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Updated: Sep 25, 2025

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
Published on: November 8, 2013
Predictors of adherence to prescribed exercise programs for older adults with medical or surgical indications for
Julia F Shaw1,2, Sophie Pilon3, Matthieu Vierula3
1Ottawa Hospital Research Institute, Clinical Epidemiology Program, 1053 Carling Ave, Ottawa, ON, K1Y 4E9, Canada.
Background And Objectives:
Prescribed exercise to treat medical conditions and to prepare for surgery is a promising intervention to prevent adverse health outcomes for older adults; however, adherence to exercise programs may be low. Our objective was to identify and grade the quality of predictors of adherence to prescribed exercise in older adults.
Methods:
Prospective observational and experimental studies were identified using a peer-reviewed search strategy applied to MEDLINE, EMBASE, Cochrane, and CINAHL from inception until October 6, 2020. Following an independent and duplicate review of titles, abstracts, and full texts, we included prospective studies with an average population age >65 years, where exercise was formally prescribed for a medical or surgical condition. We excluded studies where exercise was prescribed for a chronic musculoskeletal condition. Risk of bias was assessed using the Quality in Prognostic studies tool or Cochrane risk of bias tool, as appropriate. Predictors of adherence were identified and graded for quality using an adaptation of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework for predictor studies.
Results:
We included 19 observational studies and 4 randomized controlled trials (n=5785) Indications for exercise included cardiac (n=6), pulmonary rehabilitation (n=7), or other (n=10; surgical, medical, and neurologic). Of the 10 studies that reported adherence as the percent of prescribed sessions completed, average adherence was 80% (range 60-98%; standard deviation (SD) 11%). Of the 10 studies that reported adherence as a categorical threshold demarking adherent vs not adherent, average adherence was 57.5% (range 21-83%; SD 21%). Moderate-quality evidence suggested that positive predictors of adherence were self-efficacy and good self-rated mental health; negative predictors were depression (high quality) and distance from the exercise facility. Moderate-quality evidence suggested that comorbidity and age were not predictive of adherence.
Conclusions:
These findings can inform the design of future exercise programs as well as the identification of individuals who may require extra support to benefit from prescribed exercise.
Systematic Review Registration:
PROSPERO CRD42018108242.
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