Lizbeth ‘Libby’ Benson, PhD

@leb112358.bsky.social

assistant professor @d3center.bsky.social @UMich | EMA & intensive longitudinal methods | personalized #mHealth #DigitalHealth interventions | data viz | #BehavioralScience | she/her/rescue dog mom🦮🐕‍🦺 lizbethbenson.com, https://d3c.isr.umich.edu/

This local Wolfdog joined an Olympic ski event and triggered the finish-line camera. This is Nazgul. He snuck into a cross-country skiing sprint this morning and raced the homestretch with some competitors before being escorted home. 14/10 someone get him a medal

After 5 years of data collection, our WARN-D machine learning competition to forecast depression onset is now LIVE! We hope many of you will participate—we have incredibly rich data. If you share a single thing of my lab this year, please make it this competition. eiko-fried.com/warn-d-machi...

WARN-D machine learning competition is live » Eiko Fried

If you share one single thing of our team in 2026—on social media or per email with your colleagues—please let it be this machine learning competition. It was half a decade of work to get here, especi...

eiko-fried.com

🧪@d3center.bsky.social researchers discuss the sources and impact of missing data in microrandomized trials. They provide a conceptual framework to guide future investigators in anticipating missing data and making informed decisions to manage them myumi.ch/61MWn @leb112358.bsky.social

Missing data in microrandomized trials: Challenges and opportunities - Behavior Research Methods

The vision of leveraging digital technologies to deliver real-time psychological interventions in everyday settings is realized via just-in-time adaptive interventions (JITAI) – an intervention design...

myumi.ch

At this point, I might as well -- Here's an infographic showing different ways to include age as a predictor. The top shows two extremes, just as a plain old numerical predictor (imposes linear trajectory) vs. categorical predictor (imposes nothing whatsoever). And then three solutions in between!

Infographic illustrating different ways to model age.
First panel shows two "extreme" cases; including age as a linear numerical predictor (df = 1) or including age as a categorical predictor (df = number of years of age minus 1).
Second panel shows an intermediate solution in which age is categorized into broader bins (df = number of categories minus 1, here 5 - 1 = 4).
Third panel shows an intermediate solution in which age is included with a polynomial (df = degrees of freedom of the polynomial, here 4).
Fourth panel shows an intermediate solution in which age is modeled with the help of splines (df = degrees of freedom of the splines, here 4).
Andrew Mercer@awmercer.bsky.social · last yr.

Maybe use the same number of bins as you have knots in the spline? That’d make it easier to compare the two.

Making accessible data viz isn't just about compliance - it's about inclusion. Small changes, huge impact for 1.3 billion people with disabilities. @pavithraes.me‬ & @frank.computer‬ show you how in their recent talk at PyCon US. Watch the full session: → www.youtube.com/watch?v=WZMo...

Inclusive Data for 1.3 Billion: Designing Accessible Visualizations

According to the World Health Organization (WHO), an estimated 1.3 billion people (1 in 6 individuals) experience a disability, and nearly 2.2 billion people...

youtube.com

It matters that scientists speak out against what is being done to US science. Staying silent is neither "objective" nor "staying out of politics". Silence now *is* political - it supports the status quo, says "nothing to worry about". We need to sound the alarm. www.nature.com/articles/d41...

Trump’s siege of science: how the first 30 days unfolded and what’s next

The breakneck pace and devastating impact of the administration’s policy changes has shocked researchers.

nature.com

As a side - listing things as weaknesses w/o indicating why is ZERO helpful for career dev. candidates trying to improve a resubmission... e.g., if my Aim 2 findings don't show feasibility/acceptability, why is proposing to submit a R21 (to do intervention refinement) instead of an R01 a weakness?

Lizbeth ‘Libby’ Benson, PhD@leb112358.bsky.social · 2y ago

Got a 1, 4, 1 for Candidate scores on my K01. Across the board reviewer 2 was nasty & clearly did not spend more than the bare minimum reading my proposal. I know it's common, but it's still so frustrating a person like this can totally tank an application 🙄

Got a 1, 4, 1 for Candidate scores on my K01. Across the board reviewer 2 was nasty & clearly did not spend more than the bare minimum reading my proposal. I know it's common, but it's still so frustrating a person like this can totally tank an application 🙄

a cartoon of ariel from the little mermaid is making a sad face and says sigh .

ALT: a cartoon of ariel from the little mermaid is making a sad face and says sigh .

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