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Background: Fall prevention exercise programmes are known to be effective, but access to these programmes is not always possible. The use of eHealth solutions might be a way forward to increase access and reach a wider population. In this feasibility study the aim was to explore the choice of programme, adherence, and self-reported experiences comparing two exercise programmes - a digital programme and a paper booklet., Methods: A participant preference trial of two self-managed fall prevention exercise interventions. Community-dwelling adults aged 70 years and older exercised independently for four months after one introduction meeting. Baseline information was collected at study start, including a short introduction of the exercise programme, a short physical assessment, and completion of questionnaires. During the four months intervention period, participants self-reported their performed exercises in an exercise diary. At a final meeting, questionnaires about their experiences, and post-assessments, were completed. For adherence analyses data from diaries were used and four subgroups for different levels of participation were compared. Exercise maintenance was followed up with a survey 12 months after study start., Results: Sixty-seven participants, with mean age 77 ± 4 years were included, 72% were women. Forty-three percent chose the digital programme. Attrition rate was 17% in the digital programme group and 37% in the paper booklet group (p = .078). In both groups 50-59% reported exercise at least 75% of the intervention period. The only significant difference for adherence was in the subgroup that completed ≥75% of exercise duration, the digital programme users exercised more minutes per week (p = .001). Participants in both groups were content with their programme but digital programme users reported a significantly higher (p = .026) degree of being content, and feeling supported by the programme (p = .044). At 12 months follow-up 67% of participants using the digital programme continued to exercise regularly compared with 35% for the paper booklet (p = .036)., Conclusions: Exercise interventions based on either a digital programme or a paper booklet can be used as a self-managed, independent fall prevention programme. There is a similar adherence in both programmes during a 4-month intervention, but the digital programme seems to facilitate long-term maintenance in regular exercise., Trial Registration: ClinTrial: NCT02916849.

Background: The rate of falls in community dwelling older people with cognitive impairment (CI) is twice that of a cognitively intact population, with almost two thirds of people with CI falling annually. Studies indicate that exercise involving balance and/or a home hazard reduction program are effective in preventing falls in cognitively intact older people. However the potential benefit of these interventions in reducing falls in people with CI has not been established.This randomised controlled trial will determine whether a tailored exercise and home hazard reduction program can reduce the rate of falls in community dwelling older people with CI. We will determine whether the intervention has beneficial effects on a range of physical and psychological outcome measures as well as quality of life of participants and their carers. A health economic analysis examining the cost and potential benefits of the program will also be undertaken., Methods and Design: Three hundred and sixty people aged 65 years or older living in the community with CI will be recruited to participate in the trial. Each will have an identifiable carer with a minimum of 3.5 hours of face to face contact each week.Participants will undergo an assessment at baseline with retests at 6 and 12 months. Participants allocated to the intervention group will participate in an exercise and home hazard reduction program tailored to their cognitive and physical abilities.The primary outcome measure will be the rate of falls which will be measured using monthly falls calendars. Secondary outcome measures will include the risk of falling, quality of life, measures of physical and cognitive function, fear of falling and planned and unplanned use of health services. Carers will be followed up to determine carer burden, coping strategies and quality of life., Discussion: The study will determine the impact of this tailored intervention in reducing the rate of falls in community dwelling older people with CI as well as the cost-effectiveness and adherence to the program. The results will have direct implications for the design and implementation of interventions for this high-risk group of older people., Trial Registration: The protocol for this study is registered with the Australian New Zealand Clinical Trials Registry - ACTRN12614000603617.

