A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO 2 ] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

A new wave of wearable sensors allows frequent and continuous measurements of body functions (physiology), including heart rate, skin temperature, blood oxygen levels, and physical activity. We investigated the ability of wearable sensors to follow physiological changes that occur over the course of a day, during illness and other activities. Data from these sensors revealed personalized differences in daily patterns of activities. Interestingly, we discovered striking changes in particular environments such as airline flights. Blood oxygen levels decreased during high-altitude flights, and this decrease was associated with fatigue. By combining sensor information with frequent medical measurements, we made two important health-related observations. First, wearable sensors were useful in identifying the onset of Lyme disease and inflammation. From this observation, we then developed a computational algorithm for personalized disease detection using such sensors. Second, we found that wearable sensors can reveal physiological differences between insulin-sensitive and insulin-resistant individuals, raising the possibility that these sensors could help detect risk for type 2 diabetes. Overall, these results indicate that the information provided by wearable sensors is physiologically meaningful and actionable. Wearable sensors are likely to play an important role in managing health.

Funding: NIH (grant number UL1 TR001085). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NIH https://datascience.nih.gov/bd2k (grant number U54 EB020405). JD is funded by the Mobilize Center grant. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The Stanford Big Data Initiative. Received by MPS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Bert and Candace Forbes. Received by MPS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship in Spinal Cord Injury Medicine https://www.va.gov/oaa/specialfellows/programs/SF_SCIMinfo.asp?p=16 . Received by SMS-FR. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. NIH (grant number 8U54DK102556). Received by MPS. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Despite the revolution of wearable technology, studies to investigate their use in health care have been limited. One recent study using biosensors found no obvious benefit to users in health care costs or utilization [ 11 ]. In this work, we investigate the use of portable devices to (1) easily and accurately record physiological measurements in individuals in real time (or at high frequency), (2) quantify daily patterns and reveal interesting physiological responses to different circadian cycles and environmental conditions, (3) identify personalized baseline norms and differences among individuals, (4) detect differences in health states among individuals (e.g., people with diabetes versus people without diabetes), and (5) detect inflammatory responses and assist in medical diagnosis at the early phase of disease development, thereby potentially impacting medical care. In addition to a number of novel observations, through these analyses, we have gained considerable insight into the capabilities and value of these different devices in health and scientific research.

The popularity of wearable devices has substantially increased in recent years. As of July 2015, there are more than 500 different health care-related wearables present on the market and over 34.3 million devices sold. This is triple the number sold in 2013 [ 10 ].

Emerging wearable biosensors (hereafter called “wearables”) are a low-cost technology that either continuously or frequently measures physiological parameters and provides a promising approach to routinely monitor personalized physiological measurements and potentially identify alterations in health conditions. Wearables are capable of passive and routine recording and immediate delivery of multiple types of measurements in real time to the wearer or physician with minimal attention or training required. In addition to physiological measurements such as HR and skin temperature, wearable technology has the potential to precisely capture the wearer’s daily physical activities, such as walking, biking, running, and other activities, often in conjunction with a GPS, which provides direct information about the location of the activity.

The infrequent collection of these measurements as currently practiced is problematic. First, changes in these parameters may not be identified until many months after an initial health condition has occurred. For instance, if a healthy person with reasonable health care access visits his or her physician every 2 y for a routine visit, then a condition may arise many months, or even longer, prior to a clinical symptom onset and thus go undetected for some time. Second, physiological parameters vary among individuals depending on their gender, life stage, and physical training, among other characteristics (e.g., [ 8 , 9 ]). These parameters also vary within the same person during their daily activities and with changes in the ambient environment. Because sparse clinical measurements of an individual are often compared to the average measurements of a population, the large variation within and among individuals results in a difficult medical assessment. Thus, infrequent short measurement periods or lack of adequate health care access makes it difficult to ascertain if a significant health change has occurred in a particular person. This information is particularly valuable for caregivers responsible for the health of others.

Physiological parameters such as heart rate (HR), blood pressure, and body temperature can provide critical information about the physical health status of a person. Elevation of any of these parameters can be of concern; elevated HR and blood pressure are associated with cardiovascular disease, and elevated body temperature occurs during pathogen infection and inflammation [ 1 – 4 ]. Peripheral capillary oxygen saturation (SpO 2 ) is a measure of oxygen saturation of hemoglobin in the blood, and patients with chronic pulmonary disease often have lower resting SpO 2 and are required to use supplementary oxygen to attain a more optimal SpO 2 [ 5 ]. Skin temperature is associated with alertness levels and quality of sleep [ 6 , 7 ]. Although these different parameters are routinely measured in the physician’s office, they are not generally monitored outside of that context.

