Clinical requirements for closed-loop control systems.

Closed-loop (CL) therapy systems should be safe, efficacious, and easily manageable for type 1 diabetes mellitus patient use. For the first two clinical requirements, noninferiority and superiority criteria must be determined based on current conventional and intensive therapy outcomes. Current frequencies of hypoglycemia and diabetic ketoacidosis are reviewed and safety expectations for CL therapy systems are proposed. Glycosylated hemoglobin levels lower than current American Diabetes Association recommendations for different age groups are proposed as superiority criteria. Measures of glycemic variability are described and the recording of blood glucose levels as percentages within, above, and below a target range are suggested as reasonable alternatives to sophisticated statistical analyses. It is also suggested that Diabetes Quality of Life and Fear of Hypoglycemia surveys should be used to track psychobehavioral outcomes. Manageability requirements for safe and effective clinical management of CL systems are worth being underscored. The weakest part of the infusion system remains the catheter, which is exposed to variable and under-delivery incidents. Detection methods are needed to warn both the system and the patient about altered insulin delivery, including internal pressure and flow alarms. Glucose monitor sensor accuracy is another requirement; it includes the definition of conditions that lead to capillary glucose measurement, eventually followed by sensor recalibration or replacement. The crucial clinical requirement will be a thorough definition of the situations when the patient needs to move from CL to manual management of insulin delivery, or inversely can switch back to CL after a requested interruption. Instructions about these actions will constitute a major part of the education process of the patients before using CL systems and contribute to the manageability of these systems.

Behavioral challenges in the management of childhood diabetes.

For many individuals, the diagnosis of diabetes is accompanied by the need for significant lifestyle changes, many of which seem difficult or impossible to implement. When diabetes is diagnosed in a child, those lifestyle changes may involve radical alterations in family life and significantly impact the child's normal growth and development as well as the family's social and economic status. This article describes some of the behavioral challenges associated with childhood diabetes and the importance of identifying strong, developmentally appropriate family support. Specific emphases are given to the complexity of the treatment regimens, the physiologic and emotional challenges associated with normal growth and development, and the family's role in ensuring successful diabetes management. Challenges inherent in both type 1 and type 2 diabetes mellitus are discussed as are factors important to ensuring adherence to the treatment plan.

Neurocognitive Differences Between Drivers with Type 1 Diabetes with and without a Recent History of Recurrent Driving Mishaps.

OBJECTIVE: A subset of drivers with type 1 diabetes mellitus (T1DM) may be at significant risk of hypoglycemia-related driving collisions and moving vehicle violations due to acute and chronic neurocognitive impairment. The present study compared drivers with T1DM with and without a recent history of multiple driving mishaps on a neurocognitive battery during euglycemia, progressive mild hypoglycemia, and recovery from hypoglycemia, to determine whether neurocognitive measures differentiate the two risk groups. We hypothesized that drivers with a history of multiple recent hypoglycemia-related driving mishaps would demonstrate greater psychomotor slowing, both during hypoglycemia and euglycemia. STUDY DESIGN: Partcipants were 42 adults with T1DM and were assigned to one of two groups: those reporting no driving mishaps in the last year (-History) and those reporting two or more (+History).Neurocognitive testing was conducted before and repeated during a hyper-insulinemic clamping procedure. RESULTS: Not surprisingly, all drivers demonstrated a decrease in functioning across all neurocognitive tasks during hypoglycemia. However, in contrast to the common belief that neurocognitive functions return slowly and gradually following hypoglycemia, baseline neurocognitive functioning immediately recovered upon return of BG to euglycemia for all subjects. Between-group analyses revealed that subjects with a recent history of driving mishaps consistently demonstrated poorer performance on tasks measuring working memory. CONCLUSION: Working memory is a potential neurocognitive indicator that may help differentiate adults with T1DM with and without a history of driving mishaps, predict future risk for driving mishaps, and provide targeted intervention programs to address this critical public health issue.

Metabolic Demand of Driving Among Adults with Type 1 Diabetes Mellitus (T1DM).

