Available online at:
https://cc.arcabc.ca/islandora/object/cc%3Apsycjournal

J Camosun Psyc Res. (2022). Vol. 4(1), pp. 139-163.
Submitted Winter 2022; accepted June 2022.

Methods of Decreasing Procrastination.
Authors: Liz Snell*, Damien Tattersall, and Anna Venczel
Supervising Instructor: Michael Pollock, Psyc 110 (“Experimental Psychology”)
Department of Psychology, Camosun College, 3100 Foul Bay Road, Victoria, BC, Canada V8P 5J2
*Corresponding author email: lizannesnell@gmail.com

ABSTRACT
In this paper, we sought to learn methods to decrease procrastination and lower related stress.
Previous research has predicted that procrastination decreases by variables such as mindfulness,
reminder intervention, and cognitive behaviour therapy. In our first (correlational) study, we
tested the strength of these relationships by examining naturalistic daily changes in their
variables longitudinally over a period of one week. We measured mindfulness by each moment
of acting with intention, reminders by the number of daily external reminders received, cognitive
distortions by each time participants experienced a negative cognitive distortion, and
procrastination by a modified General Procrastination Scale (GPS). Based on the strength of
correlation found between procrastination and reminders in our correlational study, we then
conducted a second (experimental) study to test for a causal relationship between these two
variables. Over a period of one week, we randomly assigned participants each day to either a
reminder intervention condition or a non-reminder intervention condition and measured the
effect this manipulation had upon procrastination. In our correlational study, using raw data
pooled across participants procrastination showed a statistically significant correlation only with
cognitive distortions, while when using pooled standardized data reminders had the strongest
correlation. Data pooled across participants in our experimental study showed reminders
produced a significant reduction in procrastination, as measured both subjectively by the GPS
test and objectively by the number of tasks completed. Our experimental study suggests that
reminder intervention could be useful to mitigate procrastination and increase university
students’ ability to complete their many academic tasks.
1. Introduction
1.1 Research Problem
Our study’s goal was to find methods to
relieve procrastination due to our shared
struggles with procrastination and its effects
on our quality of life, especially related to
our studies. We sometimes experience
mental paralysis and anxiety when faced
with beginning or completely even small,

routine tasks. Understanding how to
alleviate this reaction would help us feel less
overwhelmed and more able to meet our
goals. People who struggle with
procrastination often suffer from hampered
career and academic achievements. Finding
solutions to mitigating procrastination may
thus have practical and concrete benefits
such as a reduction in stress and long-term
productivity gain. Tendencies to
procrastinate pose a disadvantage
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academically as well as causing feelings of
self-doubt and anxiety towards completing
schoolwork. Learning about what makes
procrastination less likely to occur will
enable us and others to implement effective
changes and become more motivated to
achieve goals.
1.2 Literature Review
One factor previously found to predict a
decrease in procrastination is mindfulness.
Gautam et al. (2019) hypothesized that
students who exhibited higher mindfulness
traits would have lower anxiety and thus
also exhibit lower procrastination. To test
this, they conducted an online survey of 801
university students, in which participants
answered 39 questions on the Five-Facet
Mindfulness Scale. This questionnaire
measured five facets of mindfulness:
observing, acting with awareness,
nonjudging, describing, and nonreactivity.
These items were measured on a 5-point
Likert scale ranging from 1 = “never to
rarely true” to 5 = “very often or always
true”. Questions included, “I do jobs or
tasks automatically without being aware of
what I’m doing,” and “I pay attention to
sensations, such as the wind in my hair or
sun on my face.” The survey also used the
Tuckman Procrastination Scale for students
to self-evaluate their procrastination levels.
On each of its 16 questions, participants
used a 4-point Likert scale where 1 = “that’s
me for sure” and 4 = “that’s not me for
sure”. Examples of questions include,
“When I have a deadline, I wait until the last
minute,” and, “When something’s too tough
to tackle, I believe in postponing it.” The
study found that nonreactivity and
describing were not significantly related to
procrastination while observing and acting
with awareness were, especially the latter.
Researchers concluded that interventions

