Psychosocial eHealth in Advanced Lung Cancer

Overview

Being diagnosed with cancer impairs many areas of a person's life. Although efficacious educational, emotional and social interventions exist in this regard, they often reach few survivors and late. This project, carried out by a specialized centre in cancer care and health research, will study the effectiveness, costs, and utility associated with a digital ecosystem tailored to meet the needs of patients with advanced lung cancer. This solution bridges the gap between patients and professionals to offer health services precisely when they are needed. The project is developed in the first year of an advanced lung cancer diagnosis, comparing the effects of the digital ecosystem with usual care in terms of their capacity to improve various psychosocial indicators. A comparative economic analysis will be carried out as well, to prove the cost-utility of the digital ecosystem presented.

Palliative Care (PC) for patients with advanced life-limiting diseases and the management of their symptoms during the trajectory of illness has evolved considerably (Clark, 2007). PC is conceptualized as an approach to improve the quality of life of patients and their relatives "through the prevention and relief of suffering by means of early identification and impeccable assessment and treatment of pain and other physical, psychosocial and spiritual problems". In recent years, survival rates have increased for most cancer diagnoses, in both early and advanced stages. Therefore, patients suffering a disease considered incurable are living longer with cumulative psychosocial comorbidity derived from both the illness itself and its associated treatments. The American and European societies for medical oncology have recently recommended integrating early psychosocial PC into standard oncological practice for patients with metastatic or advanced stage diseases like lung cancer (LC) in their professional guidelines. This decision has been recently supported by meta-analytic results as well. Those studies show that palliative interventions including physical and psychological aspects have beneficial effects on patients, both on short-term quality of life and in general symptom burden. Despite the advantages of such integrated PC interventions, healthcare systems usually encounter several barriers to implementing psychosocial care in palliative settings, like in advanced LC. The most typical include poor early detection of such needs; long waiting lists; and mobility restrictions, with many patients unable to attend visits in person. The literature strongly suggests that emotional distress is associated with worse quality of life, lower adherence to oncological treatments and adoption of unhealthy lifestyles. Actually, it is also demonstrated that stress reduction may even extend survival years. Since LC patients show great symptom variability, erratic evolution and high emotional impact along with a limited prognosis, it is urgent to increase the currently small proportion of patients with early screening, close and intensive monitoring and prompt referral to PC teams. To this aim, new approaches in psychosocial PC are needed to overcome the barriers experienced today. In the last years, two main actions have been proposed to improve the implementation of psychosocial care in PC, placing a focus on its accessibility and efficiency. For example, recent studies have introduced earlier stepped (low to high intensity) and adaptive treatments as an ingenious and sensible response to the challenge of offering proper psychosocial interventions, with high cost-effectiveness in cancer. Another comprehensive action is to make use of Information and Communication Technologies (ICT). ICT has emerged in the last few years as an innovative resource to set this new wave of health practices in motion, with an exponential increase in its use and implementation during the COVID pandemic, to guarantee continuity of care in vulnerable advanced cancer patients. ICT have also shown their capability to overcome most of the limitations expressed in conventional care settings. These tools have provided faster and more intense follow-up options to monitor patients' warning signs, facilitating better communication between patients and professionals, and also leveraging cheaper and more accessible clinical treatments compared to traditional alternatives, even at the end of life. Nevertheless, the few studies comparing ICT and usual psychosocial interventions have found mixed effectiveness results so far. Recently, ONCOMMUN, a European proposal for creating an e-health ecosystem (https://oncommun.eu/), has combined these two promising actions to facilitate early psychosocial care in an online and stepped psychosocial program. ONCOMMUN has shown promising preliminary results on breast cancer (BC) and a high potential for therapeutic application in advanced and palliative settings, like LC. The first level of care in this program is an online screening and monitoring tool, followed by a patient's campus comprising educational interventions (second level), a psychosocial support community (third level), and psychotherapeutic treatment groups through videoconference (fourth level). The current project