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. 2025 Jul 1;24(1):799.
doi: 10.1186/s12912-025-03343-y.

Nurses' perspectives on AI-Enabled wearable health technologies: opportunities and challenges in clinical practice

Haitham Alzghaibi  1
Affiliations

Nurses' perspectives on AI-Enabled wearable health technologies: opportunities and challenges in clinical practice

Haitham Alzghaibi. BMC Nurs. .

Abstract

Background: Wearable health technologies, such as smartwatches, biosensor patches, and fitness trackers, have evolved from basic monitoring tools to advanced medical-grade devices capable of continuous health tracking. The integration of artificial intelligence (AI) enhances their utility by enabling real-time data analysis, early diagnosis, and personalised disease management. Adoption accelerated during the COVID-19 pandemic, reinforcing their role in remote care. However, concerns regarding data privacy, accuracy, cost, and reduced human interaction persist. This study explores nurses' perceptions, awareness, and trust in AI-enabled wearable devices, identifies facilitators and barriers to adoption, and assesses demographic influences on attitudes.

Methods: A total of 611 nurses were recruited using purposive sampling from educational hospitals in Saudi Arabia. Data were collected through an online structured questionnaire comprising demographic items, Likert-scale statements, and multiple-choice questions. Descriptive statistics and non-parametric tests (Kruskal-Wallis and Mann-Whitney U) were used to examine group differences.

Results: Findings revealed generally positive attitudes toward AI-enabled wearables, with nurses acknowledging their potential to support personalised care, chronic disease management, and healthcare efficiency. However, data accuracy, affordability, and technical reliability emerged as prevalent concerns. Statistically significant differences were observed based on age (p < 0.001), education level (p = 0.001), and workplace setting (p < 0.05), with younger nurses and those in hospital settings expressing greater confidence in AI-driven health insights.

Conclusion: While AI-enabled wearable devices are perceived as promising tools in nursing practice, concerns regarding data reliability, cost, and over-reliance on AI must be addressed. Structured training, institutional support, and clear guidelines are essential to ensure successful integration into clinical workflows and optimise their use in patient-centred care.

Clinical trial number: Not applicable.

Keywords: AI-Enabled wearables; Artificial intelligence in healthcare; Chronic disease management; Patient-centred care; Remote monitoring; Wearable health technologies.

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Conflict of interest statement

Declarations. Ethical approval: All methods in this study were performed in accordance with the declaration of Helsinki and was approved by the Institutional Review Board (IRB) of Qassim University No. 23-19-02. All the participants provided informed consent to participate. In the case of the questionnaire-based study, all participants were informed of the voluntary nature, confidentiality, and aim of the study and the nature of their participation before they participated in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

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Fig. 1
Participants demographic information
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Nurses’ awareness and attitudes towards wearable devices in clinical practice
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Nurses’ perceptions of the primary benefits of wearable devices in chronic disease management: An UpSet plot pnalysis
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Fig. 4
Challenges in integrating patient-generated data from wearable devices into clinical practice: An UpSet plot analysis
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Nurses’ strategies for supporting patients in using wearable devices for chronic disease management: An UpSet plot analysis
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Recommended improvements to enhance the usefulness of wearable devices in chronic disease management: An UpSet plot analysis
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Nurses’ perceptions of AI in analysing wearable device data for chronic disease management: An UpSet plot analysis
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Fig. 8
Key Challenges in integrating AI for analysing wearable device data in clinical practice: An UpSet plot analysis
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