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Özden G, Parlar Kılıç S. Breathing better: A tech-monitored study of positive expiratory pressure and reading aloud for chronic obstructive pulmonary disease. Int J Nurs Pract 2023; 29:e13198. [PMID: 37653574 DOI: 10.1111/ijn.13198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/23/2023] [Accepted: 08/20/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Breathing exercises, such as diaphragmatic breathing and positive expiratory pressure (PEP), relieve breathlessness in people with chronic obstructive pulmonary disease (COPD). AIM This study aimed to investigate the effects of breathing exercises with PEP and reading aloud on vital signs, fatigue level, severity of dyspnoea and respiratory function parameters in patients with COPD. DESIGN The study followed a randomized controlled trial of COPD patients from a single hospital in eastern Turkey. METHODS The study included 103 patients who were randomly assigned to receive pre-reading exercises, breathing exercises with a PEP device or no intervention for 8 weeks. RESULTS The use of a PEP device improved oxygen saturation, forced expiratory volume in 1 s (FEV1 ) and FEV1 /forced vital capacity (FVC) values and reduced fatigue and dyspnoea severity. Reading aloud lowered the mean arterial pressure and reduced fatigue and dyspnoea severity. CONCLUSION The study concludes that PEP devices and reading aloud can improve respiratory function in patients with COPD. Additionally, reading aloud is an accessible, easy-to-implement and economically feasible method for treating COPD symptoms.
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Affiliation(s)
- Gürkan Özden
- Faculty of Nursing, Department of Internal Medicine Nursing, İnönü University, Malatya, Turkey
| | - Serap Parlar Kılıç
- Faculty of Nursing, Department of Internal Medicine Nursing, İnönü University, Malatya, Turkey
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Nathan V, Vatanparvar K, Chun KS, Kuang J. Utilizing Deep Learning on Limited Mobile Speech Recordings for Detection of Obstructive Pulmonary Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1338-1341. [PMID: 36085620 DOI: 10.1109/embc48229.2022.9871980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Passive assessment of obstructive pulmonary disease has gained substantial interest over the past few years in the mobile and wearable computing communities. One of the promising approaches is speech-based pulmonary assessment wherein spontaneous or scripted speech is used to evaluate an individual's pulmonary condition. Recent approaches in this regard heavily rely on accurate speech activity segmentation and specific, hand-crafted features. In this paper, we present an end-to-end deep learning approach for detecting obstructive pulmonary disease. We leveraged transfer learning using a network pre-trained for a different audio-based task, and employed our own additional shallow network on top as a binary classifier to indicate if a given speech recording belongs to an asthma or COPD patient. The additional network was a fully connected neural net with 2 hidden layers, and this was evaluated on two real-world datasets. We demonstrated that the system can identify subjects with obtructive pulmonary disease using their speech with 88.3 % precision, 88.8 % recall and 88.3% F-1 score using 10-fold cross-validation. The system showed improved performance in identifying the most severely affected subgroup of patients in the dataset, with an average 93.6 % accuracy.
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Alam MZ, Simonetti A, Brillantino R, Tayler N, Grainge C, Siribaddana P, Nouraei SAR, Batchelor J, Rahman MS, Mancuzo EV, Holloway JW, Holloway JA, Rezwan FI. Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach. Front Digit Health 2022; 4:750226. [PMID: 35211691 PMCID: PMC8861188 DOI: 10.3389/fdgth.2022.750226] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 01/14/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction To self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients. Methods A threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples. Results The RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%). Conclusion Our machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.
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Affiliation(s)
- Md. Zahangir Alam
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Albino Simonetti
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy
| | - Raffaele Brillantino
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy
| | - Nick Tayler
- Peter Doherty Institute, The University of Melbourne, Melbourne, VIC, Australia
| | - Chris Grainge
- Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia
- Department of Respiratory Medicine, John Hunter Hospital, Newcastle, NSW, Australia
| | - Pandula Siribaddana
- Postgraduate Institute of Medicine, University of Colombo, Colombo, Sri Lanka
| | - S. A. Reza Nouraei
- Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom
- Robert White Centre for Airway Voice and Swallowing, Poole Hospital, Poole, United Kingdom
| | - James Batchelor
- Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom
| | - M. Sohel Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Eliane V. Mancuzo
- Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - John W. Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom
| | - Judith A. Holloway
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- MSc Allergy, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Faisal I. Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom
- *Correspondence: Faisal I. Rezwan
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Pattemore PK, Liberty KA, Reid J. Changes in asthma severity in the first year of school and difficulty learning to read. J Asthma 2019; 57:799-809. [PMID: 31066318 DOI: 10.1080/02770903.2019.1609982] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objective: Asthma is a risk factor for poor early reading in children, for reasons that are unclear. This analysis examines the relationship between changes in asthma severity during the first year of school and being in the lowest quartile of reading achievement after 1 year of school.Methods: We used previously unreported data from our cohort study. Parent interviews and teacher questionnaires enquired about asthma and covariates of achievement at school entry (T1) and 12 months later (T2). Asthma severity scores at T1 and T2 showed that in 27 of 51 children with asthma, symptoms improved over the year, whereas in 24, symptoms persisted or worsened. Word and story reading were assessed at T1 and T2. We compared reading achievement at both timepoints between children with asthma and children who had no reported respiratory symptoms between birth and T2 (controls, N = 74), and between those with persistent versus improved symptoms.Results: More children with asthma than controls were in the lowest quartiles for reading. Further, significantly more children in the persistent group compared to the improved group were in the lowest quartiles for word reading (58 versus 30%, respectively) and story reading (54 versus 26%, respectively). School absences, increased behavior problems, stressful life events or parental mental health were not associated with the differences in either comparison. Logistic regression modeling identified persistent asthma as the most important variable associated with being in the lowest quartile of reading after 1 year in school.Conclusions: Active asthma symptoms during early school may influence early reading achievement.
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Affiliation(s)
| | - Kathleen A Liberty
- School of Health Sciences, College of Education, Health and Human Development, University of Canterbury, Christchurch, New Zealand
| | - James Reid
- Department of the Dean, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
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