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OBJECTIVES: Recognizing the limitations of the current pain therapies, the study aimed to explore the unique needs and obstacles related to pain management in Breast Cancer Survivors (BCs) with Chronic Pain (CP). METHODS: 4 focus groups were conducted involving 17 BCs with CP (Mage = 51, SD = 7.99) with varying pain intensities. Thematic analysis was applied to transcribed discussions. FINDINGS: Three key themes emerged: (1) Challenges to pain management, including "Doctor-patients communications barriers" and "Contextual and societal barriers"; (2) Self-management needs, encompassing "Psycho-social support," "Care-related needs," and "Shared decision-making"; (3) Treatment preferences and perceptions of pain management, with subthemes like "Treatment preferences," "Institution preference," and "Decision role perception." CONCLUSIONS: This study emphasizes tailored support systems targeting patient hesitancy, countering pain normalization, and addressing healthcare providers' attitudes. It underscores the importance of integrating caregiver and peer support. Findings advocate refining healthcare provider education, adopting a comprehensive multidisciplinary approach, and strategically incorporating eHealth tools into such care.
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BACKGROUND: Chronic pain is one of the most common and critical long-term effects of breast cancer. Digital health technologies enhance the management of chronic pain by monitoring physical and psychological health status and supporting pain self-management and patient treatment decisions throughout the clinical pathway. OBJECTIVE: This pilot study aims to evaluate patients' experiences, including usability, with a novel digital integrated health ecosystem for chronic pain named PainRELife. The sample included patients with breast cancer during survivorship. The PainRELife ecosystem comprises a cloud technology platform interconnected with electronic health records and patients' devices to gather integrated health care data. METHODS: We enrolled 25 patients with breast cancer (mean age 47.12 years) experiencing pain. They were instructed to use the PainRELife mobile app for 3 months consecutively. The Mobile Application Rating Scale (MARS) was used to evaluate usability. Furthermore, pain self-efficacy and participation in treatment decisions were evaluated. The study received ethical approval (R1597/21-IEO 1701) from the Ethical Committee of the European Institute of Oncology. RESULTS: The MARS subscale scores were medium to high (range: 3.31-4.18), and the total app quality score was 3.90. Patients with breast cancer reported reduced pain intensity at 3 months, from a mean of 5 at T0 to a mean of 3.72 at T2 (P=.04). The total number of times the app was accessed was positively correlated with pain intensity at 3 months (P=.03). The engagement (P=.03), information (P=.04), and subjective quality (P=.007) subscales were positively correlated with shared decision-making. Furthermore, participants with a lower pain self-efficacy at T2 (mean 40.83) used the mobile app more than participants with a higher pain self-efficacy (mean 48.46; P=.057). CONCLUSIONS: The data collected in this study highlight that digital health technologies, when developed using a patient-driven approach, might be valuable tools for increasing participation in clinical care by patients with breast cancer, permitting them to achieve a series of key clinical outcomes and improving quality of life. Digital integrated health ecosystems might be important tools for improving ongoing monitoring of physical status, psychological burden, and socioeconomic issues during the cancer survivorship trajectory. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/41216.
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[This corrects the article DOI: 10.2196/48852.].
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BACKGROUND: Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. OBJECTIVE: The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. METHODS: This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). RESULTS: The recruitment process started in May 2023 and is expected to conclude on December 2023. CONCLUSIONS: The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48852.
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BACKGROUND: Chronic pain (CP) and its management are critical issues in the care pathway of patients with breast cancer. Considering the complexity of CP experience in cancer, the international scientific community has advocated identifying cutting-edge approaches for CP management. Recent advances in the field of health technology enable the adoption of a novel approach to care management by developing integrated ecosystems and mobile health apps. OBJECTIVE: The primary end point of this pilot study is to evaluate patients' usability experience at 3 months of a new digital and integrated technological ecosystem, PainRELife, for CP in a sample of patients with breast cancer. The PainRELife ecosystem is composed of 3 main technological assets integrated into a single digital ecosystem: Fast Healthcare Interoperability Resources-based cloud platform (Nu platform) that enables care pathway definition and data collection; a big data infrastructure connected to the Fast Healthcare Interoperability Resources server that analyzes data and implements dynamic dashboards for aggregate data visualization; and an ecosystem of personalized applications for patient-reported outcomes collection, digital delivery of interventions and tailored information, and decision support of patients and caregivers (PainRELife app). METHODS: This is an observational, prospective pilot study. Twenty patients with early breast cancer and chronic pain will be enrolled at the European Institute of Oncology at the Division of Medical Senology and the Division of Pain Therapy and Palliative Care. Each patient will use the PainRELife mobile app for 3 months, during which data extracted from the questionnaires will be sent to the Nu Platform that health care professionals will manage. This pilot study is nested in a large-scale project named "PainRELife," which aims to develop a cloud technology platform to interoperate with institutional systems and patients' devices to collect integrated health care data. The study received approval from the Ethical Committee of the European Cancer Institute in December 2021 (number R1597/21-IEO 1701). RESULTS: The recruitment process started in May 2022 and ended in October 2022. CONCLUSIONS: The new integrated technological ecosystems might be considered an encouraging affordance to enhance a patient-centered approach to managing patients with cancer. This pilot study will inform about which features the health technological ecosystems should have to be used by cancer patients to manage CP. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41216.