Arianna R Bedoya’s research while affiliated with University of Arizona and other places

Background: Glucose-guided eating (GGE) improves metabolic markers of chronic disease risk, including insulin resistance, in adults without diabetes. GGE is a timed eating paradigm that relies on experiencing feelings of hunger and having a preprandial glucose level below a personalized threshold computed from 2 consecutive morning fasting glucose levels. The dawn phenomenon (DP), which results in elevated morning preprandial glucose levels, could cause typically derived GGE thresholds to be unacceptable or ineffective among people with type 2 diabetes (T2DM). Objective: The aim of this study is to quantify the incidence and day-to-day variability in the magnitude of DP and examine its effect on morning preprandial glucose levels as a preliminary test of the feasibility of GGE in adults with T2DM. Methods: Study participants wore a single-blinded Dexcom G6 Pro continuous glucose monitoring (CGM) system for up to 10 days. First and last eating times and any overnight eating were reported using daily surveys over the study duration. DP was expressed as a dichotomous variable at the day level (DP day vs non-DP day) and as a continuous variable reflecting the percent of days DP was experienced on a valid day. A valid day was defined as having no reported overnight eating (between midnight and 6 AM). ∂ Glucose was computed as the difference in nocturnal glucose nadir (between midnight and 6 AM) to morning preprandial glucose levels. ∂ Glucose ≥20 mg/dL constituted a DP day. Using multilevel modeling, we examined the between- and within-person effects of DP on morning preprandial glucose and the effect of evening eating times on DP. Results: In total, 21 adults (59% female; 13/21, 62%) with non-insulin-treated T2DM wore a CGM for an average of 10.5 (SD 1.1) days. Twenty out of 21 participants (95%) experienced DP for at least 1 day, with an average of 51% of days (SD 27.2; range 0%-100%). The mean ∂ glucose was 23.7 (SD 13.2) mg/dL. People who experience DP more frequently had a morning preprandial glucose level that was 54.1 (95% CI 17.0-83.9; P<.001) mg/dL higher than those who experienced DP less frequently. For within-person effect, morning preprandial glucose levels were 12.1 (95% CI 6.3-17.8; P=.008) mg/dL higher on a DP day than on a non-DP day. The association between ∂ glucose and preprandial glucose levels was 0.50 (95% CI 0.37-0.60; P<.001). There was no effect of the last eating time on DP. Conclusions: DP was experienced by most study participants regardless of last eating times. The magnitude of the within-person effect of DP on morning preprandial glucose levels was meaningful in the context of GGE. Alternative approaches for determining acceptable and effective GGE thresholds for people with T2DM should be explored and evaluated.

BACKGROUND Glucose-guided eating (GGE) improves metabolic markers of chronic disease risk, including insulin resistance, in adults without diabetes. GGE is a timed eating paradigm that relies on experiencing feelings of hunger and having a preprandial glucose level below a personalized threshold computed from 2 consecutive morning fasting glucose levels. The dawn phenomenon (DP), which results in elevated morning preprandial glucose levels, could cause typically derived GGE thresholds to be unacceptable or ineffective among people with type 2 diabetes (T2DM). OBJECTIVE The aim of this study is to quantify the incidence and day-to-day variability in the magnitude of DP and examine its effect on morning preprandial glucose levels as a preliminary test of the feasibility of GGE in adults with T2DM. METHODS Study participants wore a single-blinded Dexcom G6 Pro continuous glucose monitoring (CGM) system for up to 10 days. First and last eating times and any overnight eating were reported using daily surveys over the study duration. DP was expressed as a dichotomous variable at the day level (DP day vs non-DP day) and as a continuous variable reflecting the percent of days DP was experienced on a valid day. A valid day was defined as having no reported overnight eating (between midnight and 6 AM). ∂ Glucose was computed as the difference in nocturnal glucose nadir (between midnight and 6 AM) to morning preprandial glucose levels. ∂ Glucose ≥20 mg/dL constituted a DP day. Using multilevel modeling, we examined the between- and within-person effects of DP on morning preprandial glucose and the effect of evening eating times on DP. RESULTS In total, 21 adults (59% female; 13/21, 62%) with non–insulin-treated T2DM wore a CGM for an average of 10.5 (SD 1.1) days. Twenty out of 21 participants (95%) experienced DP for at least 1 day, with an average of 51% of days (SD 27.2; range 0%-100%). The mean ∂ glucose was 23.7 (SD 13.2) mg/dL. People who experience DP more frequently had a morning preprandial glucose level that was 54.1 (95% CI 17.0-83.9; P <.001) mg/dL higher than those who experienced DP less frequently. For within-person effect, morning preprandial glucose levels were 12.1 (95% CI 6.3-17.8; P =.008) mg/dL higher on a DP day than on a non-DP day. The association between ∂ glucose and preprandial glucose levels was 0.50 (95% CI 0.37-0.60; P <.001). There was no effect of the last eating time on DP. CONCLUSIONS DP was experienced by most study participants regardless of last eating times. The magnitude of the within-person effect of DP on morning preprandial glucose levels was meaningful in the context of GGE. Alternative approaches for determining acceptable and effective GGE thresholds for people with T2DM should be explored and evaluated.

