Howard Wolpert

Boston Children's Hospital, Boston, Massachusetts, United States

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Publications (46)495.53 Total impact

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    ABSTRACT: Traditionally, insulin bolus calculations for managing postprandial glucose levels in individuals with type 1 diabetes rely solely on the carbohydrate content of a meal. However, recent studies have reported that other macronutrients in a meal can alter the insulin required for good postprandial control. Specifically, studies have shown that high-fat (HF) meals require more insulin than low-fat (LF) meals with identical carbohydrate content. Our objective was to assess the mechanisms underlying the higher insulin requirement observed in one of these studies. We used a combination of previously validated metabolic models to fit data from a study comparing HF and LF dinners with identical carbohydrate content in seven subjects with type 1 diabetes. For each subject and dinner type, we estimated the model parameters representing the time of peak meal-glucose appearance (τm), insulin sensitivity (SI), the net hepatic glucose balance, and the glucose effect at zero insulin in four time windows (dinner, early night, late night, and breakfast) and assessed the differences in model parameters via paired Wilcoxon signed-rank tests. During the HF meal, the τm was significantly delayed (mean and standard error [SE]: 102 [14] min vs. 71 [4] min; P = 0.02), and SI was significantly lower (7.25 × 10(-4) [1.29 × 10(-4)] mL/μU/min vs. 8.72 × 10(-4) [1.08 × 10(-4)] mL/μU/min; P = 0.02). In addition to considering the putative delay in gastric emptying associated with HF meals, we suggest that clinicians reviewing patient records consider that the fat content of these meals may alter SI.
    Diabetes Technology &amp Therapeutics 08/2015; DOI:10.1089/dia.2015.0118 · 2.29 Impact Factor
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    ABSTRACT: BACKGROUND: Continuous glucose monitoring highlights the complexity of postprandial glucose patterns present in type 1 diabetes and points to the limitations of current approaches to mealtime insulin dosing based primarily on carbohydrate counting. METHODS: A systematic review of all relevant biomedical databases, including MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials, was conducted to identify research on the effects of dietary fat, protein, and glycemic index (GI) on acute postprandial glucose control in type 1 diabetes and prandial insulin dosing strategies for these dietary factors. RESULTS: All studies examining the effect of fat (n = 7), protein (n = 7), and GI (n = 7) indicated that these dietary factors modify postprandial glycemia. Late postprandial hyperglycemia was the predominant effect of dietary fat; however, in some studies, glucose concentrations were reduced in the first 2–3 h, possibly due to delayed gastric emptying. Ten studies examining insulin bolus dose and delivery patterns required for high-fat and/or high-protein meals were identified. Because of methodological differences and limitations in experimental design, study findings were inconsistent regarding optimal bolus delivery pattern; however, the studies indicated that high-fat/protein meals require more insulin than lower-fat/protein meals with identical carbohydrate content. CONCLUSIONS: These studies have important implications for clinical practice and patient education and point to the need for research focused on the development of new insulin dosing algorithms based on meal composition rather than on carbohydrate content alone.
    Diabetes care 06/2015; 38(6):1008-1015. · 8.57 Impact Factor
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    ABSTRACT: AimsArtificial pancreas systems show benefit in closely monitored at-home studies, but may not have sufficient power to assess safety during infrequent, but expected, system or user errors. The aim of this study was to assess the safety of an artificial pancreas system emulating the β–cell when the glucose value used for control is improperly calibrated and participants forget to administer pre-meal insulin boluses.Methods Artificial pancreas control was performed in a clinic research centre on three separate occasions each lasting from 10 p.m. to 2 p.m. Sensor glucose values normally used for artificial pancreas control were replaced with scaled blood glucose values calculated to be 20% lower than, equal to or 33% higher than the true blood glucose. Safe control was defined as blood glucose between 3.9 and 8.3 mmol/l.ResultsArtificial pancreas control resulted in fasting scaled blood glucose values not different from target (6.67 mmol/l) at any scaling factor. Meal control with scaled blood glucose 33% higher than blood glucose resulted in supplemental carbohydrate to prevent hypoglycaemia in four of six participants during breakfast, and one participant during the night. In all instances, scaled blood glucose reported blood glucose as safe.Conclusions Outpatient trials evaluating artificial pancreas performance based on sensor glucose may not detect hypoglycaemia when sensor glucose reads higher than blood glucose. Because these errors are expected to occur, in-hospital artificial pancreas studies using supplemental carbohydrate in anticipation of hypoglycaemia allow safety to be assessed in a controlled non-significant environment. Inpatient studies provide a definitive alternative to model-based computer simulations and can be conducted in parallel with closely monitored outpatient artificial pancreas studies used to assess benefit.This article is protected by copyright. All rights reserved.
    Diabetic Medicine 06/2015; DOI:10.1111/dme.12823 · 3.06 Impact Factor
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    ABSTRACT: Optimization of glycemic control is critical to reduce the number of diabetes mellitus-related complications, but long-term success is challenging. Although vision loss is among the greatest fears of individuals with diabetes, comprehensive personalized diabetes education and risk assessments are not consistently used in ophthalmologic settings. To determine whether the point-of-care measurement of hemoglobin A1c (HbA1c) and personalized diabetes risk assessments performed during retinal ophthalmologic visits improve glycemic control as assessed by HbA1c level. Ophthalmologist office-based randomized, multicenter clinical trial in which investigators from 42 sites were randomly assigned to provide either a study-prescribed augmented diabetes assessment and education or the usual care. Adults with type 1 or 2 diabetes enrolled into 2 cohorts: those with a more-frequent-than-annual follow-up (502 control participants and 488 intervention participants) and those with an annual follow-up (368 control participants and 388 intervention participants). Enrollment was from April 2011 through January 2013. Point-of-care measurements of HbA1c, blood pressure, and retinopathy severity; an individualized estimate of the risk of retinopathy progression derived from the findings from ophthalmologic visits; structured comparison and review of past and current clinical findings; and structured education with immediate assessment and feedback regarding participant's understanding. These interventions were performed at enrollment and at routine ophthalmic follow-up visits scheduled at least 12 weeks apart. Mean change in HbA1c level from baseline to 1-year follow-up. Secondary outcomes included body mass index, blood pressure, and responses to diabetes self-management practices and attitudes surveys. In the cohort with more-frequent-than-annual follow-ups, the mean (SD) change in HbA1c level at 1 year was -0.1% (1.5%) in the control group and -0.3% (1.4%) in the intervention group (adjusted mean difference, -0.09% [95% CI, -0.29% to 0.12%]; P = .35). In the cohort with annual follow-ups, the mean (SD) change in HbA1c level was 0.0% (1.1%) in the control group and -0.1% (1.6%) in the intervention group (mean difference, -0.05% [95% CI, -0.27% to 0.18%]; P = .63). Results were similar for all secondary outcomes. Long-term optimization of glycemic control is not achieved by a majority of individuals with diabetes. The addition of personalized education and risk assessment during retinal ophthalmologic visits did not result in a reduction in HbA1c level compared with usual care over 1 year. These data suggest that optimizing glycemic control remains a substantive challenge requiring interventional paradigms other than those examined in our study. clinicaltrials.gov Identifier:NCT01323348.
    Jama Ophthalmology 05/2015; DOI:10.1001/jamaophthalmol.2015.1312 · 3.83 Impact Factor
  • K. Bell · E. Toschi · H. Wolpert · G. Steil
    Diabetes Technology &amp Therapeutics 02/2015; 17:A23-A23. · 2.29 Impact Factor
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    ABSTRACT: Objective This qualitative study aimed to explore the experience of transition from pediatric to adult diabetes care reported by posttransition emerging adults with type 1 diabetes (T1D), with a focus on preparation for the actual transfer in care. Methods Twenty-six T1D emerging adults (mean age 26.2±2.5 years) receiving adult diabetes care at a single center participated in five focus groups stratified by two levels of current glycemic control. A multidisciplinary team coded transcripts and conducted thematic analysis. Results Four key themes on the process of transfer to adult care emerged from a thematic analysis: 1) nonpurposeful transition (patients reported a lack of transition preparation by pediatric providers for the transfer to adult diabetes care); 2) vulnerability in the college years (patients conveyed periods of loss to follow-up during college and described health risks and diabetes management challenges specific to the college years that were inadequately addressed by pediatric or adult providers); 3) unexpected differences between pediatric and adult health care systems (patients were surprised by the different feel of adult diabetes care, especially with regards to an increased focus on diabetes complications); and 4) patients’ wish list for improving the transition process (patients recommended enhanced pediatric transition counseling, implementation of adult clinic orientation programs, and peer support for transitioning patients). Conclusion Our findings identify modifiable deficiencies in the T1D transition process and underscore the importance of a planned transition with enhanced preparation by pediatric clinics as well as developmentally tailored patient orientation in the adult clinic setting.
    Adolescent Health, Medicine and Therapeutics 10/2014; 5:191-8. DOI:10.2147/AHMT.S67943
  • Timothy C Dunn · Gary A Hayter · Ken J Doniger · Howard A Wolpert
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    ABSTRACT: The objective was to develop an analysis methodology for generating diabetes therapy decision guidance using continuous glucose (CG) data. The novel Likelihood of Low Glucose (LLG) methodology, which exploits the relationship between glucose median, glucose variability, and hypoglycemia risk, is mathematically based and can be implemented in computer software. Using JDRF Continuous Glucose Monitoring Clinical Trial data, CG values for all participants were divided into 4-week periods starting at the first available sensor reading. The safety and sensitivity performance regarding hypoglycemia guidance "stoplights" were compared between the LLG method and one based on 10th percentile (P10) values. Examining 13 932 hypoglycemia guidance outputs, the safety performance of the LLG method ranged from 0.5% to 5.4% incorrect "green" indicators, compared with 0.9% to 6.0% for P10 value of 110 mg/dL. Guidance with lower P10 values yielded higher rates of incorrect indicators, such as 11.7% to 38% at 80 mg/dL. When evaluated only for periods of higher glucose (median above 155 mg/dL), the safety performance of the LLG method was superior to the P10 method. Sensitivity performance of correct "red" indicators of the LLG method had an in sample rate of 88.3% and an out of sample rate of 59.6%, comparable with the P10 method up to about 80 mg/dL. To aid in therapeutic decision making, we developed an algorithm-supported report that graphically highlights low glucose risk and increased variability. When tested with clinical data, the proposed method demonstrated equivalent or superior safety and sensitivity performance.
    Journal of diabetes science and technology 04/2014; 8(4). DOI:10.1177/1932296814532200
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    Howard A Wolpert
    Diabetes care 12/2013; 36(12):e212. DOI:10.2337/dc13-1464 · 8.57 Impact Factor
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    ABSTRACT: PurposeThe purpose of this study was to explore perceptions that emerging adults with type 1 diabetes (T1D) have of their patient-provider relationships across the transition from pediatric to adult care.Methods Twenty-six emerging adults with T1D (mean age 26.2 ± 2.5 years) participated in 5 focus groups stratified by current level of glycemic control (A1C). Coded audio-recorded data were analyzed using thematic analysis and aided by NVivo software.ResultsThree major themes emerged from the analysis: (1) loss and gain in provider relationships across the transition-patients expressed 3 key responses to leaving pediatric providers that differed by A1C levels: sad reluctance and "natural progression" (mean A1C ± SD 7.4% ± 0.6%) and wanting to go (mean A1C ± SD 9.8% ± 1.0%); (2) partners in care versus on one's own-patients valued how adult providers' collaborative conversations promoted their involvement and accountability compared to "parent-centric" interactions with pediatric providers, but they also expressed ambivalence over increased independence in adult care; (3) improving provider approaches to transition-patients recommended that pediatric providers actively promote emerging adults' autonomy while maintaining parental support, communication with adult providers, and follow-up with transitioning patients.Conclusions Findings highlight the importance of enhanced provider awareness of T1D emerging adults' complex feelings about the transition in care. Improved integration of individual- and family-centered approaches to developmentally tailored diabetes care is needed to augment patient and provider relationships.
