One Drop | Mobile on iPhone and Apple Watch: An Evaluation of HbA1c Improvement Associated With Tracking Self-Care

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Abstract

Background

The One Drop | Mobile app supports manual and passive (via HealthKit and One Drop’s glucose meter) tracking of self-care and glycated hemoglobin A _1c (HbA _1c).

Objective

We assessed the HbA _1c change of a sample of people with type 1 diabetes (T1D) or type 2 diabetes (T2D) using the One Drop | Mobile app on iPhone and Apple Watch, and tested relationships between self-care tracking with the app and HbA _1c change.

Methods

In June 2017, we identified people with diabetes using the One Drop | Mobile app on iPhone and Apple Watch who entered two HbA _1c measurements in the app 60 to 365 days apart. We assessed the relationship between using the app and HbA _1c change.

Results

Users had T1D (n=65) or T2D (n=191), were 22.7% (58/219) female, with diabetes for a mean 8.34 (SD 8.79) years, and tracked a mean 2176.35 (SD 3430.23) self-care activities between HbA _1c entries. There was a significant 1.36% or 14.9 mmol/mol HbA _1c reduction (F=62.60, P<.001) from the first (8.72%, 71.8 mmol/mol) to second HbA _1c (7.36%, 56.9 mmol/mol) measurement. Tracking carbohydrates was independently associated with greater HbA _1c improvement (all P<.01).

Conclusions

Using One Drop | Mobile on iPhone and Apple Watch may favorably impact glycemic control.

Related collections

Most cited references 16

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Defining the relationship between plasma glucose and HbA(1c): analysis of glucose profiles and HbA(1c) in the Diabetes Control and Complications Trial.

C. L. Rohlfing, H.-M. Wiedmeyer, R. Little … (2002)

To define the relationship between HbA(1c) and plasma glucose (PG) levels in patients with type 1 diabetes using data from the Diabetes Control and Complications Trial (DCCT). The DCCT was a multicenter, randomized clinical trial designed to compare intensive and conventional therapies and their relative effects on the development and progression of diabetic complications in patients with type 1 diabetes. Quarterly HbA(1c) and corresponding seven-point capillary blood glucose profiles (premeal, postmeal, and bedtime) obtained in the DCCT were analyzed to define the relationship between HbA(1c) and PG. Only data from complete profiles with corresponding HbA(1c) were used (n = 26,056). Of the 1,441 subjects who participated in the study, 2 were excluded due to missing data. Mean plasma glucose (MPG) was estimated by multiplying capillary blood glucose by 1.11. Linear regression analysis weighted by the number of observations per subject was used to correlate MPG and HbA(1c). Linear regression analysis, using MPG and HbA(1c) summarized by patient (n = 1,439), produced a relationship of MPG (mmol/l) = (1.98 . HbA(1c)) - 4.29 or MPG (mg/dl) = (35.6 . HbA(1c)) - 77.3, r = 0.82). Among individual time points, afternoon and evening PG (postlunch, predinner, postdinner, and bedtime) showed higher correlations with HbA(1c) than the morning time points (prebreakfast, postbreakfast, and prelunch). We have defined the relationship between HbA(1c) and PG as assessed in the DCCT. Knowing this relationship can help patients with diabetes and their healthcare providers set day-to-day targets for PG to achieve specific HbA(1c) goals.

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Do Mobile Phone Applications Improve Glycemic Control (HbA1c) in the Self-management of Diabetes? A Systematic Review, Meta-analysis, and GRADE of 14 Randomized Trials.

Can Hou, Ben Carter, Jonathan Hewitt … (2016)

To investigate the effect of mobile phone applications (apps) on glycemic control (HbA1c) in the self-management of diabetes.

