Technology and intensive management in youth with type 1 diabetes: state of the art

Developmental changes in the roles of patients and families in type 1 diabetes management

Developmentally-tailored diabetes self-care education and support are integral parts of contemporary multidisciplinary T1D care. The patient with T1D must have the support of the family and the diabetes team to maintain the rigors of diabetes management, but the specific roles of patients and families with regard to daily diabetes tasks change considerably throughout the developmental span of early childhood, middle childhood/school-age years, and adolescence. This review provides a framework of key normative developmental issues for each of these developmental stages. Within this context, ideal family diabetes management is reviewed within each developmental stage and anticipated challenges that can arise during these stages and that can adversely impact diabetes management are presented. This paper also summarizes empirical evidence for specific intervention and care strategies to support optimal diabetes management across these stages in order to maximize opportunities for a successful transfer of diabetes management tasks from parents to maturing youth. Finally, the review provides an emphasis on approaches to promote family teamwork and adolescent diabetes self-care adherence as well as opportunities to use novel technology platforms as a means to support optimal diabetes management.

Technology to optimize pediatric diabetes management and outcomes

Technology for diabetes management is rapidly developing and changing. With each new development, there are numerous factors to consider, including medical benefits, impact on quality of life, ease of use, and barriers to use. It is also important to consider the interaction between developmental stage and technology. This review considers a number of newer diabetes-related technologies and explores issues related to their use in the pediatric diabetes population (including young adults), with a focus on psychosocial factors. Areas include trend technology in blood glucose monitoring, continuous glucose monitoring, sensor-augmented insulin pumps and low glucose suspend functions, internet applications including videoconferencing, mobile applications (apps), text messaging, and online gaming.

Contemporary rates of severe hypoglycaemia in youth with type 1 diabetes: variability by insulin regimen

AIMS

To determine incidence rates of severe hypoglycaemia and compare incidence rates by insulin regimen in a diverse sample of youth with Type 1 diabetes from two sites.

METHODS

In this observational study, 255 youth (51% female) aged 9-15 years receiving varied insulin regimens provided data prospectively for a median of 1.2 years. Reported episodes of severe hypoglycaemia, defined as episodes requiring help from another person for oral treatment or episodes resulting in seizure/coma, and current insulin regimens were collected systematically. Incidence rates were calculated and compared according to insulin regimen in bivariate and multivariate analyses.

RESULTS

At first encounter, participants had a median age of 12.2 years (range 9.0-15.0), median diabetes duration of 4.4 years (range 1.0-13.0) and mean HbA(1c) of 67 ± 12 mmol/mol (8.3 ± 1.1%). The incidence rate was 37.6/100 patient-years for all severe hypoglycaemia and 9.6/100 patient-years for seizure/coma. The incidence rate for severe hypoglycaemia was 31.8/100 patient-years on continuous subcutaneous insulin infusion (pump therapy), 34.4/100 patient-years on basal-bolus injections and 46.1/100 patient-years on NPH (NPH vs. pump therapy: P = 0.04). The incidence rate for seizure/coma was 4.5/100 patient-years on pump therapy, 11.1/100 patient-years on basal-bolus injections and 14.4/100 patients-years on NPH (NPH vs. pump therapy: P = 0.004). In the multivariate analysis, the rate of seizure/coma was significantly higher for those on NPH vs. pump therapy (rate ratio 2.9, P = 0.03).

CONCLUSIONS

Rates of severe hypoglycaemia in youth with Type 1 diabetes remain high. Pump therapy was associated with lower rates of all severe hypoglycaemia and seizure/coma in comparison with NPH.

Impact of type 1 diabetes mellitus on the family is reduced with the medical home, care coordination, and family-centered care

OBJECTIVES

To examine whether the medical home, care coordination, or family-centered care was associated with less impact of type 1 diabetes mellitus (T1D) on families' work, finances, time, and school attendance.

STUDY DESIGN

With the 2005 to 2006 National Survey of Children with Special Health Care Needs, we compared impact in children with T1D (n = 583) with that in children with other special health care needs (n = 39 944) and children without special health care needs (n = 4945). We modeled the associations of the medical home, care coordination, and family-centered care with family impact in T1D.