Recent research suggests that the frequency of driving mishaps is increased in people with Type 1 diabetes (T1DM) as compared to those with Type 2 diabetes or their non-diabetic spouses. This study involved a sample of T1DM drivers and was designed to investigate the metabolic and physiologic demands of driving compared to sitting passively. Participants (N=38) were divided into two groups: the -History group included those reporting no driving mishaps in the past two years, and the +History group included participants reporting at least two such mishaps in the past two years. Glucose utilization rates were determined in participants while: (a) they were driving a virtual reality driving simulator for 30 minutes, and (b) watching a 30-minute video. Blood glucose (BG) levels were maintained at similar levels during both procedures. Other biological variables including heart rate (HR) were monitored. Participants rated their hypoglycemia (low BG) symptoms before and after each of the two procedures. . Participants could self-treat if they perceived they were experiencing hypoglycemia. There were no differences between the two groups. However, glucose utilization rates were significantly higher during the driving scenario (3.83mg/kg/min + 1.7 vs. 3.37 mg/kg/min + 1.6, p=0.047). HR was significantly higher during the driving scenario. Drivers reported more autonomic symptoms during driving and 32% treated perceived hypoglycemia during driving. Driving a virtual reality simulator is associated with increased glucose utilization rates suggesting that driving per se has a metabolic cost and that BG should be measured prior to driving and periodically during long drives.

Type 1 diabetic drivers with and without a history of recurrent hypoglycemia-related driving mishaps: physiological and performance differences during euglycemia and the induction of hypoglycemia.

OBJECTIVE: Collisions are more common among drivers with type 1 diabetes than among their nondiabetic spouses. This increased risk appears to be attributable to a subgroup of drivers with type 1 diabetes. The hypothesis tested is that this vulnerable subgroup is more at risk for hypoglycemia and its disruptive effects on driving. RESEARCH DESIGN AND METHODS: Thirty-eight drivers with type 1 diabetes, 16 with (+history) and 22 without (-history) a recent history of recurrent hypoglycemia-related driving mishaps, drove a virtual reality driving simulator and watched a videotape of someone driving a simulator for 30-min periods. Driving and video testing occurred in a double-blind, randomized, crossover manner during euglycemia (5.5 mmol/l) and progressive hypoglycemia (3.9-2.5 mmol/l). Examiners were blind to which subjects were +/-history, whereas subjects were blind to their blood glucose levels and targets. RESULTS: During euglycemia, +history participants reported more autonomic and neuroglycopenic symptoms (P≤0.01) and tended to require more dextrose infusion to maintain euglycemia with the same insulin infusion (P<0.09). During progressive hypoglycemia, these subjects demonstrated less epinephrine release (P=0.02) and greater driving impairments (P=0.03). CONCLUSIONS: Findings support the speculation that there is a subgroup of type 1 diabetic drivers more vulnerable to experiencing hypoglycemia-related driving mishaps. This increased vulnerability may be due to more symptom "noise" (more symptoms during euglycemia), making it harder to detect hypoglycemia while driving; possibly greater carbohydrate utilization, rendering them more vulnerable to experiencing hypoglycemia; less hormonal counterregulation, leading to more profound hypoglycemia; and more neuroglycopenia, rendering them more vulnerable to impaired driving.

Comparison of the clinical information provided by the FreeStyle Navigator continuous interstitial glucose monitor versus traditional blood glucose readings.

