Design of interactive stations for patients suffering from severe impairmentscaused byAlzheimer’sdisease (the Tipatsma device)

Les troubles de la déambulation et de la manipulation incessante d’objets sont un phénomène fréquent chez les malades à un stade avancé de la maladie d’Alzheimer. Leur prise en charge est très complexe pour les institutions et peu de solutions satisfaisantes ont jusqu’à présent été proposées. Tableau interactif pour patients atteints de troubles sévères de la maladie d’Alzheimer (Tipatsma) se veut porteur d’une solution à destination de ces patients et de leur entourage. S’inscrivant dans une proposition thérapeutique non médicamenteuse, il met à portée de main un support d’activités sensorimotrices et cognitives placé sur le parcours de déambulation. Cet article retrace le processus itératif de conception et d’évaluation de la matérialisation de ce dispositif, dans une démarche constructiviste. Les deux itérations successives ont permis de mieux cerner les besoins des patients et d’apporter de nouvelles pistes pour l’amélioration des solutions proposées

Modelling fatigue in manual and robot-assisted work for operator 5.0

Occupational Applications: Fatigue, and many other human performance factors, impact worker wellbeing, and thus production quality and efficiency. Adopting the Industry 5.0 perspective, we propose that integrating human performance models into wider industrial system models can improve modeling accuracy and lead to superior outcomes. Integrating our Worker Fatigue Model as part of their industrial system architect model allowed Airbus, a leading aircraft manufacturer, to more accurately predict system performance as a function of the workforce makeup, which could be a combination of human workers and robots, or a combination of highly experienced and less experienced workers. Our approach demonstrates the importance and value of including human performance models in trade studies for introducing robots on the shop floor, and can be used to include various aspects of human performance in industrial system models to address specific task requirements or different levels of automation. Technical Abstract: Rationale: The advent of Industry 5.0 places a heightened focus on enhancing worker wellbeing during the digital transformation of factories. System models that ignore human workers yield suboptimal results in product design and system improvement.Purpose: In the aircraft industry, worker workload is of primary concern as most tasks are performed manually, leading to general fatigue and musculoskeletal disorders. Robot assistance could improve quality, efficiency and relieve workers from fatigue. To demonstrate the feasibility and value of integrating human performance models in system design at Airbus, a Worker Fatigue Model was developed, focusing on the effects of (1) automation (manual vs semi-automated), and (2) workforce makeup (various ratios of high-skilled to low-skilled workers). Our ultimate goal was to inform the development of effective policies and strategies for human-technology integration in Industry 5.0.Methods: We developed the Worker Fatigue Model by adapting existing fatigue models for workers in industrial environments and by considering worker characteristics, tasks, and the presence or absence of robot-assistance. Two different scenarios were simulated (fully manual and semi-automated), with input variables such as worker skill, age, and motivation, and output variables including overall fatigue and error probabilities were evaluated. The Worker Fatigue Model was integrated into the Airbus system model to conduct trade studies based on workforce characteristics.Results: Our findings revealed that the composition of the workforce (i.e., various ratios of high-skilled to low-skilled workers), alongside specific manufacturing technologies, significantly reduced worker fatigue, especially with higher ratios of high-skilled workers, and improved overall industrial system performance.Conclusions: Although applying our Worker Fatigue Model effectively demonstrated the feasibility and value of integrating human factors into early industrial system design, it remains a work in progress. Future work will aim to accurately represent the workload of human workers, including operational costs, when implementing robot assistance

Development and validation of a risk model for predicting adverse drug reactions in older people during hospital stay: Brighton Adverse Drug Reactions Risk (BADRI) model

BACKGROUND: Older patients are at an increased risk of developing adverse drug reactions (ADR). Of particular concern are the oldest old, which constitute an increasingly growing population. Having a validated clinical tool to identify those older patients at risk of developing an ADR during hospital stay would enable healthcare staff to put measures in place to reduce the risk of such an event developing. The current study aimed to (1) develop and (2) validate an ADR risk prediction model. METHODS: We used a combination of univariate analysis and multivariate binary logistic regression to identify clinical risk factors for developing an ADR in a population of older people from a UK teaching hospital. The final ADR risk model was then validated in a European population (European dataset). RESULTS: Six-hundred-ninety patients (median age 85 years) were enrolled in the development stage of the study. Ninety-five reports of ADR were confirmed by independent review in these patients. Five clinical variables were identified through multivariate analysis and included in our final model; each variable was attributed a score of 1. Internal validation produced an AUROC of 0.74, a sensitivity of 80%, and specificity of 55%. During the external validation stage the AUROC was 0.73, with sensitivity and specificity values of 84% and 43% respectively. CONCLUSIONS: We have developed and successfully validated a simple model to use ADR risk score in a population of patients with a median age of 85, i.e. the oldest old. The model is based on 5 clinical variables (≥8 drugs, hyperlipidaemia, raised white cell count, use of anti-diabetic agents, length of stay ≥12 days), some of which have not been previously reported

Bedside-to-Bench Conference: Research Agenda for Idiopathic Fatigue and Aging

The American Geriatrics Society, with support from the National Institute on Aging and the John A. Hartford Foundation, held its fifth Bedside-to-Bench research conference, “Idiopathic Fatigue and Aging,” to provide participants with opportunities to learn about cutting-edge research developments, draft recommendations for future research, and network with colleagues and leaders in the field.Fatigue is a symptom that older persons, especially by those with chronic diseases, frequently experience. Definitions and prevalence of fatigue may vary across studies, across diseases, and even between investigators and patients. The focus of this review is on physical fatigue, recognizing that there are other related domains of fatigue (such as cognitive fatigue).Many definitions of fatigue involve a sensation of “low” energy, suggesting that fatigue could be a disorder of energy balance. Poor energy utilization efficiency has not been considered in previous studies but is likely to be one of the most important determinants of fatigue in older individuals. Relationships between activity level, capacity for activity, a tolerable rate of activity, and a tolerable fatigue threshold or ceiling underlie a notion of fatiguability. Mechanisms probably contributing to fatigue in older adults include decline in mitochondrial function, alterations in brain neurotransmitters, oxidative stress, and inflammation. The relationships between muscle function and fatigue are complex. A number of diseases (such as cancer) are known to cause fatigue and may serve as models for how underlying impaired physiological processes contribute to fatigue, particularly those in which energy utilization may be an important factor. A further understanding of fatigue will require two key strategies: to develop and refine fatigue definitions and measurement tools and to explore underlying mechanisms using animal and human models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79370/1/j.1532-5415.2010.02811.x.pd