RESULTS

Seventy-five percent of families of children with T1D reported a major impact compared with 45% of families of children with special health care needs (P < .0001) and 17% of families of children without special health care needs (P < .0001). In families of children with T1D, 35% reported restricting work, 38% reported financial impact, 41% reported medical expenses >$1000/year, 24% reported spending ≥11 hours/week caring or coordination care, and 20% reported ≥11 school absences/year. The medical home, care coordination, and family-centered care were associated with less work and financial impact.

CONCLUSIONS

In childhood T1D, most families experience major impact. Better systems of health care delivery may help families reduce some of this impact.

Point of care testing for pediatric following up of hemoglobin A1C: Concern on product's property

Point of care testing (POCT) is a new concept in laboratory medicine. The POCT tool helps perform the laboratory test at site giving fast result and requires only small amount of sample. It is presently and increasingly used in pediatrics. In pediatric diabetology, the use of POCT for following up of hemoglobin A1C is a new concept. At present, there are several POCT tools for this purpose. In this specific article, the author compares on important aspects (turnaround time and required amount of specimen) of 5 available POCT tools for measurement of hemoglobin A1C level. The test that is the fastest and requires the least amount of blood sample, which means the least harm to the pediatric patient is the most preferable. Here, it can be seen that there is no ideal POCT that has both properties. However, if it is standardized to compare the amount of required blood sample, the special concern in the pediatric patients, the product from multiplication shows that Siemen DCA 2000+ and Siemen DCA Vantage are the two most preferable POCT tools for hemoglobin A1C determination in pediatric diabetes mellitus management.

Contemporary management of patients with type 1 diabetes

The current standard of care for patients with type 1 diabetes (T1D) employs a system of intensive diabetes management aimed at near-normal glycemia, which reduces the risk of micro- and macrovascular complications. Optimal management is an ongoing process based on a patient-centered collaboration with a primary care clinician and a multidisciplinary diabetes team that provides diabetes management, including education and psychosocial support. Intensive diabetes therapy attempts to mimic physiologic insulin replacement. Over the past 15 years, there has been widespread use of multiple-dose insulin regimens using a variety of insulin analogs, administered either by injection or insulin pump therapy, together with medical nutrition therapy, frequent self-monitoring of blood glucose and, more recently, continuous logo glucose monitoring. It is now possible to achieve previously unattainable levels of glycemic control with less risk of severe hypoglycemia, and yet only a minority of patients achieves target hemoglobin A1c values. This review discusses contemporary management of T1D with a focus on health outcomes.

A novel method for measuring patients' adherence to insulin dosing guidelines: introducing indicators of adherence

Background

Diabetic type 1 patients are often advised to use dose adjustment guidelines to calculate their doses of insulin. Conventional methods of measuring patients' adherence are not applicable to these cases, because insulin doses are not determined in advance. We propose a method and a number of indicators to measure patients' conformance to these insulin dosing guidelines.

Methods

We used a database of logbooks of type 1 diabetic patients who participated in a summer camp. Patients used a guideline to calculate the doses of insulin lispro and glargine four times a day, and registered their injected doses in the database. We implemented the guideline in a computer system to calculate recommended doses. We then compared injected and recommended doses by using five indicators that we designed for this purpose: absolute agreement (AA): the two doses are the same; relative agreement (RA): there is a slight difference between them; extreme disagreement (ED): the administered and recommended doses are merely opposite; Under-treatment (UT) and over-treatment (OT): the injected dose is not enough or too high, respectively. We used weighted linear regression model to study the evolution of these indicators over time.

Results

We analyzed 1656 insulin doses injected by 28 patients during a three weeks camp. Overall indicator rates were AA = 45%, RA = 30%, ED = 2%, UT = 26% and OT = 30%. The highest rate of absolute agreement is obtained for insulin glargine (AA = 70%). One patient with alarming behavior (AA = 29%, RA = 24% and ED = 8%) was detected. The monitoring of these indicators over time revealed a crescendo curve of adherence rate which fitted well in a weighted linear model (slope = 0.85, significance = 0.002). This shows an improvement in the quality of therapeutic decision-making of patients during the camp.

Conclusion

Our method allowed the measurement of patients' adherence to their insulin adjustment guidelines. The indicators that we introduced were capable of providing quantitative data on the quality of patients' decision-making for the studied population as a whole, for each individual patient, for all injections, and for each time of injection separately. They can be implemented in monitoring systems to detect non-adherent patients.