Smartphone-Based Geofencing to Ascertain Hospitalizations.

BACKGROUND: Ascertainment of hospitalizations is critical to assess quality of care and the effectiveness and adverse effects of various therapies. Smartphones, mobile geolocators that are ubiquitous, have not been leveraged to ascertain hospitalizations. Therefore, we evaluated the use of smartphone-based geofencing to track hospitalizations. METHODS AND RESULTS: Participants aged ≥18 years installed a mobile application programmed to geofence all hospitals using global positioning systems and cell phone tower triangulation and to trigger a smartphone-based questionnaire when located in a hospital for ≥4 hours. An in-person study included consecutive consenting patients scheduled for electrophysiology and cardiac catheterization procedures. A remote arm invited Health eHeart Study participants who consented and engaged with the study via the internet only. The accuracy of application-detected hospitalizations was confirmed by medical record review as the reference standard. Of 22 eligible in-person patients, 17 hospitalizations were detected (sensitivity 77%; 95% confidence interval, 55%-92%). The length of stay according to the application was positively correlated with the length of stay ascertained via the electronic medical record (r=0.53; P=0.03). In the remote arm, the application was downloaded by 3443 participants residing in all 50 US states; 243 hospital visits at 119 different hospitals were detected through the application. The positive predictive value for an application-reported hospitalization was 65% (95% confidence interval, 57%-72%). CONCLUSIONS: Mobile application-based ascertainment of hospitalizations can be achieved with modest accuracy. This first proof of concept may ultimately be applicable to geofencing other types of prespecified locations to facilitate healthcare research and patient care.

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep.

BACKGROUND: Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality. AIMS: The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep. METHODS: We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index-PSQI) were obtained by survey. Linear regression was used to obtain effect estimates. RESULTS: Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency. CONCLUSIONS: These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.

Perceptions, Information Sources, and Behavior Regarding Alcohol and Heart Health.

Despite the equipoise regarding alcohol's cardiovascular effects and absence of relevant rigorous controlled trials, the lay press frequently portrays alcohol as "heart healthy." The public perception of alcohol's heart effects, the sources of those perceptions, and how they may affect behavior are unknown. We performed a cross-sectional analysis of data obtained from March 2013 to September 2014 from consecutive participants enrolled in the Health eHeart Study. Of 5,582 participants, 1,707 (30%) viewed alcohol as heart healthy, 2,157 (39%) viewed it as unhealthy, and 1,718 (31%) were unsure. Of those reporting alcohol as heart healthy, 80% cited lay press as a source of their knowledge. After adjustment, older age (odds ratio 1.11), higher education (odds ratio 1.37), higher income (odds ratio 1.07), US residence (odds ratio 1.63), and coronary artery disease (odds ratio 1.51) were associated with perception of alcohol as heart healthy (all p <0.003). Ever smokers (odds ratio 0.76, p = 0.004) and those with heart failure (odds ratio 0.5, p = 0.01) were less likely to cite alcohol as heart healthy. Those perceiving alcohol as heart healthy consumed on average 47% more alcohol on a regular basis (95% confidence interval 27% to 66%, p <0.001). In conclusion, of >5,000 consecutive Health eHeart participants, approximately 1/3 believed alcohol to be heart healthy, and the majority cited the lay press as the origin of that perception. Those with a perception of alcohol as heart healthy drink substantially more alcohol.

General strategy for biosensor design and construction employing multifunctional surface-tethered components.

Biosensors function by reversibly linking bioreceptor-target analyte binding with closely integrated signal generation and can either continuously monitor analyte concentrations or be returned to baseline readout values by removal of analyte. We present an approach for producing fully reversible, reagentless, self-assembling biosensors on surfaces. In the prototype biosensor, quencher-dye-labeled biotin-linked E. coli maltose binding protein (MBP) bound in a specific orientation to a NeutrAvidin-coated surface is employed as a bioreceptor. To complete sensor formation, a modular tether arm consisting of a flexible biotinylated DNA oligonucleotide, a fluorescence resonance energy-transfer (FRET) donor dye, and a distal beta-cyclodextrin (beta-CD) analyte analogue is bound in an equimolar amount to the same surface by means of DNA-directed immobilization. After self-assembly, a baseline level of FRET quenching is observed due to specific interaction between the beta-CD of the flexible tether arm and the sugar binding site of MBP, which brings the two dyes into proximity. Addition of the target analyte, the nutrient maltose, displaces the linked beta-CD-dye of the DNA-based tether arm, and a concentration-dependent change in FRET results. Biosensor sensitivity and dynamic range can be controlled by either using MBP variants having different binding constants or by binding of modulator DNA oligonucleotides that are complementary to the flexible DNA tether. The sensor can be regenerated and returned to baseline quenching levels by washing away analyte. A complex set of interactions apparently exists on the sensing surface that may contribute to sensor behavior and range. This approach may represent a general way to assemble a wide range of useful biosensors.