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.

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.

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.

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.

Reablement professionals' perspectives on client characteristics and factors associated with successful home-based reablement: a qualitative study.

BACKGROUND: To understand what is needed to achieve a successful Danish home-based reablement service from the perspective of reablement professionals. METHODS: Semi-structured interviews and observations were conducted with nine professionals within a municipal visitation unit in the Northern Denmark Region. Thematic analysis was used to analyze the interviews. RESULTS: Four major themes emerged during this study: "Heterogeneity of clients and mixed attitudes towards the reablement intervention", "Shared understanding and acknowledging the need for help as the first step in reablement", "Commitment and motivation are essential for successful reablement", and "Homecare helpers as most important team players". The findings indicate that the clients had both mixed characteristics and attitudes about participating in the reablement intervention. Essential factors for successful reablement included a shared understanding of the reablement intervention, commitment, and motivation in terms of client involvement and staff group collaboration. CONCLUSIONS: Shared understanding of the reablement intervention, commitment, and motivation was found to be essential factors and the driving forces in relation to successful reablement.

Preliminary Qualitative Evaluation of Patient-Related Perspectives Related to the Implementation of a Predictive Algorithm in a Telehealth System for COPD.

The aim of the present study was to evaluate patient-related perspectives from a five-week test of the implementation of a COPD prediction algorithm. The test intended to discover and avoid potential errors prior to testing the COPD prediction algorithm in a two-armed randomized controlled trial (RCT). The COPD prediction algorithm aims to predict exacerbations in COPD based on home measurements. In the present study, the algorithm was implemented in a currently deployed telehealth system. Five weeks after implementation, six interviews were conducted, including five interviews with patients with COPD and one interview with a specialized COPD nurse. The participants were overall satisfied with the telehealth system including the COPD prediction algorithm. However, technical issues must be addressed before the COPD prediction algorithm is ready to be tested in the RCT. Moreover, communication with the monitoring nurses should be clearer based on the COPD nurse's experiences. In conclusion, the participants were satisfied with the integration of the COPD prediction algorithm in the telehealth system. The identification of technical issues shows the importance of including a technical test period in a similar trial setup.

Predictors of Hospital Admissions Based on Routinely Collected Everyday Observations in a Community Care Setting.

The aim of this study was to identify predictors for hospital admissions in community-dwelling adults based on routinely collected community data. Univariate logistic regression analyses were performed to assess each variable's ability to predict preventable and all cause hospital admissions.

Models Used in Clinical Decision Support Systems Supporting Healthcare Professionals Treating Chronic Wounds: Systematic Literature Review.

BACKGROUND: Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are a massive burden to health care facilities. Many randomized controlled trials on different wound care elements have been conducted and published in the Cochrane Library, all of which have only a low evidential basis. Thus, health care professionals are forced to rely on their own experience when making decisions regarding wound care. To progress from experience-based practice to evidence-based wound care practice, clinical decision support systems (CDSS) that help health care providers with decision-making in a clinical workflow have been developed. These systems have proven useful in many areas of the health care sector, partly because they have increased the quality of care, and partially because they have generated a solid basis for evidence-based practice. However, no systematic reviews focus on CDSS within the field of wound care to chronic wounds. OBJECTIVE: The aims of this systematic literature review are (1) to identify models used in CDSS that support health care professionals treating chronic wounds, and (2) to classify each clinical decision support model according to selected variables and to create an overview. METHODS: A systematic review was conducted using 6 databases. This systematic literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement for systematic reviews. The search strategy consisted of three facets, respectively: Facet 1 (Algorithm), Facet 2 (Wound care) and Facet 3 (Clinical decision support system). Studies based on acute wounds or trauma were excluded. Similarly, studies that presented guidelines, protocols and instructions were excluded, since they do not require progression along an active chain of reasoning from the clinicians, just their focus. Finally, studies were excluded if they had not undergone a peer review process. The following aspects were extracted from each article: authors, year, country, the sample size of data and variables describing the type of clinical decision support models. The decision support models were classified in 2 ways: quantitative decision support models, and qualitative decision support models. RESULTS: The final number of studies included in the systematic literature review was 10. These clinical decision support models included 4/10 (40%) quantitative decision support models and 6/10 (60%) qualitative decision support models. The earliest article was published in 2007, and the most recent was from 2015. CONCLUSIONS: The clinical decision support models were targeted at a variety of different types of chronic wounds. The degree of accessibility of the inference engines varied. Quantitative models served as the engine and were invisible to the health care professionals, while qualitative models required interaction with the user.

Probing community nurses' professional basis: a situational case study in diabetic foot ulcer treatment.

Complicated and long-lasting wound care of diabetic foot ulcers are moving from specialists in wound care at hospitals towards community nurses without specialist diabetic foot ulcer wound care knowledge. The aim of the study is to elucidate community nurses' professional basis for treating diabetic foot ulcers. A situational case study design was adopted in an archetypical Danish community nursing setting. Experience is a crucial component in the community nurses' professional basis for treating diabetic foot ulcers. Peer-to-peer training is the prevailing way to learn about diabetic foot ulcer, however, this contributes to the risk of low evidence-based practice. Finally, a frequent behaviour among the community nurses is to consult colleagues before treating the diabetic foot ulcers.

An Iterative, Mixed Usability Approach Applied to the Telekit System from the Danish TeleCare North Trial.

Objective. The aim of the present study is to evaluate the usability of the telehealth system, coined Telekit, by using an iterative, mixed usability approach. Materials and Methods. Ten double experts participated in two heuristic evaluations (HE1, HE2), and 11 COPD patients attended two think-aloud tests. The double experts identified usability violations and classified them into Jakob Nielsen's heuristics. These violations were then translated into measurable values on a scale of 0 to 4 indicating degree of severity. In the think-aloud tests, COPD participants were invited to verbalise their thoughts. Results. The double experts identified 86 usability violations in HE1 and 101 usability violations in HE2. The majority of the violations were rated in the 0-2 range. The findings from the think-aloud tests resulted in 12 themes and associated examples regarding the usability of the Telekit system. The use of the iterative, mixed usability approach produced both quantitative and qualitative results. Conclusion. The iterative, mixed usability approach yields a strong result owing to the high number of problems identified in the tests because the double experts and the COPD participants focus on different aspects of Telekit's usability. This trial is registered with Clinicaltrials.gov, NCT01984840, November 14, 2013.

Assessing the Potential Use of Eye-Tracking Triangulation for Evaluating the Usability of an Online Diabetes Exercise System.

The Online Diabetes Exercise System was developed to motivate people with Type 2 diabetes to do a 25 minutes low-volume high-intensity interval training program. In a previous multi-method evaluation of the system, several usability issues were identified and corrected. Despite the thorough testing, it was unclear whether all usability problems had been identified using the multi-method evaluation. Our hypothesis was that adding the eye-tracking triangulation to the multi-method evaluation would increase the accuracy and completeness when testing the usability of the system. The study design was an Eye-tracking Triangulation; conventional eye-tracking with predefined tasks followed by The Post-Experience Eye-Tracked Protocol (PEEP). Six Areas of Interests were the basis for the PEEP-session. The eye-tracking triangulation gave objective and subjective results, which are believed to be highly relevant for designing, implementing, evaluating and optimizing systems in the field of health informatics. Future work should include testing the method on a larger and more representative group of users and apply the method on different system types.

Exploring User Experience of a Telehealth System for the Danish TeleCare North Trial.

The aim was to explore user experiences of using a telehealth system (Telekit) designed for the Danish TeleCare North trial. Telekit is designed for patients diagnosed with chronic obstructive pulmonary disease (COPD) in order to manage the disease and support patient empowerment. This article sums up COPD-participants' user experiences in terms of increased sense of freedom, of security, of control, and greater awareness of COPD symptoms. A consecutive sample of sixty participants (27 women, 33 men) were recruited from the TeleCare North trial. At home the participants completed a non-standardised questionnaire while a researcher was present. The questionnaire identified their health status, their use of specific technologies, and their user experiences with the telehealth system. Results from the questionnaire indicate that the majority of participants (88%) considered the Telekit system as easy to use. 43 (72%) participants felt increased sense of security, and 37 (62%) participants felt increased sense of control by using the system. 30 (50%) participants felt greater awareness of their COPD symptoms, but only 16 (27%) participants felt increased freedom. The study has provided a general picture of COPD participants' user experiences which is important to emphasise as it has a bearing on whether a given implementation will be successful or not.

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data.

BACKGROUND: People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm. METHODS: Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions. RESULTS: The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives. CONCLUSIONS: We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.

A multistep algorithm for processing and calibration of microdialysis continuous glucose monitoring data.

BACKGROUND: The deviation of continuous subcutaneous glucose monitoring (CGM) data from reference blood glucose measurements is substantial, and adequate signal processing is required to reduce the discrepancy between subcutaneous glucose and blood glucose values. The purpose of this study was to develop a multistep algorithm for the processing and calibration of continuous subcutaneous glucose monitoring data with high accuracy and short delay. Algorithm PRESENTATION: The algorithm comprises three steps: rate-limiting filtering, selective smoothing, and robust calibration. Initially, the algorithm detects nonphysiological glucose rate-of-change and corrects it with a weighted local polynomial. Noisy signal parts that require smoothing are then detected based on zero crossing count of the sensor signal first-order differences, and an exponentially weighted moving average smooths the noisy parts of the signal afterward. Finally, calibration is performed using a first-order polynomial as the conversion function, with coefficients being estimated using robust regression with a bi-square weight function. ALGORITHM PERFORMANCE: The performance of the algorithm was evaluated on 16 patients with type 1 diabetes mellitus. To compare the algorithm with state-of-the-art CGM data denoising and calibration, the rate-limiting filter and selective smoothing were replaced with an adaptive Kalman filter, and the calibration method was replaced with the calibration algorithm presented in one of the Medtronic (Northridge, CA) CGM patents. The median (mean) of the absolute relative deviation (ARD) of the sensor glucose values processed by the newly developed algorithm from capillary reference blood glucose measurements was 14.8% (22.6%), 10.6% (14.6%), and 8.9% (11.7%) in hypoglycemia, euglycemia, and hyperglycemia, respectively, whereas for the alternative algorithm, the median (mean) was 22.2% (26.9%), 12.1% (15.9%), and 8.8 (11.3%), respectively. The median (mean) ARD in all ranges was 10.3% (14.7%) for the new algorithm and 11.5% (15.8%) for the alternative algorithm. The new algorithm had an average delay of 2.1 min across the patients, and the alternative algorithm had an average delay of 2.9 min. CONCLUSIONS: The presented algorithm may increase the accuracy of CGM data.

An information and communication technology system to detect hypoglycemia in people with type 1 diabetes.

Continuous glucose monitoring (CGM) is a new technology with the potential to detect hypoglycemia in people with Type 1 diabetes. However, the inaccuracy of the device in the hypoglycemic range is unfortunately too large. The aim of this study was to develop an information and communication technology system for improving hypoglycemia detection in CGM. The system was developed as an Android application with a build-in pattern classification algorithm. The algorithm processes features from CGM and typed in data from the patient, then warns the patient about incoming hypoglycemia. The system improved the detection of hypoglycemic events by 29%, with only one 1 false alert compared to CGM alone. Furthermore, the algorithm increased the average lead-time by 14 minutes. These findings indicate that it is possible to improve the hypoglycemia detection with an information and communication technology system, but that the system must be validated on a larger dataset.

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes.

BACKGROUND: Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. MATERIALS AND METHODS: Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives. RESULTS: The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively. CONCLUSIONS: This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.

Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection.

BACKGROUND: An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM. METHOD: Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures. RESULTS: With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection. The sample-based sensitivity and specificity levels were 78% and 96%, respectively. CONCLUSIONS: The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.

Monitoring of anticoagulant therapy applying a dynamic statistical model.

Patients with an increased risk of thrombosis may require treatment with vitamin K-antagonists such as warfarin. Treatment with warfarin has been reported difficult mainly due to high inter- and intraindividual variability in response to the drug [1]. Using predictive models that can predict International Normalised Ratio (INR) values enables for a higher degree of individualised warfarin dosing regime. This paper reports the outcome of the development of a dynamic prediction model. It takes warfarin intake and INR values as inputs, and uses an individual sensitivity parameter to model response to warfarin intake. The model is set on state-space form and uses Kalman filtering technique to optimise individual parameters. Retrospective test of the model proved robustness to choices of initial parameters, and feasible prediction results of both INR values and suggested warfarin dosage, which may prove beneficial for both patients and healthcare takers.

Clinical impact of home telemonitoring on patients with chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) affects millions of people worldwide. A complication of COPD is exacerbations that result in increased utilization of healthcare services, readmissions to the hospital, and a decline in health-related quality of life. Home telehealth has been shown both to improve health-related quality of life and to reduce admission rates. Using clinical data from a home telemonitoring group, this study sought to investigate the clinical impact of telemonitoring. SUBJECTS AND METHODS: Fifty-seven subjects with COPD were included in a 4-month telemonitoring project. Differences between the clinical parameters during the first and last months of participation in the project were tested for significance, and the levels for the first month versus the difference were tested for correlation. RESULTS: Significant declines were observed in prescriptions for antibiotics and steroids (p=0.03), clinical consultations (p=0.05), mean systolic blood pressure (p<0.001), standard deviation of systolic blood pressure (p=0.03), and mean diastolic blood pressure (p=0.02). No significant differences were observed for mean of oxygen saturation (p=0.77), standard deviation of oxygen saturation (p=0.36), mean of forced expiratory volume in 1 s (p=0.17), mean of forced vital capacity (p=0.29), mean of pulse rate (p=0.78), standard deviation of pulse rate (p=0.57), and standard deviation of diastolic blood pressure (p=0.27). CONCLUSIONS: The results suggest that telemonitoring improves the condition of the patient by lowering the blood pressure, the number of prescribed antibiotics and steroids, and the number of clinical consultations.

QT Measurement and Heart Rate Correction during Hypoglycemia: Is There a Bias?

Introduction. Several studies show that hypoglycemia causes QT interval prolongation. The aim of this study was to investigate the effect of QT measurement methodology, heart rate correction, and insulin types during hypoglycemia. Methods. Ten adult subjects with type 1 diabetes had hypoglycemia induced by intravenous injection of two insulin types in a cross-over design. QT measurements were done using the slope-intersect (SI) and manual annotation (MA) methods. Heart rate correction was done using Bazett's (QTcB) and Fridericia's (QTcF) formulas. Results. The SI method showed significant prolongation at hypoglycemia for QTcB (42(6) ms; P < .001) and QTcF (35(6) ms; P < .001). The MA method showed prolongation at hypoglycemia for QTcB (7(2) ms, P < .05) but not QTcF. No difference in ECG variables between the types of insulin was observed. Discussion. The method for measuring the QT interval has a significant impact on the prolongation of QT during hypoglycemia. Heart rate correction may also influence the QT during hypoglycemia while the type of insulin is insignificant. Prolongation of QTc in this study did not reach pathologic values suggesting that QTc prolongation cannot fully explain the dead-in-bed syndrome.