Purpose: To study the association between fatigue and participation and QoL after acquired brain injury (ABI) in adolescents and young adults (AYAs). Materials & Methods: Cross-sectional study with AYAs aged 14–25 years, diagnosed with ABI. The PedsQL™ Multidimensional Fatigue Scale, Child & Adolescent Scale of Participation, and PedsQL™4.0 Generic Core Scales were administered. Results: Sixty-four AYAs participated in the study, 47 with traumatic brain injury (TBI). Median age at admission was 17.6 yrs, 0.8 yrs since injury. High levels of fatigue (median 44.4 (IQR 34.7, 59.7)), limited participation (median 82.5 (IQR 68.8, 92.3)), and diminished QoL (median 63.0 (IQR 47.8, 78.3)) were reported. More fatigue was significantly associated with more participation restrictions (β 0.64, 95%CI 0.44, 0.85) and diminished QoL (β 0.87, 95%CI 0.72, 1.02). Conclusions: AYAs with ABI reported high levels of fatigue, limited participation and diminished quality of life with a significant association between fatigue and both participation and QoL. Targeting fatigue in rehabilitation treatment could potentially improve participation and QoL.
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Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
Since the emergence of modern man some 200,000 years ago, people and technologyhave been inextricably linked to each other. However, unlike traditional technology -such as leverage (and derivative applications such as hammers, wheels and crankshafts),and control of fire - smart technology is equipped with adaptive capacity. Whereas intraditional technology people have to think and handle in terms of technology in orderto apply technology successfully and purposefully, technology with, for example, itsown learning ability adapts to humans. This means that smart technology influencesdevelopment in a different way than traditional technology. Changes in the relationship between human development (brain) and smarttechnology - technology with its own learning capacity and adaptability - have led tothe articulation of 4 requirements technology should meet: 1. it must be sustainable, 2. it must not block development and if it does it must be clear how, 3. there must bea logical argument why the technique can be used and how it can be explained, also in terms of psychological development and, finally, 4. the social and ethical discoursemust be stated in a transparent way. At a fast pace, futurologists and management gurus are presenting “theories” abouthow smart technology will change us permanently as individuals. Requirements 1(sustainability) and 2 (technology influencing human development) are at stake here.However, these ideas cannot be substantiated by scientific research. Psychology(and the other social and human sciences) have not yet been able to generate a convincing interpretation of what is going on in the area of brain and technology (living technology). In fact, there is a need for argumentation. In order to arrive at an argument-based psychology, insight into the non-linearityof processes is indispensable. The Brain & Technology research group is exploring the great possibilities to bridge the distance between people and their limitations by using smart technology, or possibilities, especially when it comes to argument based applied psychology! In this document, mainly the argument requirement is considered, because in the rapidly changing technological processes, the argument often does not sufficiently develop and the argument lies pre-eminently at the level of applied psychology, brain and technology.
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