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Optimizing and Implementing a Community-Based Group Fall Prevention Program

Falls and fall-related injuries among older adults are associated with decreased health. Therefore, fall prevention programs (FPPs) are increasingly important. However, the translation of such complex programs into clinical practice lacks insight into factors that influence implementation. Therefore, the aim of this study was to identify how to optimize and further implement a widely used group-based FPP in the Netherlands among participants, therapists and stakeholders using a mixed methods study. FPP participants and therapists filled out a questionnaire about their experiences with the FPP. Moreover, three focus groups were conducted with FPP participants, one with therapists and one with other stakeholders. Data were analysed according to the thematic analysis approach of Braun and Clarke. Overall, 93% of the 104 FPP participants were satisfied with the FPP and 86% (n = 12) of the therapists would recommend the FPP to older adults with balance or mobility difficulties. Moreover, six themes were identified regarding further implementation: (1) recruiting and motivating older adults to participate; (2) structure and content of the program; (3) awareness, confidence and physical effects; (4) training with peers; (5) funding and costs; and (6) long-term continuation. This study resulted in practical recommendations for optimizing and further implementing FPPs in practice.

Characteristics of daily life gait in fall and non fall-prone stroke survivors and controls

Background: Falls in stroke survivors can lead to serious injuries and medical costs. Fall risk in older adults can be predicted based on gait characteristics measured in daily life. Given the different gait patterns that stroke survivors exhibit it is unclear whether a similar fall-prediction model could be used in this group. Therefore the main purpose of this study was to examine whether fall-prediction models that have been used in older adults can also be used in a population of stroke survivors, or if modifications are needed, either in the cut-off values of such models, or in the gait characteristics of interest. Methods: This study investigated gait characteristics by assessing accelerations of the lower back measured during seven consecutive days in 31 non fall-prone stroke survivors, 25 fall-prone stroke survivors, 20 neurologically intact fall-prone older adults and 30 non fall-prone older adults. We created a binary logistic regression model to assess the ability of predicting falls for each gait characteristic. We included health status and the interaction between health status (stroke survivors versus older adults) and gait characteristic in the model. Results: We found four significant interactions between gait characteristics and health status. Furthermore we found another four gait characteristics that had similar predictive capacity in both stroke survivors and older adults. Conclusion: The interactions between gait characteristics and health status indicate that gait characteristics are differently associated with fall history between stroke survivors and older adults. Thus specific models are needed to predict fall risk in stroke survivors.

Vallen : incidentie en risicofactoren bij patiënten met eenberoerte, een systematisch literatuuronderzoek

Falls are common after stroke. This article presents a literature review of the incidence and risk factors of falls and the consequences for professionals working with stroke patients. It is important to consider the specific problems after stroke. Depression and cognitive impairments were found to be risk factors for fall incidents after stroke. In the relevant literature many different risk factors and circumstances are described. When patients move from bed to chair, walk to the bathroom and the first few days after the patient is discharged to another setting, - all these circumstances showed high percentages of falling. A fall during hospital stay is a significant risk factor for future fall incidents. A reliable index to measure the fall risk is not (yet) available. But scores on the Barthel Index and the Timed-Up-and-Go test can be used as fall risk indicators. Fear of falling is an important complication after a fall and therefore it is recommended prior to discharge to inquire about the patient’s self efficacy in maintaining balance. Few intervention studies use the number of falls as an outcome measure. Exercising balance following a mass training protocol seems to diminish the risk of falling.

Deep learning to predict falls in older adults based on daily-life trunk accelerometry

Trends and projections in fall death in the Netherlands from 1990 to 2045

Background: Increasing life expectancy in high-income countries has been linked to a rise in fall mortality. In the Netherlands, mortality rates from falls have increased gradually from the 1950s, with some indication of stabilisation in the 1990s. For population health and clinical practice, it is important to foresee the future fall mortality trajectories. Methods: A graphical approach was used to explore trends in mortality by age, calendar period and cohorts born in the periods of 1915–1945. Population data and the numbers of people with accidental fall fatality as underlying cause of death from 1990 to 2021 were derived from Statistics Netherlands. Age-standardised mortality rates of unintentional falls per 100 000 population were calculated by year and sex. A log-linear model was used to examine the separate effects of age, period and cohort on the trend in mortality and to produce estimates of future numbers of fall deaths until 2045. Results: While the total population increased by 17% between 1990 and 2021, absolute numbers of fall-related deaths rose by 230% (from 1584 to 5234), which was 251% (an increase of 576 deaths in 1990 to 2021 deaths in 2020) for men and 219% (from 1008 to 3213) for women. Age-standardised figures were higher for women than men and increased more over time. In 2020, 79% of those with death due to falls were over the age of 80, and 35% were 90 years or older. From 2020 to 2045, the observed and projected numbers of fall deaths were 2021 and 7073 for men (250% increase) and 3213 and 12 575 for women (291% increase). Conclusion: Mortality due to falls has increased in the past decades and will continue to rise sharply, mainly caused by growing numbers of older adults, especially those in their 80s and 90s. Contributing risk factors are well known, implementation of preventive measures is a much needed next step. An effective approach to managing elderly people after falls is warranted to reduce crowding in the emergency care and reduce unnecessary long hospital stays.

Building a Brain

Many attempts have been made to build an artificial brain. This paper aims to contribute to the conceptualization of an artificial learning system that functionally resembles an organic brain in a number of important neuropsychological aspects. Probably the techniques (algorithms) required are already available in various fields of artificial intelligence. However, the question is how to combine those techniques. The combination of truly autonomous learning, in which "accidental" findings (serendipity) can be used without supervision, with supervised learning from both the surrounding and previous knowledge, is still very challenging. In the event of changed circumstances, network models that can not utilize previously acquired knowledge must be completely reset, while in representation-driven networks, new formation will remain outside the scope, as we will argue. In this paper considerations to make artificial learning functionally similar to organic learning, and the type of algorithm that is necessary in the different hierarchical layers of the brain are discussed. To this end, algorithms are divided into two types: conditional algorithms (CA) and completely unsupervised learning. It is argued that in a conceptualisation of an artificial device that is functional similar to an organic learning system, both conditional learning (by applying CA’s), and non-conditional (supervised) learning must be applied.