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.

Automatically captured sociability and sleep quality in healthy adults.

Sleep and social interactions have been shown to have a considerable public health impact. However, little is known about how these affect each other in healthy individuals. This research is first to propose the exploration of the bidirectional relationship between technologically sensed sleep quality and quantified face-to-face social interactions. We detail a pilot study designed to study the relationship of sociability and sleep quality, both measured and perceived, of healthy adults. We capture real-world social interactions and measure sleep in a naturalistic setting using wireless sensing technologies. We find that it may not be the device-defined sleep quality (ZQ score) but our perceived sleep quality which affects our following day's sociability. Further, we also find perceived sleep quality is more strongly correlated to normalized ZQ scores than the actual scores. These intriguing insights raise several questions on how an individual's social life could be affected by sleep and indicate the usefulness of mobile sensing technologies in understanding public health phenomena.

Change in BMI accurately predicted by social exposure to acquaintances.

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R(2). This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.

Sleep, mood and sociability in a healthy population.

Sleep and mood problems have a considerable public health impact with serious societal and significant financial effects. In this work, we study the relationship between these factors in the everyday life of healthy young adults. More importantly, we look at these factors from a social perspective, studying the impact that couples have on each other and the role that face-to-face interactions play. We find that there is a significant bi-directional relationship between mood and sleep. More interestingly, we find that the spouse's sleep and mood may have an effect on the subject's mood and sleep. Further, we find that subjects whose sleep is significantly correlated with mood tend to be more sociable. Finally, we observe that less sociable subjects show poor mood more often than their more sociable contemporaries. These novel insights, especially those involving sociability, measured from quantified face-to-face interaction data gathered through smartphones, open up several avenues to enhance public health research through the use of latest wireless sensing technologies.

Trust evaluation in health information on the World Wide Web.

The impact of health information on the web is mounting and with the Health 2.0 revolution around the corner, online health promotion and management is becoming a reality. User-generated content is at the core of this revolution and brings to the fore the essential question of trust evaluation, a pertinent problem for health applications in particular. Evolving Web 2.0 health applications provide abundant opportunities for research. We identify these applications, discuss the challenges for trust assessment, characterize conceivable variables, list potential techniques for analysis, and provide a vision for future research.