A proposed multi-domain, digital model for capturing functional status and health-related quality of life in oncology.
Elena S IzmailovaJohn A WagnerJessie P BakkerRachel KilianRobert D EllisNitin OhriPublished in: Clinical and translational science (2024)
Whereas traditional oncology clinical trial endpoints remain key for assessing novel treatments, capturing patients' functional status is increasingly recognized as an important aspect for supporting clinical decisions and assessing outcomes in clinical trials. Existing functional status assessments suffer from various limitations, some of which may be addressed by adopting digital health technologies (DHTs) as a means of collecting both objective and self-reported outcomes. In this mini-review, we propose a device-agnostic multi-domain model for oncology capturing functional status, which includes physical activity data, vital signs, sleep variables, and measures related to health-related quality of life enabled by connected digital tools. By using DHTs for all aspects of data collection, our proposed model allows for high-resolution measurement of objective data as patients navigate their daily lives outside of the hospital setting. This is complemented by electronic questionnaires administered at intervals appropriate for each instrument. Preliminary testing and practical considerations to address before adoption are also discussed. Finally, we highlight multi-institutional pre-competitive collaborations as a means of successfully transitioning the proposed digitally enabled data collection model from feasibility studies to interventional trials and care management.
Keyphrases
- clinical trial
- end stage renal disease
- physical activity
- electronic health record
- high resolution
- ejection fraction
- newly diagnosed
- chronic kidney disease
- big data
- prognostic factors
- peritoneal dialysis
- mental health
- public health
- type diabetes
- risk assessment
- open label
- mass spectrometry
- artificial intelligence
- machine learning
- depressive symptoms
- acute care
- patient reported
- health information