Beyond HbA1c : using continuous glucose monitoring metrics to enhance interpretation of treatment effect and improve clinical decision-making.

Assessment of glycaemic outcomes in the management of Type 1 and Type 2 diabetes has been revolutionized in the past decade with the increasing availability of accurate, user-friendly continuous glucose monitoring (CGM). This advancement has brought a need for new techniques to appropriately analyse and understand the voluminous and complex CGM data for application in research-related goals and clinical guidance for individuals. Traditionally, HbA1c was established using the Diabetes Control and Complications Trial (DCCT) and other trials as the ultimate measure of glycaemic control in terms of efficacy and, by default, risk of microvascular complications of diabetes. However, it is acknowledged that HbA1c alone is inadequate at describing an individual's daily glycaemic variation and risks for hypo- and hyperglycaemia, and it does not provide the guidance needed to decrease those risks. CGM data provide means by which to characterize an individual's daily glycaemic excursions on a different time scale measured in minutes rather than months. As a consequence, clinical reports, such as the ambulatory glucose profile, increasingly include summary statistics related to averages (mean glucose, time in range) as well as markers related to glycaemic variability (coefficient of variation, standard deviation). However, there is a need to translate those metrics into specific risks that can be addressed in an actionable plan by individuals with diabetes and providers. This review presents several clinical scenarios of glycaemic outcomes from CGM data that can be analysed to describe glycaemic variability and its attendant risks of hyperglycaemia and hypoglycaemia, moving towards relevant interpretation of the complex CGM data streams.

Multicenter outpatient dinner/overnight reduction of hypoglycemia and increased time of glucose in target with a wearable artificial pancreas using modular model predictive control in adults with type 1 diabetes.

AIMS: To test in an outpatient setting the safety and efficacy of continuous subcutaneous insulin infusion (CSII) driven by a modular model predictive control (MMPC) algorithm informed by continuous glucose monitoring (CGM) measurement. METHODS: 13 patients affected by type 1 diabetes participated to a non-randomized outpatient 42-h experiment that included two evening meals and overnight periods (in short, dinner & night periods). CSII was patient-driven during dinner & night period 1 and MMPC-driven during dinner&night period 2. The study was conducted in hotels, where patients could move around freely. A CGM system (G4 Platinum; Dexcom Inc., San Diego, CA, USA) and insulin pump (AccuChek Combo; Roche Diagnostics, Mannheim, Germany) were connected wirelessly to a smartphone-based platform (DiAs, Diabetes Assistant; University of Virginia, Charlottesville, VA, USA) during both periods. RESULTS: A significantly lower percentage of time spent with glucose levels <3.9 mmol/l was achieved in period 2 compared with period 1: 1.96 ± 4.56% vs 12.76 ± 15.84% (mean ± standard deviation, p < 0.01), together with a greater percentage of time spent in the 3.9-10 mmol/l target range: 83.56 ± 14.02% vs 62.43 ± 29.03% (p = 0.04). In addition, restricting the analysis to the overnight phases, a lower percentage of time spent with glucose levels <3.9 mmol/l (1.92 ± 4.89% vs 12.7 ± 19.75%; p = 0.03) was combined with a greater percentage of time spent in 3.9-10 mmol/l target range in period 2 compared with period 1 (92.16 ± 8.03% vs 63.97 ± 2.73%; p = 0.01). Average glucose levels were similar during both periods. CONCLUSIONS: The results suggest that MMPC managed by a wearable system is safe and effective during evening meal and overnight. Its sustained use during this period is currently being tested in an ongoing randomized 2-month study.

Modular closed-loop control of diabetes.

Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called "artificial pancreas," modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.

Muscle microvascular recruitment predicts insulin sensitivity in middle-aged patients with type 1 diabetes mellitus.

