What Huntington's Disease Patients Say About Their Illness: An Online Direct-to-Participant Pilot Study

Abstract

Background: Direct-to-participant online reporting facilitates the conduct of clinical research by increasing access and clinically meaningful patient engagement.

Objective: We assessed feasibility of online data collection from adults with diagnosed Huntington's disease (HD) who directly reported their problems and impact in their own words.

Methods: Data were collected online from consenting United States residents who self-identified as 1) having been diagnosed with Huntington's disease, 2) able to ambulate independently, and 3) self-sufficient for most daily needs. Data for this pilot study were collected using the Huntington Study Group myHDstory online research platform. The Huntington Disease Patient Report of Problems (HD-PROP), an open-ended questionnaire, was used to capture verbatim bothersome problems and functional impact. Natural language processing, human-in-the-loop curation of verbatim reports involving clinical and experience experts, and machine learning classified verbatim-reports into clinically meaningful symptoms.

Results: All 8 questionnaires in the online pilot study were completed by 345 participants who were 60.9% men, 34.5±9.9 (mean±SD) years old, and 9.5±8.4 years since HD diagnosis. Racial self-identification was 46.4% Caucasian, 28.7% African American, 15.4% American Indian/Alaska Native, and 9.5% other. Accuracy of verbatim classification was 99%. Non-motor problems were the most frequently reported symptoms; depression and cognitive impairment were the most common.

Conclusions: Online research participation was feasible for a diverse cohort of adults who self-reported an HD diagnosis and predominantly non-motor symptoms related to mood and cognition. Online research tools can help inform what bothers HD patients, identify clinically meaningful outcomes, and facilitate participation by diverse and under-represented populations.

Keywords: Huntington’s disease; Patient-reported outcomes; machine learning; self-report.