Developing a Mobile Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events Administration System to Capture Postradiation Toxicity in Oncology: Usability and Feasibility Study

Abstract

Background Accurate self-reported symptomatic toxicity documentation via the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) is essential throughout cancer treatment to ensure safety and understand therapeutic efficacy. However, the capture of accurate toxicities from patients undergoing radiation therapy is challenging because this is generally provided only at the time of scheduled visits. Objective This study seeks to establish the usability and feasibility of a mobile PRO-CTCAE Administration System (mPROS) to capture toxicities related to radiation therapy. Methods English-speaking adult patients who were undergoing radiation therapy for cancer were enrolled and given a brief demonstration of the Say All Your Symptoms (SAYS) and Symptom Tracking Entry Program (STEP) interfaces of the mPROS app, followed by a patient-use phase where patient actions were observed as they navigated mPROS to enter toxicities. Patient feedback was captured via a semistructured interview and brief questionnaire. Results We enrolled 25 patients (age: mean 60.7 years; females: n=13, 52%; White patients: n=13; 52%; non-Hispanic patients: n=19, 76%; college graduates: n=17, 68%). Patients almost equally preferred the SAYS (n=14, 56%) or STEP (n=11, 44%) interfaces, with 21 patients (84%) agreeing that they would use mPROS to report their symptoms to their health care team and 19 patients (76%) agreeing that they would recommend mPROS to others. Conclusions The mPROS app is usable and feasible for facilitating the patient reporting of radiation therapy–related symptomatic toxicities. A revised version of mPROS that incorporates patient input and includes electronic health record integration is being developed and validated as part of a multicenter trial.

Mobile applications in radiation oncology—current choices and future potentials

“…2021 [ 37 ] ePROs in follow-up 8 weeks after RT (biweekly questionnaires) (LogPAL developed by Northwell Health Inc, Lake Success, USA) Single arm 38 Head and neck cancer patients receiving RT/RCT (curative) Feasible, regularly used, and accepted (73.2% questionnaires completed) Yes (iOS) Underwood et al. 2022 [ 39 ] Assessment of patient-related outcome after RT (“say all your symptoms” and symptom tracking CTCAE; mPROS app) Single arm 25 Patients receiving RT to head and neck, breast and pelvic areas Usable and feasible tailored assessment for patients to report symptomatic toxicities No Wöller et al. 2022 a [ 46 ] Follow-up app (Myoncare app, Oncare) Single arm 38 Prostate and breast cancer receiving radiotherapy (curative) For breast cancer patients: Interest in new communication with HCP (preliminary results) Yes (iOS and Andoid) Wong et al.…”

“…As described in Tables 1 and 2 in detail, most patient-centered studies were single-arm studies or descriptive in nature. Overall, those studies showed high compliance [ 20 , 31 , 32 ], high acceptance [ 22 , 23 , 29 , 33 – 37 ], effectiveness [ 28 ], satisfaction [ 31 ], usability [ 29 , 38 , 39 ], simplicity [ 38 ], and feasibility [ 12 , 25 , 32 , 35 – 37 , 39 ]. The randomized two-arm studies analyzing the impact of an app on the standard of care showed a significant reduction in adverse effects in head and neck cancer patients [ 13 , 15 , 17 ] and prostate cancer patients undergoing RT [ 19 ].…”

“…In the randomized study of Sundberg et al., median age was 69 years [ 19 ]. Mean ages of around 60 years were described by others [ 13 , 31 , 39 ]. In line with the demographic transformation and an increase in personal usage of smartphones in the age group ≥ 65 years [ 54 ], we believe that the elderly population benefits from technical innovations and should not be excluded.…”

Development and evaluation of a data-driven integrated management app for perioperative adverse events: protocol for a mixed-design study

“…Recent studies have investigated the potential advantages of using mobile technologies in healthcare delivery and have found them to be promising 26. Various forms of healthcare information technology have been employed in the detection, management and follow-up of AEs 27 28. Text mining has been used to detect AEs based on data-driven methods, including spontaneous warning and reporting systems 29.…”

“… 26 Various forms of healthcare information technology have been employed in the detection, management and follow-up of AEs. 27 28 Text mining has been used to detect AEs based on data-driven methods, including spontaneous warning and reporting systems. 29 Commonly, AEs are characterised as a text classification problem, where a piece of text, either an entire document or its part, is mapped to one or more predefined classes that correspond to a type of AE or its property.…”