Trait Stress Resistance and Dynamic Stress Dissipation on Health and Well-Being: The Reservoir Model

Abstract

Daily data from the NDHWB (n = 783; age range 37-90) were analyzed to produce 'dynamic characteristic' estimates of stress input and dissipation. These were used in multi-level modeling (with age and trait stress resistance) to predict depression and health trajectories. Main effects suggest that dissipation and stress resistance predict lower depression and better health, but lower stress input was only related to lower depression. Interactions revealed that subjects with above average stress resistance had lower depression irrespective of their ability to dissipate stress, but for individuals low in trait resistance those with better stress dissipation show lower depression and better health.

FIGURE 1

Sample time series from the reservoir model. Panel A shows a reservoir with liquid (e.g., a stressor) being added, and a hole in the base that leaks water (left). Panel B (right) shows a plot of the height of the liquid as it changes over time; in this example the inputs and outputs are nearly balanced, so the person’s time series appears to vary around a nonzero equilibrium. Panel C is similar to Panel A, but the hole in the bottom of the reservoir is larger. Consequently, the level of liquid the reservoir can dissipate quickly, resulting in floor effect in this person’s time series (Panel D, right).

FIGURE 2

Structural equation model of the reservoir model. The 0^th and first derivatives, x and dx/dt, respectively, are estimated by fixing the paths to pairs of subsequent observations on the construct X, that is X(t) and X(t+1). The inputs, ε_input, consist of a range of discrete values constituting a separate class c; the probability of each class, and consequently each input value, is estimated. By multiplying the input values by the probability of each value and summing, one can calculate the average of ε_input. The parameter β, the relationship between the derivatives, is a negative value related to the rate of change. The negative β, when multiplied by the positive values of x, will result in a negative value for dx/dt (velocity, i.e., linear change) thus dissipating the construct of interest. From Deboeck and Bergeman, 2013.

FIGURE 3

Histograms representing the distribution of stress dissipation and input scores at Wave 1. The black line represents the mean of the distribution (−.65 for dissipation and 4.54 for input). The gray lines signify 1st quartile, median, and 3rd quartile. This figure collapse the estimates of input and dissipation made across all three waves, for all participants.

FIGURE 4

Plots of the effects of stress resistance (dispositional resilience), stress dissipation, and average perceived input on depression (A and B) and Health (C) using unstandardized parameter estimates. Dissipation values near zero represent a low rate of dissipation, whereas large negative values represent higher rates of stress dissipation.