The Application of eHealth in Symptom Management of Patients With Breast Cancer During Endocrine Therapy: Scoping Literature Review.

The Application of eHealth in Symptom Management of Patients With Breast Cancer During Endocrine Therapy: Scoping Literature Review - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice JMIR Cancer . 2026 Jun 4;12:e92030. doi: 10.2196/92030 The Application of eHealth in Symptom Management of Patients With Breast Cancer During Endocrine Therapy: Scoping Literature Review Chengjia Gu 1 , Lili Shen 1 , Shiyu Zhang 2 , Nan Zhang 1, ✉ Editor: Matthew Balcarras Author information Article notes Copyright and License information 1 Comprehensive Breast Health Centre, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Rui Jin Er Road, Shanghai, 200025, China, 86 602268 ext 13661819697 2 Department of Nursing, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ✉ Nan Zhang, MSN, Comprehensive Breast Health Centre, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Rui Jin Er Road, Shanghai, 200025, China, 86 602268 ext 13661819697; zn21076@rjh.com.cn Received 2026 Jan 24; Revised 2026 Apr 14; Accepted 2026 Apr 15; Collection date 2026. Copyright © Chengjia Gu, Lili Shen, Shiyu Zhang, Nan Zhang. Originally published in JMIR Cancer (https://cancer.jmir.org) This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/ , as well as this copyright and license information must be included. PMC Copyright notice PMCID: PMC13235842  PMID: 42241587 Abstract Background Endocrine therapy is a fundamental treatment for hormone receptor–positive breast cancer. However, treatment-related symptoms may lead to poor adherence and premature discontinuation, adversely affecting clinical outcomes. eHealth technology interventions have been widely applied as potential tools to support patient education, symptom management, and adherence. Objective This review aimed to evaluate the application, effectiveness, and limitations of eHealth technologies in symptom management among patients with breast cancer undergoing endocrine therapy. Methods A systematic literature search was conducted across 9 databases, including MEDLINE Complete, Academic Search Complete, PubMed, and CINAHL Ultimate, covering studies published between February 2015 and February 2025. Keywords included “breast cancer,” “endocrine therapy,” and “ehealth,” along with synonyms and related terms. Eligible studies examined the use of eHealth interventions in patients with breast cancer undergoing endocrine therapy. From 1352 records, 30 (2.2%) studies were included after screening and full-text review. Results The 30 included studies comprised 13 (43.3%) randomized controlled trials, 7 (23.3%) single-arm studies, 3 (10%) retrospective analyses, 2 (6.7%) observational studies, 3 (10%) mixed methods studies, and 2 (6.7%) qualitative studies. eHealth interventions, primarily mobile apps, telemonitoring systems, and SMS text messaging, demonstrated benefits in short-term symptom management, such as reductions in hot flashes, joint pain, anxiety, and sexual distress and improved medication adherence. However, evidence regarding long-term adherence and quality of life outcomes remains inconsistent. Considerable heterogeneity existed in study designs, adherence measures, and intervention components. Nurses were considered key contributors in implementing and monitoring eHealth interventions. Conclusions eHealth interventions show promise in improving symptom management and short-term adherence among patients with breast cancer receiving endocrine therapy. Methodological limitations and variability across studies restrict the strength of conclusions. Future research should prioritize long-term outcomes, standardized measures, and integration into routine clinical practice while incorporating perspectives from multiple stakeholders. Introduction Breast cancer is heterogeneous, with approximately 80% of cases classified as hormone receptor positive [ 1 , 2 ]. Adjuvant endocrine therapy (AET), primarily selective estrogenic receptor modulators and aromatase inhibitors, is a standard treatment for patients that significantly reduces the risk of recurrence and mortality [ 3-5 ]. Clinical guidelines recommend 5 to 10 years of AET [ 6-8 ]. Despite its clinical benefits, adherence to endocrine therapy remains suboptimal. Studies indicate that up to one-third of patients discontinue treatment within 5 years, often due to treatment-related adverse effects, inadequate information, comorbidities, or patient preferences [ 9 , 10 ]. Common adverse effects include hot flashes, joint pain, osteoporosis, sexual dysfunction, weight gain, sleep disturbances, depression, anxiety, and cognitive impairment [ 6 , 11 ]. These symptoms can impair quality of life (QOL) and contribute to nonadherence, ultimately affecting prognosis [ 12 ]. Therefore, effective symptom management is critical for improving adherence and clinical outcomes. The World Health Organization has introduced the Global Strategy on Digital Health, emphasizing the integration of information and communications technologies to improve health outcomes [ 13 ]. In this review, eHealth refers to the use of digital tools such as mobile apps, SMS text messaging, telemedicine platforms, web-based portals, wearable devices, and electronic patient-reported outcome systems for real-time symptom monitoring, medication reminders, patient education, automated alerts, and remote self-management support [ 14 ]. Over the last 2 decades, eHealth has been increasingly used to support symptom monitoring, patient education, and remote care delivery [ 15 ]. Previous research has demonstrated the value of eHealth in chronic disease and cancer management, including symptom tracking, adherence support, and self-management [ 16 , 17 ]. Patients with breast cancer have used eHealth technologies for issues such as pain, sleep disorders, lymphedema, psychological distress, and chemotherapy side effects [ 18-22 ]. However, no comprehensive scoping review has synthesized evidence specifically on eHealth applications for symptom management during endocrine therapy. This review addresses that gap by evaluating the current evidence on effectiveness, strengths, limitations, and implementation considerations to inform clinical practice. Methods Search Strategy This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines ( Checklist 1 ) to ensure thorough search quality and standardized processes [ 23 , 24 ]. Nine databases were searched: MEDLINE Complete, Academic Search Complete, CINAHL Ultimate, Health Source: Nursing/Academic Edition, APA PsycInfo, PubMed, Scopus, Embase, and Web of Science. The search used combinations of terms for “breast cancer,” “endocrine therapy,” and “eHealth” supplemented with synonyms, academic terms, abbreviations, and related terminology. Boolean operators (“AND” and “OR”) were