Evidence mapPaperPMID 40317994Full record

ReviewJournal of diabetes investigation2025

Use of technology in prediabetes and precision prevention.

Jie He, Natural Chu, Heng Wan, James Ling, Yincong Xue, Kathy Leung, Aimin Yang, Jie Shen, Elaine Chow

Abstract readReview
In one paragraph

Review in Journal of diabetes investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Article
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

9 authors.

Jie HeDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Natural ChuDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Heng WanDepartment of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China.
James LingDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Yincong XueDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Kathy LeungDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Aimin YangDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.
Jie ShenDepartment of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China.
Elaine ChowDepartment of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China.ORCID https://orcid.org/0000-0002-4147-3387

Funding

Faculty of Medicine, Chinese University of Hong Kong
6 · The paper itself

Abstract

Controlling the epidemic of diabetes is an urgent global healthcare challenge. The low uptake of diabetes prevention programs highlights difficulties in scalability, partly due to the need for intensive face-to-face contact and its impact on healthcare resource utilization. In this narrative review, we will summarize the latest evidence in technology-assisted lifestyle interventions. We will appraise evidence of digital diabetes prevention programs that use internet platforms or text messaging tools to support information delivery, lifestyle coaching, or peer support. We will also discuss the use of wearables, including physical activity trackers and continuous glucose monitoring (CGM) as part of lifestyle intervention. Experience from diabetes highlights the potential for CGM as a motivational tool to promote lifestyle change. The integration of digital data may facilitate earlier detection of prediabetes, sub-phenotyping, and personalized nutritional predictions. We will highlight major gaps in research and the need for rigorous clinical trials to evaluate the acceptability and cost-effectiveness of integrating technologies as part of a multicomponent strategy in diabetes prevention.

Indexed as

Diabetes Mellitus, Type 2Precision MedicinePrediabetic StateBlood Glucose Self-MonitoringHumansLife StyleTelemedicineContinuous glucose monitoringPrediabetesWearables

Identifiers

PMID40317994
PMCPMC12209524

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.