Breast cancer is the most common cancer in women and a leading cause of cancer death worldwide [1]. Most patients with early-stage disease receive multimodal systemic therapy, and treatment has shifted from uniform protocols toward regimens tailored to tumor biology and individual risk [2]. This has improved outcomes but lengthened and intensified the treatment pathway: months of chemotherapy, surgery, radiotherapy, and often years of endocrine therapy. The associated symptom burden is substantial and is not confined to the treatment phase itself, with impairments in fatigue, sleep, and emotional and social functioning still reported years after diagnosis [3,4]. Symptom burden also has consequences for treatment delivery: delayed initiation of neoadjuvant and adjuvant chemotherapy is associated with worse survival [5,6], and patients’ own accounts show that the path toward such delays is shaped by how early symptoms and concerns are recognized and acted upon [7]. Maintaining tolerability is therefore not only a quality-of-life goal but part of delivering the planned treatment as intended.

Patient-centered care therefore requires attention not only to tumor-directed outcomes but also to patients’ preferences, values, symptoms, functioning, and lived treatment experience [8]. Patient-reported outcome measures (PROMs) provide a standardized way to capture symptom burden and health-related quality of life (HRQoL) domains that may otherwise remain underrecognized in routine oncology visits [9], covering physical, emotional, and social experiences across the cancer trajectory and helping clinical teams identify unmet needs, assess treatment tolerability, and tailor supportive interventions [3,10-15]. Collected electronically (electronic patient-reported outcomes [ePROs]), they can enable more timely monitoring, support shared decision-making, and provide structured symptom information to patients and clinicians [4,16]. The mechanisms proposed for these effects—earlier detection of deterioration, earlier supportive intervention, and better-informed consultations—depend less on the act of collecting data than on what follows once a symptom has been reported [15]. PROMs and ePROs are recommended in international clinical practice guidelines [17] and have been associated with improved symptom control and quality of care; survival effects have been reported in some trials and populations but have not been consistent across larger multicenter evaluations [13,16,18].

Despite this evidence base, important implementation questions remain. Many ePRO systems focus on scheduled symptom or HRQoL assessment, whereas fewer systems combine patient-facing monitoring with automated triage logic, tailored advice, and structured escalation to the treating care team [19,20]. Systems also differ in ways that are rarely stated explicitly: whether a predefined threshold triggers a response, who receives the signal, whether the response itself is specified rather than left to discretion, and whether it is documented. These design choices plausibly account for part of the heterogeneity in reported effects [20]. Such real-time, care team–integrated escalation is particularly relevant for early breast cancer, where the therapeutic goal differs from the metastatic setting: treatment is intensive but time-limited, dose- and schedule-dependent, and the priority is to complete it as planned [2,5,6].

Evidence on ePRO interventions in neoadjuvant and adjuvant early breast cancer remains limited. It is therefore uncertain whether benefits observed in other oncology populations translate to early breast cancer and also uncertain whether real-time triage with structured care-team escalation adds benefit beyond scheduled ePRO assessment and app-based educational support. This uncertainty justified an active digital comparator: both trial arms received the ENABLE app, educational and organizational support, and scheduled HRQoL assessments, while only the intervention arm received weekly EuroQol Visual Analogue Scale (EQ-VAS) screening, automated symptom triage, and care-team escalation. Most previous trials compared ePRO monitoring with usual care, so their effects may reflect the combined contribution of structured contact, digital information, and symptom reporting rather than the triage step alone [20,21]. An active comparator sets a higher bar and attenuates the expected effect size, but it isolates the component that would have to be built, staffed, and maintained if such a workflow was adopted in routine care.

The ENABLE trial was designed to test the incremental value of this reactive digital triage model during systemic therapy for early breast cancer in a real-world oncology setting. We tested the hypothesis that adding weekly EQ-VAS screening, rule-based digital triage, and care-team escalation to app-based ePRO monitoring and educational support would improve Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29) HRQoL trajectories, analyzed as an 8-measure end-point family, compared with app-based ePRO monitoring and educational support alone. Treatment satisfaction assessed with the Patient Satisfaction Questionnaire Short Form (PSQ-18) was evaluated as a secondary end point. Secondary objectives were to describe intervention delivery and engagement in the intervention arm and to examine time to clinically meaningful HRQoL deterioration.

