In this systematic review of predictors of exercise adherence in older adults with non-musculoskeletal indications for prescribed exercise, we found that positive predictors of adherence, supported by moderate-quality evidence, were higher self-efficacy and good self-rated mental health. Negative predictors included depression (high-quality) and distance from the exercise facility (moderate quality). Comorbidity status, sex and age did not appear to be predictive of adherence, supported by moderate- to high-quality evidence. As prescribed exercise programs are less likely to be effective without high levels of adherence, these findings provide important insights into current practice and future research. In current practice, identification of negative predictors, with a particular focus on mental health, could allow for increased personalization and targeting of support. The small number of identified predictors with at least moderate-quality evidence and sparse data available for many predictors suggest that future research is needed to better understand and predict poor exercise adherence in older adults.
Numerous studies have estimated exercise adherence rates in a variety of populations, typically reporting similar or slightly higher adherence rates than those identified in our study. For example, Bullard et al.(40) reported a pooled adherence rate of 77% (95% CI 68%, 84%) across 30 studies of adults with cancer, cardiovascular disease or diabetes. However, few studies have evaluated what patient- and program-factors predict adherence, and to our knowledge, none have evaluated the strength of this evidence using a standard framework such as GRADE. Most available data currently focuses on program-related factors. Similar to our findings, Morgan et al.(41) identified program location as a barrier to participation and adherence, while Sheill et al.(42) found that difficulties travelling to exercise locations were a substantial barrier for individuals with advanced cancer. We found no evidence that the type of exercise program (i.e., interval vs continuous exercise) was predictive of adherence, which is consistent with recommendations that the act of engaging in exercise is likely of greater importance than the specific type of exercise performed.(40, 42)
Some authors have advocated the identification of participant-level ‘red flags’ to adherence as a way to personalize exercise program design and support.(40) However, this approach requires a thorough understanding of what participant characteristics may act as red flags. At the participant level, consistent findings from our study and from others suggest that aspects of mental health are likely key predictors of adherence. Self-efficacy has previously been reported as a predictor of adherence in a systematic review of home-based physiotherapy,(43) which is consistent with our findings and aligns with other systematic reviews that have found one’s intentions to engage in health-changing behaviors to be strongly predictive of adherence.(44) We also found that the presence of depression was a strong predictor of poor adherence and the only predictor supported by high-quality evidence. The related concept of good self-rated mental health (to some degree the inverse of depression) had moderate quality evidence supporting its role as a positive predictor of adherence. Whether anxiety predicts adherence in older people remains to be determined; we found no clear evidence of an association, as the strength and quality of evidence was low and reflected findings from only 3 studies. Interestingly, we did not find evidence that comorbidities, sex, or age were important predictors of adherence, as none suggested a directional association. Obesity and multimorbidity were also the only comorbidities with at least moderate quality evidence. Many comorbidities were not assessed and the impact of frailty was not reported in any studies, suggesting a need for future research. Finally, absent from the literature and related reviews is the consideration that program factors may interact with participant factors when predicting adherence. Although we were unable to identify any evidence of this phenomenon in our review, future evaluation is likely warranted to understand how, for example, participant-level red flags such as poor mental health may potentially be modified by specifically targeted aspects of program design. Such efforts could lead to better personalization and potentially higher adherence in individuals at risk of poor participation.
Strengths and limitations
Our study’s findings should be considered in the context of its strengths and limitations. First, we conducted our review according to best-practice methodologies, which included protocol pre-registration, peer-review of our search strategy, review of multiple databases, a focus on adjusted estimates and contextualisation of our findings within the GRADE strength of evidence framework. Furthermore, our results are based on identified studies that were generally at low or moderate risk of bias (apart from blinding issues in randomized trials, which is typical of exercise studies). However, despite pre-specifying a defined population of interest, included studies represented a somewhat heterogenous group of participants who engaged in exercise for cardiovascular, pulmonary and other indications. We were also unable to identify adequately homogenous data to support quantitative meta-analyses. This may, in part, reflect the number of largely unvalidated measures used to define exercise adherence in clinical research.(45) Accordingly, we classified our studies based on whether adherence was measured using a continuous or binary definition; however, this may not have completely captured the heterogeneity in underlying adherence measures.