Improvements to executive function during exercise training predict maintenance of physical activity over the following year

John R Best, Lindsay S Nagamatsu, Teresa Liu-Ambrose, John R Best, Lindsay S Nagamatsu, Teresa Liu-Ambrose

Abstract

Previous studies have shown that exercise training benefits cognitive, neural, and physical health markers in older adults. It is likely that these positive effects will diminish if participants return to sedentary lifestyles following training cessation. Theory posits that that the neurocognitive processes underlying self-regulation, namely executive function (EF), are important to maintaining positive health behaviors. Therefore, we examined whether better EF performance in older women would predict greater adherence to routine physical activity (PA) over 1 year following a 12-month resistance exercise training randomized controlled trial. The study sample consisted of 125 community-dwelling women aged 65-75 years old. Our primary outcome measure was self-reported PA, as measured by the Physical Activity Scale for the Elderly (PASE), assessed on a monthly basis from month 13 to month 25. Executive function was assessed using the Stroop Test at baseline (month 0) and post-training (month 12). Latent growth curve analyses showed that, on average, PA decreased during the follow-up period but at a decelerating rate. Women who made greater improvements to EF during the training period showed better adherence to PA during the 1-year follow-up period (β = -0.36, p < 0.05); this association was unmitigated by the addition of covariates (β = -0.44, p < 0.05). As expected, EF did not predict changes in PA during the training period (p > 0.10). Overall, these findings suggest that improving EF plays an important role in whether older women maintain higher levels of PA following exercise training and that this association is only apparent after training when environmental support for PA is low.

Keywords: aging; executive function; exercise training; physical activity adherence; temporal self-regulation theory.

Figures

Figure 1
Figure 1
Unconditional growth model. Individual growth trajectories are indicated by the colored lines. The average growth trajectory specified by Model C in Table 4 is indicated by the thicker black line.
Figure 2
Figure 2
Conditional growth curve model of the predictive effects of change in Stroop performance during the intervention on physical activity during the 1-year follow-up period. Standardized path coefficients (standard errors) and standardized residual variances (standard errors) are shown. To simplify the depiction of the model, the PASE quadratic slope factor, the slope and intercept factor loadings, and the residual variances for PASE indicator variables have been omitted. *p < 0.05. ***p < 0.001.

