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

A personalized model and optimization strategy for estimating blood glucose concentrations from sweat measurements

BACKGROUND AND OBJECTIVE

Diabetes is one of the four leading causes of death worldwide, necessitating daily blood glucose monitoring. While sweat offers a promising non-invasive alternative for glucose monitoring, its application remains limited due to the low to moderate correlation between sweat and blood glucose concentrations, which has been obtained until now by assuming a linear relationship. This study proposes a novel model-based strategy to estimate blood glucose concentrations from sweat samples, setting the stage for non-invasive glucose monitoring through sweat-sensing technology.

METHODS

We first developed a pharmacokinetic glucose transport model that describes the glucose transport from blood to sweat. Secondly, we designed a novel optimization strategy leveraging the proposed model to solve the inverse problem and infer blood glucose levels from measured glucose concentrations in sweat. To this end, the pharmacokinetic model parameters with the highest sensitivity were also optimized so as to achieve a personalized estimation. Our strategy was tested on a dataset composed of 108 samples from healthy volunteers and diabetic patients.

RESULTS

Our glucose transport model improves over the state-of-the-art in estimating sweat glucose concentrations from blood levels (higher accuracy, p<0.001). Additionally, our optimization strategy effectively solved the inverse problem, yielding a Pearson correlation coefficient of 0.98 across all 108 data points, with an average root-mean-square-percent-error of 12%±8%. This significantly outperforms the best sweat-blood glucose correlation reported in the existing literature (0.75).

CONCLUSION

Our innovative optimization strategy, also leveraging more accurate modeling, shows promising results, paving the way for non-invasive blood glucose monitoring and, possibly, improved diabetes management.

Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals' well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects.

A Prospective Pilot Study Demonstrating Noninvasive Calibration-Free Glucose Measurement

BACKGROUND

Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Noninvasive glucose measurement would eliminate this barrier toward making glucose monitoring more accessible, extending the benefits from people living with diabetes to prediabetes and the healthy.

METHODS

A novel spectroscopy-based system for measuring glucose noninvasively was used in an exploratory, prospective, single-center clinical study (NCT06272136) to develop and test a machine learning-based computational model for continuous glucose monitoring without per-subject calibration. The study design blinded the development investigators to the validation analyses.

RESULTS

Twenty subjects were enrolled. Fifteen were used for the development set, and five in the validation set. All study participants were adults with insulin-treated diabetes and median glycated hemoglobin (HbA1c) of 7.3% (interquartile range [IQR] = 6.7-7.7). The computational model resulted in a mean absolute relative difference (MARD) of 14.5% and 96.5% of the paired glucose data points in the A plus B zones of the Diabetes Technology Society (DTS) error grid. The correlation between the average model sensitivity by wavelength and the spectrum of glucose was 0.45 (P < .001).

CONCLUSIONS

Our findings suggest that Raman spectroscopy coupled with advanced computational methods can enable continuous, noninvasive glucose measurement without per-subject invasive calibration.

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.

Diabetes distress, the mediator of the poor glycemic control and depression: A meta-analysis

BACKGROUND

Diabetes-related distress (DRD) is a common psychological disorder specifically associated with diabetes, its cross-talk with depression, and glycated hemoglobin (HbA1c) was discussed controversially. Interventions addressing DRD were shown to improve HbA1c. However, the primary concern is to investigate the association of DRD with glycemic control. No meta-analyses have compared the effects of depression and diabetes distress on HbA1c.

AIM

To assess the relationship between DRD, depression, and glycemic control.

METHODS

We systematically searched PubMed MEDLINE, Google Scholar, and Cochrane Library from inception up to May 2024. The keywords diabetes distress, depression, psychopathology, glycemic control, HbA1c, glycated hemoglobin, fasting, and postprandial blood glucose were used. A datasheet was used to extract the author’s name year and country of publication, diabetes distress, depression, and HbA1c among patients with DRD, depression, and control subjects.

RESULTS

Out of the 2046 studies retrieved, 55 full texts were screened and 22 studies were included in the final meta-analysis. Diabetes distress was associated with poor glycemic control, odd ratio = 0.42, 95% confidence interval (CI): 0.17-0.67, and P value < 0.001, and odd ratio = 0.52, 95%CI: 0.38-0.72, and P value < 0.001 respectively. No significant difference was observed between depression and DRD regarding the impact on HbA1c, odd ratio = 0.13, 95%CI: 0.15-0.41, P value = 0.37, I² for heterogeneity = 76%. However, when heterogeneity was eliminated, diabetes distress influenced the HbA1c more compared to depression, odd ratio = 0.29, 95%CI: 0.17-0.41, and P value < 0.001.

