Abstract: 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.

Abstract: e13624 Background: Manual abstraction of data from a site’s EHR to pharmaceutical sponsor’s EDC system is labor intensive and inefficient. To reduce the time and effort of this process for data managers (DM), a web-based application, Clinical Trials Data Hub (CTDH), was developed using Design Thinking methods. It extracts and consolidates AE and ConMed data from the EHR and displays it in a user friendly, automated, and consolidated view for easy entry into EDC forms. Methods: Following DT methodology to develop CTDH, we interviewed 12 DMs to identify data entry bottlenecks, and ideated solutions for what is now CTDH. To evaluate CTDH’s value, we built a functioning prototype using Splunk and conducted pilot A/B testing with 6 DMs for 2 use cases (Case I: basic easy to find ConMed linked to the AE, and Case 2: complex, where the ConMed linked to the AE was buried in a 33-page document) using their current workflow (A) versus CTDH (B) where a 5-minute training of CTDH occurred prior to testing. We hypothesized that CTDH would outperform current workflows across 3 primary outcomes: 1) correct data identification, 2) time to identify data, and 3) using a modified Single Ease Question (SEQ) rating scale to assess how difficult users found the task. This study was conducted in Jan-Aug 2022 at a large single-center cancer hospital. Results: DMs spend ~20 hours/week on data entry; the majority of which is spent searching the EHR for which ConMeds are associated with an AE. A/B testing results are noted in Table I (see below). Use case 2 showed that DMs using CTDH reduced the time to find one ConMed linked to an AE by 148%, saving ~5 minutes in one task. 5 of 6 participants preferred CTDH to existing clinical systems. Conclusions: Our findings suggest that CTDH allows DMs to 1) identify AE and ConMed data required for EDCs more quickly than in current workflow, 2) identify data more accurately to be entered in sponsor EDCs, and 3) perceive the task of identifying this data to be easier. CTDH reduces the time DMs spend searching clinical systems and documents and has the potential to save meaningful time per patient per study. CTHD will launch into production in May 2023. Digital tool product development using DT methodology has the potential to improve operational efficiency and the clinical staff user experience. This is particularly important in an industry that has struggled with burnout, cost containment, and high turnover. [Table: see text]

Abstract: Consent processes are critical for clinical care and research and may benefit from incorporating digital strategies. We compared an electronic informed consent (eIC) option to paper consent across four outcomes: (1) technology burden, (2) protocol comprehension, (3) participant agency (ability to self-advocate), and (4) completion of required document fields. METHODS: We assessed participant experience with eIC processes compared with traditional paper-based consenting using surveys and compared completeness of required fields, over 3 years (2019-2021). Participants who consented to a clinical trial at a large academic cancer center via paper or eIC were invited to either pre-COVID-19 pandemic survey 1 (technology burden) or intrapandemic survey 2 (comprehension and agency). Consent document completeness was assessed via electronic health records. RESULTS: On survey 1, 83% of participants (n = 777) indicated eIC was easy or very easy to use; discomfort with technology overall was not correlated with discomfort using eIC. For survey 2, eIC (n = 262) and paper consenters (n = 193) had similar comprehension scores. All participants responded favorably to at least five of six agency statements; however, eIC generated a higher proportion of positive free-text comments ( P 〈 .05), with themes such as thoroughness of the discussion and consenter professionalism. eIC use yielded no completeness errors across 235 consents versus 6.4% for paper ( P 〈 .001). CONCLUSION: Our findings suggest that eIC when compared with paper (1) did not increase technology burden, (2) supported comparable comprehension, (3) upheld key elements of participant agency, and (4) increased completion of mandatory consent fields. The results support a broader call for organizations to offer eIC for clinical research discussions to enhance the overall participant experience and increase the completeness of the consent process.

