1. Introduction
Boredom is an aversive
experience characterized by a desire to engage in meaningful activity, coupled
with an inability to do so (Eastwood et al., 2012). For many, boredom is a
situational, relatively brief, and unpleasant emotion that arises in monotonous
or understimulating environments and is commonly
referred to as state boredom (Hunter & Eastwood, 2018; O’Hanlon, 1981; Westgate & Steidle, 2020). Experimentally induced state
boredom is associated with heightened frustration (van Hooft & van Hoof, 2018) and increased motivation to seek
stimulation, even when that stimulation is unpleasant (Bench & Lench, 2019; Nederkoorn
et al., 2016). In contrast, some individuals
exhibit a chronic tendency toward boredom across contexts, even in situations
others find engaging. This enduring vulnerability, referred to as trait
boredom or boredom propensity (Farmer & Sundberg, 1986), has been linked to numerous
adverse outcomes, including poorer occupational and academic outcomes, reduced
intrinsic motivation, greater engagement in maladaptive behaviors, and elevated
symptoms of depression and anxiety (Carriere et al., 2008; Goldberg et al., 2011; Mercer & Eastwood, 2010; O’Hanlon, 1981; Pekrun et al., 2010; Sommers & Vodanovich, 2000; Tze et al., 2016). Furthermore, trait boredom is
particularly pronounced among individuals with attention deficit/hyperactivity
disorder (Muris et al., 2026).
Cognitive theories suggest boredom arises from failures
to regulate attention toward meaningful aspects of one’s environment (Eastwood
et al., 2012; Fisher, 1993), whereas arousal-based theories propose that
boredom occurs when environmental stimulation is insufficient to meet an
individual’s optimal level of arousal (Csikszentmihalyi, 2000; De Chenne,
1988; Mercer-Lynn et al., 2014). Accordingly, state boredom is
more likely in low arousal environments that tax sustained attention (Mikulas
& Vodanovich, 1993),
while trait boredom may reflect enduring differences in attentional capacity
interacting with environmental demands (Eastwood et al., 2012). Consistent with this view,
individuals high in trait boredom perform more poorly on tasks requiring
sustained attention and vigilance (Hunter & Eastwood, 2018; Kass et al., 2003; Malkovsky
et al., 2012) and report greater difficulty managing
attention in daily life (Carriere et al., 2008; Malkovsky
et al., 2012).
Tam et al. (2021)
proposed the Boredom Feedback Model (BFM), an integrative attentional account
that builds on both cognitive and arousal-based theories. BFM posits that
boredom arises when there is inadequate attentional engagement (IAE) which
reflects a discrepancy between one's desired and actual levels of attentional
engagement. When this discrepancy reaches a noticeable threshold, boredom is
experienced and attention shifts either outward (e.g., engaging in a more
interesting or stimulating task), inward (e.g., mind wandering) or back toward
the boring situation by actively reengaging with it, such as finding ways to
make the task more interesting or meaningful. These shifts form a regulatory
feedback loop aimed at resolving the discrepancy. When the loop fails to resolve,
which may occur in individuals with chronic attentional difficulties, boredom persists,
and adverse outcomes may follow. Importantly, BFM also proposes that trait
boredom may reflect dysfunction of this feedback loop, with different profiles
emerging depending on whether attentional deficits, lack of meaningful
engagement, or both predominate.
Trait boredom is typically measured using self-report
scales such as the Boredom Susceptibility subscale of the Sensation Seeking
Scale (BSS; Zuckerman, 1964),
the Boredom Proneness Scale (BPS; Farmer & Sundberg, 1986), and the Short Boredom Proneness
Scale (SBPS; Struk et al., 2017).
The BSS emphasizes boredom arising from low environmental stimulation and a
tendency to seek stimulation, whereas the BPS captures broader difficulties
engaging meaningfully with one’s environment, including disengagement and low
motivation. Empirical work suggests these measures capture different aspects of
the boredom experience (Mercer-Lynn et al., 2013). The SBPS was developed to address
psychometric limitations of the BPS and provides a unidimensional measure of
persistent difficulties sustaining engagement in meaningful activities.
Together, the BSS and SBPS capture complementary aspects of trait boredom:
stimulation-seeking responses to under-stimulation versus challenges in
sustaining engagement, respectively. These aspects may reflect different
behavioral manifestations of a shared attentional mechanism rather than wholly
independent constructs.
Although ADHD is typically characterized by inattention,
hyperactivity, and impulsivity, recent evidence suggests that elevated boredom
is also an important feature of the disorder. A recent meta-analysis (Muris et
al., 2026) found a robust overall effect (r=
.40) indicating that ADHD symptoms are strongly linked with higher levels of
boredom, highlighting its relevance as a core symptom rather than a peripheral
feature. Indeed, medium to strong correlations between boredom propensity and
ADHD symptoms have been documented in non-ADHD samples (Hunter & Eastwood, 2018; Kass et al., 2003; Mercer-Lynn et al., 2014) and studies of individuals with
ADHD report strong correlations between ADHD symptoms and boredom propensity
(Chou et al., 2018; Golubchik et al., 2020) as well as large group differences
in trait boredom (Hsu et al., 2025;
Orban et al., 2026) compared to non-ADHD controls. The
chronic attentional difficulties characteristic of ADHD may render individuals
particularly vulnerable to inadequate attentional engagement, predisposing them
to more frequent and intense boredom experiences and impairing their ability to
regulate out of boredom once it arises.
Several studies have examined potential mechanisms
underlying boredom proneness in ADHD. One candidate mechanism is attention
control, an executive function reflecting the ability to sustain goal-directed
behavior by maintaining task-relevant information and resisting distraction
(Draheim et al., 2022).
Research suggests that individual differences in the ability to sustain and
regulate attention influence vulnerability to boredom across settings (Tam et
al., 2021). Deficits in attention control may
therefore contribute to boredom by disrupting adequate attentional engagement.
Specifically, when individuals struggle to maintain attentional engagement in
the face of distraction, a discrepancy between desired and actual attention
engagement may emerge, producing the conditions for boredom regardless of the
objective stimulation level of the task. Although prior work has examined
cognitive processes related to boredom in ADHD, these studies have largely
focused on constructs other than attention control. For example, Golubchik et al.
