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This study aimed to explore the potential of predicting diabetes by analyzing trends in plantar thermal and plantar pressure data, either individually or in combination, using various machine learning techniques. A total of twenty-six participants, comprising thirteen individuals diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar pressure data were collected and the plantar temperature was measured both immediately before and after the walk. Each participant completed the trial three times, and the average data between the trials were calculated. The research was divided into three experiments: the first evaluated the correlations between the plantar pressure and temperature data; the second focused on predicting diabetes using each data type independently; and the third combined both data types and assessed the effect of such to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification algorithms were employed, and the performance was evaluated using a five-fold cross-validation strategy. The outcomes of the initial set of experiments indicated that the machine learning models were significant correlations between the thermal data and pressure estimates. This was consistent with the findings from the prior correlation analysis, which showed weak relationships between these two data modalities. However, a shift in focus towards predicting diabetes by aggregating the temperature and pressure data led to encouraging results, demonstrating the effectiveness of this approach in accurately predicting the presence of diabetes. The analysis revealed that, while several classifiers demonstrated reasonable metrics when using standalone variables, the integration of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at 68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the temperature and pressure data were combined, with the Extra Trees Classifier performing the best. These results suggest that combining temperature and pressure data enhances the model's predictive accuracy. This can indicate the importance of multimodal data integration and their potentials in diabetes prediction. [ABSTRACT FROM AUTHOR]

This study aimed to explore the undergraduate level at which the non-technical skills (NTS) should be taught. The objectives of the study were to explore teachers' perceptions of the effectiveness of simulation for NTS training and designing NTS simulation sessions to achieve student competence. A qualitative design was adopted, by interviewing 30 teachers and then using 5 focused group discussions for data triangulation and confirmation of responses. Thematic analysis was conducted to analyse the data by focusing on the ideas, experiences, opinions and meanings presented by the participants. Most of the participants strongly believed in the benefits of teaching NTS from pre-clinical years. Teachers' consensus was to train students in communication, situation awareness, teamwork and leadership skills from preclinical years. However decision-making and stress management were agreed to be trained from clinical years as they require clinical knowledge. The use of simulation was found to be most effective for training among other methods like didactic sessions, case-based discussions and video-assisted learning. Simulation sessions integrating technical and NTS, fewer learning outcomes and effective debriefing were considered advantageous for effective learning. Simulation-based education using a spiral curriculum approach, starting training from pre-clinical years and gradually increasing difficulty through intricate exercises in clinical years can be suggested to achieve the desired competence of NTS. [ABSTRACT FROM AUTHOR]

An increase in plantar pressure and skin temperature is commonly associated with an increased risk of diabetic foot ulcers. However, the effect of insoles in reducing plantar temperature has not been commonly studied. The aim was to assess the effect of walking in insoles with different features on plantar temperature. Twenty-six (F/M:18/8) participants—13 with diabetes and 13 healthy, aged 55.67 ± 9.58 years—participated in this study. Skin temperature at seven plantar regions was measured using a thermal camera and reported as the difference between the temperature after walking with an insole for 20 m versus the baseline temperature. The mixed analyses of variance indicated substantial main effects for the Insole Condition, for both the right [Wilks' Lambda = 0.790, F(14, 492) = 4.393, p < 0.01, partial eta squared = 0.111] and left feet [Wilks' Lambda = 0.890, F(14, 492) = 2.103, p < 0.011, partial eta squared = 0.056]. The 2.5 mm-tall dimple insole was shown to be significantly more effective at reducing the temperature in the hallux and third met head regions compared to the 4 mm-tall dimple insole. The insoles showed to be significantly more effective in the diabetes group versus the healthy group, with large effect size for the right [Wilks' Lambda = 0.662, F(14, 492) = 8.037, p < 0.000, Partial eta-squared = 0.186] and left feet [Wilks' Lambda = 0.739, F(14, 492) = 5.727, p < 0.000, Partial eta-squared = 0.140]. This can have important practical implications for designing insoles with a view to decrease foot complications in people with diabetes. [ABSTRACT FROM AUTHOR]

Aims/Introduction: This study aimed to assess if patients can be divided into different strata, and to explore if these correspond to the risk of diabetic foot complications. Materials and Methods: A set of 28 demographic, vascular, neurological and biomechanical measures from 2,284 (1,310 men, 974 women) patients were included in this study. A two‐step cluster analysis technique was utilised to divide the patients into groups, each with similar characteristics. Results: Only two distinct groups: group 1 (n = 1,199; 669 men, 530 women) and group 2 (n = 1,072; 636 men, 436 women) were identified. From continuous variables, the most important predictors of grouping were: ankle vibration perception threshold (16.9 ± 4.1 V vs 31.9 ± 7.4 V); hallux vibration perception threshold (16.1 ± 4.7 V vs 33.1 ± 7.9 V); knee vibration perception threshold (18.2 ± 5.1 V vs 30.1 ± 6.5 V); average temperature sensation threshold to cold (29.2 ± 1.1°C vs 26.7 ± 0.7°C) and hot (35.4 ± 1.8°C vs 39.5 ± 1.0°C) stimuli, and average temperature tolerance threshold to hot stimuli at the foot (43.4 ± 0.9°C vs 46.6 ± 1.3°C). From categorical variables, only impaired sensation to touch was found to have importance at the highest levels: 87.4% of those with normal sensation were in group 1; whereas group 2 comprised 95.1%, 99.3% and 90.5% of those with decreased, highly‐decreased and absent sensation to touch, respectively. In addition, neuropathy (monofilament) was a moderately important predictor (importance level 0.52) of grouping with 26.2% of participants with neuropathy in group 1 versus 73.5% of participants with neuropathy in group 2. Ulceration during follow up was almost fivefold higher in group 2 versus group 1. Conclusions: Impaired sensations to temperature, vibration and touch were shown to be the strongest factors in stratifying patients into two groups with one group having almost 5‐fold risk of future foot ulceration compared to the other. [ABSTRACT FROM AUTHOR]

