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Correlation Analysis of Nested Consumer Health Data: A New Look at an Old Problem
Abstract
Correlation analysis is one of the most popular analytic methods for discovering the relationship between two variables in consumer health data. Nevertheless, the simple sample correlation techniques (e.g., Pearson’s or Spearman’s correlation) assume the independence of the observations, which is often violated when there are repeated measures from each subject (also called nested data). In this paper, we compare three methods for correlation calculation on nested consumer health data: (1) repeated measure correlation (rmcorr), (2) subject mean correlation (smcor), and (3) simple sample correlation (sscor). We contend that rmcorr is a more appropriate technique for the correlation analysis of nested data as it could explicitly capture the within-subject variations. In contrast, the smcor and sscor, which could only capture the cross-subject variations, may produce misleading and faulty results.
It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75–0.88) and lift (4.1–11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.
A variety of self-tracking applications and devices have been developed in recent years to support users in tracking their weight, calories eaten, physical activities, sleep and productivity. The availability of all this data from multiple streams provides a rich environment for experimentation that allows users to improve certain aspects of their lives such as losing weight, getting better sleep or being more productive. In this paper we propose a framework that guides users to define, track, analyse, improve and control goals for better personal productivity. We present the outcome of a single-subject case study that was implemented over one year based on the proposed framework for academic productivity. This pilot study demonstrates how longitudinal multistream self-tracking data can be leveraged to gain actionable insights into personal productivity.
Background
Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.
Objective
The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).
Methods
This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.
ResultsThe participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P
Background and objective:
Increasing physical activity (PA) is safe and beneficial in lung cancer (LC) patients. Advanced-stage LC patients are under-studied and have worse symptoms and quality of life (QoL). We evaluated the feasibility of monitoring step count in advanced LC as well as potential correlations between PA and QoL.
Methods:
This is a prospective, observational study of 39 consecutive patients with advanced-stage LC. Daily step count over 1 week (via Fitbit Zip), QoL, dyspnea, and depression scores were collected. Spearman rank testing was used to assess correlations. Correlation coefficients (ρ) >0.3 or <-0.3 (more and less correlated, respectively) were considered potentially clinically significant.
Results:
Most (83%) of the patients were interested in participating, and 67% of those enrolled were adherent with the device. Of those using the device (n = 30), the average daily step count was 4877 (range = 504-12 118) steps/d. Higher average daily step count correlated with higher QoL (ρ = 0.46), physical (ρ = 0.61), role (ρ = 0.48), and emotional functioning (ρ = 0.40) scores as well as lower depression (ρ = -0.40), dyspnea (ρ = -0.54), and pain (ρ = -0.37) scores.
Conclusion:
Remote PA monitoring (Fitbit Zip) is feasible in advanced-stage LC patients. Interest in participating in this PA study was high with comparable adherence to other PA studies. In those utilizing the device, higher step count correlates with higher QoL as well as lower dyspnea, pain, and depression scores. PA monitoring with wearable devices in advanced-stage LC deserves further study.
It has been widely recognized that discovering potential contributing factors to personal sleep is as important as understanding sleep pattern per se. However, in large quantified-self datasets, contributing factors may only show correlations to sleep when their values are within certain ranges. Existing correlation analysis using Pearson Correlation Coefficient cannot identify such hidden dependencies. We propose a new method based on association rules mining. Our method not only can discover hidden correlations that existing methods cannot, but also provides users with actionable knowledge to guide sleep improvement through lifestyle change.
Psychology is focused on variation between cases (interindividual variation). Results thus obtained are considered to be generalizable to the understanding and explanation of variation within single cases (intraindividual variation). It is indicated, however, that the direct consequences of the classical ergodic theorems for psychology and psychometrics invalidate this conjectured generalizability: only under very strict conditions-which are hardly obtained in real psychological processes-can a generalization be made from a structure of interindividual variation to the analogous structure of intraindividual variation. Illustrations of the lack of this generalizability are given in the contexts of psychometrics, developmental psychology, and personality theory.
Wearable consumer activity trackers have become a popular tool for longitudinal monitoring of sleep quality. However, sleep data were routinely visualized in isolation from other contextual information. In this paper, we proposed a sleep analytics method to identify the associations between sleep quality and the contextual data that are readily measurable with a single Fitbit device. Different from prior studies that only focused on the daily aggregation of the contextual factors (e.g., total step counts), our method considers the intraday temporal patterns of these factors. Time-domain, frequency-domain, and nonlinear features were derived using the minute-by-minute intraday step and heart rate time series. The results showed that some of the identified contextual features such as the zero-crossing of steps and the absolute energy of heart rate could lead to actionable insights. While the nonlinear features-such as the average and longest diagonal line length derived through the recurrent quantitative analysis of the step time series-may not lead to insights that can be immediately acted on, they generated new hypotheses for further scientific studies. The results also showed that when dealing with data of consumer wearables, the individual-level analysis could generate more personally relevant insight than the cohort-level analysis.
Preventive health care is considered a promising solution to the prevalence of chronic diseases. Nevertheless,
preventive health care at the population-level adopts an one-fit-all approach. We intend to solve the problem
through promoting preventive health care at the individual level based on self-quantification. Nowadays
millions of people are tracking their health conditions and collecting huge quantity of data. We propose a Preventive
Health care on Individual Level (PHIL) framework that guides people to leverage their self-tracking
data to improve personal health, which forms a data-driven but objective-oriented methodology. The PHIL
framework consists of five phases: Define, Track, Analyze, Improve and Control (DTAIC), covering the whole
process of a complete self health care project. While the proposed PHIL framework can be implemented to
achieve various health benefit, we selectively present one case study where the subject designed and conducted
a self health care project for sleep quality improvement under the PHIL framework. We hope the proposed
framework can help change the passive role of health care receivers in traditional health care system, and
empower people to actively participate in the health care ecosystem and take the initiative in managing and
improving personal health.