Psychometric validation of the hyperglycaemia avoidance scale UK (HAS-UK).

AIMS: Hyperglycaemia aversion in type 1 diabetes can be associated with severe hypoglycaemia and impaired awareness of hypoglycaemia but is not routinely assessed clinically. This study aimed to undertake the first psychometric validation of the UK version of the Hyperglycaemia Avoidance Scale (HAS-UK). METHODS: The HAS-UK was completed by adults with type 1 diabetes in three separate research studies. Psychometric properties were evaluated, using exploratory factor analysis, internal consistency, and convergent validity. RESULTS: Of the 431 participants who completed the HAS-UK in the three studies, mean age was 49.5 years, and 58.0% were women. Mean duration of diabetes was 29 years, with 192 (44.5%) using multiple daily injections and 229 (53.1%) using an insulin pump. Five participants were excluded from analyses due to incomplete HAS-UK responses. Exploratory factor analysis revealed a 3-factor solution, with acceptable internal consistency for 'worry' and 'blood glucose decisions' factors. HAS-UK total score was higher in those using insulin pumps versus multiple daily injections, and 'blood glucose decisions' score was higher in those using a continuous blood glucose sensor versus a meter. CONCLUSIONS: The HAS-UK is a reliable measure with acceptable structural validity and is likely to be useful for evaluating hyperglycaemia aversion in people with type 1 diabetes. Future research would benefit from investigating further psychometric properties including test-retest reliability, sensitivity to change, and clinical significance of scores.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice.

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Biobehavioral Changes Following Transition to Automated Insulin Delivery: A Large Real-life Database Analysis.

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.

Hypoglycemia Subtypes in Type 1 Diabetes: An Exploration of the Hypoglycemia Fear Survey-II.

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.

Diabetes Alert Dogs (DADs): An assessment of accuracy and implications.

AIMS: To test the accuracy of Diabetes Alert Dogs (DADs) by comparing recorded alerts to continuous glucose monitoring (CGM) device readings during waking and sleeping hours. METHODS: 14 individuals (7 adults with type 1 diabetes and 7 youth with type 1 diabetes/parents) who owned DADs for ≥6 mos wore masked CGM devices over a several-week period while recording DAD alerts electronically and in paper diaries. RESULTS: During waking hours, sensitivity scores across participants were 35.9% for low BG events and 26.2% for high BG events. DAD accuracy was highly variable with 3/14 individual dogs performing statistically higher than chance. Sensitivity scores were lower during sleep hours of the person with diabetes (22.2% for low BG events and 8.4% for high BG events). DAD accuracy during sleeping hours was also highly variable, with 1/11 individual dogs performing statistically better than chance. Rate of change analyses indicated that DADs were responding to absolute BG level, rather than rapid shifts in glucose levels. CONCLUSIONS: In this study the majority of DADs did not demonstrate accurate detection of low and high BG events. However, performance varied greatly across DADs and additional studies are needed to examine factors contributing to this variability. Additionally, more research is needed to investigate the significant gap between the positive experiences and clinical outcomes reported by DAD owners and the mixed research findings on DAD accuracy.

Variability of Diabetes Alert Dog Accuracy in a Real-World Setting.

BACKGROUND: Diabetes alert dogs (DADs) are growing in popularity as an alternative method of glucose monitoring for individuals with type 1 diabetes (T1D). Only a few empirical studies have assessed DAD accuracy, with inconsistent results. The present study examined DAD accuracy and variability in performance in real-world conditions using a convenience sample of owner-report diaries. METHOD: Eighteen DAD owners (44.4% female; 77.8% youth) with T1D completed diaries of DAD alerts during the first year after placement. Diary entries included daily BG readings and DAD alerts. For each DAD, percentage hits (alert with BG ≤ 5.0 or ≥ 11.1 mmol/L; ≤90 or ≥200 mg/dl), percentage misses (no alert with BG out of range), and percentage false alarms (alert with BG in range) were computed. Sensitivity, specificity, positive likelihood ratio (PLR), and true positive rates were also calculated. RESULTS: Overall comparison of DAD Hits to Misses yielded significantly more Hits for both low and high BG. Total sensitivity was 57.0%, with increased sensitivity to low BG (59.2%) compared to high BG (56.1%). Total specificity was 49.3% and PLR = 1.12. However, high variability in accuracy was observed across DADs, with low BG sensitivity ranging from 33% to 100%. Number of DADs achieving ≥ 60%, 65% and 70% true positive rates was 71%, 50% and 44%, respectively. CONCLUSIONS: DADs may be able to detect out-of-range BG, but variability across DADs is evident. Larger trials are needed to further assess DAD accuracy and to identify factors influencing the complexity of DAD accuracy in BG detection.

Predicting and Reducing Driving Mishaps Among Drivers With Type 1 Diabetes.

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.

Psychology, technology, and diabetes management.

Use of technology in diabetes management is rapidly advancing and has the potential to help individuals with diabetes achieve optimal glycemic control. Over the past 40 years, several devices have been developed and refined, including the blood glucose meter, insulin pump, and continuous glucose monitor. When used in tandem, the insulin pump and continuous glucose monitor have prompted the Artificial Pancreas initiative, aimed at developing control system for fully automating glucose monitoring and insulin delivery. In addition to devices, modern technology, such as the Internet and mobile phone applications, have been used to promote patient education, support, and intervention to address the behavioral and emotional challenges of diabetes management. These state-of-the-art technologies not only have the potential to improve clinical outcomes, but there are possible psychological benefits, such as improved quality of life, as well. However, practical and psychosocial limitations related to advanced technology exist and, in the context of several technology-related theoretical frameworks, can influence patient adoption and continued use. It is essential for future diabetes technology research to address these barriers given that the clinical benefits appear to largely depend on patient engagement and consistence of technology use. (PsycINFO Database Record

Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems?

BACKGROUND: The relationship between daily psychological stress and BG fluctuations in type 1 diabetes (T1DM) is unclear. More research is needed to determine if stress-related BG changes should be considered in glucose control algorithms. This study in the usual free-living environment examined relationships among routine daily stressors and BG profile measures generated from CGM readings. METHODS: A total of 33 participants with T1DM on insulin pumps wore a CGM device for 1 week and recorded daily ratings of psychological stress, carbohydrates, and insulin boluses. RESULTS: Within-subjects ANCOVAs found a significant relationship between daily stress and indices of BG variability/instability (r = .172 to .185, P = .011 to .018, r(2) = 2.97% to 3.43%), increased % time in hypoglycemia (r = .153, P = .036, r(2) = 2.33%) and decreased carbohydrate consumption (r = -.157, P = .031, r(2) = 2.47%). Models accounted for more variance for individuals reporting the highest daily stress. There was no relationship between stress and mean daily glucose or low/high glucose risk indices. CONCLUSIONS: These preliminary findings suggest that naturally occurring daily stressors can be associated with increased glucose instability and hypoglycemia, as well as decreased food consumption. In addition, findings support the hypothesis that some individuals are more metabolically reactive to stress. More rigorous studies using CGM technology are needed to understand whether the impact of daily stress on BG is clinically meaningful and if it is a behavioral factor that should be considered in glucose control systems for some individuals.

A randomized controlled trial of an internet intervention for adults with insomnia: effects on comorbid psychological and fatigue symptoms.

OBJECTIVE: Insomnia is frequently comorbid with other medical and psychological disorders. This secondary data analysis investigated whether an Internet-delivered cognitive behavioral therapy for insomnia (CBT-I) intervention could also reduce comorbid psychological and fatigue symptoms. METHOD: Data from a pilot randomized controlled trial (RCT) testing the efficacy of Internet-delivered CBT-I relative to a waitlist control was used to examine changes in symptoms of depression, anxiety, mental health quality of life (QOL), and fatigue. RESULTS: Group by time interactions from repeated measures analyses revealed significant post intervention improvements in Internet participants (n = 22) relative to control participants (n = 22) on all psychological symptoms, mental health QOL, and fatigue. A small post hoc subsample of Internet participants with mild or moderate depression also showed large effect size changes in these constructs (depression, anxiety, mental health QOL, and fatigue). CONCLUSION: Internet-delivered CBT-I appears to not only improve sleep but also reduce comorbid psychological and fatigue symptoms.

Driving safety: concerns and experiences of parents of adolescent drivers with type 1 diabetes.

Driving is a dangerous activity for adolescents, perhaps being even more precarious for adolescents with type 1 diabetes due to the possibility of extreme blood glucose (BG). There is no available data on adolescent driving safety concerns and type 1 diabetes. To begin addressing this issue, we surveyed parents regarding their observations and concerns. Seventy-two parents (87.5% mothers) of adolescent drivers aged 16-19 with type 1 diabetes provided analyzable data. Females comprised 36% of their adolescents, with 74% using pump therapy. In the past year, 13 and 84% of parents reported that their adolescent had experienced severe or moderate disruptive hypoglycemia, respectively. Over half (56%) of the parents reported moderate to extreme worry about how diabetes impacted their adolescent's driving, while only 21% of parents thought their adolescents had similar concerns (p = 0.037). Almost one third (31%) of parents thought their adolescent need not treat low BG until it fell below 70 mg/dL, 13% thought their adolescent could safely drive with BG below 65 mg/dL. And, 31 and 14% of parents, respectively, reported their adolescent had been in a collision or stopped by the police in the past year, which they attributed to both hypo- and hyperglycemia. Adolescents reportedly took steps to prevent hypo- and hyperglycemia while driving, but more aggressively avoided hypoglycemia (p < 0.001). While this data is limited, lacking a non-diabetic control group and randomized sample, it does suggest that driving and adolescent type 1 diabetes deserve further attention and investigation.

