This research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS).
Results: our results overall demonstrate that stress can be reliably detected with an accuracy that varies from 75 to 100%, with an average of 84%. [...] the defined critical values are tailored to the current research, they do not serve as global values for stress detection.

The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning.
Results: results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.

This study outlines a novel machine learning model which predicts interstitial glucose levels using non-invasive measurements with impressive accuracy.
Results: the study demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively.

The aim of this study is to examine (1) whether an mHealth device integrated into a community mental health team can improve outcomes for young adults with severe mental illness and (2) whether the device detects periods of mental health versus deterioration.
Results: coming at the end of the study.

The study demonstrates that epilpetic and psychogrenic non-epileptic seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors.
Results: classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic.