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Br J Health Psychol. 2023;00:1–16. wileyonlinelibrary.com/journal/bjhp 1
1Department of Psychology, Durham University,
Durham, UK
2Institute of Work Psychology, University of
Sheffield, Sheffield, UK
3Department of Psychology, Carleton University,
Ottawa, Ontario, Canada
Correspondence
Fuschia M. Sirois, Department of Psychology,
Durham University, Upper Mountjoy, South Rd,
Durham DH1 3LE, UK.
Email: fuschia.sirois@durham.ac.uk
Abstract
Objectives: Procrastination is a common form of
self-regulation failure that a growing evidence base suggests
can confer risk for poor health outcomes, especially when it
becomes habitual. However, the proposed linkages of chronic
procrastination to health outcomes have not been tested over
time or accounted for the contributions of higher-order
personality factors linked to both chronic procrastination and
health-related outcomes. We addressed these issues by exam-
ining the role of chronic procrastination in health outcomes
over time in which the hypothesized links of procrastination
to health problems operate via stress and health behaviours.
Design: Three-wave longitudinal study with 1-month
intervals.
Methods: Participants (N = 379) completed measures
of trait procrastination at Time 1, and measures of health
behaviours, stress and health problems at each time point, in
a lab setting.
Results: Procrastination and the health variables were
inter-related in the expected directions across the three
assessments. Chronic procrastination was positively associ-
ated with stress and negatively with health behaviours at each
time point. Path analysis testing a cross-lagged longitudinal
mediation model found an indirect relationship operating
between procrastination and health problems via stress, after
accounting for the contributions of conscientiousness and
neuroticism.
Conclusions: This research extends previous work by
demonstrating that the links between chronic procrastination
and poor health are accounted for mainly by higher stress,
after accounting for other key traits, and that these associa-
tions are robust over time. The findings are discussed in terms
ARTICLE
Procrastination and health: A longitudinal test of the
roles of stress and health behaviours
Fuschia M. Sirois1 | Christopher B. Stride2 | Timothy A. Pychyl3
DOI: 10.1111/bjhp.12658
Received: 21 December 2022 Accepted: 1 March 2023
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited.
© 2023 The Authors. British Journal of Health Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.
SIROIS et al.
2
INTRODUCTION
Defined as the voluntary delay of taking action on important, necessary and intended tasks despite
knowing there will be negative consequences for this delay (Ferrari & Tice, 2000; Sirois & Pychyl, 2013),
procrastination is a ubiquitous and prevalent form of self-regulatory failure that can be a chronic tendency
for many individuals. Indeed, research estimates suggest that 50 per cent of students and 15–25 per
cent of adults chronically procrastinate (Ferrari et al., 2007; Steel, 2007). In addition to having nega-
tive consequences for academic study (Hen & Goroshit, 2014) and work life (Beheshtifar et al., 2011),
there is growing evidence that chronic or trait procrastination can also be detrimental to health and
well-being. For example, research indicates that chronic procrastination is linked to higher stress, poor
health behaviours, poor sleep and a greater number of physical illnesses and symptoms (Flett et al., 2012;
Johansson et al., 2023; Kelly & Walton, 2021; Li et al., 2020).
Theoretical accounts of why chronic procrastination may confer vulnerability for poor health are
consistent with classic models of personality and health (e.g. Smith, 2006; Suls & Rittenhouse, 1990),
and implicate heightened stress and poor practice of health-promoting behaviours as two key explana-
tory routes (Sirois et al., 2003). However, evidence supporting the procrastination–health model (Sirois
et al., 2003), the first model to explicate the links between chronic procrastination and physical health,
is mainly cross-sectional (e.g.Sirois, 2007 ; Sirois et al., 2003). This is problematic not only with respect
to making inferences regarding the directionality of the links between trait procrastination and health
outcomes but also because cross-sectional designs provide only a snapshot that cannot account for any
ongoing effects of the proposed relationships. Research on the procrastination–health model has rarely
accounted for the contributions of the higher-order personality factors linked to both procrastination
and health. In the current study, we aimed to address these important issues and provide a temporal test
of the procrastination–health model to better understand the pathways linking this chronic form of
self-regulation failure to poor health.
of the importance of addressing habitual self-regulation fail-
ure for improving health.
KEYWORDS
health behaviours, health problems, procrastination, self-regulation, stress
Statement of Contribution
What is already known on this subject?
