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BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

AimsTo describe the effects of the COVID-19 pandemic on adults with T1D or T2D in the U.S.MethodsParticipants, recruited from the Taking Control of Your Diabetes Research Registry, were ≥19 years old and diagnosed with either T1D or T2D for ≥12 months. Participants completed an online survey on a HIPAA-protected platform.ResultsCompleted surveys were received from 763 T1Ds and 619 T2Ds. Average T1D age was 53.3 (SD = 15.3); average T2D age was 64.9 (SD = 10.3). Both samples were predominantly female, non-Hispanic white and well-educated. Average self-reported HbA1c was 6.9 (SD = 1.0; 52 mmol/mol) for T1Ds and 7.1 (SD = 1.1; 54 mmol/mol) for T2Ds. About 40% of respondents reported that all of their diabetes healthcare appointments at the time were cancelled or postponed, 40% reported a switch to telehealth appointments and almost half reported lower overall satisfaction with these visits (compared to pre-pandemic). There were widespread increases in general and diabetes-related stress and social isolation, and negative effects on disease management. About 25% reported increases in highs, lows, and glucose variability in both groups.ConclusionThere has been a substantive increase in level of diabetes-related and general life stress and social isolation due to the pandemic, with a significant impact on disease management.

AimsTo assess between-group differences in participant experiences in a two-arm diabetes distress (DD) reduction RCT and to determine their relationship to clinical outcomes (reductions in DD and HbA1C).MethodsFor high DD adults with Type 1 diabetes and HbA1c ≥7.5% participating in T1-REDEEM, we evaluated post intervention 5-point ratings of overall program "helpfulness" and program component "helpfulness," along with open-ended feedback statements using 10 qualitative codes. We compared responses of those in OnTrack, a distressed-focused intervention, with KnowIt, an education/management intervention.ResultsThose in OnTrack reported significantly higher levels of overall program helpfulness and greater helpfulness of each component of the program, greater group support, far fewer negative experiences, and more active and meaningful group engagement than those who participated in KnowIt. Ratings of helpfulness were unrelated to reductions in DD and HbA1C in both study arms. As previously reported, these findings occurred despite significant reductions in both DD and HbA1C in both arms with no between-group differences.ConclusionsFindings highlight the importance of addressing the personal experience of diabetes interventions in clinical care as separate, distinct outcomes. Personal experience may not always be related to changes in traditional clinical indicators.

AimsThis study evaluated the implementation costs of two group interventions, one focused on diabetes education (KnowIt) and one focused directly on diabetes distress (OnTrack), that reduced diabetes distress and HbA1C in adults with poorly controlled type 1 diabetes (T1DM) in the T1-REDEEM trial.MethodsResources used to provide interventions were enumerated using activity-based micro-costing methods. Costs were assigned to resources in 2017 US dollars. US median wage and benefit rates were used to calculate costs of staff time. Cost per unit change was calculated for diabetes distress and HbA1C.ResultsFor both interventions, per participant implementation costs were approximately $250 and cost per 1.0 percentage point (11 mmol/mol) change in HbA1C was $1400. Cost per unit change in diabetes distress was $364 for KnowIt and $335 for OnTrack. No statistically significant differences in costs were observed.ConclusionsThis is the first study to examine the costs of implementing interventions targeting diabetes distress in the context of T1DM. Both interventions had per participant implementation costs in the lower end of the range of previously examined diabetes self-management interventions ($219 to $5390). These inventions and their costs merit further attention because reducing diabetes distress may impact long term T1DM outcomes.Clinical trials registrationClinicalTrials.govNCT02175732.

ObjectiveWe tested three models to determine how improvements in emotion regulation (ER) and cognitive skills (CS) as a result of intervention operate to affect reductions in diabetes distress DD.MethodsChange data were drawn from the baseline and 9-month T1-REDEEM trial. Adults with type 1 diabetes were recruited from several U.S. states and Toronto, Canada. A primary and two alternative structural equation models were tested to explore the directionality of effect: primary model - changes in ER and CS drive changes in DD; reverse model - changes in DD drive changes in ER and CS; and bidirectional model - changes in ER, CS and DD occur together with no directionality.ResultsAll three models displayed a good fit to the data. The primary model indicated 7 significant directional pathways: improvements in ER and CS operate together to drive reductions in DD. The reverse model only indicated that reductions in DD affected changes in one CS variable; and the bidirectional model indicated only that these results were bidirectional. Reductions in all tested domains of DD occurred together.ConclusionsImprovements in ER and CS drive reductions in DD.Practice implicationsInterventions to reduce high DD should focus on improving ER and CS.

