Evidence map›Paper›PMID 39845705›Full record

ReviewResearch (Washington, D.C.)2025

A Networked Intelligent Elderly Care Model Based on Nursing Robots to Achieve Healthy Aging.

Wenting Ji, Ruzhen Luo, Yumei Sun, Maiping Yang, Yueheng Liu, Hua Chen, Dongmei Lin, Ziyi Su, Guangming Tao, Diansheng Chen and 1 more

Abstract readReview
In one paragraph

Review in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. 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

11 authors.

Wenting JiChengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
Ruzhen LuoSchool of Nursing, Tianjin Medical University, Tianjin 300070, China.
Yumei SunPeking University, Beijing 100191, China.
Maiping YangWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Yueheng LiuWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Hua ChenPeking University, Beijing 100191, China.
Dongmei LinSchool of Nursing, Gannan Medical University, Ganzhou 341000, China.
Ziyi SuWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Guangming TaoWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.ORCID https://orcid.org/0000-0002-1371-7735
Diansheng ChenSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Hongyu SunPeking University, Beijing 100191, China.ORCID https://orcid.org/0000-0003-3294-1262

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As global populations become increasingly aged, existing elderly care models are proving insufficient. The development and application of nursing robots have shown potential in addressing the challenges of elder care in aging societies. This perspective outlines current state and potential applications of nursing robots in promoting healthy aging. Given this background, a networked intelligent elderly care model for nursing robots, which integrates technologies such as big data, artificial intelligence, the Internet of Things, and nursing robotics, is proposed. This model would synergistically combine elderly health monitoring, capability assessment, and intelligent allocation functions to revolutionize global elderly care practices and promote healthy aging.

Identifiers

PMID39845705
PMCPMC11751203

What OpenQuestion holds

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LicenceCC BY
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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.