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BackgroundCognitive deficits are among the most disabling of neurological diseases and have a serious impact on the quality of life of patients and families. Cognitive training has been proven successful in improving or compensating for neuropsychological deficits after acute brain injury, but its efficacy highly depends on the intensity of treatment over an extended period of time. Therefore, cognitive training indicates expensive human resources and renders the rehabilitation process vulnerable to physical and economic barriers for the majority of patients.ObjectiveThe aim of this study was to develop and test a new Web-based rehabilitation tool that provides intensive cognitive training at home under clinical prescription and monitoring, at affordable costs.MethodsFrom a pool of 60 original exercises, designed and used over the past 10 years for cognitive training at our center, we developed 27 exercises on a computer game format, with automatic increase or decrease of difficulty levels. These exercises were assembled in a clean, user-friendly design and covered various cognitive domains such as attention (n=4), memory (n=11), language (n=3), calculus (n=3), praxis (n=2), and executive functions (n=3). A Web 2.0 platform was also designed to provide medical prescription of cognitive training sessions, performed at the patient’s home. These sessions included continuous monitoring of compliance, performance, and evolution; algorithms for automatic adjustment and long-term learning through use, and database recording of all activities. The end-user interaction test included 80 patients from our memory clinic from several groups including subjective memory complaints (n=20), traumatic brain injury (n=20), stroke and other static brain lesions (n=20), and mild Alzheimer’s disease (n=20). During a 1-hour session, patients and their relatives were taught to use the system and allowed to practice using it. At the end of the session, they were asked to complete a questionnaire.ResultsA total of 48/80 patients (60%) attended the training session. The mean age of the patients was 60 years (SD 13.3, range 41-78), and the mean level of formal education was 6 years (range 4-16). Of all the participants, 32/48 patients (66%) have previously used a computer. All patients and their relatives made a positive evaluation of the cognitive training tool. Only 2/48 patients (4%) were not interested in performing the exercises at home; 19/48 patients (39%) mentioned the need for further coaching from a relative or health care professional. The patients who mentioned difficulties in performing the exercises have not used the computer earlier.ConclusionsThis new Web-based system was very well accepted by patients and their relatives, who showed high levels of motivation to use it on a daily basis at home. The simplicity of its use and comfort were especially outlined. This tool will have an important effect on human resource management, in increasing the patient access to specialized health care and improving the quality and national h...

BackgroundStudies exploring the association between physical activity, screen time and sleep and pain usually focus on a limited number of painful body sites. Nevertheless, pain at different body sites is likely to be of different nature. Therefore, this study aims to explore and compare the association between time spent in self-reported physical activity, in screen based activities and sleeping and i) pain presence in the last 7-days for 9 different body sites; ii) pain intensity at 9 different body sites and iii) global disability.MethodsNine hundred sixty nine students completed a questionnaire on pain, time spent in moderate and vigorous physical activity, screen based time watching TV/DVD, playing, using mobile phones and computers and sleeping hours. Univariate and multivariate associations between pain presence, pain intensity and disability and physical activity, screen based time and sleeping hours were investigated.ResultsPain presence: sleeping remained in the multivariable model for the neck, mid back, wrists, knees and ankles/feet (OR 1.17 to 2.11); moderate physical activity remained in the multivariate model for the neck, shoulders, wrists, hips and ankles/feet (OR 1.06 to 1.08); vigorous physical activity remained in the multivariate model for mid back, knees and ankles/feet (OR 1.05 to 1.09) and screen time remained in the multivariate model for the low back (OR = 2.34. Pain intensity: screen time and moderate physical activity remained in the multivariable model for pain intensity at the neck, mid back, low back, shoulder, knees and ankles/feet (Rp2 0.02 to 0.04) and at the wrists (Rp2 = 0.04), respectively. Disability showed no association with sleeping, screen time or physical activity.ConclusionsThis study suggests both similarities and differences in the patterns of association between time spent in physical activity, sleeping and in screen based activities and pain presence at 8 different body sites. In addition, they also suggest that the factors associated with the presence of pain, pain intensity and pain associated disability are different.

