Glycemic control in adolescents with type 1 diabetes: Are computerized simulations effective learning tools?
adherence
adolescence
agent-based models
computer-based simulations
patient education
type 1 diabetes mellitus
Journal
Pediatric diabetes
ISSN: 1399-5448
Titre abrégé: Pediatr Diabetes
Pays: Denmark
ID NLM: 100939345
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
29
03
2019
revised:
21
05
2019
accepted:
16
12
2019
pubmed:
31
12
2019
medline:
17
2
2021
entrez:
31
12
2019
Statut:
ppublish
Résumé
Type 1 diabetes mellitus (T1DM) in adolescent patients is often characterized by poor glycemic control. This study aimed at exploring the contribution of learning with computerized simulations to support: (a) mechanistic understanding of the biochemical processes related to diabetes; (b) diabetes self-management knowledge; and (c) glycemic control. We hypothesized that learning with such simulations might support adolescents in gaining a better understanding of the biochemical processes related to glucose regulation, and consequently improve their glycemic control. A prospective case-control study was conducted in 12- to 18-year-old adolescents with T1DM (n = 85) who were routinely treated at an outpatient diabetes clinic. While the control group (n = 45) received the routine face-to-face follow-up, the intervention group (n = 40) learned in addition with computerized simulations that were embedded in pedagogically supportive activities. Participants in both groups completed a set of questionnaires regarding sociodemographic characteristics, diabetes mechanistic reasoning and diabetes self-management. Clinical data and serum glycated hemoglobin (HbA1c) levels were gathered from medical records. All the data was collected at recruitment and 3 months later. Analysis revealed improvement HbA1c levels in the intervention group (8.7% ± 1.7%) vs the controls (9.6% ± 1.6%) after 3 months (P < .05). Regression analysis showed that levels of diabetes mechanistic understanding and diabetes self-management knowledge, in addition to sociodemographic parameters, accounted for 31% of the HbA1c variance (P < .001). These results suggest that learning with computerized simulations about biochemical processes can improve adolescents' adherence to medical recommendations and result in improved glycemic control. Implementing scientific learning into the hospital educational setting is discussed.
Substances chimiques
Glycated Hemoglobin A
0
hemoglobin A1c protein, human
0
Types de publication
Controlled Clinical Trial
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
328-338Informations de copyright
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Références
Lee V, Thurston T, Thurston C. A comparison of discovered regularities in blood glucose readings across two data collection approaches used with a type 1 diabetic youth. Methods Inf Med. 2017;56(7):e84-e91. https://doi.org/10.3414/ME16-02-0047.
Rewers MJ, Pillay K, de Beaufort C, et al. Assessment and monitoring of glycemic control in children and adolescents with diabetes. Pediatr Diabetes. 2014;15(suppl 20):102-114. https://doi.org/10.1111/pedi.12190.
Beck J, Greenwood DA, Blanton L, et al. 2017 National Standards for diabetes self-management education and support. Diabetes Care. 2017;40(10):1409-1419. https://doi.org/10.2337/dci17-0025.
Christie D, Thompson R, Sawtell M, et al. Structured, intensive education maximising engagement, motivation and long-term change for children and young people with diabetes: a cluster randomised controlled trial with integral process and economic evaluation-the CASCADE study. Health Technol Assess (Rockv). 2014;18(20):1-202. https://doi.org/10.3310/hta18200.
Lange K, Swift P, Pańkowska E, Danne T. Diabetes education in children and adolescents. Pediatr Diabetes. 2014;15(S20):77-85. https://doi.org/10.1111/pedi.12187.
Saha S, Riemenschneider H, Müller G, Levin-Zamir D, Van den Broucke S, Schwarz PEH. Comparative analysis of diabetes self-management education programs in the European Union member states. Prim Care Diabetes. 2017;11(6):529-537. https://doi.org/10.1016/j.pcd.2017.05.011.
Boren SA, Gunlock TL, Peeples MM, Krishna S. Computerized learning technologies for diabetes: a systematic review. J Diabetes Sci Technol. 2008;2(1):139-146. https://doi.org/10.1177/193229680800200121.
Hermanns N, Kulzer B, Ehrmann D, Bergis-Jurgan N, Haak T. The effect of a diabetes education programme (PRIMAS) for people with type 1 diabetes: results of a randomized trial. Diabetes Res Clin Pract. 2013;102(3):149. https://doi.org/10.1016/j.diabres.2013.10.009.
Greenwood DA, Gee PM, Fatkin KJ, Peeples M. A systematic review of reviews evaluating technology-enabled diabetes self-management education and support. J Diabetes Sci Technol. 2017;11(5):1015-1027. https://doi.org/10.1177/1932296817713506.
Zhang KM, Swartzman LC, Petrella RJ, Gill DP, Minda JP. Explaining the causal links between illness management and symptom reduction: development of an evidence-based patient education strategy. Patient Educ Couns. 2017;100(6):1169-1176. https://doi.org/10.1016/j.pec.2017.01.009.
Bolger MS, Kobiela M, Weinberg PJ, Lehrer R. Children's mechanistic reasoning. Cogn Instr. 2012;30(2):170-206. https://doi.org/10.1080/07370008.2012.661815.
