Search

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. [ABSTRACT FROM AUTHOR]

Objective: To document glycemic and user-initiated bolus changes following transition from predictive low glucose suspend (PLGS) system to automated insulin delivery (AID) system during real-life use., Research Design and Methods: We conducted analysis of 2,329,166 days (6,381 patient-years) of continuous glucose monitoring (CGM) and insulin therapy data for 19,354 individuals with type 1 Diabetes, during 1-month PLGS use (Basal-IQ technology) followed by 3-month AID use (Control-IQ technology). Baseline characteristics are as follows: 55.4% female, age (median/quartiles/range) 39/19-58/1-92 years, mean ± SD glucose management indicator (GMI) 7.5 ± 0.8. Primary outcome was time in target range (TIR) (70-180 mg/dL). Secondary outcomes included CGM-based glycemic control metrics and frequency of user-initiated boluses., Results: Compared with PLGS, AID increased TIR on average from 58.4 to 70.5%. GMI and percent time above and below target range improved as well: from 7.5 to 7.1, 39.9 to 28.1%, and 1.66 to 1.46%, respectively; all P values <0.0001. Stratification of outcomes by age and baseline GMI revealed clinically significant differences. Glycemic improvements were most pronounced in those <18 years old (TIR improvement 14.0 percentage points) and those with baseline GMI >8.0 (TIR improvement 13.2 percentage points). User-initiated correction boluses decreased from 2.7 to 1.8 per day, while user-initiated meal boluses remained stable at 3.6 to 3.8 per day., Conclusions: Observed in real life of >19,000 individuals with type 1 diabetes, transitions from PLGS to AID resulted in improvement of all glycemic parameters, equivalent to improvements observed in randomized clinical trials, and reduced user-initiated boluses. However, glycemic and behavioral changes with AID use may differ greatly across different demographic and clinical groups., (© 2022 by the American Diabetes Association.)

Objective: The Hypoglycemia Fear Survey-II (HFS-II) is a well-validated measure of fear of hypoglycemia in people with type 1 diabetes. The aim of this study was to explore the relationships between hypoglycemia worries, behaviors, and cognitive barriers to hypoglycemia avoidance and hypoglycemia awareness status, severe hypoglycemia, and HbA1c., Research Design and Methods: Participants with type 1 diabetes (n = 178), with the study population enriched for people at risk for severe hypoglycemia (49%), completed questionnaires for assessing hypoglycemia fear (HFS-II), hyperglycemia avoidance (Hyperglycemia Avoidance Scale [HAS]), diabetes distress (Problem Areas In Diabetes [PAID]), and cognitive barriers to hypoglycemia avoidance (Attitudes to Awareness of Hypoglycemia [A2A]). Exploratory factor analysis was applied to the HFS-II. We sought to establish clusters based on HFS-II, A2A, Gold, HAS, and PAID using k-means clustering., Results: Four HFS-II factors were identified: Sought Safety, Restricted Activity, Ran High, and Worry. While Sought Safety, Restricted Activity, and Worry increased with progressively impaired awareness and recurrent severe hypoglycemia, Ran High did not. With cluster analysis we outlined four clusters: two clusters with preserved hypoglycemia awareness were differentiated by low fear/low cognitive barriers to hypoglycemia avoidance (cluster 1) versus high fear and distress and increased Ran High behaviors (cluster 2). Two clusters with impaired hypoglycemia awareness were differentiated by low fear/high cognitive barriers (cluster 3) as well as high fear/low cognitive barriers (cluster 4)., Conclusions: This is the first study to define clusters of hypoglycemia experience by worry, behaviors, and cognitive barriers to hypoglycemia avoidance. The resulting subtypes may be important in understanding and treating problematic hypoglycemia., (© 2022 by the American Diabetes Association.)