BACKGROUND: The purpose of the analysis was to compare the clinical utility of data from traditional self-monitoring of blood glucose (SMBG) to that of continuous glucose monitoring (CGM). METHODS: A clinical study of the clinical accuracy of the FreeStyle Navigator CGM System (Abbott Diabetes Care, Alameda, CA), which includes SMBG capabilities, was conducted by comparison to the YSI blood glucose analyzer (YSI Inc., Yellow Springs, OH) using 58 subjects with type 1 diabetes. The Continuous Glucose-Error Grid Analysis (CG-EGA) was used as the analytical tool. RESULTS: Using CG-EGA, the "clinically accurate," "benign errors," and "clinical errors" were 86.8%, 8.7%, and 4.5% for SMBG and 92.7%, 3.7%, and 3.6% for CGM, respectively. If blood glucose is viewed as a process in time, SMBG would provide accurate information about this process 86.8% of the time, whereas CGM would provide accurate information about this process 92.7% of the time (P < 0.0001). In the hypoglycemic range, however, SMBG is more accurate as the "clinically accurate," "benign errors," and "clinical errors" were 83.5%, 6.4%, and 10.1% for SMBG and 57.1%, 8.4%, and 34.5% (P < 0.0001) for CGM, respectively. CONCLUSIONS: While SMBG produces more accurate instantaneous glucose values than CGM, control of blood glucose involves a system in flux, and CGM provides more detailed insight into the dynamics of that system. In the normal and elevated glucose ranges, the additional information about the direction and rate of glucose change provided by the FreeStyle Navigator CGM System increases the ability to make correct clinical decisions when compared to episodic SMBG tests.

Analytical and clinical performance of blood glucose monitors.

BACKGROUND: The objective of this study was to understand the level of performance of blood glucose monitors as assessed in the published literature. METHODS: Medline from January 2000 to October 2009 and reference lists of included articles were searched to identify eligible studies. Key information was abstracted from eligible studies: blood glucose meters tested, blood sample, meter operators, setting, sample of people (number, diabetes type, age, sex, and race), duration of diabetes, years using a glucose meter, insulin use, recommendations followed, performance evaluation measures, and specific factors affecting the accuracy evaluation of blood glucose monitors. RESULTS: Thirty-one articles were included in this review. Articles were categorized as review articles of blood glucose accuracy (6 articles), original studies that reported the performance of blood glucose meters in laboratory settings (14 articles) or clinical settings (9 articles), and simulation studies (2 articles). A variety of performance evaluation measures were used in the studies. The authors did not identify any studies that demonstrated a difference in clinical outcomes. Examples of analytical tools used in the description of accuracy (e.g., correlation coefficient, linear regression equations, and International Organization for Standardization standards) and how these traditional measures can complicate the achievement of target blood glucose levels for the patient were presented. The benefits of using error grid analysis to quantify the clinical accuracy of patient-determined blood glucose values were discussed. CONCLUSIONS: When examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients. There are several examples of how analytical tools used in the description of self-monitoring of blood glucose accuracy could be irrelevant to treatment decisions.

Results of a second year of therapy with the 12-month histrelin implant for the treatment of central precocious puberty.

Background. Gonadotropin releasing hormone analogs (GnRHas) are standard of care for central precocious puberty (CPP). The histrelin subcutaneous implant is safe and effective in the treatment of CPP for one year. Objective. The study evaluates a second year of therapy in children with CPP who received a new implant after one year of treatment. Methods. A prospective one-year study following an initial 12-month treatment period was conducted. Results. Thirty-one patients (29 girls) aged 7.7 +/- 1.5 years received a second implant. Eighteen were naïve to GnRHa therapy at first implantation. Peak LH declined from 0.92 +/- 0.58 mIU/mL at 12 months to 0.51 +/- 0.33 mIU/mL at 24 months (P < .0001) in naïve subjects, and from 0.74 +/- 0.50 mIU/mL at 12 months to 0.45 +/- 0.35 mIU/mL at 24 months (P = .0081) in previously treated subjects. Predicted adult height increased by 5.1 cm at 24 months (P = .0001). Minor implant site reactions occurred in 61%, while minor difficulties with explantation occurred in 32.2% of subjects. Conclusion. The histrelin implant demonstrates profound hypothalamic-pituitary-gonadal axis suppression when a new implant is placed for a second year of treatment. Prospective follow-up of this therapeutic modality for the treatment of CPP is needed.

Cognitive function is disrupted by both hypo- and hyperglycemia in school-aged children with type 1 diabetes: a field study.