Background: Despite the known glycemic benefits of continuous glucose monitoring (CGM) for adults with type 2 diabetes (T2D), the attitudinal and behavioral changes underlying these glycemic improvements remain understudied. This study aimed to qualitatively explore these changes among a sample of adults with T2D. Methods: In-depth, semistructured interviews were conducted with adults with T2D who had been using CGM for 3–6 months as part of a larger community project in Ohio. Thematic analysis was used to identify themes across participants' experiences. Results: A total of 34 participants provided insights into their experiences with CGM. Six primary themes emerged: (1) Making the Invisible Visible, highlighting the newfound awareness of T2D in daily life. (2) Effective Decision-Making, emphasizing the use of real-time glucose data for immediate and long-term choices. (3) Enhanced Self-Efficacy, describing a renewed sense of control and motivation. (4) Diabetes-Related Diet Modifications. (5) Changes in Physical Activity. (6) Changes in Medication Taking. Conclusions: Participants reported a far-reaching impact of CGM on their daily lives, with many stating that CGM fostered a greater understanding of diabetes and prompted positive behavior changes. The observed attitudinal and behavioral shifts likely contributed synergistically to the significant glycemic benefits observed over the study period. This study highlights the technology's potential to bring about meaningful attitudinal and behavioral changes. [ABSTRACT FROM AUTHOR]

ObjectiveTo compare the effectiveness of two interventions to reduce diabetes distress (DD) and improve glycemic control among adults with type 1 diabetes (T1D).Research design and methodsIndividuals with T1D (n = 301) with elevated DD and HbA1c were recruited from multiple settings and randomly assigned to OnTrack, an emotion-focused intervention, or to KnowIt, an educational/behavioral intervention. Each group attended a full-day workshop plus four online meetings over 3 months. Assessments occurred at baseline and 3 and 9 months. Primary and secondary outcomes were change in DD and change in HbA1c, respectively.ResultsWith 12% attrition, both groups demonstrated dramatic reductions in DD (effect size d = 1.06; 78.4% demonstrated a reduction of at least one minimal clinically important difference). There were, however, no significant differences in DD reduction between OnTrack and KnowIt. Moderator analyses indicated that OnTrack provided greater DD reduction to those with initially poorer cognitive or emotion regulation skills, higher baseline DD, or greater initial diabetes knowledge than those in KnowIt. Significant but modest reductions in HbA1c occurred with no between-group differences. Change in DD was modestly associated with change in HbA1c (r = 0.14, P = 0.01), with no significant between-group differences.ConclusionsDD can be successfully reduced among distressed individuals with T1D with elevated HbA1c using both education/behavioral and emotion-focused approaches. Reductions in DD are only modestly associated with reductions in HbA1c. These findings point to the importance of tailoring interventions to address affective, knowledge, and cognitive skills when intervening to reduce DD and improve glycemic control.

ObjectiveTo demonstrate how maladaptive emotion regulation (ER) can lead to diabetes distress (DD), with subsequent effects on management and metabolic outcomes among adults with type 1 diabetes.MethodsData are based on pre-intervention assessment for a random controlled trial to reduce DD. Patients were recruited in California, Oregon, Arizona and Ontario, Canada. After screening and consent, patients completed an online assessment and released their most recent laboratory HbA1C. Structural equation modeling was used to define an ER measurement model and test for significant pathways.ResultsThree ER mechanisms combined into a single construct: emotion processing, non-judgment of emotions, non-reactivity to emotions. Models indicated a significant pathway from ER and cognitions to DD to disease management to metabolic control.ConclusionsAs hypothesized, the three ER mechanisms formed a single, coherent ER construct. Patients with poor ER reported high DD; and high DD was linked to poor diabetes management and poor metabolic control.Practice implicationsIdentifying both the level of DD and the ER mechanisms that lead to high DD should be explored in clinical settings. Helping T1Ds to become more aware, less judgmental and less reactive behaviorally to what they feel about diabetes and its management may reduce DD.