Objective: Two aims of this study were to develop and validate A) a metric to identify drivers with type 1 diabetes at high risk of future driving mishaps and B) an online intervention to reduce mishaps among high-risk drivers., Research Design and Methods: To achieve aim A, in study 1, 371 drivers with type 1 diabetes from three U.S. regions completed a series of established questionnaires about diabetes and driving. They recorded their driving mishaps over the next 12 months. Questionnaire items that uniquely discriminated drivers who did and did not have subsequent driving mishaps were assembled into the Risk Assessment of Diabetic Drivers (RADD) scale. In study 2, 1,737 drivers with type 1 diabetes from all 50 states completed the RADD online. Among these, 118 low-risk (LR) and 372 high-risk (HR) drivers qualified for and consented to participate in a 2-month treatment period followed by 12 monthly recordings of driving mishaps. To address aim B, HR participants were randomized to receive either routine care (RC) or the online intervention "DiabetesDriving.com" (DD.com). Half of the DD.com participants received a motivational interview (MI) at the beginning and end of the treatment period to boost participation and efficacy. All of the LR participants were assigned to RC. In both studies, the primary outcome variable was driving mishaps., Results: Related to aim A, in study 1, the RADD demonstrated 61% sensitivity and 75% specificity. Participants in the upper third of the RADD distribution (HR), compared with those in the lower third (LR), reported 3.03 vs. 0.87 mishaps/driver/year, respectively ( P < 0.001). In study 2, HR and LR participants receiving RC reported 4.3 and 1.6 mishaps/driver/year, respectively ( P < 0.001). Related to aim B, in study 2, MIs did not enhance participation or efficacy, so the DD.com and DD.com + MI groups were combined. DD.com participants reported fewer hypoglycemia-related driving mishaps than HR participants receiving RC ( P = 0.01), but more than LR participants receiving RC, reducing the difference between the HR and LR participants receiving RC by 63%. HR drivers differed from LR drivers at baseline across a variety of hypoglycemia and driving parameters., Conclusions: The RADD identified higher-risk drivers, and identification seemed relatively stable across time, samples, and procedures. This 11-item questionnaire could inform patients at higher risk, and their clinicians, that they should take preventive steps to reduce driving mishaps, which was accomplished in aim B using DD.com., (© 2017 by the American Diabetes Association.)

Objective: To perform the first comprehensive psychometric evaluation of the Hypoglycemia Fear Survey-II (HFS-II), a measure of the behavioral and affective dimensions of fear of hypoglycemia, using modern test-theory methods, including item-response theory (IRT)., Research Design and Methods: Surveys completed in four previous studies by 777 adults with type 1 diabetes were aggregated for analysis, with 289 subjects completing both subscales of the HFS-II and 488 subjects completing only the Worry subscale. The aggregated sample (53.3% female, 44.4% using insulin pumps) had a mean age of 41.9 years, diabetes duration of 23.8 years, HbA(1c) value of 7.7%, and 1.4 severe hypoglycemic episodes in the past year. Data analysis included exploratory factor analysis using polychoric correlations and IRT. Factors were analyzed for fit, trait-level locations, point-measure correlations, and separation values., Results: Internal and test-retest reliability was good, as well as convergent validity, as demonstrated by significant correlations with other measures of psychological distress. Scores were significantly higher in subjects who had experienced severe hypoglycemia in the past year. Factor analyses validated the two subscales of the HFS-II. Item analyses showed that 12 of 15 items on the Behavior subscale, and all of the items on the Worry subscale had good-fit statistics., Conclusions: The HFS-II is a reliable and valid measure of the fear of hypoglycemia in adults with type 1 diabetes, and factor analyses and IRT support the two separate subscales of the survey.

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.

Objective: Hyperglycemia is a common event among patients with type 1 and type 2 diabetes. While the cognitive-motor slowing associated with hypoglycemia is well documented, the acute effects of hyperglycemia have not been studied extensively, despite patients' reports of negative effects. This study prospectively and objectively assessed the effects of hyperglycemia on cognitive-motor functioning in subjects' natural environment., Research Design and Methods: Study 1 investigated 105 adults with type 1 diabetes (mean age 37 years and mean duration of diabetes 20 years), study 2 investigated 36 adults with type 2 diabetes (mean age 50 years and mean duration of diabetes 10 years), and study 3 investigated 91 adults with type 1 diabetes (mean age 39 years and mean duration of diabetes 20 years). Subjects used a hand-held computer for 70 trials over 4 weeks, which required them to complete various cognitive-motor tasks and then measure and enter their current blood glucose reading., Results: Hyperglycemia (blood glucose >15 mmol/l) was associated with slowing of all cognitive performance tests (P < 0.02) and an increased number of mental subtraction errors for both type 1 and type 2 diabetic subjects. The effects of hyperglycemia were highly individualized, impacting approximately 50% of the subjects., Conclusions: Acute hyperglycemia is not a benign event for many individuals with diabetes, but it is associated with mild cognitive dysfunction.

Objective: The objective of this study was to introduce continuous glucose-error grid analysis (CG-EGA) as a method of evaluating the accuracy of continuous glucose-monitoring sensors in terms of both accurate blood glucose (BG) values and accurate direction and rate of BG fluctuations and to illustrate the application of CG-EGA with data from the TheraSense Freestyle Navigator., Research Design and Methods: We approach the design of CG-EGA from the understanding that continuous glucose sensors (CGSs) allow the observation of BG fluctuations as a process in time. We account for specifics of process characterization (location, speed, and direction) and for biological limitations of the observed processes (time lags associated with interstitial sensors). CG-EGA includes two interacting components: 1) point-error grid analysis (P-EGA) evaluates the sensor's accuracy in terms of correct presentation of BG values and 2) rate-error grid analysis (R-EGA) assesses the sensor's ability to capture the direction and rate of BG fluctuations., Results: CG-EGA revealed that the accuracy of the Navigator, measured as a percentage of accurate readings plus benign errors, was significantly different at hypoglycemia (73.5%), euglycemia (99%), and hyperglycemia (95.4%). Failure to detect hypoglycemia was the most common error. The point accuracy of the Navigator was relatively stable over a wide range of BG rates of change, and its rate accuracy decreased significantly at high BG levels., Conclusions: Traditional self-monitoring of BG device evaluation methods fail to capture the important temporal characteristics of the continuous glucose-monitoring process. CG-EGA addresses this problem, thus providing a comprehensive assessment of sensor accuracy that appears to be a useful adjunct to other CGS performance measures.

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.

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.

Objective: To evaluate the clinical/research utility of the low blood glucose index (LBGI), a measure of the risk of severe hypoglycemia (SH), based on self-monitoring of blood glucose (SMBG)., Research Design and Methods: There were 96 adults with IDDM (mean age 35+/-8 years, duration of diabetes 16+/-10 years, HbA1 8.6+/-1.8%), 43 of whom had a recent history of SH (53 did not), who used memory meters for 135+/-53 SMBG readings over a month, and then for the next 6 months recorded occurrence of SH. The SMBG data were mathematically transformed, and an LBGI was computed for each patient., Results: The two patient groups did not differ with respect to HbA1, insulin units per day, average blood glucose (BG) and BG variability. Patients with history of SH demonstrated a higher LBGI (P < 0.0005) and a trend to be older with longer diabetes duration. Analysis of odds for future SH classified patients into low- (LBGI <2.5), moderate- (LBGI 2.5-5), and high- (LBGI >5) risk groups. Over the following 6 months low-, moderate-, and high-risk patients reported 0.4, 2.3, and 5.2 SH episodes, respectively (P = 0.001). The frequency of future SH was predicted by the LBGI and history of SH (R2 = 40%), while HbA1, age, duration of diabetes, and BG variability were not significant predictors., Conclusions: LBGI provides an accurate assessment of risk of SH. In the traditional relationship history of SH-to-future SH, LBGI may be the missing link that reflects present risk. Because it is based on SMBG records automatically stored by many reflectance meters, the LBGI is an effective and clinically useful on-line indicator for SH risk.

