Evidence map›Paper›PMID 41563614›Full record

ReviewNano-micro letters2026

Flexible Polymer-Based Electronics for Human Health Monitoring: A Safety-Level-Oriented Review of Materials and Applications.

Dan Xu, Yi Yang, Keiji Numata, Bo Pang

Abstract readReview
In one paragraph

Review in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Dan XuDepartment of Material Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, 615-8510, Japan.
Yi YangDepartment of Food Science & Technology, National University of Singapore, Singapore, 117542, Singapore.
Keiji NumataDepartment of Material Chemistry, Graduate School of Engineering, Kyoto University, Kyoto, 615-8510, Japan. keiji.numata@riken.jp.
Bo PangDepartment of Food Science & Technology, National University of Singapore, Singapore, 117542, Singapore. bopang@nus.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health monitoring is becoming increasingly critical for disease prevention, early diagnosis, and high-quality living. Polymeric materials, with their mechanical flexibility, biocompatibility, and tunable biochemical properties, offer unique advantages for creating next-generation personalized devices. In recent years, flexible polymer-based platforms have shown remarkable potential to capture diverse physiological signals in both daily and clinical contexts, including electrophysiological, biochemical, mechanical, and thermal indicators. In this review, we introduce a safety-level-oriented framework to evaluate material and device strategies for health monitoring, spanning the continuum from noninvasive wearables to deeply embedded implants. Physiological signals are systematically classified by use case, and application-specific requirements such as stability, comfort, and long-term compatibility are highlighted as critical factors guiding the selection of polymers, interfacial designs, and device architectures. Special emphasis is placed on mapping material types-including hydrogels, elastomers, and conductive composites-to their most suitable applications. Finally, we propose design principles for developing safe, functional, and adaptive polymer-based systems, aiming at reliable integration with the human body and enabling personalized, preventive healthcare.

Indexed as

Flexible devicesHealth monitoringInterfacial designsPolymersSafety-level-oriented

Identifiers

PMID41563614
PMCPMC12824095

What OpenQuestion holds

Textmetadata
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.