CONCLUSION

DRD negatively influenced the HbA1c and glycemic control more than depression. Further studies using more specific measures (ecological momentary assessment) to assess DRD are recommended.

Change starts with the body: Interoceptive appreciation mediates the effect of mindfulness training on behavior change - an effect moderated by depression severity

Mindfulness catalyzes health behavior change. Yet, interoception is dysregulated in depression, potentially impairing behavioral activation. We examined the mediating role of interoceptive appreciation, as measured by how much one trusts and listens to internal bodily signals, on behavior change. Primary care patients with depression, anxiety, or stress disorders related to chronic illness were randomized to Mindfulness Training for Primary Care (MTPC) using the Mindful Behavior Change curriculum or a low-dose mindfulness comparator. Participants (N = 274) completed the Multidimensional Assessment of Interoceptive Awareness (MAIA) at 0 and 8 weeks. At week 7, participants chose a health behavior action plan. During weeks 8-10, participants reported their action plan initiation (API) level. We investigated the effect of MTPC on API level (MTPC-API), the mediating role of interoceptive appreciation (Body Listening [MAIA-BL] + Trusting [MAIA-T]), and baseline depression severity as a moderator. MTPC had a significant direct effect on API. Interoceptive appreciation (MAIA-BL + MAIA-T) had a significant indirect effect on API (CI=0.15-0.56). Without depression (n = 76), MAIA-BL partially mediated MTPC-API (CI=0.02-0.87). With moderate-to-severe depression (n = 132), MAIA-T partially mediated MTPC-API (CI=0.01-0.85). Interoceptive appreciation helps people listen to motivating bodily signals. In depression, regaining body trust may be an important step on a mindful path towards change.

Monitoring what matters to people with diabetes: Do we underestimate the importance of behaviour, attitude, and well-being?

OBJECTIVE

Despite improvements in diabetes monitoring and treatment many patients do not achieve treatment goals. Person-centred approaches have been proposed. However, their practical implementation lags. One barrier is uncertainty about which person-reported outcomes (PROs) should be considered to add the most value. We sought to identify PROs that may be prioritised.

METHODS

We used data from a multi-stakeholder Delphi study aimed at developing a person-centred diabetes outcome set and analysed which PROs patients considered important for regular monitoring but healthcare providers less so. Linear regression analyses tested whether belonging to either stakeholder group would predict the importance attributed to an outcome.

RESULTS

We found disagreement between patients and healthcare providers on eleven PROs. Stakeholder group predicted perceived importance for ten: self-management behaviours (including performance, perceived importance, motivation, and capacity), sleep quality, diabetes symptoms, screening visit attendance, health status, lifestyle behaviours, and side effects.

CONCLUSION

Our findings suggest that, according to patients' preferences, self-management behaviours, health status and sleep are currently not adequately considered in diabetes management, compromising person-centred care.

PRACTICAL IMPLICATIONS

This study suggests that prioritising these PROs can facilitate the implementation of more person-centred diabetes monitoring which may support better-informed treatment decisions to achieve treatment goals.

Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses

AIMS/HYPOTHESIS

Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up.

METHODS

In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up.

RESULTS

Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; p<0.0001) and perceived hyperglycaemia (t=13.637; p<0.0001) were the strongest predictors of daily diabetes distress, while CGM-based glucose variability was not significantly associated (t=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (β=0.32), diabetes distress (β=0.39) and hypoglycaemia fear (β=0.34) at follow-up (all p<0.001). Individuals with a stronger association between CGM-measured glucose control and daily distress had higher levels of psychosocial well-being at follow-up (depressive symptoms: β=-0.31; diabetes distress: β=-0.33; hypoglycaemia fear: β=-0.27; all p<0.001) but also higher HbA1c (β=0.12; p<0.05).

CONCLUSIONS/INTERPRETATION

Overall, subjective perceptions of glucose seem to be more influential on diabetes distress than objective CGM parameters of glycaemic control. N-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach.