    The Diabetes Educator 11/2013; 40(1). DOI:10.1177/0145721713513177 · 1.92 Impact Factor
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    ABSTRACT: To examine the impact of continuous glucose monitoring on diabetes management and marital relationships of adults with Type 1 diabetes and their spouses. Nine younger (30-49 years) and 11 older (50-70 years) patients with Type 1 diabetes and 14 spouses participated in eight focus groups specific to age and role (patient or spouse). Audio-recorded data were transcribed, coded and analysed using thematic analysis and aided by NVivo software. Qualitative analysis revealed participants perceived continuous glucose monitoring as positively influencing hypoglycaemia management by decreasing spouses' anxiety, vigilance and negative experiences. Participants also described continuous glucose monitoring as promoting collaborative diabetes management and increasing spousal understanding of diabetes, especially when planning and managing pregnancy. Couples' conflicts occurred when (1) patients assumed sole responsibility for continuous glucose monitoring and/or did not respond to night-time glucose alarms and (2) spouses did not understand alarms and felt frustrated and helpless to assist patients. Our findings suggest that continuous glucose monitoring may positively impact collaborative diabetes management and marital relationships of patients with Type 1 diabetes and spouses. However, reluctance to collaborate and lack of understanding may contribute to couples' conflicts around continuous glucose monitoring. Our findings have important implications for clinical care and point to the need for interventions that include spouses in continuous glucose monitoring training to increase their understanding of continuous glucose monitoring, minimize risk for spousal conflict and enhance collaborative diabetes management. Further studies are needed to explore these issues in more detail and depth with larger and more diverse populations. This article is protected by copyright. All rights reserved.
    Diabetic Medicine 07/2013; DOI:10.1111/dme.12276 · 3.06 Impact Factor
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    ABSTRACT: Objective: To examine barriers to health care transition reported by young adults with type 1 diabetes and associations between barriers and prolonged gaps between pediatric and adult diabetes care.Methods: We surveyed young adults with type 1 diabetes, aged 22 to 30 years, about their transition experiences, including barriers to timely establishment of adult diabetes care. We evaluated relationships between barriers and gaps in care with multivariate logistic regression.Results: The response rate was 53% (258 of 484 eligible). Respondents (62% female) were 26.7±2.4 years old and transitioned to adult diabetes care at 19.5±2.9 years. Reported barriers included lack of specific adult provider referral name (47%) or contact information (27%), competing life priorities (43%), difficulty getting an appointment (41%), feeling upset about leaving pediatrics (24%), and insurance problems (10%). In multivariate analysis, barriers most strongly associated with gaps in care >6 months were lack of adult provider name (OR 6.1, 95% CI 3.0, 12.7) or contact information (OR 5.3, 95% CI 2.0, 13.9), competing life priorities (OR 5.2, 95% CI 2.7, 10.3), and insurance problems (OR 3.5, 95% CI 1.2, 10.3). Overall, respondents reporting ≥1 moderate/major barrier (48%) had 4.7-fold greater adjusted odds of a gap >6 months (95% CI 2.8, 8.7).Conclusions: Significant barriers to transition, such as lack of specific adult provider referrals, may be addressed with more robust preparation by pediatric providers and care coordination. Further study is needed to evaluate strategies to improve young adult self-care in the setting of competing life priorities.