This trial is reported in accordance with the CONSORT (Consolidated Standards of Reporting Trials) 2025 statement (Checklist 1) [22] and the CONSORT-eHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) extension for web-based and mobile health interventions (Checklist 2) [23]. We conducted a multicenter, randomized, unblinded, parallel-group, superiority trial (1:1) in adults initiating adjuvant, neoadjuvant, or postneoadjuvant systemic therapy for early breast cancer. Participants were centrally randomized in a 1:1 ratio to reactive patient-reported outcome assessment (RPA) or control.

The trial was planned and ethics-approved as a 3-center trial at German academic breast-cancer centers (Heidelberg, Tübingen, and Mannheim). Recruitment and follow-up took place at Heidelberg and Tübingen. Mannheim University Hospital was ethics-approved and listed in the public registry (Mannheim 2021‐544, approved March 16, 2021) but ultimately enrolled no participants and was therefore not included in the analyses. Eligible sites were academic breast-cancer centers with an established adjuvant or neoadjuvant chemotherapy service and the infrastructure to operate the ENABLE platform; the intervention was delivered by trained clinical study teams (study nurses and treating oncologists) at each site. Recruitment was clinic-based and closed to enrolled participants. Participants were consecutively screened and enrolled before treatment start. The study was not an open-access web trial with anonymous self-enrollment. Because recruitment and consent occurred through study sites, multiple identities were not a relevant concern in the way typical for open online trials.

The protocol is publicly available (JMIR Research Protocols 2025 [24]), and the trial is registered with the German Clinical Trials Register (DRKS00025611). The trial was registered on July 6, 2021, after enrollment of the first participant on March 22, 2021; registration was therefore retrospective. The delay was administrative and occurred during finalization of the multicenter trial documentation and public registry entry for a 3-center trial; the registry entry lists Heidelberg, Tübingen, and Mannheim, although Mannheim subsequently enrolled no participants. The delay did not involve any change to the trial design or the outcome measures. The trial design and the outcome measures had been prespecified in the submission on August 27, 2020, to the central ethics committee, which approved the trial on October 2, 2020, more than 5 months before the first participant was enrolled (S-658/2020); both dates are recorded in the registry entry itself. The protocol was amended during the trial, most substantially to increase the target sample size in October 2022; the outcome measures were not affected.

The trial was registered retrospectively: the first participant was enrolled on March 22, 2021, and the trial was registered on July 6, 2021 (DRKS00025611); the registry entry lists Heidelberg, Tübingen, and Mannheim, although Mannheim enrolled no participants. In a change from the protocol, recruitment in the palliative setting was not pursued at the study sites because concurrent recruitment to the PRO-B trial (DRKS00024015) was ongoing; the protocol had originally allowed inclusion across settings including palliative therapy. The target sample size was increased by protocol amendment on October 27, 2022, as described under the Sample Size section; this amendment did not affect the outcome measures. The published protocol stated that the primary outcomes would be examined in an intention-to-treat analysis of all randomized individuals. The analysis reported here uses a modified intention-to-treat (mITT) population: 4 participants in whom metastatic disease was identified after randomization were excluded, and the population comprises 487 of the remaining 601 participants who provided at least 1 postrandomization assessment of the primary end point. The composition of the analysis population was determined at the analysis stage; neither the ethics-approved protocol nor the published protocol defined an analysis population beyond the sentence quoted earlier. Both the protocol and the registry name treatment satisfaction alongside HRQoL in the statement of the primary end point; treatment satisfaction (PSQ-18) is reported here as a secondary end point, consistent with the protocol’s list of secondary end points and with the instrument, timing, sample-size basis, and analysis strategy it specified (see the Outcomes section). The Benjamini-Hochberg (BH) procedure applied across the 8 PROMIS-29 measures to control the false discovery rate was chosen at the analysis stage and was not described in the protocol. The protocol designated no single PROMIS-29 domain, composite score, or binary global success rule as primary, and the primary end point is therefore reported as an 8-measure family. The protocol named 2 instruments for the assessment of patient satisfaction, the PSQ-18 and the PEF-FB-9 (Fragebogen zur Partizipativen Entscheidungsfindung). The PEF-FB-9 was not administered, and no data on it were collected. Only the PSQ-18 is reported here.

