AssociationsBetweenProcrastinationandSubsequentHealthOutcomes AmongUniversityStudentsinSweden

Original Investigation | Public Health
Associations Between Procrastination and Subsequent Health Outcomes
Among University Students in Sweden
Fred Johansson, MA; Alexander Rozental, PhD; Klara Edlund, PhD; Pierre Côté, PhD; Tobias Sundberg, PhD; Clara Onell, MSc; Ann Rudman, PhD; Eva Skillgate, PhD
Abstract
Key Points
IMPORTANCE Procrastination is prevalent among university students and is hypothesized to lead
to adverse health outcomes. Previous cross-sectional research suggests that procrastination is
associated with mental and physical health outcomes, but longitudinal evidence is currently scarce.
Question Is procrastination associated
with subsequent health outcomes
among university students?
Findings In this cohort study of 3525
OBJECTIVE To evaluate the association between procrastination and subsequent health outcomes
Swedish university students,
among university students in Sweden.
procrastination was associated with
worse subsequent mental health
DESIGN, SETTING, AND PARTICIPANTS This cohort study was based on the Sustainable University
(depression, anxiety, and stress
Life study, conducted between August 19, 2019, and December 15, 2021, in which university students
symptom levels), having disabling pain
recruited from 8 universities in the greater Stockholm area and Örebro were followed up at 5 time
in the upper extremities, unhealthy
points over 1 year. The present study used data on 3525 students from 3 time points to assess
lifestyle behaviors (poor sleep quality
whether procrastination was associated with worse health outcomes 9 months later.
and physical inactivity), and worse levels
of psychosocial health factors (higher
EXPOSURE Self-reported procrastination, measured using 5 items from the Swedish version of the
loneliness and more economic
Pure Procrastination Scale rated on a Likert scale from 1 (“very rarely or does not represent me”) to
difficulties).
5 (“very often or always represents me”) and summed to give a total procrastination score ranging
from 5 to 25.
Meaning This study suggests that
procrastination may be associated with
a range of health outcomes.
MAIN OUTCOMES AND MEASURES Sixteen self-reported health outcomes were assessed at the
9-month follow-up. These included mental health problems (symptoms of depression, anxiety, and
stress), disabling pain (neck and/or upper back, lower back, upper extremities, and lower
extremities), unhealthy lifestyle behaviors (poor sleep quality, physical inactivity, tobacco use,
cannabis use, alcohol use, and breakfast skipping), psychosocial health factors (loneliness and
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economic difficulties), and general health.
RESULTS The study included 3525 participants (2229 women [63%]; mean [SD] age, 24.8 [6.2]
years), with a follow-up rate of 73% (n = 2587) 9 months later. The mean (SD) procrastination score
at baseline was 12.9 (5.4). An increase of 1 SD in procrastination was associated with higher mean
symptom levels of depression (β, 0.13; 95% CI, 0.09-0.17), anxiety (β, 0.08; 95% CI, 0.04-0.12), and
stress (β, 0.11; 95% CI, 0.08-0.15), and having disabling pain in the upper extremities (risk ratio [RR],
1.27; 95% CI, 1.14-1.42), poor sleep quality (RR, 1.09, 95% CI, 1.05-1.14), physical inactivity (RR, 1.07;
95% CI, 1.04-1.11), loneliness (RR, 1.07; 95% CI, 1.02-1.12), and economic difficulties (RR, 1.15, 95% CI,
1.02-1.30) at the 9-month follow-up, after controlling for a large set of potential confounders.
CONCLUSIONS AND RELEVANCE This cohort study of Swedish university students suggests that
procrastination is associated with subsequent mental health problems, disabling pain, unhealthy
lifestyle behaviors, and worse psychosocial health factors. Considering that procrastination is
(continued)
Open Access. This is an open access article distributed under the terms of the CC-BY License.
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Associations Between Procrastination and Health Outcomes Among University Students
Abstract (continued)
prevalent among university students, these findings may be of importance to enhance the
understanding of students’ health.