Diabetes Technology Meeting 2023

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Diabetes distress and diabetes burnout explored in various areas of life in patients with type 1 diabetes: effect of short-term psychological intervention

INTRODUCTION

Diabetes distress (DD) and diabetes burnout (DB) are recognized psychological phenomena in patients with T1DM (type 1 diabetes mellitus). Still, there is an urgent need to create professional psychological intervention procedures to provide patients with adequate care.

AIM

The aim of the study was to assess the level of DD and DB in T1DM patients at baseline and after 5 of sessions psychological intervention in the group of participants who applied for help.

METHODS

34 T1DM patients who requested psychological support (22 females, 12 males) and 30 patients in a control group (14 females, 16 males) participated in the study. At baseline clinical test results between groups were compared. Next, in the studied group measurements were repeated after a set of five psychological face-to-face individual interventions which lasted 30-60 min each. They were support sessions with elements of cognitive-behavioral interventions done by clinical psychologists. Session 1: introduction, interview and collection of test results; session 2-4: work on the indicated by the patient and test results most problematic aspect of diabetes, session 5: a summary and plan for further treatment if needed. The control group results were obtained only at baseline. Research tools: DDS; PAID, Diabetes Burnout test by Polonsky.

RESULTS

At the baseline, significant differences were observed between the studied group and control group: in DB/DD levels: DB (3.9 ± 1.7 vs 2.4 ± 1.6; p < 0.001); DDS (3.2 ± 1.0 vs 2.7 ± 1.0; p = 0.064); PAID (62.3 ± 14.1vs 34.4 ± 21.0; p < 0.001). There were also group differences in HbA1c levels (8.7 ± 2.4 vs 7.3 ± 1.5; p = 0.028). After psychological interventions, there was a significant improvement in DB (3.9 ± 1.7vs 2.9 ± 1.2; p < 0.001; DDS (3.2 ± 1 vs 3.0 ± 0.7; p = 0.03); PAID (62.3 ± 14.1 vs 51.8 ± 12.5; p < 0.001).

CONCLUSIONS

DD and DB constitute a significant problem in the group of T1DM patients, but providing appropriate specialist care may help them accept diabetes and improve life satisfaction, as well as regain control over their diabetes management.

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.

Association of quality of life with medication adherence and glycemic control in patients with type 1 diabetes

Background and objectives

Psychological factors and patients' health-related quality of life (HRQOL) affect the outcome of patients with type 1 diabetes mellitus (T1DM). In this study, we aimed to determine the HRQOL status in patients with T1DM and its association with glycemic control and medication adherence.

Methods

In this cross-sectional study, 227 T1DM patients were selected from the diabetes clinic, Imam Ali Hospital, Alborz University of Medical Sciences, and the Gabric database registry from 2020 to 2022. Demographic and diabetes characteristic checklist, medication adherence questionnaire (8-item Morisky Medication Adherence Scale (MMAS)), and QOL questionnaires (Short-Form-12 and PedsQL) were filled. Independent sample T-test was used to assess mean of QOL subscales with glycemic control and medication adherence. A logistic regression model was used to evaluate the association between glycemic control and medication adherence with QOl.

Results

Overall QOL scores in adults and children were 33.4 ± 7.1 based on Short-Form-12 and 76.2 ± 17.8 based on PedsQL, respectively. It was demonstrated that adults with Moderate/High adherence had higher QOL (p-value = 0.007). Likewise, Children with good glycemic control had higher psychosocial health scores (0.048). Logistic regression analysis did not reveal a significant association between adherence and QOL or Glycemic control and QOL in both adjusted and crude models.

Conclusion

Better glycemic control and medication adherence in children and adults, respectively, are related to the psychological aspects of QOL. We suggest that emotional intelligence, which is replaced by other predictors during adulthood, may contribute to glycemic control in children in the early years following diagnosis.

Text messaging to enhance glucose monitoring and self-care in teens with type 1 diabetes: Teens' perceptions predict outcomes

AIMS

We assessed association between how teens with type 1 diabetes (T1D) perceived a text-messaging (TM) reminder system to check glucose levels and how their perceptions related to their responsiveness to TM reminders to check glucose levels.

METHODS

Teens received TM reminders 1-4 times daily to check glucose levels and to reply with the result. Qualitative assessments were performed quarterly. Teens were categorized by perceptions expressed at the majority of the visits and their TM responsiveness over 18 months.

