Application of Machine Learning in Multimorbidity Research: Protocol for a Scoping Review.

BACKGROUND: Multimorbidity, defined as the coexistence of multiple chronic conditions, poses significant challenges to health care systems on a global scale. It is associated with increased mortality, reduced quality of life, and increased health care costs. The burden of multimorbidity is expected to worsen if no effective intervention is taken. Machine learning has the potential to assist in addressing these challenges since it offers advanced analysis and decision-making capabilities, such as disease prediction, treatment development, and clinical strategies. OBJECTIVE: This paper represents the protocol of a scoping review that aims to identify and explore the current literature concerning the use of machine learning for patients with multimorbidity. More precisely, the objective is to recognize various machine learning models, the patient groups involved, features considered, types of input data, the maturity of the machine learning algorithms, and the outcomes from these machine learning models. METHODS: The scoping review will be based on the guidelines of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Five databases (PubMed, Embase, IEEE, Web of Science, and Scopus) are chosen to conduct a literature search. Two reviewers will independently screen the titles, abstracts, and full texts of identified studies based on predefined eligibility criteria. Covidence (Veritas Health Innovation Ltd) will be used as a tool for managing and screening papers. Only studies that examine more than 1 chronic disease or individuals with a single chronic condition at risk of developing another will be included in the scoping review. Data from the included studies will be collected using Microsoft Excel (Microsoft Corp). The focus of the data extraction will be on bibliographical information, objectives, study populations, types of input data, types of algorithm, performance, maturity of the algorithms, and outcome. RESULTS: The screening process will be presented in a PRISMA-ScR flow diagram. The findings of the scoping review will be conveyed through a narrative synthesis. Additionally, data extracted from the studies will be presented in more comprehensive formats, such as charts or tables. The results will be presented in a forthcoming scoping review, which will be published in a peer-reviewed journal. CONCLUSIONS: To our knowledge, this may be the first scoping review to investigate the use of machine learning in multimorbidity research. The goal of the scoping review is to summarize the field of literature on machine learning in patients with multiple chronic conditions, highlight different approaches, and potentially discover research gaps. The results will offer insights for future research within this field, contributing to developments that can enhance patient outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53761.

A telemonitoring intervention design for patients with poorly controlled type 2 diabetes: protocol for a feasibility study.

BACKGROUND: Maintaining optimal glycemic control in type 2 diabetes (T2D) is difficult. Telemedicine has the potential to support people with poorly regulated T2D in the achievement of glycemic control, especially if the telemedicine solution includes a telemonitoring component. However, the ideal telemonitoring design for people with T2D remains unclear. Therefore, the aim of this feasibility study is to evaluate the feasibility of two telemonitoring designs for people with non-insulin-dependent T2D with a goal of identifying the optimal telemonitoring intervention for a planned future large-scale randomized controlled trial. METHOD: This 3-month randomized feasibility study will be conducted in four municipalities in North Denmark starting in January 2024. There will be 15 participants from each municipality. Two different telemonitoring intervention designs will be tested. One intervention will include self-monitoring of blood glucose (SMBG) combined with sleep and mental health monitoring. The second intervention will include an identical setup but with the addition of blood pressure and activity monitoring. Two municipalities will be allocated to one intervention design, whereas the other two municipalities will be allocated to the second intervention design. Qualitative interviews with participants and clinicians will be conducted to gain insight into their experiences with and acceptance of the intervention designs and trial procedures (e.g., blood sampling and questionnaires). In addition, sources of differences in direct intervention costs between the two alternative interventions will be investigated. DISCUSSION: Telemonitoring has the potential to support people with diabetes in achieving glycemic control, but the existing evidence is inconsistent, and thus, the optimal design of interventions remains unclear. The results of this feasibility study are expected to produce relevant information about telemonitoring designs for people with T2D and help guide the design of future studies. A well-tested telemonitoring design is essential to ensure the quality of telemedicine initiatives, with goals of user acceptance and improved patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT06134934 . Registered November 1, 2023. The feasibility trial has been approved (N-20230026) by the North Denmark Region Committee on Health Research Ethics (June 5, 2023).