    Endocrine Practice 06/2013; 19(6):1-22. DOI:10.4158/EP13109.OR · 2.59 Impact Factor
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    ABSTRACT: OBJECTIVE Current guidelines for intensive treatment of type 1diabetes base the mealtime insulin bolus calculation exclusively on carbohydrate counting. There is strong evidence that free fatty acids impair insulin sensitivity. We hypothesized that patients with type 1 diabetes would require more insulin coverage for higher-fat meals than lower-fat meals with identical carbohydrate content.RESEARCH DESIGN AND METHODS We used a crossover design comparing two 18-h periods of closed-loop glucose control after high-fat (HF) dinner compared with low-fat (LF) dinner. Each dinner had identical carbohydrate and protein content, but different fat content (60 vs. 10 g).RESULTSSeven patients with type 1 diabetes (age, 55 ± 12 years; A1C 7.2 ± 0.8%) successfully completed the protocol. HF dinner required more insulin than LF dinner (12.6 ± 1.9 units vs. 9.0 ± 1.3 units; P = 0.01) and, despite the additional insulin, caused more hyperglycemia (area under the curve >120 mg/dL = 16,967 ± 2,778 vs. 8,350 ± 1,907 mg/dL⋅min; P < 0001). Carbohydrate-to-insulin ratio for HF dinner was significantly lower (9 ± 2 vs. 13 ± 3 g/unit; P = 0.01). There were marked interindividual differences in the effect of dietary fat on insulin requirements (percent increase significantly correlated with daily insulin requirement; R(2) = 0.64; P = 0.03).CONCLUSION This evidence that dietary fat increases glucose levels and insulin requirements highlights the limitations of the current carbohydrate-based approach to bolus dose calculation. These findings point to the need for alternative insulin dosing algorithms for higher-fat meals and suggest that dietary fat intake is an important nutritional consideration for glycemic control in individuals with type 1 diabetes.
    Diabetes care 11/2012; 36(4). DOI:10.2337/dc12-0092 · 8.57 Impact Factor
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    ABSTRACT: To examine characteristics of the transition from pediatric to adult care in emerging adults with type 1 diabetes and evaluate associations between transition characteristics and glycemic control. We developed and mailed a survey to evaluate the transition process in emerging adults with type 1 diabetes, aged 22 to 30 years, receiving adult diabetes care at a single center. Current A1C data were obtained from the medical record. The response rate was 53% (258 of 484 eligible). The mean transition age was 19.5 ± 2.9 years, and 34% reported a gap >6 months in establishing adult care. Common reasons for transition included feeling too old (44%), pediatric provider suggestion (41%), and college (33%). Less than half received an adult provider recommendation and <15% reported having a transition preparation visit or receiving written transition materials. The most recent A1C was 8.1 ± 1.3%. Respondents who felt mostly/completely prepared for transition had lower likelihood of a gap >6 months between pediatric and adult care (adjusted odds ratio 0.47 [95% CI 0.25-0.88]). In multivariate analysis, pretransition A1C (β = 0.49, P < 0.0001), current age (β = -0.07, P = 0.03), and education (β = -0.55, P = 0.01) significantly influenced current posttransition A1C. There was no independent association of transition preparation with posttransition A1C (β = -0.17, P = 0.28). Contemporary transition practices may help prevent gaps between pediatric and adult care but do not appear to promote improvements in A1C. More robust preparation strategies and handoffs between pediatric and adult care should be evaluated.
    Diabetes care 06/2012; 35(8):1716-22. DOI:10.2337/dc11-2434 · 8.57 Impact Factor
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    Julie Wagner · Howard Tennen · Howard Wolpert
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    ABSTRACT: Continuous glucose monitoring (CGM) systems collect and store glucose data in an ongoing fashion for several days at a time. The main advantage of CGM is that it can help identify fluctuations and trends that would otherwise go unnoticed with other glucose measures. Here, we provide a review of CGM for behavioral researchers. We begin with a brief review of diabetes and glucose measurement and then describe what CGM is and reference the commercial CGM systems currently available. We discuss the challenges involved in using CGM in behavioral research. We then present a broad overview of CGM in behavioral research, including data from ours and others' research programs. Finally, we cover some practical issues to be considered when using CGM, suggest reporting guidelines for the behavioral researcher, and offer suggestions for future research. Only a handful of behavioral researchers are using CGM, although its use is increasing. The main ways that CGM is being used in behavioral research is to investigate basic biobehavioral processes, to assess the effects of behavioral interventions on diabetes control, and to use CGM itself as a behavior modification and teaching tool in diabetes self-management interventions. Continuous glucose monitoring holds promise to help behavioral researchers unravel the complex relationships among glucose and intrapersonal, interpersonal, and contextual factors. However, the uptake of CGM for this purpose is limited, and the possibilities for its use are largely unmet. We encourage behavioral researchers to implement CGM in their protocols and to do so in a way that maximizes its explanatory power.
