Mobile intervention for emerging adults with regular cannabis use: a micro-randomized trial

Area of Science:

• Digital Health Interventions
• Substance Use Research
• Behavioral Science

Background:

• Cannabis use is common in emerging adults, yet few seek treatment for associated problems.
• Personalized just-in-time adaptive interventions (pJITAI) offer a novel approach to address this gap.
• Reinforcement learning (RL) algorithms can enhance real-time intervention delivery.

Purpose of the Study:

• To test the feasibility and acceptability of a pJITAI for emerging adults with regular cannabis use.
• To evaluate the impact of intervention messages on engagement and cannabis use within a micro-randomized trial (MRT).

Main Methods:

• A 30-day micro-randomized trial (MRT) involving 122 emerging adults with regular cannabis use.
• Twice-daily surveys and micro-randomizations to receive intervention messages or no message, guided by an RL algorithm.
• Assessment of feasibility, acceptability, intervention engagement, and cannabis use at 1-month and 2-month follow-ups.

Main Results:

• The pJITAI demonstrated feasibility (89.1% download rate) and acceptability (80.4% positive message ratings).
• Message delivery significantly increased proximal intervention engagement but did not directly impact proximal cannabis use.
• Greater engagement correlated with fewer cannabis use days at 2-month follow-up.

Conclusions:

• The pJITAI is a promising intervention for emerging adults not seeking treatment for cannabis use.
• Messages effectively increased user engagement, suggesting potential for future intervention refinement.
• Further optimization is warranted to enhance the intervention's impact on cannabis use reduction.