Adherence to newer second-line oral antidiabetic drugs among people with type 2 diabetes-A systematic review.

The adherence to oral antidiabetic drugs (OADs) among people with type 2 diabetes (T2D) is suboptimal. However, new OADs have been marketed within the last 10 years. As these new drugs differ in mechanism of action, treatment complexity, and side effects, they may influence adherence. Thus, the aim of this study was to assess the adherence to newer second-line OADs, defined as drugs marketed in 2012-2022, among people with T2D. A systematic review was performed in CINAHL, Cochrane Trials, Embase, PubMed, PsycINFO, and Scopus. Articles were included if they were original research of adherence to newer second-line OADs and reported objective adherence quantification. The quality of the articles was assessed using JBI's critical appraisal tools. The overall findings were reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and summarized in a narrative synthesis. All seven included articles were European retrospective cohort studies investigating alogliptin, canagliflozin, dapagliflozin, empagliflozin, and unspecified types of SGLT2i. Treatment discontinuation and medication possession ratio (MPR) were the most frequently reported adherence quantification measures. Within the first 12 months of treatment, 29%-44% of subjects on SGLT2i discontinued the treatment. In terms of MPR, 61.7%-94.9% of subjects on either alogliptin, canagliflozin, dapagliflozin, empagliflozin or an unspecified SGLT2i were adherent. The two investigated adherence quantification measures, treatment discontinuation and MPR, suggest that adherence to the newer second-line OADs may be better than that of older OADs. However, a study directly comparing older and newer OADs should be done to verify this.

The Effectiveness of Digital Health Lifestyle Interventions on People With Prediabetes: Protocol for a Systematic Review, Meta-Analysis, and Meta-Regression.

BACKGROUND: There has been an increasing interest in the use of digital health lifestyle interventions for people with prediabetes, as these interventions may offer a scalable approach to preventing type 2 diabetes. Previous systematic reviews on digital health lifestyle interventions for people with prediabetes had limitations, such as a narrow focus on certain types of interventions, a lack of statistical pooling, and no broader subgroup analysis of intervention characteristics. The identified limitations observed in previous systematic reviews substantiate the necessity of conducting a comprehensive review to address these gaps within the field. This will enable a comprehensive understanding of the effectiveness of digital health lifestyle interventions for people with prediabetes. OBJECTIVE: The objective of this systematic review, meta-analysis, and meta-regression is to systematically investigate the effectiveness of digital health lifestyle interventions on prediabetes-related outcomes in comparison with any comparator without a digital component among adults with prediabetes. METHODS: This systematic review will include randomized controlled trials that investigate the effectiveness of digital health lifestyle interventions on adults (aged 18 years or older) with prediabetes and compare the digital interventions with nondigital interventions. The primary outcome will be change in body weight (kg). Secondary outcomes include, among others, change in glycemic status, markers of cardiometabolic health, feasibility outcomes, and incidence of type 2 diabetes. Embase, PubMed, CINAHL, and CENTRAL (Cochrane Central Register of Controlled Trials) will be systematically searched. The data items to be extracted include study characteristics, participant characteristics, intervention characteristics, and relevant outcomes. To estimate the overall effect size, a meta-analysis will be conducted using the mean difference. Additionally, if feasible, meta-regression on study, intervention, and participant characteristics will be performed. The Cochrane risk of bias tool will be applied to assess study quality, and the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the certainty of evidence. RESULTS: The results are projected to yield an overall estimate of the effectiveness of digital health lifestyle interventions on adults with prediabetes and elucidate the characteristics that contribute to their effectiveness. CONCLUSIONS: The insights gained from this study may help clarify the potential of digital health lifestyle interventions for people with prediabetes and guide the decision-making regarding future intervention components. TRIAL REGISTRATION: PROSPERO CRD42023426919; http://tinyurl.com/d3enrw9j. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50340.

Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting.

BACKGROUND AND OBJECTIVES: Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting. METHODS: Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min. RESULTS: A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two. CONCLUSIONS: The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters.

OBJECTIVE: To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD). METHODS: From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores. RESULTS: The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79). CONCLUSION: In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.

Evaluation of a phosphate kinetics model in hemodialysis therapy-Assessment of the temporal robustness of model predictions.

In-depth understanding of intra- and postdialytic phosphate kinetics is important to adjust treatment regimens in hemodialysis. We aimed to modify and validate a three-compartment phosphate kinetic model to individual patient data and assess the temporal robustness. Intradialytic phosphate samples were collected from the plasma and dialysate of 12 patients during two treatments (HD1 and HD2). 2-h postdialytic plasma samples were collected in four of the patients. First, the model was fitted to HD1 samples from each patient to estimate the mass transfer coefficients. Second, the best fitted model in each patient case was validated on HD2 samples. The best model fits were determined from the coefficient of determination (R2 ) values. When fitted to intradialytic samples only, the median (interquartile range) R2 values were 0.985 (0.959-0.997) and 0.992 (0.984-0.994) for HD1 and HD2, respectively. When fitted to both intra- and postdialytic samples, the results were 0.882 (0.838-0.929) and 0.963 (0.951-0.976) for HD1 and HD2, respectively. Eight patients demonstrated a higher R2 value for HD2 than for HD1. The model seems promising to predict individual plasma phosphate in hemodialysis patients. The results also show good temporal robustness of the model. Further modifications and validation on a larger sample are needed.

A Three-Step Data-Driven Methodology to Assess Adherence to Basal Insulin Therapy in Patients With Insulin-Treated Type 2 Diabetes.

BACKGROUND: While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence. METHOD: Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged ≥18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach. RESULTS: We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases. CONCLUSION: The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support.

Quantification of insulin adherence in adults with insulin-treated type 2 diabetes: A systematic review.

AIMS: This systematic review aims to identify current methods used for the assessment of insulin adherence in adults with insulin-treated type 2 diabetes. The primary goal is to offer recommendations for clinical practice to improve quantification of adherence. METHODS: The review was conducted in accordance with PRISMA 2020 and registered at PROSPERO (CRD42022334134). PubMed, Embase, CINAHL, and PsycINFO were searched on 15 November 2022 and included three blocks: Type 2 diabetes, insulin, and adherence. We considered primary full-text studies describing an assessment method and a threshold for assessment of insulin adherence in adults with insulin-treated type 2 diabetes. RESULTS: A final sample of 50 studies were included. Identified methods fell into four categories: self-report, pharmacy claims, inulin count, and data from an insulin pen device. Commonly reported methods included: The Morisky Medication Adherence Scale, the (adjusted) Medication Possession Ratio, and the Proportions of Days Covered. A threshold of <80% was used to define non-adherence in nearly half of the studies. Yet, several thresholds were reported. CONCLUSIONS: Most available methods for assessing insulin adherence in adults with insulin-treated type 2 diabetes are severely limited in providing in-depth insights into timing, dosing size, injection patterns, and adherence behavior. However, recognizing diverse types of non-adherence is crucial, as they denote unique behavioral entities requiring targeted intervention. Employing insulin injection data (e.g., from a smart insulin pen cap) to underlie an assessment method is a potential new approach to objectively assess insulin timing and dosing adherence in adults with insulin-treated type 2 diabetes.

Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study.

AIMS: For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges. OBJECTIVE: To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework. METHODS: A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features. RESULTS: Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55. CONCLUSIONS: A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.

Implementation of a coagulation component into a phosphate kinetics model in haemodialysis therapy: A tool for detection of clotting problems?