(2021) found that children with ADHD who
had higher levels of boredom propensity demonstrated slower reaction times on a
test of sustained attention, while Hsu et al. (2025) found that delay aversion—reflecting
weaknesses in inhibitory control—partially mediated ADHD-related boredom. In
contrast, only one study to date has directly examined attention control in
relation to boredom in ADHD. Orban et al. (2026) found that young adults with elevated ADHD
symptoms and boredom propensity performed worse on tasks of attention control
and working memory, with these executive functions partially mediating the
association between ADHD group status and boredom.
Together, these findings suggest that elevated boredom in
ADHD may reflect both behavioral features of the disorder and underlying
executive dysfunction. However, the relative contributions of these processes
remain unclear. One possibility is that boredom arises from the behavioral
symptom profile of ADHD, such as difficulties with focus and distractibility,
which may increase vulnerability to disengagement from ongoing activities.
Alternatively, boredom may stem from deficits in attention control that impair
the ability to maintain attentional engagement across settings. The present
study therefore examined whether ADHD symptoms and performance-based attention
control differentially predict trait and state boredom.
State boredom is typically assessed via experimental
induction followed by self-report using measures such as the Multi-Dimensional
State Boredom Scale (MSBS; Fahlman et al., 2013), which includes multiple subscales and a
total score reflecting overall state boredom intensity. Past research indicates
a reciprocal relationship between attentional failures and state boredom, such
that commission errors on a continuous performance test (CPT) predicted
subsequent increases in state boredom, which in turn predicted further
attentional errors on later trials, though this relationship did not emerge in
the initial block of trials (Hunter & Eastwood, 2018). This suggests that momentary lapses in
attention and boredom may mutually reinforce one another over time,
contributing to the cycle of disengagement. When individuals have difficulty
sustaining attention, they may disengage from the activity, consistent with the
view that state boredom arises when a discrepancy between desired and actual
attentional engagement reaches a threshold that triggers the subjective
experience of boredom and motivates a shift toward more engaging alternatives
(Tam et al., 2021).
Therefore, individuals with ADHD, who experience chronic
difficulties regulating and sustaining attention, may be particularly
susceptible to state boredom across settings. For example, children with ADHD
exhibited reduced attention while watching a boring math video compared to
neurotypical children, and group differences in attention were mediated by
working memory such that when working memory was accounted for, attentional
differences were no longer significant (Orban et al., 2018). Similarly, Hsu et al. (2020) found that children with ADHD
reported higher levels of state boredom than controls on the MSBS, and boredom
ratings were positively correlated with inattention indices on a CPT. These
findings suggest that deficits in attentional engagement may lower the
threshold for state boredom during monotonous tasks, contributing to greater
susceptibility to boredom in ADHD.
Although relatively few studies have experimentally
examined state boredom in adults with ADHD, Matthies et al. (2012) provides an important example. In
their study, boredom was induced prior to a gambling task and self-reported
boredom increased significantly following induction, though the primary aim was
to examine risky decision-making rather than systematically measure state
boredom. As a result, it remains unclear whether individuals with ADHD
experience greater state boredom than controls across different situational
contexts, or whether elevated boredom in ADHD reflects a more general
vulnerability in attentional engagement that manifests regardless of task type.
Beyond subjective experiences of boredom, ADHD may also
be associated with behavioral responses to boredom in monotonous settings.
According to the Optimal Stimulation Model (Zentall
& Zentall, 1983), individuals with ADHD may engage in
increased motor activity (i.e., fidgeting) to regulate suboptimal arousal
levels. Although arousal-based models of boredom have traditionally emphasized
low arousal as a core feature of boredom (Mikulas & Vodanovich, 1993), the relationship between boredom
and arousal is more complex. A recent meta-analysis found that while boredom is
commonly associated with low arousal, effect sizes varied substantially across
studies, suggesting a nuanced and heterogeneous relationship between boredom
and arousal (Stempfer et al., 2025). Indeed, boredom has been
characterized as involving both sleepiness and restlessness simultaneously
(Danckert et al., 2018)
and arousal may not be a defining characteristic of boredom per se but rather
reflects the person’s attempts to respond to an unsatisfactory situation (Elpidorou, 2021).
From this perspective, motor activity may reflect the restlessness component of
the boredom experience rather than purely compensatory arousal regulation.
Neuroimaging findings indicating underarousal and
reduced prefrontal cortical activation in ADHD (Cortese et al., 2012; Hart et al., 2013) suggest that individuals with ADHD
may be particularly vulnerable to the low arousal component of boredom, while
their tendency toward hyperactivity may amplify the restless response. Although
boredom-related motor activity has not been directly examined in ADHD samples,
children with ADHD exhibit increased movement during cognitively demanding
tasks (Dekkers et al., 2021;
Kofler et al., 2016; Rapport et al., 2009; Sarver et al., 2015), suggesting that motor activity
may function as a compensatory response to boredom when attentional engagement
falters.
The present study examined ADHD-related differences in
both trait and state boredom and investigated the relative contributions of
self-reported ADHD symptoms and performance-based measures of attention
control. Specifically, we addressed four research questions: (1) whether
individuals with elevated ADHD symptoms report higher levels of trait boredom
than controls; (2) whether individuals with elevated ADHD symptoms experience
greater increases in state boredom following a boredom induction task; (3)
whether boredom is associated with increased motor activity in individuals with
elevated ADHD symptoms; and (4) whether performance-based attention control
predicts trait and state boredom beyond ADHD symptom severity. We hypothesized
that individuals with elevated ADHD symptoms would exhibit higher trait boredom
on the SBPS and greater motor activity during the boring video condition. We
also expected that both ADHD symptoms and performance-based attention control
would predict SBPS and MSBS boredom. No specific hypotheses were formulated for
state boredom during the boredom induction task or ratings on the BSS due to
limited research examining these dimensions of boredom in ADHD populations or
in relation to attention functioning.
2. Method
2.1. Participants
Ninety-seven
undergraduate students from a southeastern university in the United States were
recruited and participated in the current study. Most participants were
recruited using an undergraduate research participation pool and were
compensated with partial course credit for their general psychology course (n=
69). At the same time, additional students were recruited using on-campus
advertisements (n= 28) and received a $25 Amazon gift card as
compensation. Interested participants completed an electronic prescreening
survey to determine eligibility for inclusion in either the group of
individuals with elevated ADHD symptoms or control group (as described below).
Three participants initially assigned to the control group were excluded after
reporting a prior or current ADHD diagnosis. The final sample consisted of 94
participants with 78.7% identifying as female and 21.3% as male (M age=
19.78, SD age= 2.69). All participants provided their informed consent
and the university’s institutional review board approved the study prior to the
onset of data collection.