BACKGROUND Recent advances in personal biosensing technology support the shift from standardized to personalized health interventions, whereby biological data is used to motivate health behavior change. However, the implementation of interventions using biological feedback as a behavior change technique has not been comprehensively explored. OBJECTIVE The purpose of this review was to (1) map the domains of research where biological feedback has been used as a behavior change technique and (2) describe how it is implemented in behavior change interventions for adults. METHODS A comprehensive systematic search strategy was used to query multiple electronic databases in June 2021. Eligible studies were primary analyses of randomized controlled trials (RCTs) in adults that incorporated biological feedback as a behavior change technique. DistillerSR® was used to manage the literature search and review. RESULTS After removing 49,500 duplicates, 50,287 articles were screened, and 767 articles were included. The earliest RCT was published in 1972 with a notable increase in publications after 2000. Biological feedback was most used in RCTs aimed at preventing or managing diabetes (n=233, 30.4%), cardiovascular disease (n=175, 22.8%), and obesity (n=115, 15.0%). Feedback was often given on multiple biomarkers and targeted multiple health behaviors. The most common biomarkers used were anthropometric measures (n=297, 38.7%), blood pressure (n=238, 31.0%), and glucose (n=227, 29.6%). The most targeted behaviors were diet (n=472, 61.5%), physical activity (n=417, 54.4%), and smoking cessation (n=154, 20.1%). The frequency and type of communication by which biological feedback was provided varied by the method of biomarker measurement. Of the 493 (64.3%) studies where participants self-measured their biomarker, 476 (96.6%) received feedback multiple times over the intervention, and 468 (94.9%) received feedback through a biosensing device. CONCLUSIONS Biological feedback is increasingly being used to motivate behavior change, particularly where relevant biomarkers can be readily assessed. Yet, the methods by which biological feedback is operationalized in intervention research varies, and its effectiveness remains unclear. This scoping review serves as the foundation for developing a guiding framework for effectively implementing biological feedback as a behavior change technique. CLINICALTRIAL https://osf.io/yp5wa INTERNATIONAL REGISTERED REPORT RR2-10.2196/32579

Background Recent advancements in personal biosensing technology support the shift from standardized to personalized health interventions, whereby biological data are used to motivate health behavior change. However, the implementation of interventions using biological feedback as a behavior change technique has not been comprehensively explored. Objective The purpose of this review was to (1) map the domains of research where biological feedback has been used as a behavior change technique and (2) describe how it is implemented in behavior change interventions for adults. MethodsA comprehensive systematic search strategy was used to query 5 electronic databases (Ovid MEDLINE, Elsevier Embase, Cochrane Central Register of Controlled Trials, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global) in June 2021. Eligible studies were primary analyses of randomized controlled trials (RCTs) in adults that incorporated biological feedback as a behavior change technique. DistillerSR was used to manage the literature search and review. ResultsAfter removing 49,500 duplicates, 50,287 articles were screened and 767 articles were included. The earliest RCT was published in 1972 with a notable increase in publications after 2000. Biological feedback was most used in RCTs aimed at preventing or managing diabetes (n=233, 30.4%), cardiovascular disease (n=175, 22.8%), and obesity (n=115, 15%). Feedback was often given on multiple biomarkers and targeted multiple health behaviors. The most common biomarkers used were anthropometric measures (n=297, 38.7%), blood pressure (n=238, 31%), and glucose (n=227, 29.6%). The most targeted behaviors were diet (n=472, 61.5%), physical activity (n=417, 54.4%), and smoking reduction (n=154, 20.1%). The frequency and type of communication by which biological feedback was provided varied by the method of biomarker measurement. Of the 493 (64.3%) studies where participants self-measured their biomarker, 476 (96.6%) received feedback multiple times over the intervention and 468 (94.9%) received feedback through a biosensing device. Conclusions Biological feedback is increasingly being used to motivate behavior change, particularly where relevant biomarkers can be readily assessed. Yet, the methods by which biological feedback is operationalized in intervention research varied, and its effectiveness remains unclear. This scoping review serves as the foundation for developing a guiding framework for effectively implementing biological feedback as a behavior change technique. Trial RegistrationOpen Science Framework Registries; https://doi.org/10.17605/OSF.IO/YP5WAd International Registered Report Identifier (IRRID)RR2-10.2196/32579

