Zoekresultaten

Analysis of the features important for the effectiveness of physical activity-related apps for recreational sports

Background: A large number of people participate in individual or unorganized sports on a recreational level. Furthermore, many participants drop out because of injury or lowered motivation. Potentially, physical activity–related apps could motivate people during sport participation and help them to follow and maintain a healthy active lifestyle. It remains unclear what the quality of running, cycling, and walking apps is and how it can be assessed. Quality of these apps was defined as having a positive influence on participation in recreational sports. This information will show which features need to be assessed when rating physical activity–related app quality. Objective: The aim of this study was to identify expert perception on which features are important for the effectiveness of physical activity–related apps for participation in individual, recreational sports. Methods: Data were gathered via an expert panel approach using the nominal group technique. Two expert panels were organized to identify and rank app features relevant for sport participation. Experts were researchers or professionals in the field of industrial design and information technology (technology expert panel) and in the field of behavior change, health, and human movement sciences who had affinity with physical activity–related apps (health science expert panel). Of the 24 experts who were approached, 11 (46%) agreed to participate. Each panel session consisted of three consultation rounds. The 10 most important features per expert were collected. We calculated the frequency of the top 10 features and the mean importance score per feature (0-100). The sessions were taped and transcribed verbatim; a thematic analysis was conducted on the qualitative data. Results: In the technology expert panel, applied feedback and feedforward (91.3) and fun (91.3) were found most important (scale 0-100). Together with flexibility and look and feel, these features were mentioned most often (all n=4 [number of experts]; importance scores=41.3 and 43.8, respectively). The experts in the health science expert panels a and b found instructional feedback (95.0), motivating or challenging (95.0), peer rating and use (92.0), motivating feedback (91.3), and monitoring or statistics (91.0) most important. Most often ranked features were monitoring or statistics, motivating feedback, works good technically, tailoring starting point, fun, usability anticipating or context awareness, and privacy (all n=3-4 [number of experts]; importance scores=16.7-95.0). The qualitative analysis resulted in four overarching themes: (1) combination behavior change, technical, and design features needed; (2) extended feedback and tailoring is advised; (3) theoretical or evidence base as standard; and (4) entry requirements related to app use. Conclusions: The results show that a variety of features, including design, technical, and behavior change, are considered important for the effectiveness of physical activity–related apps by experts from different fields of expertise. These insights may assist in the development of an improved app rating scale.

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry.

Spate

Existential postdisciplinarity: personal journeys into tourism, art, and freedom

Postdisciplinarity makes claims on ontological, epistemic, and methodological levels, but it is inevitably a personal philosophical stance. This article represents an existentialist approach to the discourse on postdisciplinarity, offering reflective narratives of three academics. Tomas Pernecky discusses creativity, criticality, freedom, and methodological and epistemic pluralism; Ana María Munar reveals her journey of epistemological awakening; and Brian Wheeller underscores the importance of researchers' subjective and emotive voice. Jointly, the authors depict postdisciplinarity as an invitation to conceptual and interpretive eclecticism, critical analysis, and creative problem solving.

An evaluation of three processing methods and the effect of reduced culture times for faster direct identification of pathogens from BacT/ALERT blood cultures by MALDI-TOF MS

Matrix-assisted laser desorption/ionisation time of-flight mass spectrometry (MALDI-TOF MS) is a fast and reliable method for the identification of bacteria from agar media. Direct identification from positive blood cultures should decrease the time to obtaining the result. In this study, three different processing methods for the rapid direct identification of bacteria from positive blood culture bottles were compared. In total, 101 positive aerobe BacT/ALERT bottles were included in this study. Aliquots from all bottles were used for three bacterial processing methods, i.e. the commercially available Bruker's MALDI Sepsityper kit, the commercially available Molzym's MolYsis Basic5 kit and a centrifugation/washing method. In addition, the best method was used to evaluate the possibility of MALDI application after a reduced incubation time of 7 h of Staphylococcus aureus- and Escherichia coli-spiked (1,000, 100 and 10 colony-forming units [CFU]) aerobe BacT/ALERT blood cultures. Sixty-six (65%), 51 (50.5%) and 79 (78%) bottles were identified correctly at the species level when the centrifugation/washing method, MolYsis Basic 5 and Sepsityper were used, respectively. Incorrect identification was obtained in 35 (35%), 50 (49.5%) and 22 (22%) bottles, respectively. Gram-positive cocci were correctly identified in 33/52 (64%) of the cases. However, Gram-negative rods showed a correct identification in 45/47 (96%) of all bottles when the Sepsityper kit was used. Seven hours of pre-incubation of S. aureus- and E. coli-spiked aerobe BacT/ALERT blood cultures never resulted in reliable identification with MALDI-TOF MS. Sepsityper is superior for the direct identification of microorganisms from aerobe BacT/ALERT bottles. Gram-negative pathogens show better results compared to Gram-positive bacteria. Reduced incubation followed by MALDI-TOF MS did not result in faster reliable identification.

The relationship between pellet size and shotgun dispersion patterns

When reconstructing a shooting incident with a shotgun, the muzzle-to-target distance can be determined by relating the size of a dispersion pattern found on a crime scene to that of test shots. Ideally, the test shots are performed with the weapon and ammunition that were used in the incident. But sometimes examiners will have to resort to alternatives, such as using cartridges of the same brand and type but with another pellet size. For this reason, the relationship between pellet size and shotgun dispersion patterns was studied with both lead and steel shotgun pellets. Cartridges were loaded with identical cartridge cases, powder charges, and wads but with different pellet sizes, below size B. The cartridges were fired, and the dispersion patterns at 5 m in front of the muzzle were measured and compared. The results provide strong support for the proposition that shotgun dispersion patterns with both lead and steel shot increase with decreasing pellet size if all other relevant parameters are kept equal. The results also provide an indicative measure of the magnitude of the effect. Pattern sizes were approximately 1.7 times larger with #9 than with #0 lead shot and 1.4 times larger with #9 than with #1 steel shot. The differences between consecutive shot sizes were generally smaller. This means that cartridges of equal brand and type but with the next nearest shot number can be used for a muzzle-to-target distance determination, keeping the information of the current study in mind in the final interpretation of the results.