Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-25T01:02:04.328Z Has data issue: false hasContentIssue false

Executive Cognitive Functions and Behavioral Control Differentially Predict HbA1c in Type 1 Diabetes across Emerging Adulthood

Published online by Cambridge University Press:  11 December 2019

Yana Suchy*
Affiliation:
Department of Psychology, University of Utah, Salt Lake City, USA
Jonathan Butner
Affiliation:
Department of Psychology, University of Utah, Salt Lake City, USA
Deborah J. Wiebe
Affiliation:
Department of Psychology, University of California, Merced, USA
MaryJane Campbell
Affiliation:
Department of Psychology, University of Utah, Salt Lake City, USA
Sara L. Turner
Affiliation:
Department of Psychology, University of Utah, Salt Lake City, USA
Cynthia A. Berg
Affiliation:
Department of Psychology, University of Utah, Salt Lake City, USA
*
*Correspondence and reprint requests to: Yana Suchy, Department of Psychology, University of Utah, 380 S. 1530 E., Rm. 502, Salt Lake City, UT 84112, USA. E-mail: yana.suchy@psych.utah.edu

Abstract

Objectives:

To examine the contributions of two aspects of executive functioning (executive cognitive functions and behavioral control) to changes in diabetes management across emerging adulthood.

Methods:

Two hundred and forty-seven high school seniors with type 1 diabetes were assessed at baseline and followed up for 3 years. The baseline assessment battery included performance-based measures of executive cognitive functions, behavioral control, IQ estimate (IQ-est), and psychomotor speed; self-report of adherence to diabetes regimen; and glycated hemoglobin (HbA1c) assay kits as a reflection of glycemic control.

Results:

Linear and quadratic growth curve models were used to simultaneously examine baseline performance on four cognitive variables (executive cognitive functions, behavioral control, IQ, and psychomotor speed) as predictors of indices of diabetes management (HbA1c and adherence) across four time points. Additionally, general linear regressions examined relative contributions of each cognitive variable at individual time points. The results showed that higher behavioral control at baseline was related to lower (better) HbA1c levels across all four time points. In contrast, executive cognitive functions at baseline were related to HbA1c trajectories, accounting for increasingly more HbA1c variance over time with increasing transition to independence. IQ-est was not related to HbA1c levels or changes over time, but accounted instead for HbA1c variance at baseline (while teens were still living at home), above and beyond all other variables. Cognition was unrelated to adherence.

Conclusions:

Different aspects of cognition play a different role in diabetes management at different time points during emerging adulthood years.

Type
Regular Research
Copyright
Copyright © INS. Published by Cambridge University Press, 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