Aims: Diabetes distress (DD) refers to the emotional and behavioural challenges associated with managing this demanding chronic disease over time. DD is alarmingly common and it has a significant impact on self‐management behaviours and clinical outcomes. Thus, there is growing recognition that DD is a pressing problem that deserves careful attention in clinical care. Translating the application of validated DD assessment and intervention protocols from the research to the clinical setting, however, presents challenges that require a reconsideration of some common assumptions about what DD is, how prevalent it is, how it presents itself clinically, how it might best be assessed and by whom. Methods: We employed data from six large‐scale studies using five common DD measures. Using these data, we review and challenge several common assumptions about DD. Results: These data suggest that, because of its relative ubiquity, DD should not be viewed as a 'co‐morbidity' or 'complication' of diabetes and it should not be seen as a mental health/illness 'condition'. Furthermore, we argue that DD assessment should: (1) be accepted as a standard part of comprehensive diabetes care, (2) occur regularly using broad rather than brief screening measures and (3) be addressed directly by diabetes clinicians, rather than exclusively by behavioural specialists. Conclusions: The results form the basis of a series of suggestions to enhance the translation, adoption and implementation of DD knowledge derived from the research setting directly into the real world of clinical care. [ABSTRACT FROM AUTHOR]

Aim: Automated insulin delivery (AID) systems have demonstrated improved glycaemic outcomes in people with type 1 diabetes (T1D), yet limited data exist on these systems in very young children and their impact on caregivers. We evaluated psychosocial outcomes following use of the tubeless Omnipod® 5 AID System in caregivers of very young children. Materials and Methods: This 3‐month single‐arm, multicentre, pivotal clinical trial enrolled 80 children aged 2.0–5.9 years with T1D to use the Omnipod 5 AID System. Caregivers completed questionnaires assessing psychosocial outcomes—diabetes distress (Problem Areas in Diabetes), hypoglycaemia confidence (Hypoglycemia Confidence Scale), well‐being (World Health Organization 5 Well‐Being Index), sleep quality (Pittsburgh Sleep Quality Index), insulin delivery satisfaction (Insulin Delivery Satisfaction Survey) and system usability (System Usability Scale) at baseline with standard therapy and after 3 months of AID use. Results: Following 3 months of Omnipod 5 use, caregivers experienced significant improvements across all measures, including diabetes‐related psychosocial outcomes (Problem Areas in Diabetes; p < 0.0001, Hypoglycemia Confidence Scale; p < 0.01), well‐being (World Health Organization 5 Well‐Being Index; p < 0.0001) and perceived system usability (System Usability Scale; p < 0.0001). Significant improvements were seen in the Pittsburgh Sleep Quality Index total score and the overall sleep quality, sleep duration and efficiency subscales (all p < 0.05). Insulin Delivery Satisfaction Survey scores improved on all subscales (greater satisfaction, reduced burden and reduced inconvenience; all p < 0.0001). Conclusions: Caregivers face unique challenges when managing T1D in very young children. While glycaemic metrics have unquestioned importance, these results evaluating psychosocial outcomes reveal additional meaningful benefits and suggest that the Omnipod 5 AID System alleviates some of the burdens caregivers face with diabetes management. [ABSTRACT FROM AUTHOR]

