Download PDF
Appl Clin Inform 2018; 09(04): 919-926
DOI: 10.1055/s-0038-1676458
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes

Danielle Groat
1   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
,
Hyo Jung Kwon
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
,
Maria Adela Grando
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
3   Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
,
Curtiss B. Cook
2   Department of Biomedical Informatics, Arizona State University, Scottsdale, Arizona, United States
3   Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
,
Bithika Thompson
3   Division of Endocrinology, Mayo Clinic Arizona, Scottsdale, Arizona, United States
› Author Affiliations Funding This research was supported by the 2018 ASU-Mayo Research Accelerator Award: Data-Driven Behavioral-Change Individualized Interventions to Improve Type 1 Diabetes.
Further Information

Publication History

06 July 2018

27 October 2018

Publication Date:
26 December 2018 (online)

Also available at
   Permissions and Reprints

Abstract

Background Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise.

Objective The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels.

Methods Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM.

Results Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively (p = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM (p = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was –0.27 mg/(dL*min) and was not significantly different between data sources or intensity (p = 0.21).

Conclusion We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.

Keywords

exercise - smartphone app - wearables - type 1 diabetes - patient-generated data

Protection of Human and Animal Subjects

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


 

  • References

  • 1 Geller AI, Shehab N, Lovegrove MC. , et al. National estimates of insulin-related hypoglycemia and errors leading to emergency department visits and hospitalizations. JAMA Intern Med 2014; 174 (05) 678-686
    CrossrefPubMedGoogle Scholar
  • 2 Aiello LP. ; DCCT/EDIC Research Group. Diabetic retinopathy and other ocular findings in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care 2014; 37 (01) 17-23
    CrossrefPubMedGoogle Scholar
  • 3 Martin CL, Albers JW, Pop-Busui R. ; DCCT/EDIC Research Group. Neuropathy and related findings in the diabetes control and complications trial/epidemiology of diabetes interventions and complications study. Diabetes Care 2014; 37 (01) 31-38
    CrossrefPubMedGoogle Scholar
  • 4 American Diabetes Association. Standards of medical care in diabetes—2016. Diabetes Care 2016; 39 (Suppl. 01) S23-S25
    CrossrefPubMedGoogle Scholar
  • 5 Kourtoglou GI. Insulin therapy and exercise. Diabetes Res Clin Pract 2011; 93 (Suppl. 01) S73-S77
    CrossrefPubMedGoogle Scholar
  • 6 Riddell MC, Gallen IW, Smart CE. , et al. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes Endocrinol 2017; 5 (05) 377-390
    CrossrefPubMedGoogle Scholar
  • 7 Glasgow RE, Anderson RM. In diabetes care, moving from compliance to adherence is not enough. Something entirely different is needed. Diabetes Care 1999; 22 (12) 2090-2092
    CrossrefPubMedGoogle Scholar
  • 8 Funnell MM, Anderson RM. Empowerment and self-management of diabetes. Clin Diabetes 2004; 22 (03) 123-127
    CrossrefPubMedGoogle Scholar
  • 9 Hood KK, Peterson CM, Rohan JM, Drotar D. Association between adherence and glycemic control in pediatric type 1 diabetes: a meta-analysis. Pediatrics 2009; 124 (06) e1171-e1179
    CrossrefPubMedGoogle Scholar
  • 10 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
  • 11 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
  • 12 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
  • 13 Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport 2000; 71 (Suppl. 02) 1-14
    PubMedGoogle Scholar
  • 14 Taleb N, Emami A, Suppere C. , et al. Comparison of two continuous glucose monitoring systems, Dexcom G4 Platinum and Medtronic Paradigm Veo Enlite System, at rest and during exercise. Diabetes Technol Ther 2016; 18 (09) 561-567
    CrossrefPubMedGoogle Scholar
  • 15 Wen D, Zhang X, Liu X, Lei J. Evaluating the consistency of current mainstream wearable devices in health monitoring: a comparison under free-living conditions. J Med Internet Res 2017; 19 (03) e68
    CrossrefPubMedGoogle Scholar
  • 16 Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act; December 18, 2015;12. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683756/ . Accessed September 3, 2018
    PubMedGoogle Scholar
  • 17 Deka P, Pozehl B, Norman JF, Khazanchi D. Feasibility of using the Fitbit® Charge HR in validating self-reported exercise diaries in a community setting in patients with heart failure. Eur J Cardiovasc Nurs 2018; 17 (07) 605-611
