Nighttime Melatonin Administration and Insulin Sensitivity

Introduction: Previous studies have shown melatonin effects on insulin sensitivity, but with conflicting results. The inconsistency between these studies may be due to differences in melatonin dosage and subject age. Low melatonin dosages, generally \u3c5 mg, have been used in prior research. We studied the effect of melatonin 9 mg for 6 weeks on insulin resistance, peripheral microvascular function, and sleep in non-diabetic, non-hypertensive middle-aged and geriatric patients. Methods: Subjects with a history of hypertension or diabetes were excluded from the study. The geriatric cohort included 5 subjects 60-80 years old while the younger cohort was comprised of 14 subjects age 27-45 years old. Fifteen subjects were randomized to the melatonin treatment group and took 9 mg of controlled-release melatonin by mouth 30 minutes before bedtime for 6 weeks; the four subjects in the control did not receive any intervention. The Homeostatic Model Assessment (HOMA) and the Quantitative Insulin-Sensitivity Check Index (QUICKI) were used to assess insulin sensitivity. Results: We observed a statistically significant increase in insulin sensitivity in the melatonin treatment group (p=.008) compared to the control group after HOMA analysis. Additionally, fasting insulin levels also improved within the melatonin treatment group. We did not see any significant changes in glucose levels after melatonin usage

Selecting Useful Outcome Measures

https://repository.upenn.edu/crp/1003/thumbnail.jp

Primary Care Obstructive Sleep Apnea Screening (PCOSA)

Introduction: Obstructive sleep apnea (OSA) is a largely underdiagnosed disorder of upper airway collapse during sleep. Primary care providers do not routinely screen for OSA. This project aims to determine the yield of using the STOP-BANG questionnaire to identify previously undiagnosed OSA in a primary care population. Methods: This prospective quality improvement pilot project included 181 patients of the Jefferson Department of Family Medicine identified as high-risk for OSA based on 3 EMR-based search criteria taken from STOP-BANG: hypertension, age \u3e50 years, and BMI \u3e35 kg/m2. We attempted contact with patients by mail, followed by up to 3 weekly telephone calls to verbally screen patients with the full STOP-BANG questionnaire. A score of \u3e6 was considered high-risk for OSA. High risk patients were referred for sleep study testing. Results: From the initial 181 patients, 71 were excluded due to a prior OSA diagnosis; 3 were excluded for various other reasons; and 53 could not be reached. Of those reached, 28 patients refused participation, and 15 patients had a low-risk STOP-BANG score \u3c6. The remaining 11 patients had a high-risk STOP-BANG score \u3e6 and were referred for sleep study testing. While data collection is ongoing, all 3 patients (100%) who completed sleep studies have been newly diagnosed with OSA. Discussion: Preliminary results confirm utility of the STOP-BANG questionnaire to identify patients at high risk for OSA. The main limitation in our pilot project was difficulty contacting patients. We are adding alternate forms of communication (email, outreach at upcoming patient visits)

Identifying the Prevalence of underdiagnosed Obstructive Sleep Apnea (OSA) in the Primary Care Population via Targeted Screening Measures

Introduction: Obstructive sleep apnea (OSA) is a condition with detrimental health consequences, yet over 75% of OSA cases remain undiagnosed in the United States. This study aimed to determine the efficacy of using targeted screening measures to determine the prevalence of undiagnosed OSA in a primary care population. Methods: This prospective pilot study utilized a primary care population from Thomas Jefferson University’s family medicine department. Participants were selected using three risk criteria for OSA from STOP-BANG identifiable from their EMR records (BMI \u3e35, age over 50, and hypertension). After screening out patients previously diagnosed with OSA, patients were called and further screened with the entire STOP-BANG questionnaire; Patients who scored \u3e 6/8 were referred for sleep study testing. Results: Of the 112 patients meeting the three initial criteria, 5 were excluded for having previously undocumented OSA diagnoses, and 81 were unable to be contacted or not interested. Of the 31 remaining participants, 11 patients had a STOP-BANG score \u3e6 (35%); 3 of these patients (27%) were diagnosed with OSA after going in for a sleep study (100%). Discussion: The main obstacle in our pilot to date is low patient contact and participation. However, all of the patients who qualified for and completed sleep study testing using our screening algorithm were effectively diagnosed with OSA. We will continue to screen more patients in the upcoming months and test methodologies to increase patient participation

The Effectiveness of JeffWLP for Weight Loss and General Nutritional Knowledge in Obese Patients

PURPOSE: The increasing prevalence of obesity urgently requires effective management strategies. This study evaluates the effectiveness of Jefferson Weight Loss Program (JeffWLP), a trained medical student-delivered health education program in a predominantly African-American patient cohort. METHODS: A randomized controlled trial was performed enrolling 30 patients with an average socioeconomic status of 5.8 (10 maximum). The intervention group (n=18) completed JeffWLP, a low-cost, 12-week health coaching program combining education sessions with graded step exercises. The control group (n=12) received usual care. Mean baseline age, BMI, and General Nutritional Knowledge Questionnaire (GNKQ) scores were: 46±13 years, 38±5, and 14.7±1.9 (maximum score=17) respectively. RESULTS: Patients completing JeffWLP achieved greater weight loss, with mean weight loss of 6.1±7.8 pounds (p=0.01) compared to 4.4±7.5 pounds weight gain in controls (p=0.14). This corresponded to 2.7±3.3% weight reduction (p=0.01) and 2.0±3.5% weight gain (p=0.15). Mean endpoint GNKQ scores decreased overall slightly to 14.5±1.9, but improvement correlated with total, group, and 1:1 class attendance (R=0.81, 0.75, 0.77, p=0.0004, 0.002, 0.001 respectively). CONCLUSIONS: The significant weight reduction of 2.7±3.3% achieved in just 12 weeks of JeffWLP suggests meaningful progress towards improving cardiovascular health. Correlation of GNKQ scores to attendance suggests that patients acquired knowledge facilitating these positive outcomes. Our results support the establishment of student-delivered patient education programs to help combat the obesity epidemic.https://jdc.jefferson.edu/aoa_research_symposium_posters/1010/thumbnail.jp

Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation

Photoplethysmography (PPG) provides a low-cost, non-invasive method to continuously monitor various cardiovascular parameters. PPG signals are generated by wearable devices and frequently contain large artifacts caused by external factors, such as motion of the human subject. In order to ensure robust and accurate extraction of physiological parameters, corrupted areas of the signal need to be identified and handled appropriately. Previous methodology relied either on handcrafted feature detectors or signal metrics which yield sub-optimal performance, or relied on machine learning techniques such as deep neural networks (DNN) which lack interpretability and are computationally and memory intensive. In this work, we present a novel method to learn a small set of interpretable convolutional kernels that has performance similar to -- and often better than -- the state-of-the-art DNN approach with several orders of magnitude fewer parameters. This work allows for efficient, robust, and interpretable signal quality assessment and artifact segmentation on low-power devices.Comment: 16 pages, 6 figure