The efficacy-effectiveness gap constitutes a well-known limitation for adoption of digitally enabled integrated care services. The current report describes the co-creation process undertaken (2016–2021) to deploy a prehabilitation service at Hospital Clínic de Barcelona with the final aim of achieving sustainable adoption and facilitate site transferability. An implementation research approach with a population-based orientation, combining experience-based co-design and quality improvement methodologies, was applied. We undertook several design-thinking sessions (Oct-Nov 2017, June 2021 and December 2021) to generate and follow-up a work plan fostering service scalability. The implementation process was assessed using the Comprehensive Framework for Implementation Research, leading to the identification of key performance indicators. Personalization and modularity of the intervention according to patients’ surgical risk were identified as core traits to enhance patients’ adherence and value generation. A digitally enabled service workflow, with an adaptive and collaborative case management approach, should combine face-to-face and remotely supervised sessions with intelligent systems for patients’ and professionals’ decision support. The business model envisages operational costs financed by savings generated by the service. Evidence-based co-creation, combining appropriate methodologies and a structured evaluation framework, was key to address challenges associated with sustainable prehabilitation service adoption, scalability and transferability.

Multimodal preoperative prehabilitation has been shown to be effective in improving the functional capacity of cancer patients, reducing postoperative complications and the length of hospital and ICU stay after surgery. The availability of prehabilitation units that gather all the professionals involved in patient care facilitates the development of integrated and patient-centered multimodal prehabilitation programs, as well as patient adherence. This article describes the process of creating a prehabilitation unit in our center and the role of perioperative nursing. Initially, the project was launched with the performance of a research study on prehabilitation for gastrointestinal cancer surgery. The results of this study encouraged us to continue the implementation of the unit. Progressively, multimodal prehabilitation programs focusing on each type of patient and surgery were developed. Currently, our prehabilitation unit is a care unit that has its own gym, which allows supervised training of cancer patients prior to surgery. Likewise, the evolution of perioperative nursing in the unit is described: from collaboration and assistance in the integral evaluation of the patient at the beginning to current work as a case manager; a task that has proven extremely important for the comprehensive and continuous care of the patient.

Background/Objectives: The real impact of prehabilitation in the healthcare setting is controversial due to the efficacy–effectiveness gap. The effectiveness of prehabilitation in real-world scenarios has been associated with program attrition and adherence. This study aimed to identify factors influencing adherence to a multimodal prehabilitation program for patients undergoing major surgery. Methods: This is a analysis of a prospective trial conceived to explore the implementation of prehabilitation in a real-life setting. Participants were patients enrolled in our multimodal prehabilitation program, candidates for major surgery, and at high risk for postoperative complications. Sociodemographic and clinical variables were studied, with adherence to the program as the primary outcome. Descriptive analyses were conducted to examine associations between adherence and the study variables. A binary logistic regression model was applied to identify predictors of adherence. Results: Among the 559 patients included in the study, 356 (63.7%) were labelled as adherent. The analysis revealed significant associations between adherence and working status, type of exercise program prescribed (p < 0.001), smoking status (p = 0.023), age (t = −3.00, p = 0.003), comorbidities (t = −2.19, p = 0.029), and self-reported physical activity (t = −2.45, p = 0.015). The logistic regression identified as independent factors the type of exercise prescription, smoking status, residential area, working status, and neoadjuvant therapy. The predictive model demonstrated good specificity (86.1%) but lower sensitivity (50.6%), suggesting its utility in identifying patients at risk of non-adherence. Conclusions: Multiple factors influence adherence in prehabilitation programs. Our model exhibited good accuracy and specificity, but poor sensitivity.