2.2. Group Assignment
Thirty-nine participants
were included in the elevated ADHD symptoms group based on exceeding threshold
criteria on the Adult ADHD Self-Report Screening Scale (ASRS-5; Ustun et al., 2017)
for a total score of 14 or greater OR on the Conners’ Adult Rating Scale (T
≥60; CAARS-S:S; Conners et al., 1999).
This approach allowed us to maximize sensitivity in identifying individuals
with elevated ADHD symptoms while minimizing the risk of excluding participants
who may present with ADHD but differ in symptom reporting across instruments.
In the group of individuals with elevated ADHD symptoms, nine participants
reported a prior diagnosis of ADHD, nine participants reported a current
diagnosis of ADHD, and four participants reported taking medication for ADHD
including two who indicated medication use the morning of the research session.
Fifty-five participants were included in the control group based on having a
total score of 13 or less on the ASRS-5 AND a score within the average range on
the ADHD Index Scale of the CAARS-S:S (T<60). Exclusion criteria
included those with neurological, sensory, or serious motor impairment, a
history of seizure disorder, or psychosis. Comorbidities in the elevated ADHD
symptoms group included learning disability (n= 6) and concussion (n=
5), with no reports of autism spectrum disorder; among controls, only one
participant reported a concussion.
2.3. Measures
2.3.1. Self-Report Behavioral Measures
The Adult ADHD
Self-Report Screening Scale for DSM-5 (ASRS-5; Ustun
et al., 2017) measures symptoms of ADHD in
adults. Six items are rated on a 5-point Likert scale (0= never, 4= very
often). The scale evaluates the frequency of reported ADHD symptoms over
the last 6 months (e.g., “How often do you have difficulty concentrating on
what people are saying to you even when they are speaking to you directly?”;
“How often do you put things off until the last minute?”). Total scores ≥ 14
are suggestive of ADHD (91.4% sensitivity; 96.0% specificity; Ustun et al., 2017).
Scores below 14 reflect normal functioning.
The
Conners’ Adult ADHD Rating Scale–Self Report: Short Version (CAARS–S:S; Conners
et al., 1999) measures ADHD symptoms in adults.
The scale includes 26 items rated on a 4-point Likert scale (0= not at
all/never, 3= very much/very frequently). Items assess the frequency
and intensity of behaviors associated with ADHD recently (e.g., “I’m absent
minded in daily activities,” “I interrupt others when talking”). The CAARS–S:S
yields subscale scores for Inattention/Memory Problems, Hyperactivity/Restlessness,
Impulsivity/Emotional Lability, and Problems with Self-Concept, as well as an
overall ADHD Index. Higher T-scores indicate greater symptom severity. T-scores
≥ 60 are considered above average (Conners et al., 1999).
The
Short Boredom Proneness Scale (SBPS; Struk et al., 2017) is an eight-item self-report measure that
uses a 5-point Likert scale (0= strongly disagree, 4= strongly agree).
The scale is designed to measure one’s propensity to boredom (i.e., trait
boredom) reflecting persistent difficulties sustaining engagement in meaningful
activities over the past six months. Example items on the scale include “I find
it hard to entertain myself” and “In most situations, it is hard for me to find
something to do or see to keep me interested.” Higher scores reflect a higher
propensity to boredom. The SBPS is a widely used shortened version of the
original Boredom Proneness scale (BPS; Farmer & Sundberg, 1986) and has strong construct validity
and internal consistency indices (α=0.88; Struk et al., 2017).
The
Boredom Susceptibility Scale (BSS) is a subscale of Zuckerman’s Sensation
Seeking Scale – Form Five (SSS-V; Zuckerman et al., 1978). The BSS consists of 10 forced
choice items and is intended to measure difficulty tolerating monotonous
environmental stimulation. Example items include “I have projects in mind all
the time‚ things to do.” and “I am often trapped in situations where I have to
do meaningless things.” Prior research indicates modest internal consistency
for the Boredom Susceptibility subscale (BSS), with a median coefficient alpha
of .61 across studies (Deditius-Island & Caruso, 2002), split-half reliabilities ranging
from .38 to .75 (Vodanovich, 2003),
and acceptable test–retest reliability over three weeks (r= .70;
Zuckerman, 1979). In the present study, internal
consistency for the BSS was moderate (Cronbach’s α = .60), consistent with
prior studies. Higher scores reflect greater boredom susceptibility.
The
Multidimensional State Boredom Scale (MSBS; Fahlman et al., 2013) assesses the intensity of
momentary boredom as a transient emotional state. The 29-item self-report
measure evaluates the degree to which individuals experience feelings such as
disengagement, inattention, and lack of stimulation in the present moment
(e.g., “Time is passing by slower than usual,” “I feel bored”). Items are rated
on a 7-point Likert scale (1= strongly disagree, 7= strongly agree),
with higher scores indicating greater state boredom. Although the MSBS includes
five subscales (Disengagement, High Arousal, Low Arousal, Inattention, and Time
Perception), a total score representing overall state boredom was used in the
current study. The MSBS exhibits excellent internal consistency (α= .94 to .95)
and strong construct and convergent validity, reliably distinguishing
experimentally induced boredom from non-boredom states and capturing state
boredom beyond trait boredom and negative affect (Fahlman et al., 2013).
A
demographic questionnaire was administered to indicate the participant’s age,
gender, history of current and past ADHD diagnosis and medication use, and
history of learning disability, autism spectrum disorder, or concussion.
2.3.2. Objective
Behavioral Measures
An actigraph
is a motion-sensitive device used to measure motor activity. The ActiGraph GT3X monitor (ActiGraph,
Pensacola, FL, USA) is lightweight (27 g), compact (3.8 × 3.7 × 1.8 cm), and
powered by a rechargeable lithium polymer battery. It contains a solid-state
tri-axial accelerometer that records movement along three axes: vertical (Y),
horizontal right–left (X), and horizontal front–back (Z). In addition to
axis-specific data, the device calculates Vector Magnitude (VM), which
represents the combined magnitude of acceleration sampled across all three
axes. The GT3X captures time-varying accelerations ranging from approximately
0.05 to 2.5 G. Acceleration signals are digitized using a 12-bit
analog-to-digital converter at a sampling rate of 30 Hz. The digitized signal
is then passed through a digital filter that restricts the frequency bandwidth
to 0.25–2.5 Hz. Activity data are aggregated into 10-second epochs, and output
is expressed in activity “counts.” These counts are linearly associated with the
intensity of the participant’s physical activity during each epoch. Actigraphs were secured immediately above the participant’s
left and right ankles using Velcro watch bands. A third device was worn on the
non-dominant wrist, as participants used their dominant hand for completing
tasks and questionnaires.
