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Electronic reminder systems have been available for decades, yet medication adherence remains poor. Most systems rely on simple alarms and do not address other determinants of health-related behavior. This paper describes a collaborative awareness system for chronic disease medication adherence that relies on patient self-reflection and clinician support. Visualizations of adherence performance, including estimated plasma concentration graphs and a dynamic, personalized, disease-state simulation, are available to the patient (cell phone and internet media display) and clinician (computer) in real-time. The clinician can send asynchronous video messages of advice and encouragement to the patient regularly. A pilot was conducted with four HIV positive patients for four weeks. Three patients who started with suboptimal adherence improved (93.0% to 99.1%, 83.0% to 96.3%, and 63.9% to 81.3%). One patient who started with optimal medication adherence (>95%) maintained this level. All four patients appreciated the rich feedback and wanted to continue using the system.

The flow of messages in a message-switched data communication network is modeled in a continuous dynamical state space.The state variables represent message storage at the nodes and the control variables represent message flow rates along the links. A deterministic linear cost functional is defined which is the weighted total message delay in the network when we stipulate that all the message backlogs are emptied at the final time and the inputs are known. The desired minimization of the cost functional results in a linear optimal control problem with linear state and control variable inequality constraints. The remainder of the thesis is devoted to finding the feedback solution to the optimal control problem when all the inputs are constant in time. First, the necessary conditions of optimality are derived and shown to be sufficient. The pointwise minimization in time is a linear program and the optimal control is seen to be of the bangbang variety. Utilizing the necessary conditions it is shown that the feedback regions of interest are convex polyhedral cones in the state space.A method is then described for constructing these regions from a comprehensive set of optimal trajectories constructed backward in time from the final time.Techniques in linear programming are employed freely throughout the development, particularly the geometrical interpretation of linear programs and parametric linear programming.There are several properties of the method which complicate its formulation as a compact algorithm for general network problems. However, in the case of problems involving networks with single destinations and all unity weightings in the cost functional it is shown that these properties do not apply.A computer implementable algorithm is then detailed for the construction of the feedback solution.-2--2a-

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