References

    1. Anguera J. A., Boccanfuso J., Rintoul J. L., Al-Hashimi O., Faraji F., Janowich J., et al. (2013). Video game training enhances cognitive control in older adults. Nature 501, 97–101 10.1038/nature12486
    1. Ball K., Berch D. B., Helmers K. F., Jobe J. B., Leveck M. D., Marsiske M., et al. (2002). Effects of cognitive training interventions with older adults: a randomized controlled trial. JAMA 288, 2271–2281 10.1001/jama.288.18.2271
    1. Best J. R., Theim K. R., Gredysa D. M., Stein R. I., Welch R. R., Saelens B. E., et al. (2012). Behavioral economic predictors of overweight children's weight loss. J. Consult. Clin. Psychol. 80, 1086–1096 10.1037/a0029827
    1. Cassilhas R. C., Viana V. A., Grassmann V., Santos R. T., Santos R. F., Tufik S., et al. (2007). The impact of resistance exercise on the cognitive function of the elderly. Med. Sci. Sports Exerc. 39, 1401–1407 10.1249/mss.0b013e318060111f
    1. Colcombe S. J., Kramer A. F., Erickson K., Scalf P., McAuley E., Cohen N. J., et al. (2004). Cardiovascular fitness, cortical plasticity, and aging. Proc. Natl. Acad. Sci. U.S.A. 101, 3316–3321 10.1073/pnas.0400266101
    1. Colcombe S., Kramer A. F. (2003). Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychol. Sci. 14, 125–130 10.1111/1467-9280.t01-1-01430
    1. Costello E., Kafchinski M., Vrazel J., Sullivan P. (2011). Motivators, barriers, and beliefs regarding physical activity in an older adult population. J. Geriatr. Phys. Ther. 34, 138–147 10.1519/JPT.0b013e31820e0e71
    1. Daniel T. O., Stanton C. M., Epstein L. H. (2013). The future is now: reducing impulsivity and energy intake using episodic future thinking. Psychol. Sci. 24, 2339–2342 10.1177/0956797613488780
    1. Duckworth A. L., Kern M. L. (2011). A meta-analysis of the convergent validity of self-control measures. J. Res. Pers. 45, 259–268 10.1016/j.jrp.2011.02.004
    1. Duncan T. E., Duncan S. C., Strycker L. A. (2006). An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. 2nd edn Mahwah: Lawrence Erlbaum
    1. Erickson K. I., Voss M. W., Prakash R. S., Basak C., Szabo A., Chaddock L., et al. (2011). Exercise training increases size of hippocampus and improves memory. Proc. Natl. Acad. Sci. U.S.A. 108, 3017–3022 10.1073/pnas.1015950108
    1. Findorff M. J., Wyman J. F., Gross C. R. (2009). Predictors of long-term exercise adherence in a community-based sample of older women. J. Womens Health 18, 1769–1776 10.1089/jwh.2008.1265
    1. Groll D. L., To T., Bombardier C., Wright J. G. (2005). The development of a comorbidity index with physical function as the outcome. J. Clin. Epidemiol. 58, 595–602 10.1016/j.jclinepi.2004.10.018
    1. Hall P. A. (2013). Temporal self-regulation theory, in Encyclopedia of Behavioral Medicine, eds Gellman M. D., Turner J. R. (New York, NY: Springer; ), 1960–1963
    1. Hall P. A., Crossley M., D'Arcy C. (2010). Executive function and survival in the context of chronic illness. Ann. Behav. Med. 39, 119–127 10.1007/s12160-010-9162-z
    1. Hall P. A., Elias L. J., Crossley M. (2006). Neurocognitive influences on health behavior in a community sample. Health Psychol. 25, 778–782 10.1037/0278-6133.25.6.778
    1. Hall P. A., Fong G. T. (2007). Temporal self-regulation theory: a model for individual health behavior. Health Psychol. Rev. 1, 6–52 10.1080/17437190701492437
    1. Hall P. A., Fong G. T. (2010). Temporal self-regulation theory: looking forward. Health Psychol. Rev. 4, 83–92 10.1080/17437199.2010.487180
    1. Hare T. A., Camerer C. F., Rangel A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324, 646–648 10.1126/science.1168450
    1. Houben K., Jansen A. (2011). Training inhibitory control. A recipe for resisting sweet temptations. Appetite 56, 345–349 10.1016/j.appet.2010.12.017
    1. Houben K., Wiers R. W., Jansen A. (2011). Getting a grip on drinking behavior: training working memory to reduce alcohol abuse. Psychol. Sci. 22, 968–975 10.1177/0956797611412392
    1. Hu L.-T., Bentler P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6, 1–55 10.1080/10705519909540118
    1. Jurado M. B., Rosselli M. (2007). The elusive nature of executive functions: a review of our current understanding. Neuropsychol. Rev. 17, 213–233 10.1007/s11065-007-9040-z
    1. Kober H., Mende-Siedlecki P., Kross E. F., Weber J., Mischel W., Hart C. L., et al. (2010). Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc. Natl. Acad. Sci. U.S.A. 107, 14811–14816 10.1073/pnas.1007779107
