1,720,965 research outputs found
Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence
Full text linkBackground: Monitoring and interfacing technologies may increase physical activity (PA) program adherence in older adults, but they should account for aspects influencing older adults’ PA behavior. This study aimed at gathering preliminary wrist-based PA adherence data in free-living and relate these to the influencing factors. Methods: Ten healthy older adults (4 females, aged 70–78 years) provided health, fatigue, activity levels, attitude towards pacing, and self-efficacy information and performed a 6 min-walk test to assess their fitness. After a baseline week they followed a two-week walking and exercise intervention. Participants saw their progress via a purposely designed mobile application. Results: Walking and exercise adherence did not increase during the intervention (p = 0.38, p = 0.65). Self-efficacy decreased (p = 0.024). The baseline physical component of the Short Form Health Survey was the most predictive variable of walking adherence. Baseline perceived risk of over-activity and resting heart rate (HRrest) were the most predictive variables of exercise adherence. When the latter two were used to cluster participants according to their exercise adherence, the fitness gap between exercise-adherent and non-adherent increased after the intervention (p = 0.004). Conclusions: Risk of over-activity and HRrest profiled short-term exercise adherence in older adults. If confirmed in a larger and longer study, these could personalize interventions aimed at increasing adherence
Predicting energy expenditure from photo-plethysmographic measurements of heart rate under beta blocker therapy: Data driven personalization strategies based on mixed models
No full textEnergy expenditure have been often estimated using computational models based on heart rate (HR) and appropriate personalization strategies to account for users cardio-respiratory characteristics. However, medications like beta blockers which are prescribed to treat several cardiac conditions have a direct influence on the cardiovascular system and may impact the relationship between HR and energy expenditure during physical activity (AEE). This study proposes to estimate AEE from HR using mixed models (MIX-REG) by introducing a novel method to personalize the prediction equation. We selected as features to represent the individual random effect in the MIX-REG model those subject characteristics which minimized both estimation error (RMSE) and between-subjects error bias variability. Data from 17 patients post-myocardial infarction were collected during a laboratory protocol. AEE was measured using indirect calorimetry and HR using an innovative wrist worn activity monitor equipped with the Philips Cardio and Motion Monitoring Module (CM3-Generation-1), which is an integrated module including a photo-plethysmographic and accelerometer sensor. The presented method showed large AEE estimation accuracy (RMSE = 1.35 kcal/min) which was comparable to that of models personalized using data from laboratory calibration protocols (HR-FLEX) and was superior to multi-linear regression and MIX-REG models trained using a stepwise features selection procedure
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Energy expenditure estimation in beta-blocker-medicated cardiac patients by combining heart rate and body movement data
Full text linkBackground Accurate assessment of energy expenditure provides an opportunity to monitor physical activity during cardiac rehabilitation. However, the available assessment methods, based on the combination of heart rate (HR) and body movement data, are not applicable for patients using beta-blocker medication. Therefore, we developed an energy expenditure prediction model for beta-blocker-medicated cardiac rehabilitation patients. Methods Sixteen male cardiac rehabilitation patients (age: 55.8 ± 7.3 years, weight: 93.1 ± 11.8 kg) underwent a physical activity protocol with 11 low- to moderate-intensity common daily life activities. Energy expenditure was assessed using a portable indirect calorimeter. HR and body movement data were recorded during the protocol using unobtrusive wearable devices. In addition, patients underwent a symptom-limited exercise test and resting metabolic rate assessment. Energy expenditure estimation models were developed using multivariate regression analyses based on HR and body movement data and/or patient characteristics. In addition, a HR-flex model was developed. Results The model combining HR and body movement data and patient characteristics showed the highest correlation and lowest error (r2 = 0.84, root mean squared error = 0.834 kcal/minute) with total energy expenditure. The method based on individual calibration data (HR-flex) showed lower accuracy (i2 = 0.83, root mean squared error = 0.992 kcal/minute). Conclusions Our results show that combining HR and body movement data improves the accuracy of energy expenditure prediction models in cardiac patients, similar to methods that have been developed for healthy subjects. The proposed methodology does not require individual calibration and is based on the data that are available in clinical practice.</p
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