ActiLife software version
6.13.4 (ActiGraph LLC, Pensacola, FL, USA) was used
to initialize the device and download the time-stamped VM Average Counts. ActiLife Software calculates VM Average counts by dividing
total VM counts by the number of epochs. For example, an average VM count value
of 30 indicates that the participant generated an average of 30 activity counts
per 10-second epoch during the wear period. VM average counts were further
averaged across the three placement sites to reflect a mean activity level
during the neutral and boring video tasks. A research assistant recorded the
precise start and end times of each video, which were synchronized with actigraph time stamps. For analysis, seconds were rounded
to the nearest whole minute.
2.3.3. Performance
Based Measures
The sustained attention
to cue task (SACT) is an accuracy-based vigilance task that measures attention
control. First, participants are presented with a visual cue (+) in the center
of the screen, which displays for two seconds on half of the trials and three
seconds on the other half. After the cue, a 300-ms tone is presented and a
large white circle cue is displayed at a random location on the screen.
Participants are instructed to attend to the circle, which shrinks in size at a
variable wait time (2s, 4s, 8s, and 12s equally distributed among trials) until
it reaches a fixed size. Following the wait time, a distractor component (a
white asterisk symbol) flashes at 100ms on screen, 100ms off, and 100ms back on
for a total of 300ms. Finally, a 3 X 3 array consisting of four letters (i.e.,
B, D, P, and R) was displayed at the center of the circle cue. The target
letter was presented in dark gray font in the center of the array for 125ms
before being replaced with a ‘#’ symbol. The nontarget letters were presented
in black font and remained visible on the screen. After 1000ms, a response
screen appeared, and participants were instructed to select the correct target
letter with the option to choose between B, D, P, or R each time. Sixty-four
total trials were administered with 6 practice trials that provided feedback to
participants. Accuracy rate (i.e., number of correct trials) was used as a
dependent variable as recommended (Draheim et al., 2021).
2.4. Boredom Induction
Participants were
randomly assigned to either a neutral or boring video condition. In the neutral
condition, participants watched the first 40 minutes from the 90-minute
documentary In Search of Memory (Seeger, 2009), which chronicles the life and
research of neuroscientist Eric Kandel. In the boring condition, participants
viewed the first 90 seconds of the documentary, which looped continuously for
40 minutes. This design was informed by prior research demonstrating that
repeated exposure to a short, minimally engaging video segment reliably induces
boredom. The film has been used successfully in prior studies utilizing boredom
induction techniques (Havermans et al., 2015; Nederkoorn
et al., 2016). Although previous studies have
often employed longer durations (e.g., 60 minutes or more), a 40-minute
induction was selected to balance effectiveness with feasibility and to reduce
participant burden. All videos were presented on the same desktop computer.
2.5. Procedure
Prior to the research
session, participants completed a screening survey via Qualtrics to determine
their eligibility, which included a demographic questionnaire, the ASRS-5, and
the CAARS-S:S. Eligible participants were then scheduled for a single 90-minute
in-person laboratory session. Upon arrival, participants were seated alone at a
desk facing a desktop computer. Each session was conducted and monitored by a
research assistant who remained out of view, except when presenting
instructions for questionnaires and computer tasks. After obtaining informed
consent, actigraphs were placed on the participant's
non-dominant wrist and both ankles. The ADHD measures were then readministered
to confirm eligibility, and the responses collected during the laboratory
session were used for all subsequent analyses. Participants next completed the
computerized SACT. Following the SACT, participants completed measures of trait
boredom (i.e., SBPS, BSS) and pre-video state boredom (i.e., MSBS) to assess
baseline state boredom. Participants were then randomly assigned to view
either the boring or neutral video for 40 minutes. Immediately following the
video, participants completed the post-video MSBS to assess changes in state
boredom following the video tasks. Finally, participants were debriefed
regarding the purpose of the study and dismissed.
2.6. Power Analyses and
Missing Data
Missing data was
minimal. Actigraphy data from four participants were lost due to technical
error. SACT data from 8 participants were lost due to experimenter (n=
7) or technical error (n= 1), resulting in a sample of 86 participants
for regression analyses involving the SACT. BSS data from three participants
were missing due to questionnaire error (n= 2) and misinterpretation of
instructions (n= 1), resulting in a sample of 91 for t tests and
mixed ANCOVA analyses and 85 for regression analyses involving the BSS. These
data losses were unrelated to participant characteristics or study condition.
All analyses were conducted using SPSS Version 31.
Post hoc sensitivity analyses using G*Power determined
the minimum detectable effect size with 80% power and alpha = .05 for each
analysis (see Appendix A). For the independent samples t-tests, medium effect
sizes could be reliably detected (SBPS: d= 0.59, n= 94; BSS: d=
0.60, n= 91). For the mixed ANOVA, small-to-medium effects could be
detected for main effects and two-way interactions (f = 0.10, n=
94), though power for interaction effects involving group was limited (observed
power = .08-.23). For the mixed ANCOVA, small-to-medium within-subjects (f =
0.12) and medium between-subjects (f = 0.30), could be detected (n= 91).
For the actigraph ANOVA, medium effects could be
detected (f = 0.30, n= 90). For the regression analyses,
small-to-medium effects could be detected (Models 1 (n=86) and 2 (n=85):
f² = 0.12 Model 3 (n=85): f² = 0.15).
