Evidence map›Paper›PMID 39024559›Full record

Observational studyJMIR mHealth and uHealth2024

Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study.

Xiaolan Chen, Han Zhang, Zhiwen Li, Shuang Liu, Yuqi Zhou

Abstract readObservational Study
In one paragraph

Observational study in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 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, 2 syntheses or guidelines pooled it.

  1. Differentiating the start of an exacerbation from day-to-day variation in people with COPD: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026
    Pooled it
  2. Pooled it
  3. Article
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  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

5 authors.

Xiaolan Chen *Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University, Guangzhou, China.ORCID 0009-0007-6543-1927
Han Zhang *Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, School of Electronic and Information Engineering, South China Normal University, Foshan, China.ORCID 0000-0002-4037-3026
Zhiwen Li *Key Laboratory of Reproductive Health National Health Commission of the People's Republic of China, Institute of Reproductive and Child Health, Peking University, Beijing, China.ORCID 0000-0001-7570-3809
Shuang LiuDepartment of Pulmonary and Critical Care Medicine, Guangdong Provincial Technology Research Center of Chronic Obstructive Pulmonary Disease Rehabilitation, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0009-0007-2858-6997
Yuqi ZhouDepartment of Pulmonary and Critical Care Medicine, Guangdong Provincial Technology Research Center of Chronic Obstructive Pulmonary Disease Rehabilitation, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.ORCID 0000-0003-4988-7637

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet.

objectiveThe aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD.

methodsWe recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings.

resultsIn this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively.

conclusionsContinuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.

Indexed as

Heart RatePulmonary Disease, Chronic ObstructiveAgedFemaleHumansMaleMiddle AgedMonitoring, PhysiologicProspective StudiesRespirationRespiratory Ratechronic obstructive pulmonary diseasecontinuous monitoringCOPD diagnosisheart rateheart rate variabilitynoncontact bed sensorsprospective studyrespiratory rateROC curve

Identifiers

PMID39024559
PMCPMC11294786

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.