involving the acting with awareness would
be particularly effective in decreasing
procrastination.
Another factor previously found to
predict a decrease in procrastination is the
use of a reminder intervention. For example,
a novel study was conducted by Wessel et
al. (2020) used a modified Temporal
Motivation Theory (TMT) based
intervention with the aim to reduce
procrastination. They measured
procrastination as behavioural delay: the
amount of delay that occurs before
completing a. To measure this reliably the
Experience Sampling Method (ESM) was
used to collect data on college students and
measure the amount of behavioural delay via
progress surveys through text messaging.
Task progress for assignments among
students follows an accelerating, hyperbolic,
curve to task completion, with behavioural
delay being high initially and lower when
closer to assignment deadline, meaning
higher assignment progress the closer the
student is to the deadline, making an upward
curve. Students scoring high in trait
procrastination display steeper curves with
greater behavioural delay, making the
distribution of task progress concentrated
greater near the assignment due date, while
students scoring low in trait procrastination
would have a more gradual curve and an
earlier start. The study took place over two
weeks and split students in two groups:
those scoring high in trait procrastination
and those scoring low. The split was in both
the experimental group that received the
intervention and the control group, which
received no intervention and was only given
ESM progress surveys. ESM surveys were
sent twice daily through texts; once students
answered the survey, an intervention prompt
was sent out via text for the experimental
group. The modified TMT intervention was
based on four strategies to reduce
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procrastination, which would be used
throughout the study. They consisted of four
factors. The first was Metacognitive
Strategies, which consist of reflecting on
thinking, decision making and planning,
behind one’s task approach or time use.
Metacognitive Strategies factor was a
modification of TMT in this study. The
second factor was Expectancy; if expectancy
of positive outcomes is increased then
people will feel more motivated to progress
on the task. The third factor was Value,
which consisted of techniques of
visualization and mental time travel, such as
involving the student imagining themselves
at the end of the task with it completed vs.
having it not completed, as well as
comparing it to their current state. The
fourth factor was Delay Sensitivity, which
refers to an increased likelihood of impulsedriven behavior when deadlines are
temporarily distant. Tasks that are perceived
as large can be overwhelming and may
appear temporally distant from completion.
In order to decrease delay sensitivity and
promote more efficient use of time, large
tasks are to be broken up into smaller
chunks with less distant completion. The
authors of the study proposed that more
frequent, less time-consuming, smaller
chunks completed of a task will exploit
participants’ innate bias to interests in the
present vs. the future. The study’s novel
approach was that they used all these factors
throughout the study. Based on these four
factors, one of four prompts were sent via
text message. They would be something like
these: 1. Expectancy: “Studies show student
who score high on tests, study frequently
and well in advance of the test. Demonstrate
your understanding by telling us what
students who score high on tests do.” 2.
Value: “Imagine yourself the day before a
test, and picture yourself having done no
studying. How does that make you feel?” 3.

Delay sensitivity: “Research has shown that
breaking large tasks (like the completion of
an assignment), into smaller tasks (like
completing an assignment summary) can
increase motivation. What is your next small
step going to be?” 4. General metacognition:
“If you could do one thing to make sure that
you completed your essay on time, what
would it be?” One of these prompts would
be sent after an ESM survey of task
completion point. The prompt that would be
sent out would be chosen randomly; this was
done to avoid inattentiveness and boredom
from the students. The results of the study
were that assignment grade and completion
date of the assignment between experimental
and control groups showed no significant
change. Students in the experimental group
with high trait procrastination did not show a
significant change in behaviour delay
compared to the control group; however,
students with low trait procrastination did
show a significant reduction in behaviour
delay, displaying a less steep curve on
assignment completion in comparison to the
control group. These results showed that the
intervention was effective in reducing
procrastination in students with low trait
procrastination.
A third factor previously found to predict
a decrease in procrastination is cognitive
behaviour therapy. In a clinical trial by
Rozental et al., (2018), researchers
compared the effectiveness of self-guided
internet based cognitive behavior therapy
(ICBT) and group CBT in reducing the
effects of procrastination. The two trials
were conducted over eight weeks using
content based on a self-help book.
Intervention strategies derived from this
include psychoeducation, cost/benefit
analysis, motivation, mental fatigue, selfassertiveness strategies, value clarifications,
and relapse prevention. Group CBT included
three-hour sessions accompanied by
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clinicians trained in CBT who would
participate in psychoeducation and
discussions with participants. The ICBT
method was unguided and included little
contact with clinicians; this method included
modules and homework based on the
intervention strategies. The Pure
Procrastination Scale (PSS) was used to
assess participants on a scale of 1-5 during
different time intervals: before the trial,
post-trial, and a six-month follow up.
Participants from both methods showed an
improvement in productivity on the PSS
(33.7%). However, participants from the
group CBT continued to improve after the
six-month follow up while some
deterioration was found for those who
participated in ICBT. These results suggest
that both methods can be used to treat
procrastination short-term and that group
CBT may be a more beneficial long-term
treatment for procrastination.
1.3 Hypotheses
Based on the above literature review, we
predicted the following hypotheses:
Hypothesis #1: If acting with awareness
increases then procrastination will decrease.
Hypothesis #2: If reminders increase then
procrastination will decrease.
Hypothesis #3: If cognitive distortions
decrease then procrastination will decrease.
2. Methods
2.1 Participants
The three authors of this paper served as
the participants in its studies. The
participants ranged in age from 18-35 years
old, with an average age of 26 years, and
included both men and women. The
participants were all undergraduate students
at Camosun College who completed the

current studies as an assignment for Psyc
110 (“Experimental Psychology”) and were
grouped together due to their mutual interest
in procrastination.
2.2 Correlational Study Methods
We first performed a correlational study
to test concurrently all of our hypotheses by
examining naturalistic daily changes in their
variables longitudinally. Each participant
kept a study journal with them at all times
over this study’s one-week period in order to
record self-observations of the following
three variables: (1) acting with awareness,
(2) reminders, (3) cognitive distortions, and
(4) procrastination.
2.2.1 Acting With Awareness
To measure acting with awareness,
participants marked down in their journal
each time they exhibited acting with
awareness over the course of a week.
“Acting with awareness” is operationally
defined as a self- reminder of the
participant’s intention for a task, noncontingent on external reminders. The
intention can be written, verbalized to
oneself or another, or simply be a purposeful
thought. Examples would be thinking, “I
decided to mail these letters by noon today,”
or telling one’s mother one would respond to
her text by three o’clock, or writing in one’s
journal the intention to finish the first draft
of one’s essay tomorrow. Acting with
awareness can happen before or during the
task, either in setting the initial intention or
returning to the intention while preparing for
or engaged in the task.
2.2.2 Reminders
To measure reminders participants
recorded with a journal their daily number of
reminders. Reminders was operationally
defined as an external event that reminds
you to initiate starting, completing, and/or
progressing on any day-to-day tasks and
objectives or assignments. The tasks,
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objectives and assignments were not limited
to an academic setting and could be in any
facet of day-to-day life, like simple chores.
The reminders could be any event that
reminded the participants of something they
needed to do, including indirectly via
external factors such as a character in a
movie doing a similar task that the
participants needed to do, inadvertently
reminding participants to attend to it. Direct
reminders were also recorded, such as a
person actively reminding the participants to
do something they needed to do that they
were not currently doing. Direct reminders
included automated reminders; for example:
an automated message from a company
providing a service the participant uses,
notifying the participant their bill is
due/overdue, as well as automated
assignment due date notifications for work
or school progress.
2.2.3 Cognitive Distortions
To measure negative cognitive
distortions, participants created a tally with a
journal recording their daily amount of
cognitive distortions every time they had
one. A cognitive distortion can be define as
any form of self-talk that includes negative
generalizations about a participant’s own
ability to do a task. This includes thinking
that a certain task is impossible to complete,
even before the person has attempted the
task. A negative cognitive distortion can also
include making assumptions about a person
or situation, such as, “If I email this person
again they will get annoyed.” This can cause
the person to procrastinate when sending
emails. Another form of negative cognitive
distortions is self-defeating thoughts, such
as, “I am not smart enough,” or, “I am a
failure.” The results were recorded then
submitted at the end of each day and the
number of tally marks were analyzed to find
day-to-day variations.
2.2.4 Procrastination

