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.     

Parks/sports facilities in local communities and the onset of functional disability among older adults in Japan: The J-shaped spatial spillover effects

We examined the associations between parks/sports facilities in local communities and the onset of functional disability among Japanese older adults. We further investigated the spatial spillover effects of parks/sports facilities in 1st-, 2nd-, and 3rd-degree neighboring communities. Data on the onset of functional disability, including date of onset, sex, age, and address (school district) of all older adults aged 65 years and older living in Kobe City were obtained from the public long-term care insurance database (163,348 men [190,831 person-year] and 200,443 women [230,464 person-year]). Multilevel proportional hazard regression analyses showed that the number of parks and sports facilities were negatively associated with the onset of functional disability among men. In particular, these associations were stronger for the adjacent district than for individuals’ living districts. Our results imply that involving multiple neighboring communities as the intervention unit is effective when utilizing community built environments for long-term care prevention in men.     

实习护生情绪智力、临床实习环境与病人安全感知能力的中介效应

目的:了解实习护生情绪智力、临床实习环境与病人安全感知能力现状并探讨三者关系。方法:采用情绪智力量表、临床实习环境量表和卫生职业教育病人安全感知量表对492名实习护生进行调查。结果:实习护生情绪智力总分为(123.81±18.69)分,临床实习环境总分为(119.48±22.16)分,病人安全感知能力总分为(67.16±12.06)分,情绪智力、临床实习环境与病人安全感知能力呈正相关(r值分别为0.723和0.720,P<0.01);情绪智力在临床实习环境和病人安全感知能力间起部分中介效应,中介效应估计值为0.286(28.6%)。结论:学校要举办人文课程活动以培养情绪智力;医院应制订计划增加学习机会,选拔给予护生充分支持的带教老师,改善临床实习环境。     

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.