AI-Predictive Wearables for COPD Exacerbations: A Scoping Review of Nursing Challenges and Patient Trust

Background: The integration of artificial intelligence (AI)-enabled wearable devices for predicting COPD exacerbations presents significant opportunities for remote patient monitoring, yet implementation faces substantial nursing challenges and patient trust barriers. Objective: This scoping review aimed to map and synthesize existing evidence regarding nursing challenges and patient trust issues in utilizing AI-predictive wearables for COPD care. Methods: Following PRISMA-ScR guidelines, we searched PubMed/MEDLINE, Scopus, Google Scholar, Web of Science, and Iranian databases (SID, Magiran, IranDoc) for studies published between January ۲۰۲۰ and May ۲۰۲۶. Two independent reviewers screened ۱۰۷۹ articles, with ۱۹ studies meeting inclusion criteria. Quality assessment utilized ROBINS-I, RoB ۲, CASP, PROBAST, JBI, and MMAT tools. Findings were synthesized narratively due to study heterogeneity. Results: None of the included studies contained fully implemented AI-predictive algorithms on wearable devices for COPD exacerbation prediction. Key nursing challenges included increased workload, insufficient digital literacy training, data overload, and ethical concerns regarding accountability and dehumanization. Patient trust barriers encompassed privacy concerns, technology anxiety, digital divide issues, and fear of replacing human contact. Nurses were identified as critical facilitators when providing ongoing support and education. Conclusion: Successful implementation requires enhancing algorithm transparency, investing in nursing education, preserving human connection, and establishing clear ethical guidelines to position AI as an assistant rather than a replacement in COPD care

کلیدواژه ها:

Chronic Obstructive Pulmonary Disease (COPD) ، Artificial Intelligence (AI) ، Wearable Devices ، Nursing Challenges ، Patient Trust ، Exacerbation Prediction ، Remote Patient Monitoring (RPM) ، Digital Health Technology (DHT)