applied to combine terms for search strategy construction systematically. Limits included English-language, peer-reviewed articles focusing on the application of eHealth in breast cancer and published between February 2015 and February 2025. The full search strategy is detailed in Table 1 . Table 1. Summary of the search strategy for the literature review. Domain Description Search keywords Breast cancer “Breast Neoplasms” OR “Breast Tumor,” OR “Breast Cancer” OR “Mammary Cancer” OR “Malignant Neoplasm of Breast” OR “Breast Malignant Neoplasm” OR “Malignant Tumor of Breast” OR “Breast Malignant Tumor” OR “Cancer of Breast OR Mammary Carcinoma, Human” OR “Human Mammary Neoplasms” Endocrine therapy “endocrine therapy” OR “endocrine treatment” OR “aet” OR “hormone therapy” eHealth ehealth OR e-health OR telecare OR telemedicine OR telehealth OR “digital health” OR mHealth OR teletherapy OR “smartphone App” OR “smartphone application” OR “health apps” OR “remote monitoring” OR “mobile app” OR “mobile application” OR “wearable devices” OR telemedicine OR tele-care OR telecare OR “Medicine, Virtua” OR “Tele Referral” OR tele-referrals OR tele-referral OR “Virtual Medicine” OR “Health, Mobile” OR “Mobile Health” OR telehealth OR “Electronic App” OR “Electronic Application” OR “Application, Mobile” OR “mobile phone” OR “smartphone” OR “text-, electronic-“ OR “multimedia-message” OR “electronic mail” OR “phone applications” OR “computer” OR “podcast” OR “videos” OR “internet” OR “website” OR “chatroom” OR “message board” OR “activity tracker” OR “electronic health” OR “mobile health” OR “telemedicine” Other search parameters Search limits 2015-2025 Type of material searched English-language peer-reviewed scholarly articles Databases searched EBSCOhost databases (Academic Search Complete, Health Source: Nursing/Academic Edition, CINAHL Ultimate, MEDLINE Complete, and APA PsycInfo), PubMed, Scopus, Web of Science, and Embase Open in a new tab Eligibility Criteria Studies were eligible for inclusion if they were empirical research articles that examined the application of eHealth interventions specifically for symptom management or related outcomes, including medication adherence and QOL, among patients with breast cancer undergoing endocrine therapy in either the early or advanced disease stages. Exclusion criteria included animal studies, research protocols, review articles, conference abstracts, non–English-language publications, and studies that focused exclusively on other cancer treatments rather than endocrine therapy. Study Selection and Data Extraction A total of 1352 records were retrieved from the 9 databases, and 148 (10.9%) were removed based on the predefined inclusion and exclusion criteria. The remaining 1204 records were imported into the Zotero software (version 7; Corporation for Digital Scholarship), where 616 (51.2%) duplicates were removed. Among the remaining 588 records, title and abstract screening resulted in the exclusion of 504 (85.7%) studies, primarily due to irrelevant populations (non–breast cancer or non–endocrine therapy; n=295, 58.5%), irrelevant focus (chemotherapy only, screening, or survivorship care without endocrine therapy; n=110, 21.8%), nonempirical content (reviews, protocols, and abstracts; n=59, 11.7%), or focus on other diseases (n=40, 7.9%). Subsequently, of the remaining 84 records, 30 (35.7%) were excluded because the full texts could not be retrieved. Full-text assessment was then performed on the remaining 54 articles, of which 24 (44.4%) were excluded because they described only study protocols or were conducted on survivors of breast cancer without a specific focus on endocrine therapy. Ultimately, 30 articles met all eligibility criteria and were included in this review (see Table S1 in Multimedia Appendix 1 [ 25-54 ] for details). Data extracted included study design, participant characteristics, intervention details, outcomes (symptom management, adherence, QOL, and patient experiences), and key findings. Risk-of-Bias Assessment Two oncology nurse researchers assessed the risk of bias in randomized controlled trials (RCTs) using the Cochrane risk-of-bias tool independently [ 55 ]. Discrepancies, if any, were resolved by a third reviewer. Due to heterogeneity in designs, interventions, and outcome measures, no meta-analysis was performed. The Critical Appraisal Skills Programme checklists were used to evaluate methodological quality across study types [ 56 ]. Data Synthesis Findings were synthesized thematically into three main aspects: (1) effectiveness and limitations of eHealth for symptom management and adherence, (2) the role of nurses, and (3) patient experiences and impact on QOL. Results Overview The 30 selected studies included 13 (43.3%) RCTs, 7 (23.3%) single-arm studies, 3 (10%) retrospective analyses, 2 (6.7%) observational studies, 3 (10%) mixed methods studies, and 2 (6.7%) qualitative studies. Most studies (n=17, 56.7%) were conducted in the United States, followed by Sweden (n=3, 10%); Japan and the United Kingdom (n=2, 6.7% each); and Germany, Turkey, Singapore, South Korea, and China (n=1, 3.3% each). Three key themes emerged from the review: (1) effectiveness and limitations regarding symptom management and adherence to endocrine therapy, (2) nurses’ role in eHealth interventions, and (3) experiences of patients with breast cancer with eHealth tools and the impact on QOL. The PRISMA flow diagram is presented in Figure 1 . Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Fflow Ddiagram of the literature review. Open in a new tab Effectiveness and Limitations of eHealth in Symptom Management eHealth interventions, including mobile apps, telemonitoring, and SMS text messaging, facilitated timely self-reporting of symptoms and triggered alerts for health care providers. In total, 10% (3/10) of the studies allowed patients to self-report and record symptoms using mobile apps [ 29-31 ], whereas another 13.3% (4/30) of the studies used questionnaires to record patients’ symptoms [ 32-35 ], allowing for real-time data capture and proactive management. A total of 20% (6/30) of the studies reported positive effects on symptom control using eHealth [ 36-41 ]. One RCT combining an educational website with telephone counseling significantly reduced sexual difficulties, distress, and anxiety compared with usual care [ 36 ]. Another RCT using a mobile education program with nurse reminders improved physical well-being, pain, emotional functioning, and endocrine-related symptoms at 3 months [ 57 ]. Teleconferencing interventions in 2 RCTs enhanced self-efficacy and reduced hot flashes and depression [ 41 , 42 ]. A longitudinal study of remote tai chi sessions via an app demonstrated reductions in joint pain, anxiety, and hot flashes after 12 weeks [ 38 ]. An app-based intervention in advanced endocrine treatment improved