Curvatures of the body can disrupt fluid flow and affect hydrodynamic resistance. The purpose of this study was to evaluate the effect of a feedback intervention on glide performance and torso morphology. Eleven male and female national swimmers performed glides before and after augmented feedback. Feedback consisted of self-modelling visual feedback and verbal cuing, to manipulate body curvatures that affect hydrodynamic resistance. Two-dimensional landmark position data (knee, hip and shoulder) were used to enable computation of glide factor and glide coefficient as indicators of glide efficiency; posture (trunk incline and hip angle); and performance (horizontal velocity). Underwater images of the swimmers were manually traced to derive transverse and sagittal diameters, cross-sectional areas, and continuous form outlines (anterior and posterior) of the torso. Maximum rate of change in cross-sectional area and form gradient progressing caudally, were calculated for torso segments: shoulder-chest, chest-waist, waist-hip. Mean velocity, glide factor and glide coefficient values significantly (p< 0.001) improved due to the intervention, with large effect size (d) changes 0.880 (p= 0.015), 2.297 and 1.605, respectively. Significant changes to form gradients were related to reductions in lumbar lordosis and chest convexity. The study provides practical cuing phrases for coaches and swimmers to improve glide efficiency and performance. [ABSTRACT FROM AUTHOR]

The overall aim of this study was to determine the effects of running kinematics on the peak upper trunk segmental accelerations captured with an accelerometer embedded in a commonly used GPS device. Thirteen male participants (age: 27 ± 3.7 years, height: 1.81 ± 0.06 m, mass: 82.7 ± 6.2 kg) with extensive running experience completed a single trial of treadmill running (1 degree inclination) for 40 s at nine different speeds ranging from 10 to 18 km/h at 1 km/h increments. Three-dimensional peak upper trunk acceleration values were captured via a GPS device containing a tri-axial accelerometer. Participants' running kinematics were calculated from the coordinate data captured by an 18-camera motion capture system. A series of generalized linear mixed models were employed to determine the effects of the kinematic variables on the accelerometer acceleration peaks across the key gait phases of foot contact. Results showed that running kinematics had significant effects on peak accelerometer-measured accelerations in all axes (p < 0.05). Overall, peak segment velocities had a larger effect than joint/segment kinematics on resultant (F values = 720.9/54.2), vertical (F values = 149.8/48.1) and medial–lateral (F values = 55.4/33.4) peak accelerometer accelerations. The largest effect on peak accelerometer accelerations were observed during the impact subphase of foot contact at the adduction/abduction velocity of the shank (F value = 129.2, coefficient = −0.03) and anterior/posterior velocity of the pelvis (F value = 58.9, coefficient = 0.01). Axis-dependent effects of running kinematics were also observed, specifically at the trunk segment in the vertical and anterior–posterior peak accelerometer accelerations. This study showed the intersegmental relationship between joint/segment kinematics, segment velocities and the resulting peak accelerations of the upper trunk during running over several speeds. These findings provide insights into the lower body's GRF attenuation capacity and its contribution to trunk stability whilst running. [ABSTRACT FROM AUTHOR]

Free-swimming performance depends strongly on the ability to develop propulsive force and minimise resistive drag. Therefore, estimating resistive drag (passive or active) may be important to understand how free-swimming performance can be improved. The purpose of this narrative overview was to describe and discuss experimental methods of measuring or estimating active and passive drag relevant to competitive swimming. Studies were identified using a mixed-model approach comprising a search of SCOPUS and Web of Science data bases, follow-up of relevant studies cited in manuscripts from the primary search, and additional studies identified by the co-authors based on their specific areas of fluid dynamics expertise. The utility and limitations of active and passive drag methods were critically discussed with reference to primary research domains in this field, 'swimmer morphology' and 'technique analysis'. This overview and the subsequent discussions provide implications for researchers when selecting an appropriate method to measure resistive forces (active or passive) relevant to improving performance in free-swimming. [ABSTRACT FROM AUTHOR]

Background: Extracorporeal Membrane Oxygenation (ECMO) therapy for respiratory failure is an increasingly popular modality of support. Patient selection is an important aspect of outcome success. This review assesses the efficacy of the popular prognostic tools Respiratory ECMO Survival Prediction Score (RESP) and Predicting Death for Severe ARDS on VV-ECMO score (PRESERVE) for ECMO patient selection. Methods: A literature search was performed. Six publications were found to match the specified selection criteria. These publications were assessed and compared using the area under the receiver operating characteristic (AUROC) curve statistical method to ascertain the discriminatory ability of the models to predict treatment outcome. Results: Six articles were included in this review from 306 screened, of which all were retrospective cohort studies. Data was generated over a period of 3–9 years from 13 referring hospitals. Studies consisted of 467 male and 221 female (30 unknown) participants in total with a high heterogeneity. The PRESERVE prognostic model was found to have a higher AUROC score than the RESP model, however both models were found to be sub-optimal in their discriminatory ability. A high chance of bias was seen across all included studies. Conclusion: It was the findings of this review, indicated by analysis using the AUROC measures, that the prognostic model PRESERVE performed better than RESP for predicting post ECMO therapy outcomes, for patients presenting with Acute Respiratory Distress Syndrome within their respective validated time frames, i.e., RESP at Intensive care unit (ICU) discharge and PRESERVE at 6 months post ICU discharge. However, It was recognized that comparator groups were small thereby introducing bias into the study. Further prospective, randomized studies would be necessary to effectively assess the utility of these predictive survival scores. [ABSTRACT FROM AUTHOR]