Development of the Exercise in Cancer Evaluation and Decision Support (EXCEEDS) algorithm

Academic Article


  • Purpose: Participation in exercise or rehabilitation services is recommended to optimize health, functioning, and well-being across the cancer continuum of care. However, limited knowledge of individual needs and complex decision-making are barriers to connect the right survivor to the right exercise/rehabilitation service at the right time. In this article, we define the levels of exercise/rehabilitation services, provide a conceptual model to improve understanding of individual needs, and describe the development of the Exercise in Cancer Evaluation and Decision Support (EXCEEDS) algorithm. Methods: From literature review, we synthesized defining characteristics of exercise/rehabilitation services and individual characteristics associated with safety and efficacy for each service. We developed a visual model to conceptualize the need for each level of specialized care, then organized individual characteristics into a risk-stratified algorithm. Iterative review with a multidisciplinary expert panel was conducted until consensus was reached on algorithm content and format. Results: We identified eight defining features of the four levels of exercise/rehabilitation services and provide a conceptual model of to guide individualized navigation for each service across the continuum of care. The EXCEEDS algorithm includes a risk-stratified series of eleven dichotomous questions, organized in two sections and ten domains. Conclusions: The EXCEEDS algorithm is an evidence-based decision support tool that provides a common language to describe exercise/rehabilitation services, a practical model to understand individualized needs, and step-by-step decision support guidance. The EXCEEDS algorithm is designed to be used at point of care or point of need by multidisciplinary users, including survivors. Thus, implementation may improve care coordination for cancer exercise/rehabilitation services.
  • Published In

    Digital Object Identifier (doi)

    Pubmed Id

  • 19383602
  • Author List

  • Covington KR; Marshall T; Campbell G; Williams GR; Fu JB; Kendig TD; Howe N; Alfano CM; Pergolotti M
  • Start Page

  • 6469
  • End Page

  • 6480
  • Volume

  • 29
  • Issue

  • 11