Health Recommender Systems (HRSs) have emerged as a crucial tool in personalized healthcare, offering tailored recommendations to promote healthy behaviors and prevent diseases. The effective- ness of these systems hinges on their ability to accurately personal- ize recommendations based on contextual variables. This research investigates the contextual variables currently employed by HRSs, addressing two key research questions: (1) Which contextual vari- ables are currently used in HRSs? and (2) How can these variables be categorized? Through an extensive systematic literature review, we identified 24 commonly utilized contextual variables across existing HRSs. To provide a structured approach for understand- ing, we organized the variables with a framework that classifies contextual variables into four distinct categories: objective-static, objective-dynamic, subjective-static, and subjective-dynamic. Our findings highlight the diverse yet uneven distribution of these vari- ables within the framework, emphasizing the need for a balanced consideration of both objective and subjective data in developing comprehensive HRSs. The proposed framework serves as a robust foundation for future advancements, aiming to enhance the per- sonalization capabilities of HRSs and ultimately improve health outcomes.