RESULTS

There were 135 teens (51 % male), with a mean age of 14.8 ± 1.2 years, receiving TM reminders. Distribution of participants' perceptions was 37 % positive (POS), 35 % neutral (with both positive and negative responses (POS/NEG)), and 28 % negative (NEG). Teens with POS perceptions about TM reminders were more likely to respond with a glucose value to the TM reminders than teens with NEG or POS/NEG perceptions (p = 0.002). Youth with POS perceptions and TM responsiveness on ≥ 50 % of days had an 0.81 % improvement in their HbA1c (p = 0.004) over 18 months.

CONCLUSIONS

Teens with POS perceptions to TM reminders were likely to respond and their responsiveness yielded glycemic benefit, suggesting need to consider opinions of teens with T1D to maximize their intervention engagement and resulting benefits.

First versus second drop of capillary blood for monitoring blood glucose: a meta-analysis and systematic review

Introduction

The accuracy of the first or second drop of capillary blood for blood glucose monitoring remains unclear. This meta-analysis aimed to compare and evaluate the accuracy of the first or second drop of capillary blood for blood glucose monitoring, to provide evidence for clinical blood glucose monitoring and nursing care.

Material and methods

Two authors searched PubMed, ClinicalTrials, Cochrane Library, Clinical Evidence, EMBASE, China National Knowledge Infrastructure (CNKI), Wanfang and Weipu databases for relevant literature about the comparison of blood glucose values of the first capillary blood from the establishment of each database until November 10, 2023. After screening, extracting data and evaluating the quality of the literature, RevMan 5.4 software was used for meta-analysis.

Results

Twenty-three studies involving a total of 3121 patients were finally included in this meta-analysis. There was no significant difference in the measured value of blood glucose between the first drop and the second drop of capillary blood (MD = -0.01, 95% CI (-0.04, 0.03), p = 0.73). There was no publication bias in the synthesized outcome tested by Begg's regression analysis (p = 0.152). The result of subgroup analysis showed that there was no difference in the blood glucose values of the first two drops of blood measured by different blood glucose meters and different cleaning methods (all p > 0.05).

Conclusions

Current evidence suggests that when using capillary blood to monitor blood glucose, the first drop of capillary blood can be directly used to measure blood glucose.

Standardising personalised diabetes care across European health settings: A person-centred outcome set agreed in a multinational Delphi study

OBJECTIVE

Standardised person-reported outcomes (PRO) data can contextualise clinical outcomes enabling precision diabetes monitoring and care. Comprehensive outcome sets can guide this process, but their implementation in routine diabetes care has remained challenging and unsuccessful at international level. We aimed to address this by developing a person-centred outcome set for Type 1 and Type 2 diabetes, using a methodology with prospects for increased implementability and sustainability in international health settings.

METHODS

We used a three-round questionnaire-based Delphi study to reach consensus on the outcome set. We invited key stakeholders from 19 countries via purposive snowball sampling, namely people with diabetes (N = 94), healthcare professionals (N = 65), industry (N = 22) and health authorities (N = 3), to vote on the relevance and measurement frequency of 64 previously identified clinical and person-reported outcomes. Subsequent consensus meetings concluded the study.

RESULTS

The list of preliminary outcomes was shortlisted via the consensus process to 46 outcomes (27 clinical outcomes and 19 PROs). Two main collection times were recommended: (1) linked to a medical visit (e.g. diabetes-specific well-being, symptoms and psychological health) and (2) annually (e.g. clinical data, general well-being and diabetes self management-related outcomes).

CONCLUSIONS

PROs are often considered in a non-standardised way in routine diabetes care. We propose a person-centred outcome set for diabetes, specifically considering psychosocial and behavioural aspects, which was agreed by four international key stakeholder groups. It guides standardised collection of meaningful outcomes at scale, supporting individual and population level healthcare decision making. It will be implemented and tested in Europe as part of the H2O project.

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.

Which diabetes specific patient reported outcomes should be measured in routine care? A systematic review to inform a core outcome set for adults with Type 1 and 2 diabetes mellitus: The European Health Outcomes Observatory (H2O) programme

OBJECTIVES

The objective was to identify candidate patient reported outcomes with potential to inform individual patient care and service development for inclusion in a digital outcome set to be collected in routine care, as part of an international project to enhance care outcomes for people with diabetes.