Central ethics approval was obtained from the ethics committee of the Medical Faculty, Heidelberg University (S-658/2020). All recruiting centers obtained local approval: ethics committee at the Medical Faculty of the Eberhard Karls University and University Hospital Tübingen (896/2020BO2) and ethics committee II of Heidelberg University, Medical Faculty Mannheim (2021‐544). The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to enrollment, including consent for publication of anonymized data. Patient data were handled in accordance with the European General Data Protection Regulation (GDPR) and the German Federal Data Protection Act. Patient privacy and confidentiality were maintained throughout the study. No financial compensation was provided to participants. No images or other materials that could identify individual participants are included in this manuscript or its supplementary materials. The ethics committee approvals for this trial, each listing the documents submitted to the committee with their version numbers and dates, are provided as supplementary files.

No patients or members of the public were involved in the design, conduct, reporting, or dissemination plans of this study.

Harms were not systematically ascertained through a predefined adverse-event reporting protocol, consistent with the nonpharmacological, digital nature of the intervention; no separate safety-monitoring instrument was administered. Nonetheless, no intervention-related safety concerns, privacy breaches, or clinically relevant unintended digital effects were identified during the study.

Adults (≥18 years) with histologically confirmed, nonmetastatic, invasive breast cancer (stage I-III) scheduled to initiate systemic therapy in a neoadjuvant, adjuvant, or postneoadjuvant setting were consecutively screened and enrolled before treatment start at the university hospitals of Heidelberg and Tübingen (Germany). Key eligibility criteria were German language proficiency, capacity to provide written informed consent, and access to an internet-enabled device (smartphone, tablet, or computer).

The allocation sequence was computer-generated in R (version 3.6.3; blockrand version 1.5; R Foundation for Statistical Computing) as site-specific block-randomization lists (Heidelberg, Tübingen, and Mannheim; the Mannheim list was not used, as that site enrolled no participants) with random block sizes of 2, 4, 6, and 8, 1:1 allocation, and no stratification or minimization factors. The sequence was generated by the biostatistics unit and implemented by the study IT team in a central web portal; neither unit was involved in participant enrollment or clinical care.

Allocation was concealed by the central system until assignment. Group allocation became visible to participants and site staff only after randomization within the portal.

Study nurses enrolled participants at each site and triggered the central assignment, without access to the random allocation sequence.

Due to the nature of the digital intervention, the trial was unblinded. Participants, clinical teams, outcome assessors, and data analysts were unmasked, and no masking-success assessment was performed.

The ethics-approved protocol named change in HRQoL and treatment satisfaction, measured with PROMIS-29, in its statement of the primary end point; the trial registration (DRKS00025611) names the same 2 constructs without specifying an instrument. The protocol did not, however, provide a confirmatory framework for treatment satisfaction: the only instrument named in that statement was the PROMIS-29, which contains no treatment-satisfaction scale; the protocol’s secondary end points included the between-group comparison of patient satisfaction using the PSQ-18; the sample-size calculation rested on HRQoL measured with PROMIS-29 alone; and the protocol directed that secondary end points be analyzed descriptively and interpreted without confirmatory implication. The primary end point is therefore reported here as change in HRQoL measured with PROMIS-29. Because the protocol did not designate a single PROMIS-29 domain as primary, define a composite PROMIS-29 score, or specify a binary global success rule, the primary HRQoL end point is reported as an 8-measure family comprising the 7 PROMIS-29 domains plus the pain-intensity item. Patient satisfaction was evaluated through a single administration of the PSQ-18 at the conclusion of therapy and is reported as a secondary end point, in line with the protocol’s list of secondary end points and with what it specified operationally. This divergence from the wording of the primary end-point statement and of the registry entry is disclosed here, and no statistical analyses were changed by this clarification.

The PROMIS-29 is a standardized, self-report questionnaire designed to measure key dimensions of HRQoL across various chronic conditions and populations. It includes 7 domains: physical function, anxiety, depression, fatigue, sleep disturbance, ability to participate in social roles and activities, and pain interference. They are assessed by 4 items each, plus 1 item for pain intensity. Domain scores were expressed as T-scores on the standard Patient-Reported Outcomes Measurement Information System metric (mean 50, SD 10; calibrated to the US general population). The EORTC QLQ-C30 and EORTC QLQ-BR45 were administered at the same 6 time points as prespecified secondary HRQoL measures; their full analysis is reported separately, and here, they serve only as an internal directional concordance check against the PROMIS-29 primary end point.