Abstract Background Fall prevention exercise programmes are known to be effective, but access to these programmes is not always possible. The use of eHealth solutions might be a way forward to increase access and reach a wider population. In this feasibility study the aim was to explore the choice of programme, adherence, and self-reported experiences comparing two exercise programmes – a digital programme and a paper booklet. Methods A participant preference trial of two self-managed fall prevention exercise interventions. Community-dwelling adults aged 70 years and older exercised independently for four months after one introduction meeting. Baseline information was collected at study start, including a short introduction of the exercise programme, a short physical assessment, and completion of questionnaires. During the four months intervention period, participants self-reported their performed exercises in an exercise diary. At a final meeting, questionnaires about their experiences, and post-assessments, were completed. For adherence analyses data from diaries were used and four subgroups for different levels of participation were compared. Exercise maintenance was followed up with a survey 12 months after study start. Results Sixty-seven participants, with mean age 77 ± 4 years were included, 72% were women. Forty-three percent chose the digital programme. Attrition rate was 17% in the digital programme group and 37% in the paper booklet group (p = .078). In both groups 50–59% reported exercise at least 75% of the intervention period. The only significant difference for adherence was in the subgroup that completed ≥75% of exercise duration, the digital programme users exercised more minutes per week (p = .001). Participants in both groups were content with their programme but digital programme users reported a significantly higher (p = .026) degree of being content, and feeling supported by the programme (p = .044). At 12 months follow-up 67% of participants using the digital programme continued to exercise regularly compared with 35% for the paper booklet (p = .036). Conclusions Exercise interventions based on either a digital programme or a paper booklet can be used as a self-managed, independent fall prevention programme. There is a similar adherence in both programmes during a 4-month intervention, but the digital programme seems to facilitate long-term maintenance in regular exercise. Trial registration ClinTrial: NCT02916849 .

Background: Fall-related injuries are a significant cause of morbidity and mortality in people with dementia. There is presently little evidence to guide the management of such injuries, and yet there are potentially substantial benefits to be gained if the outcomes of these injuries could be improved. This study aimed to design an appropriate new health-care intervention for people with dementia following a fall and to assess the feasibility of its delivery in the UK NHS., Objectives: To determine whether or not it is possible to design an intervention to improve outcomes of falls in dementia, to investigate the feasibility and acceptability of the DIFRID (Developing an Intervention for Fall related Injuries in Dementia) intervention and to investigate the feasibility of a future randomised controlled trial and the data collection tools needed to evaluate both the effectiveness and the cost-effectiveness of the DIFRID intervention., Design: This was a mixed-methods feasibility study. A systematic review (using Cochrane methodology) and realist review [using Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) methodology] explored the existing evidence base and developed programme theories. Searches were carried out in November 2015 (updated in January 2018) for effectiveness studies and in August 2016 for economic studies. A prospective observational study identified service use via participant diary completion. Qualitative methods (semistructured interviews, focus groups and observation) were used to explore current practice, stakeholder perspectives of the health and social care needs of people with dementia following a fall, ideas for intervention and barriers to and facilitators of change. Each of the resulting data sets informed intervention development via Delphi consensus methods. Finally, a single-arm feasibility study with embedded process evaluation was conducted., Setting: This study was set in the community., Participants: The participants were (1) people with dementia presenting with falls necessitating health-care attention in each setting (primary care, the community and secondary care) at three sites and their carers, (2) professionals delivering the intervention, who were responsible for training and supervision and who were members of the intervention team, (3) professionals responsible for approaching and recruiting participants and (4) carers of participants with dementia., Interventions: This was a complex multidisciplinary therapy intervention. Physiotherapists, occupational therapists and support workers delivered up to 22 sessions of tailored activities in the home or local area of the person with dementia over a period of 12 weeks., Main Outcome Measures: (1) Assessment of feasibility of study procedures; (2) assessment of the acceptability, feasibility and fidelity of intervention components; and (3) assessment of the suitability and acceptability of outcome measures for people with dementia and their carers (number of falls, quality of life, fear of falling, activities of daily living, goal-setting, health-care utilisation and carer burden)., Results: A multidisciplinary intervention delivered in the homes of people with dementia was designed based on qualitative work, realist review and recommendations of the consensus panel. The intervention was delivered to 11 people with dementia. The study suggested that the intervention is both feasible and acceptable to stakeholders. A number of modifications were recommended to address some of the issues arising during feasibility testing. The measurement of outcome measures was successful., Conclusions: The study has highlighted the feasibility of delivering a creative, tailored, individual approach to intervention for people with dementia following a fall. Although the intervention required greater investment of time than usual practice, many staff valued the opportunity to work more closely with people with dementia and their carers. We conclude that further research is now needed to refine this intervention in the context of a pilot randomised controlled trial., Trial Registration: Current Controlled Trials ISRCTN41760734 and PROSPERO CRD42016029565., Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment ; Vol. 23, No. 59. See the NIHR Journals Library website for further project information., Competing Interests: Louise Robinson reports grants from the National Institute for Health Research (NIHR) Professor award and a NIHR Senior Investigator award outside the submitted work. Lynn Rochester reports grants from the Medical Research Council, the European Union, the Engineering and Physical Sciences Research Council, the Wellcome Trust (London, UK), Parkinson’s UK (London, UK), NIHR [Programme Development Grant, Physical Activity Interventions to Improve Outcomes for people with Rare Neurological Conditions (PARC), co-applicant (2018–20); and Health Technology Assessment, PDSAFE: a randomised controlled trial of the effectiveness of PDSAFE to prevent falls among people with Parkinson’s disease, co-applicant (2013–17)] and the Stroke Association (London, UK) outside the submitted work.