Hastie R. Causal thinking in judgments. In: Keren G, Wu G, eds. The Wiley Blackwell Handbook of Judgment and Decision Making. Chichester, UK: John Wiley & Sons, Ltd; 2015:590-628. https://doi.org/10.1002/9781118468333.ch21.
Koslowski B. Theory and Evidence: The Development of Scientific Reasoning; 1996. https://books.google.com/books?hl=iw&lr=&id=TbpYNhopCGoC&oi=fnd&pg=PR9&dq=Theory+and+evidence:+The+development+of+scientific+reasoning.&ots=UbG_dFQo3Q&sig=cuDmeZWkJ6gMk8iF2EHse69GbV4. Accessed August 17, 2018.
Bar-Yam Y. Dynamics of Complex Systems. The Advanced Book Program. Reading, MA: Addison-Wesley; 1997. http://www.me.sc.edu/grad/russellj/critiques/skinner3.doc. Accessed December 11, 2018.
Kaufman S. At Home in the Universe: The Search for the Laws of Self-organization and Complexity. NY: Oxford university press; 1995.
Holland JH. Hidden Order: How Adaptation Builds Complexity (Helix Books). Jackson, Tennessee: Basic Books; 1995. http://www.sidalc.net/cgi-bin/wxis.exe/?IsisScript=sibe01.xis&method=post&formato=2&cantidad=1&expresion=mfn=019378. Accessed December 11, 2018.
Levy ST, Wilensky U. Inventing a “mid level” to make ends meet: reasoning between the levels of complexity. Cogn Instr. 2008;26(1):1-47. https://doi.org/10.1080/07370000701798479.
Dickes AC, Sengupta P, Farris AV, Basu S. Development of mechanistic reasoning and multilevel explanations of ecology in third grade using agent-based models. Sci Educ. 2016;100(4):734-776. https://doi.org/10.1002/sce.21217.
Roper SO, Call A, Leishman J, et al. Type 1 diabetes: children and adolescents' knowledge and questions. J Adv Nurs. 2009;65(8):1705-1714. https://doi.org/10.1111/j.1365-2648.2009.05033.x.
Wilensky U. NetLNetLogo. Center for Connected Learning and Computer-Based Modeling. Evanston, IL: Northwestern University; 1999. http://ccl.northwestern.edu/netlogo/. Accessed August 26, 2018.
Dubovi I, Dagan E, Sader Mazbar O, Nassar L, Levy ST. Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: a quasi-experimental study. Nurse Educ Today. 2018;61:175-181. https://doi.org/10.1016/j.nedt.2017.11.022.
Beck JK, Zhang Y, Shay CM, et al. Diabetes knowledge in young adults: associations with hemoglobin A1C. Fam Syst Heal. 2015;33(1):28-35. https://doi.org/10.1037/fsh0000101.
Degroot AMB, Dannenburg L, Vanhell JG. Forward and backward word translation by bilinguals. J Mem Lang. 1994;33(5):600-629. https://doi.org/10.1006/JMLA.1994.1029.
Funnell MM, Brown TL, Childs BP, et al. National standards for diabetes self-management education. Diabetes Care. 2007;30(6):1630-1637. https://doi.org/10.2337/dc07-9923.
Haas L, Maryniuk M, Beck J, et al. National standards for diabetes self-management education and support. Diabetes Care. 2012;35(11):2393-2401. https://doi.org/10.2337/dc12-1707.
Dagan E, Dubovi I, Levy M, Zuckerman-Levin N, & Levy ST. Adherence to diabetic care: Knowledge of biochemical processes has a high impact on glycemic control among adolescents with type 1 diabetes. J Adv Nurs. 2019;75:2701-2709.
Wilensky U, Resnick M. Thinking in levels: a dynamic systems approach to making sense of the world. J Sci Educ Technol. 1999;8(1):3-19. https://doi.org/10.1023/A:1009421303064.
NGSP: Clinical use. 2018; Retrieved from http://www.ngsp.org/ADA.asp.
Bollen KA, Stine R. Direct and indirect effects: classical and bootstrap estimates of variability. Sociol Methodol. 1990;20:115-140. https//doi.org/10.2307/271084.
Hayes AF, Montoya AK, Rockwoord NJ. The PROCESS macro for SPSS and SAS. http://processmacro.org/download.html. Accessed May 21, 2019.
Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173.
Goldstone RL, Wilensky U. Promoting transfer by grounding complex systems principles. J Learn Sci. 2008;17(4):465-516. https://doi.org/10.1080/10508400802394898.
Wilensky U, Papert S. Restructurations: reformulating knowledge disciplines through new representational forms. In: Clayson J, Kalas I, eds. Proceedings of the Constructionism 2010 Conference, Paris, France. 2010;97.
Samon S, Levy ST. Micro-macro compatibility: when does a complex systems approach strongly benefit science learning? Sci Educ. 2017;101(6):985-1014. https://doi.org/10.1002/sce.21301.
Shahraz S, Pittas AG, Saadati M, Thomas CP, Lundquist CM, Kent DM. Change in testing, awareness of hemoglobin A1c result, and glycemic control in US adults, 2007-2014. JAMA. 2017;318(18):1825-1827. https://doi.org/10.1001/jama.2017.11927.