American Diabetes Association. (2019). Introduction: Standards of medical care in diabetes—2019. Diabetes Care, 42 (Supplement 1), S1 LP–S2. doi: 10.2337/dc19-Sint01CrossRefGoogle Scholar
Avci, G., Sheppard, D.P., Tierney, S.M., Kordovski, V.M., Sullivan, K.L., & Woods, S.P. (2018). A systematic review of prospective memory in HIV disease: From the laboratory to daily life. The Clinical Neuropsychologist, 32(5), 858890. doi: 10.1080/13854046.2017.1373860CrossRefGoogle ScholarPubMed
Baucom, K.J.W., Turner, S.L., Tracy, E.L., Berg, C.A., & Wiebe, D.J. (2018). Depressive symptoms and diabetes management from late adolescence to emerging adulthood. Health Psychology, 37(8), 716724.CrossRefGoogle ScholarPubMed
Bechara, A. (2007). Iowa gambling task professional manual. Lutz, FL: Psychological Assessment Resources.Google Scholar
Berg, C.A., Butner, J.E., Turner, S.L., Lansing, A.H., King, P., & Wiebe, D.J. (2016). Adolescents’, mothers’, and fathers’ reports of adherence across adolescence and their relation to HbA1c and daily blood glucose. Journal of Behavioral Medicine, 39(6), 10091019. doi: 10.1007/s10865-016-9771-5CrossRefGoogle ScholarPubMed
Berg, C.A., Butner, J., Wiebe, D.J., Lansing, A.H., Osborn, P., King, P.S., Palmer, D.L., & Butler, J.M. (2017). Developmental model of parent-child coordination for self-regulation across childhood and into emerging adulthood: Type 1 diabetes management as an example. Developmental Review, 46, 126. doi: 10.1016/j.dr.2017.09.001CrossRefGoogle Scholar
Berg, C.A., Hughes, A.E., King, P.S., Korbel, C., Fortenberry, K.T., Donaldson, D., Foster, C., Swinyard, M., & Wiebe, D.J. (2014). Self-control as a mediator of the link between intelligence and HbA1c during adolescence. Children’s Health Care, 43(2), 120131. doi: 10.1080/02739615.2013.837819CrossRefGoogle Scholar
Berg, C.A., Wiebe, D.J., Suchy, Y., Turner, S.L., Butner, J., Munion, A., Lansing, A.H., White, P.C., & Murray, M. (2018). Executive function predicting longitudinal change in type 1 diabetes management during the transition to emerging adulthood. Diabetes Care, 41(11), 22812288.CrossRefGoogle ScholarPubMed
Conners, C.K. (2000). Continuous Performance Test II. Toronto: Multi-Health System.Google Scholar
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis Kaplan Executive Function System: Examiner’s Manual. San Antonio, TX: Psychological Corporation. doi: 10.1080/09297040490911140Google Scholar
Duke, D.C., & Harris, M.A. (2014). Executive function, adherence, and glycemic control in adolescents with type 1 diabetes: A literature review. Current Diabetes Reports, 14(10), 532. doi: 10.1007/s11892-014-0532-yCrossRefGoogle ScholarPubMed
Franchow, E.I., & Suchy, Y. (2015). Naturally-occurring expressive suppression in daily life depletes executive functioning. Emotion, 15(1), 7889.CrossRefGoogle ScholarPubMed
Franchow, E.I., & Suchy, Y. (2017). Expressive suppression depletes executive functioning in older adulthood. Journal of the International Neuropsychological Society, 23(4), 341351. doi: 10.1017/S1355617717000054CrossRefGoogle ScholarPubMed
Friedman, N.P., Miyake, A., Corley, R.P., Young, S.E., DeFries, J.C., & Hewitt, J.K. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172179.CrossRefGoogle ScholarPubMed
Goethals, E.R., de Wit, M., Van Broeck, N., Lemiere, J., Van Liefferinge, D., Böhler, S., De Wulf, M, Dello, E, Laridaen, J, Van Hecke, L, Van Impe, S, Casteels, K., & Luyckx, K. (2018). Child and parental executive functioning in type 1 diabetes: Their unique and interactive role toward treatment adherence and glycemic control. Pediatric Diabetes, 19(3), 520526. doi: 10.1111/pedi.12552CrossRefGoogle ScholarPubMed
Gollwitzer, P.M., & Oettingen, G. (2011). Planning promotes goal striving. In Vohs, K.D. & Baumeister, R.F. (Eds.), Handbook of Self-Regulation: Research, Theory and Applications, (2nd ed.). New York: Guilford.Google Scholar
Gonder-Frederick, L.A., Zrebiec, J.F., Bauchowitz, A.U., Ritterband, L.M., Magee, J.C., Cox, D.J., & Clarke, W.L. (2009). Cognitive function is disrupted by both hypo- and hyperglycemia in school-aged children with type 1 diabetes: A field study. Diabetes Care, 32(6), 10011006. doi: 10.2337/dc08-1722CrossRefGoogle ScholarPubMed
