Uncovering general, shared, and unique temporal patterns in ambulatory assessment data

Abstract

Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, processes shared by a subset of the sample, and processes unique to a given individual. As this algorithm was motivated and validated for use with neuroimaging data, its performance and utility is less understood in the context of ambulatory assessment data collected by psychologists. Here, we evaluate the performance of the S-GIMME algorithm across various conditions frequently encountered with daily diary (compared to neuroimaging) data; namely, a smaller number of variables, a lower number of time points, and smaller autoregressive effects. Importantly, we demonstrate for the first time the importance of the autoregressive effects in recovering data-generating connections and directions, and the ability to use S-GIMME with lengths of data commonly seen in daily diary studies. We demonstrate the use of the S-GIMME algorithm with an empirical example evaluating the general, shared, and unique temporal processes associated with a sample of individuals with borderline personality disorder (BPD). Finally, we underscore the need for methods such as S-GIMME moving forward given the increasing use of intensive longitudinal data in psychological research, and the potential for these data to provide novel insights into human behavior and mental health.

Characterizing the role of the pre‐SMA in the control of speed/accuracy trade‐off with directed functional connectivity mapping and multiple solution reduction

“…First, the sample of participants was relatively small. However, as the time series data used in our analysis had many more observations than time series data previously used to validate the GIMME/uSEM method (60–200 time points, vs. the 950 time points used in the current study; Gates & Molenaar, ; Lane, Gates, Pike, Beltz, & Wright, ), this analysis likely had relatively high power to detect functional connections between ROIs at the individual subject level. As accurate recovery of connections present in a group can be obtained with as few as 10 subjects (Gates & Molenaar, ), we can be reasonably confident that the path counts reported in the group frequency maps closely approximate the true number of connections in the sample.…”

Foundations of idiographic methods in psychology and applications for psychotherapy

“…The second subgroup was best described by contemporaneous relationships between anxiety and mood lability and impulsivity, mood lability and urgency and anger, and urgency and emptiness. Interestingly, the third subgroup consisted of just one individual (Lane et al, 2018). Empirical results suggest that GIMME methods can be used for ecological momentary assessment data and provide direction for researchers interested in analyzing and comparing how idiographic and group level models differ from each other within the same sample.…”

“…Lane and colleagues used S-GIMME to describe results using daily diary data from a subsample of individuals with borderline personality disorder (i.e., the same sample discussed earlier in Wright et al, 2016). Daily responses regarding mood lability, anxiety, depression, anger, impulsivity, emptiness, and urgency were collected and entered into the S-GIMME model (Lane, Gates, Pike, Beltz, & Wright, 2018). Results suggested that mood lability predicted depression over the course of the day and all items exhibited some stability over time (i.e., statistically significant autoregressive relationships).…”

Latent variable GIMME using model implied instrumental variables (MIIVs).

“…These become what are considered the group-level paths. GIMME has previously only been evaluated at the 75% threshold for what defines the "majority" (Gates et al, 2017;Gates & Molenaar, 2012;S. Lane et al, 2018).…”