Using ambulatory assessment to measure dynamic risk processes in affective disorders

Abstract

Background: Rapid advances in the capability and affordability of digital technology have begun to allow for the intensive monitoring of psychological and physiological processes associated with affective disorders in daily life. This technology may enable researchers to overcome some limitations of traditional methods for studying risk in affective disorders, which often (implicitly) assume that risk factors are distal and static - that they do not change over time. In contrast, ambulatory assessment (AA) is particularly suited to measure dynamic "real-world" processes and to detect fluctuations in proximal risk for outcomes of interest.

Method: We highlight key questions about proximal and distal risk for affective disorders that AA methods (with multilevel modeling, or fully-idiographic methods) allow researchers to evaluate.

Results: Key questions include between-subject questions to understand who is at risk (e.g., are people with more affective instability at greater risk than others?) and within-subject questions to understand when risk is most acute among those who are at risk (e.g., does suicidal ideation increase when people show more sympathetic activation than usual?). We discuss practical study design and analytic strategy considerations for evaluating questions of risk in context, and the benefits and limitations of self-reported vs. passively-collected AA.

Limitations: Measurements may only be as accurate as the observation period is representative of individuals' usual life contexts. Active measurement techniques are limited by the ability and willingness to self-report.

Conclusions: We conclude by discussing how monitoring proximal risk with AA may be leveraged for translation into personalized, real-time interventions to reduce risk.

Figure 1.

Example of a single participant’s data using a person-centered approach to measuring a risk factor (heart rate variability, HRV, a marker of regulatory capacity and depression risk (Beauchaine and Thayer, 2015; Hamilton and Alloy, 2016; Stange et al., 2017a, 2017d, 2017b; Yaroslavsky et al., 2014) repeatedly over time using ambulatory assessment. Each observation of the risk factor can be compared to the individual participant’s own average (one standard deviation from participant’s mean is shaded). Deviations from the average at each time point can be used as a within-subject predictor of outcomes of interest (e.g., negative affect at the next time point) within a multilevel statistical model.

Figure 2.

Example of modeling individual differences (at the between-subject level) in the slope of the within-subject relationship between person-centered negative events and subsequent depression symptoms.

Figure 3.

Example of modeling individual differences (at the between-subject level) in the intercepts of a risk factor, measured repeatedly over time with ambulatory assessment. Two hypothetical participants’ data are shown here.

Figure 4.

Example of two hypothetical participants with high instability (Person 1), and low instability (Person 2), of a potential risk factor measured with ambulatory assessment. The two participants have the same mean value of the risk factor (50), but they differ in levels of instability (mean square successive difference).