3. Results
An independent measures
t-test was conducted to determine whether individuals with elevated ADHD
symptoms exhibit higher trait boredom than controls. A statistically
significant difference in trait boredom depending on group membership was found
for both the SBPS, t(92)= -7.97, p<
.001, two-tailed, d= 1.67 and the BSS, t(89)=
-2.20, p= .030, two-tailed, d= 0.47. Specifically, individuals
with elevated ADHD symptoms reported significantly higher levels of trait
boredom on both measures than controls. It should be noted that the observed
effect size for the BSS group difference (d= 0.47) fell slightly below
the detectable threshold for this analysis (d= 0.60), suggesting that
this finding should be interpreted with some caution and warrants replication
in larger samples. See Table 1 for means and standard deviations.[1]
|
Table 1. Means, Standard Deviations, and
Independent Samples t Tests |
|||||||||||
|
Variable |
ADHD |
Control |
Group Differences |
||||||||
|
|
n |
M |
SD |
n |
M |
SD |
t |
p |
d |
||
|
Age |
39 |
19.3 |
1.1 |
54 |
20.1 |
3.4 |
1.4 |
.169 |
0.29 |
||
|
ASRS-5 |
39 |
15.6 |
3.3 |
55 |
7.6 |
2.9 |
-12.58 |
<.001** |
2.63 |
||
|
CAARS-S:S |
39 |
66.6 |
5.8 |
55 |
45.8 |
6.8 |
-15.59 |
<.001** |
3.26 |
||
|
SBPS |
39 |
17.4 |
4.6 |
55 |
8.8 |
5.6 |
-7.97 |
<.001** |
1.67 |
||
|
BSS |
39 |
3.2 |
2.0 |
52 |
2.3 |
1.9 |
-2.20 |
.030* |
0.47 |
||
|
Pre-Video MSBS |
39 |
110.9 |
25.3 |
55 |
80.7 |
29.1 |
-5.23 |
<.001** |
1.09 |
||
|
Post-Video MSBS |
39 |
134.1 |
29.0 |
55 |
100.4 |
36.7 |
-4.77 |
<.001** |
1.00 |
||
|
Motor Activity |
36 |
77.6 |
46.5 |
54 |
58.8 |
37.9 |
-2.10 |
.038* |
0.45 |
||
|
SACT |
38 |
49.8 |
11.3 |
48 |
56.4 |
6.7 |
3.41 |
.001** |
0.74 |
||
|
|
n Male |
n Female |
n Male |
n Female |
|
||||||
|
Gender |
5 |
34 |
15 |
40 |
|
||||||
|
Note:
ASRS-5 = Adult ADHD Self-Report Rating Scale 5th Edition; BSS =
Boredom Susceptibility Scale; CAARS-S:S = Conner’s Adult ADHD Rating Scale
Self Report Short Form; M = mean; MSBS = Multidimensional State Boredom
Scale; SACT = Sustained Attention to Cue Task; SD = standard deviation; SBPS
= Short Boredom Proneness Scale. Measures reflect total scores; Motor
Activity reflects actigraph measured average vector
magnitude count; *p < .05; **p < .01. |
|||||||||||
To
examine whether individuals with elevated ADHD symptoms experience greater
state boredom during a boring video, a 2 (Group: ADHD vs. Controls) x 2 (Video
Condition: Neutral vs. Boring) x 2 (Time: pre-video state boredom [MSBS1]
vs. post-video state boredom [MSBS2]) mixed factorial ANOVA was
conducted (see Figure 1), with Group and Video condition as between subject
variables and Time as a within-subject variable. A significant main effect for
Group was found, F(1, 90)=
28.72, p< .001, η2p = .24, such that
individuals with elevated ADHD symptoms reported significantly higher state
boredom overall (M= 122.55, SE= 4.52) than those in the control
group (M= 90.86, SE= 3.81). A significant main effect of Time was
also observed, F(1, 90)=
88.99, p< .001, η2p = .50, indicating that all
participants reported significantly more state boredom after watching the
videos (M= 117.55, SE= 3.43), than before (M= 95.86, SE=
2.90). There was also a significant Time x Video Condition interaction, F(1, 90)= 21.55, p<
.001, η2p = .19, indicating that changes in boredom over
time differed by video condition. Follow-up Bonferroni-adjusted comparisons
revealed that boredom increased significantly from pre- to post-video in both
the boring (Mdiff = 32.37)
and neutral video (Mdiff =
11.02) conditions (ps < .001). As expected,
the increase in state boredom was significantly greater in the boring condition
than in the neutral condition, as reflected in the significant Time × Video
interaction, supporting the effectiveness of the boredom induction task.
However, the three-way interaction (Group x Time x Video Condition) was not
significant, which indicates individuals with elevated ADHD symptoms and
control participants were affected similarly by the boring and neutral videos
in terms of their changes in state boredom over time. In other words,
participants with elevated ADHD symptoms did not become more bored after
watching the boring video compared to control participants. In addition, there
was no significant main effect of Video Condition (collapsed across time),
Group x Time interaction, or a Group x Video Condition interaction. See Table
2.
Figure 1. Mean State Boredom Pre-
and Post-video by Group and Video Condition. Error Bars Represent Standard Errors.
MSBS= Multi-dimensional State Boredom Scale.
Because
trait boredom differed substantially between groups, ANCOVA was used to
determine whether group differences in state boredom persisted after accounting
for individual differences in trait boredom. Specifically, a 2 (Group: ADHD vs.
Control) x 2 (Video Condition: Boring vs. Neutral) x 2 (Time: pre-video vs.
post-video) mixed ANCOVA was conducted on state boredom, with SBPS and BSS
included as covariates to control for trait boredom. The main effect of Time
remained significant, F(1, 85)=
6.53, p= .012, ηp2 = .07, indicating that boredom
significantly increased from pre- to post-video across both videos controlling
for trait boredom. The Time × Video Condition interaction also remained
significant, F(1, 85)=
21.59, p< .001, ηp2 = .20, suggesting that the
increase in boredom was greater for participants in the boring video condition,
controlling for levels of trait boredom. Importantly, the previously
significant main effect of Group was no longer significant after controlling for
trait boredom, F(1, 85)=
0.63, p= .429. This indicates that differences in state boredom between
participants with elevated ADHD symptoms and control participants were
accounted for by individual differences in trait boredom. See Table 2 for ANOVA
and ANCOVA statistical results.