Diabetes is unique among chronic diseases because clinical outcomes are intimately tied to how the person living with diabetes reacts to and implements treatment recommendations. It is further characterised by widespread social stigma, judgement and paternalism. This physical, social and psychological burden collectively influences self‐management behaviours. It is widely recognised that the individual's perspective about the impact of trying to manage the disease and the burden that self‐management confers must be addressed to achieve optimal health outcomes. Standardised, rigorous assessment of mental and behavioural health status, in interaction with physical health outcomes is crucial to aid understanding of person‐reported outcomes (PROs). Whilst tempting to conceptualise PROs as an issue of perceived quality of life (QoL), in fact health‐related QoL is multi‐dimensional and covers indicators of physical or functional health status, psychological and social well‐being. This complexity is illuminated by the large number of person reported outcome measures (PROMs) that have been developed across multiple psychosocial domains. Often measures are used inappropriately or because they have been used in the scientific literature rather than based on methodological or outcome assessment rigour. Given the broad nature of psychosocial functioning/mental health, it is important to broadly define PROs that are evaluated in the context of therapeutic interventions, real‐life and observational studies. This report summarises the central themes and lessons derived in the assessment and use of PROMs amongst adults with diabetes. Effective assessment of PROMs routinely in clinical research is crucial to understanding the true impact of any intervention. Selecting appropriate measures, relevant to the specific factors of PROs important in the research study will provide valuable data alongside physical health data. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare the effectiveness of three interventions to reduce diabetes distress (DD) and improve HbA1c among adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Individuals with T1D (n = 276) with elevated DD (a score >2 on the total Type 1 Diabetes Distress Scale) and HbA1c (>7.5%) were recruited from multiple settings and randomly assigned to one of three virtual group-based programs: 1) Streamline, an educator-led education and diabetes self-management program; 2) TunedIn, a psychologist-led program focused exclusively on emotional-focused DD reduction; or 3) FixIt, an integration of Streamline and TunedIn. Assessments of the primary outcomes of DD and HbA1c occurred at baseline and at 3, 6, and 12 months. RESULTS: All three programs demonstrated substantive and sustained reductions in DD (Cohen's d = 0.58–1.14) and HbA1c (range, −0.4 to −0.72) at 12-month follow-up. TunedIn and FixIt participants reported significantly greater DD reductions compared with Streamline participants (P = 0.007). Streamline and TunedIn participants achieved significantly greater HbA1c reductions than did FixIt participants (P = 0.006). CONCLUSIONS: DD can be successfully reduced among individuals with T1D with elevated HbA1c using both the educational/behavioral and emotion-focused approaches included in the study. Although both approaches are associated with significant and clinically meaningful reductions in DD and HbA1c, TunedIn, the emotion-focused program, had the most consistent benefits across both DD and HbA1c. The study findings suggest the overall value of group-based, fully virtual, and time-limited emotion-focused strategies, like those used in TunedIn, for adults with T1D. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Previous studies have shown that individuals with similar mean glucose levels (MG) or percentage of time in range (TIR) may have different HbA1c values. The aim of this study was to further elucidate how MG and TIR are associated with HbA1c. Methods: Data from the randomised clinical GOLD trial (n=144) and the follow-up SILVER trial (n=98) of adults with type 1 diabetes followed for 2.5 years were analysed. A total of 596 paired HbA1c/continuous glucose monitoring measurements were included. Linear mixed-effects models were used to account for intra-individual correlations in repeated-measures data. Results: In the GOLD trial, the mean age of the participants (± SD) was 44±13 years, 63 (44%) were female, and the mean HbA1c (± SD) was 72±9.8 mmol/mol (8.7±0.9%). When correlating MG with HbA1c, MG explained 63% of the variation in HbA1c (r=0.79, p<0.001). The variation in HbA1c explained by MG increased to 88% (r=0.94, p value for improvement of fit <0.001) when accounting for person-to-person variation in the MG–HbA1c relationship. Time below range (TBR; <3.9 mmol/l), time above range (TAR) level 2 (>13.9 mmol/l) and glycaemic variability had little or no effect on the association. For a given MG and TIR, the HbA1c of 10% of individuals deviated by >8 mmol/mol (0.8%) from their estimated HbA1c based on the overall association between MG and TIR with HbA1c. TBR and TAR level 2 significantly influenced the association between TIR and HbA1c. At a given TIR, each 1% increase in TBR was related to a 0.6 mmol/mol lower HbA1c (95% CI 0.4, 0.9; p<0.001), and each 2% increase in TAR level 2 was related to a 0.4 mmol/mol higher HbA1c (95% CI 0.1, 0.6; p=0.003). However, neither TIR, TBR nor TAR level 2 were significantly associated with HbA1c when accounting for MG. Conclusions/interpretation: Inter-individual variations exist between MG and HbA1c, as well as between TIR and HbA1c, with clinically important deviations in relatively large groups of individuals with type 1 diabetes. These results may provide important information to both healthcare providers and individuals with diabetes in terms of prognosis and when making diabetes management decisions. [ABSTRACT FROM AUTHOR]