A coagulation component should be considered in phosphate kinetics modelling because intradialytic coagulation of the extracorporeal circuit and dialyser might reduce phosphate removal in haemodialysis. Thus, the objective of this study was to add and evaluate coagulation as an individual linear clearance reduction component to a promising three-compartment model assuming progressive intradialytic clotting. The model was modified and validated on intradialytic plasma and dialysate phosphate samples from 12 haemodialysis patients collected during two treatments (HD1 and HD2) at a Danish hospital ward. The most suitable clearance reduction in each treatment was identified by minimizing the root mean square error (RMSE). The model simulations with and without clearance reduction were compared based on RMSE and coefficient of determination (R2 ) values. Improvements were found for 17 of the 24 model simulations when clearance reduction was added to the model. The slopes of the clearance reduction were in the range of 0.011-0.632/h. Three improvements were found to be statistically significant (|observed z value| > 1.96). A very significant correlation (R2  = 0.708) between the slopes for HD1 and HD2 was found. Adding the clearance reduction component to the model seems promising in phosphate kinetics modelling and might be explained, at least in part, by intradialytic coagulation. In future studies, the model might be developed further to serve as a potentially useful tool for the quantitative detection of clotting problems in haemodialysis. NEW FINDINGS: What is the central question of this study? The aim was to add an intradialytic coagulation component to a modified version of a promising three-compartment phosphate kinetics model. The hypothesis was that circuit and dialyser clotting can be modelled by an individual linear phosphate clearance reduction component during haemodialysis treatment. What is the main finding and its importance? Improvements were found for 17 of 24 model simulations when clearance reduction was added to the model. Thus, the kinetics model seems promising and could be a useful tool for the quantitative detection of clotting problems in haemodialysis patients.

Risk of pancreatic cancer in people with new-onset diabetes: A Danish nationwide population-based cohort study.

BACKGROUND: New onset diabetes (NOD) in people 50 years or older may indicate underlying pancreatic ductal adenocarcinoma (PDAC). The cumulative incidence of PDAC among people with NOD remains uncertain on a population-based level. METHODS: This was a nationwide population-based retrospective cohort study based on the Danish national health registries. We investigated the 3-year cumulative incidence of PDAC in people 50 years or older with NOD. We further characterised people with pancreatic cancer-related diabetes (PCRD) in relation to demographic and clinical characteristics, including trajectories of routine biochemical parameters, using people with type 2 diabetes (T2D) as a comparator group. RESULTS: During a 21-year observation period, we identified 353,970 people with NOD. Among them, 2105 people were subsequently diagnosed with pancreatic cancer within 3 years (0.59%, 95% CI [0.57-0.62%]). People with PCRD were older than people with T2D at diabetes diagnosis (median age 70.9 vs. 66.0 years (P < 0.001) and had a higher burden of comorbidities (P = 0.007) and more prescriptions of medications used to treat cardiovascular diseases (all P < 0.001). Distinct trajectories of HbA1c and plasma triglycerides were observed in PCRD vs. T2D, with group differences observed for up to three years prior to NOD diagnosis for HbA1c and up to two years for plasma triglyceride levels. CONCLUSIONS: The 3-year cumulative incidence of PDAC is approximately 0.6% among people 50 years or older with NOD in a nationwide population-based setting. Compared to T2D, people with PCRD are characterised by distinct demographic and clinical profiles, including distinctive trajectories of plasma HbA1c and triglyceride levels.

Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references.

INTRODUCTION: Spirometry is associated with several diagnostic difficulties, and as a result, misdiagnosis of chronic obstructive pulmonary disease (COPD) occurs. This study aims to investigate how random forest (RF) can be used to improve the existing clinical FVC and FEV1 reference values in a large and representative cohort of the general population of the US without known lung disease. MATERIALS AND METHODS: FVC, FEV1, body measures, and demographic data from 23 433 people were extracted from NHANES. RF was used to develop different prediction models. The accuracy of RF was compared with the existing Danish clinical references, an improved multiple linear regression (MLR) model, and a model from the literature. RESULTS: The correlation between actual and predicted FVC and FEV1 and the 95% confidence interval for RF were found to be FVC = 0.85 (0.85; 0.86) (p < 0.001), FEV1 = 0.92 (0.92; 0.93) (p < 0.001), and existing clinical references were FVC = 0.66 (0.64; 0.68) (p < 0.001) and FEV1 = 0.69 (0.67; 0.70) (p < 0.001). Slope and intercept for the RF models predicting FVC and FEV1 were FVC 1.06 and -238.04 (mL), FEV1: 0.86 and 455.36 (mL), and for the MLR models, slope and intercept were FVC: 0.99 and 38.56 39 (mL), and FEV1: 1.01 and -56.57-57 (mL). CONCLUSIONS: The results point toward machine learning models such as RF have the potential to improve the prediction of estimated lung function for individual patients. These predictions are used as reference values and are an important part of assessing spirometry measurements in clinical practice. Further work is necessary in order to reduce the size of the intercepts obtained through these results.

Using machine learning to design a short test from a full-length test of functional health literacy in adults-The development of a short form of the Danish TOFHLA.

INTRODUCTION: Patients are compelled to become more involved in shared decision making with healthcare professionals in the self-management of chronic disease and general adherence to treatment. Therefore, it is valuable to be able to identify patients with low functional health literacy so they can be given special instructions about the management of chronic disease and medications. However, time spent by both patients and clinicians is a concern when introducing a screening instrument in the clinical setting, which raises the need for short instruments for assessing health literacy that can be used by patients without the involvement of healthcare personnel. This paper describes the development of a short version of the full-length Danish TOFHLA (DS-TOFHLA) that is easily applicable in the clinical context and where the use does not require a trained interviewer. MATERIALS AND METHODS: Data were collected as a part of a large-scale telehomecare project (TeleCare North), which was a randomized controlled trial that included 1225 patients with chronic obstructive pulmonary disease. The DS-TOFHLA was developed solely using an algorithm-based selection of variables and multiple linear regression. A multiple linear regression model was developed using an exhaustive search strategy. RESULTS: The exhaustive search showed that the number of items in the full-length TOFHLA could be reduced from 17 numeracy items and 50 reading comprehension items to 20 reading comprehension items while maintaining a correlation of r = 0.90 between the scores from full-length and short versions. A generic model-based approach was developed, which is suitable for development of short versions of the TOFHLA in other languages, including the original American version. CONCLUSIONS: This study demonstrated how a generic model-based approach could be applied in the development of a short version of the TOFHLA, thereby reducing the 67 items to 20 items in the short version. Furthermore, this study showed that the inclusion of numeracy items was not necessary. The development of the DS-TOFHLA presents an opportunity to reliably identify patients with inadequate functional health literacy in approximately 5 minutes without involvement of healthcare personnel. The approach may be used in the development of short versions of any scaling questionnaire.

The multidisciplinary team in diagnosing and treatment of patients with diabetes and comorbidities: A scoping review.

Background: Multidisciplinary Teams (MDTs) has been suggested as an intervention to overcome some of the complexities experienced by people with diabetes and comorbidities in terms of diagnosis and treatment. However, evidence concerning MDTs within the diabetes field remains sparse. Objective: This review aims to identify and map available evidence on key characteristics of MDTs in the context of diagnosis and treatment in people with diabetes and comorbidities. Methods: This review followed the PRISMA-ScR guidelines. Databases PubMed, EMBASE, and CINAHL were systematically searched for studies assessing any type of MDT within the context of diagnosis and treatment in adult people (≥ 18 years) with diabetes and comorbidities/complications. Data extraction included details on study characteristics, MDT interventions, digital health solutions, and key findings. Results: Overall, 19 studies were included. Generally, the MDTs were characterized by high heterogeneity. Four overall components characterized the MDTs: Both medical specialists and healthcare professionals (HCPs) of different team sizes were represented; interventions spanned elements of medication, assessment, nutrition, education, self-monitoring, and treatment adjustment; digital health solutions were integrated in 58% of the studies; MDTs were carried out in both primary and secondary healthcare settings with varying frequencies. Generally, the effectiveness of the MDTs was positive across different outcomes. Conclusions: MDTs are characterized by high diversity in their outline yet seem to be effective and cost-effective in the context of diagnosis and treatment of people with diabetes and comorbidities. Future research should investigate the cross-sectorial collaboration to reduce care fragmentation and enhance care coordination.