arm symptoms, cognitive function, anxiety, and depression [ 39 ]. The mobile app integrated daily exercise logging, medication adherence tracking, Fitbit activity data synchronization, personalized goal setting, educational modules on hormonal therapy side effects, and weekly progress visualizations, with automated feedback and nurse coach messaging. These eHealth tools enabled more accurate and timely symptom tracking, supporting personalized care plans and reducing symptom burden in the short term (typically 1 to 6 months) [ 29 , 31 , 35 , 42 ]. However, most studies focused on short-term outcomes and specific subgroups, such as patients with poor AET adherence [ 41 ], recent sexual activity [ 36 ], or aromatase inhibitor–induced arthralgia [ 38 ], limiting the generalizability of the findings. Patients with breast cancer on endocrine therapy often experience complex, interacting symptom clusters [ 22 ]; few interventions (1/30, 3.3%) addressed this holistically. Medication Adherence Medication adherence is commonly defined as taking medication as prescribed; nonadherence includes delays, dose deviations, or premature discontinuation [ 58 ]. Previous studies have shown that eHealth interventions have a significant impact on medication adherence in patients with chronic diseases and those taking medication for mental health conditions [ 59-61 ]. Medication adherence is addressed solely in relation to its direct correlation with effective symptom management; premature discontinuation is a subsequent result of inadequately controlled symptoms. Therefore, studies showing improved adherence were interpreted strictly as evidence of effective symptom control rather than as a separate end point. Retrospective studies indicated 5-year adherence rates of 48% to 53%, primarily due to adverse events and symptoms [ 43-45 ]. Access to practical information, active monitoring of side effects, and effective medication reminders have been suggested to improve adherence [ 62 ]. A total of 16.7% (5/30) of the studies found that digital health tools, including SMS text messages, mobile apps, and emails, that incorporated symptom education, self-monitoring, and alerts, improved medication adherence [ 31 , 32 , 34 , 46 , 47 ]. Another 16.7% (5/30) of the studies reported benefits from reminder-based educational interventions [ 30 , 39 , 48 , 49 ]. However, other studies reported inconsistent results. In total, 15.4% (2/13) of the RCTs with 1- and 3-year follow-ups on SMS text messaging found no significant differences in adherence [ 33 , 50 ]. One retrospective study with telephone follow-up showed no long-term benefit [ 45 ], and one RCT on a mobile program reported no improvement despite educational modules [ 40 ]. Medication adherence was measured across several aspects: prescription fill rates of more than 80% [ 32 , 39 ], self-reports [ 31 ], estrogen level monitoring, and discontinuation rates ( Table 2 ). The heterogeneity in measures and data collection and analysis methods contributed to variability in the findings. Improvements were mainly evident in short-term studies; long-term data remain limited, highlighting the need for sustained interventions. Table 2. Summary of medication adherence measures in the literature review. Mougalian et al [ 32 ] Hershman et al [ 33 ] Tan et al [ 50 ] Park et al [ 48 ] Takada et al [ 31 ] Smith et al [ 34 ] Wheeler et al [ 49 ] Chan et al [ 39 ] Krukowski et al [ 47 ] Jacobs et al [ 51 ] Prescription fill rate exceeding 80% ✓ ✓ Integrating urine analysis with self-reporting ✓ Combining self-reporting with innovative bottle technology ✓ Using estrogenic level measurements in conjunction with a simplified medication adherence report ✓ Patient self-records ✓ Discontinuation rates ✓ Integrating prescription fill data with smart bottle opening data ✓ ✓ Medication adherence reporting scale+smart pill bottle ✓ Open in a new tab Nurses’ Role in eHealth for Symptom Management Nurses were the primary implementers in most interventions, leveraging eHealth to enable real-time symptom assessment, 2-way communication, prioritization of high-risk cases, and referrals [ 63-66 ]. A total of 13.3% (4/30) of the studies found that nurses enabled 2-way communication and referral by prioritizing high-risk symptoms [ 32 , 34 , 40 , 47 ]. Nurses sharing information through eHealth tools could improve both patient satisfaction and the quality of care [ 52 , 53 ]. Nurses delivered education via SMS text messages, app modules, and teleconferences, enhancing patients’ self-management skills and symptom control [ 29 , 33 , 37 , 41 , 67 ]. Moreover, the included interventions used dynamic, standardized, and structured assessments of both cognitive and somatic symptoms, which new data indicate are superior to conventional static questionnaires for earlier detection of clinically relevant changes and timely intervention [ 67 ]. Nurse involvement improved care efficiency, multidisciplinary collaboration, and resource use [ 68-70 ]. In total, 10% (3/30) of the studies, which were nurse led, showed that nurse-led approaches improved high-quality health care and reduced high-cost contacts and outpatient visits [ 46 , 47 , 53 ]. However, challenges included increased workload, role ambiguity, and the need for process adaptation [ 69 , 71 ]. Most evidence reflects patient perspectives; fewer studies (2/30, 6.7%) examined nurses’ or physicians’ acceptance and barriers. Cost-effectiveness evaluations were largely absent, underscoring the need for health economic analyses. Patients’ Experiences With eHealth Tools and the Impact on QOL In this review, patients generally expressed high satisfaction and acceptance of SMS text message reminders [ 32 , 50 ], teleconferencing [ 37 ], and mobile apps [ 29 , 54 ], citing ease of use, helpful information, and improved self-management (satisfaction often of >90%). Combined tools such as videoconferencing applications and wearables further enhanced satisfaction, and many patients were willing to continue using them [ 38 , 47 , 62 ]. A total of 20% (6/30) of the studies examined QOL impacts. Four of these studies reported significant improvements in various domains such as physical, emotional, or endocrine-specific typically after 1 to 6 months of using eHealth tools such as mobile apps with reminders or tele-education [ 31 , 39 , 51 , 52 ]. One study providing tele-education showed significant improvements in patients’ QOL after a 3-month intervention [ 52 ]. Another study used a teleconference education format and demonstrated significant improvement in patients’ QOL after 24 weeks of intervention [ 51 ]. However, 2 RCTs found no significant differences in QOL: one study implemented a mobile app with SMS text message reminders and follow-up assessments at 6 and 12 months [ 40 ], whereas the other integrated the app with a patient e-reporting