Objective: To introduce a data transformation that enhances the power of blood glucose data analyses., Research Design and Methods: In the standard blood glucose scale, hypoglycemia (blood glucose, < 3.9 mmol/l) and hyperglycemia (blood glucose, > 10 mmol/l) have very different ranges, and euglycemia is not central in the entire blood glucose range (1.1-33.3 mmol/l). Consequently, the scale is not symmetric and its clinical center (blood glucose, 6-7 mmol/l) is distant from its numerical center (blood glucose, 17 mmol/l). As a result, when blood glucose readings are analyzed, the assumptions of many parametric statistics are routinely violated. We propose a logarithmic data transformation that matches the clinical and numerical center of the blood glucose scale, thus making the transformed data symmetric., Results: The transformation normalized 203 out of 205 data samples containing 13,584 blood glucose readings of 127 type 1 diabetic individuals. An example illustrates that the mean and standard deviation based on transformed, rather than on raw, data better described subject's blood glucose distribution. Based on transformed data: 1) the low blood glucose index predicted the occurrence of severe hypoglycemia, while the raw blood glucose data (and glycosylated hemoglobin levels) did not; 2) the high blood glucose index correlated with the subjects' glycosylated hemoglobin (r = 0.63, P < 0.001); and 3) the low plus high blood glucose index was more sensitive than the raw data to a treatment (blood glucose awareness training) designed to reduce the range of blood glucose fluctuations., Conclusions: Using symmetrized, instead of raw, blood glucose data strengthens the existing data analysis procedures and allows for the development of new statistical techniques. It is proposed that raw blood glucose data should be routinely transformed to a symmetric distribution before using parametric statistics.

Objective: To directly examine whether hypoglycemia differentially slows cognitive versus motor function, to evaluate the reliability of hypoglycemic-related slowing, and to examine factors contributing to individual differences., Research Design and Methods: IDDM subjects (n = 10) were administered a pure cognitive and a pure motor neuropsychological test at euglycemia (5.4 mmol), blood glucose nadir (2.6 mmol), postnadir (3.6 mmol), and again at euglycemia (6.7 mmol). To assess the practice effect, matched control subjects were tested at similar time intervals., Results: Concurrent and test-retest reliability for all tests was robust (r = 0.68-0.94). Only cognitive tasks demonstrated impairment at nadir (P < 0.04). Individual differences, in terms of cognitive impairment, were significantly correlated with levels of blood glucose at nadir and baseline performance., Conclusions: Cognitive tasks appear to be more sensitive to neuroglycopenia than motor tasks. Cognitive impairment caused by hypoglycemia is reliable and differs across subjects. Individuals who show reliable sensitivity to cognitive impairments of hypoglycemia should avoid moderately low blood glucose levels.

Objective: To evaluate the accuracy of blood glucose symptom recognition and subjective blood glucose estimation in insulin-dependent diabetic (IDDM) children and their parents., Research Design and Methods: Blood glucose estimation questionnaires were completed 4 times/day at home during routine activities. A sequential sample of 19 families, who attended a pediatric diabetes clinic, with IDDM children less than 12 yr old and IDDM duration of greater than or equal to 9 mo comprised the study., Results: Error grid analysis showed that both children and parents demonstrated poor accuracy, making clinically significant errors as frequently as clinically accurate estimates. The most common error was the failure to detect extreme blood glucose levels, with a significant tendency to underestimate hyperglycemia. Children often reported hypoglycemia when blood glucose was hyperglycemic. Confidence in the ability to estimate blood glucose was unrelated to measured accuracy., Conclusions: IDDM children and their parents demonstrated a higher rate of blood glucose estimation errors than IDDM adolescents and adults in previous studies. Even in families who use self-monitoring of blood glucose frequently, self-reported ability to recognize symptoms and estimate blood glucose should be viewed with caution. Families with IDDM children need more education about errors in symptom recognition and blood glucose estimation. They should also be encouraged to use self-monitoring of blood glucose before treating children's reported hypoglycemic symptoms whenever possible.