Abstract: 2066 Background: eConsent was developed to digitize the research participant consenting experience with an educational engagement model. The eConsent platform tiers consent document content in an easy-to-navigate format, using videos, images, and access to supplementary information. We hypothesize that enhancing the consenting experience improves participant engagement and comprehension. Methods: Here we present two projects: 1) qualitative assessment of patient engagement in the eConsent process using a standardized 5-question survey sent to all patients who used it during 9 months in 2019, and 2) a report of our preliminary findings from exempt protocol, Assessing Participant Engagement and Protocol Education in the Consent Process (X19-055) that quantitatively compares paper and electronic consenting and a) assesses patient agency and b) tests comprehension of key consent elements in 2 protocols: Storage and Research Use of Human Biospecimens (06-107) and Genomic Profiling in Cancer Patients (12-245). Results: 1) 940 patients completed the qualitative experience survey (27% response). Most respondents (777; 83%) indicated that electronic consenting was very easy (371) or easy (406) to use. Only 25 (3%) said electronic consenting was somewhat difficult to use, 3 indicated it was difficult (0.3%), and 64 were neutral. Most (896; 95%) recommended electronic consenting to other MSK patients. Those who reported a 1 unit increase in technology discomfort, only reported a .48 unit increase in eConsent discomfort ( P 〈 .001). 2)Quantitative 10-question electronic tests were sent to each patient’s portal account within 72h after consenting via paper or eConsent to protocols 06-107 and 12-245. To date, for 06-107: 18 paper consenters completed the test with a score of 76% vs 23 eConsent users who scored 80%. For 12-245: 43 paper consenters scored 69% vs 13 eConsent users scoring 80%. Scores are a surrogate marker for patient comprehension and show that 12-245 protocol participants’ average testing scores are higher when participants are consented with eConsent vs paper (P 〈 .01). 06-107 protocol participants’ average test scores are trending toward eConsent improving patient understanding ( P= .11). We will follow this trend as our sample size increases to a total of 500 participants. Patient agency questions received favorable responses from most patients (100%-84%). Conclusions: eConsent enhances participant engagement and understanding and does not impose a digital burden on participants.

Abstract: Introduction The current standard to assess chemotherapy tolerability relies on patient self-reporting. However, as the sole mechanism of managing symptom burden, this may be inconsistent and fraught with bias. Mobile wearable health devices have the ability to monitor and aggregate objective activity and sleep data over long periods of time, but have not been systematically used in the oncology clinic. The aim of the study was to assess whether the use of mobile wearable technology establishes patterns of "sleep" and "wake" states in newly diagnosed Multiple Myeloma (NDMM) patients receiving therapy, and whether these patterns differ over time. Methods Patients presenting to the myeloma clinic at Memorial Sloan Kettering Cancer Center (MSKCC) with a new diagnosis of Multiple Myeloma and smart phone or tablet (iOS or Android) compatible with the Garmin Vivofit device were offered to participate in a mobile wearable bio-monitoring study. All eligible participants were required to receive primary chemotherapy treatment at a MSKCC facility. Treatment was determined by physician. NDMM patients were assigned to one of two cohorts (20 in each; Cohort A - patients 〈 65 years; Cohort B - patients ≥ 65 years). Patients were given Garmin Vivofit devices and asked to download a Garmin Vivofit application and Medidata electronic patient reported outcome (ePRO) application on their phone or tablet. Patients were bio-monitored for physical activity and sleep during baseline period (1-7 days prior to chemotherapy initiation) and continuously up to 6 cycles of chemotherapy. Additionally, patients completed mobile ePRO questionnaires [(EORTC - QLQC30 and MY20) and brief pain inventory scales (BPI)] using the Medidata application at baseline and after each induction cycle. Activity, sleep data, and completed ePRO questionnaire data were automatically synced or transferred to Medidata Rave database through Medidata Sensorlink technology. In this abstract, we report initial results on prospective collection of activity measurements. Additional data from the health-related quality of life questionnaires and clinical outcomes will be presented at later date. Results Between February 2017-March 2018, 37 patients (19 males and 18 females) enrolled onto the study, with 20 in cohort A and 17 in cohort B. The mean age was 55 years (range 41-64) for cohort A and 72 years (range 65-82) for cohort B. Treatment regimens included Carfilzomib/Revlimid/Dexamethasone 14(38%), Velcade/Revlimid/Dexamethasone 10(27%), Daratumumab/Carfilzomib Revlimid/Dexamethasone, 7(19%), Cyclophosphamide/Velcade/Dexamethasone 3(8%), Revlimid/Dexamethasone 2(5%), and Velcade/Revlimid/Dexamethasone-Lite 1(3%). Twenty-four patients have completed the trial, and 7 remain active. Six patients came off-study due to the following reasons: lost devices (n=4), intolerable rash during cycle 3 (n=1), and incompletion of baseline activity (n=1). Three patients were excluded for incomplete data sets with no baseline data collection at the time of analysis. Fifteen patients were available for data review including 10 in cohort A and 5 in cohort B. Mean activity for cohort A was 6,437 steps/24 hr period (1,002 - 12,754) versus for cohort B was 3,218.37 steps/24 hr period (387 - 6,155) (p 〈 0.05). In comparing pre- and post-therapy, overall mean activity for cohort A increased from 5,995 to 6,513 steps/24 hr, 8.6% increase (p=0.78), and for cohort B mean activity increased from 2,249 to 3,420 steps/24 hr, a 52% increase (p=0.2140). We assessed short term effects therapy initiation had on activity for NDMM patients by comparing percent changes in activity (steps/24 hrs) from baseline period to cycle 1 period. We found 3 patients had a 〉 100% increase, 1 patient had 50-100% increase, and 11 patients had within +/- 50% change in activity from baseline. Conclusion Electronic mobile wearable device monitoring in symptomatic NDMM patients may be a useful tool to assess a patient's overall wellness and health as they are receiving chemotherapy. For three patients, we were able to capture a dramatic increase in activity after initiation of treatment. Overall activity in the elderly NDMM patients is decreased compared to younger patients. Mobile wearable monitoring may be an even more useful strategy for tracking elderly and unfit patients that are more prone to side effects, where the balance of response versus quality of life is paramount. Figure. Figure. Disclosures Mailankody: Physician Education Resource: Honoraria; Janssen: Research Funding; Takeda: Research Funding; Juno: Research Funding. Hassoun:Oncopeptides AB: Research Funding. Lesokhin:Squibb: Consultancy, Honoraria; Serametrix, inc.: Patents & Royalties: Royalties; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Takeda: Consultancy, Honoraria. Smith:Celgene: Consultancy, Patents & Royalties: CAR T cell therapies for MM, Research Funding. Shah:Amgen: Research Funding; Janssen: Research Funding. Landgren:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Research Funding; Pfizer: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy. Korde:Amgen: Research Funding.

