Evidence map›Paper›PMID 34459176›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2021

[Investigation on new paradigm of clinical physiological monitoring by using wearable devices].

Zhao Wang, Hong Liang, Jiachen Wang, Yaning Zang, Haoran Xu, Ke Lan, Maoqing He, Wei Yan, Desen Cao, Muyang Yan and 1 more

Abstract read
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. [Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023
    Article
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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

11 authors.

Zhao WangMedical School of Chinese PLA, Beijing 100853, P.R.China.
Hong LiangCenter for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, P.R.China.
Jiachen WangMedical School of Chinese PLA, Beijing 100853, P.R.China.
Yaning ZangSchool of kinesiology, Shanghai University of Sport, Shanghai 200438, P.R.China.
Haoran XuMedical School of Chinese PLA, Beijing 100853, P.R.China.
Ke LanBeijing SensEcho Science & Technology Co., Ltd., Beijing 100853, P.R.China.
Maoqing HeBeijing SensEcho Science & Technology Co., Ltd., Beijing 100853, P.R.China.
Wei YanDepartment of Hyperbaric Oxygen, Chinese PLA General Hospital, Beijing 100853, P.R.China.
Desen CaoDepartment of Biomedical Engineering, Chinese PLA General Hospital, Beijing 100853, P.R.China.
Muyang YanDepartment of Hyperbaric Oxygen, Chinese PLA General Hospital, Beijing 100853, P.R.China.
Zhengbo ZhangCenter for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing 100853, P.R.China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.

Indexed as

Sleep Apnea SyndromesWearable Electronic DevicesHeart RateHumansMonitoring, PhysiologicMovementclinical application researchphysiological parameter analysisreal-time monitoringwearable devices

Identifiers

PMID34459176
PMCPMC9927528

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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.