Patient satisfaction was assessed using the self-assessed PSQ-18, an 18-item measure derived from the PSQ-III (RAND Corporation), yielding 7 subscale scores and a global satisfaction score [27]. To better reflect the German health care context and the study’s focus on adverse event management, the Financial Aspects subscale was replaced with a Management of Adverse Events subscale (2 adapted items; details in the study protocol [24]).

The target sample size was 400 (200 per group), derived for the primary end point (change in HRQoL by PROMIS-29), assuming a minimally important difference (MID) of 5 points on the PROMIS T-score metric (SD 10), that is, half an SD, 2-sided α=.05, and 80% power for a repeated-measures (mixed) analysis; the base requirement of n=250 (125 per arm) was inflated by 37.5% to account for anticipated missingness or attrition, yielding the final target n=400. Calculations followed the protocol’s statistical design for the interventional subprotocol. Following a favorable recruitment trajectory, the target was increased to n=600 (ethics committee amendment S-658/2020, approved 27 October 2022); this increase was independent of the interim analysis of the secondary end points and was not informed by its results. The protocol did not designate a single visit as the primary time point; the primary analysis is the longitudinal group×visit comparison across the 6 assessments, with end-of-treatment contrasts interpreted against the 5-point MID.

For each PROMIS-29 domain, Cox proportional-hazards models were fitted with treatment group (RPA vs control) as the sole covariate. Time origin was baseline; follow-up was restricted to the first 6 scheduled visits. Deterioration was defined as a ≥5-point change in the domain T-score consistent with worse health (+5 for symptom domains and −5 for functioning domains), in line with established MIDs [31]. Patients were included if they had a valid baseline and ≥1 postbaseline measurement, and deterioration was possible at baseline (ie, not at ceiling or floor). Participants without an event were censored at last assessment; hazard ratio (HR) <1 favors RPA. To assess robustness to the choice of MID, this analysis was repeated using alternative deterioration thresholds of 3, 5, 7, and 10 T-score points.

Completion of the scheduled outcome questionnaires was analyzed separately from intervention engagement. This analysis concerned the 6 fixed HRQoL assessment visits completed by participants in both trial arms and was not intended to measure adoption of the RPA intervention itself. Complete discontinuation of scheduled outcome assessment was defined as the first missed visit from which all subsequent scheduled questionnaires remained unanswered. Patients who continued to complete at least 1 scheduled questionnaire until the end of follow-up were right-censored at their last observation. Within the intervention arm, delivered intervention dose was summarized separately: the number of patients triggering at least 1 reactive symptom alert, alerts per patient, the proportion of acknowledged alerts followed by documented care-team contact, and adherence to the weekly EQ-VAS self-assessment.

Recruitment ran from March 22, 2021, to September 8, 2023, ending after the amended target sample size of 600 had been reached (605 patients were centrally randomized); the last study visit occurred in February 2024.

In total, 1066 patients were assessed for eligibility (Figure 3). Of these, 461 were excluded: 302 did not meet inclusion criteria, 72 declined participation, and 87 were eligible but enrolled in a competing trial. The remaining 605 patients were centrally randomized. A total of 4 participants were retrospectively excluded after randomization due to newly identified metastatic disease, yielding 601 patients in the relevant allocation set (Figure 3). A total of 57 participants discontinued after randomization (withdrawal of consent or no activation of the study app; RPA: n=25; control: n=32). A further 57 participants had no postrandomization HRQoL assessment (RPA: n=33; control: n=24) and are shown in Figure 3 as lost to follow-up for the primary outcome. The mITT population for HRQoL analyses comprised 487 participants (RPA: n=242; control: n=245).

Completion of scheduled outcome questionnaires was examined as a process measure across the 6 fixed HRQoL assessment visits. Time to complete discontinuation of scheduled outcome assessment was analyzed using Kaplan-Meier curves and univariable Cox models. Continued completion of scheduled questionnaires was similar between trial arms, with no evidence of a between-group difference in time to discontinuation (HR 1.06, 95% CI 0.75‐1.48; P=.75; Figure S1 in Multimedia Appendix 1). At 26 weeks, corresponding to the median treatment duration in the sample, estimated continued completion was 78.2% in the control group and 79.8% in the RPA group. These estimates refer to scheduled outcome-questionnaire completion in both groups and are distinct from weekly EQ-VAS adherence and reactive-alert engagement in the intervention arm.

