AI Nursing Interventions in Stress Management: A Review

Assistant Professor, HOD Department of Psychiatric Nursing, JSS College of Nursing, Mysuru, India

*Corresponding author: Mrs Purohit Saraswati, Assistant Professor, HOD Department of Psychiatric Nursing, JSS College of Nursing, Mysuru, India, Email: [email protected]

Received Date: June 23, 2025

Published Date: July 28, 2025

Citation: Saraswati P. (2025). AI Nursing Interventions in Stress Management: A Review. Mathews J Nurs. 7(2):62.

Copyrights: Saraswati P. © (2025).

ABSTRACT

Artificial Intelligence (AI) is transforming healthcare, particularly in mental health and stress management. Nursing professionals increasingly utilize AI-driven tools to assess, monitor, and intervene in patient stress-related conditions. This review explores the emerging role of AI in nursing interventions aimed at stress management. The integration of AI-powered chatbots, predictive analytics, wearable technology, and mental health applications supports nurses in delivering timely, personalized, and effective interventions. Current trends suggest significant benefits in early detection and management of stress, but also highlight ethical, technological, and implementation challenges. This review aims to map the current evidence and future directions for AI-assisted nursing interventions in stress care. AI integration in nursing empowers a proactive approach to stress management by enabling early identification of high-risk individuals through predictive modeling and behavioral analytics. Tools such as emotional recognition systems and AI-assisted virtual reality therapy not only enhance therapeutic engagement but also support continuous care beyond traditional clinical settings. This convergence of technology and compassionate nursing care underscores the importance of interdisciplinary collaboration and ongoing research to refine AI applications that are both ethically sound and clinically effective.

Keywords: Artificial Intelligence, Nursing Interventions, Stress Management, Mental Health, Predictive Analytics, Chatbots.

INTRODUCTION

Stress is a growing global health concern, influencing both physical and psychological well-being. Nurses play a pivotal role in identifying and managing stress, especially in clinical and community settings. With the increasing demand for mental health services, integrating AI into nursing practices provides scalable, efficient, and individualized care solutions. Technologies like AI chatbots, mobile apps, wearable sensors, and machine learning models assist nurses in screening, intervention, and follow-up care.

Stress has emerged as a major public health challenge, affecting individuals across all age groups and professions. It is a physiological and psychological response to perceived challenges or threats, and if left unmanaged, it can lead to serious mental health conditions such as anxiety, depression, and burnout. In healthcare settings, patients often experience heightened stress due to illness, hospitalization, and uncertainty about outcomes. Nurses, being at the forefront of patient care, are uniquely positioned to identify, assess, and intervene in cases of stress-related disorders.

Traditional nursing interventions in stress management have included therapeutic communication, relaxation techniques, patient education, and referral to mental health professionals. While effective, these approaches are often limited by time constraints, high patient loads, and a shortage of mental health specialists. In this context, Artificial Intelligence (AI) offers innovative solutions to enhance the delivery and efficiency of stress management interventions in nursing.

AI refers to computer systems that simulate human intelligence, including learning, reasoning, and problem-solving. In the field of healthcare, AI is being utilized for diagnostic support, predictive analytics, virtual consultations, and personalized treatment recommendations. When applied to nursing, especially in mental health and stress management, AI tools can assist in screening, continuous monitoring, risk assessment, and even direct patient interaction through chatbots and mobile applications.

The integration of AI into nursing care is particularly valuable for stress management due to its capacity to process large amounts of data, detect subtle behavioral changes, and deliver timely, evidence-based interventions. AI-enabled wearable devices, for example, can monitor physiological indicators of stress such as heart rate variability and sleep patterns, alerting nurses to potential issues before they escalate. Chatbots can engage patients in therapeutic conversations based on cognitive-behavioral therapy (CBT) principles, offering support outside of clinical hours.

In addition, predictive analytics can identify patients at risk of developing chronic stress or related conditions, allowing for targeted intervention. This is especially relevant in settings like intensive care units, oncology wards, and high-stress environments, where early identification and management of stress can significantly improve patient outcomes.

OBJECTIVES OF THE REVIEW

• To explore the role of AI in nursing interventions for stress management.
• To identify AI tools used in stress assessment and care planning.
• To assess the outcomes and effectiveness of AI-assisted stress interventions.
• To analyze current trends, challenges, and future potential.

METHODOLOGY

A systematic literature review was conducted using PubMed, Scopus, CINAHL, and Google Scholar databases from 2015 to 2025. Keywords used were: “AI in nursing,” “stress management,” “mental health,” “predictive models in nursing,” “chatbots for stress,” and “nursing interventions and artificial intelligence.” Inclusion criteria included peer-reviewed articles, English language, and studies involving AI tools in stress management involving nursing practice.

AI TOOLS IN STRESS MANAGEMENT

AI Chatbots

Chatbots such as Woebot and Wysa provide real-time emotional support and cognitive behavioral therapy (CBT)-based interactions. These tools reduce patient anxiety, provide coping strategies, and allow nurses to monitor mental health remotely.

Wearable Devices

Smart wearables like Fitbit, Apple Watch, and Oura Ring use AI algorithms to track heart rate variability, sleep patterns, and physical activity—key stress indicators. Nurses use this data to create personalized intervention plans.

Predictive Analytics

AI algorithms analyze patient data to predict stress-related conditions, including burnout, depression, and anxiety. Nurses utilize these predictions to target high-risk individuals and implement preventive strategies.

Virtual Reality (VR) & Augmented Reality (AR)

Used in nursing education and practice, VR simulates stress-inducing environments for training and therapeutic exposure, aiding in stress desensitization.

