Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors

Groat, Danielle; Soni, Hiral; Grando, Adela; Thompson, Bithika; Kaufman, David R.; Cook, Curtiss B.

doi:10.1055/s-0038-1660438

Cited by 13 publications

(31 citation statements)

References 28 publications

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“…That sample, recruited through advertisements in print media and one Web site, certainly was self-selected in contrast to our consecutive patient recruitment in the clinic. Similarly, in a recent pilot 33 of an app to monitor meals and self-care actions in participants with diabetes, 13 of 14 participants logged meal events over 30 days; it is unclear whether the participants were prompted in any way.…”

Section: Discussionmentioning

confidence: 99%

Self-Care Support for Patients with Gastrointestinal Cancer: iCancerHealth

et al. 2018

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Background Patient-centered symptom assessment and management tools allow patients to perform self-assessments and engage in self-symptom management. Efficacious tools exist for reducing symptom distress; however, little is known about feature-specific use. Objectives This article evaluates the feasibility of the iCancerHealth app as an adjunct to usual patient education regarding cancer symptoms and medication management. Methods We conducted a single-arm, pilot study grounded in the health outcomes model. Our evaluation included (1) enrollment rates, (2) 2-month utilization rates, (3) patient acceptability, and (4) clinician satisfaction with the provider-side application. English-speaking, adult patients receiving care in the gastrointestinal oncology service of a comprehensive cancer center were invited to participate. Research coordinators enrolled consenting participants who had a personal, Internet-connected device; participants registered and used the platform to complete the baseline symptom assessment in clinic. Participants were reminded weekly to use the app and to perform a symptom report 4 to 6 weeks later. Results A total of 64 patients were approached, of which 57 (89%; 95% exact confidence interval [CI], 79-96%) enrolled. About half were 60 years old and 40% were women. Fifty-three patients (93%; 95% exact CI, 85-99%) accessed at least one app feature, at least once, from home. The most frequently used (86%) feature was Health Tracker in which participants monitored and reported symptoms; followed by My Inbox (63%) and My Medications features (60%). The mean acceptability score was 24.8 (standard deviation ¼ 4.2), indicating good acceptability. Clinicians reported that the app was most acceptable with regard to facilitating in-person interactions that occurred after app use. Conclusion In a sample of adults with various stages of gastrointestinal malignancies, the iCancerHealth app was utilized at a high rate. Features that focused on symptoms and medication side effects plus communication with clinicians were used most frequently. This extends our understanding of preferences and specific feature use with patient-centered technologies.

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Section: Discussionmentioning

confidence: 99%

Self-Care Support for Patients with Gastrointestinal Cancer: iCancerHealth

et al. 2018

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“…10,28 Our previous work in T1DM patients on CSII therapy using both data from a survey in conjunction with self-tracked data collection using a mHealth app indicated a high intra-and interpatient variability of SMBs. 19,20,22,24 Findings on variability of SMBs from the phenotyping algorithms used were discussed in follow-up personalized interventions. Our interviews asked about SMBs and practices discovered in the created SMB reports to manage diabetes when eating, drinking alcohol, and exercising.…”

Section: Discussionmentioning

confidence: 99%

“…Previous research by the study team examined and characterized SMBs in adults of all ages with T1D. [19][20][21][22]24 Building on knowledge gleaned from that work, SMBs identified and quantified by the biomedical informatics team and a statistician were presented to a diabetes care team for review. An iterative process with feedback from all members of the research team was used to refine the report templates to present the behaviors in a meaningful format for health providers at the individual patient level.…”

Section: Design Of the Report Templatementioning

confidence: 99%

“…Next, the iOS mHealth app, iDECIDE, was installed on the participants' smartphones and they were taught how to use the app to self-track exercise, meal, and alcohol intake. 24 Participants were instructed to otherwise maintain their normal self-care routine for 1 month. When tracking exercise (ExerFreq), the app required that users provide the start time, duration in minutes, and intensity rating as light, moderate, or vigorous using the "talk test" as a guide (►Fig.…”

Section: Subject Recruitment Self-tracking and Data Collectionmentioning

confidence: 99%

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Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy

et al. 2020

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Objective It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices. Methods This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted. Results Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38–100% range), and frequency of changing the bolus waveform, median 11 (7–95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control. Conclusion The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current “one size fits all” approach.

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“…[22][23][24] The authors have previously published data on a noncommercial smartphone application (app) called iDECIDE. 25 To the best of our knowledge, iDECIDE is the first app that allows users to track the techniques they employ to compensate for meals, alcohol, and exercise, as illustrated in ►Fig. 1.…”

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confidence: 99%

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

et al. 2018

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Type 1 diabetes (T1D) is a complex, chronic disease which requires patients to engage in certain self-management strategies to avoid acute and long-term complications. 1-3 For instance, adherence to insulin therapy, medical nutrition therapy, and physical activity are the ways and means by which individuals with T1D achieve the objective of optimizing glycemic control. [4][5][6] Patients are more likely to adhere to selfmanagement regimens that incorporate personal lifestyle Keywords ► exercise ► smartphone app ► wearables ► type 1 diabetes ► patient-generated data AbstractBackground 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 ¼ 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 ¼ 0.45). Intensity had a low concordance between the data sources (Cohen's kappa ¼ 0.2). The mean rate of change of glucose during exercise was -0.27 mg/(dL Ã min) and was not significantly different between data sources or intensity (p ¼ 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.

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Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors

Cited by 13 publications

References 28 publications

Self-Care Support for Patients with Gastrointestinal Cancer: iCancerHealth

Self-Care Support for Patients with Gastrointestinal Cancer: iCancerHealth

Data-Driven Diabetes Education Guided by a Personalized Report for Patients on Insulin Pump Therapy

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

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