No abstract

PURPOSE: 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.

No 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]

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.