AIMS/HYPOTHESIS: Insulin delivery to muscle is rate-limiting for insulin's metabolic action and is regulated by insulin's own action to increase skeletal muscle blood flow and to recruit microvasculature. Microvascular dysfunction has been observed in insulin resistant states. We investigated the relation between insulin's action to recruit microvasculature and its metabolic action in type 1 diabetes. METHODS: Near euglycaemia was obtained by an overnight insulin infusion during 17 inpatient admissions of participants with type 1 diabetes. This was followed by a 2 h 1 mU kg⁻¹ min⁻¹ euglycaemic-hyperinsulinaemic clamp. Microvascular blood volume (MBV) was assessed using contrast-enhanced ultrasound 10 min before and 30 min after starting the clamp. RESULTS: We observed that, after overnight modest hyperinsulinaemia (average ≈ 286 pmol/l), MBV was positively related to the steady-state insulin sensitivity measured during the subsequent clamp (r = 0.62, p = 0.008). The more marked hyperinsulinaemia during the clamp (average steady-state insulin ≈ 900 pmol/l) increased MBV in the more insulin resistant participants within 30 min but not in the insulin sensitive participants. The change in MBV during the clamp was negatively correlated to the insulin sensitivity (r = -0.55, p = 0.022). As a result, MBV after 30 min of marked hyperinsulinaemia was comparable between the insulin sensitive and resistant participants. CONCLUSIONS/INTERPRETATION: We conclude that moderate overnight hyperinsulinaemia recruited microvasculature in the more sensitive participants, while higher levels of plasma insulin were needed for more insulin resistant participants. This suggests that microvascular responsiveness to insulin is one determinant of metabolic insulin sensitivity in type 1 diabetes.

Anticipating the next meal using meal behavioral profiles: a hybrid model-based stochastic predictive control algorithm for T1DM.

Automatic control of Type 1 Diabetes Mellitus (T1DM) with subcutaneous (SC) measurement of glucose concentration and subcutaneous (SC) insulin infusion is of great interest within the diabetes technology research community. The main challenge with the so-called "SC-SC" route to control is sensing and actuation delay, which tends to either destabilize the system or inhibit the aggressiveness of the controller in responding to meals and exercise. Model predictive control (MPC) is one strategy for mitigating delay, where optimal insulin infusions can be given in anticipation of future meal disturbances. Unfortunately, exact prior knowledge of meals can only be assured in a clinical environment and uncertainty about when and if meals will arrive could lead to catastrophic outcomes. As a follow-on to our recent paper in the IFAC symposium on Biological and Medical Systems (MCBMS 2009), we develop a control law that can anticipate meals given a probabilistic description of the patient's eating behavior in the form of a random meal (behavioral) profile. Preclinical in silico trials using the oral glucose meal model of Dalla Man et al. show that the control strategy provides a convenient means of accounting for uncertain prior knowledge of meals without compromising patient safety, even in the event that anticipated meals are skipped.

Driving mishaps and hypoglycaemia: risk and prevention.

Driving is a complex, multi-task activity that can be affected by cognitive impairment resulting from episodes of severe hypoglycaemia. Intensive insulin therapy increases the likelihood of severe hypoglycaemia but there have been few studies examining effects on driving skills. A survey carried out recently indicated that patients with type 1 diabetes had twice the incidence of driving accidents than their non-diabetic spouses or patients with type 2 diabetes. The motor accidents were associated with more frequent low blood glucose while driving and less frequent self-monitoring. In driving simulation tests it was found that driving has an intrinsic metabolic demand that can contribute to hypoglycaemia. Driving performance began to deteriorate at around 3.6 mmol/l but drivers frequently did not recognise and failed to treat the hypoglycaemia. Those who did self-treat had more driving relevant symptoms and less neuroglycopenia quantified by EEG alpha-theta differences. Patients should be recommended not to begin driving if blood glucose is below 4.5 mmol/l and should not continue to drive if they suspect that blood glucose has fallen below 4 mmol/l while driving. If hypoglycaemia is suspected patients should immediately pull off the road, measure blood glucose if possible, treat themselves as necessary and not resume driving until glucose and cognitive-motor function return to normal. The problems of driving and hypoglycaemia should be discussed with patients with diabetes and behavioural interventions instigated. To this end, Blood Glucose Awareness Training (BGAT) and Hypoglycaemia Anticipation, Awareness and Treatment Training (HAATT) have been developed and shown to markedly reduce incidence of driving mishaps.