Abstract: 1514 Background: Based on our previous research with patient satisfaction for electronic consenting (95% of 940 respondents would recommend it another patient), we hypothesized that telemedicine (telemed) would be received as well as or better than in-person clinical research (CR) consent encounters for complex early-phase clinical trial (Phase I-II) and clinical genetic consent discussions by patients. Oncologist experiences to date have shown that telemed works well for uncomplicated clinical scenarios, but its performance alongside increased care complexity is less clear from the patient perspective. Methods: We conducted a one-time survey of adult patients having a telemed consent visit between 8/31/21 and 2/13/22 and an in-person clinic visit. Nine CR specific questions covered visit preference and empowerment across 6 high value consent agency domains. Results: 513 patients completed the survey and consented across 96 Clinical trials (CT), including genetic, therapeutic, diagnostic, and quality of life. Consent discussions were performed by 75 clinicians and 41 non-clinicians, with the majority (64%) for clinical genetic and Phase I-II CTs. Most patients (52%) preferred telemed over in-person clinic visits (19%) when all visit related factors (time, cost, convenience, quality of care, healthcare team interaction) were considered ( P 〈 .05) (Table). Comparing their last in-person visit with telemed, patients reported feeling either less stressed/overwhelmed (16%) for their consent discussion or about the same (39%) using telemed, and 6% were more stressed ( P 〈 .05). Patients expressed equal comfort taking agency-supported action across 6 domains regardless of consent setting. Conclusions: Electronic consenting via telemed is the preferred method for consent in complex early-phase clinical trials when all visit factors are considered and performs as well across 6 key agency domains when compared with in-person visits. Telemed does not contribute additional stress to consent appointments for most patients and performs well across complex clinical genetic and Phase I-II clinical trial discussions. Our findings suggest telemed and electronic consent should be offered as an option for patients throughout their treatment continuum. Beyond MSK, our data support a broader call for organizations to offer telemed platforms for CT discussions to increase overall patient satisfaction and potentially increase participation. [Table: see text]

Abstract: Introduction The current standard to assess chemotherapy tolerability and health related quality of life (HRQOL) relies on patient (PT) self-reporting. Continuous passive monitoring using mobile wearable devices can objectively aggregate and monitor "activity" over long periods of time without potential reporting bias. Due to the nature of the disease, multiple myeloma (MM) PTs are often ridden with bone disease and pain, thereby limiting activity while impacting HRQOL. In this prospective clinical study, we enrolled 40 newly diagnosed MM PTs and remotely monitored their activity (steps/24 hrs) while administering electronic PT reported outcome (ePRO) surveys at baseline (BL) and through induction therapy. The study aim was to assess whether wearables can establish patterns of physical activity while receiving therapy and how these activity bioprofiles correlate with HRQOL outcomes. Methods PTs were eligible for the study if they had newly diagnosed MM, not having received any systemic therapy, and if they owned a device (iOS or Android) compatible with Garmin Vivofit® (GV) device. Regimens were determined by treating physicians. PTs were given GV® devices and asked to download a GV® application and Medidata ePRO app. PTs were assigned to either Cohort A - PTs & lt;65 years or Cohort B - PTs ≥ 65 years. PTs were remotely monitored for physical activity and sleep at BL (1-7 days prior to therapy) and continuously up to 6 cycles of therapy. Additionally, PTs completed ePRO surveys [(EORTC - QLQC30 and MY20) at BL and after each cycle. Activity data and completed ePRO surveys were synced to Medidata Rave through Medidata Sensorlink technology. Responses at the end of cycle 6 were scored by IMWG response categories: & gt; VGPR Responders (Res) vs. & lt; PR Sub-responders (Sub-Res). 