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- Horwich, A, et al.
(författare)
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EAU–ESMO consensus statements on the management of advanced and variant bladder cancer - an international collaborative multi-stakeholder effort : under the auspices of the EAU and ESMO Guidelines Committees
- 2019
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Ingår i: Annals of Oncology. - : Oxford University Press. - 0923-7534 .- 1569-8041. ; 30:11, s. 1697-1727
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Although guidelines exist for advanced and variant bladder cancer management, evidence is limited/conflicting in some areas and the optimal approach remains controversial.OBJECTIVE: To bring together a large multidisciplinary group of experts to develop consensus statements on controversial topics in bladder cancer management.DESIGN: A steering committee compiled proposed statements regarding advanced and variant bladder cancer management which were assessed by 113 experts in a Delphi survey. Statements not reaching consensus were reviewed; those prioritised were revised by a panel of 45 experts before voting during a consensus conference.SETTING: Online Delphi survey and consensus conference.PARTICIPANTS: The European Association of Urology (EAU), the European Society for Medical Oncology (ESMO), experts in bladder cancer management.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Statements were ranked by experts according to their level of agreement: 1-3 (disagree), 4-6 (equivocal), 7-9 (agree). A priori (level 1) consensus was defined as ≥70% agreement and ≤15% disagreement, or vice versa. In the Delphi survey, a second analysis was restricted to stakeholder group(s) considered to have adequate expertise relating to each statement (to achieve level 2 consensus).RESULTS AND LIMITATIONS: Overall, 116 statements were included in the Delphi survey. Of these, 33 (28%) statements achieved level 1 consensus and 49 (42%) statements achieved level 1 or 2 consensus. At the consensus conference, 22 of 27 (81%) statements achieved consensus. These consensus statements provide further guidance across a broad range of topics, including the management of variant histologies, the role/limitations of prognostic biomarkers in clinical decision making, bladder preservation strategies, modern radiotherapy techniques, the management of oligometastatic disease and the evolving role of checkpoint inhibitor therapy in metastatic disease.CONCLUSIONS: These consensus statements provide further guidance on controversial topics in advanced and variant bladder cancer management until a time where further evidence is available to guide our approach.
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- Dias, SB, et al.
(författare)
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Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease
- 2021
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Ingår i: Frontiers in psychology. - : Frontiers Media SA. - 1664-1078. ; 11, s. 612835-
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Tidskriftsartikel (refereegranskat)abstract
- Human-Computer Interaction (HCI) and games set a new domain in understanding people’s motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people’s health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson’s Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients’ quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.
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- Konstantinidis, D, et al.
(författare)
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Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos
- 2020
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Ingår i: Nutrients. - : MDPI AG. - 2072-6643. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel “Rapid Automatic Bite Detection” (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen’s kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.
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