A comprehensive mobile health intervention to prevent and manage the complexities of opioid use. (August 2022)

• Record Type: Journal Article
• Authors: Singh, Neetu; Dube, Shanta R.; Varshney, Upkar; Bourgeois, Anu G.
• Is Part Of: International journal of medical informatics. Volume 164(2022)
• Publication Date: 2022-08
• Subjects: Opioid Use -- M-health intervention -- Design -- Analytical model -- Evaluation; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2022.104792
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Multi-functional opioid M-health interventions for patients and other stakeholders. O-INT addresses three-level prevention and overdose detection and response. Analytical modeling results show specific preferences for intervention functions. The Opioid Cube visualizes opioid states and influencing and interacting factors. Abstract: 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.