Participants used the General
Procrastination Scale (GPS; see the
Appendix for a complete list of its items),
developed by Lodha et al. (2019), to
measure their level of procrastination each
day at 9:00pm. The scale was modified by
one of the current authors (D.T.) to include a
prompt at the beginning instructing the
participants that “The following is to be
answered only pertaining to the last 24
hours”, and with no other modifications.
The scale totals 23 items and measures
procrastination in these 4 areas: academic,
medical, workplace and civic
responsibilities, with the areas all relating to
procrastination. The items require a 5-point
Likert scale ranging from 1 to 5, to be rated,
with scores being defined as; 1 being: never,
2 being: rarely, 3 being: sometimes, 4 being:
often, 5 being: always. The items #5, 8, 12,
16, 18, 21 and 23 are scored in reverse. So,
for example, if your score was 1 for item 23
it would be tallied as a score of 5. A lower
sum of scores obtained on all items indicates
a lower level of procrastination with higher
sum scores indicating a higher level of
procrastination for the participants taking the
test.
2.3 Correlational Study Planned Analyses
To assess the strength and statistical
significance of associations between
variables predicted by our three hypotheses,
we performed Pearson product moment
correlations of their predictor variables
acting with awareness, reminders, and
cognitive distortions with their outcome
variable procrastination. For testing
Hypothesis #1, we correlated the tally of
acting with awareness with the GPS score.
For testing Hypothesis #2, we correlated the
number of reminders with the GPS score.
For testing Hypothesis #3, we correlated the
number of cognitive distortions with the
GPS score. We performed all of the above
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correlations separately for each participant
as well as using data pooled across all of the
participants. For the correlations using
pooled data, in addition to using the raw
data, we also performed correlations after
we had first transformed the data from each
participant into z-scores in order to
standardize differences in averages and
variability seen between the participants in
their data and thus make them more
comparable. A correlation coefficient was
considered statistically significant if the
probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).
2.4 Experimental Study Methods
Based on the strength of the correlation
between reminders and procrastination
found in our correlational study, we then
chose to conduct an experimental study to
test for a causal relationship between these
two variables from Hypothesis #2.
We manipulated the independent
variable, reminders, over a one-week period
by randomly assigning participants each day
to either a condition with only naturallyoccurring reminders or a condition involving
daily reminders from a cellphone app. To
avoid order effects, we used an alternating
(ABAB) design, alternating days between
control and experimental conditions. On
control days, participants received only
whichever reminders occurred naturally in
their daily lives. On experimental days,
participants used an app that gave them five
random reminders throughout the day for
specific tasks they felt the urge to
procrastinate.
We could not use a blind procedure
to control for placebo effects since
participants were required to set reminders
themselves and required to have conscious
knowledge of the procedure. Despite these
limitations, we were able to somewhat

minimize the possibility of subjective bias in
our measurements by having participants
measure their productivity objectively via
recording in a journal how many significant
tasks they had completed on both
experimental and control days. The
operational definition of “significant tasks”
is tasks participants feel tempted to
procrastinate in these three categories: work,
school, or personal life. Examples include
academic assignments, work projects, house
maintenance, appointments, personal
projects, relational maintenance, exercise,
etc. We then compared the number of tasks
completed between control and experimental
days, with a higher number of tasks
completed indicating higher productivity.
We also controlled for the possible effects of
knowing the time of day when reminders
would occur by having participants using an
app (Yapp Reminders for participants using
an iPhone and Randomly Remind Me for
participants using android) that would
randomly remind them without their
knowledge of when the reminders would
happen. Participants also measured
themselves each evening using the same
GPS scale described above for the
correlational study.
2.5 Experimental Study Planned Analyses
To assess the statistical significance of
differences seen in procrastination on
reminder intervention experimental days vs.
non-reminder intervention control days,
Student’s t-tests were performed. We
performed t-tests separately for each
participant as well as using data pooled
across all of the participants. For the t-tests
using pooled data, in addition to using the
raw data, we also performed t-tests after we
had first transformed the data from each
participant into z-scores in order to
standardize differences in averages and
variability seen between the participants in
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their data and thus make them more
comparable. An average difference between
conditions was considered statistically
significant if, using a one-tailed distribution
(i.e., to determine if there is a difference
between groups in a specific direction), the
probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).
3. Results
3.1 Correlational Study Results
As shown in Table 1, after testing
correlations for eight days between our three
variables, we found that none of the
variables showed statistical significance in
the pooled standardized data. In the pooled
raw data, only cognitive distortions was
significant. As shown in Figures 1 and 2,
acting with awareness was not significant in
the pooled raw data (r =0.03, p = 0.91) or
the pooled standardized data (r = -0.14, p =
0.53). The pooled raw data also had a
positive r value, which contradicts the
hypothesis for acting with awareness being
associated with a decrease in
procrastination. The p value for participant 1
(p = 0.04) was significant; however, again
the direction of the correlation did not match
the hypothesis (r = 0.73). The other
participants’ results were not significant (p ≥
0.09). As shown in Figures 3 and 4, neither
the pooled raw data (r = -0.21, p = 0.34) nor
the pooled standardized data (r = -0.37, p =
0.07) showed significant correlations
between reminders and procrastination.
None of the participants’ values were
significant (p ≥ 0.21). As shown in Figures 5
and 6, cognitive distortions were significant
for the pooled raw data (r = 0.44, p = 0.03)
but the standardized data was not significant
(r = 0.27, p = 0.20). None of the
participants’ individual data was significant

