Associations of Time in Range and Other Continuous Glucose Monitoring-Derived Metrics With Well-Being and Patient-Reported Outcomes: Overview and Trends

Treatment satisfaction and time in range after 16 weeks of treatment with iGlarLixi in insulin-naive adults with suboptimally controlled type 2 diabetes

AIMS

In Soli-CGM, treatment with iGlarLixi (insulin glargine 100 U/mL and lixisenatide 33 μg/mL) in insulin-naive adults with suboptimally controlled type 2 diabetes (T2D; haemoglobin A1c 9%-13% on ≥2 oral antihyperglycaemic agents (OADs) ± glucagon-like peptide-1 receptor agonist (GLP-1 RA) therapy) increased time in range (TIR; primary endpoint) from 26.4% at baseline to 52.7% at Week 16. This exploratory analysis examined the impact of treatment with iGlarLixi on patient-reported treatment satisfaction.

MATERIALS AND METHODS

Soli-CGM was a single-arm, 16-week, multicentre, interventional, open-label, phase 4 study using blinded continuous glucose monitoring (CGM; FreeStyle Libre Pro) to assess glycaemic metrics (N = 124). CGM data were collected for a 2-week period before initiation of iGlarLixi, and after treatment with iGlarLixi (Weeks 14-16). Treatment satisfaction was assessed using the Diabetes Medication Treatment Satisfaction Tool (DM-SAT, which comprises four domains: well-being, medical control, lifestyle and convenience), at baseline and end-of-treatment. Association of TIR and overall satisfaction (sum of all items) was also assessed.

RESULTS

Overall, 118 (95.9%) and 107 (87.0%) participants completed the DM-SAT at baseline and Week 16, respectively. Mean overall score increased by 0.18, from 0.59 (baseline) to 0.78 (Week 16). A trend in improvement in score was observed in all domains. Improvement in TIR had a positive, but weak, trend of association with improvement in overall treatment satisfaction (mean r = 0.14).

CONCLUSIONS

In people with T2D suboptimally controlled on ≥2 OADs ± GLP-1 RA, 16 weeks' treatment with iGlarLixi resulted in a trend of improvement in treatment satisfaction.

Glucometrics and Patient-Reported Outcomes in Individuals With Type 1 Diabetes Mellitus: Insights From the Correlation of Time in Range (CorrelaTIR) Study in Real-World Settings

Background This study aimed to measure the association between time in range (TIR) and other continuous glucose monitoring (CGM)-derived glucometrics, quality of life (QoL), healthcare resource use (HCRU), and costs in persons with type 1 diabetes mellitus (T1DM) in routine clinical practice in Spain. Methods This observational, cross-sectional, multicentre study evaluated persons with T1DM who received insulin via multiple daily injections. The study collected data on the participants (demographic and clinical), the use of the CGM devices, patient-reported outcomes (PROs) for general and diabetes-related QoL, treatment satisfaction, work productivity and activity impairment, HCRU, and costs. Data were analysed descriptively. The Spearman correlation coefficient was used to measure the association between glucometrics and PROs, HCRU and costs. Results Participants (N=114) had a mean age (standard deviation) of 44.53 (14.39) years, were 50.88% men, and 53.51% had glycated haemoglobin ≤7%. A higher TIR was significantly associated with better diabetes-related QoL but not with general QoL. HCRU and PRO scores for treatment satisfaction and work productivity and activity impairment showed no correlation with TIR. Higher TIR correlated with a lower number of emergency room visits. Conclusion Good glycaemic control (high TIR) is favourably associated with some aspects of diabetes-related QoL.

Somatic and mental symptoms associated with dysglycaemia, diabetes-related complications and mental conditions in people with diabetes: Assessments in daily life using continuous glucose monitoring and ecological momentary assessment

AIM

To analyse the potential drivers (glucose level, complications, diabetes type, gender, age and mental health) of diabetes symptoms using continuous glucose monitoring (CGM) and ecological momentary assessment.

