Medications for substance use disorders are limited, especially for stimulants and cannabis, due to addiction's biological complexity, regulatory hurdles, and pharmaceutical industry disinterest. The opioid crisis has spurred urgent efforts to find new treatments. Several neurobiological systems newly implicated in drug reward offer novel medication targets. This review covers ongoing clinical trials and authors' research on psychedelics targeting serotonin 2A receptors, glucagon-like peptide 1 receptor agonists, cannabidiol, dynorphin/kappa opioid receptor, orexin/hypocretin, and oxytocin receptor systems, plus agonist therapies for stimulant use disorders. These innovations suggest an improved therapeutic landscape is near.
A large-scale digital experiment on the Spotify mobile app tested whether messages addressing specific barriers to meditation could encourage people to try meditation. Among approximately 1.33 million U.S. adults aged 18 and older, the most effective message—which countered the barrier that meditation requires being alone in a quiet place—had a click-through rate 1.57 times higher and an activation rate 1.55 times higher than a control message with no barrier-focused content. The least effective message, targeting knowledge barriers, performed worse than the control, with a click-through rate odds ratio of 0.91 and activation rate odds ratio of 0.66. After seven days, engagement differences between conditions largely disappeared. Theory-driven messaging can boost initial interest but may not sustain long-term behavior change.
A representative survey of 2000 US adults mapped onto the Sussex Mindfulness Meditation (SuMMed) model of habit formation. Most adults (69.7%) were in the Pre-Intention stage, 19.1% in Preparation, 2.3% in Action, and 9.0% in Maintenance. Among those preparing to meditate, 34.5% planned to do so within the next week, but only 21.9% intended to start a regular practice within the next month. People with less established meditation practices were more likely to plan using meditation apps than were experienced meditators. The findings suggest the SuMMed model is useful for understanding meditation habits, with a proposed refinement to the Preparation stage to distinguish trying meditation from committing to a regular practice.