Huge thanks to @nigeljaffe.bsky.social, @christianwebb.bsky.social and our amazing team Habiballah Rahimi-Eichi, Erika Forbes, @diegopizzagalli.bsky.social and @drjbake.bsky.social

These patterns appeared at the individual level, showing the potential for personalized, real-time monitoring of therapy progress. In the future, such tools could help clinicians track clients’ progress outside the therapy room and deliver just-in-time interventions that enhance ongoing treatment.

• Smartphone mobility features predicted weekly improvements in anhedonia and depressive symptoms.

• GPT “activation” ratings correlated with both self-report of activation and mobility data (e.g., time away from home, number of places visited).
• Increases in GPT-rated activation were associated with higher daily positive and lower negative affect.

BA aims to increase activation, encouraging engagement in rewarding, goal-directed activities, which is assumed to reduce anhedonia and depressive symptoms.

Using GPT-4o-based ratings of daily EMA text responses and smartphone data, we found:

In this study, we tested whether passive smartphone data (GPS, accelerometer) and large language models (LLMs) like GPT could capture meaningful change among adolescents receiving behavioral activation (BA) therapy for depression and anhedonia.

My hope is that, with the right boundaries, knowledge, and caution, and with the incredible work of so many researchers in the field, AI can advance our long-standing efforts to close the urgent gap in mental health access.

I argue that we can’t stop this train, but as clinicians and researchers, we can and *should* shape its track. Rather than resist change, we should be more active in guiding it, wisely, ethically, and collaboratively (with the developers and the users).

In this letter, I respond to Ziv Ben-Zion’s important World View column, which recommended ways to stop, or at least slow down, the use of AI (such as ChatGPT) for emotional support.
👉Ben Zion’s world View column: www.nature.com/articles/d41...

Thanks so much for tagging me, Christian! This sounds like an absolutely amazing dataset. @eikofried.bsky.social I’m traveling now with little reception (and lots of kids’ noise 😅), but once I’m somewhere quiet I’ll send an email with my thoughts on de-identification and some collab suggestions.

How emotions unfold over time, their flexibility and adaptability, may be just as important as what people feel for understanding depression vulnerability.
This opens new doors for early identification and prevention.

📉 The results: Participants with more rigid emotional systems were significantly more likely to develop depressive symptoms later, even after controlling for risk factors, sex, emotional intensity, and variability.

This was specific to depression! Emotion rigidity didn't predict anxiety symptoms.

We followed adolescents without depression and had them report emotions 4x daily for a month. Then we tracked depressive symptoms for 2 years.
We used dynamic systems methods to build individual emotion networks and calculated emotional rigidity (how densely interconnected emotional states were).

We know that emotions help us make sense of the world, guide decisions, and navigate (social) life. But what happens when emotions become too rigid to flexibly adapting to changes?
In this study, we asked whether emotion rigidity might precede and predict the onset of depressive symptoms.

This means we can use digital tools to provide therapists with objective, real-time measures of therapeutic progress, potentially personalizing treatments and improving outcome.

While LLM ratings predicted immediate daily emotional changes (more positive, less negative affect), passive sensors (places visited (↑), time at home(↓))predicted weekly improvements in depression and anhedonia.

Both LLM-derived activation (daily texts) and passive sensing features (places visited ↑, time at home ↓) correlated significantly with self-reported activation —supporting their convergent validity.

Passive sensing features (higher activity scores (↑), more places visited (↑) less time spent at home (↓)) correlated with daily LLM-rated activation.