27 research outputs found
Analysis of the sentiments of the participants in a clinical study to evaluate a balance rehabilitation intervention delivered by a Virtual Coach
Multiple studies for balance rehabilitation interventions have been accomplished aiming to demonstrate that sensory interventions and cognitive functionality are crucial for postural control and improvement of the quality of patient's daily life. However, none of the existing studies is filling the lack of expert physiotherapists availability. A pilot randomized study was conducted to assess the acceptability of the HOLOBalance telerehabilitation system. HOLOBalance is an interactive AR rehabilitation system which encompasses multi-sensory training program to enhance balance and cognitive coaching, for older adults at falls risk. In this work, we present a sentiment analysis of the patients participating in this study using the VADER methodology to evaluate and quantify their attitude towards the HOLOBalance system. Our results highlight the importance of findings positive polarity towards the AR interaction, which is based on the use of a holographic virtual physiotherapist. The compound score of 0.185 indicates the valuable positive feedback gained from the user experience
Diabetes self-management arrangements in Europe: a realist review to facilitate a project implemented in six countries
Background: Self-management of long term conditions can promote quality of life whilst delivering benefits to the financing of health care systems. However, rarely are the meso-level influences, likely to be of direct relevance to these desired outcomes, systematically explored. No specific international guidelines exist suggesting the features of the most appropriate structure and organisation of health care systems within which to situate self-management approaches and practices. This review aimed to identify the quantitative literature with regard to diabetes self-management arrangements currently in place within the health care systems of six countries (The United Kingdom, The Netherlands, Norway, Spain, Bulgaria, and Greece) and explore how these are integrated into the broader health care and welfare systems in each country.
Methods: The methodology for a realist review was followed. Publications of interest dating from 2000 to 2013 were identified through appropriate MeSH terms by a systematic search in six bibliographic databases. A search diary was maintained and the studies were assessed for their quality and risk of bias.
Results: Following the multi-step search strategy, 56 studies were included in the final review (the majority from the UK) reporting design methods and findings on 21 interventions and programmes for diabetes and chronic disease self-management. Most (11/21, 52%) of the interventions were designed to fit within the context of primary care. The majority (11/21, 52%) highlighted behavioural change as an important goal. Finally, some (5/21, 24%) referred explicitly to Internet-based tools.
Conclusions: This review is based on results which are derived from a total of at least 5,500 individuals residing in the six participating countries. It indicates a policy shift towards patient-centred self-management of diabetes in a primary care context. The professional role of diabetes specialist nurses, the need for multidisciplinary approaches and a focus on patient education emerge as fundamental principles in the design of relevant programmes. Socio-economic circumstances are relevant to the capacity to self-manage and suggest that any gains and progress will be hard to maintain during economic austerity. This realist review should be interpreted within the wider context of a whole systems approach regarding self-care support and chronic illness management
A novel approach to estimate the weight of food items based on features extracted from an image using boosting algorithms
Abstract Managing daily nutrition is a prominent concern among individuals in contemporary society. The advancement of dietary assessment systems and applications utilizing images has facilitated the effective management of individuals' nutritional information and dietary habits over time. The determination of food weight or volume is a vital part in these systems for assessing food quantities and nutritional information. This study presents a novel methodology for evaluating the weight of food by utilizing extracted features from images and training them through advanced boosting regression algorithms. Α unique dataset of 23,052 annotated food images of Mediterranean cuisine, including 226 different dishes with a reference object placed next to the dish, was used to train the proposed pipeline. Then, using extracted features from the annotated images, such as food area, reference object area, food id, food category, and food weight, we built a dataframe with 24,996 records. Finally, we trained the weight estimation model by applying cross validation, hyperparameter tuning, and boosting regression algorithms such as XGBoost, CatBoost, and LightGBM. Between the predicted and actual weight values for each food in the proposed dataset, the proposed model achieves a mean weight absolute error 3.93 g, a mean absolute percentage error 3.73% and a root mean square error 6.05 g for the 226 food items of the Mediterranean Greek Food database (MedGRFood), setting new perspectives in food image-based weight and nutrition estimate models and systems
15+ MILLION TOP 1% MOST CITED SCIENTIST 12.2% AUTHORS AND EDITORS FROM TOP 500 UNIVERSITIES Chapter from the book Knowledge-Oriented Applications in Data Mining Glucose Prediction in Type 1 and Type 2 Diabetic Patients Using Data Driven Techniques
Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression.