Self-treatment of hypoglycemia while driving.

OBJECTIVE: While it is clear that progressive diabetic hypoglycemia leads to neuroglycopenia, which impairs driving, it is not clear what contributes to patients' detection and subsequent self-correction of hypoglycemia/driving impairments. Drivers with Type 1 Diabetes Mellitus (T1DM) who did and did not engage in self-treatment during experimental hypoglycemia driving are compared physiologically and psychologically. METHOD: 38 drivers with T1DM drove a sophisticated driving simulator during euglycemia and progressive hypoglycemia. Subjects were continually monitored for driving performance, EEG activity and whether they self-treated with a glucose drink. Every 5 min measures were taken of blood glucose (BG) and epinephrine levels, perceived neurogenic and neuroglycopenic symptoms and driving ability. For the four weeks prior to this hospital study, subjects participated in a field study. Using a hand-held computer just prior to routine self-measurements of BG, subjects rated neurogenic and neuroglycopenic symptoms and made judgements about BG level and ability to drive as they did in the hospital. RESULTS: Drivers who did and did not self-treat did not differ in terms of their pre-hospital exposure to hypoglycemia, their depth and rate of BG fall during experimental testing, or their epinephrine response to hypoglycemia. Subjects who self-treated detected more neurogenic and neuroglycopenic symptoms than those who did not self-treat. They also experienced less EEG defined neuroglycopenia during the progressive hypoglycemic drive as compared to those who did not self-treat. Perceived need to self-treat and EEG parameters correctly classified 88% of those who did treat from those who did not self-treat. Further, subjects who self-treated were more aware of hypoglycemia and when not to drive while hypoglycemic in the field study. CONCLUSION: There is a narrow window between a patient's detection of hypoglycemic symptoms and the need to self-treat, and neuroglycopenia, which impairs self-treatment. Consequently, drivers with T1DM should be vigilant for signs of hypoglycemia and driving impairment (e.g. trembling, uncoordination, visual difficulties) and encouraged to treat themselves immediately when they suspect hypoglycemia while driving.

A psychophysiological marker of attention deficit/hyperactivity disorder (ADHD)--defining the EEG consistency index.

This study continues our research to further validate the idea that ADHD (Attention Deficit/Hyperactivity Disorder) interferes with transition from one task to another and this interference can be quantified by a Consistency Index (CI) derived from a specific mathematical representation of EEG data. We reanalyze 32 previously reported data sets present new data for 35 boys and girls, ages 7-12, ADHD or control. Each data set contains EEG, recorded and digitized while participants perform consecutive 10-min tasks: video, reading, and math. For boys, the CI in ADHD was four times lower than in controls, p < .005, for girls this difference was two times, p < .05. ADHD/control classification based on the CI coincided with the DSM-IV criteria for 88% of the boys and for 67% of the girls. Post hoc analysis indicated that the classification utility of the CI diminished with age. A CI below 40% could be a discriminating, reliable, and reproducible marker of ADHD in young boys.

A construct of interactive feedback control of the GH axis in the male.

Growth hormone (GH) secretion is controlled by GH-releasing hormone (GHRH), the GH release-inhibiting hormone somatostatin (SRIF), and autofeedback connections. The ensemble network produces sexually dimorphic patterns of GH secretion. In an effort to formalize this system, we implemented a deterministically based autonomous feedback-driven construct of five principal dose-responsive regulatory interactions: GHRH drive of GH pituitary release, competitive inhibition of GH release by SRIF, GH autofeedback via SRIF with a time delay, delayed GH autonegative feedback on GHRH, and SRIF inhibition of GHRH secretion. This formulation engenders a malelike pattern of successive GH volleys due jointly to positive time-delayed feedback of GH on SRIF and negative feedback of SRIF on GH and GHRH. The multipeak volley is explicated as arising from a reciprocal interaction between GH and GHRH during periods of low SRIF secretion. The applicability of this formalism to neuroendocrine control is explored by initial parameter sensitivity analysis and is illustrated for selected feedback-dependent experimental paradigms. The present construct is not overparameterized and does not require an ad hoc pulse generator to achieve pulsatile GH output. Further evolution of interactive constructs could aid in exploring more complex feedback postulates that confer the vivid sexual dimorphism of female GH profiles.

