Compliance for completion of forms was 97%. The system facilitated the educational management of our training program along multiple dimensions. The small perceptual differences among a highly selected group of residents have made the unambiguous validation of the system challenging. The instruments and approach warrant further study. Improvements are likely best achieved in broad consultation among other otolaryngology programs.

In patients with SAH, hyponatremia is associated with a significantly greater risk of developing CVS and may precede CVS by at least one day.

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Background: We describe the impact of influenza on medical outcomes and daily activities among people with and without type 2 diabetes mellitus (T2DM). Methods: Retrospective cohort analysis of a US health plan offering a digital wellness platform connecting wearable devices capable of tracking steps, sleep, and heart rate. For the 2016 to 2017 influenza season, we compared adults with T2DM to age and gender matched controls. Medical claims were used to define cohorts and identify influenza events and outcomes. Digital tracking data were aggregated at time slices of minute-, day-, week-, and year-level. A pre-post study design compared the peri-influenza period (two weeks before and four weeks after influenza diagnosis) to the six-week preceding period (baseline). Results: A total of 54 656 T2DM and 113 016 non-DM controls were used for the study. People with T2DM had more influenza claims, vaccinations, and influenza antivirals per 100 people (1.96% vs 1.37%, 34.3% vs 24.3%, and 27.1 vs 22 respectively, P < .001). A total of 1086 persons with T2DM and 1567 controls had an influenza claim (47.4% male, median age 54, 6.4% vs 7.8% trackers, respectively). Glycemic events, pneumonia, and ischemic heart disease increased over baseline during the peri-influenza period for T2DM (1.74-, 7.4-, and 1.6-fold increase respectively, P < .01). In a device wearing subcohort, we observed 10 000 fewer steps surrounding the influenza event, with the lowest (5500 steps) two days postinfluenza. Average heart rate increased significantly (+5.5 beats per minute) one day prior to influenza. Conclusion: Influenza increases rates of pneumonia, heart disease, and abnormal glucose levels among people with T2DM, and negatively impacts daily activities compared to controls.

ObjectiveTo describe population-level response to influenza-like illness (ILI) as measured by wearable mobile health (mHealth) devices across multiple dimensions including steps, heart rate, and sleep duration and to assess the potential for using large networks of mHealth devices for influenza surveillance.IntroductionInfluenza surveillance has been a major focus of Data Science efforts to use novel data sources in population and public health [1]. This interest reflects the public health utility of timely identification of flu outbreaks and characterization of their severity and dynamics. Such information can inform mitigation efforts including the targeting of interventions and public health messaging. The key requirement for influenza surveillance systems based on novel data streams is establishing their relationship with underlying influenza patterns [2]. We assess the potential utility of wearable mHealth devices by establishing the aggregate responses to ILI along three dimensions: steps, sleep, and heart rate. Surveillance based on mHealth devices may have several desirable characteristics including 1) high resolution individual-level responses that can be prospectively analyzed in near real-time, 2) indications of physiological responses to flu that should be resistant to feedback loops, changes in health seeking behavior, and changes in technology use, 3) a growing user-base often organized into networks by providers or payers with increasing data quality and completeness, 4) the ability to query individual users underlying aggregate signals, and 5) demographic and geographic information enabling detailed characterization. These features suggest the potential of mHealth data to deliver “faster, more locally relevant” surveillance systems [3].MethodsDuring the 2017/2018 influenza season, surveys were conducted within the Achievement platform, a health app that integrates with a variety of wearable trackers and consumer health applications [4]. The Achievement population has given consent agreeing to participation in studies like the one presented here and permitting access to their data. Surveys queried users as to whether they had experienced flu-like (ILI) symptoms in the preceding 14 days. Respondents who had experienced symptoms were then asked to identify symptom days. Those who had not experienced symptoms were queried again two weeks later. Positive responses were re-indexed to align by date of symptom onset. Individual respondent’s measures were standardized on a per-individual level in the 6 week period centered on the index date. Population-level mean signals were directly computed across several dimensions including steps, sleep, and heart rate. Uncertainty was quantified using resampling.ResultsBeginning February 17th, 2018, surveys were distributed to Achievement users. Within the first week 31,934 users had responded to the survey. Over a 12-week period, 124,892 individuals completed the survey with 25,512 reporting flu-like symptoms in a two week period prior to the survey. Of these, 9,495 had wearable device data in the 90-day window surrounding their symptom dates and 3,362 respondents had “dense” data defined as no more than 4 consecutive missing days in the 6-week period surrounding the index date.Population-level signals to ILI were clearly evident for five measures across the three dimensions. Step count [fig. 1] and time spent active [fig. 2] decreased 1 day prior to reported symptom onset date (index date), with a minimum at day 2 of -.24 std. dev. for step count and -.25 std. dev for time spent active, and a return to baseline at day 8. Sleeplessness [fig.3] and time spent in bed [fig. 4] increased one day prior to index, peaking 4 days after index at a mean increase of .16 std. dev. for sleeplessness and .13 std. dev. for time spent in bed, and returning to baseline at 7 days. Heart rate was elevated from 1 day before index to day 6 with a peak increase of .18 std. dev. on days 2 and 3 after index.ConclusionsThe potential of mHealth devices to register illness has been recognized [5]. This study is the first to present population-level influenza signals in a large network of mHealth users. Mobile health device data linked to ILI-specific survey responses taken during the 2017/18 flu season demonstrate clear aggregate patterns across several dimensions including sleep, steps, and heart rate. These signals suggest the potential for systems to rapidly process individual-level responses to classify ILI and to use such classifiers for ILI surveillance. The data described here, high resolution individual-level behavioral and physiological data linked to timely survey responses, suggests the potential to further enhance outbreak detection and improve characterization of ILI patterns. The setting of our study, a very large network of mobile health device users who have consented to the prospective use of their data and to being queried about their health status, could provide a framework for automated prospective influenza surveillance using “real world evidence” [6]. Employed over a population-representative sample, this approach could provide adjunct to standard clinically-based sentinel systems.References[1] Althouse, Benjamin M., et al. "Enhancing disease surveillance with novel data streams: challenges and opportunities." EPJ Data Science 4.1 (2015): 17.[2] Henning KJ. What is syndromic surveillance?. Morbidity and Mortality Weekly Report. 2004 Sep 24:7-11[3] Simonsen L, Gog JR, Olson D, Viboud C. Infectious disease surveillance in the big data era: towards faster and locally relevant systems. The Journal of infectious diseases. 2016 Nov 14;214(suppl_4):S380-5.[4] https://www.myachievement.com/[5] Li, Xiao, et al. "Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information." PLoS biology 15.1 (2017): e2001402.[6]https://www.fda.gov/scienceresearch/specialtopics/realworldevidence/default.htm