Aims: To describe the development of a novel, conceptually sound instrument with contemporary content for assessing diabetes distress (DD) among adults with type 1 diabetes. Methods: Qualitative interviews with 15 adults and 7 clinicians were used to develop Core (intensity of DD emotional burden) and primary Source (key DD contributors) items. These were administered to a national sample recruited from the TCOYD Research Registry, T1D Exchange and our previous studies. Exploratory and confirmatory factor analyses were undertaken, along with reliability and construct validity studies, and cut‐point analyses to determine elevated DD. Results: Analyses based on 650 respondents yielded an 8‐item Core DD scale (α = 0.95) and 10 2‐ or 3‐item DD Source Scales (α range = 0.53–0.88): Financial Worries, Interpersonal Challenges, Management Difficulties, Shame, Hypoglycemia Concerns, Healthcare Quality, Lack of Diabetes Resources, Technology Challenges, Burden to Others and Worries about Complications. Core and Source scores were significantly associated with criterion variables: Higher DD scores were significantly linked with higher HbA1C, more frequent episodes of severe hypoglycaemia, missed boluses, and poorer quality of life (p > 0.001). A ≥2.0 scale cut‐point to define elevated DD is suggested. Conclusions: The new T1‐Diabetes Distress Assessment System demonstrated good reliability and validity, and with measures of both Core emotional burden and Sources of DD, it provides a contemporary, flexible and practical approach to assessing DD that can be used seamlessly to inform intervention for clinicians and researchers. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) is a complex, chronic autoimmune disease that affects over 1.6 million people in the United States. It is now understood that T1D may be undetected for many years while the disease progresses quietly without producing symptoms. T1D can be identified through diabetes-related autoantibody screening and staged accordingly, enabling healthcare providers to identify high-risk individuals in the early stages of the disease and either provide a stage-specific intervention or offer clinical trial opportunities to preserve beta cell function and anticipate the onset of clinical T1D. Evidence-based clinical practice guidelines currently do not exist for routine diabetes-related autoantibody screening of individuals at risk of developing T1D or of the general population. The purpose of this article is to help clinicians acquire an understanding of the rationale and protocols recommended for identifying patients at risk of developing T1D and monitoring such patients for autoimmune markers and progression of disease from Stage 1 to Stage 3 (clinical disease). Plain Language Summary: Type 1 diabetes (T1D) is a life-long condition where the body's immune system (which normally fights infection) mistakenly attacks cells in the pancreas that make insulin. Insulin allows one to use energy from food and controls blood sugar levels. Without early recognition and treatment, high blood sugar can cause serious symptoms and life-threatening complications, such as diabetic ketoacidosis (DKA). DKA happens when there is very low insulin, and if not spotted early, it can cause coma and death. T1D can occur at any age. The chance of getting T1D is higher if another family member has it. T1D progresses silently for months or years before symptoms appear such as increased thirst, frequent urination, and unintentional weight loss. Healthcare providers can now screen and identify people who are at early stages of T1D (without symptoms) with blood tests called autoantibodies. Early detection through screening allows people to 1) learn about the disease before symptoms start and insulin is needed, 2) potentially receive treatments that delay T1D progression, and 3) participate in research trials. By detecting T1D at early stages, people can connect with the right care team and develop the skills needed to manage later stage T1D. Early detection has been shown to prevent hospitalization and life-threatening conditions. Screening for T1D will help people maximize their opportunities to delay T1D onset while preparing for diabetes care. [ABSTRACT FROM AUTHOR]

AimsTo document the prevalence and 9-month incidence of elevated diabetes distress (DD) and the stability of DD over time using both single threshold and minimal clinically important differences (MCID) approaches.MethodsAdults with type 1 diabetes (T1D) (N=224) completed the 28-item T1-Diabetes Distress Scale (T1-DDS) at baseline and 9months. A T1-DDS threshold was identified with spline analysis and MCID was calculated from the standard error of measurement.ResultsAnalyses supported a cut-point of ≥2.0 for elevated DD. The prevalence and 9-month incidence of elevated DD was 42.1% and 54.4%, respectively. MCID was ±0.19 but varied by subscale (.26 to .50). Elevated DD was stable: only 20% crossed 2.0 over 9months. MCID analyses showed that change also occurred among those who remained either below or above 2.0 over time. Change varied by source of distress, with Powerlessness the most prevalent and stable. Using MCID, only participant age, gender and number of complications predicted change.ConclusionsThe prevalence, 9-month incidence and stability of elevated DD are high among adults with T1D, with change based on source of DD. We propose a combined cut-point/MCID framework for measuring change in DD, since each approach reflects unique characteristics of change over time.

