arXiv subjects

Manuel Spitschan

Publications and source records attributed to Manuel Spitschan.

2 recordsLinked to original sources

arXiv2026

momenTUM: A schema-driven platform for designing, deploying, and exploring ecological momentary assessment studies

Ecological momentary assessment (EMA) is widely used to collect repeated self-report data in participants' everyday lives using mobile devices. EMA studies often involve multiple questionnaires, flexible schedules, and longitudinal data collection, requiring reliable systems for study setup, deployment, monitoring, and data management. Existing workflows are often fragmented across tools, making studies difficult to reproduce and maintain. We present momenTUM, an open-source platform for designing, deploying, and managing mobile EMA studies. Its central principle is that a structured study specification serves as the shared representation across the full workflow. The same specification supports authoring in the Study Designer, execution in the participant-facing mobile application, backend synchronization and storage, REDCap-linked data handling, and researcher monitoring. This makes protocols reusable, inspectable, and consistent across system components without requiring study-specific app implementations. momenTUM integrates with REDCap to automate project setup and synchronize responses. Its authenticated dashboard provides tabular and calendar-based views, filtering by study components, and visual summaries. The platform has been deployed in real-world studies, including AMBIENT-BD, which examines mood, sleep, and circadian rhythms in bipolar disorder, and the EcoSleep cohort study. We also describe an exploratory LLM-assisted extension that generates draft structured study specifications for researcher review. These deployments show that momenTUM can support complex ambulatory assessment protocols while reducing technical overhead and enabling reproducible, reusable, and extensible EMA workflows.

q-bio.OT↗

arXiv2026

Scene-based field validation of wearable light loggers

Wearable light loggers are increasingly used to measure personal light exposure. However, there is no standardised method to test how well these devices perform in real-world settings. To address this, we developed a scene-based field validation framework combining wearable light loggers, laboratory-grade spectral reference instruments, and image-based scene characterisation to evaluate wearable performance in the field. We applied the validation framework to ActTrust2 and ActLumus light loggers using 433 natural, everyday scenes across two sites: T\"ubingen, Germany (n=210), and Izmir, T\"urkiye (n=223), spanning indoor and outdoor environments in daylight, artificial light and mixed scenarios, across wide range of photopic and melanopic equivalent daylight illuminances. Both light loggers exhibited high agreement with the reference instruments (R^2=0.988-0.990), but they systematically underestimated light exposure. The lighting condition, scene complexity, time of day, and study site contributed significantly to measurement bias. Resampling under specific lighting conditions indicated that bias ranged from -0.065 log units in daylight-only situations to -0.195 log units in artificial-only situations. This suggested that single-condition assessments with limited spectra or scene categories can underestimate or overestimate overall wearable performance. Bootstrap resampling demonstrated that performance estimates stabilised at approximately 100 scenes, indicating that a diverse sample of this size is sufficient for reliable field validation. Finally, cross-site validation showed consistent performance across two sites, supporting the framework's reproducibility. Overall, these findings establish our validation framework as an effective tool for guiding scene diversity and sampling design for ecologically valid field validation of wearable light loggers.

physics.ins-det↗