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Introduction
Procrastination is defined as voluntarily delaying an intended course of action despite expecting to
be worse off because of the delay1 and is common, especially among younger people.2,3 It is
estimated that at least half of university students engage in consistent and problematic
procrastination, such as postponing studying for examinations or writing papers.1,4 Procrastination is
described as a form of self-regulatory failure linked to personality traits such as impulsiveness,5,6
distractibility, and low conscientiousness.1,7 An individual’s tendency to procrastinate is relatively
stable over time, but specific procrastination behaviors are influenced by contextual factors such as
task aversiveness.1 For some students, procrastination is occasional and related to specific academic
tasks, while for others it is more of a general disposition, potentially affecting academic
achievements8 and health.
Cross-sectional studies suggest that procrastination is associated with symptoms of depression,
anxiety, and stress as well as loneliness and reduced life satisfaction.2,9-11 Procrastination is also
associated with prevalent general physical health problems,12 cardiovascular disease,13 and
unhealthy lifestyle behaviors.12,14,15
Students engaged in university studies have high levels of freedom and low structure, which
places high demands on their capacity to self-regulate.16 These high demands on self-regulation may
explain the high prevalence of procrastination among university students and make persons who are
prone to procrastinate more vulnerable to the negative consequences of procrastination while at
the university.16
The procrastination health model12,17 suggests that a general tendency to procrastinate is
associated with negative health outcomes by increasing levels of stress, reducing healthy behaviors,
and delaying treatment.12,17,18 However, the causal direction between procrastination and health
outcomes is not well understood and could be bidirectional, with poor physical or mental health
reducing energy levels and motivation, potentially leading to more procrastination.
Some of the strongest support for the causal claims of the procrastination health model comes
from studies on the effects of treating procrastination. Results from randomized clinical trials suggest
that intervening on procrastination with psychological treatments can reduce subsequent levels of
depression and anxiety and improve quality of life.19 However, the evidence is limited as there are
only a few studies, with a focus solely on mental health outcomes.19
The procrastination health model provides a useful theoretical foundation for how
procrastination could affect different health outcomes.12,17 However, longitudinal evidence from
studies of procrastination and subsequent health outcomes is scant and focuses on only 1 or a few
health outcomes.20-23 Furthermore, most studies have limited control for important confounders,
including other health problems, age, and sociodemographic factors. Also, no study has, to our
knowledge, controlled for the potential reverse causation that may arise if health problems increase
procrastination.
In this study, we investigated the associations between procrastination and a range of
subsequent health outcomes while controlling for a large set of potential confounders, including
prior levels of the outcomes. This method allows for stronger conclusions regarding the causal
direction24 between procrastination and health outcomes and can provide a broader picture of the
potential associations between procrastination and health.
We aimed to evaluate the associations between procrastination and 16 health outcomes at the
end of a 9-month period. The outcomes include mental health, disabling pain, and several unhealthy
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Associations Between Procrastination and Health Outcomes Among University Students
lifestyle behaviors and psychosocial health factors. Following the procrastination health model,12,17
we hypothesized that higher levels of procrastination would be associated with worse health
outcomes at follow-up.
Methods
Design and Study Population
The cohort for this study came from the Sustainable University Life study,25 which followed up
Swedish university students for 1 year using web-based surveys. Undergraduate or graduate students
(up to and including masters’ level) at full-time educational programs, with at least 1 year left of their
education were eligible to participate. Students were recruited from 8 universities in the greater
Stockholm area and Örebro. Data collection was ongoing from August 19, 2019, to December 15,
2021. The targeted universities represent a convenience sample and were selected to provide a
variety of different types of disciplines, such as medicine, technology, social sciences, and economics
and were restricted to universities in a limited geographical area to enable physical presence by study
staff. The study was approved by the Swedish Ethical Review Authority and all participants provided
informed consent electronically. More information about the study methods and data collection is
available in the study protocol.25 This study is reported following the Strengthening the Reporting of
Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.