Frigault, J.S., and Giles, A.R. Understanding Fall Risk Factors for Inuvialuit Elders in Inuvik, Northwest Territories, Canada. Gallagher, C.P., and Lea, E.V. Overwintering Habitat of American Dipper, Cinclus mexicanus, in [...]

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

Objective: Knee osteoarthritis (OA) has been suggested to increase the risk of falls. Low back pain (LBP) is a potential risk factor for falls in people with knee OA, but this issue has not been addressed adequately in previous studies. The objective of this study was to investigate the relationship between LBP and falls in people with knee OA in a 12-month period., Methods: Participants with knee OA (Kellgren/Lawrence [K/L] grade ≥1) completed questionnaires for LBP and falls that occurred in the preceding 12 months. Binary and ordinal logistic regression analyses were performed to assess the relationship between LBP or moderate-to-severe LBP (numeric rating scale ≥4 points) and any fall (≥1 fall) or recurrent falls (≥2 falls) after adjustment for age, sex, K/L grade, knee pain severity, and quadriceps strength. Sensitivity analyses were performed excluding people with sciatica, nonchronic LBP, K/L grade 1, and those receiving pain medications., Results: We included 189 participants (ages 61-90 years, 78.3% women) in this study. Of these participants, 41 (21.6%) reported falls in the preceding 12 months. People with any LBP (n = 101) and those with moderate-to-severe LBP (n = 45) had 2.7- and 3.7-times higher odds of recurrent falls, respectively. Sensitivity analyses revealed a strong correlation between moderate-to-severe LBP and recurrent falls., Conclusion: Thorough investigation of LBP as a risk factor for recurrent falls in people with knee OA may provide a novel insight into the pathomechanics of recurrent falls in this population., (© 2020, American College of Rheumatology.)

The article presents a commentary on how group-format dual-task training reduces dual-task interference in mobility tasks in people with chronic stroke. Topics discussed include potential advantages in terms of increasing social interactions, improving adherence, and reducing costs; simultaneous performance practice of walking and concurrent tasks across numerous participants; and reductions in dual-task interference effects were observed only in the group receiving dual-task training.

The article presents a synopsis on group-format dual-task training which reduces dual-task interference in mobility tasks in people with chronic stroke. Topics discussed include potential advantages in terms of increasing social interactions, improving adherence, and reducing costs; simultaneous performance practice of walking and concurrent tasks across numerous participants; and reductions in dual-task interference effects were observed only in the group receiving dual-task training.

