Evidence map›Paper›PMID 41181565›Full record

ArticleDigital health

Analysis of health factors on artificial intelligence and digital twin telemedicine: Improving the health conditions of elderly adults in Chinese townships.

Chaoqi Chang, Lamei Yan, Renzhi Qiu, Xiaogang Jin, Xun Hu, Fengqiong Li, Yan Shi, Lin Pan, Youwei Yuan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Chaoqi ChangSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.ORCID https://orcid.org/0009-0000-2640-2305
Lamei YanSchool of Media and Design, Hangzhou Dianzi University, Hangzhou, China.
Renzhi QiuSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.ORCID https://orcid.org/0009-0003-2847-1517
Xiaogang JinSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.ORCID https://orcid.org/0009-0001-7904-5177
Xun HuSchool of Media and Design, Hangzhou Dianzi University, Hangzhou, China.
Fengqiong LiSchool of Media and Design, Hangzhou Dianzi University, Hangzhou, China.
Yan ShiSchool of Media and Design, Hangzhou Dianzi University, Hangzhou, China.
Lin PanSchool of Media and Design, Hangzhou Dianzi University, Hangzhou, China.
Youwei YuanSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to evaluate the efficacy of an artificial intelligence (AI) and digital twin (DT)-based telemedicine system in enhancing health outcomes among elderly adults in China's townships. Methods: A six-month controlled trial was conducted involving 252 adults aged ≥60 from 12 townships in Zhejiang Province. Participants were allocated to either an intervention group using the AI-DT telemedicine system or a control group receiving routine health monitoring. Health indicators including blood pressure, glucose levels, body mass index (BMI), and health anxiety were assessed pre- and postintervention. Statistical analyses were performed using SPSS, incorporating Chi-square tests, Results: The intervention group demonstrated significant improvements in systolic blood pressure (136.30-130.07 mmHg, Conclusion: The AI-DT telemedicine system significantly enhances both physiological and psychological health outcomes among elderly adults in townships, demonstrating high acceptability and potential for broader implementation.

Indexed as

controlled trialDigital twinKano modelolder adultstelemedicine system

Identifiers

PMID41181565
PMCPMC12576213

What OpenQuestion 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 OpenQuestion graph.