A framework for smartphone-enabled, patient-generated health data analysis

Introduction

Empowered patients and health care consumers (Topol, 2015) have aligned with health data tracking technologies to create the quantified self movement (Swan, 2013). Quantified self involves the use of tracking one’s own health-related data to understand trends and potentially alter behavior in order to achieve a health goal. The size and scope of health tracking has and continues to expand with the advent of digital medicine and mobile health (mHealth) technologies enabled by smartphones and connected device infrastructures (Steinhubl, Muse & Topol, 2015). For example, in certain individuals traditional daily weight monitoring has been supplemented by apps that track food intake and devices that monitor physical activity in order to achieve this health goal—a part of the 58% of mobile phone users that have downloaded a health-related mobile app (Krebs & Duncan, 2015). Researchers have shown that in some cases interventions using this technology alone can improve health outcomes, though this result is far from universal (see Free et al. (2013) for review) with the disparity likely due to numerous factors including poor adherence and fatigue (Shaw et al., 2016), and failure of the intervention to change behavior (Patel, Asch & Volpp, 2015).

While new, better, more user-friendly technologies will continue to be developed, vying for the appreciable forecasted growth of the industry (Statista, 2016), we and others believe the future of the field is not simply in the devices and software themselves, but in the data they generate (Gibbs, 2015). Such data can help guide individual health decisions—the crux of the quantified self movement—but can also be used to address novel human health and biology questions (Steinhubl et al., 2015). Yet there exists a sizable gap between the data that is generated and the methods available to analyze and interpreted such data (Fawcett, 2015). Indeed, most such devices and apps simply display the data, leaving any inference up to the user and anyone the user wishes to share the data with (e.g., their physician). However, even the most data experienced users and health care providers may find identifying subtle trends in such complex data a daunting task. These challenges also extend to researchers who may wish to examine data captured from these technologies as, in addition to inherent technical obstacles such as data collection and data security, few analytic methods are established and general software packages are not readily available.

These technologies present the opportunity to examine health data in nontraditional ways. Rather than large intermittent gaps in health measures between doctor or study visits, data can be collected in relatively high resolution. Such high resolution data allows users and researchers to detect unique trends and relationships that were never before possible. For example, a diabetic patient using a continuous glucose monitor can now assess their minute-to-minute health rather than rely on single, low resolution measures such as hemoglobin A1c levels, which does not accurately assess health variability (Virtue et al., 2004). Furthermore, access to such high-resolution human health data in nontraditional settings (e.g., normal, at home environment) allows us to evaluate “real world” health and not be relegated to artificial worlds created in clinical trials that suffer from poor clinical practice adoption (Goss, Elmore & Lessler, 2003). Yet in order to form scientific conclusions in this new frontier, novel methods and adaptations of existing approaches must be developed to account for the intricacies of patient-generated data.

While numerous digital medicine biosensors, devices, and applications have been manufactured to measure various aspects of human physiology and exposome, perhaps no metric epitomizes both the contemporary challenge and opportunity of this field more than blood pressure. Heart disease is the leading cause of death in the United States, with hypertension being the leading contributor of disease (National Center for Health Statistics, 2012). With proper management, hypertension can be controlled and the health consequences of uncontrolled hypertension can largely be avoided. Nevertheless, in the United States only 48% of individuals with hypertension have their condition under control (Farley et al., 2010). It is unsurprising that hypertension has been a target for patient-centric, mHealth disease management (Logan, 2013). Yet the technology to continuously monitor blood pressure in the outpatient setting is still developing as manufacturers have not entirely solved the technical aspects of truly passive monitoring. Thus, the current state of the field largely includes mobile blood pressure cuffs in which readings are initiated by the user. As users may take a reading at any time, evaluating temporal trends in this data requires added consideration.

In this study, we present a hypothesis testing framework in which we examined blood pressure readings taken at variable, uncontrolled time points in individuals enrolled in a smartphone-based health monitoring intervention trial—though the approach we present can be adapted to other similarly structured data. In total, 38 study participants recorded and provided us with blood pressure data on 6,290 occasions. We find that by leveraging all data across individuals we were able to detect an approximately 2 mmHg decrease in blood pressure over a six month trial, despite considerable intra- and inter-individual variation. We then discuss how this and other techniques can be implemented in data analyses of the quantified self and in future study designs.