Reflectance meters containing memory chips were used in a study that addressed several questions concerning routine use of self-monitoring of blood glucose (SMBG), including accuracy of patient blood glucose (BG) diaries, reliability of self-reported frequency of SMBG, and adherence to recommended SMBG regimen. Thirty adults with insulin-dependent diabetes used memory meters and recorded test results in diaries for 2 wk while performing their normal SMBG regimen. Analysis of glucose diaries showed that only 23% of the subjects had no diary errors and 47% had clinically accurate diaries (less than 10% error rate). The most common types of errors were omissions of values contained in meter memory and additions of values not contained in meter memory, with significantly more omissions than additions. Alterations of test values (e.g., changing a 300-mg/dl reading to 200 mg/dl) were extremely rare. There was no difference in the rate of errors that resulted in a more positive clinical profile (omitting unacceptable values and adding acceptable values) or a more negative clinical profile (omitting acceptable values and adding unacceptable values). Examination of the actual frequency of SMBG showed that most subjects (56.6%) measured their BG an average of two to three times each day. Self-report of SMBG frequency correlated with both actual frequency and HbA1. Although actual frequency of SMBG was not related to physicians' recommendations, the majority (64%) of subjects were self-testing as often or more often than they had been instructed.

Whereas self-monitoring of blood glucose (SMBG) is the recommended source of information on which to make self-care decisions, patients frequently use estimates of their own blood glucose (BG). This study evaluated whether patients with insulin-dependent diabetes mellitus (IDDM) could learn to improve accuracy of BG estimations and whether this would lead to improved metabolic control. Subjects in BG awareness training improved both their BG-estimation accuracy and glycosylated hemoglobin (HbA1) compared with the control group. Initial BG-estimation accuracy was marginally associated with pretreatment HbA1 and months of previous SMBG experience. Posttreatment improvement was associated with pretreatment BG-estimation accuracy and the ability to counterregulate to insulin-induced hypoglycemia.

Although the scientific literature contains numerous reports of the statistical accuracy of systems for self-monitoring of blood glucose (SMBG), most of these studies determine accuracy in ways that may not be clinically useful. We have developed an error grid analysis (EGA), which describes the clinical accuracy of SMBG systems over the entire range of blood glucose values, taking into account 1) the absolute value of the system-generated glucose value, 2) the absolute value of the reference blood glucose value, 3) the relative difference between these two values, and 4) the clinical significance of this difference. The EGA of accuracy of five different reflectance meters (Eyetone, Dextrometer, Glucometer I, Glucometer II, Memory Glucometer II), a visually interpretable glucose reagent strip (Glucostix), and filter-paper spot glucose determinations is presented. In addition, reanalyses of a laboratory comparison of three reflectance meters (Accucheck II, Glucometer II, Glucoscan 9000) and of two previously published studies comparing the accuracy of five different reflectance meters with EGA is described. EGA provides the practitioner and the researcher with a clinically meaningful method for evaluating the accuracy of blood glucose values generated with various monitoring systems and for analyzing the clinical implications of previously published data.

Eighteen insulin-dependent diabetic subjects [age (mean +/- SD) 33.2 +/- 10.6 yr] participated in a study designed to determine the metabolic and cutaneous parameters associated with activation of the nocturnal hypoglycemia monitor Sleep Sentry. Plasma glucose, glucagon, epinephrine, norepinephrine, and pancreatic polypeptide concentrations were determined every 10 min during a 2-h constant intravenous insulin infusion (40 mU.kg-1.h-1). In addition, skin temperature and electrical conductance were monitored at the same time intervals, and subjects were asked to rate the degree to which they felt cold and/or sweaty. Ten of the subjects (alarmers) activated the device with a mean plasma glucose nadir of 52.8 +/- 13.8 mg/dl, whereas eight (nonalarmers) failed to do so despite a mean plasma glucose nadir of 50.5 +/- 8.2 mg/dl. There were no significant differences between alarmers and nonalarmers with respect to initial or nadir plasma glucose levels, rate of fall of plasma glucose, or changes in plasma epinephrine, norepinephrine, or pancreatic polypeptide concentrations. In addition, changes in skin temperature and conductance were similar in both groups as were descriptive variables including age, disease duration, gender, and level of glucose control. No subject reported an increase in coldness, whereas 80% of both groups reported an increase in sweatiness. Three subjects studied on more than one occasion over a year failed to exhibit consistent activation of the alarm. This study suggests that it may not be possible to identify patients for whom the Sleep Sentry would be a reliable addition to their self-management regimen and that physicians should exercise caution in recommending its use.