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Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire healthrelated information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patientcentric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients' functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson's disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.

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A case is presented of angina manifesting itself initially solely as vertex and occipital headache, accompanied by EKG changes, provoked by exercise and relieved by rest. It was totally relieved by coronary bypass surgery and, later, by angioplasty. Its mechanism is probably a variation on the neural convergence usually invoked to explain the more typical chest pain angina.

Establishing meaningful change thresholds for Clinical Outcome Assessments (COA) is critical for score interpretation. While anchor- and distribution-based statistical methods are well-established, qualitative approaches are less frequently used. This commentary summarizes and expands on a symposium presented at the International Society for Quality of Life Research (ISOQOL) 2017 annual conference, which provided an overview of qualitative methods that can be used to support understanding of meaningful change thresholds on COAs. Further published literature and additional examples from multiple disease areas which have also qualitatively explored the concept of meaningful change are presented. Semi-structured interviews conducted independently from a clinical trial, exit interviews conducted in the context of a clinical trial, focus groups, vignettes and the Delphi panel method can be used to obtain data regarding meaningful change thresholds, with advantages and disadvantages to each method. Semi-structured interviews using concept elicitation (CE) or cognitive debriefing (CD) methods conducted independently from a clinical trial can be an efficient way to gain in-depth patient/caregiver insights. However, there can be challenges with reconciling heterogeneous data across diverse samples and in interpreting the qualitative insights in the context of quantitative score changes. Semi-structured qualitative interviews using CE/CD methods embedded as exit interviews in a clinical trial context with patients/caregivers can provide insights which can augment quantitative findings based on analysis of clinical trial data. However, there are logistical challenges relating to embedding the interviews in a clinical trial. Focus groups and the Delphi panel method can be valuable for reaching consensus regarding meaningful change thresholds; however, for face-to-face interactions, social desirability bias can affect responses. Finally, using vignettes and taking a mixed methods approach can aid in achieving consensus on the minimum score change endorsed by respondents as a meaningful improvement/decrement. However, the approach can be cognitively challenging for participants and reaching a consensus is not guaranteed. Anchor- and distribution- based methods remain critical in establishing responder definitions. Nonetheless, qualitative data has the potential to provide complementary support that a certain level of change on the target COA, which has been statistically supported, is truly important and meaningful for the target population.