    Psychosomatic Medicine 05/2012; 74(4):356-65. DOI:10.1097/PSY.0b013e31825769ac · 4.09 Impact Factor
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    ABSTRACT: The aim was to formulate practice guidelines for determining settings where patients are most likely to benefit from the use of continuous glucose monitoring (CGM). The Endocrine Society appointed a Task Force of experts, a methodologist, and a medical writer. This evidence-based guideline was developed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system to describe both the strength of recommendations and the quality of evidence. One group meeting, several conference calls, and e-mail communications enabled consensus. Committees and members of The Endocrine Society, the Diabetes Technology Society, and the European Society of Endocrinology reviewed and commented on preliminary drafts of these guidelines. The Task Force evaluated three potential uses of CGM: 1) real-time CGM in adult hospital settings; 2) real-time CGM in children and adolescent outpatients; and 3) real-time CGM in adult outpatients. The Task Force used the best available data to develop evidence-based recommendations about where CGM can be beneficial in maintaining target levels of glycemia and limiting the risk of hypoglycemia. Both strength of recommendations and quality of evidence were accounted for in the guidelines.
    The Journal of Clinical Endocrinology and Metabolism 10/2011; 96(10):2968-79. DOI:10.1210/jc.2010-2756 · 6.31 Impact Factor
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    ABSTRACT: To determine the individual persistence of the relationship between mean sensor glucose (MG) concentrations and hemoglobin A(1c) (A1C) from the Juvenile Diabetes Research Foundation Continuous Glucose Monitoring (CGM) Randomized Trial. MG was calculated using CGM data for 3 months before A1C measurements at 3, 6, 9, and 12 months for the CGM group and at 9 and 12 months for the control group. An MG-to-A1C ratio was included in analysis for subjects who averaged ≥4 days/week of CGM use. Spearman correlations of the MG-to-A1C ratio between consecutive visits 3 months apart ranged from 0.70 to 0.79. The correlations for children and youth were slightly smaller than those for adults. No meaningful differences were observed by device type or change in A1C. Individual variations in the rate of hemoglobin glycation are persistent and contribute to the inaccuracy in estimating MGs calculated from A1C levels.
    Diabetes care 06/2011; 34(6):1315-7. DOI:10.2337/dc10-1661 · 8.57 Impact Factor
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    ABSTRACT: Identify factors predictive of severe hypoglycemia (SH) and assess the clinical utility of continuous glucose monitoring (CGM) to warn of impending SH. In a multicenter randomized clinical trial, 436 children and adults with type 1 diabetes were randomized to a treatment group that used CGM (N = 224), or a control group that used standard home blood glucose monitoring (N = 212) and completed 12 months of follow-up. After 6 months, the original control group initiated CGM while the treatment group continued use of CGM for 6 months. Baseline risk factors for SH were evaluated over 12 months of follow-up using proportional hazards regression. CGM-derived indices of hypoglycemia were used to predict episodes of SH over a 24-h time horizon. The SH rate was 17.9 per 100 person-years, and a higher rate was associated with the occurrence of SH in the prior 6 months and female sex. SH frequency increased eightfold when 30% of CGM values were ≤ 70 mg/dL on the prior day (4.5 vs. 0.5%; P < 0.001), but the positive predictive value (PPV) was low (<5%). Results were similar for hypoglycemic area under the curve and the low blood glucose index calculated by CGM. SH in the 6 months prior to the study was the strongest predictor of SH during the study. CGM-measured hypoglycemia over a 24-h span is highly associated with SH the following day (P < 0.001), but the PPV is low.