AimsTo identify the unique sources of diabetes distress (DD) for adults with type 1 diabetes (T1D).MethodsSources of DD were developed from qualitative interviews with 25 T1D adults and 10 diabetes health care providers. Survey items were then developed and analyzed using both exploratory (EFA) and confirmatory CFA) analyses on two patient samples. Construct validity was assessed by correlations with depressive symptoms (PHQ8), complications, HbA1C, BMI, and hypoglycemia worry scale (HWS). Scale cut-points were created using multiple regression.ResultsAn EFA with 305 U.S. participants yielded 7 coherent, reliable sources of distress that were replicated by a CFA with 109 Canadian participants: Powerlessness, Negative Social Perceptions, Physician Distress, Friend/Family Distress, Hypoglycemia Distress, Management Distress, Eating Distress. Prevalence of DD was high with 41.6% reporting at least moderate DD. Higher DD was reported for women, those with complications, poor glycemic control, younger age, without a partner, and non-White patients.ConclusionsWe identified a profile of seven major sources of DD among T1D using a newly developed assessment instrument. The prevalence of DD is high and is related to glycemic control and several patient demographic and disease-related patient characteristics, arguing for a need to address DD in clinical care.

The article highlights the intersection of type 1 diabetes and dementia in aging populations, emphasizing the challenges faced by patients and caregivers. Topics discussed include the increased risk of cognitive decline in individuals with type 1 diabetes, the reluctance of patients to share diabetes management details, and strategies for healthcare providers to assist with diabetes management and support caregivers effectively.

Aims: While peer support research is growing in the Type 1 diabetes (T1D) community, the peer supporter training (PST) process is rarely documented in detail. This study provides a comprehensive description of PST and evaluation for the REACHOUT mental health support intervention, and examines the feasibility and perceived utility of PST. Methods: Fifty‐three adults with T1D were recruited to participate in a 6‐hour, zoom‐based PST program for mental health support. The program was structured in three parts: (1) internal motivation, resilience and empathy; (2) mindfulness, emotions and diabetes distress; and (3) active listening and deferring clinical questions to professionals. Candidates were evaluated based on eight pre‐established competency criteria during a 5‐day support trial with an assigned standardized T1D participant. Perceived usefulness of training skills was also assessed 3 months into the REACHOUT mental health support intervention. Results: Fifty‐one of the fifty‐three candidates who completed training achieved the criteria to graduate. Mean scores for the eight competency domains were: listens actively (4.55); asks open‐ended questions (4.12); expresses empathy (4.42); avoids passing judgment (4.67); sits with strong emotions (4.44); refrains from giving advice (4.38); makes reflections (4.5); and defers medical questions (4.58). Of the skills learned during the PST, 95% rated interpreting and discussing diabetes distress profile and expressing empathy as moderately to extremely useful. Conclusions: Findings demonstrate that it is feasible to recruit and graduate the number of trainees needed using a rigorous process. Only by making training protocols available can the PST be replicated and translated to other T1D populations (e.g. adolescents, parents of children with T1D). [ABSTRACT FROM AUTHOR]

OBJECTIVE: The effect of therapeutic strategies on cardiovascular (CV) disease can be evaluated by monitoring changes in CV risk biomarkers. This study investigated the effect of a structured self-monitoring of blood glucose (SMBG) protocol and the resulting improvements in glycemic control on changes in high-sensitivity C-reactive protein (hs-CRP) in insulin-naïve patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: The Structured Testing Program (STeP) study was a prospective, cluster-randomized, multicenter trial in which 483 poorly controlled, insulin-naïve patients with type 2 diabetes were randomized to active control (ACG) or structured testing (STG) that included quarterly structured SMBG. Changes in A1C, hs-CRP, and glycemic variability (STG subjects only) were measured at baseline and quarterly. RESULTS: Reductions in geometric mean hs-CRP values were significantly greater in the STG group at months 3 (P = 0.005), 6 (P = 0.0003), and 12 (P = 0.04) than in the ACG group. STG patients at high CV risk (>3 mg/L) showed significantly greater reductions in hs-CRP levels than ACG patients at high CV risk: -3.64 mg/dL (95% CI -4.21 to -3.06) versus -2.18 mg/dL (-2.93 to -1.43), respectively (P = 0.002). There was a strong correlation between reductions in hs-CRP and A1C in both groups: standardized coefficient (β) was 0.25 for the entire cohort (P < 0.0001), 0.31 for STG (P < 0.0001), and 0.16 for ACG (P = 0.02). CONCLUSIONS: Reductions in hs-CRP level are associated with reductions in A1C but not reductions in lipids or glycemic variability. Comprehensive structured SMBG-based interventions that lower A1C may translate into improvements in CV risk, as evidenced by levels of the biomarker hs-CRP.