Among the 487 patients included in the mITT population, 329 (67.6%) were scheduled to receive neoadjuvant chemotherapy at the time of enrollment, 115 (23.6%) adjuvant chemotherapy, 33 (6.8%) adjuvant endocrine therapy, and 10 (2.1%) postneoadjuvant therapy. Patient characteristics for the control and intervention groups are detailed in Table 1.

No relevant differences in age, therapy situation, type of systemic therapy, Eastern Cooperative Oncology Group status at enrollment, trial site, tumor characteristics, or other important confounders were observed between the 2 study groups following randomization.

Table 2 presents the global likelihood-ratio tests derived from the linear mixed-effects models for the 8 PROMIS-29 measures. These models address whether the RPA intervention altered the longitudinal trajectory of each PROMIS-29 measure across the 6 scheduled assessments. After BH adjustment across the 8-measure family, statistically significant trajectory differences were observed for anxiety, fatigue, and social participation. Physical function, depression, sleep disturbance, pain interference, and pain intensity did not show adjusted trajectory differences. Because no binary global success rule was prespecified for the family, these findings are reported as domain-specific trajectory differences rather than as a single global statement that the primary end point was achieved.

Figure 4 presents the estimated marginal means from the post-hoc linear mixed-effects models for each PROMIS-29 subscale over all study visits (regression coefficients in Table S3 in Multimedia Appendix 1). By the sixth visit, the RPA group showed more favorable trajectories for 5 of 8 domains, with the largest effects observed for fatigue (b=–3.90, 95% CI –5.77 to –2.03) and social participation (b=2.84, 95% CI 1.18‐4.49). No relevant group differences were observed for pain interference and pain intensity. The marginal R² ranged from 0.8% (sleep disturbance and pain intensity) to 6.5% (physical function). Although statistically significant, the between-group differences at the final visit for all 3 affected domains remained below the prespecified 5-point MID—anxiety −2.10 (95% CI −3.65 to −0.55), fatigue −3.90 (95% CI −5.77 to −2.03), and social participation 2.84 (95% CI 1.18 to 4.49; Table S3 in Multimedia Appendix 1)—indicating statistically detectable but subthreshold effects on these domains. The treatment effect was consistent across therapy pathways: no group×therapy-pathway interaction reached statistical significance for any PROMIS-29 domain (all P≥.22), although power to detect interaction was limited in the small adjuvant-endocrine and postneoadjuvant subgroups.

No intervention-related safety concerns, privacy breaches, or clinically relevant unintended digital effects were identified during the trial. Harms were not ascertained through a predefined adverse-event reporting protocol (see Harms in the Methods section).

In this multicenter randomized trial, adding real-time ePRO monitoring with rule-based digital triage and care-team integration to app-based support improved selected PROMIS-29 trajectories compared with an active digital-support comparator. The pattern was domain-specific: anxiety, fatigue, and ability to participate in social roles and activities showed statistically detectable trajectory differences, whereas physical function, sleep disturbance, pain, and depression did not show adjusted trajectory differences in the primary models. All end-of-treatment between-group differences remained below the prespecified PROMIS MID, and treatment satisfaction did not differ between groups. The findings therefore support a targeted and modest benefit for psychological and social well-being rather than a broad or global improvement in HRQoL.

The active comparator is central to interpretation. Unlike trials comparing ePRO monitoring with usual care, both arms in ENABLE received digital app-based information, organizational support, and scheduled HRQoL assessment. This design is closest conceptually to ePRO studies that test a more responsive or interactive intervention against lower-intensity digital support, such as PreCycle, while asking a different question in early breast cancer [32,33]. The modest effect sizes observed here are therefore consistent with an incremental-effect trial: the contrast isolated real-time triage and care-team escalation rather than digital support or ePRO collection alone.

Our findings are broadly consistent with evidence that ePRO monitoring can improve symptom burden, HRQoL, self-efficacy, or supportive-care processes in oncology, including adjuvant-treatment settings [21,34,35]. At the same time, the literature does not support a uniform expectation of large clinical effects across all settings and outcomes. Earlier metastatic-disease studies suggested possible survival benefits of web-based symptom monitoring [13,16], whereas the final Patient-Reported Outcomes to Enhance Cancer Treatment results did not show an overall-survival benefit despite improvements in symptom control, physical function, HRQoL, and emergency-visit outcomes [18]. This mixed evidence supports the cautious framing used here and argues against extrapolating metastatic-disease outcomes directly to a predominantly nonmetastatic early breast cancer cohort.