The proportion of aged populations is increasing worldwide. Exercise has a palliating effect on some adverse implications of aging. Multicomponent training (MCT) is a recommended form of exercise for the aged population. The aims of this research were to (1) study the number of publications regarding MCT in the aged population following an exponential growth rate; (2) identify the journals, authors, and countries that stand out the most in this area; and (3) describe the most common themes and used keywords in this field. The analysis was performed through the traditional laws of bibliometrics, including, Price's, Lotka's, Bradford's, and Zipf's law. All documents published in journals indexed in the Web of Science (WoS) Core Collection from 2001 to November 2023 that met the inclusion criteria were included. The 485 documents included in this review revealed that the number of annual publications experienced an exponential growth phase, 15 journals with six or more publications formed the core journals on this topic, and the author Mikel Izquierdo and his collaborative network topped the lists of prominent and prolific co-authors. Spain was the leading country in number of publications. Various thematic lines and keywords regarding strength, sarcopenia, quality of life, falls, balance, dual-task exercise, and cognitive and physical functioning were identified. In conclusion, this work confirmed that research on this topic is going through an exponential growth phase and provided detailed information about the journals, authors, and countries involved in the subject, as well as the keywords most frequently used in the subject matter. [ABSTRACT FROM AUTHOR]

Introduction: Blind and visually impaired individuals, an under-represented population of the emergency department (ED), possess comorbidities and have a higher chance of in-hospital sequelae, including falls. This potentially vulnerable population, if not treated mindfully, can be subject to decreased quality of care, recurrent and/or longer hospitalizations, persistence of health issues, increased incidence of falls, and higher healthcare costs. For these reasons, it is crucial to implement holistic practices and train clinicians to treat blind and visually impaired patients in the ED setting. Methods: We identified and used a comprehensive article describing best practices for the care of blind and visually impaired patients to establish the ED-specific recommendations presented in this paper. A scoping review of the literature was then performed using PubMed to identify additional articles to support each recommendation. To ensure that recommendations could be implemented in a representative, scalable, and sustainable manner, we consulted an advocate for the blind to help refine and provide additional suggestions. Results: We identified 14 recommendations that focus on communication strategies, ED resource access, and continuity of care. The main recommendation is for the clinician to support the unique healthcare needs of the visually impaired individual and maintain the patient's autonomy. Another recommendation is the consistent use of assistive devices (eg, canes, guide dogs) to aid patients to safely ambulate in the ED. Also identified as best practices were discharge education with the use of a screen reader and timely follow-up with a primary care physician. Conclusion: While we summarize a variety of recommendations in this article, it is important to implement only the strategies that work best for the patients, personnel, and environment specific to your ED. After implementation, it is vital to refine (as frequently as needed) the interventions to optimize the strategies. This will enable the provision of exceptional and equal care to blind and visually impaired patients in the ED. [ABSTRACT FROM AUTHOR]

Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.

To address the challenges in recognizing various types of falls, which often exhibit high similarity and are difficult to distinguish, this paper proposes a human fall classification system based on the SE-Residual Concatenate Network (SE-RCNet) with adaptive weighted fusion. First, we designed the innovative SE-RCNet network, incorporating SE modules after dense and residual connections to automatically recalibrate feature channel weights and suppress irrelevant features. Subsequently, this network was used to train and classify three types of radar images: time-distance images, time-distance images, and distance-distance images. By adaptively fusing the classification results of these three types of radar images, we achieved higher action recognition accuracy. Experimental results indicate that SE-RCNet achieved F1-scores of 94.0%, 94.3%, and 95.4% for the three radar image types on our self-built dataset. After applying the adaptive weighted fusion method, the F1-score further improved to 98.1%.