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BackgroundIt is estimated that 23% of adults and 55% of older adults do not meet the recommended levels of physical activity. Thus, improving the levels of physical activity is of paramount importance, but it requires the use of low-cost resources that facilitate universal access without depleting the health system. The high number of apps available constitutes an opportunity, but it also makes it quite difficult for the layperson to select the most appropriate app. Furthermore, the information available in the app stores is often insufficient, lacks quality, and is not evidence based, and the systematic reviews fail to assess app quality using standardized and validated instruments.ObjectiveThe objective of this study was to systematically assess the features, content, and quality of the most popular apps that can be used to measure and, potentially, promote physical activity.MethodsSystematic searches were conducted on Apple App Store, Google Play, and Windows Phone Store between December 2017 and January 2018. Apps were included if their primary objective was to assess the aspects of physical activity, if they had a user rating of at least 4, if their number of ratings was ≥100, and if they were free. Apps meeting these criteria were independently assessed by two reviewers regarding their general and technical information, aspects of physical activity, presence of behavioral change techniques, and quality. Data were analyzed using means and SDs or frequencies and percentages.ResultsOf 51 apps included, none specified the age of the target group and only one mentioned the involvement of health professionals. Most apps offered the possibility to work in background (n=50) and allowed data sharing (n=40). Regarding physical activity, most apps measured steps and distance (n=11) or steps, distance, and time (n=17). Only 18 apps, all of which measured number of steps, followed the guidelines on recommendations for physical activity. On average, 5.5 (SD 1.8) behavioral change techniques were identified per app; the most frequently used techniques were “provide feedback on performance” (n=50) and “prompt self-monitoring of behavior” (n=50). The overall quality score was 3.88 (SD 0.34).ConclusionsAlthough the overall quality of the apps was moderate, the quality of their content, particularly the use of international guidelines on physical activity, should be improved. Additionally, a more in-depth assessment of apps should be performed before releasing them for public use, particularly regarding their reliability and validity.

This study was aimed at assessing a new computer‐based system to promote task performance in three adults with severe developmental disabilities. The new system, which was small and battery powered, could provide auditory and vibratory prompts in addition to pictorial instructions concerning the task steps. To control for the effects of the computer‐based system, the subjects were also exposed to a card system. The results showed that the subjects had a significantly higher level of correct performance on the tasks presented with the computer‐based system than on the tasks presented with the card system. Two of the subjects, moreover, seemed to prefer the computer‐based system to the card system. © 1998 John Wiley & Sons, Ltd.

O objetivo deste estudo é identificar, analisar e classificar os métodos descritos na literatura para a avaliação de usabilidade de produtos e serviços baseados em tecnologias de informação e comunicação.A metodologia utilizada foi a revisão sistemática da literatura, onde os estudos incluídos na análise foram classificados segundo os modelos (empírico ou analítico), os métodos (teste, inquérito, inspeção ou experiência controlada) e técnicas utilizadas. Na pesquisa foram incluídos 2116 estudos, dos quais 1308 foram classificados. O método inquérito foi o método mais frequente nesta revisão, seguido dos métodos teste e inspeção e, por fim, do método baseado em experiências controladas. A combinação de métodos é relativamente frequente, especialmente a combinação dos métodos teste e inquérito, provavelmente porque a utilização dos dois permite recolher informação quantitativa e qualitativa contribuindo para uma avaliação mais completa

Objectives-To identify the technologies that are being used in the remote care of patients with chronic conditions, and their most relevant outcomes. Methods-A systematic review of reviews and meta-analyses. Results-Fifty-one systematic reviews and meta-analyses related to diabetes, congestive heart failure, chronic obstructive pulmonary disease, mental and behavioral diseases, cancer, hypertension, asthma, multiple sclerosis, chronic renal disease, and obesity were retrieved; these studies compared the use of remote care technology with usual care. Conclusion-Remote care technology has positive effects in various health-related outcomes, but further research is required to allow its use in clinical practice.