#PassiveSensing

NuRelm

@nurelm.com

3 months ago

A list of the Software Services for Researchers NuRelm provides. Images of apps.

Have an idea for a mobile or web app? We'd love to hear more! 👂✨ Stop by booth #314 at #ABCT2025.

#nihgrants #digitalinterventions #studyportals #passivesensing

JMIR Publications

@jmirpub.bsky.social

9 months ago

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study Background: Depression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% among college students. Passive, device-based sensing further enables detection of depressive symptoms at a low burden to the individual. Objective: We leveraged an ensemble machine learning method (light gradient boosting machine) to detect depressive symptoms entirely through passive sensing. Methods: A diverse sample of undergraduate students (N=28; mean age 19.96, SD 1.23 y; 15/28, 54% women; 13/28, 46% Latine; 10/28, 36% Asian; 4/28, 14% non-Latine White; 11/28, 4% other) participated in an intensive longitudinal study. Participants wore 2 devices (an Oura ring for sleep and physiology data, and a Samsung smartwatch for physiology and movement data) and installed the AWARE software on their mobile devices, which collects passive sensing data such as screen time. Participants were derived from a randomized controlled trial of a positive psychology mobile health intervention. They completed a self-report measure of depressive symptoms administered weekly over a 19- to 22-week period. Results: The light gradient boosting machine model achieved an F1-score of 0.744 and a Cohen κ coefficient of 0.474, indicating moderate agreement between the predicted labels and the ground truth. The most predictive features of depressive symptoms were sleep quality and missed mobile interactions. Conclusions: Findings suggest that data collected from passive sensing devices may provide real-time, low-cost insight into the detection of depressive symptoms in college students and may present an opportunity for future prevention and perhaps intervention.

JMIR Formative Res: Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study #DepressionAwareness #MentalHealthMatters #CollegeMentalHealth #PassiveSensing #MachineLearning

Egon Dejonckheere

@dejonckheeregon.bsky.social

9 months ago

🚨 New m-Path feature alert!

Trigger #ESM / #EMA prompts based on proximity to a Bluetooth beacon 🔵📱

Perfect for dyadic studies 👥 — deliver mobile surveys or interventions real-time, when participants are near their partner, friend or family member.

#passivesensing @saa2025leuven.bsky.social

Anna Langener

@annalangener.bsky.social

1 year ago

✨ Excited to share our new preprint: osf.io/preprints/os... ✨

In this paper, Nick Jacobson and I dive into the challenges researchers face when developing prediction models for just-in-time adaptive interventions (JITAI). 🧵👇

#EMA #JITAI #CrossValidation #PassiveSensing