Eligible students were informed about the study during an in-class presentation and/or by an
email with a link to the survey. Students who agreed to participate were followed up every 3 months
for 1 year at 5 time points. In the present data analysis, the first time point was defined as prebaseline
and was used for measures of the covariates. The second time point was defined as the baseline and
was used for the procrastination measure and the fifth time point was defined as the 9-month
follow-up and used for measurement of the health outcomes (Figure 1). The follow-up period of 9
months represents the length of an academic year, which we believe is adequate for procrastination
to manifest its potential associations with different health outcomes. The sample was restricted to
those responding at baseline and included 3525 participants; the follow-up rate was 73% (n = 2587)
at the 9-month follow-up (Figure 2).
Measures
Exposure: Procrastination
Procrastination was measured using 5 items from the Swedish version of the Pure Procrastination
Scale (PPS).26 The items were rated on a Likert scale from 1 (“very rarely or does not represent me”)
to 5 (“very often or always represents me”) and summed to give a total procrastination score ranging
from 5 to 25. The short version of the PPS includes items 4 through 8 from the full PPS and has shown
adequate psychometric properties in nonclinical samples.26,27 In this sample, the scale had a
Cronbach α of 0.92 at baseline and a test-retest reliability (Pearson r) of 0.79 over 3 months and of
0.75 over 9 months.
Figure 1. Timeline of Data Collection in the Sustainable University Life (SUN) Cohort and How Data
Were Organized for the Present Study
Time point in the SUN cohort
(time since enrollment)
Definition in
present study
Measures in
the present study
T1
(0)
T2
(3 mo)
Prebaseline
Baseline
Covariates
Procrastination
T3
(6 mo)
T4
(9 mo)
T5
(12 mo)
9-mo Follow-up
(9 mo since baseline)
Health
outcomes
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T indicates time point.
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Associations Between Procrastination and Health Outcomes Among University Students
Outcomes
We assessed a range of health outcomes at the 9-month follow-up, including mental health problems
(symptoms of depression, anxiety, and stress), disabling pain (neck and/or upper back, lower back,
upper extremities, and lower extremities), unhealthy lifestyle behaviors (poor sleep quality, physical
inactivity, weekly tobacco use, monthly cannabis use, daily alcohol use, and breakfast skipping),
psychosocial health factors (loneliness and economic difficulties), and general health. For more
information on the outcome measures, see eMethods 1 in Supplement 1.
Confounders
Potential confounders were selected using the modified disjunctive cause criterion.28 This approach
is a feasible alternative to directed acyclic graphs when the causal knowledge needed to produce a
reliable directed acyclic graph is unavailable.28 We used the same set of covariates in all the outcome
models, as has been suggested for outcome-wide studies.29 The covariate set included all outcome
variables at prebaseline to limit the risk of reverse causation,30 as well as age, gender, highest
parental level of education, previous physical and psychiatric diagnoses, civil status, place of birth,
and university education type. All covariates were measured at prebaseline to ensure that none were
mediators between procrastination and the outcomes. Information on covariate measurement and
coding is provided in eMethods 1 in Supplement 1.
Statistical Analysis
The characteristics of the sample are presented in Table 1, both for the full sample and across
quartiles of procrastination (procrastination score range, 5-25; first quartile, 5-9; second quartile,
10-12; third quartile, 13-17; and fourth quartile, 18-25). To assess whether procrastination was
associated with the health outcomes at follow-up, we used the outcome-wide approach,29 a newly
developed analytic approach to study associations between an exposure and multiple outcomes. We
built separate multivariable regression models for each of the outcomes to assess the association
between baseline procrastination and the different outcomes at the 9-month follow-up. For
continuous outcomes, linear regression models were used. For binary outcomes, modified Poisson
regressions31 were used (rather than logistic regression models because some outcomes were
relatively common).
Procrastination and all continuous outcome measures were standardized (mean = 0, SD = 1).
