UNASSIGNED: Accidental falls among adult cancer survivors are a health concern. Falls impose economic burdens and detrimental consequences to cancer survivors. This review aimed to synthesize findings from published research to explore the relationship between falls and cancer diagnosis and treatment among cancer survivors.
UNASSIGNED: A scoping review was conducted using four databases (Medline, EMBASE, CINAHL, and Scopus) for the years 2001-2021. A total of 425 abstracts were identified after removing duplicates. A second search for the years 2022-2023 was completed where 80 abstracts were identified. Abstract screening, full-text review, and data extraction were conducted. Study characteristics and key findings were extracted from full texts. Descriptive numerical summaries were presented, and narrative analyses were performed.
UNASSIGNED: A total of 42 articles were included in the scoping review which demonstrated (1) an increased prevalence of falls among cancer survivors, (2) the presence of cancer-specific fall risk factors, (3) a lack of cancer-specific fall prediction tools, and (4) few fall prevention interventions as part of usual care among cancer survivors. Younger cancer survivors were underrepresented. Cancer survivors should be aware of their risk of falls, and health professionals should ensure that fall prevention is part of usual care.
Falls are associated with cancer survivorship and as there are more people living with and beyond cancer, falls are becoming more significant.There are cancer-specific fall risk factors relevant to cancer survivors which can contribute to increased fall risk.However, fall prevention may not be addressed in standard care for cancer survivors.This review suggests cancer-specific fall risk tools are needed, and that fall prevention should be part of oncologic care.

Falls in older people are a result of a combination of multiple risk factors. There are few studies involving predictive models in a community context. The aim of this study was to determine the validation of a new model for predicting fall risk in older adults (65+) living alone in community dwellings (n = 186; n = 117) with a test-retest reliability study. We consider in the predictive model the significant factors emerged from the bivariate analysis: age, zone, social community resources, physical exercise, self-perception of health, difficulty to keep standing, difficulty to sit and get up from a chair, strain to see, use of technical devices, hypertension and number of medications. The final model explained 28.5% of the risk of falling in older adults living alone in community dwellings. The AUC = 0.660 (se = 0.065, IC 95% 0.533-0.787, p = 0.017). The predictive model developed revealed a satisfactory discriminatory performance of the model and can contribute to clinical practice, with respect to the evaluation of risk of falling in this frailty group and preventing falls.

Postal screening has not previously been validated as a method for identifying fall and fracture risk in community-dwelling populations. We examined prognostic performance of a postal risk screener used in the UK Prevention of Falls Injury Trial (PreFIT; ISRCTN71002650), to predict any fall, recurrent falls, and fractures over 12 months. We tested whether adding variables would improve screener performance.
Nine thousand eight hundred and eight community-dwelling participants, aged 70 years and older, and 63 general practices in the UK National Health Service (NHS) were included in a large, pragmatic cluster randomised trial comparing screen and treat fall prevention interventions. The short postal screener was sent to all participants in the trial intervention arms as an A4 sheet to be completed and returned to the GP (n = 6,580). The postal screener items were embedded in the baseline pre-randomisation postal questionnaire for all arms of the trial (n = 9,808). We assessed discrimination and calibration using area under the curve (AUC). We identified additional predictors using data from the control arm and applied these coefficients to internal validation models in the intervention arm participants. We used logistic regression to identify additional predictor variables.
A total of 10,743 falls and 307 fractures were reported over 12 months. Over one third of participants 3,349/8,136 (41%) fell at least once over 12 month follow up. Response to the postal screener was high (5,779/6,580; 88%). Prediction models showed similar discriminatory ability in both control and intervention arms, with discrimination values for any fall AUC 0.67 (95% CI 0.65 to 0.68), and recurrent falls (AUC 0.71; 95% CI 0.69, 0.72) but poorer discrimination for fractures (AUC 0.60; 95% CI 0.56, 0.64). Additional predictor variables improved prediction of falls but had modest effect on fracture, where AUC rose to 0.71 (95% CI 0.67 to 0.74). Calibration slopes were very close to 1.
A short fall risk postal screener was acceptable for use in primary care but fall prediction was limited, although consistent with other tools. Fracture and fall prediction were only partially reliant on fall risk although were improved with the additional variables.

Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an integrated approach consisting of body kinematics and machine learning. The model data consist of video recordings collected in the UP-Fall Detection dataset experiment. Three models based on long-short-term memory (LSTM) network-4p-SAFE, 5p-SAFE, and 6p-SAFE for four, five, and six parameters-were developed in this work. The parameters needed for these models consist of some coordinates and angles extracted from videos. These models are easy to apply to the sequential images collected by ordinary cameras, which are installed everywhere, especially on aged-care premises. The accuracy of predictions was as good as 98%. Finally, the authors discuss that, by applying these models, the health and wellness of adults and elderlies will be considerably promoted.

Falls are associated with impairment in postural control in people with Parkinson\'s disease (PwPD). We aimed to predict the fall risk through models combining postural responses with clinical and cognitive measures. Also, we compared the center of pressure (CoP) between PwPD fallers and non-fallers after unpredictable external perturbations. We expected that CoP parameters combined with clinical and cognitive measures would predict fall risk. Seventy-five individuals participated in the study. CoP parameters were measured during postural responses through five trials with unpredictable translations of the support-surface in posterior direction. Range and peak of CoP were analyzed in two periods: early and late responses. Time to peak (negative peak) and recovery time were analyzed regardless of the periods. Models included the CoP parameters in early (model 1), late responses (model 2), and temporal parameters (model 3). Clinical and cognitive measures were entered into all models. Twenty-nine participants fell at least once, and 46 PwPD did not fall during 12 months following the postural assessment. Range of CoP in late responses was associated with fall risk (p = .046). However, although statistically non-significant, this parameter indicated low accuracy in predicting fall risk (area under the curve = 0.58). Fallers presented a higher range of CoP in early responses than non-fallers (p = .033). In conclusion, although an association was observed between fall risk and range of CoP in late responses, this parameter indicated low accuracy in predicting fall risk in PwPD. Also, fallers demonstrate worse postural control during early responses after external perturbations than non-fallers, measured by CoP parameters.

Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults\' independent living and well-being.