OBJECTIVE: We developed a field procedure using personal digital assistant (PDA) technology to test the hypothesis that naturally occurring episodes of hypo- and hyperglycemia are associated with deterioration in cognitive function in children with type 1 diabetes. RESEARCH DESIGN AND METHODS: A total of 61 children aged 6-11 years with type 1 diabetes received a PDA programmed with two brief cognitive tests (mental math and choice reaction time), which they completed just before home glucose readings. The computer recorded time to complete each test and number of correct responses. Children completed several trials per day over 4-6 weeks for a total of 70 trials. Performance variables were compared across glucose ranges. Individual impairment scores (IISs) were also computed for each child by calculating the SD between performance during euglycemia and that during glucose extremes. RESULTS: Time to complete both mental math and reaction time was significantly longer during hypoglycemia. During hyperglycemia, time to complete math was significantly longer and reaction time was marginally significant (P = 0.053). There were no differences on task accuracy. Decline in mental math performance was equivalent at glucose levels <3.0 and >22.2 mmol/l. IISs varied greatly across children, with no age or sex differences. CONCLUSIONS: A decrease in mental efficiency occurs with naturally occurring hypo- and hyperglycemic glucose fluctuations in children with type 1 diabetes, and this effect can be detected with a field procedure using PDA technology. With blood glucose levels >22.2 mmol/l, cognitive deterioration equals that associated with significant hypoglycemia.

Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience.

BACKGROUND: Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. METHODS: Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70-140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared. RESULTS: With closed-loop control, once BG levels achieved the target range (70-140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced (p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control. CONCLUSION: Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This "artificial pancreas" controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.

Clinical application of emerging sensor technologies in diabetes management: consensus guidelines for continuous glucose monitoring (CGM).

Continuous glucose monitoring (CGM) is an evolving technology poised to redefine current concepts of glycemic control and optimal diabetes management. To date, there are few randomized studies examining how to most effectively use this new tool. Therefore, a group of eight diabetes specialists heard presentations on continuous glucose sensor technology and then discussed their experience with CGM in order to identify fundamental considerations, objectives, and methods for applying this technology in clinical practice. The group concluded that routine use of CGM, with real-time data showing the rate and direction of glucose change, could revolutionize current approaches to evaluating and managing glycemia. The need for such progress is indicated by the growing prevalence of inadequately treated hyperglycemia. Coordinating financial and educational resources and developing clear protocols for using glucose sensor technology are urgent priorities in promoting wide adoption of CGM by patients and health care providers. Finally, researchers, manufacturers, payers, and advocacy groups must join forces on the policy level to create an environment conducive to managing continuous data, measuring outcomes, and formalizing best practices.

Evaluating clinical accuracy of continuous glucose monitoring systems: Continuous Glucose-Error Grid Analysis (CG-EGA).

Continuous Glucose Sensors (CGS) generate rich and informative continuous data streams which have the potential to improve the glycemic condition of the patient with diabetes. Such data are critical to the development of closed loop systems for automated glycemic control. Thus the numerical and clinical accuracy of such must be assured. Although numerical point accuracy of these systems has been described using traditional statistics, there are no requirements, as of yet, for determining and reporting the rate (trend) accuracy of the data generated. In addition, little attention has been paid to the clinical accuracy. of these systems. Continuous Glucose-Error Grid Analysis (CG-EGA) is the only method currently available for assessing the clinical accuracy of such data and reporting this accuracy for each of the relevant glycemic ranges, - hypoglycemia, euglycemia, hyperglycemia. This manuscript reviews the development of the original Error Grid Analysis (EGA) and describes its inadequacies when used to determine point accuracy of CGS systems. The development of CG-EGA as a logical extension of EGA for use with CGS is described in detail and examples of how it can be used to describe the clinical accuracy of several CGS are shown. Information is presented on how to obtain assistance with the use of CG-EGA.

Safe at school: a Virginia experience.

