Health and well-being are shaped by how lifestyle and the environment interact with biological machines. A navigational paradigm can help users reach a specific health goal by using constantly captured measurements to estimate how their health is continuously changing and provide actionable guidance.

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Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geo-spatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.

Dietary choices are the primary determinants of prominent dis- eases such as diabetes, heart disease, and obesity. Human health care providers, such as dietitians, cannot be at the side of every user at all times to manually guide them towards optimal choices. Automated adaptive guidance fused with expert knowledge can use multimedia data to technologically scale health guidance without human intervention. Addressing the correct granularity of recommendations (in this case meal dishes) is essential for effortless decision making. Thus we make a decision support system using multi-modal data relying on timely, contextually aware, personalized data to find local restaurant dishes to satisfy a user’s needs. Algorithms in this system take nutritional facts regarding products, efficiently calculate which items are healthiest, then re-rank and filter results to users based on their personalized health data streams and environmental context. Our recommendation engine is driven by the primary goal of lowering the barriers to a personalized healthy choice when eating out, by distilling dish suggestions to a single contextually aware and easily understood score.

Purpose: Our study fills the spatiotemporal gaps in dry eye disease (DED) epidemiology by using Google Trends as a novel epidemiological tool for geographically mapping DED in relation to environmental risk factors. Methods: We used Google Trends to extract DED-related queries estimating users' intent from 2004 to 2019 in the United States. We incorporated national climate data to generate heat maps comparing geographic, temporal, and environmental relationships of DED. Multivariable regression models were constructed to generate quadratic forecasts predicting DED and control searches. Results: Our results illustrated the upward trend, seasonal pattern, environmental influence, and spatial relationship of DED search volume across the US geography. Localized patches of DED interest were visualized in urban areas. There was no significant difference in DED queries across the US census regions (P = 0.3543). Regression model 1 predicted DED queries per state (R2 = 0.61), with the significant predictor being urban population [r = 0.56, adjusted (adj.) P < 0.001, n = 50]; model 2 predicted DED searches over time (R2 = 0.97), with significant predictors being control queries (r = 0.85, adj. P = 0.0169, n = 190), time (r = 0.96, adj. P < 0.001, n = 190), time2 (r = 0.97, adj. P < 0.001, n = 190), and seasonality (winter r = −0.04, adj. P = 0.0196, n = 190; spring r = 0.10, adj. P < 0.001, n = 190). Conclusions: Our study used Google Trends as a novel epidemiologic approach to geographically mapping the US DED. Importantly, urban population and seasonality were stronger risk factors of DED searches than temperature, humidity, sunshine, pollution, or region. Our work paves the way for future exploration of geographic information systems for locating DED and other diseases through online population metrics.

Calorie restriction (CR) delays aging and extends lifespan in numerous organisms, including mice. Down-regulation of the somatotropic axis, including a reduction in insulin-like growth factor-1 (IGF-1), likely plays an important role in CR-induced lifespan extension, possibly by reducing cell proliferation rates, thereby delaying replicative senescence and inhibiting tumor promotion. Accordingly, elucidating the mechanism(s) by which IGF-1 is reduced in response to CR holds therapeutic potential in the fight against age-related diseases. Up-regulation of fibroblast growth factor 21 (FGF21) is one possible mechanism given that FGF21 expression is induced in response to nutritional deprivation and has been implicated as a negative regulator of IGF-1 expression. Here we investigated alterations in hepatic growth hormone (GH)-mediated IGF-1 production and signaling as well as the role of FGF21 in the regulation of IGF-1 levels and cell proliferation rates in response to moderate CR in adult mice. We found that in response to moderate CR, circulating GH and hepatic janus kinase 2 (JAK2) phosphorylation levels are unchanged but that hepatic signal transducer and activator of transcription 5 (STAT5) phosphorylation levels are reduced, identifying STAT5 phosphorylation as a potential key site of CR action within the somatotropic axis. Circadian measurements revealed that the relative level of FGF21 expression is both higher and lower in CR vs. ad libitum (AL)-fed mice, depending on the time of measurement. Employing FGF21-knockout mice, we determined that FGF21 is not required for the reduction in IGF-1 levels or cell proliferation rates in response to moderate CR. However, compared to AL-fed WT mice, AL-fed FGF21-knockout mice exhibited higher basal rates of cell proliferation, suggesting anti-mitotic effects of FGF21. This work provides insights into both GH-mediated IGF-1 production in the context of CR and the complex network that regulates FGF21 and IGF-1 expression and cell proliferation rates in response to nutritional status.

A root cause of chronic disease is a lack of timely informed decision power in everyday lifestyle choices, such as in diets. Users are unable to clearly delineate and demand healthy food in a quantitative manner. To scale the benefit of health nutrition coaching in broad real-world scenarios, we need a technological solution that is constantly able to interpret nutrition information. We ingest nutritional facts about products to efficiently calculate which items are healthiest. We deliver these results to users based on their location context. Our ranking algorithm outperforms major nutrition score metrics, and is more consistent than human dietitians in real world scenarios. Most importantly, our system gives the user a rapid way to connect with healthy food in their vicinity, reducing the barriers to a healthy diet.

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