Gillian Gresham, PhD
Using wearable activity monitors may eventually supplement standard assessments of performance status (PS) and functionality that could inform clinicians, especially because objective evaluation of PS is difficult to determine. Patients spend most of their time outside of the clinic, self-report to providers, and undergo changes throughout treatment. Findings from a recent study at Cedars-Sinai Medical Center (CSMC) in Los Angeles, California, demonstrated the feasibility of using these wrist-worn devices to correlate with Eastern Cooperative Oncology Group (ECOG) PS and Karnofsky PS (KPS) scales.1 PS is affected by bias, resulting in patient activity being over- or underreported, which can affect short- and long-term treatment plans and clinical trial eligibility.
“Before we can ask if these devices can change practice or replace these standard assessments, we need to determine if it’s feasible for the patient to use them, [and] to wear them, and if there are any challenges with their use,” said Gillian Gresham, PhD, lead author and postdoctoral student at CSMC. “After establishing short-term feasibility, we can begin to explore whether these measurements correlate with standard assessments, patient-reported outcomes, and predict clinical outcomes,” she added.
Additional objectives of the study included measuring patient reported outcomes (PROs). “We were able to correlate PROs such as improved physical functioning, better pain tolerance, improved sleep, and lower levels of depression with increased activity, as measured using the devices,” Gresham said.
Thirty-seven patients (20 men) with stage IV or unresectable advanced stage III cancer agreed to participate in the single-center, single-cohort study that evaluated the Fitbit Charge HR device to measure daily activity. Patients of varying ECOG PS and KPS ratings participated, with participants agreeing to wear the Fitbit for 3 consecutive clinic visits over 2 weeks, in which ECOG PS and KPS were assessed. Associations between metrics (steps, distance, and stair climbing) and PS, clinical outcomes (adverse events [AEs], hospitalizations, and survival), and PROs were determined.
Heat Map of Average Activity Intensity for Each Patient Over a 24-hour Period, as Measured by a Wearable Activity Monitor and Sorted by ECOG PS Categories
Median age was reported as 62 years (range, 34-81). At baseline, patients’ ECOG-PS scores were 0 (24%), 1 (35%), 2 (24%), or 3 (16%). The majority of patients were diagnosed with gastrointestinal cancer (n = 27) and had stage IV disease (n = 34). There were 2 patients with locally advanced stage IV pancreatic disease and 1 patient with stage IIIB endocervical serous carcinoma.
The highest correlations were observed between average daily steps and both PS scores. Each 1000 steps/day increase was associated with reduced odds for AEs (odds ratio [OR], 0.34; 95% CI, 0.13-0.94), hospitalizations (OR, 0.21; 95% CI, 0.56-0.79), and hazard for death (hazard ratio [HR], 0.48; 95% CI, 0.28-0.83). On average, patients walked about 3700 steps, or 1.7 miles, a day, climbed 3 flights of stairs daily, and slept 8 hours/night as measured by their wearable device (Figure
Although the device doesn’t measure heart rate the same way electrocardiograms do, it estimates heart rate by measuring blood flow through pulse readings. “We lined up measurements of the heart rate at a specific time in the clinic and matched it during the time that the patients were wearing the Fitbit and looked at the comparison of those 2 findings for each clinic visit,” said Gresham. “They were very closely related. That was encouraging but warrants further investigation.”
The investigators were, however, intrigued by the poor correlation between sleep quality and duration (as measured with the device), said Gresham. “I think we need more granular-level data with regards to sleep,” Gresham said. “[Although] Fitbit has improved its sleep recording quality since we completed the study, it opens up a new and interesting facet of research,” she said. “It also highlights the importance of the patient’s voice in their management, and is an example of how a patient’s perception of their sleep quality may not always match what is measured. Perhaps in this case, quality does not mean quantity.”