    Diabetes care 03/2011; 34(3):586-90. DOI:10.2337/dc10-1111 · 8.57 Impact Factor
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    ABSTRACT: Background: The evaluation of patient-reported outcomes (e. g. impact, satisfaction) is important in trials of continuous glucose monitoring (CGM). We evaluated psychometric properties of the CGM Satisfaction Scale (CGM-SAT) and the Glucose Monitoring Survey (GMS). Methods: CGM-SAT is a 44-item scale on which patients (n -224) or parents (n 102) rated their experience with CGM over the prior 6 months. GMS is a 22-item scale on which patients (n 447) or parents (n 221) rated the blood glucose monitoring system they were using (home glucose meter with or without CGM) at baseline and 6 months. Results: The alpha coefficient for the CGM-SAT was >= 0.94 for all respondents and for the GMS was >= 0.84 for all respondents at baseline and 6 months. Parent-youth agreement was 0.52 for the CGM-SAT at 6 months and 0.24 and 0.20 for the GMS at baseline and 6 months for the Standard Care Group, respectively. Test-retest reliability of the GMS at 6 months for controls was r = 0.76 for adult patients, 0.63 for pediatric patients, and 0.43 for parents. Factor analysis isolated measurement factors for the CGM-SAT labeled Benefits of CGM and Hassles of CGM, accounting for 33% and 9% of score variance, respectively. For the GMS, two factors emerged: Glucose Control and Social Complications, accounting for 28% and 9% of variance, respectively. Significant correlations of CGM-SAT with frequency of CGM use between 6 months and baseline and GMS with frequency of conventional daily self-monitoring of blood glucose at baseline support their convergent validity. Conclusions: The CGM-SAT and GMS are reliable and valid measures of patient-reported CGM outcomes.
    Diabetes Technology &amp Therapeutics 09/2010; 12(9):679-684. DOI:10.1089/dia.2010.0015 · 2.29 Impact Factor
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    ABSTRACT: To identify psychosocial factors associated with the use of continuous glucose monitoring by adults with Type 1 diabetes. Twenty adult patients (aged 45 +/- 15 years, diabetes duration 25 +/- 19 years, 50% female) followed at our site in the multi-centre Juvenile Diabetes Research Foundation continuous glucose monitoring trial were divided into three groups: Glycated haemoglobin (HbA(1c)) Responders who demonstrated an improvement in glycaemic control with continuous glucose monitoring (baseline HbA(1c)> or = 7.0%, HbA(1c) reduction greater than or equal to 0.5%), Hypoglycaemia Responders (baseline HbA(1c) < 7.0%) who demonstrated decreased time < 3.9 mmol/l while remaining within target HbA(1c), and HbA(1c) Non-Responders (baseline HbA(1c)> or = 7.0%, HbA(1c) reduction less than 0.5%). Subjects participated in semi-structured interviews focusing on their psychosocial experiences with continuous glucose monitoring. Three major themes were identified that differentiated Responders (including both the HbA(1c) and Hypoglycaemia groups) from Non-Responders: (i) coping with frustrations-Responders used self-controlled rather than emotions-based coping when faced with continuous glucose monitoring frustrations; (ii) use of information-Responders used retrospective pattern analysis, not just minute-by-minute data analysis, in glycaemic management; (iii) 'significant other'/spousal involvement-Responders endorsed interest, encouragement and participation by their loved ones. Both Responders and Non-Responders expressed body image concerns when wearing continuous glucose monitoring devices. This qualitative study points to the importance of coping skills, retrospective review of data, and 'significant other' involvement in the effective use of continuous glucose monitoring. These findings will inform clinical initiatives to improve patient selection and training in the use of this new technology and have served as the basis for development of quantitative surveys to be used in clinical practice.
    Diabetic Medicine 09/2010; 27(9):1060-5. DOI:10.1111/j.1464-5491.2010.03061.x · 3.06 Impact Factor

Publication Stats

1k Citations
495.53 Total Impact Points

Institutions

  • 2015
    • Boston Children's Hospital
      Boston, Massachusetts, United States
  • 2001–2015
    • Joslin Diabetes Center
      • Section on Genetics and Epidemiology
      Boston, Massachusetts, United States
  • 2005–2013
    • Harvard University
      Cambridge, Massachusetts, United States
    • University of New Mexico
      Albuquerque, New Mexico, United States
  • 2011
    • Stanford University
      • Division of Pediatric Endocrinology
      Stanford, CA, United States
  • 2008
    • University of Washington Seattle
      • Department of Medicine
      Seattle, WA, United States
  • 2003
    • University of Toronto
      Toronto, Ontario, Canada
    • George Washington University
      Washington, Washington, D.C., United States