METHODS

PubMed, COSMIN and COMET databases were searched. Published studies were included if they recommended patient reported outcomes that were clinically useful and/or important to people with diabetes. To aid selection decisions, recommended outcomes were considered in terms of the evidence endorsing them and their importance to people with diabetes.

RESULTS

Twenty-seven studies recommending 53 diabetes specific outcomes, and patient reported outcome measures, were included. The outcomes reflected the experience of living with diabetes (e.g. psychological well-being, symptom experience, health beliefs and stigma) and behaviours (e.g. self-management). Diabetes distress and self-management behaviours were most endorsed by the evidence.

CONCLUSIONS

The review provides a comprehensive list of candidate outcomes endorsed by international evidence and informed by existing outcome sets, and suggestions for measures.

PRACTICE IMPLICATIONS

The review offers evidence to guide clinical application. Integrated measurement of these outcomes in care settings holds enormous potential to improve provision of care and outcomes in diabetes.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

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.

Within-person relationships of sleep duration with next-day stress and affect in the daily life of adults with Type-1 diabetes

OBJECTIVE

The objective of this study is to examine the within-person relationships between sleep duration and next-day stress and affect in the daily life of individuals with T1D.

METHODS

Study participants were recruited in the Function and Emotion in Everyday Life with Type 1 Diabetes (FEEL-T1D) study. Sleep duration was derived by synthesizing objective (actigraphy) and self-report measures. General and diabetes-specific stress and positive and negative affect were measured using ecological momentary assessment. Multilevel regression was used to examine the within-person relationships between sleep duration and next-day stress and affect. Cross-level interactions were used to explore whether gender and baseline depression and anxiety moderated these within-person relationships.

RESULTS

Adults with T1D (n = 166) completed measurements for 14 days. The average age was 41.0 years, and 91 participants (54.8%) were female. The average sleep duration was 7.3 h (SD = 1.2 h). Longer sleep was significantly associated with lower general stress (p < 0.001) but not diabetes-specific stress (p = 0.18) on the next day. There were significant within-person associations of longer sleep with lower levels on next-day negative affect (overall, p = 0.002, disappoint, p = 0.05; sad, p = 0.05; tense, p < 0.001; upset, p = 0.008; anxious, p = 0.04). There were no significant associations with positive affect. Examination of the interaction effects did not reveal significant differential relationships for men and women and for individuals with and without depression or anxiety at baseline.

CONCLUSION

Findings from this study suggest optimizing sleep duration as an important interventional target for better managing general stress and improving daily emotional wellbeing of individuals with T1D.

Assessing the Content Validity, Acceptability, and Feasibility of the Hypo-METRICS App: Survey and Interview Study

BACKGROUND

The Hypoglycaemia - MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone app was developed to investigate the impact of hypoglycemia on daily functioning in adults with type 1 diabetes mellitus or insulin-treated type 2 diabetes mellitus. The app uses ecological momentary assessments, thereby minimizing recall bias and maximizing ecological validity. It was used in the Hypo-METRICS study, a European multicenter observational study wherein participants wore a blinded continuous glucose monitoring device and completed the app assessments 3 times daily for 70 days.

OBJECTIVE

The 3 aims of the study were to explore the content validity of the app, the acceptability and feasibility of using the app for the duration of the Hypo-METRICS study, and suggestions for future versions of the app.

METHODS

Participants who had completed the 70-day Hypo-METRICS study in the United Kingdom were invited to participate in a brief web-based survey and an interview (approximately 1h) to explore their experiences with the app during the Hypo-METRICS study. Thematic analysis of the qualitative data was conducted using both deductive and inductive methods.

RESULTS

A total of 18 adults with diabetes (type 1 diabetes: n=10, 56%; 5/10, 50% female; mean age 47, SD 16 years; type 2 diabetes: n=8, 44%; 2/8, 25% female; mean age 61, SD 9 years) filled out the survey and were interviewed. In exploring content validity, participants overall described the Hypo-METRICS app as relevant, understandable, and comprehensive. In total, 3 themes were derived: hypoglycemia symptoms and experiences are idiosyncratic; it was easy to select ratings on the app, but day-to-day changes were perceived as minimal; and instructions could be improved. Participants offered suggestions for changes or additional questions and functions that could increase engagement and improve content (such as providing more examples with the questions). In exploring acceptability and feasibility, 5 themes were derived: helping science and people with diabetes; easy to fit in, but more flexibility wanted; hypoglycemia delaying responses and increasing completion time; design, functionality, and customizability of the app; and limited change in awareness of symptoms and impact. Participants described using the app as a positive experience overall and as having a possible, although limited, intervention effect in terms of both hypoglycemia awareness and personal impact.

