[[Digital Interventions]] [[HOPES Project Index]] [[EMA]] [[Systematic Review of Digital Interventions Project]] ----- [[Article - Technology and Mental Health. State of the Art for Assessment and Treatment]] "Goldsack et al. (97) proposed the V3 framework for determining the validity of passive digital biomarkers, which involves three components: verification, analytical validation, and clinical validation. These components, as reviewed below, provide a useful heuristic for determining whether the level of validity achieved for various passive measures meets clinical standards." - 97 - Goldsack JC, Coravos A, Bakker JP, et al: Verification, analytical validation, and clinical validation (V3): the foundation of deter- mining fit-for-purpose for biometric monitoring technologies (BioMeTs). NPJ Digit Med 2020; 3:55 --- 1. **Verification** - 1. Determine whether the sensor capturing data accurately, and software accurately outputs data 2. According to manufacturers, industry guidelines. 3. Such as FDA, Good Manufacturing Practice Standards for medical devices.. **1. Analytical Validation -** 1. Determine whether sample-level data output by device is properly received, and algorithms calculating on that data as expected. 2. Should be evaluated against relevant reference, although no agreed-upon reference standards. 3. e.g "For example, phone-based geolocation and accelerometry recorded on the ExpoApp have been validated in relation to a reference wrist-worn actigraph and a travel/activity diary; time in microenvironments and physical activity from the diary demonstrated high agreement with phone-based geo- location and accelerometry measures (98). Huang and Onnela (92) analytically validated a phone accelerometer and gyroscope using a ground-truth standard. They had human participants engage in specific physical activities (e.g., sitting, standing, walking, and ascending and descending stairs) with a phone in their front and back pockets. Behavior was filmed throughout as an objective reference. The sensors accurately predicted video-recorded behavior in the reference standard. One ongoing challenge is that as smartphones are updated with new software and phone models with new sensors, prior validation efforts cannot be assumed to be valid. 1. **Clinical validation** (i.e., implementation), involves determining whether the passive digital phenotyping variable of interest adequately predicts a specific clinical outcome within the population of interest. 1. Which is what we are talking about, whether DP is clinical validated to predict specific clinical outcomes.