Evidence map›Paper›PMID 41337361›Full record

ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2025

Automated Monitoring and Clinical Notifications of Patient State for Neuropsychiatric Neuromodulation Studies.

Tomasz M Fraczek, Yewen Zhou, Thomas P Kutcher, Raphael A Bechtold, Saipravallika Chamarthi, Nora Vanegas Arroyave, Wayne K Goodman, Sameer A Sheth, Jeffrey A Herron, Nicole R Provenza

Abstract read
In one paragraph

Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tomasz M Fraczek
Yewen Zhou
Thomas P Kutcher
Raphael A Bechtold
Saipravallika Chamarthi
Nora Vanegas Arroyave
Wayne K Goodman
Sameer A Sheth
Jeffrey A Herron
Nicole R Provenza

Funding

Measuring Automated Behavioral Observations & Vocal Expressions (ABOVE) while Recording from the BrainUH3NS100549 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI GOODMAN, WAYNE K · 2016 to 2022
$7.8M
Building Mood State Classifiers to Inform Deep Brain Stimulation (DBS) of Treatment-Resistant Bipolar DepressionUH3NS136631 · NINDS · BAYLOR COLLEGE OF MEDICINE · PI Wayne K Goodman, Jeffrey A. Herron · 2024 to 2026
$5.8M
NINDS NIH HHS UH3 NS100549NINDS NIH HHS UH3 NS136631
6 · The paper itself

Abstract

The proliferation of wearable technologies and the internet of things has revolutionized clinical research by opening the door to continuous at-home monitoring. These systems give clinicians better insight into patient well-being throughout daily life. Concurrently, new deep brain stimulation (DBS) devices have opened avenues for continuous neural recordings in the background of everyday activities. Behavioral data passively collected from wearables could lead to new insights into the neural mechanism underlying the pathophysiology of neurological or psychiatric disorders. Additionally, reliable multi-device monitoring systems could be used to detect or predict symptoms of a disorder or side effects of an intervention. This is particularly important in investigational neuromodulation trials in which stimulation parameters can be adjusted in the clinic to manage symptoms and side effects but the chronic response of the patient's symptoms to stimulation out of the clinic is still under investigation. Here we demonstrate a prototype continuous monitoring and email-based warning notification system, designed to incorporate data from multiple wearable devices and detect changes in clinical status for patients with neuropsychiatric disorders enrolled in DBS studies. The system is built with custom Python packages that collect data from multiple third-party APIs and synchronize different data modalities to a common second-resolution database. This backend enables automated analysis and visualization, supporting both real-time monitoring and retrospective review of patients undergoing deep brain stimulation treatment., It has been tested and deployed to the real world with several patients who wore Oura rings while undergoing deep brain stimulation treatment for obsessive-compulsive disorder. The system has been able to reliably detect changes in measurable changes in the individual's behavior and has operated successfully for over 8 months.Clinical RelevanceThis is a prototype of a continuous monitoring and email-notification system that will allow clinicians to automatically monitor patient states and receive notifications of behavioral changes, thereby improving patient outcomes and increasing the safety of clinical trials, especially those where stimulation may have an unknown effect on the patient while outside of the clinic.

Indexed as

Deep Brain StimulationMental DisordersAutomationHumansMonitoring, PhysiologicWearable Electronic Devices

Identifiers

PMID41337361
PMCPMC13404756

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.