<p>Biosensor-based, real-time stress detection has generated clinical interest for the purpose of driving just-in-time interventions that support recovery from mental disorders. Most stress detection models to date, however, have been trained with laboratory-based data from homogenous samples of healthy adults, and do not perform as well in clinical populations. As an initial step toward the development of a stress detection algorithm that functions well in clinical populations, we tested a series of stress-detection machine learning models on ambulatory electrocardiogram (ECG) and daily ecological momentary assessment (EMA) data collected from a sample of individuals in early recovery from alcohol use disorder (AUD). Forty-four individuals ages 18–65 in the first year of a current AUD recovery attempt wore an ECG monitor for 4 days, while concurrently completing 3-times-daily EMA of stress. Data were segmented and normalized. Target features were identified using unsupervised learning models (e.g., <i>t</i>-SNE, cluster analysis) and supervised learning models were tuned to optimize model performance. As a comparator, we also tested these models with laboratory-derived stress data from a sample of healthy young adults. Before accounting for individual characteristics, we achieved a modest accuracy of 63% in our clinical sample, which compared to 94% accuracy in the laboratory-derived healthy young adult sample. After accounting for age and body-mass-index (BMI) we increased model accuracy up to 80% in our clinical sample. Stress detection is challenging in clinical populations; however, better prediction is possible with data normalization and stratification considering age and BMI.</p>