|
Table 2. Mixed Factorial ANOVA and
ANCOVA Examining State Boredom. |
|||||||||
|
|
ANOVA |
|
ANCOVA |
||||||
|
Source |
df |
F |
p |
|
|
df |
F |
p |
|
|
Between-Subjects Effects |
|
|
|
|
|
|
|
|
|
|
Group |
1, 90 |
28.72 |
<.001** |
.24 |
|
1, 85 |
0.63 |
.429 |
.01 |
|
Video |
1, 90 |
0.40 |
.531 |
.01 |
|
1, 85 |
1.57 |
.213 |
.02 |
|
Group x Video |
1, 90 |
1.49 |
.226 |
.02 |
|
1, 85 |
1.06 |
.306 |
.01 |
|
Within-Subjects Effects |
|
|
|
|
|
|
|||
|
Time |
1, 90 |
88.99 |
<.001** |
.50 |
|
1, 85 |
6.53 |
.012* |
.07 |
|
Time × Group |
1, 90 |
0.30 |
.586 |
.01 |
|
1, 85 |
0.31 |
.581 |
.01 |
|
Time × Video |
1, 90 |
21.55 |
<.001** |
.19 |
|
1, 85 |
21.59 |
<.001** |
.20 |
|
Time × Group × Video |
1, 90 |
1.30 |
.257 |
.01 |
|
1, 85 |
0.31 |
.582 |
.01 |
|
Note: Group= ADHD vs Control; Time= pre-video vs
post-video; Video= Neutral vs Boring. ANCOVA covariates: SBPS and BSS;* p < .05; **p < .01. |
|||||||||
A 2 (Group: ADHD vs. Control) x 2 (Video Condition:
Boring vs. Neutral) ANOVA on actigraph-measured
activity level revealed a significant main effect of Video Condition, F(1,
90)= 10.57, p= .002, ηp2
= .11, indicating that participants moved more during the boring
video (M= 82.55, SE= 6.12) than the neutral video (M=
54.82, SE= 5.94). There was also a significant main effect of Group, F(1, 90)= 4.37, p=
.040, with the elevated ADHD symptoms group (M= 77.59, SE= 6.60)
exhibiting more motor activity than the control group (M= 59.77, SE=
5.40). The Group x Video Condition interaction was not significant, F(1, 90)= 0.05, p=
.826, suggesting that the increase in activity from neutral to boring videos
was similar across groups (See Figure 2). These results suggest that
individuals with elevated ADHD symptoms exhibit generally higher motor
activity, while all participants increase their activity levels in response to
a boring task.
We next examined whether performance-based attention
control predicts boredom above and beyond ADHD behavioral symptoms using
separate multiple linear regression analyses. Prior to conducting regression
analyses, we ran zero-order bivariate correlations to examine relations between
our measure of performance-based attention control (i.e., SACT) and behavioral
measures of boredom and ADHD (see Table 3). Results revealed significant,
negative correlations between performance on the SACT and the SBPS (r=
-.358), the BSS (r= -.291), MSBS1 (r= -.424) and the
ASRS-5 (r= -.459), indicating that worse performance on the SACT was
associated with increased trait boredom, state boredom, and ADHD symptoms.
Consistent with prior research, the two measures of trait boredom were not
correlated (r= .136). Strong positive correlations were found between
the SBPS and MSBS1 (r= .709) and the ASRS-5 (r= .721);
whereas the BSS was not significantly related to the MSBS1 (r=
.078). The correlation between BSS and ASRS-5 was marginally significant (r=
.205; p= .051). Finally, a significant, positive correlation was found
between the MSBS1 and ASRS-5 (r= .527).
Figure 2. Actigraph
Average Vector Magnitude (VM) Counts by Group and Video Condition. Error Bars Represent
Standard Errors.
Three
linear regression models were conducted, testing SACT performance as a
predictor separately for SBPS, BSS, and MSBS1 (i.e., pre-video MSBS
scores), controlling for ASRS-5 scores. In Model 1, SACT and ASRS-5 were
entered in as predictors of SBPS. The model was significant, F(2,
85)= 43.93, p< .001, and explained 51% of
the variance in trait boredom as measured by the SBPS, R2=
.514. ADHD symptoms as measured by the ASRS-5 served as a significant predictor
of SBPS (b= 0.92, SE= 0.11, ß= .70, t= 8.13, p <.001),
but SACT did not (b= -0.03, SE= 0.06, ß= -.04, t= -0.42, p=
.677). In other words, ADHD behavioral symptoms, rather than performance-based
attention control predicted trait boredom as measured by the SBPS. In Model 2,
SACT and ASRS-5 were entered in as predictors of BSS. The model was
significant, F(2, 84)=
4.05, p= .021, and explained 9% of the variance in trait boredom as
measured by the BSS, R2= .090. In contrast to the results of
the SBPS, performance-based attention control as measured by the SACT served as
a significant predictor of BSS (b= -0.05, SE= 0.03, ß= -.26, t=
-2.16, p= .034), but ASRS-5 did not (b= 0.03, SE= 0.05, ß=
.08, t= 0.66, p= .511). In other words, when controlling for ADHD
symptoms, performance-based attention control independently predicted BSS. In
Model 3, SACT, ASRS-5, SBPS, and BSS were entered in as predictors of state
boredom as measured by MSBS1. The model was significant, F(4, 84)= 23.43, p
< .001, and explained 54% of the variance in state boredom as measured by
the MSBS1, R2 = .54. Interestingly,
both performance-based attention control as measured by the SACT and trait
boredom as measured by the SBPS served as significant predictors of MSBS1
(SACT: b= -0.73, SE= 0.29, ß= -.23, t= -2.56, p=
.012; SBPS: b= 3.21, SE= 0.51, ß= .68, t= 6.31, p<
.001), but ASRS-5 (b= -0.44, SE= 0.71, ß= -.07, t= -0.62, p=
.534) and BSS (b= -0.63, SE= 1.23, ß= -.04, t= 0.52, p=
.607) did not. In other words, performance-based attention control and trait
boredom as measured by the SBPS predict state boredom above and beyond ADHD
symptoms.
Table 3. Zero-Order Bivariate
Correlations.
|
Variable |
ASRS-5 |
SBPS |
BSS |
MSBS1 |
SACT |
|
1. ASRS-5 |
- |
|
|
|
|
|
2. SBPS |
.721** |
- |
|
|
|
|
3. BSS |
.205 |
.136 |
- |
|
|
|
4. MSBS1 |
.527** |
.709** |
.078 |
- |
|
|
5. SACT |
-.459** |
-.358** |
-.291** |
-.424** |
- |
Note: * indicates p < .05, ** indicates p
< .01; ASRS-5 = Adult ADHD Self-Report Rating Scale 5th Edition;
BSS = Boredom Susceptibility Scale; MSBS = Multidimensional State Boredom
Scale; SACT = sustained attention to cue task; SBPS = Short Boredom Proneness
Scale.
