XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Prospective Cohort Study

The COVID-19 pandemic altered many facets of life. We aimed to evaluate the impact of COVID-19-related public health guidelines on physical activity (PA), sedentary behavior, mental health, and their interrelations. Cross-sectional data were collected from 3052 US adults 3–8 April 2020 (from all 50 states). Participants self-reported pre- and post-COVID-19 levels of moderate and vigorous PA, sitting, and screen time. Currently-followed public health guidelines, stress, loneliness, positive mental health (PMH), social connectedness, and depressive and anxiety symptoms were self-reported. Participants were grouped by meeting US PA guidelines, reporting ≥8 h/day of sitting, or ≥8 h/day of screen time, pre- and post-COVID-19. Overall, 62% of participants were female, with age ranging from 18–24 (16.6% of sample) to 75+ (9.3%). Self-reported PA was lower post-COVID among participants reporting being previously active (mean change: −32.3% [95% CI: −36.3%, −28.1%]) but largely unchanged among previously inactive participants (+2.3% [−3.5%, +8.1%]). No longer meeting PA guidelines and increased screen time were associated with worse depression, loneliness, stress, and PMH (p < 0.001). Self-isolation/quarantine was associated with higher depressive and anxiety symptoms compared to social distancing (p < 0.001). Maintaining and enhancing physical activity participation and limiting screen time increases during abrupt societal changes may mitigate the mental health consequences.

https://www.mdpi.com/1660-4601/17/18/6469/pdf

Changes in Physical Activity and Sedentary Behavior in Response to COVID-19 and Their Associations with Mental Health in 3052 US Adults.

Mobile Devices and Health Mobile health involves sensors, mobile apps, social media, and location-tracking technology used in disease diagnosis, prevention, and management. This article provides an...

https://www.nejm.org/doi/pdf/10.1056/NEJMra1806949

Mobile Devices and Health.

BACKGROUND: The COVID-19 pandemic has posed an unprecedented threat to global mental health. Children and adolescents may be more susceptible to mental health impacts related to their vulnerable developmental stage, fear of infection, home confinement, suspension of regular school and extracurricular activities, physical distancing mandates, and larger scale threats such as global financial recessions and associated impacts. Our objective was to review existing evidence of the COVID-19 pandemic's global impact on the mental health of children and adolescents 3,000 chart reviews. A high prevalence of COVID-19-related fear was noted among children and adolescents, as well as more depressive and anxious symptoms compared with prepandemic estimates. Older adolescents, girls, and children and adolescents living with neurodiversities and/or chronic physical conditions were more likely to experience negative mental health outcomes. Many studies reported mental health deterioration among children and adolescents due to COVID-19 pandemic control measures. Physical exercise, access to entertainment, positive familial relationships, and social support were associated with better mental health outcomes. CONCLUSIONS: This review highlights the urgent need for practitioners and policymakers to attend to and collaborate with children and adolescents, especially those in higher risk subgroups, to mitigate short- and long-term pandemic-associated mental health effects.

Review: Mental health impacts of the COVID-19 pandemic on children and youth - a systematic review.

Author(s): Tison, Geoffrey H; Avram, Robert; Kuhar, Peter; Abreau, Sean; Marcus, Greg M; Pletcher, Mark J; Olgin, Jeffrey E

/pdf/worldwide-effect-of-covid-19-on-physical-activity-a-3r66j55w5y.pdf

Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study.

Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18–45, ≤100 in those aged 45–60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data.

/pdf/real-world-heart-rate-norms-in-the-health-eheart-study-5ayp3jiwsv.pdf

Real-world heart rate norms in the Health eHeart study.

Background— The 6-minute walk test (6MWT) independently predicts congestive heart failure severity, death, and heart failure hospitalizations, but must be administered in clinic by qualified staff on a premeasured course. As part of the Health eHeart Study, we sought to develop and validate a self-administered 6MWT mobile application (SA-6MWTapp) for independent use at home by patients.

Methods and Results— We performed a validation study of an SA-6MWTapp in 103 participants. In phase 1 (n=52), we developed a distance-estimation algorithm for the SA-6MWTapp by comparing step counts from an Actigraph and measured distance on a premeasured 6MWT course with step counts and estimated distance obtained simultaneously from our SA-6MWTapp (best estimation algorithm, r=0.89 [95% confidence interval 0.78–0.99]). In phase 2, 32 participants (including those with congestive heart failure and pulmonary hypertension) used the SA-6MWTapp independently in clinic, and the distance estimated by the SA-6MWTapp was compared with the measured distance (r=0.83 [95% confidence interval 0.79–0.92]). In phase 3, 19 patients with congestive heart failure and pulmonary hypertension consecutively enrolled from clinic performed 3.2±1 SA-6MWTapp tests per week at home over 2 weeks. Distances estimated from the SA-6MWTapp during home 6MWTs were highly repeatable (coefficient of variation =4.6%) and correlated with in-clinic–measured distance (r=0.88 [95% confidence interval 0.87–0.89]). Usability surveys performed during the second (in-clinic) and third (at-home) phases demonstrated that the SA-6MWTapp was simple and easy to use independently.

Conclusions— An SA-6MWTapp is easy to use and yields accurate repeatable measurements in the clinic and at home.

Accuracy and Usability of a Self-Administered 6-Minute Walk Test Smartphone Application

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the ‘primary cohort’), which we then validated in a separate cohort of 7,806 individuals (the ‘contemporary cohort’) and a cohort of 181 prospectively enrolled individuals from three clinics (the ‘clinic cohort’). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750–0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723–0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. A deep neural network applied to smartphone-based vascular imaging can detect diabetes, opening new possibilities for non-invasive diagnosis.

Peter Kuhar

Papers

Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study.

Real-world heart rate norms in the Health eHeart study.

Accuracy and Usability of a Self-Administered 6-Minute Walk Test Smartphone Application

A digital biomarker of diabetes from smartphone-based vascular signals

Predicting diabetes from photoplethysmography using deep learning