Estimates for continuous outcomes are thus interpreted as the difference in the outcome as
measured in SDs associated with a 1-SD increase in procrastination, and estimates for the binary
outcomes are interpreted as the risk ratio (RR) of the outcome associated with a 1-SD increase in
procrastination. All models were adjusted for the set of prebaseline confounders described. For
Figure 2. Flowchart of the Inclusion of Participants
18 973 Invited to participate
14 711 Did not respond
Included in the SUN cohort
4262 Prebaseline (T1)
96 Withdrew from study
641 Did not respond
Study base
3525 Baseline (T2)
85 Withdrew from study
853 Did not respond
Analytic sample
2587 9-mo Follow-up (T5)
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The definition of each time point (T) in the present
study (T1, T2, and T5) refers to the time point in the
Sustainable University Life (SUN) Cohort (Figure 1).
Note that T3 and T4 are not shown here, because data
from these time points were not used for the main
analyses.
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Associations Between Procrastination and Health Outcomes Among University Students
unadjusted estimates, see eTable 1 in Supplement 1. Visual inspection of the residuals from all models
indicated fairly linear associations between procrastination and the outcomes. The homoscedasticity
and distribution of the residuals were not assessed because they typically have minor impact on the
regression estimates,32 but the modified Poisson models still applies robust SEs. This analytic
Table 1. Characteristics of Participants Prebaseline for the Full Sample and by Quartiles of Procrastination Levelsa
Participants, No. (%)
Procrastination quartileb
Characteristic
Full sample
(N = 3525)
First
(n = 1052)
Second
(n = 713)
Third
(n = 1003)
Fouth
(n = 757)
Age, mean (SD), y
24.8 (6.2)
25.3 (6.7)
24.8 (6.3)
24.7 (6.2)
24.0 (5.5)
Female
2229 (63)
674 (64)
458 (64)
640 (64)
457 (60)
Male
1274 (36)
NRc
NRc
NRc
NRc
Other
22 (1)
NRc
NRc
NRc
NRc
Gender
Education type
Medical or health
1688 (48)
556 (53)
343 (48)
471 (47)
318 (42)
Technical
1407 (40)
354 (34)
274 (38)
413 (41)
366 (48)
Social science or humanities
294 (8)
100 (10)
68 (10)
79 (8)
47 (6)
Economic
85 (2)
28 (3)
16 (2)
28 (3)
13 (2)
Other
51 (1)
14 (1)
12 (2)
12 (1)
13 (2)
Civil status
Single
1975 (56)
494 (47)
395 (55)
594 (59)
492 (65)
Cohabiting partner
1133 (32)
435 (41)
227 (32)
294 (29)
177 (23)
Noncohabiting partner
417 (12)
123 (12)
91 (13)
115 (11)
88 (12)
Sweden
2826 (80)
859 (82)
579 (81)
797 (79)
591 (78)
Other Nordic countries
118 (3)
38 (4)
23 (3)
31 (3)
26 (3)
Other Europe
192 (5)
57 (5)
41 (6)
61 (6)
33 (4)
Outside Europe
389 (11)
98 (9)
70 (10)
114 (11)
107 (14)
Place of birth
Highest parental educational level
University
2553 (72)
747 (71)
513 (72)
737 (73)
556 (73)
Abbreviation: NR, not reported.
Less than university
972 (28)
305 (29)
200 (28)
266 (27)
201 (27)
a
1.0 (1.3)
0.9 (1.2)
1.0 (1.3)
1.1 (1.2)
1.2 (1.4)
Procrastination was measured at baseline, while all
covariates and outcomes were measured
prebaseline. Percentages of some categorical
variables do not add up to 100% due to rounding.