Methods

Results

Demographic information on study participants is presented in Table S1. Among the 59 original study participants assigned to the monitoring intervention arm that completed the study, 38 had complete device readings data. This cohort was predominantly Caucasian (87%) and largely female (74%) with an average age of 57. A number of participants did not own a smartphone prior to enrollment in the study (21%) with half owning an iPhone. Responses to health-related survey questions are presented in Table S2. We saw a general increase in overall health by the end of the study. There was notable decrease in smoking frequency and increase in exercise frequency. However, we note that these health improvements were also observed in the control arm of the original study (data not presented), suggesting that the monitoring intervention itself had no discernible impact on these traits.

The implied goal of individuals participating in the study is better management of their condition. In this regard, we considered observed blood pressure device readings collected over the course of the study as outcomes of interest. Single blood pressure readings taken at, for example, the enrollment and end of study visit can provide some level of inference on blood pressure changes. However, this approach ignores all data that could be generated between these time points and is vulnerable to biases and natural variation. Rather, we feel approaches which leverage the entirety of the data are preferential. By utilizing Qualcomm’s cloud-to-cloud data integration capability, we were able to capture the measure and time recorded of each blood pressure reading on 38 individuals in the monitoring group. While our inference is based on these 38 individuals and is limited, we present our mathematical framework and modeling below in attempt to answer the seemingly simple question: was blood pressure changing over the course of the study?

In total, we collected 6,290 systolic and 6,265 diastolic blood pressure readings from these individuals (Data S1 and S2). Device readings were recorded roughly uniform over the course of the study (Fig. S1). The number of readings taken varied between individuals, with an average of 165 readings per person (sd = 70, min = 61, max = 416). The time of day that readings were taken was also variable, with a large proportion of measurements taken in the early morning and afternoon. Few readings were taken in the late morning and at night, though this was not necessarily surprising given we asked participants to use the device in the morning, presumably before day time activities (Fig. S2).

Multiple N-of-1 approach

We first assessed the effect of time on blood pressure on each study participant individually (Figs. 1 and S3). Among the 38 participants, 18 had nominally statistically significant (p < 0.05) changes in systolic blood pressure and 21 had significant changes in diastolic blood pressure. However, the number of participants with a significant decrease in systolic blood pressure (n = 9) was equal to the number with an increase (n = 9), and was similarly true for diastolic blood pressure (decrease: n = 12, increase: n = 7; p = 0.36). This result also held when we examined the estimated effects from all participants, regardless of p-value. There were 20 individuals with a decrease (i.e. negative slope) in systolic blood pressure against 18 with an increase (p = 0.87), and 21 with a decrease and 17 with an increase in diastolic blood pressure (p = 0.62). In efforts to summarize these results across individuals, we calculated the mean slope and mean slope weighted by the square root of the number of readings each participant recorded. We found no evidence towards an overall decrease in systolic or diastolic blood pressure. The weighted mean change in systolic blood pressure was −1.7 mmHg (95% CI: −4.7, 1.4) and diastolic blood pressure was −1.9 mmHg (95% CI: −4.0, 0.1). Results from the unweighted calculations were similar.

Mixed model approach

We then pooled device readings data from all study participants, and assessed the effect of time on blood pressure using the mixed model framework described previously with three possible covariance structures of R, where Σ_R was: (1) compound symmetric; (2) first-order autoregressive; or (3) generalized autoregressive for unequally spaced data (i.e. spatial power law).

We encountered one issue when applying the spatial power law structure mixed model. As opposed to the first-order autoregressive model in which ${\displaystyle {\Sigma }_{\text{R}}={\sigma }^2\left(\begin{array}{cccc}1& \rho & {\rho }^2& \\ \rho & 1& \rho & \cdots \\ {\rho }^2& \rho & 1& \\ & ⋮& & \ddots \end{array}\right)}$ where ρ is the correlation between two successive measures on the same subject, the spatial power law structure is a generalized form of the first-order autoregressive model in which ${\Sigma }_{\text{R}}={\sigma }^2\left(\begin{array}{cccc}1& {\rho }^{t_2-t_1}& {\rho }^{t_3-t_1}& \\ {\rho }^{t_2-t_1}& 1& {\rho }^{t_3-t_2}& \cdots \\ {\rho }^{t_3-t_1}& {\rho }^{t_3-t_2}& 1& \\ & ⋮& & \ddots \end{array}\right)$ where t_k is the time of the k^th measurement, and the difference t_i − t_j is the lag between the i^th and j^th measure. When measurements are equally spaced, the spatial power law models simplifies to a first-order autoregressive model as the lag between measures is constant. Though we instructed study participants to measure their blood pressure at certain intervals, the time individuals chose to take these readings, and thus the lag between measures, varied considerably. In this context, we expected the spatial power law structure to be appropriate for the data collected. However, while the majority of consecutive measures had lags of six hours or more (Fig. 2), we discovered a small number of consecutive measures with short lag that led to issue we alluded to above. To demonstrate this issue between consecutive measurements with short lag, consider the following example: three measurements are recorded at times labeled t₁, t₂, and t₃. In this case, ${\displaystyle {\Sigma }_{\text{R}}={\sigma }^2\left(\begin{array}{ccc}1& {\rho }^{t_2-t_1}& {\rho }^{t_3-t_1}\\ {\rho }^{t_2-t_1}& 1& {\rho }^{t_3-t_2}\\ {\rho }^{t_3-t_1}& {\rho }^{t_3-t_2}& 1\end{array}\right)}$

