  The technical capacity to monitor patients with a mobile device has
drastically expanded, but data produced from this approach are often difficult
to interpret. We present a solution to produce a meaningful representation of
patient status from large, complex data streams, leveraging both a data-driven
approach, and use clinical knowledge to validate results. Data were collected
from a clinical trial enrolling chronic pain patients, and included
questionnaires, voice recordings, actigraphy, and standard health assessments.
The data were reduced using a clustering analysis. In an initial exploratory
analysis with only questionnaire data, we found up to 3 stable cluster
solutions that grouped symptoms on a positive to negative spectrum. Objective
features (actigraphy, speech) expanded the cluster solution granularity. Using
a 5 state solution with questionnaire and actigraphy data, we found significant
correlations between cluster properties and assessments of disability and
quality-of-life. The correlation coefficient values showed an ordinal
distinction, confirming the cluster ranking on a negative to positive spectrum.
This suggests we captured novel, distinct Pain Patient States with this
approach, even when multiple clusters were equated on pain magnitude. Relative
to using complex time courses of many variables, Pain Patient States holds
promise as an interpretable, useful, and actionable metric for a clinician or
caregiver to simplify and provide timely delivery of care.
