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Older adults face growing risks of depression and anxiety, yet stigma, comorbidities, cost, and limited access impede receipt of conventional care. Digital mental health interventions (DMHIs), including immersive virtual reality (VR), exergaming, and mobile apps, may reduce these barriers.
To evaluate the efficacy of DMHIs in reducing depressive and anxiety symptoms among adults aged ≥ 50 years.
We conducted a PRISMA adherent systematic review and meta-analysis of randomized controlled trials. Interventions included immersive VR, exergaming/physical digital platforms, mobile applications, and digital cognitive training. Standardized mean differences (SMDs) were pooled with random effects models; heterogeneity was assessed with I 2.
Nineteen RCTs (n = 718; mean ages 50.9–84.7 years) met inclusion criteria. Across studies, DMHIs significantly reduced depressive symptoms (SMD = −0.656, 95% CI = −0.932 to −0.380; p < 0.001) and anxiety symptoms (SMD = −0.559, 95% CI = −0.740 to −0.380; p < 0.0001). Immersive and physically engaging modalities (e.g., VR, exergaming) outperformed app-based approaches. Heterogeneity ranged from moderate to high (I 2 ≈ 69.6%–97%).
Offer DMHIs: especially VR or exergaming when access to in-person therapy is limited or as an adjunct to usual care. Provide brief onboarding and, when feasible, caregiver support to boost adherence and confidence with technology. Select or configure age-friendly interfaces (e.g., large fonts, simple navigation) to address common usability barriers. Integrate DMHIs into stepped-care or rehabilitation pathways and monitor outcomes with validated tools (e.g., GDS, STAI). Address equity by supplying devices/connectivity solutions and consider cost-effectiveness and long-term engagement in implementation plans.
Trial Registration: PROSPERO ID: CRD420250655153
Artificial Intelligence is revolutionizing healthcare by addressing complex challenges and enhancing patient care. AI technologies, such as machine learning, natural language processing, and predictive analytics, offer significant potential to impact nursing practice and patient outcomes.
This systematic review aims to assess the impact of Artificial Intelligence applications in healthcare on nursing practice and patient outcomes. The goal is to evaluate the effectiveness of these technologies in improving nursing efficiency and patient care and to identify areas requiring further research.
This review, conducted in August 2024, followed PRISMA guidelines. We searched PubMed, GOOGLE SCHOLAR, and Web of Science for studies published up to August 2024. The inclusion criteria were original research on AI in nursing and healthcare practice published in English. A two-stage screening process was used to select relevant studies, which were then analyzed for their impact on nursing practice and patient outcomes.
A total of 5975 studies were surveyed from the previously mentioned databases, which met the inclusion criteria. Findings show that AI applications, including machine learning, robotic process automation, and natural language processing, have improved diagnostic accuracy, patient management, and operational efficiency. Machine learning enhanced disease detection, reduced administrative tasks for nurses, NLP improved documentation accuracy, and physical robots increased patient safety and comfort. Challenges identified include data privacy concerns, integration into existing workflows, and methodological variability.
AI technologies have substantially improved nursing practice and patient outcomes. Addressing challenges related to data privacy and integration, as well as standardizing methodologies, is essential for optimizing AI's potential in healthcare. Further research is needed to explore the long-term impacts, cost-effectiveness, and ethical implications of Artificial Intelligence in this field.
Artificial Intelligence (AI) is revolutionizing healthcare by enhancing nursing practices and improving patient outcomes. Tools such as Clinical Decision Support Systems (CDSS), predictive analytics, robotic process automation (RPA), and remote monitoring empower nurses to make informed decisions, optimize workflows, and monitor patients more effectively. AI enhances decision-making, boosts efficiency, and facilitates personalized care, while aiding in early detection and real-time data analysis. It also contributes to better nurse education and patient safety by minimizing errors and enabling remote consultations. However, for AI to be successfully integrated into healthcare, it is essential to tackle challenges related to training, ethical considerations, and data privacy to guarantee its effective implementation and positive impact on the quality and safety of healthcare.
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