Results

Summary and Validation of the Devices Several of the devices, including the Basis device, used most frequently in our study had been validated for clinical-grade accuracy by the manufacturer (See Materials and Methods for details). Nonetheless, we performed extensive testing to assess the accuracy of the different devices against gold standard measurements and/or our instrument (Welch Allyn [WA] 6000 series), which is routinely used at the clinical laboratory services at Stanford University. We found that HR and SpO 2 data collected using four devices (Scanadu Scout, iHealth-finger, Masimo, and Basis) were very close to that of the WA instrument over a wide range of values using the Bland–Altman method of comparison [16,17] and the Pearson correlation test (see S1 Fig). For example, HR measurements were within five beats per minute (BPM) and 10% of the WA instrument for all devices. SpO 2 measurements were within 3% for all devices except for the Scanadu, which still yielded similar trends (see Material and Methods). Similarly we found that activity measurements were also close to standards for the conditions measured (e.g., MOVES App: steps: 0.79 +/- 0.16 standard deviation [SD] of the actual value; running: 0.96 +/- 0.05 SD of the actual value; details for all methods are presented in Material and Methods). Thus, we deemed the wearable biosensor measurements to be suitable for these studies.

Circadian and Diurnal Patterns in Physiological Parameters In order to understand deviation from normal patterns, we first analyzed the collected data for systematic normal patterns, such as circadian rhythms, beginning with Participant #1. To reduce effects due to travel, our analyses focused on days lacking distance travel (defined as trips taken using airlines, assessed using GPS data from MOVES, and validated by comprehensive personal logs/calendars; see Material and Methods). Fig 2A–2D shows the circadian patterns of HR, skin temperature, and activity for 71 nontraveling d of Participant #1. As expected, we detected clear cyclical fluctuations over 24-h periods. For example, HR (measured using the Basis Peak) is generally lower at night (mean of 69.2 +/- 7.7 SD BPM from 10 p.m. to 6 a.m.) and higher during the day (mean of 84.5 +/- 11.3 SD BPM from 6 a.m. to 10 p.m.), with daily fluctuations (peak/trough or max/min) of 46.4 +/- 11.6 SD BPM (Fig 2B, S2A Fig), consistent with the sleep–wake cycle indicated by the Basis device (Fig 2A). Skin temperature measurements also generally followed a similar day-and-night pattern. Unlike that reported for core temperature [18], we found that skin temperature increases during sleep (a mean of 91.3 +/- 2.0°F for 10 p.m. to 6 a.m.; a mean of 86.6 +/- 3.2°F for 6 a.m. to 10 p.m., with daily fluctuations of 11.5 +/- 2.9 SD°F on average; Fig 2C, S2B Fig). PPT PowerPoint slide

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larger image TIFF original image Download: Fig 2. Circadian and diurnal patterns in physiological parameters. Participant #1 hourly summaries in (A) sleep, (B) HRs, (C) skin temperature, and (D) steps as measured using the Basis Peak device over 71 nontravel d. (E) Summaries of 43-person cohort for daily HR and skin temperature from all data and (F) differences in resting (fewer than five steps) nighttime and daytime HRs (Note: one person did not have nighttime measurements and is not included) and skin temperature. (G) Daily activity plots for 43 individuals. Based on number of peaks in the curves, four general patterns of activity behavior are evident. The plots in Fig 2G were aligned according to the first increase in activity. https://doi.org/10.1371/journal.pbio.2001402.g002 Comparison of physiological data with physical activity information revealed obvious activity-related physiological responses during specific time windows. Participant #1 often has an elevated HR during the 7 a.m.-to-8 a.m. and 6 p.m.-to-7 p.m. time windows, which included the typical time for bike commuting on weekdays (confirmed with daily calendar and consistent with MOVES information). On weekends, elevated HR was often evident in the 4 p.m.-to-6 p.m. window (S2 Fig), which is consistent with running activity measured using both the Basis device and MOVES. Overall, the correspondence of patterns detected with known activities indicates that the wearable devices can readily capture physiological information.

Physiological Parameters Change Dynamically with Human Activity We also directly compared the physiological response in relation to different daily activities using data from the Basis device and MOVES apps (see Material and Methods). As shown in S3 Fig, our results replicate well-known patterns of physiological responses to events [19–22], including significantly faster HRs during exercise and significantly slower HRs during sleep compared to activity-free times, the mean of which is 78.4 ± 14.7 (SD) BPM; a mean of 67.6 ± 8.3 (SD) BPM, 101.1 ± 15.4 (SD) BPM, 114.1 ± 14.1 (SD) BPM, and 145.2 ± 18.1 (SD) BPM were observed during sleep, walking, cycling, and running, respectively (two-sided Wilcoxon rank sum p < 10−32). As expected, the measurements of HR, steps, calories, and skin temperature are very consistent for most of the activities, except the step measurement during cycling, which is not accurately detected using the Basis device (S3 Fig). Importantly, as described below, examination of recorded notes revealed a significant decrease in SpO 2 levels measured by both the forehead and finger devices when Participant #1 reported fatigue (two-sided Wilcoxon rank-sum test p < 0.05; see below), and this finding was validated using systematic fatigue testing as described in the section on Airline Flights. Overall, these results indicate that our devices capture data as expected and also serve as a useful baseline to detect outlying measurements, as described below.