The domain-specific pattern may reflect the mechanism of the intervention, but this interpretation remains hypothesis-generating. The RPA pathway combined weekly screening, rule-based symptom triage, tailored patient-facing advice, and care-team alerting, features that align with elements associated with stronger ePRO effects in recent syntheses [20]. Such a pathway may be more likely to influence psychological distress, fatigue management, and social participation than pain or physical limitations driven primarily by systemic therapy exposure [3,4]. Because the trial was powered for the PROMIS-29 HRQoL end-point family rather than for domain-specific mechanistic hypotheses, this selective pattern should be confirmed in future studies with explicit end-point hierarchies and mechanism-focused process measures.

The PROMIS-29 and TTD analyses were not identical in what they captured. Fatigue and social participation showed the most consistent pattern across the longitudinal trajectory and exploratory deterioration analyses, whereas anxiety and depression differed by analytic approach. This is plausible because longitudinal mixed models estimate average trajectory differences over scheduled assessments, whereas TTD analyses focus on first clinically meaningful worsening among participants at risk [31,33]. Directional concordance with the prespecified secondary European Organisation for Research and Treatment of Cancer (EORTC) emotional-functioning, social-functioning, and fatigue scales provides some support for the PROMIS-29 pattern, although the full EORTC analyses are reported separately and should not be overinterpreted here.

Treatment satisfaction did not differ between the RPA and control groups. This null finding is plausible because both arms received the ENABLE app and structured digital support, which may have reduced the contrast available for PSQ-18 differences. Trials such as CAPRI (Cancérologie Parcours Région Île-de-France) reported improved patient satisfaction when digital monitoring was compared with usual care, but the comparator and disease context differed from ENABLE [36]. The high overall PSQ-18 scores in this trial are also consistent with prior reports showing generally high satisfaction among patients with oncological disease, which can limit sensitivity to additional intervention effects [37-39].

Exploratory differences in satisfaction between neoadjuvant and adjuvant chemotherapy groups should be interpreted cautiously. These comparisons were not randomized, were not part of the primary end-point hierarchy, and may reflect differences in treatment timing, expectations, tumor biology, perioperative context, and assessment timing. The observation is clinically interesting because neoadjuvant treatment provides an in vivo assessment of tumor response and informs subsequent surgical and systemic decisions [2], but the present trial was not designed to isolate the determinants of satisfaction by therapy pathway. These analyses therefore generate hypotheses for future work rather than evidence for a causal effect of therapy setting on satisfaction.

We are not aware of direct comparisons of treatment satisfaction between neoadjuvant and adjuvant chemotherapy contexts; the available satisfaction literature in oncology addresses age, disease stage, and care setting rather than this contrast [37,38]. The present exploratory findings should therefore be viewed as descriptive. They may nevertheless help explain why intervention effects on satisfaction were difficult to detect: satisfaction appears to be shaped by the broader care relationship and treatment context as well as by digital-support features [8]. Future studies could examine whether ePRO-supported care has different perceived value at distinct points in the early breast cancer pathway, particularly around diagnosis, chemotherapy initiation, surgery, and transition to survivorship. Such work would require a design and end-point hierarchy tailored to patient-experience outcomes rather than post-hoc subgroup comparison.

Taken together, the comparison with prior work supports a bounded conclusion. ENABLE adds evidence that real-time, care team–integrated ePRO triage can produce small benefits in selected HRQoL domains even when the comparator includes digital support and scheduled ePRO assessment. It does not show a broad HRQoL effect, a treatment-satisfaction effect, or effects on clinical or health-service outcomes. This distinction is important for implementation: the intervention should be considered a supportive-care workflow tool with targeted patient-reported benefits, not a stand-alone solution for all treatment-related morbidity.

By design, several limitations should be considered. First, the intervention was compared with an active digital-support comparator rather than usual care, which strengthens the mechanistic question but likely attenuates observable effect sizes. Second, the trial enrolled patients across neoadjuvant chemotherapy, adjuvant chemotherapy, adjuvant endocrine therapy, and postneoadjuvant therapy pathways that differ in duration, clinical context, and assessment timing. Randomization balanced these pathways between arms, and the primary mixed-effects models used visit number as a categorical factor, but the small adjuvant endocrine and postneoadjuvant subgroups limit pathway-specific inference. Third, the trial was designed around patient-reported HRQoL and satisfaction end points; emergency visits, unplanned outpatient visits, hospitalizations, febrile neutropenia, chemotherapy delays, dose reductions, and treatment discontinuations were not collected as comparative outcomes and cannot be inferred from these data.