MATERIALS AND METHODS

Participants used a smartphone application to rate 25 diabetes symptoms in their daily lives over 8 days. These symptoms were grouped into four blocks so that each symptom was rated six times on 2 days (noon, afternoon and evening). The symptom ratings were associated with the glucose levels for the previous 2 hours, measured with CGM. Linear mixed-effects models were used, allowing for nested random effects and the conduct of N = 1 analysis of individual associations.

RESULTS

In total, 192 individuals with type 1 diabetes and 179 with type 2 diabetes completed 6380 app check-ins. Four symptoms showed a significant negative association with glucose values, indicating higher ratings at lower glucose (speech difficulties, P = .003; coordination problems, P = .00005; confusion, P = .049; and food cravings, P = .0003). Four symptoms showed a significant positive association with glucose values, indicating higher scores at higher glucose (thirst, P = .0001; urination, P = .0003; taste disturbances, P = .021; and itching, P = .0120). There were also significant positive associations between microangiopathy and eight symptoms. Elevated depression and diabetes distress were associated with higher symptom scores. N = 1 analysis showed highly idiosyncratic associations between symptom reports and glucose levels.

CONCLUSIONS

The N = 1 analysis facilitated the creation of personalized symptom profiles related to glucose levels with consideration of factors such as complications, gender, body mass index, depression and diabetes distress. This approach can enhance precision monitoring for diabetes symptoms in precision medicine.

The emotional burden of type 1 diabetes: A cross-sectional study to understand associations between diabetes distress and glucose metrics in adulthood

AIMS

Advancements in type 1 diabetes (T1D) management, such as continuous glucose monitoring (CGM), have helped people achieve narrower glucose ranges, but associations between CGM and diabetes distress are unclear. Although higher HbA1c is associated with higher distress, associations with other glucose metrics are unknown. To better understand this relationship, we characterized diabetes distress in a sample of CGM users and compared differences in glucose metrics (measured via CGM) between those with higher versus lower distress.

METHODS

CGM users with T1D from the T1D Exchange Registry completed an online survey including diabetes distress (DDS-2) and shared CGM data (N = 199). CGM metrics were computed from all available data within 3 months prior to survey completion. Participants were grouped by distress level: lower (DDS-2 < 3, n = 120) or higher (DDS-2 ≥ 3, n = 79). Welch's t-tests were used to compare mean differences in CGM metrics between groups and MANCOVA was used to further probe mean differences.

RESULTS

Approximately 39.7% participants reported higher diabetes distress. Welch's t-tests revealed participants with higher distress spent significantly more time in higher glucose ranges (above 180 mg/dL and above 250 mg/dL), less time in target glucose ranges (between 70 and 180 mg/dL and between 70 and 140 mg/dL) and had higher glucose management index values compared to those with lower distress (p < 0.01). MANCOVA models showed similar results.

CONCLUSIONS

CGM users continue to experience diabetes distress. Moreover, higher distress appears to be associated with hyperglycaemia. These findings provide support for broader screening efforts for diabetes distress.

Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review

INTRODUCTION

Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain.

METHODS

The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018-2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain.

RESULTS

A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as "glucodensities," could help patients to achieve better glycemic control in the future.

CONCLUSION

By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.

Associations between continuous glucose monitoring (CGM) metrics and psycholinguistic measures: a correlational study

AIM

Recently, the relationship between diabetes and mental health has been widely studied. With the advent of continuous glucose monitoring (CGM), some researchers have been interested in exploring the association between glucose-related metrics and psychological aspects. These studies have primarily relied on self-report questionnaires which present some limitations. Therefore, the present multicenter study aims at testing potential associations between CGM metrics and affective processes derived from narratives about using a CGM sensor.

METHODS

An exploratory correlational design was used. Fifty-eight adults with type 1 diabetes using CGM were enrolled and invited to complete an online survey, where they replied to an open-ended question regarding their personal experience with the CGM sensor. Texts derived from the answers were analyzed through Linguistic Inquiry and Word Count, a widely used text analysis tool that can automatically identify and quantify linguistic patterns related to various psychological dimensions. Psycholinguistic measures were correlated with CGM metrics.