An Interactive Communication Model for Self-Care-Regardless of Health Literacy: Protocol for a Quasi-Experimental Study.

BACKGROUND: Clear dialogue-based (interactive) communication that ensures comprehension and recall becomes more important in patient-provider interactions, especially in relation to patients with chronic diseases, where self-management education and counseling are cornerstones in managing these diseases. If patients with chronic disease experience challenges in obtaining, understanding, and applying health-related information (necessary to make informed health decisions and sufficiently manage their health), clear communication and ensuring comprehension become even more critical in the patient-provider interactions. Furthermore, patient-provider communication has been proposed as a potential pathway through which health literacy might influence health outcomes, especially in individuals with chronic diseases. Hence, adjusting communication to the individual level of health literacy might have a positive influence on health outcomes. On this basis, the authors have developed a web-based interactive communication model that both seeks to accommodate health literacy by allowing tailored communication and ensure comprehension and recall between nurses and patients. OBJECTIVE: This study seeks to examine the use of an IT solution that comprises an interactive communication model that seeks to accommodate health literacy in communication and ensure comprehension and recall between nurses and patients. METHODS: A quasi-experimental control group study including full economic evaluation with 6-month follow-up. Based on power calculation, a total of 82 participants will be included. Participants are assigned either the interactive communication model (intervention) or usual nursing care. It will be assessed if the model influences the level of health literacy and participants experience a higher health-related quality of life. Further, cost-effectiveness will be evaluated. Overall, the statistical methods will follow an intention-to-treat principle. Results will be presented in accordance with the Transparent Reporting of Evaluations with Non-randomized Designs guidelines for nonrandomized designs as well as the Consolidated Health Economic Evaluation Reporting Standards. RESULTS: This paper describes a protocol for a clustered quasi-experimental control study that seeks to evaluate the effectiveness of the interactive communicative model. Most studies in the field of health literacy are epidemiological studies that seek to address the effects of poor health literacy in populations and its potential impact on health inequity. A total of 82 participants, who receive community nursing will be included. The final trial day is May 1, 2022, with the first report of results in the final quarter of 2022. CONCLUSIONS: The results of the trial can create the base for conducting a large-scale study and inspire the conduction of more studies that seeks to create and evaluate interventions aimed at enhancing the level of health literacy and reducing the usage of health resources. TRIAL REGISTRATION: ClinicalTrials.gov NCT04929314; https://clinicaltrials.gov/ct2/show/NCT04929314. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37673.

Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited.

BACKGROUND AND OBJECTIVE: It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates. METHODS: We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed. RESULTS: A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used (P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%. CONCLUSIONS: Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment.

The Effectiveness of Telemedicine Solutions for the Management of Type 2 Diabetes: A Systematic Review, Meta-Analysis, and Meta-Regression.

BACKGROUND: Previous systematic reviews have aimed to clarify the effect of telemedicine on diabetes. However, such reviews often have a narrow focus, which calls for a more comprehensive systematic review within the field. Hence, the objective of the present systematic review, meta-analysis, and meta-regression is to evaluate the effectiveness of telemedicine solutions versus any comparator without the use of telemedicine on diabetes-related outcomes among adult patients with type 2 diabetes (T2D). METHODS: This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We considered telemedicine randomized controlled trials (RCT) including adults (≥18 years) diagnosed with T2D. Change in glycated hemoglobin (HbA1c, %) was the primary outcome. PubMed, EMBASE, and the Cochrane Library Central Register of Controlled Trials (CENTRAL) were searched on October 14, 2020. An overall treatment effect was estimated using a meta-analysis performed on the pool of included studies based on the mean difference (MD). The revised Cochrane risk-of-bias tool was applied and the certainty of evidence was graded using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. RESULTS: The final sample of papers included a total of 246, of which 168 had sufficient information to calculate the effect of HbA1c%. The results favored telemedicine, with an MD of -0.415% (95% confidence interval [CI] = -0.482% to -0.348%). The heterogeneity was great (I2 = 93.05%). A monitoring component gave rise to the higher effects of telemedicine. CONCLUSIONS: In conclusion, telemedicine may serve as a valuable supplement to usual care for patients with T2D. The inclusion of a telemonitoring component seems to increase the effect of telemedicine.

