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This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbour (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).

People with severe persistent mental illness pose a significant challenge to managed care organizations and society in general. The financial costs are staggering as is the community impact including homelessness and incarceration. This population also has a high incident of chronic comorbid disorders that not only drives up healthcare costs but also significantly shortens longevity. Traditional case management approaches are not always able to provide the intense and direct interventions required to adequately address the psychiatric, medical and social needs of this unique population. This article describes a Medicare Advantage Chronic Special Needs Program that provides a Medical Home, Active Community Treatment, and Integrated Care. A comparison of utilization and patient outcome measures of this program with fee for service Medicare found significant reduction in utilization and costs, as well as increased adherence to the management of chronic medical conditions and preventative services.

This study aims to test the effects of Celliant armbands on grip strength in subjects with chronic wrist and elbow pain. Celliant® is a functional textile fabric containing minerals that emit infrared radiation (IR) in response to body heat. IR-emitting fabrics have biological effects including the reduction of pain and inflammation and the stimulation of muscle function.

A randomized placebo-controlled trial recruited 80 subjects (40 per group) with a six-month history of chronic wrist or elbow pain (carpal tunnel syndrome, epicondylitis or arthritis) to wear an armband (real Celliant or placebo fabric) on the affected wrist or elbow for two weeks. Grip strength was measured by a dynamometer before and after the two-week study.

For the placebo group, the mean grip strength increased from 47.95 ± 25.14 (baseline) to 51.69 ± 27.35 (final), whereas for the Celliant group, it increased from 46.3 ± 22.02 to 54.1 ± 25.97. The mean per cent increase over the two weeks was +7.8% for placebo and +16.8% for Celliant (p = 0.0372). No adverse effects was observed.

Limitations include the wide variation in grip strength in the participants at baseline measurement, which meant that only the percentage increase between baseline and final measurements showed a significant difference. Moreover, no subjective measurements of pain or objective neurophysiology testes was done.

Celliant armbands are easy to wear and have not been shown to produce any adverse effects. Therefore, there appears to be no barrier to prevent widespread uptake.

IR-emitting textiles have been studied for their beneficial effects, both in patients diagnosed with various disorders and also in healthy volunteers for health and wellness purposes. Although there are many types of textile technology that might be used to produce IR-emitting fabrics, including coating of the fabric with a printed layer of ceramic material, incorporating discs of mineral into the garment, the authors feel that incorporating ceramic particles into the polymer fibers from which the fabric is woven is likely to be the most efficient way of achieving the goal.

Celliant armbands appear to be effective in painful upper limb inflammatory disorders, and further studies are warranted. The mechanism of action is not completely understood, but the hypothesis that the emitted IR radiation is absorbed by nanostructured intracellular water provides some theoretical justification.

The purpose of this study was to investigate diabetes self-care behaviors, stigmatization and A1C as predictors of a negative perception of insulin treatment in insulin-treated type 2 diabetic patients.

A descriptive cross-sectional and relational design was used. The study was carried out in the Diabetes Training Centre and Endocrine and Metabolism Clinic of a university hospital in the southeast of Turkey between May and October 2017. The research sample consisted of 100 type 2 diabetic patients determined by using a convenience sampling method. An introductory information form for type 2 diabetic patients, the Insulin Treatment Appraisal Scale (ITAS), Diabetes Self-Care Activities Survey (DSCAS) and Barriers to Insulin Treatment Scale (BIT) were used to collect the research data. The data were analyzed using descriptive statistics, correlations and step wise multi-linear regression.

The number of daily insulin injections, training received about insulin and stigmatization was significant predictors of a negative perception of insulin treatment.

Strategies to decrease diabetic individuals' fear of stigmatization should be utilized to minimize their negative insulin treatment perception (giving diabetic individuals training about diabetes, planning public training to inform society and using mass media tools). Diabetes educators should know that diabetic individuals' perception of the severity of the illness could influence the daily number of injections applied and decrease the negative perception regarding insulin.