Depression symptoms
4.7 (4.7)
2.9 (3.6)
4.1 (4.2)
5.0 (4.4)
7.3 (5.4)
b
Anxiety symptoms
3.0 (3.3)
2.1 (2.9)
2.7 (3.1)
3.2 (3.2)
4.2 (4)
Stress symptoms
6.3 (4.6)
5.0 (4.3)
6.0 (4.3)
6.7 (4.4)
7.9 (4.8)
Neck and/or upper back
405 (11)
90 (9)
84 (12)
117 (12)
114 (15)
Lower back
364 (10)
86 (8)
62 (9)
117 (12)
99 (13)
Upper extremities
436 (12)
90 (9)
98 (14)
137 (14)
111 (15)
The procrastination score ranged from 5 to 25, with
the quartiles 9 (25th percentile), 12 (50th
percentile), and 17 (75th percentile). Participants
were categorized based on their procrastination
score as follows: 5 to 9, first quartile; 10 to 12, second
quartile; 13 to 17, third quartile; and 18 to 25, fourth
quartile.
c
Lower extremities
545 (15)
123 (12)
106 (15)
162 (16)
154 (20)
Poor sleep quality
1893 (54)
380 (36)
351 (49)
605 (60)
557 (74)
Physically inactive
1831 (52)
438 (42)
364 (51)
548 (55)
481 (64)
Daily alcohol use
51 (1)
7 (1)
17 (2)
12 (1)
15 (2)
The gender categories “male” and “other” are not
presented for procrastination quartiles because the
“other” category had low cell counts (<5). The gender
category “other” included participants responding
“Other” or “Do not want to identify with either of
these identities.”
d
Weekly tobacco use
542 (15)
136 (13)
130 (18)
141 (14)
135 (18)
Monthly cannabis use
72 (2)
12 (1)
17 (2)
16 (2)
27 (4)
Breakfast skipping
878 (25)
188 (18)
160 (22)
246 (25)
284 (38)
Loneliness
1485 (42)
273 (26)
275 (39)
477 (48)
460 (61)
Economic difficulties
555 (16)
110 (10)
110 (15)
172 (17)
163 (22)
Poor general health
113 (3)
14 (1)
11 (2)
34 (3)
54 (7)
The previous diagnoses variable is a count indicating
how many of the following diagnoses the
participants had received: rheumatic diseases;
allergies; respiratory diseases; cardiovascular
diseases; gastrointestinal diseases; diabetes;
diseases in urinary tracts or internal or external
genitals; diseases in the nervous system, eyes, or
ears; mental disorders; tumors or cancers; and
attention deficits or learning disabilities.
Previous diagnoses, mean (SD), No.d
Mental health score, mean (SD)
Disabling pain
Lifestyle behaviors
Psychosocial health factors
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strategy deviates from the ones prespecified in our study protocol25 but was selected as it was
deemed most appropriate for the research questions. Analyses were conducted using R, version 4.1.2
(R Group for Statistical Computing).
Missing Data
Outcome data were missing for 938 participants not responding at the 9-month follow-up, and the
main analyses were performed as complete-case analyses (n = 2587). Prebaseline characteristics
stratified by missingness at the 9-month follow-up are presented in eTable 2 in Supplement 1. We had
complete data on the exposure and all covariates, except for the sleep quality variable. The
Pittsburgh Sleep Quality Index (PSQI) had missing data on 3 items for sleep disturbances for 9%
(n = 225) of the respondents in the analytic sample due to a technical error at the beginning of data
collection. These missing data were handled by imputing the person-mean of the items for sleep
disturbances.
Sensitivity Analyses
First, we computed the E-value33,34 that assesses how strongly unmeasured and residual
confounding would need to be associated with the exposure and the outcomes on the RR scale to
reduce the point estimates to the null. When the 95% CIs did not include the null, we also computed
the E-value of the 95% CI bound closest to the null, to assess the strength of unmeasured and
residual confounding needed to shift the 95% CI to include the null. Second, we calculated the pointbiserial correlation between procrastination at baseline and missingness at follow-up to investigate
the possibility of selection bias in relation to the internal validity. No association between the
exposure and loss to follow-up indicates that selection bias cannot affect the internal validity, but
only the external validity.35 Third, we conducted analyses controlling for prior levels of
procrastination, as has been suggested for outcome-wide studies.29 These analyses had to be
performed with a 6-month follow-up because we did not have data on procrastination at prebaseline
(eMethods 2 and eTable 3 in Supplement 1). Fourth, the robustness of our results to the personmean imputation of values on the PSQI was assessed by performing multiple imputation for the 3
missing items (eMethods 3 in Supplement 1).