However, when two measurements are taken relatively close together, say at t₁ and t₂ compared to t₃, then t₂ − t₁ is relatively close to zero and t₃ − t₁ ≈ t₃ − t₂. The result on Σ_R is: ${\displaystyle {\Sigma }_{\text{R}}\approx {\sigma }^2\left(\begin{array}{ccc}1& 1& {\rho }^{t_3-t_1}\\ 1& 1& {\rho }^{t_3-t_1}\\ {\rho }^{t_3-t_1}& {\rho }^{t_3-t_2}& 1\end{array}\right)}$

As can be seen, the first and second columns and rows are roughly equivalent, leading to singularity in this matrix, non-convergence, and inestimable effects. It should be noted that this issue does not manifest in the compound symmetric and first-order autoregressive models.

In order to solve this issue, we merged data readings taken within short periods of each other by taking the average over that time. We found that merging readings within a one hour period of each other eliminated the singularity of Σ_R while minimizing the number of readings manipulated. We still ran into singularity issues when using shorter time intervals (e.g., 15 and 30 min minimums). In total, we merged 826 systolic and 801 diastolic blood pressure readings within an hour of each other, with most merging (61%) being two readings recorded within an hour. The subsequent dataset resulted in 5,464 systolic and diastolic readings.

Mixed model results from the one-hour merged dataset are presented in Table 1. For each model assessed, there was an approximately 2 mmHg decrease in systolic and diastolic blood pressure across the sample over the study period (p < 0.001 in all cases). While both the first-order autoregressive and spatial power law models with random effects had better model fit than the random effects model alone, the first-order autoregressive model had slightly better model fit (the estimate of ρ was near 0.2 regardless of the approach). It should be noted that when we tested various merging strategies to eliminate the singularity in the spatial power law Σ matrix (e.g., averaging blood pressure across the entire day) sometimes the spatial power law model had the better fit. This suggests that while accounting for time dependencies was important, the precise structure appeared to be less so. This was true even though the spatial power law model appeared to be more appropriate, given the readings were generally unequally spaced.

Table 1:

Mixed model results.

	Σ	AIC	BIC	Estimate (CI)	p
Systolic	RE	41,604	41,608	−2.11 (−3.13, −1.09)	5.19 × 10−5
	RE + AR(1)	41,495	41,500	−2.11 (−3.29, −0.93)	4.45 × 10−4
	RE + SP	41,546	41,550	−2.04 (−3.11, −0.98)	1.80 × 10−4
Diastolic	RE	36,725	36,729	−2.04 (−2.69, −1.39)	9.41 × 10−10
	RE + AR(1)	36,620	36,624	−2.06 (−2.81, −1.31)	8.17 × 10−8
	RE + SP	36,705	36,710	−2.05 (−2.72, −1.37)	2.83 × 10−9

DOI: 10.7717/peerj.2284/table-1

Note:

RE, random effects only (compound symmetric Σ_R); RE + AR(1), first-order autoregressive model with random effects; RE + SP, spatial power law with random effects.

Sequential analysis approach

We next implemented a sequential analysis approach in which we modeled a spatial power law structure with random effects on sequential subsets of the one-hour merged datasets. The goal of this approach was to determine if and when we could have arrived at our primary conclusion (i.e. systolic and diastolic blood pressure decrease 2 mmHg over the study) prior to the end of the study. While the device readings data across all study participants was collected over ten and a half months, after the first seven months of the study we would have arrived at a similar conclusion (Figs. 3 and S4). Though all 38 participants had completed the study or were enrolled by that time, and 4,686 blood pressure measures (86%) had taken place by then, the prospect of arriving at a conclusion prior to the designed end of the study has some benefit—particularly so when data is collected using digital medicine devices as we discuss below. Furthermore, when applying a sequential analysis approach in an N-of-1 framework, we were able to determine that 13 of the 15 individuals with nominally significant systolic blood pressure changes, and 14 of the 18 individuals with diastolic blood pressure changes over six months demonstrated this trend (p < 0.05) by the fifth month of their study enrollment.