OBJECTIVE: The Commonwealth of Virginia passed legislation in 1999 requiring nonmedical school personnel to assist students with the management of type 1 diabetes when school nurses were unavailable. This study was designed to determine which school personnel in Virginia currently assist type 1 diabetic students with insulin administration and management of hypoglycemia and to determine if these students are being cared for in a safe manner. RESEARCH DESIGN AND METHODS: Parents of children with type 1 diabetes who attended public school in Virginia during the previous year and who were receiving their diabetes care at the University of Virginia diabetes clinics were asked to participate in an anonymous survey. The survey asked parents which school personnel were responsible for their child's diabetes care while at school and which persons helped with specific care tasks including blood glucose monitoring, insulin administration, and assistance with treatment of hypoglycemia. Questions were asked regarding the occurrence and treatment of hypoglycemia and any adverse effects of such treatment. RESULTS: A total of 185 parents whose children attended 153 different schools responded, 69% of whom reported that a full-time school nurse was assigned to their child's school. In other schools, teachers, administrators, coaches, and cafeteria workers supplemented part-time nurses in assisting students with diabetes management tasks. Although hypoglycemia was not a rare event (75% of students experienced a median of five episodes per year), only one severe event requiring the use of glucagon was reported. In that case, glucagon was administered appropriately by a part-time school nurse, and the student experienced no adverse effects related to the treatment. CONCLUSIONS: Students with type 1 diabetes can be cared for safely during the school day by a variety of trained medical and nonmedical personnel. The occurrence of one severe hypoglycemic event among 185 students suggests that as many as 3% of students could experience severe hypoglycemia in a given school year. Legislation that permits nonmedical school personnel to assist students with their diabetes management could make the diabetic children's school day safer and improve their overall glucose control.

Continuous Glucose Sensors: Continuing Questions about Clinical Accuracy.

Continuous glucose sensors (CGS) offer the potential to greatly change the lives of people with diabetes. Even though two of these systems (Guardian RT, Medtronic, Northridge, CA, and DexCom STS, DexCom, San Diego, CA) have been approved by the Food and Drug Administration for use as adjuncts to self-blood glucose monitoring (SBGM), questions remain concerning the accuracy of these devices. When considering accuracy, two distinct approaches should be emphasized: (1) numerical and (2) clinical. Because CGS data are a process in time, each of these two approaches includes two subtypes of accuracy: point and rate. Conventional statistics such as correlation coefficients, mean and median relative absolute differences, and International Standards Organization criteria are measures of numerical point accuracy. A new measure, the R deviation, is introduced to quantify numerical rate accuracy. Error-grid analysis (Clarke EGA) measures clinical point accuracy. The only measure of both clinical point accuracy and rate accuracy is continuous glucose error-grid analysis. This analysis is a combination of two components, P-EGA measuring point accuracy and R-EGA measuring rate accuracy, which are designed to assess the information that distinguishes continuous glucose measurements from intermittent SBGM determinations. Further, a better understanding of the source of the error associated with time lag and its effect on CGS readings may improve sensor output. Finally, the reliability of the CGS sensors, in terms of initial calibration and long-term application, needs to be assessed carefully if current CGS systems are to be used as hypoglycemia monitors or incorporated in the future design of closed loop (artificial pancreas) systems.

A model of self-treatment behavior, glucose variability, and hypoglycemia-associated autonomic failure in type 1 diabetes.

