Abstract 1871P
Background
Evidence suggests that introducing eHealth ecosystems improves the clinical management of chronic pain (CP) in breast cancer patients (BCPs). These tools contribute to overcoming established contextual, social, and individual barriers by monitoring patients' physical health and psychological well-being and early recognizing high-risk patients. Moreover, eHealth ecosystems increase patient participation in treatment decisions through specific decision aids modules, thus enabling shared decisions.
Methods
This pilot study is nested in PainRELife Project (ID:1173269). The primary endpoint was investigating patients' usability experience using an eHealth ecosystem constituted by Nu Platform joined with the PainRELife mobile application. Twenty-five female BCPs (Mage=47, SD=8.41) were enrolled and utilized the mobile application for three months. A set of self-report measures were administered at scheduled time points evaluating the following constructs: physical and mental characteristics; pain experience and intensity; pain self-efficacy; and patients’ preferences for participating in clinical decision-making and shared decision-making. The usability was evaluated by means of the Mobile Application Rating Scale (MARS).
Results
A reduction in pain intensity was observed from baseline (M=5) to 3 months (M=3.72), p<.04. The BCPs reported high values in all subscales of the MARS: engagement (M=3.31); functionality (M=4.14); aesthetics (M= 3.98); quality of the information (M=4.18); subjective quality (M=3.5); influence to change patient behaviors (M=4.05). A positive correlation was found between shared decision-making and engagement (r=.445*), quality of the information (r=.427*), and subjective quality attributed to the mobile application (r=.548**).
Conclusions
The eHealth ecosystems could be valuable aids in supporting clinicians and patients to accomplish the physical, social, and psychological challenges associated with the disease’s trajectory, also improving patients’ participation in treatment decisions allowing the implementation into clinical practice of shared decision-making models.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Applied Research Division for Cognitive and Psychological Science, European Institute of Oncology, IRCCS.
Funding
This work is supported by a grant from Regione Lombardia - “PainRELife, Sustainable and integrat-ed big data ecosystem for continuity of care and decision support for patients with pain" (ID: 1173269). The European Institute of Oncology (Istituto Europeo di Oncologia - IEO, Italy) monitors the scientific, legal, and ethical aspects of the study. Participants are recruited at IEO.
Disclosure
All authors have declared no conflicts of interest.
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