Several experiments have demonstrated that focusing a performer's attention externally (i.e., on the effects of a movement) rather than internally (i.e., on specific parts of the body) enhances performance when the task requires object manipulation (i.e., throwing a ball to a target). The purpose of this experiment was to investigate if whole-body movements (e.g., standing long jump), without object manipulation, are influenced by an internal or external focus of attention. After participants (n = 120) completed a short warm-up, they were assigned to either an internal (INT) or external (EXT) focus of attention group. All participants completed 5 standing long jumps separated by a 2-minute seated rest. Before each jump, participants in the INT condition were read the following instructions: "When you are attempting to jump as far as possible, I want you to focus your attention on extending your knees as rapidly as possible." Participants in the EXT condition were read the following instructions: "When you are attempting to jump as far as possible, I want you to focus your attention on jumping as far past the start line as possible." An independent samples t-test revealed a significant difference (p = 0.003) in the average distance jumped between the EXT (187.37 +/- 42.66 cm) group and the INT group (177.33 +/- 40.97 cm). The results suggest that providing instructions that focus attention externally enhances standing long-jump performance compared with instructions that focus attention internally. This finding is valuable for strength and conditioning professionals that use jumping tests to evaluate performance.

The primary purpose of this study was to investigate if focusing attention externally produced faster movement times compared to instructions that focused attention internally or a control set of instructions that did not explicitly focus attention when performing an agility task. A second purpose of the study was to measure participants’ focus of attention during practice by use of a questionnaire. Participants (N = 20) completed 15 trials of an agility “L” run following instructions designed to induce an external (EXT), internal (INT) attentional focus or a control (CON) set of instructions inducing no specific focus of attention. Analysis revealed when participants followed the EXT instructions they had significantly faster movement times compared to when they followed the INT and CON set of instructions; consistent with previous research the INT and CON movement times were not significantly different from each other. Qualitative data showed when participants were in the external condition they focused externally 67% of the time. When they were in the internal condition they focused internally 76% of the time, and when they were in the control condition they did not use an internal or external focus of attention 77% of the time. Qualitative data also revealed participants in the EXT, INT, and CON conditions switched their focus of attention at a frequency of 27, 35, and 51% respectively.

Ultrasound imaging is one of the most prominent technologies to evaluate the growth, progression, and overall health of a fetus during its gestation. However, the interpretation of the data obtained from such studies is best left to expert physicians and technicians who are trained and well-versed in analyzing such images. To improve the clinical workflow and potentially develop an at-home ultrasound-based fetal monitoring platform, we present a novel fetus phantom ultrasound dataset, FPUS23, which can be used to identify (1) the correct diagnostic planes for estimating fetal biometric values, (2) fetus orientation, (3) their anatomical features, and (4) bounding boxes of the fetus phantom anatomies at 23 weeks gestation. The entire dataset is composed of 15, 728 images, which are used to train four different Deep Neural Network models, built upon a ResNet34 backbone, for detecting aforementioned fetus features and use-cases. We have also evaluated the models trained using our FPUS23 dataset, to show that the information learned by these models can be used to substantially increase the accuracy on real-world ultrasound fetus datasets. We make the FPUS23 dataset and the pre-trained models publicly accessible at https://github.com/bharathprabakaran/FPUS23, which will further facilitate future research on fetal ultrasound imaging.

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