Results
At prebaseline, the study included 3525 participants (2229 women [63%] and 1274 men (36%);
mean [SD] age, 24.8 [6.2] years) (Table 1). The mean (SD) procrastination score at baseline was 12.9
(5.4). Gender and age were similar across levels of procrastination, but participants with higher levels
of procrastination tended to be more likely to study technical sciences, be single, and have been born
outside of Europe. All outcome variables showed higher prevalence or mean levels at prebaseline
among participants with higher procrastination levels at baseline.
Table 2 presents the results from the outcome regression models. At the 9-month follow-up, a
1-SD increase in procrastination was associated with higher mean symptom levels of depression (β,
0.13; 95% CI, 0.09-0.17), anxiety (β, 0.08; 95% CI, 0.04-0.12), and stress (β, 0.11; 95% CI, 0.08-0.15)
as well as having disabling pain in the upper extremities (RR, 1.27; 95% CI, 1.14-1.42), poor sleep
quality (RR, 1.09, 95% CI, 1.05-1.14), physical inactivity (RR, 1.07; 95% CI, 1.04-1.11), loneliness (RR,
1.07; 95% CI, 1.02-1.12), and economic difficulties (RR, 1.15, 95% CI, 1.02-1.30). The results showed no
clear evidence of associations between procrastination and subsequent disabling pain in other body
regions; use of tobacco, alcohol, or cannabis, breakfast skipping; or general health afer adjustment
for the prebaseline covariates.
For estimates with a 95% CI that excluded the null, the E-values suggested that unmeasured
confounders would need to increase the risk of both procrastination and the outcomes by 35% to
86%, depending on the outcome, to move the point estimates to the null (Table 2). The point-biserial
correlation between procrastination at baseline and missingness at the follow-up was 0.05. Outcome
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levels at prebaseline were generally somewhat higher in the group that was missing at the 9-month
follow-up (eTable 2 in Supplement 1). The associations between procrastination and health outcomes
6 months later, while controlling for prior procrastination levels, were similar to the results in the
main analysis (eMethods 2 and eTable 3 in Supplement 1). Using multiple imputation for the 3 missing
items on the PSQI gave results identical to the main analysis when rounded to the second decimal
place (eMethods 3 in Supplement 1).
Discussion
In this cohort of Swedish university students, higher levels of procrastination were associated with
worse subsequent mental health (depression, anxiety, and stress symptom levels), having disabling
pain in the upper extremities, unhealthy lifestyle behaviors (poor sleep quality and physical
inactivity), and worse levels of psychosocial health factors (higher loneliness and more economic
difficulties) 9 months later. We found no clear associations between procrastination and subsequent
disabling pain in other body regions (neck and/or upper back, lower back, or lower extremities), other
unhealthy lifestyle behaviors (alcohol, tobacco, or cannabis use and breakfast skipping), or
general health.
The identified associations are in the same direction, but to a lesser magnitude, than those
reported in most previous studies on procrastination and health outcomes.1,2,9,10,12 There are several
potential reasons for this difference in magnitude, such as the use of different scales to measure
procrastination and the health outcomes. However, the arguably largest difference between this
study and most previous studies is that we controlled for an extensive set of potential confounders.