Blood glucose awareness training (BGAT-2): long-term benefits.

OBJECTIVE: Blood glucose awareness training (BGAT) has been shown to improve awareness of blood glucose (BG) fluctuations among adults with type 1 diabetes. This study investigates the long-term (12-month) benefits of BGAT-2. RESEARCH DESIGN AND METHODS: A total of 73 adults with type 1 diabetes participated in a 6-month repeated baseline design with a 12-month follow-up. At 6 months and 1 month before BGAT-2 and at 1,6, and 12 months after BGAT-2, subjects used a handheld computer for 50 trials and completed psychological tests. Throughout assessment, subjects completed diaries, recording occurrences of diabetic ketoacidosis, severe hypoglycemia, and motor vehicle violations During follow-up, 50% of the subjects received booster training. RESULTS: During the first and last halves of both the baseline period and the follow-up period, dependent variables were generally stable. However, from baseline to follow-up, BGAT-2 led to 1) improved detection of hypoglycemia and hyperglycemia; 2) improved judgment regarding when to lower high BG, raise low BG, and not drive while hypoglycemic; 3) reduction in occurrence of diabetic ketoacidosis, severe hypoglycemia, and motor vehicle violations; and 4) improvement in terms of worry about hypoglycemia, quality of life, and diabetes knowledge. Reduction in severe hypoglycemia was not associated with a worsening of metabolic control (HbA1). The presence or absence of booster training did not differentially affect these benefits. CONCLUSION: BGAT has sustained broad-ranging benefits, independent of booster intervention.

Episodes of severe hypoglycemia in type 1 diabetes are preceded and followed within 48 hours by measurable disturbances in blood glucose.

This study quantifies blood glucose (BG) disturbances occurring before and after episodes of severe hypoglycemia (SH). For 6-8 months, 85 individuals with type 1 diabetes and a history of SH (age, 44+/-10 yr; 41 women and 44 men; duration of diabetes, 26+/-11 yr; hemoglobin A1c, 7.7+/-1.1%) used Lifescan One Touch BG meters for self-monitoring three to five times daily and recorded the date and time of SH episodes in diaries. For each subject, the timing of SH episodes was located in the temporal stream of SMBG readings recorded by the meter, and characteristics, including the Low BG index (LBGI), were computed in 24-h increments. In the 24-h period before the SH episode LBGI rose (P < 0.001), average BG was lower (P = 0.001), and BG variance increased (P = 0.001). In the 24 h after SH, LBGI and BGvariance remained elevated (P < 0.001), but average BG returned to baseline. These disturbances disappeared in 48 h. On the basis of LBGI we identified subjects at low, moderate, and high risk of SH, who reported, on the average, 1.7, 3.4, and 7.4 SH episodes (P < 0.005) during the study. In addition, we designed an algorithm that predicted 50% of all SH episodes that occurred in this subject group. We conclude that episodes of SH are preceded and followed by quantifiable BG disturbances, which could be used to devise warnings of imminent SH.

The EEG consistency index as a measure of ADHD and responsiveness to medication.

The primary diagnostic procedure for Attention-Deficit/Hyperactivity Disorder (ADHD) is the clinical interview, because psychological, neuropsychological, and neurological tests to date have not had sufficient specificity. Currently, there is no objective means to measure severity of ADHD, or the extent to which it is benefited by various dosages of medication. We recently reported that a certain EEG profile, the Consistency Index, occurring during the transition between two easy cognitive tasks clearly differentiated ADHD from non-ADHD boys between the ages of 8 and 12. The current study replicated this with older males (19-25) using different tasks, and a double blind, placebo versus Ritalin controlled crossover design. Seven ADHD subjects were found to have a significantly lower Consistency Index than 6 non-ADHD males while transitioning from 2 Simple tasks during placebo condition, while only the ADHD subjects demonstrated a significant improvement in their Consistency Index while on Ritalin. Similar but nonsignificant trends were observed while transitioning across Hard tasks.