Graham, P., Young, J., & Penny, R. (2009). Multiply imputed synthetic data: Evaluation of hierarchical bayesian imputation models. Journal of Official Statistics, 25(2), 245.Google Scholar
Guy, S.C., Isquith, P.K., & Gioia, G.A. (2004). Behavior Rating Inventory of Executive Function – Self-report Version: Professional Manual. Lutz, FL: PAR.Google Scholar
Hackman, D.A., Gallop, R., Evans, G.W., & Farah, M.J. (2015). Socioeconomic status and executive function: Developmental trajectories and mediation. Developmental Science, 18(5), 686702. doi: 10.1111/desc.12246CrossRefGoogle ScholarPubMed
Helgeson, V.S., Vaughn, A.K., Seltman, H., Orchard, T., Libman, I., & Becker, D. (2018). Featured article: Trajectories of glycemic control over adolescence and emerging adulthood: An 11-year longitudinal study of youth with type 1 diabetes. Journal of Pediatric Psychology, 43(1), 818. doi: 10.1093/jpepsy/jsx083CrossRefGoogle ScholarPubMed
Hood, K.K., Peterson, C.M., Rohan, J.M., & Drotar, D. (2009). Association between adherence and glycemic control in pediatric type 1 diabetes: A meta-analysis. Pediatrics, 124(6), e1171e1179. doi: 10.1542/peds.2009-0207CrossRefGoogle ScholarPubMed
Karr, J.E., Areshenkoff, C.N., Rast, P., Hofer, S.M., Iverson, G.L., & Garcia-Barrera, M.A. (2018). The unity and diversity of executive functions: A systematic review and re-analysis of latent variable studies. Psychological Bulletin, 144(11), 11471185. doi: 10.1037/bul0000160CrossRefGoogle ScholarPubMed
King, P.S., Berg, C.A., Butner, J.E., Butler, J.M., & Wiebe, D.J. (2014). Longitudinal trajectories of parental involvement in type 1 diabetes and adolescents’ adherence. Health Psychology, 33(5), 424432. doi: 10.1037/a0032804CrossRefGoogle ScholarPubMed
Köstering, L., Leonhart, R., Stahl, C., Weiller, C., & Kaller, C.P. (2016). Planning decrements in healthy aging: Mediation effects of fluid reasoning and working memory capacity. The Journals of Gerontology: Series B: Psychological Sciences and Social Sciences, 71(2), 230242. doi: 10.1093/geronb/gbu107CrossRefGoogle ScholarPubMed
La Greca, A.M. (1990). Issues in adherence with pediatric regimens. Journal of Pediatric Psychology, 15(4), 423436. doi: 10.1093/jpepsy/15.4.423CrossRefGoogle ScholarPubMed
Lezak, M.D., Howieson, D.B., Bigler, E.D., & Tranel, D. (2012). Neuropsychological Assessment (5th ed.). New York: Oxford University Press.Google Scholar
Luyckx, K., & Seiffge-Krenke, I. (2009). Continuity and change in glycemic control trajectories from adolescence to emerging adulthood. Diabetes Care, 32(5), 797801. doi: 10.2337/dc08-1990.CrossRefGoogle ScholarPubMed
Majumder, E., Cogen, R.R., & Monaghan, M. (2017). Self-management strategies in emerging adults with Type 1 Diabetes. Journal of Pediatric Health Care, 31(1), 2936.CrossRefGoogle ScholarPubMed
McNally, K., Rohan, J., Pendley, J.S., Delamater, A., & Drotar, D. (2010). Executive functioning, treatment adherence, and glycemic control in children with type 1 diabetes. Diabetes Care, 33(6), 11591162. doi: 10.2337/dc09-2116CrossRefGoogle ScholarPubMed
Miller, M.M., Rohan, J.M., Delamater, A., Shroff-Pendley, J., Dolan, L.M., Reeves, G., & Drotar, D. (2013). Changes in executive functioning and self-management in adolescents with type 1 diabetes: A growth curve analysis. Journal of Pediatric Psychology, 38(1), 1829. doi: 10.1093/jpepsy/jss100CrossRefGoogle ScholarPubMed
Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., & Howerter, A. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49100. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2000-00487-002&site=ehost-liveCrossRefGoogle ScholarPubMed
Panwar, K., Rutherford, H.J.V, Mencl, W.E., Lacadie, C.M., Potenza, M.N., & Mayes, L.C. (2014). Differential associations between impulsivity and risk-taking and brain activations underlying working memory in adolescents. Addictive Behaviors, 39(11), 16061621. doi: 10.1016/j.addbeh.2013.12.007CrossRefGoogle ScholarPubMed
Plomin, R., & Deary, I.J. (2015). Genetics and intelligence differences: Five special findings. Molecular Psychiatry, 20(1), 98108. doi: 10.1038/mp.2014.105CrossRefGoogle ScholarPubMed
Rechenberg, K., Whittemore, R., Grey, M., & Jaser, S. (2016). Contribution of income to self-management and health outcomes in pediatric type 1 diabetes. Pediatric Diabetes, 17(2), 120126. doi: 10.1111/pedi.12240CrossRefGoogle ScholarPubMed