Validation of the insomnia severity index as a web-based measure.

Although the Insomnia Severity Index (ISI) is already administered online, this frequently used instrument has not been validated for Web delivery. This study compares online and paper-and-pencil ISI versions completed by participants in a randomized controlled trial testing an Internet-delivered intervention for insomnia. Forty-three adults with insomnia completed both ISI versions during pre- (Assessment 1) and post-intervention (Assessment 2). Correlations between total scores of both versions were significant (rs ≥ .98, ps < .001). For both ISI versions, internal consistency was acceptable (Assessment 1, α = .61; Assessment 2, α ≥ .88). Among participants not receiving the parent study intervention, correlations between 1 format at Assessment 1 and the alternative format at Assessment 2 were generally significant (rs = .26-.82). Together, findings suggest the ISI can be delivered online.

Psychometric properties of the hypoglycemia fear survey-ii for adults with type 1 diabetes.

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.

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.

A behavior change model for internet interventions.

BACKGROUND: The Internet has become a major component to health care and has important implications for the future of the health care system. One of the most notable aspects of the Web is its ability to provide efficient, interactive, and tailored content to the user. Given the wide reach and extensive capabilities of the Internet, researchers in behavioral medicine have been using it to develop and deliver interactive and comprehensive treatment programs with the ultimate goal of impacting patient behavior and reducing unwanted symptoms. To date, however, many of these interventions have not been grounded in theory or developed from behavior change models, and no overarching model to explain behavior change in Internet interventions has yet been published. PURPOSE: The purpose of this article is to propose a model to help guide future Internet intervention development and predict and explain behavior changes and symptom improvement produced by Internet interventions. RESULTS: The model purports that effective Internet interventions produce (and maintain) behavior change and symptom improvement via nine nonlinear steps: the user, influenced by environmental factors, affects website use and adherence, which is influenced by support and website characteristics. Website use leads to behavior change and symptom improvement through various mechanisms of change. The improvements are sustained via treatment maintenance. CONCLUSION: By grounding Internet intervention research within a scientific framework, developers can plan feasible, informed, and testable Internet interventions, and this form of treatment will become more firmly established.

Efficacy of an Internet-based behavioral intervention for adults with insomnia.

CONTEXT: Insomnia is a major health problem with significant psychological, health, and economic consequences. However, availability of one of the most effective insomnia treatments, cognitive behavioral therapy, is significantly limited. The Internet may be a key conduit for delivering this intervention. OBJECTIVE: To evaluate the efficacy of a structured behavioral Internet intervention for adults with insomnia. DESIGN, SETTING, AND PARTICIPANTS: Forty-five adults were randomly assigned to an Internet intervention (n = 22) or wait-list control group (n = 23). Forty-four eligible participants (mean [SD] age, 44.86 [11.03] years; 34 women) who had a history of sleep difficulties longer than 10 years on average (mean [SD], 10.59 [8.89] years) were included in the analyses. INTERVENTION: The Internet intervention is based on well-established face-to-face cognitive behavioral therapy incorporating the primary components of sleep restriction, stimulus control, sleep hygiene, cognitive restructuring, and relapse prevention. MAIN OUTCOME MEASURES: The Insomnia Severity Index and daily sleep diary data were used to determine changes in insomnia severity and the main sleep variables, including wake after sleep onset and sleep efficiency. RESULTS: Intention-to-treat analyses showed that scores on the Insomnia Severity Index significantly improved from 15.73 (95% confidence interval [CI], 14.07 to 17.39) to 6.59 (95% CI, 4.73 to 8.45) for the Internet group but did not change for the control group (16.27 [95% CI, 14.61 to 17.94] to 15.50 [95% CI, 13.64 to 17.36]) (F(1,42) = 29.64; P < .001). The Internet group maintained their gains at the 6-month follow-up. Internet participants also achieved significant decreases in wake after sleep onset (55% [95% CI, 34% to 76%]) and increases in sleep efficiency (16% [95% CI, 9% to 22%]) compared with the nonsignificant control group changes of wake after sleep onset (8% [95% CI, -17% to 33%) and sleep efficiency (3%; 95% CI, -4% to 9%). CONCLUSIONS: Participants who received the Internet intervention for insomnia significantly improved their sleep, whereas the control group did not have a significant change. The Internet appears to have considerable potential in delivering a structured behavioral program for insomnia. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00328250.

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.