4. Discussion
The present study
examined whether adults with elevated ADHD symptoms exhibit higher trait
boredom, greater state boredom and motor activity during a boredom induction
task, and whether performance-based attention control predicts trait and state
boredom beyond ADHD symptom severity. Consistent with prior research,
individuals with elevated ADHD symptoms reported significantly higher levels of
trait boredom than controls on both the SBPS and BSS, with a particularly large
effect for the SBPS (d= 1.67) and a medium effect for the BSS (d=
0.47). These findings align with previous work documenting strong associations
between ADHD symptoms and trait boredom in both ADHD and non-ADHD samples (Chou
et al., 2018; Golubchik et al., 2020; Hunter & Eastwood, 2018; Kass et al., 2003; Mercer-Lynn et al., 2014; Muris et al., 2026), and extend prior findings of
large group differences in trait boredom between individuals with and without
ADHD (Hsu et al., 2025; Orban et al., 2026). These findings are consistent
with the Boredom Feedback Model (BFM; Tam et al., 2021) which posits that chronic difficulties
maintaining adequate attentional engagement may predispose individuals to more
frequent and intense boredom experiences, and that chronic boredom may reflect
dysfunction of the attentional feedback loop. Collectively, these findings
support the robust link between ADHD and trait boredom and provide a foundation
for examining how different aspects of boredom may differentially relate to
ADHD symptomatology and attentional control.
Consistent with prior literature, our findings suggest
that although the SBPS and BSS both assess trait boredom, they show divergent
patterns of association with ADHD symptoms, attention control, and state
boredom, consistent with the possibility that they capture different aspects of
trait boredom (Gerritsen et al., 2014; Mercer-Lynn et al., 2013, 2014). Further supporting this
interpretation, the two measures were not correlated in the present sample,
consistent with prior research (Mercer-Lynn et al., 2013). The SBPS is generally
characterized by disengagement, low motivation, and withdrawal, whereas the BSS
reflects frustration, desire for stimulation, and sensation-seeking (Gerritsen
et al., 2014; Greenson, 1951; Mercer-Lynn et al., 2013). These aspects of boredom also
appear to differ in reinforcement sensitivity: BPS/SBPS scores have been linked
to punishment sensitivity and internalizing symptoms such as depression
(Gerritsen et al., 2014;
Goetz et al., 2014; Malkovsky
et al., 2012), whereas the BSS has been
associated with reward sensitivity, sensation seeking, and externalizing
tendencies including anger and hyperactivity (Dahlen et al., 2004; Gerritsen et al., 2014; Malkovsky
et al., 2012). This pattern is consistent with
BFM’s account of chronic boredom, which suggests that trait boredom may stem
from (1) trait-like attentional factors, including individual differences in
the ability to sustain attention and regulate attention allocation and (2)
long-term factors related to whether one can engage in sufficiently meaningful
activities (Tam et al., 2021).
The present findings tentatively suggest that the BSS may
be particularly sensitive to trait-like attentional factors, as it was
predicted by objective attention control performance independent of ADHD
symptoms. Individuals with high BSS scores may be motivated to engage with
their environment but experience frustration when attentional resources are
insufficient to sustain engagement, leading to feelings of anger and motivation
to escape the current situation (Dahlen et al., 2004; Goetz et al., 2014; Greenson, 1951). In this way, stimulation-seeking
boredom (i.e., BSS) may reflect a mismatch between reward motivation and the
ability to maintain cognitive engagement. The SBPS, predicted by behavioral
ADHD symptoms, may tap into trait-like attentional factors, long-term
difficulties engaging meaningfully with activities, or both – as ADHD is
characterized by chronic weaknesses in attention systems (Tsal
et al., 2005) and difficulties engaging in
meaningful activities (Kazemy-Pour et al., 2025). It is worth noting that Orban et
al. (2026) found that performance-based
attention control, measured using a composite of multiple tasks, partially
mediated the relation between ADHD symptoms and SBPS scores, suggesting that
executive dysfunction may also contribute to disengagement-related boredom when
assessed more comprehensively than in the present study. To our knowledge,
these findings provide preliminary empirical support for the BFM’s framework
while also highlighting the complexity of mapping specific boredom measures
onto different sources of trait boredom, an important direction for future
research.
The boredom induction task successfully increased state
boredom across all participants. Individuals with elevated ADHD symptoms did
not report larger increases in state boredom than controls during the boring
condition; rather, they reported higher overall state boredom across both
videos. Importantly, this group difference was no longer significant after
controlling for trait boredom, suggesting that elevated state boredom in those
with elevated ADHD symptoms may reflect enduring individual differences in
trait boredom rather than heightened sensitivity to specific monotonous
situations. This is consistent with the BFM’s framework that chronic
difficulties maintaining adequate attentional engagement may predispose
individuals to elevated boredom across settings rather than heightened
reactivity to specific situational demands.
The absence of significant Group x Video interactions
should be interpreted with caution given that the present study had limited
statistical power to detect small interaction effects involving group (observed
power = .20). It is therefore possible that true differences in boredom
reactivity between individuals with elevated ADHD symptoms and control
participants exist but went undetected in the present study. That said, one
theoretical explanation for the absence of these interactions is that both videos
may have been experienced as relatively under-stimulating by participants with
elevated ADHD symptoms. These individuals reported higher overall boredom
across conditions and demonstrated elevated trait boredom, suggesting a
generalized susceptibility to boredom. If the neutral video was also perceived
as somewhat monotonous, the relative difference in stimulation between
conditions may have been attenuated for this group, reducing the likelihood of
detecting differences in reactivity. This interpretation is consistent with
theoretical accounts proposing that individuals with ADHD require higher levels
of environmental stimulation to maintain optimal arousal and engagement (Zentall & Zentall, 1983). A promising direction for future
research is to examine boredom reactivity across tasks that vary in stimulation
(e.g., neutral, boring, and highly engaging) to determine whether state boredom
systematically differs as a function of task stimulation in individuals with
ADHD, ideally using larger samples adequately powered to detect interaction
effects.
Performance-based attention control and SBPS scores
uniquely predicted baseline state boredom, whereas ADHD symptom severity and
BSS scores did not. This pattern is consistent with BFM’s framework, which
suggests that inadequate attentional engagement is a proximal trigger for state
boredom, such that individuals who struggle to maintain attentional engagement
objectively, as well as those who with a chronic tendency toward disengagement,
may be particularly vulnerable to experiencing boredom in the moment. This
finding also partially aligns with prior work demonstrating that sustained
attention performance predicts baseline state boredom in children with ADHD
(Hsu et al., 2020) and extends these findings to
adults by showing that both SBPS scores and attention control performance
contribute to state boredom. The absence of a significant effect for BSS is
also noteworthy, as stimulation-seeking boredom may function more as a behavioral
response to state boredom than as a predictor of it, consistent with BFM’s
framework that boredom motivates attention shifts outward toward more
stimulating alternatives (Tam et al., 2021).