    1. Kramer A. F., Hahn S., Cohen N. J., Banich M. T., McAuley E., Harrison C. R., et al. (1999). Ageing, fitness and neurocognitive function. Nature 400, 418–419 10.1038/22682
    1. Lautenschlager N. T., Cox K. L., Flicker L., Foster J. K., van Bockzmeer F. M., Xiao J., et al. (2008). Effect of physical activity on cognitive function in older adults at risk for alzheimer disease: a randomized trial. JAMA 300, 1027–1037 10.1001/jama.300.9.1027
    1. Liu-Ambrose T., Nagamatsu L. S., Graf P., Beattie B. L., Ashe M. C., Handy T. C. (2010). Resistance training and executive functions: a 12-month randomized controlled trial. Arch. Intern. Med. 170, 170–178 10.1001/archinternmed.2009.494
    1. Liu-Ambrose T. Y., Nagamatsu L. S., Voss M. W., Khan K. M., Handy T. C. (2012). Resistance training and functional plasticity of the aging brain: a 12-month randomized controlled trial. Neurobiol. Aging 33, 1690–1698 10.1016/j.neurobiolaging.2011.05.010
    1. McClure S. M., Laibson D. I., Loewenstein G., Cohen J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science 306, 503–507 10.1126/science.1100907
    1. Muthen B., Asparouhov T., Hunter A. M., Leuchter A. F. (2011). Growth modeling with nonignorable dropout: alternative analyses of the STAR*D antidepressant trial. Psychol. Methods 16, 17–33 10.1037/a0022634
    1. Muthén L. K., Muthén B. (2012). Mplus: Statical Analysis with Latent Variables. Los Angeles, CA: Muthén & Muthén
    1. Nagamatsu L. S., Handy T. C., Hsu C. L., Voss M. W., Liu-Ambrose T. Y. (2012). Resistance training promotes cognitive and functional brain plasticity in seniors with probable mild cognitive impairment. Arch. Intern. Med. 172, 666–668 10.1001/archinternmed.2012.379
    1. Nasreddine Z. S., Phillips N. A., Bédirian V. R., Charbonneau S., Whitehead V., Collin I., et al. (2005). The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53, 695–699 10.1111/j.1532-5415.2005.53221.x
    1. Roshanaei-Moghaddam B., Katon W. J., Russo J. (2009). The longitudinal effects of depression on physical activity. Gen. Hosp. Psychiatry 31, 306–315 10.1016/j.genhosppsych.2009.04.002
    1. Shamosh N. A., Deyoung C. G., Green A. E., Reis D. L., Johnson M. R., Conway A. R., et al. (2008). Individual differences in delay discounting: relation to intelligence, working memory, and anterior prefrontal cortex. Psychol. Sci. 19, 904–911 10.1111/j.1467-9280.2008.02175.x
    1. Shimamura A. P. (2000). The role of the prefrontal cortex in dynamic filtering. Psychobiology 28, 207–218 10.3758/BF03331979
    1. Smith P. J., Blumenthal J. A., Hoffman B. M., Cooper H., Strauman T. A., Welsh-Bohmer K., et al. (2010). Aerobic exercise and neurocognitive performance: a meta-analytic review of randomized controlled trials. Psychosom. Med. 72, 239–252 10.1097/PSY.0b013e3181d14633
    1. Stroop J. R. (1935). Studies of interference in serial verbal reactions. J. Exp. Psychol. 18, 643 10.1037/h0054651
    1. Tak E. C. P. M., Van Uffelen J. G. Z., Paw M., Van Mechelen W., Hopman-Rock M. (2012). Adherence to exercise programs and determinants of maintenance in older adults with mild cognitive impairment. J. Aging Phys. Act. 20, 32–46
    1. Voss M. W., Prakash R. S., Erickson K. I., Basak C., Chaddock L., Kim J. S., et al. (2010). Plasticity of brain networks in a randomized intervention trial of exercise training in older adults. Front. Aging Neurosci. 2:32 10.3389/fnagi.2010.00032
    1. Washburn R. A., McAuley E., Katula J., Mihalko S. L., Boileau R. A. (1999). The physical activity scale for the elderly (PASE): evidence for validity. J. Clin. Epidemiol. 52, 643–651 10.1016/S0895-4356(99)00049-9
    1. Washburn R. A., Smith K. W., Jette A. M., Janney C. A. (1993). The Physical Activity Scale for the Elderly (PASE): development and evaluation. J. Clin. Epidemiol. 46, 153–162 10.1016/0895-4356(93)90053-4
    1. Washburn R., Ficker J. (1999). Physical Activity Scale for the Elderly (PASE): the relationship with activity measured by a portable accelerometer. J. Sports Med. Phys. Fitness 39, 336–340
    1. Willis S. L., Tennstedt S. L., Marsiske M., Ball K., Elias J., Mann Koepke K., et al. (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. JAMA 296, 2805–2814 10.1001/jama.296.23.2805
    1. Yesavage J. A., Brink T. L., Rose T. L., Lum O., Huang V., Adey M., et al. (1982). Development and validation of a geriatric depression screening scale: a preliminary report. J. Psychiatr. Res. 17, 37–49 10.1016/0022-3956(82)90033-4

Source: PubMed

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies

3
Abonneren