New technologies for art therapy interventions tailored to severe disabilities.

Individuals with multiple disabilities can have a wide range of characteristics depending on the combination and severity of the disabilities, such as intellectual disability, mobility issues, sensorial impairment, language issues and brain injury. New technologies can help therapists find an alternative way to engage and interact with clients by opening a communication window and starting to build the therapeutic relationship. The need to use more customized technological tools led us to develop the Painteraction system, an intuitive tool based on Augmented Reality that allows clients to be immersed in their own images. Just by moving their bodies individuals are able to make drawings and receive visual feedback, both from themselves and their therapists, as it appears on the screen. The pilot testing of Painteraction was performed on 21 inpatients at Istituto Serafico (Assisi, Italy) with severe/multiple disabilities in order to explore and assess reaction and responsiveness in a semi-structured art therapy setting. The sample was formed by 14 males and 7 females (N=21) between the ages of 7 and 35. All participants attended three twenty-minute individual art therapy sessions which were approximately one week apart. Through direct and indirect (video recordings) observation of the sessions, it appeared that the specific Augmented Reality tool introduced in the art therapy setting was easily accepted by most of the clients involved and generally allowed the development of an interpersonal therapist-client relationship. The present study therefore gave us the opportunity to test new digital tools in the challenging setting of severe/multiple disabilities and observe the huge potential of new media to empower clients to express themselves and their creativity, and ultimately overcome mental and physical barriers. We propose that Augmented Reality tools are particularly well-suited to art therapy and create an equally suitable therapeutic environment to address specific client needs.

Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes.

BACKGROUND: The development of an artificial pancreas requires an accurate representation of diabetes pathophysiology to create effective and safe control systems for automatic insulin infusion regulation. The aim of the present study is the assessment of a previously developed mathematical model of insulin and glucose metabolism in type 1 diabetes and the evaluation of its effectiveness for the development and testing of control algorithms. METHODS: Based on the already existing "minimal model" a new mathematical model was developed composed of glucose and insulin submodels. The glucose model includes the representation of peripheral uptake, hepatic uptake and release, and renal clearance. The insulin model describes the kinetics of exogenous insulin injected either subcutaneously or intravenously. The estimation of insulin sensitivity allows the model to personalize parameters to each subject. Data sets from two different clinical trials were used here for model validation through simulation studies. The first set had subcutaneous insulin injection, while the second set had intravenous insulin injection. The root mean square error between simulated and real blood glucose profiles (G(rms)) and the Clarke error grid analysis were used to evaluate the system efficacy. RESULTS: Results from our study demonstrated the model's capability in identifying individual characteristics even under different experimental conditions. This was reflected by an effective simulation as indicated by G(rms), and clinical acceptability by the Clarke error grid analysis, in both clinical data series. CONCLUSIONS: Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.

Control oriented model of insulin and glucose dynamics in type 1 diabetics.

The aim of the study was to realize a mathematical model of insulin-glucose relationship in type I diabetes and test its effectiveness for the design of control algorithms in external artificial pancreas. A new mathematical model, divided into glucose and insulin sub-models, was developed from the so-called "minimal model". The key feature is the representation of insulin sensitivity so as to permit the personalisation of the parameters. Real-time applications are based on an insulin standardised model. Clinical data were used to estimate model parameters. Root mean square error between simulated and real blood glucose profiles (G(rms)) was used to evaluate system efficacy. Results from parameter estimation and insulin standardisation showed a good capability of the model to identify individual characteristics. Simulation results with a G(rms) 1.30 mmol/l in the worst case testified the capacity of the model to accurately represent glucose-insulin relationship in type 1 diabetes allowing self tuning in real time.

Closing the loop: the adicol experience.

The objective of the project Advanced Insulin Infusion using a Control Loop (ADICOL) was to develop a treatment system that continuously measures and controls the glucose concentration in subjects with type 1 diabetes. The modular concept of the ADICOL's extracorporeal artificial pancreas consisted of a minimally invasive subcutaneous glucose system, a handheld PocketPC computer, and an insulin pump (D-Tron, Disetronic, Burgdorf, Switzerland) delivering subcutaneously insulin lispro. The present paper describes a subset of ADICOL activities focusing on the development of a glucose controller for semi-closed-loop control, an in silico testing environment, clinical testing, and system integration. An incremental approach was adopted to evaluate experimentally a model predictive glucose controller. A feasibility study was followed by efficacy studies of increasing complexity. The ADICOL project demonstrated feasibility of a semi-closed-loop glucose control during fasting and fed conditions with a wearable, modular extracorporeal artificial pancreas.

Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

A nonlinear model predictive controller has been developed to maintain normoglycemia in subjects with type 1 diabetes during fasting conditions such as during overnight fast. The controller employs a compartment model, which represents the glucoregulatory system and includes submodels representing absorption of subcutaneously administered short-acting insulin Lispro and gut absorption. The controller uses Bayesian parameter estimation to determine time-varying model parameters. Moving target trajectory facilitates slow, controlled normalization of elevated glucose levels and faster normalization of low glucose values. The predictive capabilities of the model have been evaluated using data from 15 clinical experiments in subjects with type 1 diabetes. The experiments employed intravenous glucose sampling (every 15 min) and subcutaneous infusion of insulin Lispro by insulin pump (modified also every 15 min). The model gave glucose predictions with a mean square error proportionally related to the prediction horizon with the value of 0.2 mmol L(-1) per 15 min. The assessment of clinical utility of model-based glucose predictions using Clarke error grid analysis gave 95% of values in zone A and the remaining 5% of values in zone B for glucose predictions up to 60 min (n = 1674). In conclusion, adaptive nonlinear model predictive control is promising for the control of glucose concentration during fasting conditions in subjects with type 1 diabetes.