Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety

Abstract

The use of data generated passively by personal electronic devices, such as smartphones, to measure human function in health and disease has generated significant research interest. Particularly in psychiatry, objective, continuous quantitation using patients' own devices may result in clinically useful markers that can be used to refine diagnostic processes, tailor treatment choices, improve condition monitoring for actionable outcomes, such as early signs of relapse, and develop new intervention models. If a principal goal for digital phenotyping is clinical improvement, research needs to attend now to factors that will help or hinder future clinical adoption. We identify four opportunities for research directed toward this goal: exploring intermediate outcomes and underlying disease mechanisms; focusing on purposes that are likely to be used in clinical practice; anticipating quality and safety barriers to adoption; and exploring the potential for digital personalized medicine arising from the integration of digital phenotyping and digital interventions. Clinical relevance also means explicitly addressing consumer needs, preferences, and acceptability as the ultimate users of digital phenotyping interventions. There is a risk that, without such considerations, the potential benefits of digital phenotyping are delayed or not realized because approaches that are feasible for application in healthcare, and the evidence required to support clinical commissioning, are not developed. Practical steps to accelerate this research agenda include the further development of digital phenotyping technology platforms focusing on scalability and equity, establishing shared data repositories and common data standards, and fostering multidisciplinary collaborations between clinical stakeholders (including patients), computer scientists, and researchers.

Keywords: Biomarkers; Information technology; Psychiatric disorders.

Fig. 1

Two models of integration between digital phenotyping and digital interventions. Figures and letters refer to those shown in the diagram. Model (A) describes a “learn-then-implement” approach where (1) multi-modal digital signals (e.g. sensor data) are combined with (2) ground-truth data (such as self-reported mental health) and used to learn a digital phenotyping predictive model, for example, predicting a change in mental health status from GPS and activity data. This model can then be deployed into future interventions (4) to trigger intervention components based on changes in mental health state predicted by digital signals alone. Model (B) describes a “continuous learning” approach, where (1) digital signals are automatically collected alongside intervention outcomes data. These are used to (2) continuously update and refine an intervention model conditioned on some goal, for example achieving a positive change in mental health status. This model is then used to trigger and tailor different aspects of the intervention (3). The resultant outcomes feed back into the learning process. Data collected via this approach can also be extracted for analysis (4)

Fig. 2

Black Dog Institute/Deakin model for a scalable, integrated multi-user platform for digital phenotyping research Figures and letters refer to those shown in the diagram. In this model, (1) researchers specify the study design, define which questionnaires and sensors are required to deliver a digital phenotyping study (and optionally how these are integrated with any intervention components, such as self-guided therapy.) This specification is then hosted alongside others in a secure online repository. When each study commences, the specification is automatically downloaded (2) to users’ devices by a digital phenotyping app. This app can be a multi-study coordination tool that acts to coordinate data collection, a bespoke, study-specific data collection app, or a hybrid data collection intervention. Collected (3) self-report (e.g. questionnaires and momentary assessments) and (4) digital data (e.g. sensor measurements and device interaction data) is uploaded automatically to a secure online registry. Platform modules automatically manage potential barriers to data collection, such as user battery life and limited connectivity, through smart scheduling and caching. Automated processing pipeline (5) normalizes and converts raw data into standardized intermediate features and labelled outputs using machine learning. Researchers can start to extract registry data (6) as soon as it is received, accelerating analysis, permitting study designs that involve expert feedback, and allowing any data collection issues to be identified and addressed early in the research process. Rights management enables future researchers to request from users’ access to previously-collected data