Adaptation and Evaluation of a Symptom-Monitoring Digital Health Intervention for Patients With Relapsed and Refractory Multiple Myeloma: Pilot Mixed-Methods Implementation Study

Abstract

Background Relapsed and refractory multiple myeloma (RRMM) is a bone marrow cancer that requires systemic treatment, which often results in severe symptom burden. Recent studies have found that electronic patient-reported outcome (ePRO) interventions implemented in the clinic setting have had positive outcomes for other oncology populations. Evidence of the efficacy of a similar approach is lacking for patients with RRMM. Objective Recent recommendations for digital health interventions call for the publication of descriptions of iterative development processes in order to improve reproducibility and comparability. This study is an implementation pilot aiming to evaluate the acceptability and appropriateness of an ePRO intervention for patients with RRMM and to explore its impact on clinic workflow. Methods A total of 11 patients with RRMM were recruited from the John Theurer Cancer Center in Hackensack, New Jersey. Patients used a mobile app to report on 17 symptoms at 4 sessions, each a week apart. Patients could also report symptoms ad hoc. When reports met predefined thresholds, the clinic was alerted and patients received automated guidance. Study end points were assessed using qualitative and quantitative methods. Results A total of 9 patients (mean age 69.7 years) completed the study. Overall, 83% (30/36) of weekly sessions were completed. Patients found the frequency and time required to complete reporting acceptable. All patients agreed that the app was easy to use and understand. Providers felt the alerts they received required refinement. Patients and providers agreed it would be beneficial for patients to report for longer than 4 weeks. Patients felt that the training they received was adequate but contained too much information for a single session. All patients found the symptoms tracked to be appropriate; providers suggested shortening the list. All patients understood how to use the app for weekly reporting but had confusion about using it ad hoc. Providers felt the ad hoc feature could be removed. Neither patients nor providers viewed the in-app data reports but agreed on their potential value. Patients reported benefitting from symptom reporting through increased awareness of their symptoms. Clinic staff reported that app alerts were too numerous and redundant. They had difficulty responding to alerts within their existing workflow, partially because the data were not integrated into the electronic medical record system. Conclusions Overall, the intervention was found to be acceptable and appropriate for patients with RRMM. Points of friction integrating the intervention into the clinic workflow were identified. Clinic staff provided recommendations for addressing these issues. Once such modifications are implemented, ePRO data from patients with RRMM could be used to inform and improve clinical research and care. This study underlines the importance of an iterative approach to implementation that includes all stakeholders in order to ensure successful adoption.

Evaluating Barriers and Facilitators to the Uptake of mHealth Apps in Cancer Care Using the Consolidated Framework for Implementation Research: Scoping Literature Review

“…The 91 studies included for analysis describe 73 mHealth apps, of which 29 (40%) were designed for supporting any cancer types [ 9 , 12 , 47 , 48 , 50 , 54 , 56 - 58 , 65 - 69 , 71 , 73 , 74 , 76 , 81 - 83 , 85 - 88 ,​ 90 , 91 , 93 , 105 - 109 , 111 - 113 , 116 , 117 , 121 , 122 , 125 , 128 ], followed by 17 (23%) on breast cancer [ 49 , 64 , 70 , 72 , 77 ,​ 78 , 80 , 95 - 98 , 103 , 114 , 115 , 118 , 124 , 126 , 129 - 131 ], 5 (7%) [ 52 , 53 , 63 , 99 , 101 , 102 ] on gastric and colon cancer types, 3 (4%) [ 75 , 79 , 89 , 119 ] on lung cancer, 3 (4%) on thyroid cancer [ 84 , 100 , 123 ] type, and 2 (3%) on hematological cancer types [ 9 , 55 , 56 ,…”

“…Regarding the intervention source, the literature reported that participating in the development phase increased the likelihood of later embracing the technology. Most analyzed apps have been developed collaboratively [8,53,[55][56][57][58]62,72,73,75,77,87,101,103,104,107,109,114,[116][117][118]122,127,131], often including HCPs, potential patients, and external technology partners responsible for actual software development [56,66,71,78,89,97,105,125,127,129,130]. For instance, the development of eOncoSalud was carefully planned during a series of 7 nominal consensus meetings involving a wide range of stakeholders [56].…”

“…mHealth users can be patients, clinicians, a broader pool of health care professionals (HCPs), or different combinations of users. Most commonly, apps are designed for both patients and clinicians (42/73, 58%)[12,16,[48][49][50][51][52][53][54][55]58,59,61,63,66,67,69,75,77,[84][85][86]89,[91][92][93][94]96,98,100,101,[104][105][106]108,110,[112][113][114][115][116]119,120,[122][123][124][125][127][128][129]…”

Digital Health for Patients With Multiple Myeloma: An Unmet Need

“…One small study of 11 patients with relapsed or refractory MM examined the value of patientreported symptoms after one to three prior lines of therapy. 41 Patients were asked to report symptom presence, severity, and interference via the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) weekly or, if desired, spontaneously via cell phone app or web portal. At predefined thresholds, patients received guidance and HCPs were alerted.…”

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

“… 5 , 6 Digital health technology applications may bridge the gap between academic lengthy PRO questionnaires and real-time monitoring of HRQoL. Current strategies on integrating digital health technology applications in the care of MM patients include digital health coaching and actigraphy during autologous stem cell transplantation, 7 , 8 artificial intelligence-based symptom monitoring, 9 and medication adherence when transitioning subcutaneous treatments to oral-based treatments. 10 To our knowledge, actigraphy has not yet been studied to evaluate physical activity trends in newly diagnosed symptomatic MM patients receiving induction chemotherapy as it correlates with HRQoL.…”