BackgroundPre-exposure prophylaxis (PrEP) provides a strong preventative benefit to individuals at risk for HIV. While PrEP adherence is highly correlated with its efficacy, adherence rates are variable both across and within persons.ObjectiveThe objective of this study was to develop and pilot-test a smartphone-based intervention, known as mSMART, that targets PrEP adherence. mSMART provides contingency management in the form of monetary incentives for daily PrEP adherence based on a real-time adherence assessment using a camera-based medication event-monitoring tool as well as medication reminders, PrEP education, individualized behavioral strategies to address PrEP adherence barriers, and medication adherence feedback.MethodsThis was a 4-week open-label, phase I trial in a community sample of young men who have sex with men already on PrEP (N=10).ResultsAlthough adherence composite scores corresponding to PrEP biomarkers indicated that 90% (9/10) of the sample already had an acceptable baseline adherence in the protective range, by the end of the 4-week period, the scores improved for 30% (3/10) of the sample—adherence did not worsen for any participants. Participants reported mean PrEP adherence rates of 91% via daily entries in mSMART. At the end of the 4-week period, participants indicated acceptable ratings of satisfaction, usability, and willingness to recommend mSMART to others. There were no technical difficulties associated with smartphone compatibility, user misunderstandings about mSMART features that interfered with daily use, or study attrition.ConclusionsThis study is the first to apply contingency management to PrEP adherence. Findings indicated that mSMART is feasible and acceptable. Such an adherence intervention administered via a user-friendly smartphone app can allow for widespread dissemination. Future efficacy trials are needed.Trial RegistrationClinicalTrials.gov NCT02895893; https://clinicaltrials.gov/ct2/show/NCT02895893 (Accessed by Webcite at http://www.webcitation.org/72JskjDJq)

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The development of an infrastructure for multiple autonomous agents, with an application to urban traffic signal control, is described. The agent-based infrastructure, Cybele, allows for distributed computing, interagent communication, agent migration, and computational resource allocation. The agents that are used to solve the traffic signal control problem are known collectively as DAARTS (Decentralized Adaptive Agents for contRol of Traffic Signals). DAARTS adopts a hierarchical multiagent-based architecture in which the lowest level (intersection agents) involves individual intersection-traffic dynamics and phase selection based on “local” information, while higher levels take into account the supervisory (network-level) dynamics. The controller design is based on a receding-horizon model predictive control approach. Coordination between intersections is achieved in a decentralized manner at the lowest level. The agents are integrated into a simulation test bed with the microsimulator CORSIM, using the DAARTS simulation tool kit. This kit enables communications between the CORSIM real-time extension, the communications management functions of Cybele, and the traffic control agents. The control process is truly distributed, and each of these components can reside on a different computer. Descriptions of all of the software components are given, and the control algorithm is discussed in detail. Some encouraging results from the simulation of a small network are included. Ongoing and future research activities are discussed.

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