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International Journal of
eISSN: 2577-8269

Family & Community Medicine

Research Article Volume 10 Issue 2

The emotional Arc of recovery and relapse: AI and mind genomics illuminate how people navigate change

Dipak Paul, Howard Moskowitz, David Stevens, Sharon Wingert

Received: March 25, 2026 | Published: April 9, 2026

Citation: Paul D, Moskowitz H, Stevens D, et al. The emotional Arc of recovery and relapse: AI and mind genomics illuminate how people navigate change. Int J Fam Commun Med. 2026;10(2):73-76. DOI: 10.15406/ijcam.2026.10.00416

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Abstract

Recovery is rarely a straight line. Whether the goal is weight reduction, glycemic control, or healthier eating patterns, patients describe recovery as a shifting emotional landscape— hope, fear, momentum, fatigue, pride, shame, and the quiet pull of old habits. Relapse is not a failure of willpower; it is a predictable part of the behavioral cycle, shaped by stress, identity, environment, and emotional regulation. Yet clinical conversations often treat relapse as a deviation rather than an expected phase. AI-supported narrative simulation now allows clinicians to rehearse the emotional realities of recovery and relapse, generating patient voices that express ambivalence, frustration, renewed hope, and the subtle drift back toward old patterns. Mind Genomics complements this by mapping the decision rules that govern how people interpret progress, setbacks, and self-evaluation. This paper integrates AI-generated narratives with Mind Genomics segmentation to illuminate the emotional arc of recovery and relapse. The goal is to help clinicians understand not only what patients do, but how they feel as they attempt to change. By the end of this paper, the reader will see recovery not as a linear ascent but as a dynamic psychological process that requires empathy, structure, and individualized communication.

Keywords: recovery and relapse, mind genomics, artificial intelligence, behavioral change, emotional processing, clinical communication, patient-centered care

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©2026 Paul, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.