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Appl Clin Inform 2020; 11(01): 070-078
DOI: 10.1055/s-0039-1701002
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Self-Management Behaviors of Patients with Type 1 Diabetes: Comparing Two Sources of Patient-Generated Data

George Karway
1   College of Health Solutions, Arizona State University, Scottsdale, Arizona, United States
,
Maria Adela Grando
1   College of Health Solutions, Arizona State University, Scottsdale, Arizona, United States
,
Kevin Grimm
2   Department of Psychology, Arizona State University, Scottsdale, Arizona, United States
,
Danielle Groat
1   College of Health Solutions, Arizona State University, Scottsdale, Arizona, United States
3   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Curtiss Cook
4   Department of Endocrinology, Mayo Clinic, Scottsdale, Arizona, United States
,
Bithika Thompson
4   Department of Endocrinology, Mayo Clinic, Scottsdale, Arizona, United States
› Author Affiliations Funding This research was supported by the 2018 Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery and the Arizona State University Research Acceleration Grant.
Further Information

Publication History

14 June 2019

09 December 2019

Publication Date:
22 January 2020 (online)

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Abstract

Objectives This article aims to evaluate adult type 1 diabetes mellitus (T1DM) self-management behaviors (SMBs) related to exercise and alcohol on a survey versus a smartphone app to compare self-reported and self-tracked SMBs, and examine inter- and intrapatient variability.

Methods Adults with T1DM on insulin pump therapy were surveyed about their alcohol, meal, and exercise SMBs. For 4 weeks, participants self-tracked their alcohol, meal, and exercise events, and their SMBs corresponding with these events via an investigator-developed app. Descriptive statistics and generalized linear mixed-effect models were used to analyze the data

Results Thirty-five participants self-tracked over 5,000 interactions using the app. Variability in how participants perceived the effects of exercise and alcohol on their blood glucose was observed. The congruity between SMBs self-reported on the survey and those self-tracked with the app was measured as mean (SD). The lowest congruity was for alcohol and exercise with 61.9% (22.7) and 66.4% (20.2), respectively. Congruity was higher for meals with 80.9% (21.0). There was significant daily intra- and interpatient variability in SMBs related to preprandial bolusing: recommended bolus, p < 0.05; own bolus choice, p < 0.01; and recommended basal adjustment, p < 0.01.

Conclusion This study highlights the variability in intra- and interpatient SMBs obtained through the use of a survey and app. The outcomes of this study indicate that clinicians could use both one-time and every-day assessment tools to assess SMBs related to meals. For alcohol and exercise, further research is needed to understand the best assessment method for SMBs. Given this degree of patient variability, there is a need for an educational intervention that goes beyond the traditional “one-size-fits-all” approach of diabetes management to target individualized treatment barriers.

Keywords

diabetes mellitus - surveys or interviews - self-care - smartphone - patient-generated data

Protection of Human and Animal Subjects

This study was reviewed by the Mayo Clinic and the Arizona State University Institutional Review Boards.


 

