TIC en actividad física y parámetros cardiovasculares en mayores: una revisión

IntroductionAgeing is a period of physical and psychological changes. Inactivity is one of the biggest problems among the older adult population increasing the risk of sarcopenia and chronic diseases. Physical activity is an effective intervention to improve health outcomes. In recent years, there has been an increase in the use of technology, with health technology tools (ICT) appearing as an intervention to increase physical activity and improve associated health problems.ObjectiveIn this review, we evaluated the effectiveness of health technology to increase physical activity and to improve cardiovascular parameters in older adults.MethodologyStudies with a great variety of health technology tools to increase physical activity levels, and that evaluated the effect of that increase on cardiovascular parameters were included by searching the main databases.ResultsEleven studies reporting the use of a variety of ICT tools were included in this review. Despite these differences, the effectiveness of health technology tool interventions has been demonstrated in increasing physical activity and reducing cardiovascular parameters.DiscussionThe lack of adherence of older adults to health technology would be a disadvantage, but it has been shown that younger older adults are more familiar with health technology tools and the number using them is increasing.ConclusionHealth technology tools show effectiveness in increasing physical activity in older adults and improving cardiovascular parameters.     

Understanding How Newly Implemented Rehabilitation Best Practices Are Sustained: A Realist Review

ObjectiveWe conducted a realist review to understand how (mechanism) and in what circumstances (context) evidence-based practices are sustained in rehabilitation (outcome).Data SourcesMEDLINE, Embase, reference lists, and targeted websites.Study SelectionTwo independent reviewers calibrated study selection; then 1 reviewer screened all titles and abstracts, while the second reviewer screened a random 20%. We repeated this process for full texts. We included 115 documents representing 61 implementation projects (8.9% of identified documents). Included documents described implementation projects in which physical therapists, occupational therapists, and/or speech-language pathologists were the target users of an evidence-based practice.Data ExtractionTwo reviewers repeated the independent process described in study selection to extract basic study and sustainability characteristics as well as context, mechanism, outcome, and strategy text.Data SynthesisUsing basic numerical analyses, we found that only 54% of evidence-based practices in rehabilitation are sustained. Furthermore, while authors who reported sustainability planning sustained the practice 94% of the time, sustainability planning in rehabilitation is rare (only reported 26% of the time). Extracted text was synthesized using the realist technique of inductive and deductive retroduction in which context, mechanism, outcome, and strategy text are combined into narrative explanations of how sustainability works. To inform these explanations, we applied normalization process theory and the theory of planned behavior. Collectively, the 52 identified narratives provide evidence for 3 patterns: (1) implementation and sustainability phases are interconnected, (2) continued use of the evidence-based practice can be interpreted as the ultimate sustainability outcome, and (3) intermediate sustainability outcomes (ie, fit/alignment, financial support, benefits, expertise) can become contextual features influencing other sustainability outcomes.ConclusionsImplementation teams can use the narrative explanations generated in this review to optimize sustainability planning. This can sustain practice changes and improve quality of care and patient outcomes. Future research should seek to iteratively refine the proposed narrative explanations.     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.     

Physical activity and mental well-being during COVID-19 pandemic

The corona virus disease 2019 (COVID-19) pandemic has resulted in most nations deciding upon self-isolation and social distancing policies for their citizens to control the pandemic and reduce hospital admission. This review aimed at evaluating the effect of physical activity on mental well-being during the COVID-19 pandemic. It was concluded that the COVID-19 pandemic may lead to augmented levels of angiotensin-converting enzyme (ACE)-2 that led to cardiovascular and neurological disorders associated with highly inflammatory effects of viral infection affecting the brain tissues leading to damage of the nervous system and resulting in cognition dysfunction, insulin sensitivity reduction, and behavioral impairments. Anxiety and depression may lead to negative effects on various quality of life domains, such as being physically inactive. Regular physical activities may reduce inflammatory responses, improve ACE-2 responses, and improve mental well-being during self-isolation and social distancing policies related to the COVID-19 pandemic. Further studies should be conducted to assess the different intensities of physical activities on cardiovascular function, and mental well-being during the COVID-19 pandemic.     

Effects of exogenous factors on spatial accuracy in neurosurgery

The study aimed to assess the effect of exogenous factors such as surgeon posture, surgical instrument length, fatigue after a night shift, exercise and caffeine consumption on the spatial accuracy of neurosurgical manipulations. For the evaluation and simulation of neurosurgical manipulations, a testing device developed by the authors was used. The experimental results were compared using nonparametric analysis (Wilcoxon test) and multivariate analysis, which was performed using mixed models. The results were considered statistically significant at p < 0.05. The study included 11 first-year neurosurgery residents who met the inclusion criteria. Hand support in the sitting position (Wilcoxon test p value = 0.0033), caffeine consumption (p = 0.0058) and the length of the microsurgical instrument (p = 0.0032) had statistically significant influences on the spatial accuracy of surgical manipulations (univariate analysis). The spatial accuracy did not significantly depend on the type of standing position (Wilcoxon test p value = 0.2860), whether the surgeon was standing/sitting (p = 0.1029), fatigue following a night shift (p = 0.3281), or physical exertion prior to surgery (p = 0.2845).When conducting the multivariate analysis, the spatial accuracy significantly depended on the test subject (p < 0.0001), the use of support during the test (p = 0.0001), and the length of the microsurgical instrument (p = 0.0397). To increase the spatial accuracy of microsurgical manipulations, hand support and shorter tools should be used. Caffeine consumption in high doses should also be avoided prior to surgery.