in long-term care facilities, are more often affected by the loss of their mobility. However, the prevalence of the mobility disabilities of nursing home residents and their association with demographic and care-specific/medical factors have so far been studied less. This prevalence study should investigate a possible association of mobility disability with age, BMI, multimorbidity, fall risk, and social participation. Methods: The prevalence study covered the data of residents in 30 German residential care facilities (RCFs). Trained staff nurses used a standardised instrument to collect data about the age, obesity, medical diagnoses, the fall risk, the level of mobility disability, and the need for supporting the social participation of the residents. Results: The representative sample included 2,066 nursing home residents, of whom 81.5% required support in their mobility. The level of a mobility disability or the need for a support in matters of mobility by others was not associated either with age, with the BMI, or with the fall risk of the participants. The groups with higher mobility disability included the more multimorbid participants with more difficult medical diagnoses, and these had a higher need for support in their social participation. Conclusions: It may be that professional care must reflect the premature link between older and obese patients with mobility disability. In addition to nursing diagnoses, medical diagnoses should necessarily be integrated into the nursing assessment. The association between the level of mobility disability and the fall risk, which is often mentioned in the literature, should be critically reflected. Nursing home residents should be informed that there is a possible association between their mobility disability and their social participation.

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“…Sixty per cent of residents in NHs fall at least once a year (Kosse, de Groot, Vuillerme, Hortobagyi, & Lamoth, ), and these falls can have serious consequences, including fractures, traumatic brain injury or depression (Terroso, Rosa, Torres Marques, & Simoes, ). Falls are also associated with a higher risk of future falls in the next year (Kwan & Straus, ).…”

Section: Introductionmentioning

confidence: 99%

Conditions and ethical challenges that could influence the implementation of technologies in nursing homes: A qualitative study

Bourbonnais

Rousseau

Lalonde

et al. 2019

Int J Older People Nursing

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Aim To explore the conditions that may influence the implementation of an interactive mobile application (app) and an intelligent videomonitoring system (IVS) in nursing homes (NHs) and the ethical challenges of their use. Background There is a lack of knowledge about implementing technologies in NHs and the ethical challenges that might arise. In past studies, nursing care teams expressed the need for technologies offering clinical support. Technologies like an IVS and an app could prove useful in NHs to prevent and manage falls and responsive behaviours. Design An exploratory qualitative study was conducted with care managers, family caregivers and formal caregivers in five NHs. Methods Each participant was shown a presentation of a potential app and a short video on an IVS. It was followed by an individual semi‐structured interview. A conventional content analysis was performed. Findings Potential users found it would be possible to implement these technologies in NHs even if resistance could be expected. To facilitate adoption and achieve clinical benefits, the implementation of technologies should be pilot‐tested, and coaching activities should be planned. Ethical risks were considered already present in NHs even without technologies, for example, risks to privacy. Strategies were proposed, for instance, to adapt the code of ethics and procedures. Some potential prejudices about the interest and abilities of older staff, nurses' aides, and family caregivers to use technology were identified. Conclusions Through rigorous and ethical implementation, technologies supporting clinical care processes could benefit older people living in NHs, as well as their relatives and the staff. Implications for practice Various strategies are proposed to successfully implement technologies. Effort should be made to avoid prejudices during implementation, and procedures should be adapted to mitigate possible ethical challenges.

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Factors related to the high fall rate in long-term care residents with dementia

Cited by 44 publications

References 27 publications

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Prevalence of mobility disabilities in nursing home residents and the association with age, BMI, fall risk and social participation

Conditions and ethical challenges that could influence the implementation of technologies in nursing homes: A qualitative study

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