intervention and assessed QOL at 3 months after the intervention [ 35 ]. Heterogeneity and variations in QOL study results likely stem from differences in QOL instruments [ 31 , 35 , 37 , 39 , 40 ] ( Table 3 ). Various QOL measurement scales may yield different results when evaluating due to differences in study design, cultural appropriateness, and assessment dimensions [ 72-74 ]. The assessment of QOL in survivors of breast cancer requires a multidimensional approach encompassing physical, psychological, social, and spiritual well-being, indicating that the use of a single assessment tool may not fully capture the heterogeneity in their QOL trajectories [ 75 ]. This review found that eHealth interventions effectively improved patients’ QOL in the short term. However, the long-term effects and trends for patients with breast cancer undergoing endocrine treatment remain to be thoroughly researched and explored. Table 3. Summary of questionnaires assessing quality of life (QOL) in the literature review. Study FACT-ES QLS a EORTC QLQ-C30 b EORTC QLQ-BR23 c FACT-B d 12-Item Short Form Health Survey Validity and reliability Measurement results Çınar et al [ 52 ], 2021 ✓ ✓ There was a statistically significant difference in the total FACT-ES QLS scores ( P <.05). Jacobs et al [ 51 ], 2022 ✓ ✓ There was a significant improvement in QOL in the intervention group at 24 wk ( P <.01). Takada et al [ 31 ], 2022 ✓ ✓ The intervention did not negatively affect QOL. Chan et al [ 39 ], 2024 ✓ ✓ ✓ Subscale scores were significant: P =.02 for cognitive functioning, P =.04 for future prospective functioning, and P =.04 for arm symptom score. Graetz et al [ 40 ], 2024 ✓ Not statistically significant. Okuyama et al [ 35 ], 2024 ✓ ✓ Not statistically significant. Open in a new tab a FACT-ES QLS: Functional Assessment of Cancer Therapy–Endocrine Subscale Quality of Life Scale. b EORTC QLQ-C30: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30. c EORTC QLQ-BR23: European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire–Breast Cancer Module. d FACT-B: Functional Assessment of Cancer Therapy–Breast. However, several limitations remain in sample selection when examining patients’ experience with eHealth interventions. In terms of sample inclusion, the mixed methods study by Jacobs et al [ 37 ] of patients with high levels of anxiety involved predominantly White patients with high symptom burden. Additionally, the single-arm study by Gomaa et al [ 38 ] only included patients who reported joint pain. Some studies (4/30, 13.3%) predominantly selected patients who were younger, White individuals, more educated, and economically privileged [ 32 , 38 , 40 , 47 ]. In terms of sample size, 2 mixed methods studies and 1 single-arm study lacked a control group. They had small sample sizes of 15 [ 29 ], 10 [ 54 ], and 39 [ 47 ]. Furthermore, several studies with low response rates (3/30, 10%) were noted [ 46 , 52 , 53 ]. The main reasons for this problem included lack of personalized interventions, poor user interface design, time constraints, technical difficulties, and privacy issues [ 32 , 47 , 49 ]. Therefore, the characteristics of nonresponding patients and patients who declined to be enrolled may introduce selection bias. Discussion Principal Findings This is the first scoping review to synthesize evidence on eHealth applications specifically for symptom management in patients with breast cancer undergoing endocrine therapy, incorporating effectiveness, adherence, nurse roles, patient experience, and QOL. Interventions varied (mobile apps: 8/30, 26.7%; SMS text messaging: 5/30, 16.7%; telemedicine: 6/30, 20%; online portals: 3/30, 10%; hybrid electronic patient-reported outcomes+wearables: 2/30, 6.7%; remote tai chi: 1/30, 3.3%) but shared fundamental features: real-time data capture, threshold-triggered alerts, and remote feedback. Short-term benefits for symptom burden were consistent across app- and telemedicine-based approaches, although direct comparisons were limited. This comprehensive perspective not only facilitates an in-depth understanding of the mechanisms and actual effects of eHealth interventions but also provides valuable reference information for future clinical practice and research. This review demonstrated eHealth’s efficacy in symptom monitoring and short-term management, with some positive effects on psychological symptoms and potential indirect benefits for adherence. However, many studies of symptom monitoring and management (4/30, 13.3%) applied single-arm designs with short follow-up periods, and adherence outcomes relied heavily on self-report, introducing bias [ 30-32 , 39 , undefined , undefined ]. The RCTs included in this review often lacked blinding, and interventions frequently targeted single symptoms or specific subgroups, overlooking complex symptom clusters and socioeconomic factors [ 33 , 40 , 47 , 48 , 50 ]. Nevertheless, this review found that eHealth interventions could improve patients’ medication adherence and short-term symptom management. However, current research relies heavily on one-sided quantitative data from patients while neglecting to assess the early warning systems of physicians and nurses. This review revealed that nurses played a pivotal role in implementation, education, and coordination, contributing to efficiency and patient engagement. However, challenges such as workload, role clarity, and limited exploration of health care providers’ perspectives persist. Patient experiences were largely positive with high satisfaction, but generalizability is constrained by limited studies and equipment requirement issues [ 76 , 77 ]. QOL improvements were evident in the short term but inconsistent in the long term, partly due to measurement heterogeneity. In addition, most existing studies have examined the roles and functions of physicians and nurses in eHealth interventions primarily from the patient’s perspective. However, as the primary implementers of these technologies, health care providers’ own acceptance of eHealth tools and the barriers they encounter in daily practice remain underexplored. This review indicates that eHealth interventions are generally associated with favorable patient experiences, high uptake rates, and positive short-term effects on QOL among patients with breast cancer receiving endocrine therapy. Prior literature similarly underscores this population’s expressed need for digital support. Nevertheless, several limitations constrain the generalizability of these findings. Many quantitative studies (4/30, 16.6%) featured small, relatively homogeneous samples, often restricted to participants with the necessary devices and digital access [ 31 , 38 , 39 , 49 ]. Low response rates to eHealth applications in some studies (6/30, 20%) further restricted applicability across diverse populations. These issues can hinder