To our knowledge, this is the first study of procrastination and health outcomes to adjust for prior
levels of the outcomes. As shown in eTable 1 in Supplement 1, when controlling for prior levels of the
outcome variables, the unadjusted estimates of associations between procrastination and the health
outcomes corresponded more closely to those of some previous studies.2,9,10 By adjusting for
prebaseline levels of the outcome variables, we reduced the risk of reverse causality.30 Still,
interpreting these association as causal effects requires the assumptions of no unmeasured or
Table 2. Procrastination and Subsequent Health Outcomesa
E-value
Outcome
β or RR (95% CI)
PE
LCL
Depression symptoms
0.13 (0.09-0.17)
1.57
1.45
Anxiety symptoms
0.08 (0.04-0.12)
1.41
1.27
Stress symptoms
0.11 (0.08-0.15)
1.53
1.41
Neck and/or upper back
1.09 (0.96-1.24)
1.41
NA
Lower back
1.03 (0.90-1.18)
1.22
NA
Upper extremities
1.27 (1.14-1.42)
1.86
1.53
Lower extremities
1.10 (0.99 to 1.23)
1.44
NA
Poor sleep quality
1.09 (1.05-1.14)
1.41
1.27
Physical inactivity
1.07 (1.04-1.11)
1.35
1.24
Daily alcohol use
1.05 (0.71-1.56)
1.29
NA
Weekly tobacco use
0.96 (0.89-1.04)
1.24
NA
Monthly cannabis use
1.21 (0.91-1.62)
1.71
NA
Breakfast skipping
1.03 (0.97-1.09)
1.19
NA
Mental health, β (95% CI)
Disabling pain, RR (95% CI)
Lifestyle behaviors, RR (95% CI)
Abbreviations: LCL, lower confidence limit; NA, not
applicable; PE, point estimate; RR, relative risk.
a
Psychosocial health factors, RR (95% CI)
Loneliness
1.07 (1.02-1.12)
1.35
1.17
Economic difficulties
1.15 (1.02-1.30)
1.57
1.17
Poor general health
1.14 (0.97-1.34)
1.54
NA
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All models were adjusted for the set of prebaseline
covariates described, including all outcomes
prebaseline. Procrastination and all continuous
outcomes were standardized (mean = 0 and SD = 1),
and β is the standardized effect size.
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Associations Between Procrastination and Health Outcomes Among University Students
residual confounding, selection bias, or other biases affecting the associations,36 assumptions that
are unlikely to be fully met in the present study. We do believe, however, that our results may
correspond more closely to the consequences that interventions targeting procrastination would
have for different health outcomes, than those of earlier cross-sectional studies.
Our estimates indicate that the associations between procrastination and subsequent health
outcomes are weak. For instance, a 1-SD increase in procrastination was associated with a mean
increase in subsequent depression symptoms of only 0.13 SDs, and many of the associations were
weaker than this. The follow-up time of 9 months represents 1 academic year and was chosen
because we believe this is sufficient induction time for procrastination to lead to health problems. It
is possible, however, that these estimates would be stronger for a longer follow-up because the
potential negative associations of procrastination with health outcomes could accumulate over time.
Still, even though the associations are not very strong, it seems that procrastination could have
associations with many different aspects of health, including mental health, physical pain, lifestyle
behaviors, and psychosocial health factors. Thus, although it seems that intervening on
procrastination is unlikely to produce large associations with any specific health outcome, it could
possibly produce small associations with a diverse set of different health outcomes.
Limitations
This study has some limitations. The E-value analysis suggests that unmeasured and residual
confounding (from, for instance, neuroticism, conscientiousness, or genetic factors) could
potentially explain away the observed associations. This potential unmeasured or residual
confounding would, however, need to be moderately associated with procrastination and the
outcomes, independent of all measured covariates29 (ie, associated with procrastination and the
outcomes even after adjusting for all covariates). Given the large number of confounders adjusted for
in these analyses, it is likely that a substantial proportion of any unmeasured confounding is already
accounted for in our analyses. Controlling for prior procrastination levels gave results similar to those
in the main analysis, limiting the risk of reverse causality and unmeasured confounding29 (eMethods
2 and eTable 3 in Supplement 1).
We also have the risk of misclassification of exposure and outcomes, which is probably not an
issue for the procrastination measure given that the PPS showed excellent reliability. The outcomes,
however, differ somewhat in their psychometric properties, and misclassification may have
attenuated the observed associations for some of the outcomes.