Progressive hypoglycemia's impact on driving simulation performance. Occurrence, awareness and correction.

OBJECTIVE: Progressive hypoglycemia leads to cognitive-motor and driving impairments. This study evaluated the blood glucose (BG) levels at which driving was impaired, impairment was detected, and corrective action was taken by subjects, along with the mechanisms underlying these three issues. RESEARCH DESIGN AND METHODS: There were 37 adults with type 1 diabetes who drove a simulator during continuous euglycemia and progressive hypoglycemia. During testing, driving performance, EEG, and corrective behaviors (drinking a soda or discontinuing driving) were continually monitored, and BG, symptom perception, and judgement concerning impairment were assessed every 5 min. Mean +/- SD euglycemia performance was used to quantify z scores for performance in three hypoglycemic ranges (4.0-3.4, 3.3-2.8, and <2.8 mmol/l). RESULTS: During all three hypoglycemic BG ranges, driving was significantly impaired, and subjects were aware of their impaired driving. However, corrective actions did not occur until BG was <2.8 mmol/l. Driving impairment was related to increased neurogenic symptoms and increased theta-wave activity. Awareness of impaired driving was associated with neuroglycopenic symptoms. increased beta-wave activity, and awareness of hypoglycemia. High beta and low theta activity and awareness of both hypoglycemia and the need to treat low BG influenced corrective behavior. CONCLUSIONS: Driving performance is significantly disrupted at relatively mild hypoglycemia, yet subjects demonstrated a hesitation to take corrective action. The longer treatment is delayed, the greater the neuroglycopenia (increased theta), which precludes corrective behaviors. Patients should treat themselves while driving as soon as low BG and/or impaired driving is suspected and should not begin driving when their BG is in the 5.0-4.0 mmol/l range without prophylactic treatment.

Effect of stimulant medication on driving performance of young adults with attention-deficit hyperactivity disorder: a preliminary double-blind placebo controlled trial.

Driving performance of adult males with attention-deficit hyperactivity disorder (ADHD) was compared with matched controls in a double-blind (Ritalin vs. placebo) cross-over design, using a high-fidelity driving simulator. Seven ADHD and six non-ADHD drivers (mean age 22) were screened to rule out comorbidity and assess for ADHD, and then admitted to the General Clinical Research Center to control diet and sleep before testing. At 0800 and 1530, subjects consumed either a placebo or Ritalin pill in a counter-balanced manner, and at 0930 and 1700, subjects drove the simulator. After both drives, subjects rated their driving performance. Compared with non-ADHD subjects, ADHD subjects had more career driving accidents (p < .04) and motor vehicle violations (p = .059), drove worse on the simulator under placebo condition (p < .05), demonstrated significant improvement under the Ritalin condition (p < .05), rated themselves as driving poorer during the placebo condition (p = .05), and tended to perceive their driving to be better during the Ritalin condition (p = .07). This would suggest that individuals with ADHD should have the therapeutic benefit of a stimulant medication when operating a vehicle.

Biopsychobehavioral model of severe hypoglycemia. II. Understanding the risk of severe hypoglycemia.