(p ≥ 0.08); the data for participant 3 showed
an opposite correlation to the hypothesis (r =
-0.30) and the other participants (r ≥ 0.47).
Using the pooled standardized data,
reminders showed the strongest correlation
with procrastination.
3.2 Experimental Study Results
As shown in Table 2, when participants
recorded procrastination using the GPS for
nine days, both the pooled raw data and
pooled standardized data showed significant
differences between the pooled mean on
experimental days, where participants had
five daily reminders, and control days,
where they had no experimental condition
reminders. The degree of significance for
these differences between control days and
experimental days was p = 0.0097 using the
raw pooled data (see Figure 7) and p = 0.002
using the standardized pooled data (see
Figure 9). On experimental days when
participants used the reminders app, the
mean GPS showed a z-score of -0.46,
compared to a z-score of 0.57 on control
days.
As shown in Table 3, when participants
recorded the number of tasks completed, the
pooled raw data and the pooled standardized
data also showed significant differences
between the pooled mean on experimental
and control days. The degree of significance
for these differences between the mean on
control days and experimental days was p =
0.0077 using the raw pooled data (see Figure
8) and p = 0.0021 using the standardized
pooled data (see Figure 10). The number of
tasks completed was higher on experimental
days, with a z-score of 0.43, as compared to
a z-score of -0.54 on control days.
4. Discussion
4.1 Summary of Results
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In our correlational study, we
hypothesized that two variables, acting with
awareness and reminders, would increase as
procrastination decreased (Hypothesis #1 &
#2) while one variable, cognitive distortions,
would increase as procrastination increased
(Hypothesis #3). In our results we found that
only cognitive distortions showed a
statistically significant correlation with
procrastination using the pooled raw data,
but not using the pooled standardized data.
Acting with awareness and cognitive
distortions did not show statistically
significant correlation with procrastination.
However, while not statistically significant,
reminders had the strongest correlation with
a decrease in procrastination out of any of
the variables examined in this study.
In our experimental study, we
hypothesized that days where participants
received experimental reminders would
show a decrease in procrastination, as
opposed to control days without
experimental reminders. Our results
confirmed this hypothesis. Both of our
measurements of procrastination, the GPS
and number of tasks completed, showed a
statistically significant difference between
conditions with both the pooled and raw
data.
4.2 Relation of Results to Past Research
In previous research, acting with
awareness was shown to have a negative
correlation with procrastination (Gautam et
al., 2019). In our research, we found that
only pooled standardized data, and not
pooled raw data, showed a negative
correlation between acting with awareness
and procrastination, and neither of these
correlations showed a statistical
significance. At least three differences in our
methodology could have resulted in these

