Evidence map›Paper›PMID 31616691›Full record

ArticleWorld journal of clinical cases2019

Respiratory training interventions improve health status of heart failure patients: A systematic review and network meta-analysis of randomized controlled trials.

Mei-Hua Wang, Mei-Ling Yeh

Open access · diamondAbstract read
In one paragraph

Article in World journal of clinical cases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
5.8field-weighted citation impact, top 4% of its field
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

7 citing papers in PubMed, 26 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Mei-Hua WangSchool of Nursing, National Taipei University of Nursing and Health Sciences, Taipei 11219, Taiwan.
Mei-Ling YehSchool of Nursing, National Taipei University of Nursing and Health Sciences, Taipei 11219, Taiwan. meiling@ntunhs.edu.tw.
National Taipei University of Nursing and Health Science · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrior studies indicate that doing breathing exercises improves physical performance and quality of life (QoL) in heart failure patients. However, these effects remain unclear and contradictory.

aimTo determine the effects of machine-assisted and non-machine-assisted respiratory training on physical performance and QoL in heart failure patients.

methodsThis was a systematic review and network meta-analysis study. A literature search of electronic databases was conducted for randomized controlled trials (RCTs) on heart failure. Respiratory training interventions were grouped as seven categories: IMT_Pn (inspiratory muscle training without pressure or < 10% maximal inspiratory pressure, MIP), IMT_Pl (inspiratory muscle training with low pressure, 10%-15% MIP), IMT_Pm (inspiratory muscle training with medium pressure, 30%-40% MIP), IMT_Ph (inspiratory muscle training with high pressure, 60% MIP or MIP plus aerobics), Aerobics (aerobic exercise or weight training), Qi_Ex (tai chi, yoga, and breathing exercise), and none. The four outcomes were heart rate, peak oxygen uptake (VO

resultsA total of 1499 subjects from 31 RCT studies were included. IMT_Ph had the highest effect sizes for VO

conclusionThis study supports that high- and medium-intensity machine-assisted training improves exercise capacity and QoL in hospital-based heart failure patients. After hospital discharge, non-machine-assisted training continuously improves cardiac function.

Indexed as

Cardiac functionExercise capacityHeart failureNetwork meta-analysisQuality of lifeRespiratory training

Identifiers

PMID31616691
PMCPMC6789387
OpenAlexW2976518232

What OpenQuestion holds

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Registered trials

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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.