An analytical model to evaluate reminders for medication adherence

A comprehensive mobile health intervention to prevent and manage the complexities of opioid use

OBJECTIVES

The Opioid Use crisis continues to be an epidemic with multiple known influencing and interacting factors. With the need for suitable opioid use interventions, we present a conceptual design of an m-health intervention that addresses the various known interacting factors of opioid use and corresponding evidence-based practices. The visualization of the opioid use complexities is presented as the "Opioid Cube".

METHODS

Following Stage 0 to Stage IA of the NIH Stage Model, we used guidelines and extant health intervention literature on opioid apps to inform the Opioid Intervention (O-INT) design. We present our design using system architecture, algorithms, and user interfaces to integrate multiple functions including decision support. We evaluate the proposed O-INT using analytical modeling.

RESULTS

The conceptual design of O-INT supports the concept of collaborative care, by providing connections between the patient, healthcare professionals, and their family members. The evaluation of O-INT shows a preference for specific functions, such as overdose detection and potential for high system reliability with minimal side effects. The Opioid Cube provides a visualization of various opioid use states and their influencing and interacting factors.

CONCLUSIONS

O-INT is a promising design with a holistic approach to manage opioid use and prevent and treat misuse. With several needed functionalities, O-INT design serves as a decision support system for patients, healthcare professionals, researchers, and policy makers. Together, O-INT and the Opioid Cube may serve as a foundation for development and adoption of highly effective m-health interventions for opioid use.