BACKGROUND: Type 1 diabetes patients face a lifelong behaviorally controlled optimization problem: maintaining strict glycemic control without increasing the risk of hypoglycemia. Because internal insulin secretion in type 1 diabetes (T1DM) is practically absent, this optimization is entirely dependent on the interplay among (i) self-treatment behavior, (ii) interaction between exogenous insulin and carbohydrates utilization, and (iii) internal defenses against hypoglycemia. This article presents a mathematical model and a computer simulation of the relationship among self-treatment in T1DM, blood glucose (BG) variability, and hypoglycemia-associated autonomic failure (HAAF). METHOD: A stochastic behavioral self-control process was coupled with a dynamical system simulation of the dampening effect of counterregulation on BG oscillations. The resulting biobehavioral control system was compared to data from a field clinical trial (85 T1DM patients, 21-62 years old, T1DM of at least 2 years duration, and at least two documented severe hypoglycemia episodes during the previous year). RESULTS: The mathematical simulation was able to reproduce characteristics of hypoglycemic events observed during a field clinical trial, such as temporal clustering of hypoglycemic episodes associated with HAAF and occurrence of severe hypoglycemia as a result of periods of HAAF augmented by increased BG variability. CONCLUSION: This investigation offers a mathematical model of HAAF-the primary barrier to intensive insulin treatment. This combined modeling/computer simulation/data analysis approach explains the temporal relationship among behaviorally induced hypoglycemia, glucose variability, and autonomic failure in T1DM. This explanation is valuable not only because it indicates that signs of HAAF can be detected in patients' natural environment via self-monitoring or continuous glucose monitoring, but also because it allows for tracking of the risk of severe hypoglycemia over time.

Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp.

BACKGROUND: Models of the dynamics of interstitial fluid-based continuous glucose sensors imply a variable sensor deviation from reference blood glucose (BG), depending on both sensor calibration procedure and BG dynamics. These effects could have a significant effect on the cross-interpretation of nonidentical accuracy studies. METHODS: Hyperinsulinemic euglycemic and hypoglycemic clamps were performed on 39 subjects with type 1 diabetes wearing the Medtronic Continuous Glucose Monitoring System®. Sensor calibration and interstitial glucose (IG) dynamics were modeled and analyzed as potential confounders of sensor deviation from reference BG. RESULTS: The mean absolute deviation (MAD) of sensor data was 20.9 mg/dl during euglycemia and 24.5 mg/dl during descent into and recovery from hypoglycemia. Computer-generated recalibration reduced MAD to 10.6 and 14.6 mg/dl, respectively. Modeling of IG dynamics reduced the MAD further to 10.0 and 10.4 mg/dl (using idiosyncratic parameters) or to 10.6 and 11.5 mg/dl (using model parameters common for all subjects), respectively. CONCLUSIONS: The sensor MAD from reference is strongly influenced by the choice of calibration points. Thus, cross-experiment comparisons of sensor accuracy are likely to be heavily dependent on the employed calibration procedures. Demanding calibration points substantially differing in value was found to improve calibration effectiveness. Simulation using existing IG models and population parameters reduced the bias resulting from BG-IG dynamics.

Predictors of fear of hypoglycemia in adolescents with type 1 diabetes and their parents.

OBJECTIVES: This study tested the hypothesis that both trait anxiety and hypoglycemic history contribute to fear of hypoglycemia (FOH) both in adolescents with type 1 diabetes mellitus (T1DM) and in their parents, and relationships between FOH and other variables including metabolic control, symptom perception, and use of insulin pump therapy. STUDY DESIGN: Thirty-nine parent-adolescent pairs completed questionnaires assessing background and clinical information, hypoglycemic episodes, FOH, and trait anxiety. Adolescent blood was also sampled for glycosylated hemoglobin A1c (HbA1c) measurement. RESULTS: In adolescents, both trait anxiety and frequency of severe hypoglycemic episodes were significant predictors of FOH, together accounting for almost 50% of the variance. Parental FOH was not predicted by their own trait anxiety or their child's hypoglycemic history but by whether they believed that their child carried emergency glucose. FOH was not related to metabolic control, although adolescents who experienced recent severe hypoglycemia (SH) with unconsciousness had significantly higher HbA1c. Parental trait anxiety significantly correlated with child trait anxiety, but parent-child levels of FOH were unrelated. Neither trait anxiety nor FOH related to reported symptoms, and FOH was not lower in parents with insulin pump therapy. CONCLUSIONS: Consistent with findings from adult patient populations, trait anxiety levels and recent experiences with hypoglycemia predict FOH in adolescents with T1DM. In parents, however, beliefs about their adolescents' ability to cope with hypoglycemic episodes predicted FOH. FOH in adolescents with T1DM and their parents is a complex construct influenced by multiple personality and situational and behavioral factors, and its impact on diabetes management remains unclear.