    CrossrefPubMedGoogle Scholar
  • 18 Gonzalez JS, Schneider HE. Methodological issues in the assessment of diabetes treatment adherence. Curr Diab Rep 2011; 11 (06) 472-479
    CrossrefPubMedGoogle Scholar
  • 19 Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc 2011; 43 (08) 1561-1567
    CrossrefPubMedGoogle Scholar
  • 20 Butte NF, Ekelund U, Westerterp KR. Assessing physical activity using wearable monitors: measures of physical activity. Med Sci Sports Exerc 2012; 44 (01) (Suppl. 01) S5-S12
    CrossrefPubMedGoogle Scholar
  • 21 Blackwell M, Wheeler BJ. Clinical review: the misreporting of logbook, download, and verbal self-measured blood glucose in adults and children with type I diabetes. Acta Diabetol 2017; 54 (01) 1-8
    CrossrefPubMedGoogle Scholar
  • 22 Trawley S, Baptista S, Browne JL, Pouwer F, Speight J. The use of mobile applications among adults with type 1 and type 2 diabetes: results from the Second MILES-Australia (MILES-2) Study. Diabetes Technol Ther 2017; 19 (12) 730-738
    CrossrefPubMedGoogle Scholar
  • 23 Dobson R, Whittaker R, Murphy R. , et al. The use of mobile health to deliver self-management support to young people with type 1 diabetes: a cross-sectional survey. JMIR Diabetes 2017; 2 (01) e4
    CrossrefPubMedGoogle Scholar
  • 24 Lithgow K, Edwards A, Rabi D. Smartphone app use for diabetes management: evaluating patient perspectives. JMIR Diabetes 2017; 2 (01) e2
    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 Centers for Disease Control and Prevention. Measuring Physical Activity Intensity. CDC 24/7: Saving Lives, Protecting People; 2015 . Available at: https://www.cdc.gov/physicalactivity/basics/measuring/index.html . Accessed June 7, 2018
    PubMed
  • 27 Reed JL, Pipe AL. The talk test: a useful tool for prescribing and monitoring exercise intensity. Curr Opin Cardiol 2014; 29 (05) 475-480
    CrossrefPubMedGoogle Scholar
  • 28 FitBit. Charge HR 101. Available at: https://www.fitbit.com/c/chargehr/chargehr-101 . Accessed June 17, 2017
    PubMed
  • 29 Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol 2001; 37 (01) 153-156
    CrossrefPubMedGoogle Scholar
  • 30 Sylvia LG, Bernstein EE, Hubbard JL, Keating L, Anderson EJ. Practical guide to measuring physical activity. J Acad Nutr Diet 2014; 114 (02) 199-208
    CrossrefPubMedGoogle Scholar
  • 31 Kantor M, Wright A, Burton M. , et al. Comparison of computer-based clinical decision support systems and content for diabetes mellitus. Appl Clin Inform 2011; 2 (03) 284-303
    Article in Thieme ConnectPubMedGoogle Scholar
  • 32 Kumar RB, Goren ND, Stark DE, Wall DP, Longhurst CA. Automated integration of continuous glucose monitor data in the electronic health record using consumer technology. J Am Med Inform Assoc 2016; 23 (03) 532-537
    CrossrefPubMedGoogle Scholar
  • 33 O'Connor P. Opportunities to increase the effectiveness of EHR-based diabetes clinical decision support. Appl Clin Inform 2011; 2 (03) 350-354
    Article in Thieme ConnectPubMedGoogle Scholar
  • 34 Wang R, Blackburn G, Desai M. , et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol 2017; 2 (01) 104-106
    CrossrefPubMedGoogle Scholar
  • 35 García-García F, Kumareswaran K, Hovorka R, Hernando ME. Quantifying the acute changes in glucose with exercise in type 1 diabetes: a systematic review and meta-analysis. Sports Med 2015; 45 (04) 587-599
    CrossrefPubMedGoogle Scholar
  • 36 Matuleviciene V, Joseph JI, Andelin M. , et al. A clinical trial of the accuracy and treatment experience of the Dexcom G4 sensor (Dexcom G4 system) and Enlite sensor (guardian REAL-time system) tested simultaneously in ambulatory patients with type 1 diabetes. Diabetes Technol Ther 2014; 16 (11) 759-767
    CrossrefPubMedGoogle Scholar
  • 37 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
  • 38 Pyatak EA, Carandang K, Vigen C. , et al. Resilient, Empowered, Active Living with Diabetes (REAL Diabetes) study: methodology and baseline characteristics of a randomized controlled trial evaluating an occupation-based diabetes management intervention for young adults. Contemp Clin Trials 2017; 54: 8-17
    CrossrefPubMedGoogle Scholar
  • 39 Yardley JE, Hay J, Abou-Setta AM, Marks SD, McGavock J. A systematic review and meta-analysis of exercise interventions in adults with type 1 diabetes. Diabetes Res Clin Pract 2014; 106 (03) 393-400
    CrossrefPubMedGoogle Scholar
  • 40 Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol 2013; 166 (01) 15-29
    CrossrefPubMedGoogle Scholar
  • 41 Mazze RS, Shamoon H, Pasmantier R. , et al. Reliability of blood glucose monitoring by patients with diabetes mellitus. Am J Med 1984; 77 (02) 211-217
    CrossrefPubMedGoogle Scholar
  • 42 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