Evidence map›Paper›PMID 39185844›Full record

ReviewACS nano2024

Diving into Sweat: Advances, Challenges, and Future Directions in Wearable Sweat Sensing.

Andre Childs, Beatriz Mayol, José A Lasalde-Ramírez, Yu Song, Juliane R Sempionatto, Wei Gao

Abstract readReview
In one paragraph

Review in ACS nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 pooled it
–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

40 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026
    Review
  3. Review
  4. Review
  5. Review
  6. Microfluidic valves for wearable sweat analysis: a review.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026
    Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Making Sweat Measurable: Induction, Sampling, and Refreshment in Wearable Biofluid Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  16. Article
  17. Article
  18. Review
  19. Ionic-Bionic Interfaces: Advancing Iontronic Strategies for Bioelectronic Sensing and Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  20. 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

6 authors.

Andre ChildsDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.
Beatriz MayolDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.
José A Lasalde-RamírezAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
Yu SongDepartment of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China.ORCID 0000-0002-4185-2256
Juliane R SempionattoDepartment of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.
Wei GaoAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California 91125, United States.ORCID 0000-0002-8503-4562

Funding

Laser-Engraved Wearable Sweat Sensors to Detect and Monitor Cardiometabolic DiseaseR01HL155815 · NHLBI · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI GAO, WEI · 2021 to 2025
$2.0M
Cutaneous uric acid and metabolite monitoring to improve individual response to pharmaceutical and dietary treatment in patients with goutR21DK132666 · NIDDK · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI FITZGERALD, JOHN D, GAO, WEI · 2022 to 2023
$461k
NHLBI NIH HHS R01 HL155815NIDDK NIH HHS R21 DK132666
6 · The paper itself

Abstract

Sweat analysis has advanced from diagnosing cystic fibrosis and testing for illicit drugs to noninvasive monitoring of health biomarkers. This article introduces the rapid development of wearable and flexible sweat sensors, highlighting key milestones and various sensing strategies for real-time monitoring of analytes. We discuss challenges such as developing high-performance nanomaterial-based biosensors, ensuring continuous sweat production and sampling, achieving high sweat/blood correlation, and biocompatibility. The potential of machine learning to enhance these sensors for personalized healthcare is presented, enabling real-time tracking and prediction of physiological changes and disease onset. Leveraging advancements in flexible electronics, nanomaterials, biosensing, and data analytics, wearable sweat biosensors promise to revolutionize disease management, prevention, and prediction, promoting healthier lifestyles and transforming medical practices globally.

Indexed as

Biosensing TechniquesSweatWearable Electronic DevicesHumansMachine LearningNanostructures

Identifiers

PMID39185844
PMCPMC12129723

What OpenQuestion holds

Textmetadata
LicenceTDM
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.