OBJECTIVE: To evaluate the clinical/research utility of the biopsycho-behavioral model of severe hypoglycemia in differentiating patients with and without a history of severe hypoglycemia and in predicting occurrence of future severe hypoglycemia. RESEARCH DESIGN AND METHODS: A total of 93 adults with type 1 diabetes (mean age 35.8 years, duration of diabetes 16 +/- 10 years, HbA1 8.6 +/- 1.8%), 42 of whom had a recent history of recurrent severe hypoglycemia (SH) and 51 who did not (NoSH), used a handheld computer for 70 trials during 1 month recording cognitive-motor functioning, symptoms, blood glucose (BG) estimates, judgments concerning self-treatment of BG, actual BG readings, and actual treatment of low BG. For the next 6 months, patients recorded occurrence of severe hypoglycemia. RESULTS: SH patients demonstrated significantly more frequent and extreme low BG readings (low BG index), greater cognitive-motor impairments during hypoglycemia, fewer perceived symptoms of hypoglycemia, and poorer detection of hypoglycemia. SH patients were also less likely to treat their hypoglycemia with glucose and more likely to treat with general foods. Low BG index, magnitude of hypoglycemia-impaired ability to do mental subtraction, and awareness of neuroglycopenia, neurogenic symptoms, and hypoglycemia correlated separately with number of SH episodes in the subsequent 6 months. However, only low BG index, hypoglycemia-impaired ability to do mental subtraction, and awareness of hypoglycemia entered into a regression model predicting future severe hypoglycemia (R2 = 0.25, P < 0.001). CONCLUSIONS: Patients with a history of severe hypoglycemia differed on five of the seven steps of the biopsychobehavioral model of severe hypoglycemia. Helping patients with a recent history of severe hypoglycemia to reduce the frequency of their low-BG events, become more sensitive to early signs of neuroglycopenia and neurogenic symptoms, better recognize occurrence of low BG, and use fast-acting glucose more frequently in the treatment of low BG, may reduce occurrence of future severe hypoglycemia.

Hypoglycemia and the decision to drive a motor vehicle by persons with diabetes.

CONTEXT: Laboratory studies have shown impairments in driving performance among subjects with type 1 diabetes mellitus when their blood glucose (BG) level is between 2.6 and 3.6 mmol/L (47-65 mg/dL). However, to our knowledge, no data exist examining subjects' decisions to drive at various BG levels during their daily routine. OBJECTIVE: To examine type 1 diabetic subjects' decisions to drive during their daily routine based on perception of BG levels compared with actual measured BG levels. DESIGN AND SETTING: Two separate groups of patients were recruited 2 years apart from 4 academic medical centers. PARTICIPANTS: All subjects were adults with type 1 diabetes who were drivers and who performed at least 2 BG tests per day. Group 1 (initial) subjects (n = 65) had a mean (SD) age of 38.6 (8.9) years with a mean (SD) diabetes duration of 20.5 (10.6) years, were taking 38.8 (16.8) U/d of insulin, and had a mean (SD) glycosylated hemoglobin (HbA1) level of 10.0% (1.9%). Group 2 (replication) subjects (n = 93) were 35.8 (8.0) years old with a mean diabetes duration of 17.0 (10.6) years, were taking 40.0 (15.5) U/d of insulin, and had a mean (SD) HbA1 level of 8.5% (1.6%). Each subject used a handheld computer to record data on symptoms, cognitive function, insulin dosage, food, activity, estimated and actual BG levels, and whether he/she would drive. Data were entered 3 to 6 times per day for a total of 50 to 70 collections per subject during a 3- to 4-week period. MAIN OUTCOME MEASURES: Decisions to drive when subjects estimated their BG level to be less than 2.2 mmol/L (40 mg/dL), 2.2 to 2.8 mmol/L (40-50 mg/dL), 2.8 to 3.3 mmol/L (50-60 mg/dL), 3.3 to 3.9 mmol/L (60-70 mg/dL), 3.9 to 10 mmol/L (70-180 mg/dL), and more than 10 mmol/L (>180 mg/dL), and driving decisions when actual BG levels were in these ranges. RESULTS: Subjects stated they would drive 43% to 44% of the time when they estimated their BG level to be 3.3 to 3.9 mmol/L (60-70 mg/dL), and 38% to 47% of the time when their actual BG level was less than 2.2 mmol/L (40 mg/dL). Logistic regression analysis demonstrated that number of autonomic symptoms, degree of impairment on cognitive function tests, and BG level estimate predicted 76% to 80% of decisions to drive (P<.01 for all). Approximately 50% of subjects in each group decided to drive at least 50% of the time when their BG level was less than 3.9 mmol/L (70 mg/dL). CONCLUSIONS: Our data suggest that persons with type 1 diabetes may not judge correctly when their BG level is too low to permit safe driving and may consider driving with a low BG level even when they are aware of the low level. Health care professionals should counsel their patients about the risk of driving with hypoglycemia and the importance of measuring BG level before driving.