discrepancies. First, the small number of
participants (3) in our study vs. 801 in the
Gautem et al. (2019) study gave us a small
sample size and much less data. Secondly, in
the Gautem et al. (2019) study, the
participants did not conduct the study, which
would have controlled for experimenter
expectancy and placebo effects. We
recommend that future research into acting
with awareness and procrastination use a
larger sample size and a double-blind
procedure.
In our correlational study, we failed to
confirm the negative relationship between
reminders and procrastination previously
reported by Wessel et al. (2020). Wessel et
al. (2020) found that the participants scoring
lowest in trait procrastination when put in
the intervention condition resulted in
lowered procrastination as reduced
behaviour delay. Our study’s findings,
differing in statistical significance to the
study Wessel et al. (2020), could be due to
two notable differences. A difference in
duration of the study was one notable
difference. The study done by Wessel et al.
(2020) was two weeks in duration in
comparison to our study being eight days,
suggesting the potential need for more data
collection. The other notable difference in
our study was how Wessel et al. (2020)
measured delay associated with
procrastination through the Experience
Sampling Method (ESM) with short surveys
performed twice daily. In our study
procrastination was measured with a
modified General Procrastination Scale
(GPS) test taken once at the end of the day.
This difference in measurement of
procrastination could have been less precise
in the day-to-day variation in procrastination
that participants can have. Contributing to
that, the items that the modified GPS uses
may have posed questions that were not
specific enough for day-to-day
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measurements as some of the questions, like,
“I often delay medical issues concerning my
health,” may have been less relevant as the
modified GPS was to be scored only
pertaining to the last 24 hours. GPS may be
more suited in measuring procrastination
when the items are scored in the context of a
larger time frame. This suggests that
measuring day-to-day procrastination could
give inaccurate results if certain factors are
to event-specific like attending to medical
issues, which may give you an instant low
score if you have no medical issues to attend
to that day. Future studies could be used to
see if procrastination can be measured more
precisely for day-to-day variation with a
focus in relation to how procrastination
impacts whatever variety of tasks
participants face that day rather than in
relation to a set of tasks that may not have
any reason to occur that day.
However, in contrast to our correlational
study, our experimental study concluded that
on days participants received reminder
intervention, their procrastination decreased.
We were not able to entirely control for
experimenter expectancy or placebo effects,
although we measured tasks completed to
reduce these effects. Therefore, participants
may have been affected by experimenter
expectancy or placebo effects. We
recommend that further research increase
number of participants and find better ways
to control for experimental expectancy and
placebo effects.
Our study failed to confirm the findings
of Rozental et al. (2018), which found CBT
to decrease procrastination. The CBT used
in the previous study included a module for
“managing maladaptive thoughts and
beliefs” (Rozental et al., 2018). Therefore,
cognitive distortions may be correlated to an
increase in procrastination. Much like the
study by Rozental et al. (2018), participants
in our study self-reported using a

procrastination scale; however, the two
studies differ by size, duration, and
resources, possibly resulting in
discrepancies. Future studies could observe
the remaining factors treated by CBT in the
study by Rozental et al. (2018); these factors
may correlate to the amount of
procrastination in participants.
4.3 Implications of Results
Based on our correlational study, we did
not find statistical significance in the
standardized data of any of the three
variables (acting with awareness, reminders,
and cognitive distortions). However,
because reminders showed the strongest
correlation, we tested reminders in our
experimental study and found a strong effect
upon procrastination. Based on the data, we
suggest that a reminder app could be a
helpful tool to mitigate procrastination. This
could be particularly useful in scenarios
where people have a number of tasks that
require long-range planning and therefore
are easier to procrastinate.
We originally decided to conduct this
study on procrastination because we were
concerned about the negative effects of
procrastination on people’s daily lives,
particularly around academics. Given the
results of our study, we believe that
intervention reminders could provide an
effective means to decrease student
procrastination. For example, students may
begin writing papers sooner or studying
earlier for exams if they receive reminder
interventions, as well as balancing their
personal tasks with academic goals.
Regardless of whether one is a student or
not, using a reminder app could help with
productivity and possibly also contribute to
a decrease in procrastination-related anxiety.

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References
Gautam, A., Polizzi, C. P., & Mattson, R. E.
(2019). Mindfulness, procrastination, and
anxiety: Assessing their
interrelationships. Psychology of
Consciousness: Theory, Research, and
Practice.
https://doi.org/10.1037/cns0000209
Lodha, P., Sharma, A., Dsouza, G., Marathe,
I., Dsouza, S., Rawal, S., Pandya, V., &
De Sousa, A. (2019). General
procrastination scale: Development of
validity and reliability. International
Journal of Medicine and Public Health,
9(3), 74-80.