Forensic reconstruction and scenario evaluation are crucial in investigations of suspicious deaths related to falls from a height. In such cases, distinguishing between accidental falls, being pushed or jumping is an important but difficult task, since objective methods to do so are currently lacking. This paper explores the possibility of repurposing a passive rigid body model of a human from commercially available crash simulation software for forensic reconstruction and scenario evaluation of humans dropping from heights. To use this approach, a prerequisite is that the human body model can produce realistic movements compared to those of a real human, given similar environmental conditions. Therefore, this study assessed the validity of the commercially available Simcenter Madymo Pedestrian Model (MPM) for simulating human fall movements. Experimental kinematic and kinetic data was collected from nine participants, who dropped from a height in three different ways: passively tilting over, getting pushed, and jumping. Next, the performance of the MPM in reproducing the kinematics of the experimental falls was assessed by comparing the orientation of the body 0.3 s after platform release. The results show that the MPM currently does not consistently reproduce the experimentally recorded falling movements across multiple falling conditions and outcome measures. The MPM must therefore be adapted if to be used for forensic reconstruction and scenario evaluation, for example by implementing active movement., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network's recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos.

Background: Identifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challenge in analysing vast amounts of data to identify factors that could enhance patient safety., Objective: To determine fall-associated factors and develop high-performance prediction tools for at-risk patients in acute and sub-acute care services in Australia., Methods: A retrospective study of 672,400 patients admitted to acute and sub-acute care services within a large metropolitan tertiary health service in Victoria, Australia, between January 1, 2019, and December 31, 2021. Data were obtained from four sources: the Department of Health Victorian Admitted Episodes Dataset, RiskMan TM , electronic health records, and the health workforce dataset. Machine learning techniques, including Random Forest and Deep Neural Network models, were used to analyse the data, predict patient falls, and identify the most important risk factors for falls in this population. Model performance was evaluated using accuracy, F1-score, precision, recall, specificity, Matthew's correlation coefficient, and the area under the receiver operating characteristic curve (AUC)., Results: The deep neural network and random forest models were highly accurate in predicting hospital patient falls. The deep neural network model achieved an accuracy of 0.988 and a specificity of 0.999, while the RF achieved an accuracy of 0.989 and a specificity of 1.000. The top 20 variables impacting falls were compared across both models, and 12 common factors were identified. These factors can be broadly classified into three categories: patient-related factors, staffing-related factors, and admission-related factors. Although not all factors are modifiable, they must be considered when planning fall prevention interventions., Conclusion: The study demonstrated machine learning's potential to predict falls and identify key risk factors. Further validation across diverse populations and settings is essential for broader applicability., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait., Method: The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset., Results: VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications., Conclusion: The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common., (© 2024. The Author(s).)

Multiple sclerosis (MS) is a chronic inflammatory condition that causes demyelination of the central nervous system accompanied by a wide range of symptoms. The high prevalence of falls among patients diagnosed with MS within the initial six months highlights the importance of this issue. The objective of this study is to identify factors associated with falls in MS patients in order to increase awareness and reduce the risk of falls. This scoping review used specific Mesh terms to formulate the literature search around falls and MS using Medline, Google Scholar, Scopus, and Embase search engines. English papers published between 2012 and 2022, studies with a clear definition of falls, McDonald's diagnostic criteria for MS, and those with Expanded Disability Status Scale (EDSS) or Patient Determined Disease Steps (PDDS) scores were included. Critical data from the selected articles were extracted and classified according to the different factors associated with falls in MS patients. Eighteen articles were included in this review. The most important factors associated with falls in MS patients identified were the severity and progression of the disease, mobility and balance problems, bladder dysfunction, fear of falling, fatigue, and cognitive dysfunction. In conclusion, this scoping review yielded the most common factors associated with falls in patients with MS. Study findings can be used to develop future interventions focusing on improving mobility, proprioception, and balance to decrease fall risk and injury amongst MS patients., Competing Interests: The authors report no conflicts of interest in this work., (© 2024 Kaddoura et al.)