  • References

  • 1 Aschner P, Horton E, Leiter LA, Munro N, Skyler JS. ; Global Partnership for Effective Diabetes Management. Practical steps to improving the management of type 1 diabetes: recommendations from the Global Partnership for Effective Diabetes Management. Int J Clin Pract 2010; 64 (03) 305-315
    CrossrefPubMedGoogle Scholar
  • 2 American Diabetes Association. 1. Improving care and promoting health in populations: Standards of Medical Care in Diabetes-2018 . Diabetes Care 2018; 41 (Suppl. 01) S7-S12
    CrossrefPubMedGoogle Scholar
  • 3 O'Connell MA, Donath S, Cameron FJ. Poor adherence to integral daily tasks limits the efficacy of CSII in youth. Pediatr Diabetes 2011; 12 (06) 556-559
    PubMedGoogle Scholar
  • 4 Ziegler R, Rees C, Jacobs N. , et al. Frequent use of an automated bolus advisor improves glycemic control in pediatric patients treated with insulin pump therapy: results of the Bolus Advisor Benefit Evaluation (BABE) study. Pediatr Diabetes 2016; 17 (05) 311-318
    CrossrefPubMedGoogle Scholar
  • 5 Asche C, LaFleur J, Conner C. A review of diabetes treatment adherence and the association with clinical and economic outcomes. Clin Ther 2011; 33 (01) 74-109
    CrossrefPubMedGoogle Scholar
  • 6 Kourtoglou GI. Insulin therapy and exercise. Diabetes Res Clin Pract 2011; 93 (Suppl. 01) S73-S77
    CrossrefPubMedGoogle Scholar
  • 7 Marliss EB, Vranic M. Intense exercise has unique effects on both insulin release and its roles in glucoregulation: implications for diabetes. Diabetes 2002; 51 (Suppl. 01) S271-S283
    CrossrefPubMedGoogle Scholar
  • 8 Toni S, Reali MF, Barni F, Lenzi L, Festini F. Managing insulin therapy during exercise in type 1 diabetes mellitus. Acta Biomed 2006; 77 (Suppl. 01) 34-40
    PubMedGoogle Scholar
  • 9 American Diabetes Association. Evidence-based nutrition principles and recommendations for the treatment and prevention of diabetes and related complications. Diabetes Care 2002; 25 (01) 202-212
    CrossrefPubMedGoogle Scholar
  • 10 Heinrich E, Schaper NC, de Vries NK. Self-management interventions for type 2 diabetes: a systematic review. Eur Diabetes Nurs 2010; 7 (02) 71-76
    CrossrefPubMedGoogle Scholar
  • 11 Hendricks M, Monaghan M, Soutor S, Chen R, Holmes CS. A profile of self-care behaviors in emerging adults with type 1 diabetes. Diabetes Educ 2013; 39 (02) 195-203
    CrossrefPubMedGoogle Scholar
  • 12 Ravert RD. Alcohol management strategies of college students with diabetes. Patient Educ Couns 2009; 77 (01) 97-102
    CrossrefPubMedGoogle Scholar
  • 13 Miller-Hagan RS, Janas BG. Drinking perceptions and management strategies of college students with diabetes. Diabetes Educ 2002; 28 (02) 233-244
    CrossrefPubMedGoogle Scholar
  • 14 Barnard KD, Dyson P, Sinclair JMA. , et al. Alcohol health literacy in young adults with type 1 diabetes and its impact on diabetes management. Diabet Med 2014; 31 (12) 1625-1630
    CrossrefPubMedGoogle Scholar
  • 15 Pinsker JE, Kraus A, Gianferante D. , et al. Techniques for exercise preparation and management in adults with type 1 diabetes. Can J Diabetes 2016; 40 (06) 503-508
    CrossrefPubMedGoogle Scholar
  • 16 Pettus J, Price DA, Edelman SV. How patients with type 1 diabetes translate continuous glucose monitoring data into diabetes management decisions. Endocr Pract 2015; 21 (06) 613-620
    CrossrefPubMedGoogle Scholar
  • 17 Groat D, Grando MA, Soni H. , et al. Self-management behaviors in adults on insulin pump therapy. J Diabetes Sci Technol 2017; 11 (02) 233-239
    CrossrefPubMedGoogle Scholar
  • 18 Grando MA, Groat D, Soni H. , et al. Characterization of exercise and alcohol self-management behaviors of type 1 diabetes patients on insulin pump therapy. J Diabetes Sci Technol 2017; 11 (02) 240-246
    CrossrefPubMedGoogle Scholar
