Citation
Wood, William A.; Dilip, Deepika; Derkach, Andriy; Grover, Natalie S.; Elemento, Olivier; Levine, Ross; Thanarajasingam, Gita; Batsis, John A.; Bailey, Charlotte; & Kannappan, Arun, et al. (2023). Wearable Sensor-Based Performance Status Assessment in Cancer: A Pilot Multicenter Study from the Alliance for Clinical Trials in Oncology. PLOS Digital Health, 2(1), e0000178. PMCID: PMC9931326Abstract
Clinical performance status is designed to be a measure of overall health, reflecting a patient's physiological reserve and ability to tolerate various forms of therapy. Currently, it is measured by a combination of subjective clinician assessment and patient-reported exercise tolerance in the context of daily living activities. In this study, we assess the feasibility of combining objective data sources and patient-generated health data (PGHD) to improve the accuracy of performance status assessment during routine cancer care. Patients undergoing routine chemotherapy for solid tumors, routine chemotherapy for hematologic malignancies, or hematopoietic stem cell transplant (HCT) at one of four sites in a cancer clinical trials cooperative group were consented to a six-week prospective observational clinical trial (NCT02786628). Baseline data acquisition included cardiopulmonary exercise testing (CPET) and a six-minute walk test (6MWT). Weekly PGHD included patient-reported physical function and symptom burden. Continuous data capture included use of a Fitbit Charge HR (sensor). Baseline CPET and 6MWT could only be obtained in 68% of study patients, suggesting low feasibility during routine cancer treatment. In contrast, 84% of patients had usable fitness tracker data, 93% completed baseline patient-reported surveys, and overall, 73% of patients had overlapping sensor and survey data that could be used for modeling. A linear model with repeated measures was constructed to predict the patient-reported physical function. Sensor-derived daily activity, sensor-derived median heart rate, and patient-reported symptom burden emerged as strong predictors of physical function (marginal R2 0.429-0.433, conditional R2 0.816-0.822).URL
http://dx.doi.org/10.1371/journal.pdig.0000178Reference Type
Journal ArticleYear Published
2023Journal Title
PLOS Digital HealthAuthor(s)
Wood, William A.Dilip, Deepika
Derkach, Andriy
Grover, Natalie S.
Elemento, Olivier
Levine, Ross
Thanarajasingam, Gita
Batsis, John A.
Bailey, Charlotte
Kannappan, Arun
Devine, Steven M.
Artz, Andrew S.
Ligibel, Jennifer A.
Basch, Ethan
Kent, Erin
Glass, Jacob