Effectiveness of Telemedicine in Managing Diabetes in Pregnancy: A Systematic Review and Meta-Analysis.

BACKGROUND: Strict monitoring of blood glucose during pregnancy is essential for ensuring optimal maternal and neonatal outcomes. Telemedicine could be a promising solution for supporting diabetes management; however, an updated meta-analysis is warranted. This study assesses the effects of telemedicine solutions for managing gestational and pregestational diabetes. METHODS: PubMed, EMBASE, Cochrane Library Central Register of Controlled Trials, and CINAHL were searched up to October 14, 2020. All randomized trials assessing the effects of telemedicine in managing diabetes in pregnancy relative to any comparator without the use of telemedicine were included. The primary outcome was infant birth weight. A meta-analysis comparing the mean difference (MD) in birth weight across studies was applied, and subgroup and sensitivity analyses were performed. The revised Cochrane tool was applied to assess the risk of bias, and the certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. RESULTS: From a total of 18 studies, ten (totaling 899 participants) were used to calculate the effect on infant birth weight. The results nonsignificantly favored the control (MD of 19.34 g; [95% confidence interval, CI -47.8; 86.47]), with moderate effect certainty. Heterogeneity was moderate (I2 = 37.39%). Statistically significant secondary outcomes included differences in two-hour glucose tolerance postpartum (gestational diabetes; two studies: standardized mean difference 9.62 mg/dL [95% CI: 1.95; 17.28]) that favored the control (GRADE level, very low) and risk of shoulder dystocia (four studies: log odds -1.34 [95% CI: -2.61; -0.08]) that favored telemedicine (GRADE, low). CONCLUSIONS: No evidence was found to support telemedicine as an alternative to usual care when considering maternal and fetal outcomes. However, further research is needed, including economic evaluations.

The Effectiveness of Telemedicine Solutions in Type 1 Diabetes Management: A Systematic Review and Meta-analysis.

BACKGROUND: Telemedicine holds a potential to strengthen self-management support outside health care settings in the everyday management of type 1 diabetes (T1D). However, existing effectiveness reviews are older or include a relatively narrow focus on specific definitions of telemedicine or included databases. OBJECTIVE: To conduct a systematic review of the effectiveness of telemedicine solutions versus any comparator on diabetes-related outcomes among people with T1D. METHODS: Studies including adults (≥18 years) with T1D published before October 14, 2020, were eligible. Primary outcome was glycated hemoglobin (HbA1c, %). The Cochrane Library, PubMed, EMBASE, and CINAHL were searched. Meta-analysis based on the mean difference in HbA1c% was used to pool effects. The Cochrane tool was applied to assess risk-of-bias, and the certainty of evidence was graded using the GRADE approach. RESULTS: A total of 22 studies were included (with 1615 participants). Treatment effect for HbA1c% favored telemedicine (mean difference of -0.26% [95% confidence interval:-0.37% to -0.15%]) with moderate effect certainty. Heterogeneity was moderate (I2 = 33.30%). Although not significant, secondary outcomes were all in favor of telemedicine except number of severe hypoglycemic events and diabetes knowledge, but the certainty of the evidence for those outcomes was all low or very low. DISCUSSION: Reducing average HbA1c% levels are important to combat the risk of diabetic complications and premature death. However, the evidence mostly consist of small studies with a relative short duration and the estimated pooled effect is smaller than could be expected from quality improvement strategies in general for diabetes management. PROSPERO NUMBER: CRD42020123565.