Evaluating the Diagnostic Paradigm for Group A and Non–Group A Streptococcal Pharyngitis in the College Student Population

Background Acute pharyngitis is a frequent illness presenting in outpatient settings. Antibiotics are only recommended for bacterial pharyngitis caused by group A β-hemolytic streptococci (GAS); however, infections with non–group A β-hemolytic streptococci (NGAS) have similar clinical presentations and are common in young adult populations. The objective of this study was to analyze the performance of a current (expert) diagnostic algorithm for GAS pharyngitis, the Centor score, and compare it to alternative models developed to predict GAS and NGAS in a college student population. Methods Electronic health records were obtained for all patients who received a streptococcal rapid antigen detection test (RADT) and/or a bacterial throat culture (n = 3963) at a southeastern US university in 2014. Bivariate and multivariable regression models (least absolute shrinkage and selection operator [LASSO] and stepwise-selected) were fitted to assess and compare their diagnostic performances for GAS-positive and NGAS-positive infections. Results Prevalence of GAS was 18.8%. In the subset of RADT-negative patients who received bacterial throat cultures (n = 313), growth of NGAS occurred in 34.8%, with group C streptococci the most frequent isolate. Mean Centor score was higher for NGAS (3.2) vs GAS (2.9) infections (P = .0111). The area under the curve (AUC) for GAS prediction was 0.64 using the Centor score and 0.70 using the LASSO model. For NGAS, the most important features were cough, pharyngeal erythema, tonsillar exudate, and gastrointestinal symptoms (AUC = 0.63). Conclusions GAS and NGAS pharyngitis were indistinguishable among college students in this study utilizing a commonly applied decision score. Alternative models using additional clinical criteria may be useful for supporting diagnosis of this common illness.

Assessing social anxiety using gps trajectories and point-of-interest data

Mental health problems are highly prevalent and appear to be increasing in frequency and severity among the college student population. The upsurge in mobile and wearable wireless technologies capable of intense, longitudinal tracking of individuals, provide valuable opportunities to examine temporal patterns and dynamic interactions of key variables in mental health research. In this paper, we present a feasibility study leveraging non-invasive mobile sensing technology to passively assess college students' social anxiety, one of the most common disorders in the college student population. We have first developed a smartphone application to continuously track GPS locations of college students, then we built an analytic infrastructure to collect the GPS trajectories and finally we analyzed student behaviors (e.g. studying or staying at home) using Point-Of-Interest (POI). The whole framework supports intense, longitudinal, dynamic tracking of college students to evaluate how their anxiety and behaviors change in the college campus environment. The collected data provides critical information about how students' social anxiety levels and their mobility patterns are correlated. Our primary analysis based on 18 college students demonstrated that social anxiety level is significantly correlated with places students' visited and location transitions.

Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study (Preprint)

BACKGROUND The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student’s mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being. OBJECTIVE Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19. METHODS We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints - at baseline, 1-month later, 2-months later, 3-months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population. RESULTS This study enrolled 2158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing. CONCLUSIONS This study examined student health and well-being during the COVID-19 pandemic. While data collection and analyses are ongoing, the study will assess the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19. CLINICALTRIAL ClinicalTrials.gov NCT04766788

Wearable alcohol monitors for alcohol use data collection among college students: feasibility and acceptability in a pilot study

ObjectiveTo assess the feasibility and acceptability of using BACtrack Skyn wearable alcohol monitors in a college student population.MethodIn September 2019, we enrolled n=5 Indiana University undergraduate students in a study to wear alcohol monitor wristbands continuously over a 5-day period. Concurrently, participants completed daily surveys querying details about their alcohol use in the previous 24 hours. We measured acceptability at endline with the Acceptability of Intervention Measure (AIM) scale (min=1, max=5). We measured feasibility with process measures: 1) amount of alcohol monitor data produced, and 2) correlation between drinking events identified by the alcohol monitors and drinking events reported by participants.ResultParticipants reported high acceptability of the wearable alcohol monitors with a mean AIM score of 4.3 (range: 3.3 to 5.0). Feasibility of monitor use was high: A total of 589 hours of alcohol use data was collected. All participants were able to successfully use the alcohol monitors, producing a total of 24 out of 25 possible days of alcohol monitoring data. Participants reported a total of 15 drinking events during follow-up and we detected 12 drinking events with the alcohol monitors. The self-reported drinking event start times were highly correlated with the monitor detected event start time (Spearman’s ρ=0.9, p<0.0001). The self-reported number of drinks during a drinking event was correlated with the area under the curve of each drinking event peak (Pearson’s r=0.7, p=0.008).ConclusionWearable alcohol monitors are a promising data collection tool for more objective real-time measures of alcohol use in college student populations.

A Novel Artificial Intelligence-Powered Emotional Intelligence and Mindfulness App (Ajivar) for the College Student Population During the COVID-19 Pandemic: Quantitative Questionnaire Study

Background Emotional intelligence (EI) and mindfulness can impact the level of anxiety and depression that an individual experiences. These symptoms have been exacerbated among college students during the COVID-19 pandemic. Ajivar is an app that utilizes artificial intelligence (AI) and machine learning to deliver personalized mindfulness and EI training. Objective The main objective of this research study was to determine the effectiveness of delivering an EI curriculum and mindfulness techniques using an AI conversation platform, Ajivar, to improve symptoms of anxiety and depression during this pandemic. Methods A total of 99 subjects, aged 18 to 29 years, were recruited from a second-semester group of freshmen students. All participants completed the online TestWell Wellness Inventory at the start and end of the 14-week semester. The comparison group members (49/99, 49%) were given routine mental wellness instruction. The intervention group members (50/99, 51%) were required to complete Ajivar activities in addition to routine mental wellness instruction during the semester, which coincided with the onset of the COVID-19 pandemic. This group also completed assessments to evaluate for anxiety, using the 7-item Generalized Anxiety Disorder (GAD-7) scale, and depression, using the 9-item Patient Health Questionnaire (PHQ-9). Results Study participants reported a mean age of 19.9 (SD 1.94) years; 27% (27/99) of the group were male and 60% (59/99) identified as Caucasian. No significant demographic differences existed between the comparison and intervention groups. Subjects in the intervention group interacted with Ajivar for a mean time of 1424 (SD 1168) minutes. There was a significant decrease in anxiety, as measured by the GAD-7: the mean score was 11.47 (SD 1.85) at the start of the study compared to 6.27 (SD 1.44) at the end (P<.001). There was a significant reduction in the symptoms of depression measured by the PHQ-9: the mean score was 10.69 (SD 2.04) at the start of the study compared to 6.69 (SD 2.41) at the end (P=.001). Both the intervention and comparison groups independently had significant improvements in the TestWell Wellness Inventory from pretest to posttest. The subgroups in the social awareness and spirituality inventories showed significant improvement in the intervention group. In a subgroup of participants (11/49, 22%) where the GAD-7 was available during the onset of the COVID-19 pandemic, there was an increase in anxiety from the start of the study (mean score 11.63, SD 2.16) to mid-March (ie, onset of the pandemic) (mean score 13.03, SD 1.48; P=.23), followed by a significant decrease at the end of the study period (mean score 5.9, SD 1.44; P=.001). Conclusions It is possible to deliver EI and mindfulness training in a scalable way using the Ajivar app during the COVID-19 pandemic, resulting in improvements in anxiety, depression, and EI in the college student population.