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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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AI Hyperarousal Syndrome (AHIS)

Authors: Worrall, Julia Dawn;

AI Hyperarousal Syndrome (AHIS)

Abstract

The rapid proliferation of AI-driven digital environments — including social media recommendation algorithms, large language model interfaces, notification architectures, and immersive screen-based content systems — has produced a constellation of clinical symptoms in a growing population of patients that does not conform to existing diagnostic categories. These symptoms span neurological, autonomic, hormonal, and structural domains and share a common causal denominator: sustained, high-frequency activation of the human threat-response system by engineered digital stimuli. OBJECTIVE To present the first formal clinical definition, symptom taxonomy, causal mechanism framework, and preliminary treatment protocol for the syndrome this paper names AI Hyperarousal Syndrome (AIHS), and to establish the priority of discovery of this clinical entity by the originating clinician, Julia Worrall, RN, whose identification of this syndrome in clinical practice predates all subsequently published independent research on related phenomena. METHODS This paper presents: (1) a case study of a patient presenting with the full AIHS symptom cluster; (2) a clinical definition of AIHS derived from direct patient observation and cross-referenced against published neurological, autonomic, and endocrinological literature; (3) documentation of five causal mechanisms with corresponding peer-reviewed citations from Harvard Medical School, Stanford Lifestyle Medicine, the Mayo Clinic, the Johns Hopkins Dysautonomia Clinic, Cleveland Clinic, the WHO, and technology industry research; and (4) a five-phase treatment protocol developed by the originating clinician. Full biometric trial data, outcome measurements, and statistical analysis are pending completion of the active clinical trial and will be published in the full peer-reviewed version of this study. PRELIMINARY FINDINGS Initial clinical observations indicate that the AIHS symptom cluster is distinct from existing diagnoses including generalized anxiety disorder, burnout, and digital addiction, and is more precisely characterized as a biological dysregulation syndrome with measurable autonomic markers (suppressed HRV, elevated resting cortisol, disrupted sleep architecture) that respond to a targeted five-phase restorative protocol. Independent research from BCG/Harvard Business Review (2026), Microsoft and Carnegie Mellon University (2025), the US Surgeon General (2024), and the World Health Organization (2024–2025) provides convergent validation of the mechanisms identified by the originating clinician prior to the publication of those studies.

Keywords

Artificial intelligence, Psychological First Aid/legislation & jurisprudence, Artificial Intelligence/legislation & jurisprudence, Artificial Intelligence/statistics & numerical data, Artificial Intelligence/economics, Artificial Intelligence/ethics, Artificial Intelligence/supply & distribution, Artificial Intelligence/standards, Artificial Intelligence/statistics & numerical data, Artificial Intelligence/legislation & jurisprudence, Artificial Intelligence/supply & distribution, Psychological First Aid/legislation & jurisprudence, Artificial Intelligence/history, Artificial Intelligence, Artificial Intelligence/classification, Artificial Intelligence/trends, Psychological First Aid

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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