RESULTS

Higher levels of sadness/depression correlated with lower %TIR (r = - 339; p < .01) and higher %TAR (r = .342; p < .01).

CONCLUSIONS

The study highlights the relationship between CGM metrics and psychological variables derived from patients' narratives. In particular, it is possible to hypothesize a positive role of %TIR in reducing depressive feelings in individuals with diabetes, as well as a negative role of depressive feelings in achieving desirable CGM outcomes. Additionally, there is a potential role of glycemic variability, particularly hyperglycemia, in the expression of depressive and sad feelings, which has been less studied compared to the effects of hypoglycemia.

Person-reported outcomes in diabetes care: What are they and why are they so important?

In this review, we aim to show how person-reported outcomes (PROs) and person-reported experiences (PREs) can significantly contribute to the way diabetes care is delivered, the involvement of people with diabetes in diabetes care, and the collaboration between health care professionals and people with diabetes. This review focuses on the definition and measurement of PROs and PREs, the importance of PROs and PREs for person-centred diabetes care, and integrating the perspectives of people with diabetes in the evaluation of medical, psychological and technological interventions. PROs have been increasingly accepted by Health Technology Assessment bodies and are therefore valued in the context of reimbursement decisions and consequently by regulators and other health care stakeholders for the allocation of health care resources. Furthermore, the review identified current challenges to the assessment and use of PROs and PREs in clinical care and research. These challenges relate to the combination of questionnaires and ecological momentary assessment for measuring PROs and PREs, lack of consensus on a core outcome set, limited sensitivity to change within many measures and insufficient standardization of what can be considered a minimal clinically important difference. Another issue that has not been sufficiently addressed is the involvement of people with diabetes in the design and development of measures to assess PROs and PREs.

Glucose variability and mood in people with type 1 diabetes using ecological momentary assessment

OBJECTIVE

Mood fluctuations related to blood glucose excursions are a commonly reported source of diabetes-distress, but research is scarce. We aimed to assess the relationship between real-time glucose variability and mood in adults with type 1 diabetes (T1D) using ecological momentary assessments.

METHODS

In this prospective observational study, participants wore a masked continuous glucose monitor and received prompts on their smartphone 6 times a day to answer questions about their current mood (Profile Of Mood States (POMS)-SF (dimensions: Anxiety, Depressive symptoms, Anger, Fatigue, Vigor)) for 14 days. Mixed model analyses examined associations over time between daily Coefficient of Variation (CV) of blood glucose and mean and variability (CV) of POMS scores. Further, within-person differences in sleep and nocturnal hypoglycemia were explored.

RESULTS

18 people with T1D (10 female, mean age 44.3 years) participated. A total of 264 out of 367 days (70.2%) could be included in the analyses. No overall significant associations were found between CV of blood glucose and mean and CV of POMS scores, however, nocturnal hypoglycemia moderated the associations between CV of blood glucose and POMS scales (mean Fatigue Estimate 1.998, p < .006, mean Vigor Estimate -3.308, p < .001; CV Anger Estimate 0.731p = 0.02, CV Vigor Estimate -0.525, p = .006).

CONCLUSION

We found no overall relationship between real-time glycemic variability and mood per day. Further research into within-person differences such as sleep and nocturnal hypoglycemia is warranted.

Coordination of glucose monitoring, self-care behaviour and mental health: achieving precision monitoring in diabetes

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes.

Time With Diabetes Distress and Glycemia-Specific Distress: New Patient-Reported Outcome Measures for the Psychosocial Burden of Diabetes Using Ecological Momentary Assessment in an Observational Study

OBJECTIVE

To estimate time with diabetes distress using ecological momentary assessment (EMA) in people with type 1 diabetes and analyze its associations with glycemic management based on continuous glucose monitoring (CGM).

