Evidence map›Paper›PMID 37100995›Full record

ReviewNature metabolism2023

Wearable and digital devices to monitor and treat metabolic diseases.

Ayya Keshet, Lee Reicher, Noam Bar, Eran Segal

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 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

23 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  11. Longitudinal big biological data in the AI era.Molecular systems biology · 2025
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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.

Ayya Keshet *Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Lee Reicher *Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Noam BarDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Eran SegalDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. Eran.segal@weizmann.ac.il.ORCID http://orcid.org/0000-0002-6859-1164

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.

Indexed as

Metabolic DiseasesMonitoring, PhysiologicWearable Electronic DevicesCardiovascular SystemContinuous Glucose MonitoringData CollectionFitness TrackersHumansLife StylePolysomnographyTime Factors

Identifiers

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.