Rozental, F. A. D., Lindner, P., Nilsson, S.,
Mårtensson, L., Rizzo, A., Andersson, G.,
& Carlbring, P. (2018). Treating
procrastination using cognitive behavior
therapy: A pragmatic randomized
controlled trial comparing treatment
delivered via the internet or in groups.
Behavior Therapy, 49(2), 180–197.
https://doi.org/10.1016/j.beth.2017.08.00
2
Wessel, J., Bradley, G. L., & Hood, M.
(2020). A low‐intensity, high‐frequency
intervention to reduce procrastination.
Applied Psychology: An International
Review, 70(4), 1669–1690.
https://doi.org/10.1111/apps.12293

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Table 1
Correlations for Study Variables

Variables

Participant
#1

Participant
#2

Participant
#3

Pooled raw
data

r

n

r

n

r

n

r

n

Pooled
standardized
data
r
n

0.73*

8

-0.50

8

-0.64

8

0.03

24

-0.14

24

-0.51

8

-0.21

8

-0.41

8

-0.21

24

-0.37

24

0.47

8

0.65

8

-0.30

8

0.44*

24

0.27

24

Acting with
awareness and
procrastination
Reminders and
procrastination
Cognitive distortions
and procrastination
* p < .05.

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Table 2
Descriptive Statistics for Procrastination (Measured by GPS) Across Different Reminder
Conditions

M

43.40

55.00*

53.80

Pooled
raw
data
50.73*

SD

5.18

5.43

10.64

8.80

0.78

n

5

5

5

15

15

No Experimental

M

53.5

67.50

58.25

59.75

0.57

Reminders

SD

9.61

6.45

8.66

9.69

0.88

n

4

4

4

12

12

Condition
Five Daily
Reminders

Statistic

Participant Participant Participant
#1
#2
#3

Pooled
standardized
data
-0.46*

Note. M, SD, and n, represent mean, standard deviation, and sample size, respectively. The
General Procrastination Scale was used to measure procrastination (with possible range of 23115; 23 = minimal procrastination; 115 = high procrastination).
* p < .05 for comparison of a five daily reminders condition with its respective no artificial
reminders condition.

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Table 3
Descriptive Statistics for Procrastination (Measured by Tasks Completed) Across Different
Reminder Conditions

M

5.20*

4.20*

2.80

Pooled
raw
data
4.07*

SD

1.92

0.84

1.92

1.83

1.00

n

5

5

5

15

15

No Experimental

M

3.00

3.00

2.00

2.67

-0.54

Reminders

SD

0.82

0.82

0.82

0.89

0.59

n

4

4

4

12

12

Condition
Five Daily
Reminders

Statistic

Participant Participant Participant
#1
#2
#3

Pooled
standardized
data
0.43*

Note: M, SD, and n represent mean, standard deviation, and sample size, respectively. The
number of tasks completed each day was used to measure procrastination (higher values indicate
less procrastination).
* p < .05 for comparison of the five daily reminders condition with its respective no
experimental reminders condition.

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Figure 1
Association Between Acting with Awareness and Procrastination Using Pooled Raw Data

Procrastination (GPS) (Scale 23-115; 23
minimal procrastination; 115 high
procrastination)

90
80
70
60
50
40
30
20
10
0
0

1

2

3
4
Acting with awareness (Tally)

5

6

7

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 2
Association Between Acting with Awareness and Procrastination Using Pooled Standardized
Data
2.5

Procrastination (GPS) (z-score)

2

-1.5

1.5
1
0.5
0
-1

-0.5

0

0.5

1

1.5

2

2.5

-0.5
-1
-1.5

-2
Acting with awareness (z-score)

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 3
Association Between Reminders and Procrastination Using Pooled Raw Data

Procrastination (GPS) (Scale 23-115; 23
minimal procrastination; 115 high
procrastination)

90
80
70
60
50
40
30
20
10
0
0

0.5

1

1.5
2
Reminders (Tally)

2.5

3

3.5

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 4
Association Between Reminders and Procrastination Using Pooled Standardized Data
2.5

Procrastination (GPS) (z-score)

2

-2.5

1.5
1
0.5
0
-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-0.5
-1
-1.5
-2
Reminders (z-score)

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 5
Association Between Cognitive Distortions and Procrastination Using Pooled Raw Data

Procrastination (GPS) (Scale 23-115; 23
minimal procrastination; 115 high
procrastination)

90
80
70
60
50
40
30
20
10
0
0

1

2
3
4
Cognitive distortions (Tally)

5

6

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 6
Association Between Cognitive Distortions and Procrastination Using Pooled Standardized
Data
2.5

Procrastination (GPS) (z-score)

2

-2

1.5
1
0.5
0
-1.5

-1

-0.5

0

0.5

1

1.5

2

-0.5
-1
-1.5

-2
Cognitive distortions (z-score)

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 7
Average Procrastination (Measured by GPS) Across Different Reminder Conditions Using
Pooled Raw Data

Procrastination (Scale (GPS) 23 - 115; 23
minimal procrastination; 115 high
procrastination)

80
70
60
50
40
30
20
10
0
Five daily reminders

No artificial reminders

Reminders

Notes. Procrastination scores are shown for five daily reminders and no experimental reminders
conditions using pooled raw data from all participants. Errors bars show ± 95% confidence
levels. Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3.