We found that procrastination at baseline had a near-null association with loss to follow-up, so
it is unlikely that the internal validity of observed associations is subject to selection bias.35 There
were, however, small differences in prebaseline characteristics, especially prior outcome levels,
between responders and nonresponders, which could affect external validity (eTable 2 in
Supplement 1). Furthermore, the sample is not fully representative of the overall Swedish student
population (for instance, we have more medical and health and technical students than average).
Thus, it is uncertain whether our estimates are generalizable to Swedish students overall or to other
populations. Furthermore, our measures were collected during the COVID-19 pandemic, which could
affect generalizability to other time periods.
Conclusions
This cohort study of Swedish university students suggests that procrastination is associated with
subsequent mental health problems, disabling pain, unhealthy lifestyle behaviors, and worse
psychosocial health factors. Considering that procrastination is prevalent among university students,
these findings may be of importance to enhace the understanding of students’ health.
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ARTICLE INFORMATION
Accepted for Publication: November 13, 2022.
Published: January 4, 2023. doi:10.1001/jamanetworkopen.2022.49346
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Johansson
F et al. JAMA Network Open.
Corresponding Author: Fred Johansson, MA, Sophiahemmet Högskola, PO Box 5605, 114 86 Stockholm, Sweden
([email protected]).
Author Affiliations: Department of Health Promotion Science, Sophiahemmet University, Stockholm, Sweden
(Johansson, Edlund, Sundberg, Onell, Skillgate); Department of Psychology, Uppsala University, Uppsala, Sweden
(Rozental); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden (Rozental, Rudman);
Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine,
Karolinska Institutet, Stockholm, Sweden (Edlund, Skillgate); Institute for Disability and Rehabilitation Research
and Faculty of Health Sciences, Ontario Tech University, Oshawa, Ontario, Canada (Côté); Department of Caring
Sciences, Dalarna University, Falun, Sweden (Rudman).
Author Contributions: Mr Johansson had full access to all of the data in the study and takes responsibility for the
integrity of the data and the accuracy of the data analysis.
Concept and design: Johansson, Edlund, Côté, Rudman, Skillgate.
Acquisition, analysis, or interpretation of data: Johansson, Rozental, Côté, Sundberg, Onell, Skillgate.
Drafting of the manuscript: Johansson, Edlund, Côté, Sundberg, Skillgate.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Johansson, Côté.
Obtained funding: Rudman, Skillgate.
Administrative, technical, or material support: Edlund, Sundberg, Onell.
Supervision: Rozental, Edlund, Côté, Sundberg, Rudman, Skillgate.
Conflict of Interest Disclosures: Mr Johansson and Drs Edlund, Sundberg, and Skillgate reported receiving grants
from the Swedish Research Council for Health, Working Life and Welfare (FORTE) during the conduct of the study.
No other disclosures were reported.
Funding/Support: This research project was funded by grant number FORTE2018-00402 from the Swedish
Research Council for Health, Working Life and Welfare (FORTE). The Sustainable University Life study also received
financial support from the Public Health Agency of Sweden.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection,
management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and
decision to submit the manuscript for publication.
Data Sharing Statement: See Supplement 2.
Additional Contributions: We would also like to express our appreciation to ACTIC (health club chain), the Tim
Bergling Foundation, and all the participating students for their contribution to the Sustainable University
Life study.
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SUPPLEMENT 1.
eMethods 1. Assessment of the Outcome and Confounder Variables
eTable 1. Comparisons of Estimates Under Different Adjustments
eTable 2. Characteristics of Participants at Pre-baseline Stratified by Missingness at the Nine-Month Follow-up
eMethods 2. Sensitivity Analysis Controlling for Prior Levels of Procrastination
eTable 3. Procrastination (T3) and Subsequent Health Outcomes Six Months Later (T5), Adjusted for Prior Levels
of Procrastination (T2)
eMethods 3. Sensitivity Analysis for the Imputation of the Three Missing Items on the Pittsburgh Sleep Quality
Inventory
eReferences
SUPPLEMENT 2.
Data Sharing Statement
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