Christiansenfeldman6990

Findings supported the proposed model among boys and girls and extend existing theoretical knowledge to encompass male body image and well-being. Interventions which target internalization and comparisons in the context of social media are likely to be valuable in improving body satisfaction and subjective well-being in co-educational settings.As yoga continues to increase in global popularity, idealized representations of a thin, athletic 'yoga body' have also become more prominent across commercial media. To examine how yoga is typically represented on social media, a content analysis of the posts of female yoga practitioners on Instagram was undertaken. Images were sourced using hashtags #yoga, #yogabody, #yogapractice, and #yogawoman, and 200 females per hashtag were then coded on demographic factors, body shape, activity, objectification, and practice of yoga. Results showed that over 90 % of women in the images were coded as being under 40 years of age with the vast majority in their 20 s. Almost three-quarters of women were perceived to be white, 100 % appeared able bodied. More than 80 % were classed as thin and/or athletic, while less than 15 % displayed average levels of visible body fat. More than 50 % of yoga poses were advanced while a quarter displayed potentially unsafe alignment. The findings demonstrate that the typical 'yoga body' on Instagram was perceived to conform to the young, thin/athletic ideal and that overall yoga is not being represented as an inclusive physical practice that can be adapted for women of diverse ages, body types, and abilities.

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability.

Can a structured machine learning approach achieve a more accurate prediction of running gait event timings from tibial accelerometry, compared to the previously utilised heuristic approaches?

Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. 3D tibial acceleration and ground reaction force data from 93 rearfoot runners were captured. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration sirate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.

Limb length discrepancy (LLD) is common and is associated with musculoskeletal disorders. Selection of adaptation strategies, the side more susceptible to complications, and the relationships between LLD magnitude and musculoskeletal complications are unclear. To elucidate these ambiguities, studies on gait parameters in LLD have been conducted. However, studies on inter-limb difference of mechanical work in LLD are rare.

To investigate whether inter-limb differences in mechanical work in LLD and the relationship between LLD magnitude and mechanical work performed by each lower limb are significant.

Thirty-seven participants with LLD and without neuromuscular disorders disturbing normal gait were included. Three-dimensional motion analysis was conducted to obtain data on mechanical work, including joint work and the individual limb method (ILM) work. Mechanical work performed by the longer and shorter limbs was compared using paired t-test. Relationships between LLD and mechanical work were investigatednd different adaptation strategies between LG and SG. These differences attribute to the decrease in ILM work performed by the shorter limb with the increase in LLD. Mechanical work including ILM work should be included in future studies to prevent complications and development of treatment methods for LLD.

Mammography (MG) is widely used for screening examinations. Dense breast reduces MG screening sensitivity, possibly delaying diagnosis. However, little is known about the characteristics of breast cancers without MG findings indicative of malignancy. Hence, we investigated breast cancer patients with tumors not detected by MG.

In total, 1758 Japanese patients with breast cancer, undergoing curative surgery between 2012 and 2018 without neo-adjuvant chemotherapy, were retrospectively investigated. Clinicopathological features were compared between patients without (MG-negative) and with (MG-positive) cancer-specific findings on MG. The current study included cases who came to our hospital after experiencing subjective symptoms, or whose tumors were detected by MG and/or US-screening. We reviewed results of both MG and US conducted at our institution.

There were 201MG-negative cases (11.4%). In patients with invasive disease, multivariate analysis revealed MG-negative patients to have higher breast density on MG (p<0.001). Tumors of MG-negative patients were smaller (p<0.001), showed less lymph node involvement (p=0.011), and were of lower grade (p=0.027). The majority of MG-negative tumors were found by ultrasound screening, being smaller than tumors in patients with subjective symptoms. In the MG-negative group, tumor characteristics such as tumor grade did not differ between those detected by screening versus subjective symptoms.

Most tumors in MG-negative group patients were identified by US screening and the diseases were found at early stages with low malignancy. The usefulness of additional ultrasound with MG-screening might merit further investigations.

Most tumors in MG-negative group patients were identified by US screening and the diseases were found at early stages with low malignancy. The usefulness of additional ultrasound with MG-screening might merit further investigations.

Early EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values.

We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest. We calculated Link Rates (LR) and Link Durations (LD) in the α, δ, and θ band, based on similarity of instantaneous frequencies in five-minute EEG epochs, hourly, during 3days after cardiac arrest. We studied associations of LR and LD with good (Cerebral Performance Category (CPC) 1-2) or poor outcome (CPC 3-5) with univariate analyses. selleck With random forest classification, we established EEG-based predictive models. We used receiver operating characteristics to estimate additional values of dynamic connectivity measures for outcome prediction.

Of 683 patients, 369 (54%) had poor outcome. Patients with poor outcome had significantly lower LR and longer LD, with largest differences 12h after cardiac arrest (LR

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