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. A Wald-test was used to compute p-values for the significance of association. Results Between Feb 2017 and 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). Treatment regimens included KRd 14(35%), RVd 12(30%), Dara-KRd 8(20%), VCd 5(12.5%), and Rd 1(2.5%). Activity bioprofiles were compiled among 24/40(60%) PTs: 14 full sets (7/7 cycle periods) and 10 partial sets [1 PT - 2/7(28.5%) cycle periods; 2 PTs - 3/7(42.8%); 1 PT - 4/7(57.1%); 2 PTs- 5/7(71.4%); 4 PTs 6/7(85.7%)]. PT activity increased over time by 179 steps/24 hrs per cycle (p=0.001, 95% CI: 68-289) for the entire study. Mean activity pre- vs. post- for cohort A was 6,041 vs. 7,266 steps/24 hrs, respectively with an increase of 116 steps/24 hrs per cycle (p=0.2, 95% CI: -60-293), and for cohort B 2,984 steps/24 hrs vs. 5,007 steps/24 hrs with an increase of 260 steps/24 hrs per cycle (p & lt;0.001, 95% CI: 154-366) (fig 1). There was improvement in activity levels in both Res 169 steps/24 hrs per cycle (p= 0.02, 95% CI: -31-305) and Sub-Res 212 steps/24 hrs per cycle (p= 0.01, 95% CI: 53-371) groups. PTs reported improvement in ePRO MY20 disease burden symptoms over time, -1.6 score/cycle (p=0.001, 95% CI -2.6- -0.6). There was no observed change in time over self-body image (p=0.5), while PTs reported worsening of future perspective, -2.8 score/cycle (p & lt;0.001, 95% CI -2.6- -0.6). Similarly, there was an observed improvement of QLQC30 global health status, + 1.7 score/cycle (p=0.02, 95% CI: 0.3-3.1) and physical functioning over time +2.1 score/cycle (p & lt;0.001, 95% CI: 1.2-3.0). An association between increased PT activity (steps/24 hrs) and decreased symptom burden was observed (p=0.04). Increased PT activity was also associated with improved global health status (p=0.02) and physical functioning (p & lt;0.001) scores. Conclusion Our study demonstrates that passive wearable monitoring can successfully capture PT activity in newly diagnosed MM, and that PT activity bioprofiles correlate well with traditional HRQOL measurements. Of clinical relevance, our study shows that activity bioprofiles improve with therapy, regardless of depth of response. Significant gains in activity were attributable to the older cohort, suggesting a greater functional impact at BL in this population. Future studies are needed to elucidate how mobile wearables may aid the clinician in passive monitoring of therapy tolerability in the outpatient setting. Figure 1 Disclosures Korde: Amgen: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees. Mailankody:PleXus Communications: Honoraria; Juno Therapeutics, a Bristol-Myers Squibb Company: Research Funding; Allogene Therapeutics: Research Funding; Janssen Oncology: Research Funding; Takeda Oncology: Research Funding; Physician Education Resource: Honoraria. Hassoun:Novartis: Consultancy; Celgene: Research Funding; Takeda: Research Funding. Lesokhin:Takeda: Consultancy, Honoraria; GenMab: Consultancy, Honoraria; Serametrix Inc.: Patents & Royalties; BMS: Consultancy, Honoraria, Research Funding; Janssen: Research Funding; Juno: Consultancy, Honoraria; Genentech: Research Funding. Lendvai:Janssen: Current Employment. Smith:Bristol Myers Squibb: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics: Consultancy; Precision Biosciences: Consultancy. Hultcrantz:Daiichi Sankyo: Research Funding; GSK: Research Funding; Intellisphere LLC: Consultancy; Amgen: Research Funding. Shah:Physicians Education Resource: Honoraria; Celgene/BMS: Research Funding. Shah:Amgen: Research Funding; Janssen Pharmaceutica: Research Funding. Giralt:Jazz: Research Funding; Kite: Research Funding; Actinuum: Research Funding; CSL Behring: Research Funding; Pfizer: Research Funding; Quintiles: Research Funding; Janssen: Research Funding; Amgen: Research Funding; Sanofi: Research Funding; Celgene: Research Funding; Adienne: Research Funding. Landgren:Adaptive: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Glenmark: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Research Funding; Janssen: Consultancy, Honoraria, Other: Independent Data Monitoring Committees for clinical trials, Research Funding; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; Takeda: Other: Independent Data Monitoring Committees for clinical trials, Research Funding; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Glenmark: Consultancy, Honoraria, Research Funding; Juno: Consultancy, Honoraria; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria; Merck: Other; Karyopharma: Research Funding; Binding Site: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Cellectis: Consultancy, Honoraria; Juno: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Merck: Other.