Abstract
Data-driven techniques have recently drawn significant interest in the predictive modeling of subcutaneous (s.c.) glucose concentration in type 1 diabetes. In this study, the s.c. glucose prediction is treated as a multivariate regression problem, which is addressed using support vector regression (SVR). The proposed method is based on variables concerning: (i) the s.c. glucose profile, (ii) the plasma insulin concentration, (iii) the appearance of meal-derived glucose in the systemic circulation, and (iv) the energy expenditure during physical activities. Six cases corresponding to different combinations of the aforementioned variables are used to investigate the influence of the input on the daily glucose prediction. The proposed method is evaluated using a dataset of 27 patients in free-living conditions. 10-fold cross validation is applied to each dataset individually to both optimize and test the SVR model. In the case where all the input variables are considered, the average prediction errors are 5.21, 6.03, 7.14 and 7.62 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively. The results clearly indicate that the availability of multivariable data and their effective combination can significantly increase the accuracy of both short-term and long-term predictions
Inflammatory and oxidative stress biomarkers in allergic rhinitis: the effect of smoking
Accumulating evidence confirms the presence of pan-airway inflammation in allergic rhinitis patients. Smoking is known to affect the asthmatic airway inflammation. However, no study has evaluated the impact of smoking on airway inflammation of allergic rhinitis patients. The aim of the present study was to evaluate the impact of smoking on inflammatory and oxidative stress biomarkers in patients with seasonal allergic rhinitis, using non-invasive methods for sample collection. Forty patients with seasonal allergic rhinitis (20 smokers and 20 non-smokers) and 30 healthy subjects (15 smokers and 15 non-smokers) were recruited for the study during pollen season. All subjects were submitted to measurement of the fraction of exhaled NO (FeNO), exhaled breath condensate (EBC) collection, nasal lavage collection, pre- and post- bronchodilation spirometry and metacholine bronchial challenge testing. pH, leukotriene B(4) (LTB(4)) and 8-isoprostane were determined in EBC and nasal lavage samples. Patients with allergic rhinitis presented higher LTB(4) and 8-isoprostane levels in nasal lavage (P < 0.0001 for both comparisons), with no significant differences between smokers and non-smokers. Patients with allergic rhinitis also presented higher LTB(4) levels and lower pH in EBC (P < 0.001 and P=0.004, respectively), with prominent differences between smokers and non-smokers (P < 0.0001 and P=0.003, for LTB(4) and pH, respectively). A significant correlation between nasal lavage and EBC LTB(4) values was observed (r(s)=0.313, P=0.048). Patients with allergic rhinitis present increased LTB(4) and 8-isoprostane in their nasal cavity, however, with no significant differences between smokers and non-smokers. In contrast, smokers with allergic rhinitis present higher LTB(4) levels and lower pH in EBC, suggesting that these patients may be more susceptible to the deleterious effects of smoking, compared with non-smokers
Plasma high sensitivity C-reactive protein and its relationship with cytokine levels in children with newly diagnosed type 1 diabetes and ketoacidosis
Background: High-sensitivity C-reactive protein (hs-CRP) and pro-inflammatory cytokines have been suggested as sensitive markers of endothelial dysfunction. Our aim was to monitor plasma hs-CRP levels at different time-points and in different degrees of ketoacidosis severity, its association with cytokine levels and its role as a marker of severe ketoacidosis complications. Patients and methods: We studied in 38 newly diagnosed children with type 1 diabetes and ketoacidosis, aged 7.7. ±. 3.1. years, hs-CRP, white blood cell count (WBC), and plasma levels of cytokines IL-1β (interleukin-1β), IL-2, IL-6, IL-8, IL-10, TNF-α (tumor necrosis factor-α) prior to and during DKA management. Results: On admission, the levels of WBC, PMN, IL-6 and IL-10 were elevated, but were all reduced within 120. h after ketoacidosis management. In the group with moderate/severe ketoacidosis, but not in mild ketoacidosis, hs-CRP levels were significantly reduced at 24. h (p= 0.021), WBC and IL-6 at 120. h (p= 0.003), while IL-10 was prematurely reduced at 6-8. h (p= 0.008). Moreover hs-CRP was significantly associated with WBC (p= 0.023) and IL-6 (p= 0.028) on admission, with IL-6 (p= 0.002) and IL-8 (p= 0.014) at 24. h and with IL-10 (p= 0.027) at 120. h. The above were not observed in the group with mild ketoacidosis. Conclusions: In the children with moderate/severe diabetic ketoacidosis of our study, increased levels of hs-CRP and IL-6 were observed, together with leukocytosis and neutrophilia, without the presence of infection. As hs-CRP was found to be strongly associated with the inflammatory IL-6, the prolonged elevation of hs-CRP levels in children with severe ketoacidosis could serve as a marker for the development of its severe complications. © 2012 The Canadian Society of Clinical Chemists