Abstract
The rise in dementia and the evidence of cognitive benefits of exercise for the older adult population together make salient the research into variables affecting cognitive benefit and exercise behavior. One promising avenue for increasing exercise participation has been the introduction of exergaming, a type of exercise that works in combination with virtual reality to enhance both the exercise experience and health outcomes. Past research has revealed that executive function (EF) was related to greater use of self-regulatory strategies, which in turn was related to greater adherence to exercise following an intervention (McAuley et al., 2011). Best et al. (2014) found improvement in EF related to adherence to exercise post- intervention. Anderson-Hanley et al. (2012) found that for older adults aerobic exergaming yielded greater cognitive benefit than traditional exercise alone; however, questions remain as to the possible impact of greater cognitive benefit and other factors on participants’ involvement in exercise following the end of an intervention. The current study presents follow-up data exploring the relationship between EF, self-regulation, and exercise behavior in the post-intervention (naturalistic) period. Herein, it was predicted that higher EF at the start of the naturalistic window, would predict subsequent exercise with an exergame. Contrary to expectations, results suggest that those with poorer EF are likely to exergame more frequently. The results of this study contradict previous literature, but suggest an interesting relationship between EF, self-regulation, and exercise behaviors when exergaming is employed, particularly with older adults with some cognitive decline. We hypothesize that other factors may be at work, perhaps expectation of cognitive benefit might act as a unique motivator.
Highlights
The positive impact of exercise on cognition has been well documented in the scientific literature (Kramer et al, 1999; Colcombe et al, 2004; Hogan et al, 2013)
Given the well-established evidence-base suggesting that exercise offers cognitive benefit for the older adult population in particular (Colcombe et al, 2004; Hindin and Zelinski, 2012), and the concomitant rise in rates of dementia (Steffens et al, 2009), research efforts must turn to understanding variables which may predict this populations’ participation in and adherence to a fitness regimen
No significant differences were found on key variables between the sample of those continuing to be followed in the study after the Randomized controlled trials (RCTs) and those from the original sample who did not continue on to participate in the naturalistic exercise window
Summary
The positive impact of exercise on cognition has been well documented in the scientific literature (Kramer et al, 1999; Colcombe et al, 2004; Hogan et al, 2013). Given the well-established evidence-base suggesting that exercise offers cognitive benefit for the older adult population in particular (Colcombe et al, 2004; Hindin and Zelinski, 2012), and the concomitant rise in rates of dementia (Steffens et al, 2009), research efforts must turn to understanding variables which may predict this populations’ participation in and adherence to a fitness regimen. An understanding of the variables that predict involvement in exercise behavior (both during and after prescribed interventions) could lead to more targeted interventions for older adults and, to more exercise and cognitive benefit
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