Rogers, M.A., Kasai, K., Koji, M., Fukuda, R., Iwanami, A., Nakagome, K., Fukuda, M., & Kato, N. (2004). Executive and prefrontal dysfunction in unipolar depression: A review of neuropsychological and imaging evidence. Neurosci Res, 50(1), 111. doi: 10.1016/j.neures.2004.05.003S0168010204001117 [pii]CrossRefGoogle ScholarPubMed
Ross, L.A., Frier, B.M., Kelnar, C.J., & Deary, I.J. (2001). Child and parental mental ability and glycaemic control in children with Type 1 diabetes. Diabetes Medicine, 18(5), 364369.CrossRefGoogle ScholarPubMed
Ryan, C.M., van Duinkerken, E., & Rosano, C. (2016). Neurocognitive consequences of diabetes. American Psychologist, 71(7), 563576.CrossRefGoogle ScholarPubMed
Schwandt, A., Hermann, J.M., Rosenbauer, J., Boettcher, C., Dunstheimer, D., Grulich-Henn, J., Kuss, O., Rami-Merhar, B., Vogel, C., & Holl, R.W. (2017). Longitudinal trajectories of metaboliccontrol from childhood to young adulthood in type 1 diabetes from a large German/Austrian registry: A group-based modeling approach. Diabetes Care, 40(3), 309316. doi: 10.2337/dc16-1625CrossRefGoogle ScholarPubMed
Spearman, C.E. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72101.CrossRefGoogle Scholar
Steinberg, L. (2005). Cognitive and affective development in adolescence. Trends in Cognitive Sciences, 9(2), 6974. doi: 10.1016/j.tics.2004.12.005CrossRefGoogle ScholarPubMed
Stuss, D.T. (2011). Functions of the frontal lobes: Relation to executive functions. Journal of the International Neuropsychological Society, 17, 759765. doi: 10.1017/S1355617711000695CrossRefGoogle ScholarPubMed
Suchy, Y. (2009). Executive functioning: Overview, assessment, and research issues for non-neuropsychologists. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 37(2), 106116. doi: 10.1007/s12160-009-9097-4CrossRefGoogle ScholarPubMed
Suchy, Y. (2015). Executive Functions: A Comprehensive Guide for Clinical Practice. New York: Oxford University Press.Google Scholar
Suchy, Y. (2019). Contextually Valid Executive Assessment (ConVExA): A new approach to addressing ecological validity in assessment of executive functions. Workshop presented at the annual meeting of the International Neuropsychological Society in New York.Google Scholar
Suchy, Y., Queen, T.L., Huntbach, B., Wiebe, D.J., Turner, S.L., Butner, J., Kelly, C.S., White, P.C., Murray, M., Swinyard, M., & Berg, C.A. (2017). Iowa gambling task performance prospectively predicts changes in glycemic control among adolescents with type 1 diabetes. Journal of the International Neuropsychological Society, 23(3), 204213. doi: 10.1017/S135561771600103XCrossRefGoogle ScholarPubMed
Suchy, Y., Turner, S.L., Queen, T.L., Durracio, K., Wiebe, D.J., Butner, J., Franchow, E.I., White, P.C., Murray, M.A., Swinyard, M., & Berg, C.A. (2016). The relation of questionnaire and performance-based measures of executive functioning with type 1 diabetes outcomes among late adolescents. Health Psychology, 35(7), 661. doi: 10.1037/hea0000326CrossRefGoogle Scholar
Swanson, H.L., & Fung, W. (2016). Working memory components and problem-solving accuracy: Are there multiple pathways? Journal of Educational Psychology, 108(8), 11531177. doi: 10.1037/edu0000116CrossRefGoogle Scholar
Van Hoeck, N., Watson, P.D., & Barbey, A.K. (2015). Cognitive neuroscience of human counterfactual reasoning. Frontiers in Human Neuroscience, 9, 420. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-42125-001&site=ehost-liveCrossRefGoogle Scholar
Wasserman, R.M., Hilliard, M.E., Schwartz, D.D., & Anderson, B.J. (2015). Practical strategies to enhance executive functioning and strengthen diabetes management across the lifespan. Current Diabetes Reports, 15(8), 52. doi: 10.1007/s11892-015-0622-5CrossRefGoogle ScholarPubMed
Wechsler, D. (2008). Wechsler Adult Intelligence Scale-4th edition: Technical and Interpretative Manual. San Antonio, TX: Psychological Corporation.Google Scholar
Wiebe, D.J., Baker, A.C., Suchy, Y., Stump, T.K., & Berg, C.A. (2018). Individual differences and day-to-day fluctuations in goal planning and type 1 diabetes management. Health Psychology, 37(7), 638646. doi: 10.1037/hea0000624CrossRefGoogle ScholarPubMed