timely symptom detection and intervention for certain patient groups. Moreover, few studies have thoroughly explored patients’ specific needs in terms of tool design, particularly with respect to cultural adaptability. The strengths of this review lie in its comprehensive literature search and the inclusion of diverse study designs, including RCTs, single-arm studies, retrospective analyses, observational studies, mixed methods studies, and qualitative research. Quality appraisal was conducted using the Critical Appraisal Skills Programme checklists, and the complementary nature of these methodologies, where RCTs help establish causality and qualitative studies provide rich insights into patient experiences, enhances the overall credibility of the synthesis. However, notable limitations were evident in the primary studies. Most (23/30, 76.6%) lacked adequate sample diversity and representativeness. In addition, the evidence base was heavily skewed toward high-income countries, with minimal data from low- and middle-income settings or rural areas, thereby limiting global applicability and scalability. Although the use of theories, models, and frameworks has been shown to strengthen the scientific rigor and explanatory depth of digital health research [ 78 ], only 13.3% (4/30) of the studies in this review were explicitly guided by such frameworks. Furthermore, most of the included RCTs (10/13, 76.9%) lacked blinding of participants or researchers, and 26.7% (8/30) of the studies used single-arm designs without control groups, both of which increase the risk of bias and reduce the robustness of the findings. In addition, the search period ended in February 2025, which may have omitted more recent high-impact publications. The restriction to English-language peer-reviewed articles may also have excluded relevant studies published in other languages. Despite these constraints, eHealth interventions demonstrate considerable potential to reduce symptom burden and improve adherence during endocrine therapy. Future research should prioritize long-term efficacy, greater equity in access, multi-stakeholder perspectives, and integration into routine oncology workflows to maximize real-world impact and sustainability. Conclusions eHealth interventions offer promising support for symptom management and short-term adherence among patients with breast cancer receiving endocrine therapy, with generally high patient acceptance. Nurses are central to successful implementation. However, evidence is limited by methodological heterogeneity, short follow-ups, inconsistent long-term outcomes, and a lack of diversity. By addressing individualized patient needs and incorporating experiences from multiple stakeholders (patients, physicians, and nurses), eHealth tools can be optimized to reduce symptom burden, improve adherence, and enhance clinical outcomes. Future studies should prioritize rigorous designs, standardized measures, long-term follow-up, and equitable implementation to realize the full potential of digital health in oncology care. Supplementary material Multimedia Appendix 1. Summary of main characteristics of identified research, including research aim, outcome measures, and results. cancer-v12-e92030-s001.docx (93.6KB, docx) DOI: 10.2196/92030 Checklist 1. PRISMA-ScR Checklist. cancer-v12-e92030-s002.pdf (180.9KB, pdf) DOI: 10.2196/92030 Acknowledgments The authors declare that none of them used generative artificial intelligence in the research and writing process. Abbreviations AET adjuvant endocrine therapy PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses QOL quality of life RCT randomized controlled trial Footnotes Funding: The authors declared no financial support was received for this work. Authors’ Contributions: Conceptualization: CG Data curation: CG, LS, SZ Investigation: CG, LS, SZ Methodology: CG Formal analysis: CG, LS, SZ Supervision: NZ Writing – original draft: CG Writing – review & editing: LS, SZ, NZ Conflicts of Interest: None declared. References 1. Zhang Z, Wang J, Skinner KA, et al. Pathological features and clinical outcomes of breast cancer according to levels of oestrogen receptor expression. Histopathology. 2014 Oct;65(4):508–516. doi: 10.1111/his.12412. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Rosso R, D’Alonzo M, Bounous VE, et al. Adherence to adjuvant endocrine therapy in breast cancer patients. Curr Oncol. 2023 Jan 21;30(2):1461–1472. doi: 10.3390/curroncol30020112. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Record H, Clelland E, Rothschild HT, et al. Tamoxifen or aromatase inhibitors with ovarian function suppression in pre-menopausal stage I-III lobular breast cancer. NPJ Breast Cancer. 2023 Oct 26;9(1):88. doi: 10.1038/s41523-023-00594-3. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Schiavon G, Smith IE. Status of adjuvant endocrine therapy for breast cancer. Breast Cancer Res. 2014;16(2):206. doi: 10.1186/bcr3636. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Sasada S, Kondo N, Hashimoto H, et al. Prognostic impact of adjuvant endocrine therapy for estrogen receptor-positive and HER2-negative T1a/bN0M0 breast cancer. Breast Cancer Res Treat. 2023 Dec;202(3):473–483. doi: 10.1007/s10549-023-07097-6. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Burstein HJ, Lacchetti C, Anderson H, et al. Adjuvant endocrine therapy for women with hormone receptor-positive breast cancer: ASCO clinical practice guideline focused update. J Clin Oncol. 2019 Feb 10;37(5):423–438. doi: 10.1200/JCO.18.01160. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 7. Cardoso F, Kyriakides S, Ohno S, et al. Early breast cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019 Aug;30(8):1194–1220. doi: 10.1093/annonc/mdz173. doi. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Gradishar WJ, Moran MS, Abraham J, et al. Breast cancer, version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2022 Jun;20(6):691–722. doi: 10.6004/jnccn.2022.0030. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 9. Yussof I, Mohd Tahir NA, Hatah E, Mohamed Shah N. Factors influencing five-year adherence to adjuvant endocrine therapy in breast cancer patients: a systematic review. Breast. 2022 Apr;62:22–35. doi: 10.1016/j.breast.2022.01.012. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Cavazza M, Banks H, Ercolanoni M, et al. Factors influencing adherence to adjuvant endocrine therapy in breast cancer-treated women: using real-world data to inform a switch from acute to chronic disease management. Breast Cancer Res Treat. 2020 Aug;183(1):189–199. doi: 10.1007/s10549-020-05748-6. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Franzoi MA, Agostinetto E, Perachino M, et al. Evidence-based approaches for the management of side-effects of adjuvant endocrine therapy in patients with breast cancer. Lancet Oncol. 