  • 19 Gandhi K, Vu BK, Eshtehardi SS, Wasserman RM, Hilliard ME. Adherence in adolescents with Type 1 diabetes: strategies and considerations for assessment in research and practice. Diabetes Manag (Lond) 2015; 5 (06) 485-498
    PubMedGoogle Scholar
  • 20 Borus JS, Laffel L. Adherence challenges in the management of type 1 diabetes in adolescents: prevention and intervention. Curr Opin Pediatr 2010; 22 (04) 405-411
    CrossrefPubMedGoogle Scholar
  • 21 Guilfoyle SM, Crimmins NA, Hood KK. Blood glucose monitoring and glycemic control in adolescents with type 1 diabetes: meter downloads versus self-report. Pediatr Diabetes 2011; 12 (06) 560-566
    PubMedGoogle Scholar
  • 22 Datye KA, Moore DJ, Russell WE, Jaser SS. A review of adolescent adherence in type 1 diabetes and the untapped potential of diabetes providers to improve outcomes. Curr Diab Rep 2015; 15 (08) 51
    CrossrefPubMedGoogle Scholar
  • 23 Ateya MB, Aiyagari R, Moran C, Singer K. Insulin bolus calculator in a pediatric hospital. Safety and user perceptions. Appl Clin Inform 2017; 8 (02) 529-540
    Article in Thieme ConnectPubMedGoogle Scholar
  • 24 Beverly EA, Ganda OP, Ritholz MD. , et al. Look who's (not) talking: diabetic patients' willingness to discuss self-care with physicians. Diabetes Care 2012; 35 (07) 1466-1472
    CrossrefPubMedGoogle Scholar
  • 25 Groat D, Soni H, Grando MA, Thompson B, Kaufman D, Cook CB. Design and testing of a smartphone application for real-time self-tracking diabetes self-management behaviors. Appl Clin Inform 2018; 9 (02) 440-449
    Article in Thieme ConnectPubMedGoogle Scholar
  • 26 Bellei EA, Biduski D, Cechetti NP, De Marchi ACB. Diabetes mellitus m-health applications: a systematic review of features and fundamentals. Telemed J E Health 2018; 24 (11) 839-852
    CrossrefPubMedGoogle Scholar
  • 27 Hood M, Wilson R, Corsica J, Bradley L, Chirinos D, Vivo A. What do we know about mobile applications for diabetes self-management? A review of reviews. J Behav Med 2016; 39 (06) 981-994
    CrossrefPubMedGoogle Scholar
  • 28 Hayakawa M, Uchimura Y, Omae K, Waki K, Fujita H, Ohe K. A smartphone-based medication self-management system with real time medication monitoring. Appl Clin Inform 2013; 4 (01) 37-52
    Article in Thieme ConnectPubMedGoogle Scholar
  • 29 Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005; 353 (05) 487-497
    CrossrefPubMedGoogle Scholar
  • 30 Sabaté E. Adherence to Long-Term Therapies: Evidence for Action. Geneva: World Health Organization; 2003: 198
    Google Scholar
  • 31 Chesney MA, Ickovics JR, Chambers DB. , et al. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. Patient Care Committee & Adherence Working Group of the Outcomes Committee of the Adult AIDS Clinical Trials Group (AACTG). AIDS Care 2000; 12 (03) 255-266
    CrossrefPubMedGoogle Scholar
  • 32 Driscoll KA, Johnson SB, Hogan J, Gill E, Wright N, Deeb LC. Insulin bolusing software: the potential to optimize health outcomes in type 1 diabetes mellitus. J Diabetes Sci Technol 2013; 7 (03) 646-652
    CrossrefPubMedGoogle Scholar
  • 33 Patton SR, Driscoll KA, Clements MA. Adherence to insulin pump behaviors in young children with type 1 diabetes mellitus. J Diabetes Sci Technol 2017; 11 (01) 87-91
    CrossrefPubMedGoogle Scholar
  • 34 Glooko [Internet]. Glooko | Diabetes Remote Monitoring | Population Management. [cited April 10, 2019]. Available at: https://www.glooko.com/ . Accessed December 26, 2019
    PubMedGoogle Scholar
  • 35 Tidepool | Liberate your diabetes data [Internet]. [cited April 10, 2019]. Available at: https://tidepool.org/ . Accessed December 26, 2019
    PubMedGoogle Scholar
  • 36 Lewinski AA, Drake C, Shaw RJ. , et al. Bridging the integration gap between patient-generated blood glucose data and electronic health records. J Am Med Inform Assoc 2019; 26 (07) 667-672
    CrossrefPubMedGoogle Scholar