Traditional assessment of affective and behavioral functioning relies almost entirely on questionnaires, self-report interviews, and laboratory-based measurements. Although each of these approaches has important strengths, they are also subject to limitations. Recently, technological advances in mobile computing have allowed for the widespread adoption of consumer mobile technologies that may ameliorate many methodological limitations of traditional assessment methods as these devices contain a multitude of sensors enabling the scalable, unobtrusive, and ecologically valid collection of biobehavioral variables. Despite many review articles delineating the promise of these devices, research has largely been limited to single symptom profiles and homogenous populations. This symposium will address these gaps by presenting novel findings that utilize multimethod approaches (e.g., actigraphy, GPS, photoplethysmography, camera and light sensors) to examine how intensively longitudinal study designs leveraging consumer smartphone and wearable technology can be used to index mental health profiles, acute stress, and socioeconomic disparities across the lifespan and in diverse populations. First, Dr. Nelson will present a preregistered assessment of multiple clinical profiles using a computational psychiatry machine learning approach with large scale wearable data collection in a large nationally representative sample of adolescents. Second, Dr. Lockwood, will present a large-scale longitudinal study using newly-validated smartphone-based optic sensor to assess socioeconomic disparities. Third, Vega will present on how 6 months of smartphone sensor data during COVID-19 predicts weekly levels of depression and anxiety. Lastly, Harvie will present on how smartphone-based measures of photoplethysmography using a consumer wearable device tracks self-reported increases in perceived stress in children and adults.