ABSTRACT
As cancer progresses, patients may experience physical decline, which can impair their ability to carry out essential daily tasks. The aim of this study was to analyze the levels of physical activity in patients with advanced cancer undergoing systemic treatment and its relationship with sociodemographic, clinical, and psychological factors. A prospective, cross-sectional, multicenter study was carried out in 15 oncology departments in Spain. Patients with locally advanced, unresectable, or metastatic cancer who were candidates for systemic treatment were included. Participants completed demographic information and psychological scales. In total, 508 patients were included in the study, the majority of whom were male, over the age of 65, and diagnosed with bronchopulmonary tumors (36%) and metastatic disease. Based on their physical activity levels, participants were categorized as sedentary (20%, n = 190), engaging in light physical activity (43%, n = 412), or demonstrating moderate physical activity (37%, n = 351). Patients who were over 65 years old; had a worse baseline status (ECOG ≥ 1); lacked a partner; had a lower educational level; or were retired or unemployed were found to have lower levels of physical activity. Those with sedentary physical activity reported higher levels of psychological distress, anxiety, depression, somatization, and physical symptoms, as well as worse functional status, global health status, and well-being. Understanding the complex interplay between physical activity and sociodemographic, clinical, and psychological factors can help neuroscientists develop tailored exercise interventions that address the unique needs of advanced cancer patients.
ABSTRACT
OBJECTIVE: Reducing backlogs for elective care is a priority for healthcare systems. We conducted an interrupted time series analysis demonstrating the effect of an algorithm for placing automatic test order sets prior to first specialist appointment on avoidable follow-up appointments and attendance rates. DESIGN: Interrupted time series analysis. SETTING: 4 academic hospitals from Madrid, Spain. PARTICIPANTS: Patients referred from primary care attending 10 033 470 outpatient appointments from 16 clinical specialties during a 6-year period (1 January 2018 to 30 June 2023). INTERVENTION: An algorithm using natural language processing was launched in May 2021. Test order sets developed for 257 presenting complaints from 16 clinical specialties were placed automatically before first specialist appointments to increase rates of diagnosis and initiation of treatment with discharge back to primary care. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcomes included rate of diagnosis and discharge to primary care and follow-up to first appointment index. The secondary outcome was trend in 'did not attend' rates. RESULTS: Since May 2021, a total of 1 175 814 automatic test orders have been placed. Significant changes in trend of diagnosis and discharge to primary care at first appointment (p=0.005, 95% CI 0.5 to 2.9) and 'did not attend' rates (p=0.006, 95% CI -0.1 to -0.8) and an estimated attributable reduction of 11 306 avoidable follow-up appointments per month were observed. CONCLUSION: An algorithm for placing automatic standardised test order sets can reduce low-value follow-up appointments by allowing specialists to confirm diagnoses and initiate treatment at first appointment, also leading to early discharge to primary care and a reduction in 'did not attend' rates. This initiative points to an improved process for outpatient diagnosis and treatment, delivering healthcare more effectively and efficiently.
