Thank you CBS @wbz for covering our work on teen depression on your show this evening! via @wbz.

RT @ChrisWebbPhD: 🚨 New research alert. We applied machine learning to the large MYRIAD trial (84 schools and 8,376 UK teens) to ask:. 👉 Wh….

Co-authors @WillemKuyken @HinzeVerena @TimDalgleish and twitter/x-less Drs. Ren, Ford, Greenberg, and Montero-Marin.

Focusing only on individual-level interventions risks overlooking broader influences. Future precision prevention should integrate multilevel data—capturing both individual and contextual drivers of outcomes. 6/n.

Youth mental health ≠ just individual factors. School climate, poverty & inequities matter too. Prevention must move beyond one-size-fits-all to integrate both personal + systemic influences. 5/n.

Machine learning can identify which adolescents are more likely to benefit from SBMT vs. TAU. This may point to a path toward precision prevention in schools. Next step: prospective trials testing whether algorithm-guided intervention assignment improves outcomes. 4/n.

We used ML on baseline student + school characteristics to predict change in depression in SBMT vs. TAU. Results:.• 19% of students fared better with SBMT (d = 0.22).• 37% fared better with TAU (d = 0.22).• Compared to negligible average between-group effects (d = 0.03).3/n.

The MYRIAD trial compared school-based mindfulness training (SBMT) vs. teaching as usual (TAU) in > 8,000 teens in 84 UK schools. SBMT did *not* outperform TAU in the overall trial. Background via NYT: 2/n.

🚨 New research alert. We applied machine learning to the large MYRIAD trial (84 schools and 8,376 UK teens) to ask:. 👉 Which adolescents benefit most from mindfulness delivered in schools?. Bottom line: One-size-fits-all doesn’t work. Paper link: 1/n.

RT @ErikNook: We're hiring!!! @PsychPrinceton has an Assistant Prof search in cog neuro (joint with @PrincetonNeuro). Apply apply apply! ht….

RT @demishassabis: Genie 3 is here - it can generate an entire world simulation that you can interact with in real-time, just from a text p….

Congrats to lab member @hadarfisher and her new paper showing that emotion rigidity (assessed via EMA) in youth predicts future depressive symptoms over a 2 year follow-up.

RT @hadarfisher: Check out our thoughts on the growing use of passive sensing for digital phenotyping. In this commentary, we reflect on ex….

RT @aidangcw: For those interested in the target paper, see here for the link (it's open access). We use a much larger sample than is typi….

🧠📱Excited to share our invited commentary in @JAMANetworkOpen on the promise and pitfalls of using smartphones (and wearables) to track psychopathology. w/ @hadarfisher and linked to excellent paper by @WhitneyRingwald @aidangcw & team. Link ->

If you’re heading to @SPRtweets meeting, check out our panels on tech, personalization, & precision in psychotherapy!. 📱Enhancing Clinical Practice w/ EMA (Fri, 11:30am).📊Innovations in Personalization/Precision in therapy (Sat, 2pm).🧠Neuroscience in therapy (Sat, 3:15pm).

preprint here:

RT @ChrisWebbPhD: New paper out from the lab led by @hadarfisher and @NigelJaffe using smartphone sensors tracking activity levels and LLMs….

New paper out from the lab led by @hadarfisher and @NigelJaffe using smartphone sensors tracking activity levels and LLMs applied to teen text data to track activity levels in behavioral activation.

RT @hadarfisher: 🚨 Preprint alert! 🚨. Can we track therapeutic change outside the clinic?.In a new preprint, we explored whether digital to….