ECG collection and RSA-R quantification
RSA-R was extracted from each participant’s electrocardiogram (ECG)
using the following steps. ECG was acquired continuously at 2000 Hz
using a BIOPAC MP150 system with ECG 100C amplifiers and analyzed
offline using the AcqKnowledge 5.0.6 software (Biopac Systems, 2018).
The ECG signal was recorded during the MacArthur Reactivity Protocol
using the ECG100C amplifier with three pre-gelled Ag/AgCl electrodes
with a lead II configuration.
Offline, ECG signals were bandpass filtered using an FIR filter from 0.5
to 35 Hertz. Next, R-R intervals were extracted for the ECG signal using
a modified Pan-Tompkins QRS detector (Pan & Tompkins, 1985) and
resampled to 8hz after cubic spline interpolation. Data were then
visually inspected for movement artifacts or incorrect identification of
the QRS complex by the scoring algorithm.
RSA values were computed using a fast Fourier transform with a hamming
window within the respiratory frequency band that corresponded to the
participant’s age (0.2-1.0Hz for children under six, 0.15-0.5Hz for
children six and older; Boyce et al., 2001, Gentzler et al. 2009). RSA
values were computed in 60 second epochs and log transformed. RSA during
the first and last 60 seconds was averaged for each baseline and
challenge task, respectively. RSA reactivity (RSA-R) reflected the
difference score for each challenge task minus its corresponding
comparison task11The psychometric properties of using a
regression residual are preferable, but more difficult to interpret.
We computed all analyses with both difference scores and with the
regression residual and all results were identical. Difference scores
are reported to increase ease of interpretation..
Analytic plan
We characterized the feasibility of ECG collection in this sample (Aim
1) by computing the percent of collected, and subsequently usable, ECG
data for each MRP task across both visits. For participants with usable
ECG data at both visits, we ran intraclass correlations with RSA values
for corresponding tasks at visits 1 and 2 to assess the test-retest
stability of each RSA measure (Aim 1). ICC values were interpreted as
poor (< 0.4), fair (0.4-0.59), good (0.6-0.74), and excellent
(0.75-1.0; Cicchetti, 1994). Finally, for tasks with adequate ICCs, we
tested whether RSA-R related with concurrent externalizing behavior (Aim
2) at visit 1 via bivariate Pearson’s correlations. Before conducting
analyses, we first confirmed that age and developmental level were not
related to RSA or externalizing scores.
According to Bujang & Baharum (2017) with 80% power, 15 subjects with
data at both timepoints are required to detect an ICC of 0.6 (i.e.,
good). Seven subjects with data at both timepoints are required to
detect an ICC of 0.8 (i.e., excellent reliability). For Aim 2, data from
25 children would enable detection of a large effect size (ƒ2 = 0.35)
with power of 0.8 and α = 0.05. Fewer children with required language
levels were available to participate given the COVID pandemic, so we may
not detect all large effects for concurrent validity estimates. Given
the study was designed to pilot the initial feasibility and stability,
we believe the data reported could still be useful for planning future
studies.
Results