LAUSR

115 tionalized spatial contexts. Imagine if, for example, a patient’s psychosis could be understood using an interface similar to online geographic maps. One could “zoom out” (decrease the resolution) to observe psychosis symptoms over days, weeks and months, and could “zoom in” (increase the resolution) to observe whether psychosis systematically change as a function of time (e.g., worse in the evening) or spatial conditions (e.g., worse when interacting with certain peers). This sort of dynamic data and interface would provide unprecedented opportunities for understanding psychiatric disorders and for personalizing pharmacological, psychosocial and emergency interventions. Just as the reliability and validity of biomedical measures of, for example, glucose or heart rate are only reported and evaluated during specific and controlled circumstances, so too should the reliability and validity of digital phenotyping technologies be understood as a function of time and space. Digital phenotyping technologies are not “reliable and valid” per se, but rather can have reliability and validity under specific circumstances and for specific purposes. Reporting psychometric features with regard to relevant temporal and spatial characteristics can help guide implementation of digital phenotyping technologies, improve interpretation of their data, and potentially help optimize signal and reduce noise. Conceivably, this can improve reliability and validity parameters such that they approximate those of biomedical tests more generally. To illustrate how resolution can improve digital phenotyping validation efforts, consider natural language processing technologies used to quantify psychosis. A cursory review of the literature reveals that “validity” has been established, in that modest convergence is documented between various computationallyderived semantic speech features and “gold-standard” clinical symp tom ratings. This approach to validation seems inappropriate when one considers the mismatch in resolution between these measures – with the former being derived from systematic analysis of brief language samples procured during a fairly-contrived clinical interaction or cognitive task, and the latter representing an ordinal rating assigned by a clinician based on an extended clinical interview. These ratings reflect very different temporal and spatial characteristics, and hence, failures to find large convergence is unsurprising. While machine learning-based algorithms connecting digital phenotyping technologies and clinical ratings have shown impressive accuracy, they have generally also ignored the overt resolution mismatch between these variables and have not demonstrated generalizability to new samples, speaking tasks or clinical measures. To our knowledge, resolution is not generally considered in digital phenotyping research. In order for digital phenotyping of psychiatric disorders to be considered on-par with that of biomedical disorders more generally, their psychometrics need to be similarly precise. This precision can be achieved through deliberate consideration of “resolution” .