We are currently recruiting participants for our WARN-D study on student mental health. If you are in 🇳🇱 & over 18, we'd love for you to participate warn-d.com/participate. Thanks to Mare, LINDA. & Leidsch Dagblad for the great press coverage in the last days!

Een #depressie komt meestal niet zomaar uit de lucht vallen. Psycholoog Eiko Fried ontwikkelt een soort buitenradar voor depressies. Ben je student en wil je (betaald) meewerken aan dit urgente onderzoek? Geef je op 👉youtu.be/uvCZXrtjd8I

Thanks Association for Psychological Science for the opportunity to present our work developing and delivering personalized feedback from EMA! Sat @ 11:30am Me, Carlotta Rieble Lino von Klipstein Zachary Cohen, and Eiko Fried will share how EMA can help folks understand their health/behavior

So happy to have been a part of #apschi22! I talked about the personalized reports we give our EMA participants in the WARN-D study as a reward for participating. Huge shout out to Aljoscha Rimpler for his hard work on the underlying tool FRED (Feedback Reports on EMA Data).

🥁🥁🥁 After 1.5 years of hard work, I'm super duper excited to share the preprint of our protocol paper for WARN-D (with Ricarda Proppert & Carlotta Rieble), our study to build a personalized early warning system for depression. 1/4 psyarxiv.com/9qcvs

🥁 Researchers & teachers in 🇳🇱: we need your help! We are researchers at Universiteit Leiden, following 2000 students for 2 years using smartphones & -watches to build an early warning system for depression. We want to recruit a diverse pool of participants. How can you help? ⬇️ (1/4)

🥁 Calling all network enthusiasts and critics 🥁 Have you ever engaged with the network approach to psychopathology or had reasons not to? We are curious to hear your thoughts on the (non)applicability of network analyses and the network approach: tinyurl.com/network-survey

Always been in still admiration for Eiko Fried‘s WARN-D study - thanks for the update Carlotta Rieble, and for hosting this wonderful symposium Dr. Hanna Reich! 🦊 #dgppn #dgppn2022

We'll be hiring a 3 year postdoc for our WARN-D project to build a warning system for #depression, based on 2 years of data in ~2000 students (incl. smartwatches & smartphones). Would appreciate if you shared w colleagues :). Thx! Short summary 🧵 universiteitleiden.nl/vacatures/2022…

Please help us find participants!

📣amazing symposium I’m chairing Fri 4pm Holme’s West ‘The power of integration: leveraging multimodal data & methods to increase description, prediction, & understanding of mental health problems’ Zachary Cohen Nick Jacobson Carlotta Rieble @MyrtheVeenman Eiko Fried @CheriLev

Honored to bring brilliant scientists together for an incredible symposium Association for Psychological Science #aps23dc today Zachary Cohen Nick Jacobson Carlotta Rieble Eiko Fried @MyrtheVeenman @CheriLev & getting to visit Shirley B. Wang Matt Southward @MiriamHehlmann Björn Siepe Brian Schwartz

Preprint on Bayesian estimation and testing for idiographic networks (psyarxiv.com/uwfjc/)🎉 Networks on n=1 data are popular, but they often contain a lot of noise. How can we test if networks that look different reflect more than sampling variability? 🧵below

Excited that our WARN-D protocol paper is now published #OpenAccess, introducing the rationale, design, objectives, & measures of WARN-D. It took us a year to build the measurement battery: it's available in supplementary & we hope others will use it. cpe.psychopen.eu/index.php/cpe/…

1/4 FRED is online, our free open source software to provide feedback to participants in EMA studies. We (led by Aljoscha Rimpler) programmed the software to provide feedback at scale to our ~2000 WARN-D participants. Hope it will be useful to researchers & clinicians alike!

1/8 New tutorial preprint led by Björn Siepe in which we present different descriptive statistics & data visualization techniques with the goal to better understand EMA item functioning. Preprint: osf.io/preprints/psya… Brief overview thread 🧵:

New preprint by Björn Siepe & WARN-D team. We collected information on sleep, tiredness & stress with both EMA and sensor data, and try to find out to which degree these are associated contemporaneously in a large dataset (n~800) with many timepoints (t~360). Brief 🧵

1/2 Our new preprint shows how to estimate internal consistency reliability in EMA data: ➡️n~1150, 3 months data, 4 scales ➡️6 nomothetic & idiographic methods ➡️2 timescales (4/day, 1/week) ➡️2 languages (ENG vs NL) ➡️separation of between & within person reliability.