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Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation

Kathryn Mercer, Lora Giangregorio, Eric Schneider … (2016)

Background Physical inactivity and sedentary behavior increase the risk of chronic illness and death. The newest generation of “wearable” activity trackers offers potential as a multifaceted intervention to help people become more active. Objective To examine the usability and usefulness of wearable activity trackers for older adults living with chronic illness. Methods We recruited a purposive sample of 32 participants over the age of 50, who had been previously diagnosed with a chronic illness, including vascular disease, diabetes, arthritis, and osteoporosis. Participants were between 52 and 84 years of age (mean 64); among the study participants, 23 (72%) were women and the mean body mass index was 31 kg/m2. Participants tested 5 trackers, including a simple pedometer (Sportline or Mio) followed by 4 wearable activity trackers (Fitbit Zip, Misfit Shine, Jawbone Up 24, and Withings Pulse) in random order. Selected devices represented the range of wearable products and features available on the Canadian market in 2014. Participants wore each device for at least 3 days and evaluated it using a questionnaire developed from the Technology Acceptance Model. We used focus groups to explore participant experiences and a thematic analysis approach to data collection and analysis. Results Our study resulted in 4 themes: (1) adoption within a comfort zone; (2) self-awareness and goal setting; (3) purposes of data tracking; and (4) future of wearable activity trackers as health care devices. Prior to enrolling, few participants were aware of wearable activity trackers. Most also had been asked by a physician to exercise more and cited this as a motivation for testing the devices. None of the participants planned to purchase the simple pedometer after the study, citing poor accuracy and data loss, whereas 73% (N=32) planned to purchase a wearable activity tracker. Preferences varied but 50% felt they would buy a Fitbit and 42% felt they would buy a Misfit, Jawbone, or Withings. The simple pedometer had a mean acceptance score of 56/95 compared with 63 for the Withings, 65 for the Misfit and Jawbone, and 68 for the Fitbit. To improve usability, older users may benefit from devices that have better compatibility with personal computers or less-expensive Android mobile phones and tablets, and have comprehensive paper-based user manuals and apps that interpret user data. Conclusions For older adults living with chronic illness, wearable activity trackers are perceived as useful and acceptable. New users may need support to both set up the device and learn how to interpret their data.

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Author and article information

Contributors

Chandra Y Osborn:

Informed Data Systems Inc85 Delancey St, Ste 71New York, NY, 10002United States1 8604242858chandra@onedrop.today

ORCID: http://orcid.org/0000-0002-7668-028X

Journal

Journal ID (nlm-ta): JMIR Mhealth Uhealth

Journal ID (iso-abbrev): JMIR Mhealth Uhealth

Journal ID (publisher-id): JMU

Title: JMIR mHealth and uHealth

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-5222

Publication date Collection: November 2017

Publication date (Electronic): 29 November 2017

Volume: 5

Issue: 11

Electronic Location Identifier: e179

Affiliations

[1] ¹ Informed Data Systems Inc New York, NY United States

[2] ² Leiden University Leiden Netherlands

[3] ³ The University of Arizona Health Sciences Tucson, AZ United States

[4] ⁴ Biomedical Informatics Consultants LLC Potomac, MD United States

[5] ⁵ Informed Data Systems Inc Austin, TX United States

Author notes

Corresponding Author: Chandra Y Osborn chandra@ 123456onedrop.today

Author information

Chandra Y Osborn http://orcid.org/0000-0002-7668-028X

Joost R van Ginkel http://orcid.org/0000-0002-4137-0943

David G Marrero http://orcid.org/0000-0003-2112-7812

David Rodbard http://orcid.org/0000-0002-5547-3564

Brian Huddleston http://orcid.org/0000-0001-7375-6552

Jeff Dachis http://orcid.org/0000-0001-8654-8293

Article

Publisher ID: v5i11e179

DOI: 10.2196/mhealth.8781

PMC ID: 5729227

PubMed ID: 29187344

SO-VID: 1f35cc66-9d81-47df-9810-bead14f1ad31

Copyright © ©Chandra Y Osborn, Joost R van Ginkel, David G Marrero, David Rodbard, Brian Huddleston, Jeff Dachis. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 29.11.2017.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 18 August 2017

Date revision requested : 15 September 2017

Date revision received : 6 October 2017

Date accepted : 29 October 2017

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