RESEARCH DESIGN AND METHODS

We used EMA to assess diabetes distress in a sample of recently hospitalized adults with type 1 diabetes once a day for 17 consecutive days in an ambulatory setting. Additionally, participants were asked daily about hypoglycemia distress (<70 mg/dL [3.9 mmol/L]), hyperglycemia distress (>180 mg/dL [10 mmol/L]), and variability distress (glucose fluctuations). Per person, the percentage of days with elevated distress was calculated (time with distress). Multilevel regression was used to analyze daily associations of distress ratings with CGM-derived parameters. EMA-derived associations between diabetes distress and glycemic outcomes were compared with questionnaire-derived associations.

RESULTS

Data of 178 participants were analyzed. Participants spent a mean (SD) of days in a state of diabetes distress, 54.6 ± 26.0% in hyperglycemia distress, 45.2 ± 27.5% in variability distress, and 23.0 ± 19.3% in hypoglycemia distress. In multilevel analyses, higher daily ratings of diabetes distress were significantly associated with hyperglycemia (β = 0.41). Results showed high between-person variability as explanation of variance of the models ranged between 22.2 and 98.8%. EMA-derived diabetes distress showed a significant association with mean glucose (r = 0.25), while questionnaire-based diabetes distress did not (r = 0.10). Prospectively, time with diabetes distress was associated with HbA1c at the 3-month follow-up (r = 0.27), while questionnaire-based distress showed no association (r = 0.11).

CONCLUSIONS

Time with distress as assessed with EMA showed a comparative advantage over distress as determined by questionnaire-based assessment of diabetes distress regarding associations with glycemic management.

Demographic and Glycemic Factors Linked With Diabetes Distress in Teens With Type 1 Diabetes

OBJECTIVE

Diabetes distress (DD) is a negative emotional response related to the burdens of living with type 1 diabetes (T1D) and is linked with diabetes outcomes, such as hemoglobin A1c (A1c). Yet, less is known about how other glycemic indicators, average blood glucose and time in range, relate to DD, and which demographic characteristics are associated with higher DD.

METHODS

In total, 369 teens (Mage 15.6 ± 1.4, 51% female, MT1D duration 6.7 ± 3.8 years) screened for DD using The Problem Areas in Diabetes-Teen Version to determine eligibility for an ongoing multi-site behavioral trial. The associations of DD, demographic factors, and glycemic indicators (A1c, average blood glucose, and time in range) were analyzed.

RESULTS

Twenty-nine percent of teens (n = 95) scored above the clinical cutoff (≥44) for DD. Females scored significantly higher on average than males. Black/African American, non-Hispanic youth screened significantly higher compared to youth from other racial/ethnic groups. Higher DD scores were related to higher A1c and average blood glucose, and lower time in range. Logistic regression models revealed that females were significantly more likely to report clinically elevated DD than males, and teens with higher A1c were 1.3 times more likely to report DD. Age and diabetes duration were not significantly associated with clinically elevated DD scores.

CONCLUSIONS

Results demonstrated that DD is most prevalent in Black, non-Hispanic and female teens, and DD is associated with higher average blood glucose and lower time in range. Further investigation into these disparities is warranted to promote optimal health outcomes for teens with T1D.

Empfehlungen zur Diabetes-Behandlung mit automatischen Insulin-Dosierungssystemen

