Self-Management Behaviors in Adults on Insulin Pump Therapy

AdultCarbQuiz for the Portuguese population with type 1 diabetes mellitus: translation, cultural adaptation and validation of its metabolic impact

AIMS

In type 1 diabetes mellitus (T1DM), functional insulin therapy, based on carbohydrate (CH) counting and individualized insulin-to-carbohydrate ratio, is essential to achieve an adequate metabolic control. However, to date, few tools have been validated to assess patients' knowledge about CH counting, with the AdultCarbQuiz questionnaire having proved to be a reliable method in an American cohort. The aim of this study was to translate, culturally adapt and validate the AdultCarbQuiz questionnaire for the Portuguese population.  METHODS: This was a cross-sectional study of patients with T1DM on functional insulin therapy through continuous subcutaneous insulin infusion (CSII). Prior to its application, the AdultCarbQuiz questionnaire was translated and culturally adapted to the Portuguese context. Statistical analyses include descriptive, correlation and intern consistency analysis using IBM® SPSS® Statistics, version 27.

RESULT

One hundred patients were included, 58% of female sex, with a mean age of 31.09 ± 10.77 years. Mean disease duration was 15.04 ± 9.23 years, and mean CSII usage time was 4.02 ± 3.90 years. The average value of glycated haemoglobin (HbA1c), time in range (TIR), time above range (TAR) and time below range was, respectively, 7.32 ± 0.87, 59.75 ± 14.13, 34.38 ± 15.40 and 5.75 ± 6.58%. The average score of the questionnaire was 30.86 points ± 3.58 points, considered high. The Kuder-Richardson 20 coefficient value was 0.63 for the total score, with a Spearman-Brown value for the half-split of 0.63. Individuals with lower HbA1c values scored significantly higher on knowledge about hypoglycaemia prevention and correction (r = - 0.269, p = 0.007) and on the total questionnaire score (r = - 0.205, p = 0.041). A higher TIR and a lower TAR were also associated with a higher total score (r = 0.274, p = 0.007 and r = - 0.274, p = 0.007, respectively).

CONCLUSIONS

In this study, the AdultCarbQuiz questionnaire, translated and culturally adapted to the Portuguese context, proved to be a useful tool in assessing knowledge about CH counting in patients with T1DM, allowing to optimize, individually, the therapeutic strategy in consultation.

Proof-of-concept Study of Using Supervised Machine Learning Algorithms to Predict Self-care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy

OBJECTIVE

Using supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus (T1DM) patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within six months.

METHODS

This was a single-center retrospective chart review of 100 adult T1DM patients on insulin pump therapy (>6 months). Three SMLAs were deployed: multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included AUC-ROC for discrimination and Brier scores for calibration.

RESULTS

Variables predictive of adherence with IPSMB criteria were baseline HbA1c, continuous glucose monitoring (CGM), and sex. The models had comparable discriminatory power (LR=0.74; RF=0.74; k-NN=0.72), with the random forest model showing better calibration (Brier=0.151). Predictors of the good glycemic response included baseline HbA1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR=0.81, RF=0.80, k-NN=0.78) but the random forest model being better calibrated (Brier=0.099).

CONCLUSION

These proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within six months. Subject to further study, non-linear prediction models may perform better.

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

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.

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

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.

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

BACKGROUND

Type 1 diabetes (T1D) care requires multiple daily self-management behaviors (SMBs). Preliminary studies on SMBs rely mainly on self-reported survey and interview data. There is little information on adult T1D SMBs, along with corresponding compensation techniques (CTs), gathered in real-time.

OBJECTIVE

The article aims to use a patient-centered approach to design iDECIDE, a smartphone application that gathers daily diabetes SMBs and CTs related to meal and alcohol intake and exercise in real-time, and contrast patients' actual behaviors against those self-reported with the app.

METHODS

Two usability studies were used to improve iDECIDE's functionality. These were followed by a 30-day pilot test of the redesigned app. A survey designed to capture diabetes SMBs and CTs was administered prior to the 30-day pilot test. Survey results were compared against iDECIDE logs.

RESULTS

Usability studies revealed that participants desired advanced features for self-tracking meals and alcohol intake. Thirteen participants recorded over 1,200 CTs for carbohydrates during the 30-day study. Participants also recorded 76 alcohol and 166 exercise CTs. Comparisons of survey responses and iDECIDE logs showed mean% (standard deviation) concordance of 77% (25) for SMBs related to meals, where concordance of 100% indicates a perfect match. There was low concordance of 35% (35) and 46% (41) for alcohol and exercise events, respectively.

CONCLUSION

The high variability found in SMBs and CTs highlights the need for real-time diabetes self-tracking mechanisms to better understand SMBs and CTs. Future work will use the developed app to collect SMBs and CTs and identify patient-specific diabetes adherence barriers that could be addressed with individualized education interventions.

Self-Reported Compensation Techniques for Carbohydrate, Exercise, and Alcohol Behaviors in Patients With Type 1 Diabetes on Insulin Pump Therapy

Studies have found variability in self-care behaviors in patients with type 1 diabetes, particularly when incorporating exercise and alcohol consumption. The objective of this study was to provide results from a survey to understand (1) insulin pump behaviors, (2) reported self-management behaviors for exercise and alcohol, and (3) perceptions of the effects of exercise and alcohol on blood glucose (BG) control. Fourteen participants from an outpatient endocrinology practice were recruited and administered an electronic survey. Compensation techniques for exercise and alcohol, along with reasons for employing the techniques were identified. Also identified were factors that participants said affected BG control with regard to exercise and alcohol. These results confirm the considerable inconsistency patients have about incorporating exercise and alcohol into decisions about self-management behaviors.

A Methodology to Compare Insulin Dosing Recommendations in Real-Life Settings

BACKGROUND

We propose a methodology to analyze complex real-life glucose data in insulin pump users.

METHODS

Patients with type 1 diabetes (T1D) on insulin pumps were recruited from an academic endocrinology practice. Glucose data, insulin bolus (IB) amounts, and self-reported alcohol consumption and exercise events were collected for 30 days. Rules were developed to retrospectively compare IB recommendations from the insulin pump bolus calculator (IPBC) against recommendations from a proposed decision aid (PDA) and for assessing the PDA's recommendation for exercise and alcohol.

RESULTS

Data from 15 participants were analyzed. When considering instances where glucose was below target, the PDA recommended a smaller dose in 14%, but a larger dose in 13% and an equivalent IB in 73%. For glucose levels at target, the PDA suggested an equivalent IB in 58% compared to the subject's IPBC, but higher doses in 20% and lower in 22%. In events where postprandial glucose was higher than target, the PDA suggested higher doses in 25%, lower doses in 13%, and equivalent doses in 62%. In 64% of all alcohol events the PDA would have provided appropriate advice. In 75% of exercise events, the PDA appropriately advised an IB, a carbohydrate snack, or neither.

CONCLUSIONS

This study provides a methodology to systematically analyze real-life data generated by insulin pumps and allowed a preliminary analysis of the performance of the PDA for insulin dosing. Further testing of the methodological approach in a broader diabetes population and prospective testing of the PDA are needed.