Flu@home: the Comparative Accuracy of an At-Home Influenza Rapid Diagnostic Test Using a Prepositioned Test Kit, Mobile App, Mail-in Reference Sample, and Symptom-Based Testing Trigger.

At-home testing with rapid diagnostic tests (RDTs) for respiratory viruses could facilitate early diagnosis, guide patient care, and prevent transmission. Such RDTs are best used near the onset of illness when viral load is highest and clinical action will be most impactful, which may be achieved by at-home testing. We evaluated the diagnostic accuracy of the QuickVue Influenza A+B RDT in an at-home setting. A convenience sample of 5,229 individuals who were engaged with an on-line health research platform were prospectively recruited throughout the United States. "Flu@home" test kits containing a QuickVue RDT and reference sample collection and shipping materials were prepositioned with participants at the beginning of the study. Participants responded to daily symptom surveys. If they reported experiencing cough along with aches, fever, chills, and/or sweats, they used their flu@home kit following instructions on a mobile app and indicated what lines they saw on the RDT. Of the 976 participants who met criteria to use their self-collection kit and completed study procedures, 202 (20.7%) were positive for influenza by qPCR. The RDT had a sensitivity of 28% (95% CI = 21 to 36) and specificity of 99% (98 to 99) for influenza A, and 32% (95% CI = 20 to 46) and 99% (95% CI = 98 to 99), for influenza B. Our results support the concept of app-supported, prepositioned at-home RDT kits using symptom-based triggers, although it cannot be recommended with the RDT used in this study. Further research is needed to determine ways to improve the accuracy and utility of home-based testing for influenza.

Early Insights From a Digitally Enhanced Diabetes Self-Management Education and Support Program: Single-Arm Nonrandomized Trial.

BACKGROUND: Translation of diabetes self-management education and support (DSMES) into a digital format can improve access, but few digital programs have demonstrated outcomes using rigorous evaluation metrics. OBJECTIVE: The aim of this study was to evaluate the impact of a digital DSMES program on hemoglobin A1c (HbA1c) for people with type 2 diabetes. METHODS: A single-arm, nonrandomized trial was performed to evaluate a digital DSMES program that includes remote monitoring and lifestyle change, in addition to comprehensive diabetes education staffed by a diabetes specialist. A sample of 195 participants were recruited using an online research platform (Achievement Studies, Evidation Health Inc). The primary outcome was change in laboratory-tested HbA1c from baseline to 4 months, and secondary outcomes included change in lipids, diabetes distress, and medication adherence. RESULTS: At baseline, participants had a mean HbA1c of 8.9% (SD 1.9) and mean BMI of 37.5 kg/m2 (SD 8.3). The average age was 45.1 years (SD 8.9), 70% were women, and 67% were White. At 4-month follow up, the HbA1c decreased by 0.8% (P<.001, 95% CI -1.1 to -0.5) for the total population and decreased by 1.4% (P<.001, 95% CI -1.8 to -0.9) for those with an HbA1c of >9.0% at baseline. Diabetes distress and medication adherence were also significantly improved between baseline and follow up. CONCLUSIONS: This study provides early evidence that a digitally enhanced DSMES program improves HbA1c and disease self-management outcomes.