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Figure 8
Average Procrastination (Measured by Tasks Completed) Across Different Reminder Conditions
Using Pooled Raw Data

Procrastination (Number of tasks
completed each day)

8
7
6
5
4
3
2
1
0
Five daily reminders

No artificial reminders

Reminders

Notes. Procrastination scores are shown for five daily reminders and no experimental reminders
conditions using pooled raw data from all participants. Errors bars show ± 95% confidence
levels. Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3. Note that
higher number of tasks completed should be interpreted as less procrastination.

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Figure 9
Average Procrastination (Measured by GPS Scores) Across Different Reminder Conditions
Using Pooled Standardized Data
2

Procrastination (z-score)

1.5
1
0.5
0
-0.5
-1
-1.5
-2
Five daily reminders

No artificial reminders
Reminders

Notes. Procrastination scores are shown for five daily reminders and no experimental reminders
conditions using pooled raw data from all participants. Errors bars show ± 95% confidence
levels. Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3.

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Figure 10
Average Procrastination (Measured by Tasks Completed) Across Different Reminder Conditions
Using Pooled Standardized Data
3

Procrastination (z-score)

2.5
2
1.5

1
0.5
0
-0.5
-1
-1.5
-2
Five daily reminders

No artificial reminders
Reminders

Notes. Procrastination scores are shown for five daily reminders and no experimental reminders
conditions using pooled raw data from all participants. Errors bars show ± 95% confidence
levels. Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, and yellow = participant #3. Note that
higher number of tasks completed should be interpreted as less procrastination.

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Appendix
The General Procrastination Scale (GPS)
The General Procrastination Scale was developed by Lodha et. (2019). With 23 items in total, the
scale measures procrastination in 4 domains- academic, workplace, medical and civic
responsibilities related procrastination. All items are required to be rated on a 5-point Likert
scale ranging from 1 to 5. The scores reveal a Procrastination Quotient (PQ).
Items 5, 8, 12, 16, 18, 21 and 23 are reversed scored. Scores are obtained as a sum of
response to each item and they range from 23 to 115. A higher sum of scores obtained
on all items indicates higher level of procrastination for the individual test taker in
term of a higher Procrastination Quotient (P.Q.) obtained.
Scores of each item are calculated according to the following order:
Never
1

Rarely
2

Sometimes
3

Often
4

Always
5

The following is to be answered only pertaining to the last 24 hours:
1.
2.
3.
4.

5.
6.
7.
8.
9.
10.
11.
12.
13.
14.

I often try to avoid doing a task that I have little or no interest in.
I often delay tasks that are desirable to me.
When a task is highly stressful, I'm likely to put in more effort.
I think that certain problems can subside or be solved on their own, with
a passage of time.
I begin work immediately on a task once it has been given to me.
I have often had services terminated because of unpaid bills.
I often delay attending to medical issues concerning my health.
I prefer submitting an assignment before the deadline.
I generally don't start working on a project or assignment immediately.
I am usually late when I have to go out and meet friends for a movie or
dinner or other such plans.
I often put off doing tasks until urgency develops.
Whenever I make a plan of action, I follow it.
I think too much about things I would like to do but rarely get around to
doing them.
I tend to work at the eleventh hour for a task or project.
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15.
16.
17.
18.
19.
20.
21.
22.
23.

I postpone my chores to a later time when something more interesting
comes up.
I prefer planning ahead for tasks and events.
I needlessly delay finishing jobs, even when they're important.
I prefer working on one assignment at a time.
I do not complete tasks until I am insisted to complete them.
I am generally late at the workplace or college
I try to avoid any backlog of work.
I delay the tasks that distress me.
I feel guilty when I delay doing tasks.

163