Abstract
Increasingly, persons with self-reported health symptoms are using mobile health technologies to better understand, validate, and manage their symptoms. These off-the-shelf devices primarily utilize actigraphy to estimate sleep and activity. The purpose of this study was to describe qualitatively the experience of using a personal sleep monitoring device for sleep self-management in adults 65 years or older with self-reported sleep disturbances. This study followed a hybrid qualitative design using deductive and emergent coding derived from open-ended interviews (n = 25) after a period of 4 weeks using a wearable personal sleep monitoring device. Results expanded existing theoretical models on usability with the theme of personal meaning in the interaction between health and self-monitoring technology that were associated with age and technology use, privacy, and capability. Future studies for sleep health self-management and personally tailored interventions using personal sleep monitoring devices should continue to collect qualitative information in extending the understanding of user experience across different symptom clusters, such as sleep disturbances, that manifest more commonly in older age populations. This research is important for application in the use of mobile health technologies for nursing led health self-management interventions.
Original language | English (US) |
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Pages (from-to) | 598-605 |
Number of pages | 8 |
Journal | CIN - Computers Informatics Nursing |
Volume | 40 |
Issue number | 9 |
DOIs | |
State | Published - Sep 27 2022 |
Externally published | Yes |
Bibliographical note
Funding Information:The research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under award number p20NR016599. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© Lippincott Williams & Wilkins.
Keywords
- Activity tracking
- mHealth
- Nursing
- Personal self-monitoring
- Sleep
- Sleep disturbances
PubMed: MeSH publication types
- Journal Article