Das Prinzip der automatischen Insulindosierung, kurz „AID“ genannt, zeigt in Zulassungsstudien und Real-World-Erfahrungen ausgezeichnete Behandlungsergebnisse. Beim AID wird eine Insulinpumpe mit einem System zur kontinuierlichen Glukosemessung zusammengeschaltet, während ein Rechenprogramm, der sogenannte Algorithmus, die Steuerung der Insulingabe nach Bedarf übernimmt. Idealerweise wäre das System ein geschlossener Kreis, bei dem die Menschen mit Diabetes keine Eingabe mehr machen müssten. Jedoch sind bei den heute verfügbaren Systemen verschiedene Grundeinstellungen und Eingaben erforderlich (insbesondere von Kohlenhydratmengen der Mahlzeiten oder körperlicher Aktivität), die sich von den bisherigen Empfehlungen der sensorunterstützten Pumpentherapie in einzelnen Aspekten unterscheiden. So werden die traditionellen Konzepte von „Basal“ und „Bolus“ mit AID weniger nützlich, da der Algorithmus beide Arten der Insulinabgabe verwendet, um die Glukosewerte dem eingestellten Zielwert zu nähern. Daher sollte bei diesen Systemen statt der Erfassung von „Basal“ und „Bolus“, zwischen einer „nutzerinitiierten“ und einer „automatischen“ Insulindosis unterschieden werden. Gemeinsame Therapieprinzipien der verschiedenen AID-Systeme umfassen die passgenaue Einstellung des Kohlenhydratverhältnisses, die Bedeutung des Timings der vom Anwender initiierten Insulinbolusgaben vor der Mahlzeit, den korrekten Umgang mit einem verzögerten oder versäumten Mahlzeitenbolus, neue Prinzipien im Umgang mit Sport oder Alkoholgenuss sowie den rechtzeitigen Umstieg von AID zu manuellem Modus bei Auftreten erhöhter Ketonwerte. Das Team vom Diabetes-Zentrum AUF DER BULT in Hannover hat aus eigenen Studienerfahrungen und der zugrunde liegenden internationalen Literatur praktische Empfehlungen zur Anwendung und Schulung der gegenwärtig und demnächst in Deutschland kommerziell erhältlichen Systeme zusammengestellt. Für den Erfolg der AID-Behandlung scheint das richtige Erwartungsmanagement sowohl beim Behandlungsteam und als auch beim Anwender von großer Bedeutung zu sein.

Function and Emotion in Everyday Life With Type 1 Diabetes (FEEL-T1D): Protocol for a Fully Remote Intensive Longitudinal Study

BACKGROUND

Although short-term blood glucose levels and variability are thought to underlie diminished function and emotional well-being in people with type 1 diabetes (T1D), these relationships are poorly understood. The Function and Emotion in Everyday Life with T1D (FEEL-T1D) study focuses on investigating these short-term dynamic relationships among blood glucose levels, functional ability, and emotional well-being in adults with T1D.

OBJECTIVE

The aim of this study is to present the FEEL-T1D study design, methods, and study progress to date, including adaptations necessitated by the COVID-19 pandemic to implement the study fully remotely.

METHODS

The FEEL-T1D study will recruit 200 adults with T1D in the age range of 18-75 years. Data collection includes a comprehensive survey battery, along with 14 days of intensive longitudinal data using blinded continuous glucose monitoring, ecological momentary assessments, ambulatory cognitive tasks, and accelerometers. All study procedures are conducted remotely by mailing the study equipment and by using videoconferencing for study visits.

RESULTS

The study received institutional review board approval in January 2019 and was funded in April 2019. Data collection began in June 2020 and is projected to end in December 2021. As of June 2021, after 12 months of recruitment, 124 participants have enrolled in the FEEL-T1D study. Approximately 87.6% (7082/8087) of ecological momentary assessment surveys have been completed with minimal missing data, and 82.0% (82/100) of the participants provided concurrent continuous glucose monitoring data, ecological momentary assessment data, and accelerometer data for at least 10 of the 14 days of data collection.

CONCLUSIONS

Thus far, our reconfiguration of the FEEL-T1D protocol to be implemented remotely during the COVID-19 pandemic has been a success. The FEEL-T1D study will elucidate the dynamic relationships among blood glucose levels, emotional well-being, cognitive function, and participation in daily activities. In doing so, it will pave the way for innovative just-in-time interventions and produce actionable insights to facilitate tailoring of diabetes treatments to optimize the function and well-being of individuals with T1D.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/30901.