The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported; no quantitative synthesis performed

• Primary Outcome: Mapping of five core functional roles of AI-enabled tools in self-directed stress management: (1) psychological intervention, (2) behavioural support, (3) psychoeducation, (4) companionship and emotional support, and (5) stress monitoring, detection, and triage

• Nnt Or Sensitivity: Not applicable; no pooled efficacy or diagnostic accuracy data reported. The review is descriptive/mapping in nature. Stress detection and triage functions were identified but sensitivity/specificity data for AI-based stress detection tools were not pooled.

• Confidence Interval: Not reported

Clinical Application

AI-enabled self-directed stress management tools are technically feasible and increasingly accessible via smartphones and wearables. However, clinical integration requires attention to data privacy, regulatory oversight, user engagement sustainability, and appropriate triage pathways for individuals with clinical-level mental health needs. Feasibility in resource-limited or digitally excluded populations remains uncertain. In Australia, AI-enabled mental health tools are not currently listed on the PBS and are not subject to TGA therapeutic goods regulation unless they meet the definition of a medical device under the Therapeutic Goods Act 1989. The RACGP does not yet have specific guidelines for AI-assisted self-directed stress management, though digital mental health tools are acknowledged in the RACGP's mental health care framework. Relevant Australian context includes: (1) the national digital mental health platform (Head to Health) which aggregates digital mental health resources; (2) MindSpot and This Way Up as established digitally-delivered CBT platforms; (3) the Australian Digital Health Agency's ongoing work on AI governance in healthcare. Clinicians should exercise caution recommending unregulated AI tools and should ensure patients with clinical-level presentations are directed to evidence-based, regulated services. The review's findings may inform future TGA regulatory considerations for AI-based mental health tools as Software as a Medical Device (SaMD). General adult population seeking self-directed stress management support outside formal clinical care; potentially applicable to individuals with subclinical stress, occupational stress, or those with barriers to accessing clinician-led services (cost, stigma, geographic remoteness)

Abstract

BACKGROUND: Stress is widespread and carries substantial mental health, social, and economic burdens. Yet, access to clinician-led stress management remains constrained by service capacity, cost, and stigma. In response, artificial intelligence (AI)-enabled tools have rapidly proliferated as scalable, self-directed options. However, evidence on how these systems support stress management outside formal clinical settings remains fragmented. OBJECTIVE: This systematic review aimed to synthesize empirical evidence on how AI-enabled technologies are used for self-directed stress management. We mapped the emerging functions of these tools, the psychological frameworks informing their design, the populations and settings studied, and the outcomes reported. METHODS: We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-compliant systematic review of English-language studies published between 2000 and 2025. Six databases were searched (APA PsycINFO, PubMed, MEDLINE, Scopus, Web of Science Core Collection, ProQuest, and Google Scholar). RESULTS: Of 3008 records identified, 35 studies met the inclusion criteria. The methodological quality of included studies was critically appraised using the Mixed Methods Appraisal Tool (version 2018). Findings illustrated that AI-supported stress management can operate through 5 core functions, including psychological intervention, behavioral support, psychoeducation, companionship, and emotional support, and stress monitoring, detection, and triage. Across the reviewed studies, these functions supported self-directed stress management by helping users identify stress, regulate responses, and engage in coping outside formal clinical care. CONCLUSIONS: AI-enabled systems show preliminary promise for supporting self-directed stress management through multiple user-facing functions grounded in established psychological frameworks.

References

1. 1.Reyes, M. K. G., Teo, S. S. M., & Hartanto, A. (2026). The emerging roles of AI in self-directed stress management: Systematic review. Journal of Medical Internet Research. https://doi.org/10.2196/90709