Digital phenotyping studies predominantly used wearable devices for assessing chronic stress with modest classification accuracy ranging 56.8%–79%. HRV was the most common physiological measure used across studies.
Systematic Review (n=18)
Digital phenotyping via wearables and smartphones is a promising but early-stage approach for assessing chronic stress, self-regulation, and interoception, with most current work focused on short-term wearable data and heart rate variability.
Introduction Digital phenotyping, the real-time quantification of human phenotype in situ via digital devices, offers opportunities to understand how behavior change interventions influence brain and mental health. Interoception, chronic stress, and self-regulation are key domains, benefiting from real-world, continuous assessment beyond what traditional methods can provide. Objective The aim of this scoping review was to map and synthesize the literature of the last five years on the use of digital phenotyping to measure or predict interoception, chronic stress, and self-regulation in adults. We focused on the types of devices and sensors employed, the psychological domains targeted, the nature of the data collected, feature extraction, data processing methods, and technological platforms utilized. Methods Following Joanna Briggs Institute methodology and PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus, complemented with Google Scholar. Eligibility criteria included studies published since 2018, using smartphones or commercial wearables to assess or predict interoception, chronic stress, or self-regulation in adults. Results From 850 retrieved records, 18 studies met inclusion criteria. Of these, 11 addressed chronic stress or stress reactivity, five self-regulation, and two interoception. Thirteen studies used wearable devices, three used smartphones, and two combined both approaches. Ecological momentary assessment (EMA) via smartphones was applied in eight studies. Heart rate variability (HRV) was the most common physiological measure ( n = 14), followed by electrodermal activity and heart rate ( n = 4 each). Nine studies analyzed behavioral data, including smartphone use, sleep, and activity. Six studies applied machine learning models, though only three reported classification accuracy (56.8%–79%). Eight used statistical methods to link features with stress or interoception, while four examined self-regulation using predefined features without identifying new biomarkers. Discussion This review highlights that the field is still in its early stages, with most work focused on chronic stress and predominantly reliant on wearable devices. Integration of smartphone sensing and long-term monitoring remains limited, and analytical performance is modest. Nevertheless, the ubiquity of smartphones and wearables positions digital phenotyping as a promising, scalable approach for assessing brain and mental health in daily life. Future research should emphasize multimodal, longer-term data collection, innovative analytic methods, and transparent reporting.
Alvarez-Ambrosio et al. (2026) conducted a systematic review in Adults primarily aged 21-65 years studied for digital phenotyping assessment or prediction of interoception, chronic stress, and self-regulation (n=18). Digital phenotyping using smartphones and wearable devices was evaluated on Assessment or prediction of interoception, chronic stress, and self-regulation using digital phenotyping. Digital phenotyping studies predominantly used wearable devices for assessing chronic stress with modest classification accuracy ranging 56.8%–79%. HRV was the most common physiological measure used across studies.