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BACKGROUND: Monitoring physical activity with consumers wearables is one of the possibilities to control a patient's self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking. OBJECTIVE: This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine. METHODS: Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices. RESULTS: Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters. CONCLUSIONS: Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials. [ABSTRACT FROM AUTHOR]

BACKGROUND: The traditional rheumatoid arthritis (RA) diagnosis is very complicated because it uses many clinical and image data. Therefore, there is a need to develop a new method for diagnosing RA using a consolidated set of blood analysis and thermography data. OBJECTIVE: The following issues related to RA are discussed: 1) Which clinical data are significant in the primary diagnosis of RA? 2) What parameters from thermograms should be used to differentiate patients with RA from the healthy? 3) Can artificial neural networks (ANN) differentiate patients with RA from the healthy? METHODS: The dataset was composed of clinical and thermal data from 65 randomly selected patients with RA and 104 healthy subjects. Firstly, the univariate logistic regression model was proposed in order to find significant predictors. Next, the feedforward neural network model was used. The dataset was divided into the training set (75% of data) and the test set (25% of data). The Broyden-Fletcher-Goldfarb-Shanno (BFGS) and non-linear logistic function to transformation nodes in the output layer were used for training. Finally, the 10 fold Cross-Validation was used to assess the predictive performance of the ANN model and to judge how it performs. RESULT: The training set consisted of the temperature of all fingers, patient age, BMI, erythrocyte sedimentation rate, C-reactive protein and White Blood Cells (10 parameters in total). High level of sensitivity and specificity was obtained at 81.25% and 100%, respectively. The accuracy was 92.86%. CONCLUSIONS: This methodology suggests that the thermography data can be considered in addition to the currently available tools for screening, diagnosis, monitoring of disease progression. [ABSTRACT FROM AUTHOR]

Background and Objectives: Lateral compression injuries of the pelvic ring are most common among young and elderly patients. Of all pelvic ring fracture injuries, the B2.1 type—involving lateral compression of the pelvic ring—is the most common. Despite this, we still have no high-level evidence to consult when choosing between the surgical and non-operative approaches. The purpose of this research was to compare the short-term functional and quality of life outcomes between operatively and non-operatively treated young patients after a B2.1 type pelvic fracture. Materials and Methods: Patients aged 18 to 65 years with pelvic B2.1 type fractures—according to AO/Tile classification—that were hospitalized in a single trauma center between 2016 November and 2019 September were included in the research. Patients were retrospectively divided into two groups regarding their treatment: nonoperative and operative. Functional outcomes were evaluated using Majeed score, and SF-36 was used for the evaluation of quality of life. Patients completed these questionnaires twice: first during hospitalization, regarding their pre-traumatic condition (timepoint I); and again 10 weeks after the injury, regarding their current condition (timepoint II). Results: A total of 55 patients (70.6% of whom were female) with type B2.1 pelvic fractures were included in the analysis, with an average age of 37.24 ± 13.78 years. There were 21 (38.18%) patients with high injury severity, and 37 (67.3%) patients were treated operatively versus 18 (32.7%) non-operatively. Between the two timepoints, Majeed score reduced by 34.08 ± 18.95 for operatively and 31.44 ± 14.41 for non-operatively treated patients. For operatively and non-operatively treated patients, the physical component summary (PCS) of the SF-36 questionnaire reduced by 19.45 ± 9.95 and 19.36 ± 7.88, respectively, while the mental component summary (MCS) reduced by 6.38 ± 11.04 and 7.23 ± 10.86, respectively. Conclusions: We observed that operative treatment of B2.1 type pelvic fractures for young patients is not superior to non-operative in the short-term, because the functional outcomes and quality of life are similar in both groups. [ABSTRACT FROM AUTHOR]