Given the importance of glycemic control in the development of diabetes complications, the plethora of tools now available to monitor the day-to-day trends in glycemia is remarkable. In this regard, self-monitoring of blood glucose (SMBG) has been considered a key component of patient management. Arguably, there remains almost universal agreement that SMBG should be available to all diabetic patients regardless of current treatment strategy. However, recently there have been reports that have challenged the current paradigm that all patients should use SMBG and concluded that SMBG for type 2 diabetic patients not on insulin may not be beneficial on glycemic control and must be weighed against the expense and inconvenience. In the counterpoint narrative following the contribution by Malanda et al., Drs. Polonsky and Fisher provide a compelling argument suggesting that while it is evident that implementing SMBG in unstructured ways without training patients and clinicians is likely to be a waste of resources, there are effective and powerful ways to use structured SMBG in insulin-naïve type 2 diabetic patients. -William T. Cefalu, MD Editor in Chief, Diabetes Care.

ObjectiveTo identify the pattern of relationships between the 17-item Diabetes Distress Scale (DDS17) and diabetes variables to establish scale cut points for high distress among patients with type 2 diabetes.Research design and methodsRecruited were 506 study 1 and 392 study 2 adults with type 2 diabetes from community medical groups. Multiple regression equations associated the DDS17, a 17-item scale that yields a mean-item score, with HbA(1c), diabetes self-efficacy, diet, and physical activity. Associations also were undertaken for the two-item DDS (DDS2) screener. Analyses included control variables, linear, and quadratic (curvilinear) DDS terms.ResultsSignificant quadratic effects occurred between the DDS17 and each diabetes variable, with increases in distress associated with poorer outcomes: study 1 HbA(1c) (P < 0.02), self-efficacy (P < 0.001), diet (P < 0.001), physical activity (P < 0.04); study 2 HbA(1c) (P < 0.03), self-efficacy (P < 0.004), diet (P < 0.04), physical activity (P = NS). Substantive curvilinear associations with all four variables in both studies began at unexpectedly low levels of DDS17: the slope increased linearly between scores 1 and 2, was more muted between 2 and 3, and reached a maximum between 3 and 4. This suggested three patient subgroups: little or no distress,

Abstract Background The value and utility of self-monitoring of blood glucose (SMBG) in non-insulin treated T2DM has yet to be clearly determined. Findings from studies in this population have been inconsistent, due mainly to design differences and limitations, including the prescribed frequency and timing of SMBG, role of the patient and physician in responding to SMBG results, inclusion criteria that may contribute to untoward floor effects, subject compliance, and cross-arm contamination. We have designed an SMBG intervention study that attempts to address these issues. Methods/design The Structured Testing Program (STeP) study is a 12-month, cluster-randomised, multi-centre clinical trial to evaluate whether poorly controlled (HbA1c ≥ 7.5%), non-insulin treated T2DM patients will benefit from a comprehensive, integrated physician/patient intervention using structured SMBG in US primary care practices. Thirty-four practices will be recruited and randomly assigned to an active control group (ACG) that receives enhanced usual care or to an enhanced usual care group plus structured SMBG (STG). A total of 504 patients will be enrolled; eligible patients at each site will be randomly selected using a defined protocol. Anticipated attrition of 20% will yield a sample size of at least 204 per arm, which will provide a 90% power to detect a difference of at least 0.5% in change from baseline in HbA1c values, assuming a common standard deviation of 1.5%. Differences in timing and degree of treatment intensification, cost effectiveness, and changes in patient self-management behaviours, mood, and quality of life (QOL) over time will also be assessed. Analysis of change in HbA1c and other dependent variables over time will be performed using both intent-to-treat and per protocol analyses. Trial results will be available in 2010. Discussion The intervention and trial design builds upon previous research by emphasizing appropriate and collaborative use of SMBG by both patients and physicians. Utilization of per protocol and intent-to-treat analyses facilitates a comprehensive assessment of the intervention. Use of practice site cluster-randomisation reduces the potential for intervention contamination, and inclusion criteria (HbA1c ≥ 7.5%) reduces the possibility of floor effects. Inclusion of multiple dependent variables allows us to assess the broader impact of the intervention, including changes in patient and physician attitudes and behaviours. Trial Registration Current Controlled Trials NCT00674986.