Resistance Training of Inspiratory Muscles After Coronary Artery Disease May Improve Obstructive Sleep Apnea in Outpatient Cardiac Rehabilitation: RICAOS Study

Respiratory muscle training for obstructive sleep apnea: Systematic review and meta-analysis

Obstructive sleep apnea is the most common sleep disorder. This review aims to evaluate the effectiveness and safety of respiratory muscle training in the treatment of patients with obstructive sleep apnea. The study protocol was registered in Prospero Platform (CRD42018096980). We performed searches in the main databases: Medical Literature Analysis and Retrieval System Online (MEDLINE) via Pubmed; Excerpta Medica dataBASE (Embase) via Elsevier; Cochrane Central Register of Controlled Trials (CENTRAL) via Cochrane Library; Latin American and Caribbean Literature on Health Sciences (LILACS) through the Portal of the Virtual Health Library and Physiotherapy Evidence Database (PEDro) for all randomised-controlled trials published before July 2022. The randomised-controlled trials were assessed for risk of bias and certainty of evidence. Thirteen randomised-controlled trials were included. All studies had an overall high risk of bias. Inspiratory muscle training probably improves systolic blood pressure and sleepiness when compared with sham. However, inspiratory muscle training probably does not improve diastolic blood pressure and maximum expiratory pressure, and may not be superior to sham for apnea-hypopnea index, forced expiratory volume in 1 s, forced vital capacity, sleep quality and quality of life. In addition, it is uncertain whether there is any effect of inspiratory muscle training on maximum inspiratory pressure and physical capacity. Inspiratory muscle training may also improve maximum inspiratory pressure and maximum expiratory pressure compared with oropharyngeal exercises. However, it may not be superior for apnea-hypopnea index, sleep quality, sleepiness, quality of life and functional capacity. When associated with physical exercise, inspiratory muscle training may not be superior to physical exercise alone for maximum inspiratory pressure, maximum expiratory pressure, systolic and diastolic blood pressure, and functional capacity. At the same time, when associated with cardiac rehabilitation exercises, inspiratory muscle training may reduce apnea-hypopnea index, improve inspiratory muscle strength, sleepiness and sleep quality compared with cardiac rehabilitation alone. However, it may not be superior for improving quality of life. Regarding expiratory muscle training, it may improve expiratory muscle strength and sleep quality, but not sleepiness when compared with sham. The evidence on the effects of expiratory muscle training in apnea-hypopnea index is very uncertain.

Effects of Respiratory Muscle Training on Exercise Capacity, Quality of Life, and Respiratory and Pulmonary Function in People With Ischemic Heart Disease: Systematic Review and Meta-Analysis

OBJECTIVE

This systematic review and meta-analysis aimed to evaluate the effects of respiratory muscle training (RMT) on functional exercise capacity, health-related quality of life (HRQoL), respiratory muscle function, and pulmonary function in individuals with ischemic heart disease (IHD).

METHODS

The MEDLINE, Web of Science, Scopus, PEDro, CINAHL, Science Direct, and CENTRAL electronic databases were searched in January 2023. Randomized controlled trials published in English, Spanish, or Portuguese that were conducted to determine the effect of RMT versus passive control and/or sham RMT on the target variables in individuals with IHD, irrespective of age or sex were included. Two reviewers performed the searches and extraction of the most relevant data. The quality and risk of bias for each included study were examined with the PEDro scale and Cochrane risk-of-bias tool.

RESULTS

Thirteen studies (849 participants) were included. The meta-analysis showed a significant increase in peak oxygen consumption (mean difference [MD] = 2.18 mL·kg-1·min-1 [95% CI = 0.54 to 3.83]), inspiratory muscle strength (MD = 16.62 cm H2O [95% CI = 12.48 to 20.77]), inspiratory muscle endurance (standardized mean difference = 0.39 [95% CI = 0.19 to 0.60]), and expiratory muscle strength (MD = 14.52 cm H2O [95% CI = 5.51 to 23.53]). There were no benefits in 6-minute walking distance (MD = 37.57 m [95% CI = -36.34 to 111.48]), HRQoL (standardized mean difference = 0.22 [95% CI = -0.16 to 0.60]), pulmonary function (forced vital capacity; MD = 2.07% of predicted value [95% CI = -0.90 to 5.03], or forced expiratory volume at the first second (MD = -0.75% of predicted value [95% CI = -5.45 to 3.95]).

CONCLUSION

This meta-analysis provided high- and moderate-quality evidence that inspiratory muscle training (IMT) improves inspiratory muscle strength and endurance, respectively; and very low-quality evidence for effects on peak oxygen consumption and expiratory muscle strength in individuals with IHD. No superior effects were found in the 6-minute walking test, HRQoL, or pulmonary function compared with the control group.

IMPACT

The results shown in this systematic review with meta-analysis will provide clinicians a better understanding of the effects of IMT in people with IHD. IMT could be integrated into the cardiac rehabilitation management, although more research is needed.

Non-pharmacological interventions for improving sleep outcomes among patients with a diagnosis of coronary artery disease: a systematic review and meta-analysis

AIMS

To determine the effectiveness of non-pharmacological interventions on sleep outcomes among patients with coronary artery disease and recognize pertinent characteristics that potentially affect the effectiveness of such interventions.

METHODS AND RESULTS

Relevant studies conducted before 27 April 2022 were identified through four core electronic databases using terms related to coronary artery disease, sleep outcomes, and randomized controlled trials. Two authors independently conducted study selection, data extraction, and risk-of-bias assessment. Meta-analysis, sub-group analysis, publication bias analysis, and sensitivity analysis were conducted using R version 4.2.2. Of the 4755 retrieved articles, 42 studies were selected for systematic review and 30 studies were included in the meta-analysis. The findings of this study revealed that non-pharmacological interventions significantly improved self-reported sleep quality (standardized mean difference = -0.85, 95% confidence interval -1.08, -0.63), but had no effects on objectively measured sleep efficiency and duration. Regarding the types of interventions involved, environmental control was the most effective in improving self-reported sleep quality, followed by relaxation, self-care behaviour management, and relaxation and cognitive/psychological complex interventions. Through subgroup analysis, we did not find any covariates that were significantly related to self-reported sleep quality.

CONCLUSION

Non-pharmacological interventions have been shown to play beneficial roles in improving self-reported sleep quality among patients with coronary artery disease. Additional studies are required to elucidate the effect of non-pharmacological interventions on objectively measured sleep outcomes and to characterize their optimal frequencies and durations.

REGISTRATION

PROSPERO CRD42022366851.

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.