Finally, the finding that ADHD symptom severity did not independently predict
state boredom after accounting for attention control and trait boredom suggests
that the relationship between ADHD and state boredom may be better explained by
these underlying mechanisms than by behavioral symptoms alone, consistent with
the ANCOVA finding that group differences in state boredom were accounted for
by trait boredom.
Actigraph-measured motor
activity increased in all participants during the boring video compared to the
neutral video, consistent with prior research linking motor restlessness to
boredom (Danckert et al., 2018).
Participants with elevated ADHD symptoms also exhibited higher overall motor
activity across both videos, although both groups increased movement to a
similar degree during the boring condition. These findings are consistent with
recent evidence suggesting that boredom involves both low and high arousal
experiences (Danckert et al., 2018; Stempfer
et al., 2025), with motor restlessness
reflecting attempts to reengage with the environment in response to an
unsatisfactory situation (Elpidorou, 2021). Rather than simply compensating
for low arousal, motor activity during boredom may reflect a broader attempt to
restore engagement with the environment. For example, adults with ADHD
exhibited greater fidgeting during later and correct trials of a sustained
attention task, consistent with the idea that motor movements help maintain
engagement over time (Son et al., 2024).
Similarly, fidgeting has been associated with improved task performance in
individuals with ADHD, indicating that movement may play a functional role in
attentional regulation rather than merely reflecting ubiquitous or disruptive
behavior (Dekkers et al., 2021; Hudec
et al., 2015; Kofler et al., 2016; Rapport et al., 2009; Sarver et al., 2015). The finding that both groups
increased motor activity similarly during the boring video suggests that this
restlessness response to boredom may be a general phenomenon, with individuals
with elevated ADHD symptoms showing an elevated baseline level of motor
activity across both conditions.
Several limitations warrant consideration. First,
reliance on self-report measures introduces the possibility of shared method
variance and response biases, though actigraph and
performance-based measures partially address this concern. Second, ADHD status
was determined using self-report symptom scales rather than gold-standard
diagnostic procedures (e.g., clinical interview, confirmation of impairment
across settings), and comorbid psychiatric conditions were not systematically
assessed. For example, affective symptoms such as depression or anxiety may
influence both trait and state boredom. Therefore, future research should
employ gold-standard diagnostic procedures and assess psychiatric
comorbidities. Third, the sample consisted of primarily young adult female
university students, which limits generalizability to other age groups and
clinical populations. Because ADHD is more commonly diagnosed in males, the
female-skewed sample may not fully capture the boredom profile of individuals
with ADHD more broadly. Future research should prioritize more diverse and
clinically representative samples. Fourth, the between-subjects design, while
appropriate for avoiding order and carryover effects in the boredom induction
task, resulted in approximately 19–29 participants per cell in the mixed
factorial ANOVA. As a result, the study had limited statistical power to detect
interaction effects involving group, with observed power ranging from .08 to
.23 for these effects. The absence of significant Group x Video Condition and
three-way interactions should therefore be interpreted cautiously, as true
differences in boredom reactivity between ADHD and control participants may
exist but have gone undetected. Future research should employ larger samples or
within-subjects designs where feasible to improve power for detecting
interaction effects. Fifth, attention control was assessed using a single
performance-based task, which may not fully capture the breadth of executive
processes relevant to boredom in ADHD. Prior work using composite measures of
attention control has found stronger associations with boredom proneness (Orban
et al., 2026), suggesting future research should
employ multiple indices of cognitive control. Sixth, findings involving the BSS
should be interpreted with some caution. Its forced-choice format may reduce
internal consistency compared to Likert-based measures (Zuckerman, 2007), though internal consistency in
the current sample was comparable to prior studies (Deditius-Island
& Caruso, 2002). Additionally, while the BSS group
difference was statistically significant, the observed effect size (d=
0.47) fell slightly below the detectable threshold for this analysis (d=
0.60), suggesting that replication in larger samples is warranted to confirm
the reliability of this finding. Finally, the present study did not assess
arousal directly, which limits interpretation of the motor activity findings in
relation to theoretical accounts of boredom and arousal. Future research should
include physiological measures of arousal to better understand the relationship
between boredom, motor activity, and arousal in ADHD.
Despite these limitations, the present findings have
important implications for intervention strategies targeting boredom in
individuals with ADHD. The results suggest that boredom may serve as a
meaningful signal of underlying attentional difficulties, highlighting the
potential utility of helping individuals recognize and respond to boredom in a
more adaptive manner. Consistent with BFM, interventions may be most effective
when tailored to the underlying source of boredom: targeting attentional regulation
for individuals whose boredom stems primarily from trait-like attentional
difficulties and supporting meaningful engagement for those whose boredom
reflects long-term difficulties finding satisfying activities (Tam et al., 2021). For example, training in emotion
awareness may enable individuals to identify boredom as a cue to re-engage,
prompting the use of strategies such as scheduling brief breaks, alternating
tasks, or incorporating social elements (e.g., studying with a partner).
Interventions may also benefit from reducing attentional demands during
cognitively effortful activities, such as breaking tasks into smaller, more
manageable components, distributing work across shorter, structured intervals,
and introducing external incentives to support sustained engagement.
Additionally, given that boredom is often associated with tasks perceived as
monotonous or lacking personal relevance (Danckert & Merrifield, 2018; Mercer-Lynn et al., 2014), strategies that enhance task
meaning or introduce optimal levels of challenge may further mitigate
disengagement. Finally, the observed patterns of motor activity suggest that
allowing for movement or incorporating structured physical activity may support
reengagement with the environment during tasks with insufficient environmental
stimulation. Together, these approaches highlight the importance of targeting
both attentional control and environmental modification to reduce boredom and
improve functioning in individuals with ADHD.
Acknowledgements
This research was funded
by an Undergraduate Research and Inquiry (URI) grant awarded by the University
of Tampa (GR3266 22/23).
Declaration of
Interest Statement
The authors have no
conflicts of interest to disclose.
Use of Artificial
Intelligence
During the
reviewing-editing stage, the authors used ChatGPT (OpenAI, March 2026 version)
to improve grammatical clarity. No AI tools were used to generate research
data, interpret results, or draft substantive content.
Authorship
Contribution (CRediT)
Jenna Santer and Sarah
A. Orban: Conceptualization, Data curation, Formal analysis, Fundraising,
Research, Methodology, Management of project, Resources, Software, Supervision,
Visualization, Writing-first draft, Writing-reviewing and editing.
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