Discussion

As digital, smartphone-enabled, and other patient-centric medical and health technologies have the potential to improve individual health and the overall health care system, the quest to develop the “best” technologies will remain ongoing. However, we and others feel the future of this field is not simply in the devices, sensors, software, and wearables per se (Gibbs, 2015), but in what the data generated from these tools can tell us about human health and biology. Above, we present a framework for hypothesis testing on unequally spaced time series data—a common feature of data generated from these technologies. Applied to a subset of hypertensive individuals enrolled in an interventional trial, we discovered that individuals participating had, on the whole, a roughly 2 mmHg decrease of systolic and diastolic blood pressure over a six month period. Using the methods presented we were able to observe this distinction despite considerable intra- and inter-individual variation in blood pressure measures, and without a rigorously structured readings schedule.

The context of this discovery should, however, be considered. As stated above, we detected a statistical increase in systolic (n = 9) and diastolic blood pressure (n = 7) in some study participants, while device readings were (unsurprisingly) quite variable even among study participants that clearly showed a trend towards lowering blood pressure. We have recently demonstrated some explanation for this heterogeneity in study outcomes (Kim, Wineinger & Steinhubl, 2016). But this observation demonstrates the advantage, yet caution, needed when performing analyses and interrupting results that leverage large amounts of high-dimensional patient-generated data. On one hand, this presents an opportunity to identify subtle trends in health data that might otherwise be difficult to observe—such as single measurements intermittently collected during a physician visit. On the other hand, large amounts of data can make it possible to detect any trend even if this trend does not substantially contribute to our knowledge of human health and biology. For these reasons we find it more informative to reflect on the magnitude of the effect and less on the magnitude of the p-value. Complementary approaches which specify the ‘null’ model as a minimally meaningful effect (i.e., non-zero contrast matrix) may be more appropriate in such cases.

Importantly, while we focused on time enrolled in the study as the independent variable of interest, the framework we present can also be adapted to examine other temporal relationships in similarly generated data. For example, this framework can be used to compare data captured between discrete conditions such as intervention versus control, or to differentiate based on other quantitative measures. In these cases, the X matrix presented above can be remodeled to reflect the desired design matrix. Additionally, while our mixed model approach modeled data across all 38 study participants collectively, this framework can be also adapted to examine temporal trends on a single individual, such as that of an N-of-1 crossover design or how we examined individual trends in the N-of-1 sequential analysis approach above. In this case, the Z matrix can be omitted on account of no between subject effects, while R would remain.

One of the more intriguing aspects of this technology as a tool to enhance individual health is that data is collected, stored, and presented digitally without the need for direct interaction between the user and (as traditional) health professional. Likewise, we feel the processes of data inference can also be built into the technology to bypass the need for data interpretation by a professional data analyst. Certainly many technologies have such tools. Yet as new methods and extension of existing approaches, such as the framework we presented, are developed, these will need to be implemented into the technology in order to provide users with the best opportunity to make informed health decisions based on this data. The most immediate way this can be accomplished is by coding these methods directly on the device or software, or accessible to a cloud server where computations can be performed. However, other options include crowd-sourced initiatives such as an app store, where the public can design specialized software which provides automated analyses and interpretation of data back to the user.

Alternatively, the digital nature of data obtained from this technology opens up a number of interesting possibilities for researchers. Again, because data can be continuously collected without the need for personnel (e.g., study coordinators) to interact with users/study participants, approaches which benefit from data analyses over the course of the study may prove beneficial. We attempted to show this in the sequential analysis approaches above in which we were able to arrive at the primary study result over three months prior to the end of the study. Methods like this can be implemented directly into the study design, such as those in adaptive clinical trials. Moreover, because data on individuals can be recorded, collected, analyzed, and interpreted in real time, concepts such as early stoppage due to success or futility can apply not only to the study itself, but study participants as well—thereby minimizing risks, reducing costs, and forming conclusions earlier.

Data collected from digital medicine and smartphone-enabled health technologies offers tremendous potential to learn more about human health and biology. We applaud manufacturers for striving towards more comprehensive monitoring technologies, and when applicable encourage researchers to use this data source to help address research questions of interest.

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