JOURNAL ARTICLE
Peak flow monitoring using digital technology to improve patient self-management and asthma control in primary care: a pilot.
Published In: Primary Health Care, 2025, v. 35, n. 3. P. 37 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Natasha Smith, Sara-Louise; Swift, Hiedi; Tweddle, Sarah; Coupe, Sarah 3 of 3
Abstract
Why you should read this article: • To refresh your knowledge of the main symptoms of asthma, such as shortness of breath, wheeze and chest tightness • To understand why a self-management plan should be offered to all patients with asthma • To appreciate the benefits of identifying exacerbations of asthma and enabling early interventions. Asthma is one of the two most common chronic respiratory diseases diagnosed and treated in primary care and is the cause of most hospital admissions for chronic respiratory illness. The authors set up a pilot service review which used a digital platform to enable nurses to review participants’ peak flow entries and make therapeutic adjustments to treatment without the need for a face-to-face appointment. It also enabled the nurses to react quickly to any changes in patients’ peak flow readings or concerns. The objective of this pilot was to demonstrate how digital health technologies in primary care can enhance patient outcomes by improving their understanding of their condition, so that they can recognise early warning signs of exacerbations. By enhancing self-management, it was hoped that there would be a reduction in primary care contact and acute hospital interventions. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Primary Health Care. 2025/06, Vol. 35, Issue 3, p37
- Document Type:Article
- Subject Area:Technology
- Publication Date:2025
- ISSN:0264-5033
- DOI:10.7748/phc.2024.e1833
- Accession Number:185631818
- Copyright Statement:Copyright of Primary Health Care is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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