has been designed as a randomized non-inferiority controlled trial to compare an e-health ecosystem of psychosocial care, based on the ONCOMMUN proposal, against traditional in-person psychosocial treatment in PC during advanced LC. Our group proposes the development and adaptation of this digital ecosystem by integrating screening and monitoring tools with educational and psychological interventions, building upon the results of its recent implementation in BC. This innovative e-health ecosystem intends to foster healthy experiences, integrating a four-stepped psychosocial program of early PC focused on patients with a diagnosis of non-small cell lung carcinoma (NSCLC) at advanced stages (III and IV). OBJECTIVES (3 years) General 1. To offer early detection and tailored treatment of psychosocial and physical needs of advanced LC survivors. 2. To implement and assess an online stepped ecosystem for psychosocial and educational screening, monitoring and care, through the use of an e-health platform specifically developed for advanced LC patients. Specific objectives 1. To compare the capacity of the digital ecosystem to detect and deliver early psychosocial care compared to treatment as usual. 2. To improve or cushion the consequences of the advanced lung cancer course and treatment in terms of emotional distress, demoralization and quality of life. 3. To explore the potential mediating and facilitating role of spirituality in an optimal psychosocial adaptation. 4. To study the cost-utility associated with both interventions in terms of Quality-Adjusted Life Years (QALY), through the estimation of patients' use of additional health services, their degree of pharmacological adherence, intake of psychotropic medication, and time spent in disability leaves. 5. To disseminate to cancer patients, professionals and the general public the results of the study. Procedure and data acquisition 1. Development of an LC platform: LC platform will be adapted from the BC platform and fed by the results of the pilot LC system and focus groups with professionals and patients. The LC solution will share a set of structural items (e.g., measurement instruments, instant symptom management), to which specific resources for LC will be added. Patients' experience will be at the centre of this development stage to maximize usability and understanding of all resources. 2. Validation with users: Patients with advanced LC diagnosis will be invited to participate by their medical team. If interested, they will be contacted by our group and a face-to-face meeting will be scheduled, where the study will be described and informed consent signed in case of acceptance. Participants will be then randomized to 1) e-health ecosystem or 2) usual psychosocial care by an external researcher blind to the research questions and treatment conditions, using a random sequence of numbers generated by REDCap software. In the eHealth ecosystem group, participants lacking equipment will be offered webcams and tablets as needed. There are specific processes of the system that will only apply to ICOnnecta't branch as they will measure the platform usability and their associate emotional state during the study (see Interventions section below). All other measures will be administered from T0 to T3 through a professional online survey platform compliant with the latest European General Data Protection Regulation (GDPR; EC/2016/679). Data collection and analysis Two databases will be created: The first one will associate participants' identifiable personal data (e.g., names, patient ID) with an alphanumeric code, and will be saved in an encrypted external hard drive stored in a key-protected closet within the office of the PI. The second database, created via REDCap system, will record all data to be analyzed making use of alphanumeric codes and will be stored in a secure collaborative cloud also GDPR-compliant. This procedure will allow us to conduct the analyses anonymously. Data will be monthly downloaded from REDCap and backed up in a second encrypted external hard drive. Every 3 months one researcher will conduct a data integrity check. While online systems automatically keep a registry of users' access, a notebook will remain next to the hard drives for researchers authorized by PIs to record their name, date and time when drives are retrieved and returned. Finally, the information collected through the eHealth ecosystem will also be stored in a GDPR-compliant server. Descriptive results will be provided for sociodemographic and clinical variables, as well as for education, usability and satisfaction indicators, while between-group differences will be assessed with Student's t-test and chi-square tests as appropriate. Multilevel linear models (MLM) will be used to compare both groups in outcome variables, while effect sizes (Hedges' g) will be reported and non-inferiority tested. For QALY analyses, results from the EQ-5D-3L will be used together with costs associated with professional salaries, adherence, infrastructure, psychotropic medication and sick leaves. The effect of any potential confounding variable will be analyzed. Analyses will be conducted using SPSS v24.021 (IBM SPSS Statistics 21, 2017) by the IDIBELL biostatistics department.