BACKGROUND Type 2 diabetes mellitus (T2D) can be managed through diet and lifestyle changes. The American Dietetics Association acknowledges that knowing what and when to eat is the most challenging aspect of diabetes management. While current recommendations for self-monitoring of diet and glucose levels aim to improve glycemic control among people with T2D, tracking all intake is burdensome and unsustainable. Equally effective, but lower burden, dietary self-monitoring approaches should be explored. OBJECTIVE To examine the feasibility of abbreviated dietary self-monitoring in T2D where only carbohydrate-containing foods are recorded into a diet tracker. METHODS We used a mixed methods approach to quantitatively and qualitatively assess general and diet-related diabetes knowledge and the acceptability of reporting only carbohydrate-containing foods in N=30 men and women with T2D. RESULTS The mean Diabetes Knowledge Test score was 83.9±14.2%. Only 6 of 30 (20%) participants correctly categorized 5 commonly-consumed carbohydrate-containing and 5 non-carbohydrate containing foods. The mean perceived difficulty of reporting only carbohydrate-containing foods was 5.3 on a 10-point scale. Approximately half of the participants (53.3%, n=16) preferred to record all foods. A lack of knowledge about carbohydrate-containing foods was the primary cited barrier to acceptability (40%, n=12). CONCLUSIONS Abbreviated dietary self-monitoring, where only carbohydrate-containing foods are reported, is likely not feasible due to limited carbohydrate-specific knowledge and a preference of the majority to report all foods. Other approaches to reduce the burden of dietary self-monitoring for people with T2D that do not rely on food specific knowledge could be more feasible.

Background Type 2 diabetes mellitus (T2D) can be managed through diet and lifestyle changes. The American Diabetes Association acknowledges that knowing what and when to eat is the most challenging aspect of diabetes management. Although current recommendations for self-monitoring of diet and glucose levels aim to improve glycemic stability among people with T2D, tracking all intake is burdensome and unsustainable. Thus, dietary self-monitoring approaches that are equally effective but are less burdensome should be explored. Objective This study aims to examine the feasibility of an abbreviated dietary self-monitoring approach in patients with T2D, in which only carbohydrate-containing foods are recorded in a diet tracker. Methods We used a mixed methods approach to quantitatively and qualitatively assess general and diet-related diabetes knowledge and the acceptability of reporting only carbohydrate-containing foods in 30 men and women with T2D. Results The mean Diabetes Knowledge Test score was 83.9% (SD 14.2%). Only 20% (6/30) of participants correctly categorized 5 commonly consumed carbohydrate-containing foods and 5 noncarbohydrate-containing foods. The mean perceived difficulty of reporting only carbohydrate-containing foods was 5.3 on a 10-point scale. Approximately half of the participants (16/30, 53%) preferred to record all foods. A lack of knowledge about carbohydrate-containing foods was the primary cited barrier to acceptability (12/30, 40%). Conclusions Abbreviated dietary self-monitoring in which only carbohydrate-containing foods are reported is likely not feasible because of limited carbohydrate-specific knowledge and a preference of most participants to report all foods. Other approaches to reduce the burden of dietary self-monitoring for people with T2D that do not rely on food-specific knowledge could be more feasible.