The exploratory therapy pathway and satisfaction analyses have additional limitations. Patients were not randomized to neoadjuvant versus adjuvant treatment, and these pathways differ by tumor subtype, clinical indication, timing of surgery, and expectations of response [2]. Patients receiving neoadjuvant therapy were assessed in a different clinical context than those receiving adjuvant chemotherapy, and the adjuvant subgroup with available satisfaction data was relatively small. These factors preclude causal interpretation of the observed satisfaction differences between treatment settings.

Generalizability is also bounded. The trial enrolled adults with stage I-III invasive, nonmetastatic breast cancer at 2 German university hospitals, and eligibility required German fluency and access to an internet-enabled device. This likely reflects patients treated in comprehensive academic breast units but may underrepresent community settings, older patients, patients with language barriers, and patients with lower digital access or health literacy. The relatively young cohort and the high proportion receiving neoadjuvant chemotherapy mirror contemporary practice in high-volume centers [2] but may differ from other regions or care systems. Evidence from older oncology populations shows that lack of internet access can strongly limit ePRO feasibility, reinforcing the need to test such workflows in more diverse populations [40].

The retrospective registration is an additional reporting limitation. The first participant was enrolled before public trial registration, although the outcome measures had been specified in the ethics committee submission before recruitment began. The protocol was amended during the trial, most substantially to increase the target sample size in October 2022; these amendments did not affect the outcome measures. The paper discloses this timeline and is accompanied by an Editorial Notice explaining the rationale for the exception. Readers should nevertheless interpret primary outcome and effectiveness claims with the caution appropriate for retrospectively registered randomized trials.

In our cohort, feasibility findings should likewise be interpreted within this selected context. The screening and retention data suggest that app-based ePRO assessment was acceptable for many eligible patients in academic early breast cancer care, and completion of scheduled outcome assessments was similar between groups. However, the analyzed mITT sample reflects participants who provided at least 1 HRQoL assessment, and trial entry required digital access. Although the mITT sample exceeded the original sample size derived for the PROMIS-29 HRQoL analysis, the later amended recruitment target was not fully reflected in the final analyzable cohort. These features support internal interpretation of the randomized comparison while limiting claims about universal implementation readiness.

Future research should move beyond the question of whether ePRO data can be collected and toward how digital triage should be integrated into oncology workflows. Independent prospectively registered trials should specify the end-point hierarchy before enrollment; distinguish patient-reported, implementation, and clinical-service outcomes; and define how care-team responses will be categorized. Mechanism-focused analyses of alert types, contact modes, counseling, referral pathways, and response timing may clarify which components drive benefit. Adaptive prediction models may ultimately help personalize alert thresholds and anticipate deterioration, but such approaches require transparent validation and careful integration into clinical governance [41,42].

In this trial, the PROMIS-29 was evaluated as an 8-measure end-point family rather than as a single global score. Real-time digital triage and care-team escalation improved trajectories in anxiety, fatigue, and social participation, while the remaining PROMIS-29 measures did not show adjusted trajectory differences. Treatment satisfaction, assessed as a secondary end point, did not differ between groups. The observed effects were modest and below the prespecified MID, indicating a targeted benefit for selected psychological and social HRQoL domains rather than a global improvement; because both arms received app-based support and scheduled ePRO assessment, they speak to the incremental value of real-time triage and care-team escalation. The trial therefore supports the feasibility and potential value of embedding automated symptom-triggered triage into early breast cancer care pathways, especially where the goal is responsive supportive care rather than passive data collection; its value appears most likely where symptom signals can trigger timely, structured, and documented responses from the care team, and ENABLE offers a model that can be adapted and tested in other oncology settings. The results should not be interpreted as evidence for effects on clinical outcomes, health-service use, or broad HRQoL improvement, and the modest sub-MID effects argue for careful implementation and evaluation rather than overstatement. Confirmation should come from independent prospectively registered studies with a clear end-point hierarchy and prespecified implementation and response-process outcomes, which should also determine which patients benefit most, which alert-response pathways are most effective, and how such systems can be integrated equitably into routine care.