BACKGROUND: The emergence and ubiquitous availability of personal miniaturized technologies, including self-tracking mobile and wearable devices, enable continuous, longitudinal data collection and facilitate “self-knowledge through numbers,” fulfilling the vision put forward by the “quantified-self” founders. Motivated individuals leverage these technologies, as well as self-reporting tools to track their behaviors, including those related to physical activity, sleep, alcohol consumption, foods, the presence of psychological stress, air travel, or more. Additionally, these technologies enable capturing certain physiological signals like body temperature (temp), respiration rate (RR), heart rate (HR), heart rate variability (HRV), or galvanic skin response (GSR) corresponding to the physical or psychological state of the individual.

Individuals can track a single behavior at its simplest, and use their self-tracking data for self-experimentation, changing it in the desired direction, like walking more steps or sleeping enough. However, these technologies can also enable more complex interventions and, if paired with disciplined scientific approaches to data analysis, they can provide more robust personalized insights. They are also able to help detect or even predict health issues by the mean of more advanced measurements like an electrocardiogram (ECG). When combining wearable ECG signals with artificial intelligence algorithms, illness prediction is possible, transforming these ubiquitous and accessible devices into a powerful source of self-information.

This study employs an n-of-1 observational study (N1OS) design and integrates data from two different technological touchpoints: a consumer-grade behavior and physiology tracking device; and an electronic self-reporting tool. We use the data to characterize nonathlete individuals and test our main hypotheses on the correlation of daily stressors with nighttime HR, an important health concerning the context of cardiovascular health. The nighttime HR is specifically defined as a nighttime resting heart rate when the body returns to a baseline, and no daily-life stressors are present. We will sometimes use the term “correlation” interchangeably with the broader and more statistically accurate term “association” for ease of understanding. However, note that the statistical definition of “correlation” is narrower than is commonly meant; i.e. a non-linear statistical association or dependence is not a statistical correlation.

Additionally, we evaluate the analytic impact of model-twin randomization (MoTR) on our inferences and conclusions. MoTR (“motor”) is a new causal inference method that artificially emulates an n-of-1 randomized trial (i.e. the gold standard due to randomization) from the N1OS dataset. It does so by first modeling the outcome of interest as a function of the exposure of interest, along with an individual’s assumed recurring confounders (i.e. daily observed variables thought to influence or affect both the exposure and the outcome). MoTR then randomly shuffles (i.e. permutes) the exposures, which were originally only observed, thereby simulating an n-of-1 randomized trial. This allows us to infer more accurately a suggested effect of daily stressors beyond just correlation.

Note that this study is not a case report, an observational study of a single participant. Unlike a case report, which has limited internal validity, our study uses MoTR to improve the veracity of findings of possible causal effects. In this way, an N1OS enables the discovery of findings for a given individual that is hard to achieve with standard group based observational study designs — and MoTR adjusts these findings to suggest possible interventions. These causal inference methods also facilitate the subsequent design and testing of the suggested effects in an n-of-1 randomized trial of these discovered effects.

The operational objective of this paper is to establish the feasibility of the N1OS design augmented with MoTR for generating and evaluating hypotheses about the idiographic (i.e. individual-specific) recurring average effect of an exposure (e.g. daily stressors) on the self-tracked outcome of each participant (e.g. nighttime HR). The analogous nomothetic (i.e. group-level) effects in randomized controlled trials (RCTs) are called “average causal effects” or “average treatment effects” (ATEs).

We chose daily life stressors like physical activity, insufficient sleep, excess alcohol consumption, certain foods, presence of psychological stress, and air travels as the exposure variables because they have a profound and acute effect on several aspects of health in a short, as well as long-term, especially when repeated and are behaviors that may be commonly tracked on current consumer devices or via a minimum self-reporting efforts. The nighttime HR is our selected health biomarker because it is affected by daily stressors in nonathletes and is also an important outcome measure of cardiovascular health.

This hypothesis exploration is based on the relevant literature on the importance of managing daily life stressors for short-term and long-term health outcomes. The intentional choice of nonathletic individuals was with an eye for preventing a chronic disease involving the cardiovascular system. The additional objective was to evaluate the MoTR method for generating and testing such idiographic hypotheses, potentially facilitating personalized management of stressors in daily life. As a result, we demonstrate an observational study design and analysis plan to contribute to and help guide rigorous self-tracking and n-of-1 study designs.

Read the full paper here.