Association of patient activity bio-profiles with health-related quality of life in patients with newly diagnosed multiple myeloma: a prospective observational cohort study

BACKGROUND

Due to the nature of their disease, patients with multiple myeloma (MM) often have bone disease-related pain that limits physical activity and diminishes health-related quality of life (HRQOL). Digital health technology with wearables and electronic patient reported outcome (ePRO) tools can provide insights into MM HRQoL.

METHODS

In this prospective observational cohort study conducted at Memorial Sloan Kettering Cancer in NY, NY, USA, patients with newly diagnosed MM (n = 40) in two cohorts (Cohort A - patients <65 years; Cohort B - patients ≥65 years) were passively remote-monitored for physical activity at baseline and continuously for up to 6 cycles of induction therapy from Feb 20, 2017 to Sep 10, 2019. The primary endpoint of the study was to determine feasibility of continuous data capture, defined as 13 or more patients of each 20-patient cohort compliant with capturing data for ≥16 h of a 24-hr period in ≥60% of days of ≥4 induction cycles. Secondary aims explored activity trends with treatment and association to ePRO outcomes. Patients completed ePRO surveys (EORTC - QLQC30 and MY20) at baseline and after each cycle. Associations between physical activity measurements, QLQC30 and MY20 scores, and time from the start of treatment were estimated using a linear mixed model with a random intercept.

FINDINGS

Forty patients were enrolled onto study, and activity bioprofiles were compiled among 24/40 (60%) wearable user participants (wearing the device for at least one cycle). In an intention to treat feasibility analysis, 21/40 (53%) patients [12/20 (60%) Cohort A; 9/20 (45%) Cohort B] had continuous data capture. Among data captured, overall activity trended upward cycle over cycle for the entire study cohort (+179 steps/24 h per cycle; p = 0.0014, 95% CI: 68-289). Older patients (age ≥65 years) had higher increases in activity (+260 steps/24 h per cycle; p < 0.0001, 95% CI: -154 to 366) compared to younger patients (+116 steps/24 h per cycle; p = 0.21, 95% CI: -60 to 293). Activity trends associated with improvement of ePRO domains, including physical functioning scores (p < 0.0001), global health scores (p = 0.02), and declining disease burden symptom scores (p = 0.042).

INTERPRETATION

Our study demonstrates that feasibility of passive wearable monitoring is challenging in a newly diagnosed MM patient population due to patient use. However, overall continuous data capture monitoring remains high among willing user participants. As therapy is initiated, we show improving activity trends, mainly in older patients, and that activity bioprofiles correlate with traditional HRQOL measurements.

FUNDING

Grants -National Institutes of HealthP30 CA 008748, Awards - Kroll Award 2019.

Using mobile wearables to establish sleep bioprofiles in newly diagnosed multiple myeloma (MM) patients

8040

Background: Passive monitoring using wearables can objectively measure sleep over extended time periods. MM patients (PTs) are susceptible to fluctuating sleep patterns due to pain and dexamethasone (dex) treatment. In this prospective study, we remotely monitored sleep patterns on 40 newly diagnosed MM (NDMM) PTs while administering electronic PT reported outcome (ePRO) surveys. The study aim was to establish sleep bioprofiles during therapy and correlate with ePROs. Methods: Eligible PTs for the study had untreated NDMM and assigned to either Cohort A – PTs < 65 years or Cohort B – PTs ≥ 65 years. PTs were remotely monitored for sleep 1-7 days at baseline [BL] and continuously up to 6 therapy cycles. PTs completed ePRO surveys (EORTC - QLQC30 and MY20) at BL and after each cycle. Sleep data and completed ePRO surveys were synced to Medidata Rave through Sensorlink technology. Associations between sleep measurement trends and QLQC30 scores were estimated using a linear mixed model with a random intercept. Results: Between Feb 2017 - Sep 2019, 40 PTs (21 M and 19 F) were enrolled with 20 in cohort A (mean 54 yrs, 41-64) and 20 in cohort B (mean 71 yrs, 65-82). Regimens included KRd 14(35%), RVd 12(30%), Dara-KRd 8(20%), VCd 5(12.5%), and Rd 1(2.5%). Sleep data was compiled among 23/40 (57.5%) PTs. BL mean sleep was 578.9 min/24 hr for Cohort A vs. 544.9 min/24 hr for Cohort B (p = 0.41, 95% CI -51.5, 119.5). Overall median sleep trends changed for cohort A by -6.3 min/24 hr per cycle (p = 0.09) and for cohort B by +0.8 min/24 hr per cycle (p = 0.88). EPRO data trends include global health +1.5 score/cycle (p = 0.01, 95% CI 0.31, 3.1), physical +2.16 score/cycle (p < 0.001, 95% CI 1.26, 3.07), insomnia -1.6 score/cycle (p = 0.09, 95% CI [-3.47, 0.26]), role functioning +2.8 score/cycle (p = 0.001, 95% CI 1.15, 4.46), emotional +0.3 score/cycle (p = 0.6, 95% CI -0.73, 1.32), cognitive -0.36 score/cycle (p = 0.44, 95% CI -1.29,0.56), and fatigue -0.36 score/cycle (p = 0.4, 95% CI -1.65, 0.93). No association between sleep measurements and ePRO were detected. Difference in sleep on dex days compared to all other days during the sample cycle period for cohort A was 81.4 min/24 hr (p = 0.004, 95% CI 26, 135) and for cohort B was 37.4 min/24 hr (p = 0.35, 95% CI -41, 115). Conclusions: Our study provides insight into wearable sleep monitoring in NDMM. Overall sleep trends in both cohorts do not demonstrate significant gains or losses, and these trends fit with HRQOL ePRO insomnia responses. Upon further examination, we demonstrate objective differences (younger PTs) in intra-cyclic sleep measurements on dex days compared to other cycle days (less sleep by > 1 hr). For older patients, less variation in sleep profiles was detected during dex days, possibly due to higher levels of fatigue or longer sleep duration. Sleep is an integral part of well-being in the cancer patient. Future studies should continue to characterize sleep patterns as it relates to HRQOL.