Evidence map›Paper›PMID 41765978›Full record

ReviewActa epileptologica2026

The research progress of wearable digital health technologies in epilepsy management.

Xinyi Zhao, Tiancheng Wang

Abstract readReview
In one paragraph

Review in Acta epileptologica, 2026. 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

2 authors.

Xinyi ZhaoDepartment of Neurology, Epilepsy Center, The Second Hospital & Clinical Medical School, Lanzhou University, 82 Cuiyingmen, Chengguan District, Lanzhou City, Gansu Province, 730030, China.ORCID http://orcid.org/0009-0008-7560-1608
Tiancheng WangDepartment of Neurology, Epilepsy Center, The Second Hospital & Clinical Medical School, Lanzhou University, 82 Cuiyingmen, Chengguan District, Lanzhou City, Gansu Province, 730030, China. wangtch@lzu.edu.cn.ORCID http://orcid.org/0000-0001-9701-8845

Funding

Lanzhou Science and Technology Bureau 2024-3-85Science and Technology Program of Gansu Province 23JRRA1504
6 · The paper itself

Abstract

Epilepsy is one of the most common neurological disorders, characterized by recurrent, unpredictable seizures. Due to the unpredictability of seizures, epilepsy presents unique challenges in monitoring and management. While video electroencephalogram (EEG) monitoring is the gold standard for diagnosing epilepsy, its application is limited to clinical settings and is not suitable for long-term monitoring in daily life. In recent years, the development of wearable digital health technologies has provided new solutions for epilepsy management. These technologies, utilizing artificial intelligence algorithms, can monitor the physiological state of epilepsy patients in real time, predict and record seizures, thereby optimizing the management and response to seizures, reducing injuries, and potentially lowering the risk of sudden unexpected death in epilepsy (SUDEP). This article reviews the current applications and challenges of wearable technology in epilepsy monitoring and management, and explores the future directions of its development in epilepsy care, aiming to provide insights for effective monitoring and prevention.

Indexed as

Artificial intelligenceEpilepsyEpilepsy managementWearable technology

Identifiers

PMID41765978
PMCPMC12951947

What Socratic 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 Socratic graph.