Self-Management Behaviors in Adults on Insulin Pump Therapy

Groat, Danielle; Grando, Adela; Soni, Hiral; Thompson, Bithika; Boyle, Mary; Bailey, Marilyn; Cook, Curtiss B.

doi:10.1177/1932296816666538

Cited by 13 publications

(23 citation statements)

References 17 publications

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“…272,273 In patients on insulin pump therapy, the rate of bolus calculator use has been correlated with improved glycemic control in both the adult and pediatric populations. 268,274,275 Overall, these studies show that for MDI and conventional insulin pump patients, use of a bolus calculator reduces burden, improves glycemic control, and improves quality of life.…”

Section: Bolus Calculatorsmentioning

confidence: 81%

“…A recent study showed a significant increase in the number of patients achieving HbA1c targets and a reduction in hypoglycemia in the bolus calculator use group compared to the active control group . In patients on insulin pump therapy, the rate of bolus calculator use has been correlated with improved glycemic control in both the adult and pediatric populations . Overall, these studies show that for MDI and conventional insulin pump patients, use of a bolus calculator reduces burden, improves glycemic control, and improves quality of life.…”

Section: Diabetes Apps Automated Decision Support and Bolus Calculamentioning

confidence: 93%

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ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

et al. 2018

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Section: Bolus Calculatorsmentioning

confidence: 81%

Section: Diabetes Apps Automated Decision Support and Bolus Calculamentioning

confidence: 93%

ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

et al. 2018

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“…The 18 studies included in this review consist of 5 qualitative studies [40][41][42][43][44], 5 experimental studies [31][32][33][34][35], 3 observational studies [28][29][30], 4 cross-sectional studies [36][37][38][39] and 1 mixed methods [45]. Five of the studies were smaller samples drawn from parent clinical trials [29,32,38,40,41].…”

Section: Resultsmentioning

confidence: 99%

“…Groat et al [28] analyzed individual participant IP address data (Medtronic) to characterize the relationship between adherence to insulin bolus dosing, logging of CHO intake and BG monitoring and glycemic control for a one month period.…”

Section: The Relationship Between Adherence To Data Logging and Measumentioning

confidence: 99%

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review (Preprint)

Waite¹,

Martin²,

Franklin³

et al. 2017

Preprint

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Background: People with T1DM are confronted with self-management tasks and strategies to reduce risks of long-term complications against the risk of hypoglycemic events. The potential of advanced and evolving technology to address these issues involves consideration of psychological and behavioral constructs alongside the usability of devices. Access and uptake of advanced technology is further influenced by economic factors and health care provider capacity to support such interventions. Previous reviews have focused upon either clinical outcomes or descriptively scoped the literature and sometimes synthesize studies on adults with children and young people where human factors are different. Objective:The objective of this review was to describe the relationship between human factors and adherence with technology for data logging processes in adults with T1DM and to explore the factors which influence this association. Methods: A systematic literature review.Results: 18 studies were included in the review following a literature search using the PRISMA guidelines and the quality appraisal process. Six constructs emerged from the data analysis:. The relationship between adherence to data logging and measurable outcomes; Satisfaction with the transition to advanced technology for self-management; Use of advanced technology and time spent on diabetes related activities; Strategies to mediate the complexities of diabetes and the use of advanced technology; Cognition in the wild and Meanings, views and perspectives from the users of technology.There was evidence of increased treatment satisfaction on transition to advanced technology (insulin pump and CGM), the most significant contributing factor was when BG levels were consistently < 7.00mmol/l (P =.009). However, participants spent considerable time on their diabetes self-care. There was evidence that 1 logging of data was positively correlated with increasing age with the use of an app that provided meaningful feedback (regression coefficient = 55.8 recordings/ year; P = 0.009). Furthermore, there were benefits of CGM for older people in mediating complexities and their fears of hypoglycemia with reported significant differences in well-being (P= .009).Qualitative studies within the review aimed to explore the use and uptake of technology within the context of everyday lives. There were 'frustrations' with CGM, CSII, calibration of devices and alarms. In addition, the implications of 'body image' and the way in which 'significant' others impacted on the behavior and attitude of the individual towards technology use. There were wide variations in the normal use of and interaction with technology across a continuum of sociocultural contexts. This has implications for the way in which future technologies are designed. Conclusions:Many of the quantitative studies in the review were limited by small sample sizes. This may make it difficult to generalize findings to other contexts. This is further limited by a sample that was predominantly Caucasian, well-contr...

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“…Moreover, and partly as a result of these challenges, there are scarce data regarding adherence to CSII therapy and SMBs among adults with T1DM, as prior qualitative and quantitative studies have focused primarily on children or young adults. 5,6,[9][10][11][12][16][17][18] While recent evidence shows that there is considerable variability of SMBs within and between adult patients with T1DM on insulin pump therapy who use CSII, [19][20][21][22] current behavioral interventions are mostly based on the "one size fits all" approach of standardized diabetes self-management education. 1 This is unfortunate since individuals are more likely to follow self-management protocols that take their lifestyle preferences into account.…”

Section: Background and Significancementioning

confidence: 99%

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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Self-Management Behaviors in Adults on Insulin Pump Therapy

Cited by 13 publications

References 17 publications

ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review (Preprint)

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

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