CONCLUSIONS

The Hypo-METRICS app shows promise as a new research tool to assess the impact of hypoglycemia on an individual's daily functioning. Despite suggested improvements, participants' responses indicated that the app has satisfactory content validity, overall fits in with everyday life, and is suitable for a 10-week research study. Although developed for research purposes, real-time assessments may have clinical value for monitoring and reviewing hypoglycemia symptom awareness and personal impact.

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

PURPOSE

Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.

METHODS

We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords 'continuous glucose monitor,' 'nutrition,' 'physical activity,' and 'numerical modeling.'

RESULTS

Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.

CONCLUSION

CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Influence of wearing complete denture on the glycemic control, serum lipid, and proteins in patients with diabetes

Aims

The aim of this study was to assess the impact of prosthodontic rehabilitation on glycemic and lipid control in functionally and completely edentulous patients with diabetes.

Setting and Design

An in vivo study conducted with the intention of studying the potential link between edentulism and impaired masticatory efficiency with the nutritional status in diabetic patients.

Materials and Methods

A total of 20 diabetic patients based on the inclusion criteria were selected. They were rehabilitated using a removable prosthesis, and observations were made across three parameters - glycosylated hemoglobin (HbA1C), serum cholesterol (S col), and serum protein (SP) at three stages - baseline, 3 months, and 6 months posttreatment. This was done to gauge the impact of the prosthetic rehabilitation on their health due to an increased masticatory efficiency potentially causing changes in dietary patterns.

Statistical Analysis Used

•Inter group comparison (>2 groups) was done using one way ANOVA followed by pair wise comparison using post hoc test. •Intra group comparison was done using repeated measures ANOVA (for>2 observations) followed by post Hoc test. For all the statistical tests, P < 0.05 was considered to be statistically significant, keeping α error at 5% and β error ati20%, thus giving a power to the study as 80%.

Results

Hba1c at the baseline had a mean value of 8.04%, which reduced to 7.87% at the 3-month stage and 7.38% at the 6-month stage. S col at the baseline had a mean of 151.6 mg/dL; at the 3-month follow-up, it was 166.5 mg/dL, and at the 6-month follow-up, it was 173.95 mg/dL. SP had a mean baseline value of 6.38 mg/dL, which progressed to 6.67 mg/dL at the 3-month stage and 6.97 at the 6-month stage.

Conclusion

Within the limitations of this study, it can be concluded that after 6 months of prosthetic rehabilitation in edentulous/functionally edentulous patients: There was a reduction in HbA1c (8.04%-7.38%); however, it was found to be statistically insignificant at that stage There was an increase in S col (151.6 mg/dL-173.95 mg/dL); it was found to be statistically significant There was an increase in SP (6.38 mg/dL-6.97 mg/dL); however, it was found to be statistically insignificant at that stage.

Insulin detection in diabetes mellitus: challenges and new prospects

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

A Roadmap to an Equitable Digital Diabetes Ecosystem

OBJECTIVES

Diabetes management presents a substantial burden to individuals living with the condition and their families, health care professionals, and health care systems. Although an increasing number of digital tools are available to assist with tasks such as blood glucose monitoring and insulin dose calculation, multiple persistent barriers continue to prevent their optimal use.

METHODS

As a guide to creating an equitable connected digital diabetes ecosystem, we propose a roadmap with key milestones that need to be achieved along the way.

RESULTS

During the Coronavirus 2019 pandemic, there was an increased use of digital tools to support diabetes care, but at the same time, the pandemic also highlighted problems of inequities in access to and use of these same technologies. Based on these observations, a connected diabetes ecosystem should incorporate and optimize the use of existing treatments and technologies, integrate tasks such as glucose monitoring, data analysis, and insulin dose calculations, and lead to improved and equitable health outcomes.

CONCLUSIONS

Development of this ecosystem will require overcoming multiple obstacles, including interoperability and data security concerns. However, an integrated system would optimize existing devices, technologies, and treatments to improve outcomes.