2021 Jul;22(7):e303–e313. doi: 10.1016/S1470-2045(20)30666-5. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 12. Wang L, Wei T, Liu J, et al. Experiences and management needs of endocrine therapy-related symptoms in patients with breast cancer: a systematic review and qualitative evidence synthesis protocol. BMJ Open. 2023 Dec;13(12):e073915. doi: 10.1136/bmjopen-2023-073915. doi. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Global strategy on digital health 2020-2027. World Health Organization. 2025. [26-04-2026]. https://www.who.int/publications/i/item/9789240116870 URL. Accessed. 14. Ahern DK, Kreslake JM, Phalen JM. What is eHealth (6): perspectives on the evolution of eHealth research. J Med Internet Res. 2006 Mar 31;8(1):e4. doi: 10.2196/jmir.8.1.e4. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Boogerd EA, Arts T, Engelen LJ, van de Belt TH. “What is eHealth”: time for an update? JMIR Res Protoc. 2015 Mar 12;4(1):e29. doi: 10.2196/resprot.4065. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Vaffis S, Whaley S, Axon DR, et al. Features of cancer mHealth apps and evidence for patient preferences: scoping literature review. JMIR Cancer. 2023 Apr 28;9:e37330. doi: 10.2196/37330. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Li X, Zhang N, Yang J, Geng Z, Zhou J, Zhang J. Weight management personas of breast cancer patients undergoing chemotherapy in China: a multi-method study. BMC Med Inform Decis Mak. 2024 Apr 25;24(1):108. doi: 10.1186/s12911-024-02515-1. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Reeves MM, Terranova CO, Winkler EA, et al. Effect of a remotely delivered weight loss intervention in early-stage breast cancer: randomized controlled trial. Nutrients. 2021 Nov 15;13(11):4091. doi: 10.3390/nu13114091. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Du X, Li Y, Fu L, et al. Strategies in activating lymphatic system to promote lymph flow on lymphedema symptoms in breast cancer survivors: a randomized controlled trial. Front Oncol. 2022;12:1015387. doi: 10.3389/fonc.2022.1015387. doi. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Oswald LB, Morales-Cruz J, Eisel SL, et al. Pilot randomized controlled trial of eHealth cognitive-behavioral therapy for insomnia among Spanish-speaking breast cancer survivors. J Behav Med. 2022 Jun;45(3):503–508. doi: 10.1007/s10865-022-00313-6. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Shi N, Wong AK, Wong FK, Zhang N, Lai X, Gan L. A nurse-led mHealth self-management program (mChemotherapy) for breast cancer patients undergoing chemotherapy: study protocol of a randomized controlled pilot study. Cancer Control. 2022;29:10732748221115469. doi: 10.1177/10732748221115469. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Jing F, Zhu Z, Qiu J, et al. Symptom clusters and quality of life in patients with breast cancer receiving endocrine therapy in China. Semin Oncol Nurs. 2023 Aug;39(4):151443. doi: 10.1016/j.soncn.2023.151443. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 23. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009 Jul 21;6(7):e1000097. doi: 10.1371/journal.pmed.1000097. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Haddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and open synthesis. Campbell Syst Rev. 2022;18(2):e1230. doi: 10.1002/cl2.1230. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Krok-Schoen JL, Naughton MJ, Young GS, et al. Increasing adherence to adjuvant hormone therapy among patients with breast cancer: a smart phone app-based pilot study. Cancer Control. 2019;26(1):1073274819883287. doi: 10.1177/1073274819883287. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Lally P, May CN, Mitchell ES, McCallum M, Michaelides A, Fisher A. Prototype of an app designed to support self-management for health behaviors and weight in women living with breast cancer: qualitative user experience study. JMIR Cancer. 2024 Dec 20;10:e48170. doi: 10.2196/48170. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Brett J, Boulton M, Watson E. Development of an e-health app to support women prescribed adjuvant endocrine therapy after treatment for breast cancer. Patient Prefer Adherence. 2018;12:2639–2647. doi: 10.2147/PPA.S187692. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Harbeck N, Kates R, Schinköthe T, et al. Favorable impact of therapy management by an interactive eHealth system on severe adverse events in patients with hormone receptor-positive, HER2-negative locally advanced or metastatic breast cancer treated by palbociclib and endocrine therapy. Cancer Treat Rev. 2023 Dec;121:102631. doi: 10.1016/j.ctrv.2023.102631. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Bergqvist J, Lundström S, Wengström Y. Patient interactive digital support for women with adjuvant endocrine therapy in order to increase compliance and quality of life. Support Care Cancer. 2021 Jan;29(1):491–497. doi: 10.1007/s00520-020-05476-z. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Neuner J, Weil E, Fergestrom N, et al. Feasibility of a pharmacist-led symptom monitoring and management intervention to improve breast cancer endocrine therapy adherence. J Am Pharm Assoc (2003) 2022;62(4):1321–1328. doi: 10.1016/j.japh.2022.03.001. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 31. Takada F, Okuyama H, Nakamura S, Fujita KI. Application of personal health record in enhancing the quality of life in patients with breast cancer who received adjuvant hormonal therapy. Eur J Breast Health. 2022 Apr;18(2):155–162. doi: 10.4274/ejbh.galenos.2022.2021-12-2. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Mougalian SS, Epstein LN, Jhaveri AP, et al. Bidirectional text messaging to monitor endocrine therapy adherence and patient-reported outcomes in breast cancer. JCO Clin Cancer Inform. 2017 Nov;1:1–10. doi: 10.1200/CCI.17.00015. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 33. Hershman DL, Unger JM, Hillyer GC, et al. Randomized trial of text messaging to reduce early discontinuation of adjuvant aromatase inhibitor therapy in women with early-stage breast cancer: SWOG S1105. J Clin Oncol. 2020 Jul 1;38(19):2122–2129. doi: 10.1200/JCO.19.02699. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Smith KL, Tsai HL, Lim D, et al. Feasibility of symptom monitoring during the first year of endocrine therapy for early breast cancer using patient-reported outcomes collected via smartphone app. JCO Oncol Pract. 2023 Nov;19(11):981–989. doi: 10.1200/OP.23.00038. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Okuyama H, Takada F, Taira N, Nakamura S. A randomized trial of the impact of symptom monitoring using an electronic patient-reported outcome app on health-related quality of life in postmenopausal breast cancer patients receiving adjuvant endocrine therapy. Breast Cancer. 2024 Sep;31(5):787–797. doi: 10.1007/s12282-024-01592-4. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 36. Advani P, Brewster AM, Baum GP, Schover LR. A pilot randomized trial to prevent sexual dysfunction in postmenopausal breast cancer survivors starting adjuvant aromatase inhibitor therapy. J Cancer Surviv. 2017 Aug;11(4):477–485. doi: 10.1007/s11764-017-0606-3. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Jacobs JM, Walsh EA, Rapoport CS, et al. Development and refinement of a telehealth intervention for symptom management, distress, and adherence to adjuvant endocrine therapy after breast cancer. J Clin Psychol Med Settings. 2021 Sep;28(3):603–618. doi: 10.1007/s10880-020-09750-4. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Gomaa S, West C, Lopez AM, et al. A telehealth-delivered tai chi intervention (TaiChi4Joint) for managing aromatase inhibitor-induced arthralgia in patients with breast cancer during COVID-19: longitudinal pilot study. JMIR Form Res. 2022;6(6):e34995. doi: 10.2196/34995. doi. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Chan WL, Wong YL, Tai YL, et al. Digital rehabilitation program for breast cancer survivors on adjuvant hormonal therapy: a feasibility study. Cancers (Basel) 2024 Dec 5;16(23):4084. doi: 10.3390/cancers16234084. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Graetz I, Hu X, Kocak M, et al. Remote monitoring app for endocrine therapy adherence among patients with early-stage breast cancer: a randomized clinical trial. JAMA Netw Open. 2024 Jun 3;7(6):e2417873. doi: 10.1001/jamanetworkopen.2024.17873. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Walsh LE, Dunderdale L, Horick N, Temel JS, Greer JA, Jacobs JM. Intervention-related changes in coping ability drives improvements in mood and quality of life for patients taking adjuvant endocrine therapy. Psychooncology. 2024 Dec;33(12):e70049. doi: 10.1002/pon.70049. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Richardson D, Zhan L, Mahtani R, et al. A prospective observational study of patient-reported functioning and quality of life in advanced and metastatic breast cancer utilizing a novel mobile application. Breast Cancer Res Treat. 2021 May;187(1):113–124. doi: 10.1007/s10549-020-06082-7. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Harrell M, Fabbri D, Levy M. Analysis of adjuvant endocrine therapy in practice from electronic health record data of patients with breast cancer. JCO Clin Cancer Inform. 2017 Nov;1:1–8. doi: 10.1200/CCI.16.00044. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Yin Z, Harrell M, Warner JL, Chen Q, Fabbri D, Malin BA. The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation. J Am Med Inform Assoc. 2018 Nov 1;25(11):1444–1451. doi: 10.1093/jamia/ocy118. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Aurell C, Haidar A, Giglio D. Follow-up routines matter for adherence to endocrine therapy in the adjuvant setting of breast cancer. Breast Cancer (Auckl) 2024;18:11782234241240171. doi: 10.1177/11782234241240171. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Graetz I, McKillop CN, Stepanski E, Vidal GA, Anderson JN, Schwartzberg LS. Use of a web-based app to improve breast cancer symptom management and adherence for aromatase inhibitors: a randomized controlled feasibility trial. J Cancer Surviv. 2018 Aug;12(4):431–440. doi: 10.1007/s11764-018-0682-z. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Krukowski RA, Hu X, Arshad S, et al. Symptom monitoring app use associated with medication adherence among woman survivors of breast cancer on adjuvant endocrine therapy. JCO Clin Cancer Inform. 2024 Dec;8:e2400179. doi: 10.1200/CCI-24-00179. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Park HR, Kang HS, Kim SH, Singh-Carlson S. Effect of a smart pill bottle reminder intervention on medication adherence, self-efficacy, and depression in breast cancer survivors. Cancer Nurs. 2022;45(6):E874–E882. doi: 10.1097/NCC.0000000000001030. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Wheeler SB, Spencer J, Drier SW, Fray N, Reeder-Hayes KE. Motivational interviewing counseling to increase endocrine therapy adherence in diverse patients. Cancers (Basel) 2023 Mar 25;15(7):1973. doi: 10.3390/cancers15071973. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Tan EH, Wong AL, Tan CC, et al. Improving medication adherence with adjuvant aromatase inhibitor in women with breast cancer: a randomised controlled trial to evaluate the effect of short message service (SMS) reminder. Breast. 2020 Oct;53:77–84. doi: 10.1016/j.breast.2020.06.012. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Jacobs JM, Post K, Massad K, et al. A telehealth intervention for symptom management, distress, and adherence to adjuvant endocrine therapy: a randomized controlled trial. Cancer. 2022 Oct 1;128(19):3541–3551. doi: 10.1002/cncr.34409. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Çınar D, Karadakovan A, Erdoğan AP. Effect of mobile phone app-based training on the quality of life for women with breast cancer. Eur J Oncol Nurs. 2021 Jun;52:101960. doi: 10.1016/j.ejon.2021.101960. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 53. Ahlstedt Karlsson S, Henoch I, Olofsson Bagge R, Wallengren C. Person-centred support programme (RESPECT intervention) for women with breast cancer treated with endocrine therapy: a feasibility study. BMJ Open. 2022 Oct 5;12(10):e060946. doi: 10.1136/bmjopen-2022-060946. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Nathan M, Beatty L, Coborn J, et al. Targeting psychological and menopausal factors linked to non-adherence to anti-estrogen therapy in women with early-stage hormone-receptor positive breast cancer: a web-based feasibility study of Finding My Way. Psychooncology. 2023 Sep;32(9):1461–1465. doi: 10.1002/pon.6195. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 55. Higgins JPT, Savović J, Page MJ, Elbers RG, Sterne JAC. In: Cochrane Handbook for Systematic Reviews of Interventions. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. The Cochrane Collaboration; 2019. Assessing risk of bias in a randomized trial; pp. 205–228. doi. [ DOI ] [ Google Scholar ] 56. Long HA, French DP, Brooks JM. Optimising the value of the critical appraisal skills programme (CASP) tool for quality appraisal in qualitative evidence synthesis. Res Methods Med Health Sci. 2020;1(1):31–42. doi: 10.1177/2632084320947559. doi. [ DOI ] [ Google Scholar ] 57. ÇInar D, Karadakovan A, Erdoğan AP. The effect of e-mobile education on the quality of life in women with breast cancer. Ann Oncol. 2019 Oct;30(Supplement 5):v818–v819. doi: 10.1093/annonc/mdz272.010. doi. [ DOI ] [ Google Scholar ] 58. Vrijens B, De Geest S, Hughes DA, et al. A new taxonomy for describing and defining adherence to medications. Br J Clin Pharmacol. 2012 May;73(5):691–705. doi: 10.1111/j.1365-2125.2012.04167.x. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 59. Basit SA, Mathews N, Kunik ME. Telemedicine interventions for medication adherence in mental illness: a systematic review. Gen Hosp Psychiatry. 2020;62:28–36. doi: 10.1016/j.genhosppsych.2019.11.004. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 60. Chilala CI, Kassavou A, Sutton S. Evaluating the effectiveness of remote behavioral interventions facilitated by health care providers at improving medication adherence in cardiometabolic conditions: a systematic review and meta-analysis. Ann Behav Med. 2023 Feb 4;57(2):99–110. doi: 10.1093/abm/kaac037. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 61. Kim SK, Park SY, Hwang HR, Moon SH, Park JW. Effectiveness of mobile health intervention in medication adherence: a systematic review and meta-analysis. J Med Syst. 2025 Jan 17;49(1):13. doi: 10.1007/s10916-024-02135-2. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 62. Cahir C, Bennett K, Dombrowski SU, et al. Informing interventions to improve uptake of adjuvant endocrine therapy in women with breast cancer: a theoretical-based examination of modifiable influences on non-adherence. Support Care Cancer. 2023 Mar 4;31(3):200. doi: 10.1007/s00520-023-07658-x. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Farokhzadian J, Khajouei R, Hasman A, Ahmadian L. Nurses’ experiences and viewpoints about the benefits of adopting information technology in health care: a qualitative study in Iran. BMC Med Inform Decis Mak. 2020 Sep 21;20(1):240. doi: 10.1186/s12911-020-01260-5. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Paatela S, Kyytsönen M, Saranto K, Kinnunen UM, Vehko T. Experiences of electronic health records’ and client information systems’ use on a mobile device and factors associated with work time savings among practical nurses: cross-sectional study. J Med Internet Res. 2024 May 29;26:e46954. doi: 10.2196/46954. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Alipour J, Mehdipour Y, Zakerabasali S, Karimi A. Nurses’ perspectives on using mobile health applications in southeastern Iran: awareness, attitude, and obstacles. PLoS One. 2025;20(3):e0316631. doi: 10.1371/journal.pone.0316631. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Yada H, Soejima K. Digital transformation in cardiology ― mobile health. Circ J. 2026;90(1):3–11. doi: 10.1253/circj.CJ-24-0654. doi. [ DOI ] [ PubMed ] [ Google Scholar ] 67. Trojan A, Kühne C, Kiessling M, et al. Impact of electronic patient-reported outcomes on unplanned consultations and hospitalizations in patients with cancer undergoing systemic therapy: results of a patient-reported outcome study compared with matched retrospective data. JMIR Form Res. 2024 May 6;8:e55917. doi: 10.2196/55917. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. van Steenbergen G, van Veghel D, van Lieshout D, Sperwer M, Ter Woorst J, Dekker L. Effects of video-based patient education and consultation on unplanned health care utilization and early recovery after coronary artery bypass surgery (IMPROV-ED): randomized controlled trial. J Med Internet Res. 2022 Aug 26;24(8):e37728. doi: 10.2196/37728. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Theodore BR, Whittington J, Towle C, et al. Transaction cost analysis of in-clinic versus telehealth consultations for chronic pain: preliminary evidence for rapid and affordable access to interdisciplinary collaborative consultation. Pain Med. 2015 Jun;16(6):1045–1056. doi: 10.1111/pme.12688. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. van der Meij E, Anema JR, Otten RH, Huirne JA, Schaafsma FG. The effect of perioperative e-health interventions on the postoperative course: a systematic review of randomised and non-randomised controlled trials. PLoS One. 2016;11(7):e0158612. doi: 10.1371/journal.pone.0158612. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 71. Theiler RN, Butler-Tobah Y, Hathcock MA, Famuyide A. OB Nest randomized controlled trial: a cost comparison of reduced visit compared to traditional prenatal care. BMC Pregnancy Childbirth. 2021 Jan 21;21(1):71. doi: 10.1186/s12884-021-03557-3. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Bok SK, Song Y, Lim A, Choi H, Shin H, Jin S. Korean translation and psychometric evaluation of Korean version EORTC QLQ-BRECON23. Int J Environ Res Public Health. 2020 Dec 8;17(24):9163. doi: 10.3390/ijerph17249163. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Clarijs ME, Oemrawsingh A, Bröker ME, Verhoef C, Lingsma H, Koppert LB. Quality of life of caregivers of breast cancer patients: a cross-sectional evaluation. Health Qual Life Outcomes. 2022 Feb 19;20(1):29. doi: 10.1186/s12955-022-01930-0. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Dewi IP, Gartika N, Sanusi S, Suryatiningsih D. Determinants of spiritual well-being that impact breast cancer patients’ quality of life. J Holist Nurs. 2024 Jun;42(2_suppl):S40–S46. doi: 10.1177/08980101231181272. doi. Medline. [ DOI ] [ PubMed ] [ Google Scholar ] 75. Chopra I, Kamal KM. A systematic review of quality of life instruments in long-term breast cancer survivors. Health Qual Life Outcomes. 2012 Jan 31;10:14. doi: 10.1186/1477-7525-10-14. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Frennert S, Petersson L, Muhic M, et al. Materiality and the mediating roles of eHealth: a qualitative study and comparison of three cases. Digit Health. 2022;8:20552076221116782. doi: 10.1177/20552076221116782. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 77. Frennert S, Petersson L, Erlingsdottir G. “More” work for nurses: the ironies of eHealth. BMC Health Serv Res. 2023 Apr 27;23:411. doi: 10.1186/s12913-023-09418-3. doi. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 78. Rouleau G, Wu K, Ramamoorthi K, et al. Mapping theories, models, and frameworks to evaluate digital health interventions: scoping review. J Med Internet Res. 2024 Feb 5;26:e51098. doi: 10.2196/51098. doi. Medline. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Multimedia Appendix 1. Summary of main characteristics of identified research, including research aim, outcome measures, and results. cancer-v12-e92030-s001.docx (93.6KB, docx) DOI: 10.2196/92030 Checklist 1. PRISMA-ScR Checklist. cancer-v12-e92030-s002.pdf (180.9KB, pdf) DOI: 10.2196/92030 Articles from JMIR Cancer are provided here courtesy of JMIR Publications Inc. ACTIONS View on publisher site PDF (271.0 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Non-pharmacological symptom self-management in non-malignant chronic disease: A scoping review.#162123
• Mind-Body Medicine Day Care Clinic in Breast Cancer Patients Undergoing Endocrine Therapy#840580
• Internet of things and health monitoring of patients with cancer: A scoping review.#421179
• Factors Affecting Telehealth Consultation in Diabetic Patients: A Systematic Scoping Review.#465229
• Talking to Employers and Medical Staff About Breast Cancer Treatment and Your Job#968134