The article discusses a study which describes the Multi-Angle Snowflake Camera (MASC) instrument for capturing hydrometeors in freefall. The study involves stereoscopic photographs captures by continous measurements from high altitude location in Wasatch Front in Utah for two months. It also illustrates the importance of MASC in refining microwave scattering calculations.

Objective: The consequences of falls in the elderly are severe, ranging from skin abrasion to hip fracture, which is very easy to cause death. Using advanced technology to develop anti-fall clothing that meets the needs of the elderly can play a significant role in protecting the elderly. By reviewing and analyzing the existing literature on the importance of fall protection clothing in reducing falls and protecting the body of the elderly, it is hoped to explore further research that needs improvement. Methods: Guided by the preferred reporting items for systematic reviews and meta-analyses, eight related studies were identified through Web of Science, Scopus and Chinese National Knowledge Infrastructure. The research objects, approaches, material and equipment, protection principle, and survey results are extracted. Results: Two articles verified the fall detection algorithm adopted in the research through experiments, which significantly improved fall detection accuracy. Six papers found that selecting appropriate cushioning materials can effectively reduce the consequences of falls of the elderly through experimental comparative analysis. Finally, three attributes for significant design value are drawn: (1) size and fit; (2) cushioning materials; (3) wearable sensing elements. Conclusions: Anti-fall clothing can effectively protect the elderly when they fall. Further design experiments are needed to select appropriate cushioning materials and wearable sensing elements based on anthropometry and aesthetics to design the style and structure of the garment to achieve the purpose of protecting the elderly. [ABSTRACT FROM AUTHOR]

Background: Around 10–25% of emergency calls for adults aged over 65 are attributed to falls. Regardless of whether injuries are caused, quality of life is often affected by fear of falling, leading to reduced confidence and activity, negatively impacting mobility and risking depression and isolation. Ambulance service staff are well placed to identify falls risk factors so patients can be directed to falls prevention services. This article aims to determine how the referral by paramedics of uninjured falls patients to community falls services may reduce future falls and emergency services use. Methods: The CINAHL, MEDLINE and AMED electronic databases, grey literature sources and reference lists of relevant papers were systematically searched to identify primary research of an experimental design. Studies were eligible if they included elderly patients, aged over 65, who had received a paramedic response following a fall, were found to be uninjured and who were referred to local falls services rather than being transported to hospital. The study outcomes were required to include the rate of subsequent falls and emergency service use. Results: Four papers from three studies were included in the review following quality assessment. Results were not always statistically significant but showed a reduction in subsequent falls, particularly in the high-risk population, and in emergency service call-outs. A consistent positive effect was seen on the patients' well-being and independence related to activities of daily living across the studies. Conclusions: The relationship between paramedics and local falls services has changed the pre-hospital management of these patients. Generally, access to a falls-specific care package has proved beneficial in supporting independence and reducing unnecessary transport to hospital. Further research into the uptake of this care pathway by paramedics now it is more established may be useful, as would research into barriers to adherence of the elderly to such an intervention. [ABSTRACT FROM AUTHOR]

Postural control impairment is one of the primary motor symptoms in patients with Parkinson’s disease, leading to an increased risk of falling. Several studies have been conducted on postural control disorders in Parkinson’s disease patients, but no relevant bibliometric analysis has been found. In this paper, the Web of Science Core Collection database was searched for 1,295 relevant papers on postural control in Parkinson’s disease patients from December 2011 to December 2021. Based on the Citespace knowledge graph, these relevant papers over the last decade were analyzed from the perspectives of annual publication volume, countries and institutes cooperation, authors cooperation, dual-map overlay of journals, co-citation literature, and keywords. The purpose of this study was to explore the current research status, research hotspots, and frontiers in this field, and to provide a reference for further promoting the research on postural control in Parkinson’s disease patients. [ABSTRACT FROM AUTHOR]