High-Resolution Mapping of Inflammatory Disease To examine the resolution at which illness might be confidently identified, we developed a computational approach called “Change-of-Heart” or COH to identify periods with abnormal HR patterns. HR was chosen because, as described above, it reliably detected all periods with elevated CRP levels in each of the individuals. We were unable to reliably map elevated skin temperature at high resolution during these periods across all individuals, and thus this parameter was not pursued. Specifically, we focused on deviations in resting HRs relative to an inactive period and applied a peak-finding–based algorithm to the smoothed continuous HR signal to search for peaks different from a global and local distribution (see Material and Methods). This peak-finding method is optimal for identifying times of transition from healthy to ill states, and thus preferentially detects early periods of infection, which is most desirable. As shown in Fig 5F, during the 679 d when Participant #1 was monitored, we identified 11 periods with elevated HR. These periods successfully tagged all of the four sick periods indicated above, sometimes with multiple peaks, and also revealed four other periods during which no illness was reported. Application of this approach to the other three individuals also revealed peaks during each of their ill periods. For all four individuals, we are able to identify all of the sick periods using this method with area under the receiver operating characteristic curves larger than 0.9 for each individual (S8B Fig). Importantly, each illness period is identified (100% sensitivity), and for most of the sick periods, significant signals were evident at the very beginning of the illness period. Overall, these results indicate that elevated HRs are present during illness and can be detected using wearable devices.

Physiological Differences in IR and Insulin Sensitivity are Detectable using Wearables The availability of clinical measurements on our participants enabled us to investigate associations between information collected from wearables with clinically important data. We focused on diabetes-related measurements because many of our participants were at risk for T2D. Diabetes is a significant rising global health problem, and IR is highly correlated with progression to T2D [33]. Twenty individuals in our cohort underwent measurement of their SSPG, a direct measurement of resistance to insulin-mediated glucose uptake (See Material and Methods) [12,13]. We performed a stepwise modeling approach to examine the relationships between SSPG values and HR, activity, and BMI, beginning with a simple univariate model and then building to bi- and trivariate models. We first examined the associations between daytime, nighttime, and delta (daytime minus nighttime) HR and SSPG (Fig 6A, 6C and 6D) because of evidence that diabetes is associated with changes in diurnal variation of HR [34]. Both daytime HR (Fig 6C) and delta HR (Fig 6A) were positively correlated with SSPG (Daytime HR: β = 4.5, 95% CI 1.2–7.8), p = 0.0107; Delta HR: β = 4.1 (95% CI 1.1–7.1), p = 0.0098), but nighttime HR (Fig 6D) was not. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 6. HR differences in IR and sensitivity. Fit plots (A–D) of the linear regression models showing the associations between daytime (C), nighttime (D), delta (daytime minus nighttime) HR (A), and average daily steps (B) with SSPG levels. Higher SSPG levels indicate increased IR. An increase in delta HR and daytime HR is associated with a higher SSPG level, whereas an increase in average number of steps taken a day is associated with a lower SSPG level. There was no association with nighttime HR. Contour fit plot (E) and 3-D plot (F) of the multivariate regression demonstrating that delta HR and average daily steps each have an independent inverse association with SSPG. Parameter estimates were obtained using restricted maximum likelihood estimation with a robust variance estimator to account for unequal variances. https://doi.org/10.1371/journal.pbio.2001402.g006 Because our previous results showed a relationship between overall activity and resting HR (Fig 3), we wanted to evaluate whether the relationship we discovered between daytime or delta HR and SSPG was due to differences in study participant activity. We first assessed whether there was a relationship between daily activity and SSPG (Fig 6B) and found that average daily steps had an inverse relationship (β = -0.012, 95% CI -0.022–-0.002, p = 0.0183) with SSPG. We also evaluated the relationship between average daily steps and HR and found that daily steps was not significantly associated with daytime HR (β = -0.0008, 95% CI -0.0021–0.0005, p = 0.1943) but did have a significant inverse relationship with nighttime HR (β = -0.0017, 95% CI -0.0030–-0.0004, p = 0.0115). Thus, the association of higher daytime HR with higher SSPG levels is unlikely to be due to differences in participant daily activity. Including overall activity in a multivariate regression model with delta HR to predict SSPG resulted in an improved adjusted R2 to 0.41 from 0.17 in the univariate model with delta HR as the only predictor. These results suggest that information from different wearable sensor data types in combination can improve the ability to detect important physiological changes as compared to information from a single sensor. To assess whether BMI plays a role in the relationship between delta HR and SSPG, we further expanded our multivariate regression to include BMI. BMI is known to have a positive correlation with HR [35], and IR and is negatively correlated with overall activity levels (Kruger et al., 2016). In this model, delta HR remained a strong predictor of SSPG levels (β = 5.05, 95% CI 2.73–7.37, p = 0.0003) independent of daily activity (β = -0.010, 95% CI -0.021–0.000, p = 0.0509) and BMI (β = 7.58, 95% CI 1.83–13.33, p = 0.0130, adjusted R2 = 0.52). Thus, combining information from multiple wearable sensors and electronic medical records to capture the relevant underlying physiological parameters enables enhanced prediction of SSPG. Overall, these results indicate that individuals with different degrees of IR and insulin sensitivity have important physiological differences and that these differences can be measured using wearable devices.