Attuazione e Valutazione del Programma BE-FIT (BE-FIT)

Frailty and post-operative complications are the predictors of increased cost-of-care among older patients who underwent emergency abdominal surgery in Alberta. In addition, older patients are often more vulnerable to the physical stressors following major surgery including risks of falls and disability. There is increasing evidence that daily exercise and physical activities can enhance the recovery and discharge process for older patients. Employing a two-arm trial design we will test whether the initiation of an early mobilization program can decrease the rate of functional decline in post-surgical older adults' patients.

The trial will be conducted in an acute care hospital setting in Alberta, Canada. The investigators will implement a quality improvement program BE-FIT (Bedside reconditioning for functional improvements) based on the Mobilization of Vulnerable Elders (MOVE) implementation strategy. BE-FIT is an evidence-based exercise program that focuses on early mobilization and recovery by minimizing bed rest, promoting functional tasks, and encouraging self-management. The intervention consists of a bedside exercise plan completed throughout the hospitalization. It is done independently without the need for additional rehabilitation or healthcare staff. The investigators will implement educational intervention to support the patients, staff, and families in bringing and sustaining change in practice and behavior from sedentary to physically active mobilization approach in the surgical area of the acute care setting. Control patients will receive usual care without the added exercise plan.

BE-FIT will be implemented in surgical units at 3 demonstrator sites (University of Alberta Hospital (UAH), Foothills Medical Centre (FMC) and the Misericordia Community Hospital (MCH)). Units may include General surgery, Otolaryngologic surgery, and Urology. The research will include all patients ≥65 years admitted to the hospital (emergency or elective) units.

The study is guided by the Knowledge-to-Action Cycle theory (K2A) and the Quality Implementation Framework (QIF) with interrupted time-series (IT) design. Prior to the implementation of the program, the investigators will used readiness assessments, identification of facilitators and barriers to post-operative mobility and co-designs with patients will be conducted. To measure and analyze the impact of our program, the primary outcome will evaluate the percentage of 'out of bed' occurrences as measured by an interrupted time series of mobility audits; secondary outcomes will include: time-to-mobility, length of stay, complication incidence and hospitalization costs. The quasi-experimental unblinded interrupted time-series (ITS) design will be conducted in three phases: pre-intervention (10 weeks); during intervention (16 weeks); post-intervention (20 weeks). A pre-intervention model will be created by using the first time-points over 10 weeks to analyze the primary outcome. To evaluate whether this model describes the baseline adequately, an estimate of the time-series models for the entire 84-time points (pre- and post-intervention) will be performed. To examine the impact of the BE-FIT exercise program on patient outcome, the investigators will use segmented linear regression models and correct the models for autocorrelation as required using the Cochrane-Orcutt method. This will be used to calculate the median daily length-of-stay for each week in the study period, classified into pre-, during and post-intervention. Regression methods will be used to assess mobilization outcome to investigate and compare the trend in median length-of-stay. A before-and-after analysis methodology will be adopted for complications, functional performance, and patient satisfaction. Regression models will be constructed using the parsimonious approach, multivariable linear (or transformed linear) regression, and logistic regression. Potential confounding baseline variables (i.e., age, sex, patient location before admission, admitting diagnosis, etc.) will be entered into the model and kept if deemed statistically (p<0.05) and/or clinically important. Health economic assessment will estimate mean cost for different sub-categories using appropriate regressions models for skewed cost data, adjusted for potential risk differences. The impact on patient's function and quality-of-life will be assessed with Short Performance Physical Battery (SPPB) and EQ-5D questionnaires.