Table 1. Major Findings from Literature

INNOVATIVE EXAMPLES OF AI NURSING INTERVENTIONS IN STRESS MANAGEMENT

1. Wysa and Woebot: AI Mental Health Chatbots

Wysa and Woebot are AI-powered mental health chatbots trained in cognitive-behavioral therapy (CBT). Nurses use these tools to offer emotional support to patients experiencing stress, anxiety, or depressive symptoms. These chatbots are available 24/7, providing patients with guided self-help techniques, journaling support, mindfulness activities, and cognitive reframing.

Example in Practice: In post-operative wards, nurses refer patients to Woebot for daily check-ins. Patients report improved sleep, reduced anxiety, and increased satisfaction due to consistent emotional support.

2. Mindstrong App Integration with Nursing Follow-up

The Mindstrong app uses AI to analyze smartphone usage patterns (such as typing speed and navigation behavior) to detect cognitive changes linked to stress and mental health deterioration. Nurses receive real-time alerts when the system detects behavioral red flags, prompting proactive follow-up.

Example in Practice: Community health nurses monitor patients with chronic illness using Mindstrong. When a user’s data shows cognitive decline or high stress, the nurse conducts an early intervention via phone or home visit.

3. BioBeat AI Wearable in ICU Patients

BioBeat is an FDA-approved AI-powered wearable device that continuously monitors patient vitals like blood pressure, heart rate variability, and oxygen saturation. In ICUs and oncology settings, nurses use it to detect stress-related changes and prevent complications like hypertensive crises or arrhythmias triggered by stress.

Example in Practice: In oncology wards, nurses integrate BioBeat to monitor chemo patients. Stress spikes detected through heart rate variability are addressed with prompt relaxation therapy or pharmacologic intervention.

4. Virtual Reality-Based Stress Exposure Therapy

AI-enhanced Virtual Reality (VR) systems simulate calming or stress-triggering environments and adjust based on biofeedback data. Nurses use VR for stress desensitization, especially in patients with PTSD, phobias, or chronic anxiety.

Example in Practice: Psychiatric nurses use AI-VR therapy to expose patients with social anxiety to controlled social situations. The system adapts in real-time based on the patient's physiological stress markers and facial expressions.

5. IBM Watson for Predictive Stress Risk Analysis

IBM Watson Health uses predictive analytics to analyze electronic health records and behavioral data, helping nurses identify patients at risk of burnout or psychological stress before clinical symptoms manifest.

Example in Practice: Nurse administrators use Watson to monitor healthcare staff burnout risk. Based on shift hours, emotional tone in clinical notes, and biometric data, the system recommends targeted wellness interventions or modified schedules.

6. AI-Powered Emotional Recognition Systems

These systems use facial recognition and natural language processing to detect emotional distress. Deployed in elderly care and psychiatric settings, they help nurses intervene when patients cannot verbalize their stress.

Example in Practice: In geriatric care homes, emotional AI tools analyze residents’ expressions and tone. Nurses are alerted when the system detects sadness or anxiety, prompting human interaction and support.

DISCUSSION

AI enables personalized, scalable interventions in stress management, enhancing nursing efficiency and patient outcomes. Tools like chatbots and wearables facilitate continuous monitoring and self-management. However, nurse training in AI technologies and ethical concerns regarding data privacy remain barriers. The use of AI should complement—not replace—human empathy and critical judgment [5-10].

CURRENT TRENDS

• Mental Health Apps with AI-powered tracking and recommendations.
• AI in Telehealth for rural and underserved populations.
• Machine Learning Algorithms in EHR for predicting nurse burnout.
• AI-Driven CBT Modules integrated into nursing care plans.

CHALLENGES

• Data privacy and consent issues.
• Technological literacy among nurses.
• Risk of algorithmic bias.
• Integration into existing healthcare systems.

FUTURE DIRECTIONS

• Development of nurse-AI co-management platforms.
• AI in nursing curriculum and continuing education.
• Real-time biofeedback systems integrated with AI.
• More large-scale randomized controlled trials (RCTs) on AI tools in nursing.

CONCLUSION

AI presents a transformative approach to stress management in nursing, providing tools that support early detection, continuous monitoring, and effective intervention. While its benefits are evident, responsible implementation, ethical governance, and nurse preparedness are essential to leverage AI’s full potential in mental health care.

Artificial Intelligence is rapidly evolving as a powerful ally in modern nursing practice, especially in the domain of stress management. This review highlights that AI-enabled tools like chatbots, predictive algorithms, wearable devices, and virtual reality platforms are reshaping how nurses assess, monitor, and intervene in stress-related conditions. These tools enhance real-time decision-making, personalize patient care, and enable early intervention strategies that are both cost-effective and scalable.

AI supports nurses in reducing manual workload and improving patient engagement, particularly in mental health care. It facilitates continuous stress monitoring through physiological indicators, identifies patterns through data analysis, and offers interventions tailored to individual needs. Such approaches empower nurses to not only address symptoms but also promote long-term mental well-being in patients.

Despite the potential, successful implementation of AI in stress management demands addressing significant challenges. These include ensuring patient data privacy, improving AI literacy among nurses, avoiding algorithmic bias, and integrating AI seamlessly into existing workflows without undermining the humanistic essence of nursing care.

As AI technology continues to evolve, its collaboration with nursing professionals will be crucial for developing empathetic, ethical, and effective mental health interventions. Future research, policy-making, and education must prioritize a balanced approach where AI complements human judgment, thus enriching the quality of nursing care in stress management.

ACKNOWLEDGEMENTS

None.

CONFLICT OF INTEREST

The author declares that there is no conflict of interest.

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