The effects of age and alcohol intoxication on simulated driving performance, awareness and self-restraint.

AIMS: To investigate whether, compared with middle-aged men (aged 30-50), older men (age > or =60) (i) perform more poorly on a driving simulator and (ii) are more sensitive to the effects of ethanol in terms of blood alcohol concentration (BAC) and driving performance, but more aware of their driving difficulties, and therefore exercise better driving judgement. METHODS: 14 Healthy middle-aged men (mean age 36 years) were compared with 14 healthy older men (mean age 69 years) on an interactive driving simulator, while sober and while legally intoxicated (BAC >80 mg/dl). RESULTS: Older age was associated with poorer driving performance on the simulator. While sober, older men exhibited more improper braking, slower driving, greater speed variability, fewer appropriate full stops and more crashes, and spent more time executing left turns (across oncoming traffic); all values < or =0.02. BACs > or =80 mg/dl were associated with impaired driving, with more inappropriate braking, fewer appropriate full stops and more time executing left turns (all values > or =0.02) and trends towards more speed variability, more low speed collisions and more wrong turns (values <0.1). However, similar ethanol consumption did not produce higher peak BAC or more driving impairments in older drivers. While there were no differences between age groups in terms of awareness of intoxication or driving difficulties, older men were unwilling to drive while legally intoxicated because of fear of physical injury, whereas middle-aged men were more likely to avoid driving when intoxicated due to fear of legal ramifications. CONCLUSION: While both age and legal intoxication affected driving performance, older men were no more sensitive to ethanol in terms of peak BACs, driving performance or awareness/judgement than middle-aged men.

Biopsychobehavioral model of risk of severe hypoglycemia. Self-management behaviors.

OBJECTIVE: To identify self-management antecedents of low blood glucose (BG) (< 3.9 mmol/l) that might be easily recognized, treated, or avoided altogether. RESEARCH DESIGN AND METHODS: Ninety-three adults with type 1 diabetes (age, 35.8 +/- 8 years [mean +/- SD]; duration of diabetes, 17.0 +/- 11 years; daily insulin dose, 0.58 +/- 0.18 U/kg; and HbAlc, 8.6 +/- 1.8%) were recruited to participate in the study. Of the 93 subjects, 42 had a history of severe hypoglycemia (SH), defined as two or more hypoglycemic episodes in the preceding 12 months, and 51 subjects had no history of SH (No-SH) in the same time period. Before each of 70 BG measurements obtained over a 3-week period, subjects used a handheld computer to record whether their most recent insulin, food, and exercise was more than, less than, or the same as usual. Associations among self-management behaviors preceding BG readings < 3.9 mmol/l versus those preceding BG readings of 5.6-7.8 mmol/l were determined using chi 2 tests, analyses of variance, and logistic regression analyses. RESULTS: Analysis of 6,425 self-management/self-monitoring of BG events revealed that the usual amounts of insulin, food, and exercise preceded the events 58.3% of the time. No significant differences were observed for changes in insulin before readings of BG < 3.9 mmol/l versus 7.8 < BG > 5.6 mmol/l, but significantly less food (P < 0.01) was eaten and more exercise (P < 0.001) was performed before the low BG measurement. No interactions between SH and No-SH groups and management behaviors were observed. However, each of the three management variables entered significantly in a logistic model that predicted 61% of all readings of BG < 3.9 mmol/l. CONCLUSIONS: Subjects with a history of SH did not report managing their diabetes differently from those with no such history. Specifically, when low BG occurred, the preceding management behaviors, although predictive of low BG, were not different in SH and No-SH subjects. Overall, self-management behaviors did not distinguish SH from No-SH subjects. Thus, even though it might be beneficial for all patients to review their food and exercise management decisions to reduce their frequency of low BG, an educational intervention whose content stresses insulin, food, and exercise would be unlikely by itself to be sufficient to reduce the frequency of SH.