BACKGROUND: Falls and fall-related injuries among older adults are a serious threat to the quality of life and result in high healthcare and societal costs. Despite evidence that falls can be prevented by fall prevention programmes, practical barriers may challenge the implementation of these programmes. In this study, we will investigate the effectiveness and cost-effectiveness of In Balance, a fourteen-week, low-cost group fall prevention intervention, that is widely implemented in community-dwelling older adults with an increased fall risk in the Netherlands. Moreover, we will be the first to include cost-effectiveness for this intervention. Based on previous evidence of the In Balance intervention in pre-frail older adults, we expect this intervention to be (cost-)effective after implementation-related adjustments on the target population and duration of the intervention.METHODS: This study is a single-blinded, multicenter randomized controlled trial. The target sample will consist of 256 community-dwelling non-frail and pre-frail adults of 65 years or older with an increased risk of falls. The intervention group receives the In Balance intervention as it is currently widely implemented in Dutch healthcare, which includes an educational component and physical exercises. The physical exercises are based on Tai Chi principles and focus on balance and strength. The control group receives general written physical activity recommendations. Primary outcomes are the number of falls and fall-related injuries over 12 months follow-up. Secondary outcomes consist of physical performance measures, physical activity, confidence, health status, quality of life, process evaluation and societal costs. Mixed model analyses will be conducted for both primary and secondary outcomes and will be stratified for non-frail and pre-frail adults.DISCUSSION: This trial will provide insight into the clinical and societal impact of an implemented Dutch fall prevention intervention and will have major benefits for older adults, society and health insurance companies. In addition, results of this study will inform healthcare professionals and policy makers about timely and (cost-)effective prevention of falls in older adults.TRIAL REGISTRATION: Netherlands Trial Register: NL9248 (registered February 13, 2021).
BackgroundA key factor in successfully preventing falls, is early identification of elderly with a high risk of falling. However, currently there is no easy-to-use pre-screening tool available; current tools are either not discriminative, time-consuming and/or costly. This pilot investigates the feasibility of developing an automatic gait-screening method by using a low-cost optical sensor and machinelearning algorithms to automatically detect features and classify gait patterns.MethodParticipants (n = 204, age 27 ± 7 yrs.) performed a gait test under two conditions: control and with distorted depth perception (induced by wearing special goggles). Each test consisted of 4x 3m walking at comfortable speed. Full-body 3D kinematics were captured using an optical sensor (Microsoft Xbox One Kinect). Tests were conducted in a public space to establish relatively 'natural' conditions. Data was processed in Matlab and common spatiotemporal variables were calculated per gait section. The 3D-time series data of the centre of mass for each section was used as input for a neural network, that was trained to discriminate between the two conditions.ResultsWearing the goggles affected the gait pattern significantly: gait velocity and step length decreased, and lateral sway increased compared to the control condition. A 2-layer neural network could correctly classify 79% of the gait segments (i.e. with or without distorted vision).ConclusionsThe results show that gait patterns of healthy people with distorted vision could automatically be classified with the proposed approach. Future work will focus on adapting this model for identification of specific physical risk-factors in elderly.
BackgroundA valuable opportunity for reducing the fall incidence in hospitals, is alerting nurses when a patient is about to fall. For such a fall prevention system, more knowledge is needed on what occurs right before a fall. This can be achieved with a stereo camera that automatically detects (and records) dangerous situations.MethodsInpatients with a high risk of falling are selected for inclusion. A fall-risk questionnaire is administered and falls are logged during their stay. A stereo-camera (3D BRAVO-EagleEye system) is mounted in the ceiling and monitors the bed with surroundings. A baseline recording is made to improve the algorithms behind the alert system. When a fall or dangerous situation is detected, monitoring data preceding the incident is stored. Data is analyzed to assess 1) the quality of the system and 2) the prevalence of dangerous situations. Interviews with senior nurses are included in the evaluation.ResultsData collection is ongoing (Currently n=18; falls=1), and currently consists of ±62 hour of baseline recordings and ±24 hour of event-based recordings. These recordings include false positives as well as actual high risk situations.ConclusionsDespite the initial enthusiasm of the participating departments, inclusion of participants is slow, and the number of falls lower than expected. Possible explanations for this have been discussed with the involved senior nurses. With the monitoring data we gained more insight into the occurrence of dangerous situations, but to be able to reliably predict falls, more data on actual fallsshould be recorded.