• Chronic procrastination is associated with poor health outcomes including higher stress, poor
health behaviours and poor health.
• The procrastination–health model posits that higher stress and poor health behaviours are two
routes through which chronic procrastination can confer risk for poor health.
• Previous research testing the procrastination–health model is limited and has used
cross-sectional designs.
What does this study add?
• This study used a three-wave longitudinal design to test the procrastination–health model.
• Chronic procrastination was associated with health problems over time via stress but not
health behaviours.
• Chronic self-regulation failure may confer short-term risk for poor health due to higher stress.
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PROCRASTINATION AND HEALTH OVER TIME 3
Chronic procrastination and health
The relationship between procrastination and health was first noted in a longitudinal study by Tice and
Baumeister (1997) in which students who habitually procrastinated reported lower stress and fewer
health problems at the beginning of the term relative to non-procrastinators. However, by the end of the
term, they reported higher stress and more health problems. Although the reasons for the links between
procrastination and health were not empirically tested, it was speculated that increased stress associated
with chronic procrastination may be one possible explanation for the poor health of procrastinators.
Building on this initial study, the procrastination–health model (Sirois, 2007; Sirois et al., 2003) offered
a theoretically driven answer to the question of why chronic procrastination may be linked to a greater
number of health issues.
Consistent with extant models linking personality to health in general (Friedman, 2000;
Sergerstrom, 2000; Suls & Rittenhouse, 1990), the procrastination–health model (Sirois et al., 2003)
proposed that procrastination confers risk for poor health through both a stress-related route and a
behavioural route. The stress-related route refers to the health risks posed by the activation of physiolog-
ical systems involved in the stress response and their role in the development of physical health issues
both in the short and long term. For example, activation of the stress system creates increased risk of
infections and autonomic nervous system arousal, which can elevate heart rate, increase muscle tension
and disrupt digestive functioning and sleep (Taylor et al., 2020). The behavioural route involves poor health
behaviours including avoidance of health-promoting behaviours, such as healthy eating and regular exer-
cise. Although lack of exercise and eating unhealthy foods may have minor immediate effects on health,
this pattern of poor health-promoting behaviours is known to increase risk for disease, especially among
those with pre-existing risk factors (World Health Organization, 2015).
The stress-related route in the procrastination–health model captures the contribution of chronic
procrastination for generating unnecessary stress via behavioural and cognitive pathways. An enduring
tendency to put off important and intended tasks across a variety of life domains can generate unnec-
essary stress as the procrastinator has to deal with the personal (Solomon & Rothblum, 1984) and social
(Ferrari et al., 1999; Giguère et al., 2016) consequences of delaying tasks. Theory and research also indi-
cate that chronic procrastination is associated with a tendency towards stressogenic thoughts (Sirois, 2016),
and negative, harsh self-evaluations (Flett et al., 1995; McCown et al., 2012) that can maintain stress (Flett
et al., 2012; Sirois, 2014).
A general tendency to procrastinate also includes unnecessarily delaying the practice of impor-
tant health-promoting behaviours. For example, in one study of adults in Israel, procrastinating on
health-promoting behaviours was the most common life domain in which people procrastinated, with
40 per cent reporting doing so (Hen & Goroshit, 2018). Changing health behaviours can be challeng-
ing for many individuals, as it often requires breaking unhealthy habits by drawing on more conscious
and effortful self-regulation resources to resist temptations and monitor goals in order to bridge the
intention–behaviour gap (Allom et al., 2013; Sheeran & Webb, 2016). However, for those with chronic
self-regulation difficulties, engaging in and maintaining a practice of healthy behaviours can be especially
challenging (Sirois & Giguère, 2018).
Evidence for the procrastination–health model
Research over the past two-plus decades has found evidence for the proposed links of chronic procras-
tination with stress, health behaviours and physical health. Trait procrastination is associated with higher
levels of stress across both student (Flett et al., 2012; Jackson et al., 2000; Johansson et al., 2023; Sirois
et al., 2003; Sirois & Tosti, 2012; Stead et al., 2010; Tice & Baumeister, 1997) and community adult
samples (Sirois, 2007, 2015; Sirois & Kitner, 2015). Research has also found that chronic procrastination
is associated with poor health behaviours in the form of less practice and weaker intentions to engage in
health-promoting behaviours, such as healthy eating, physical activity and healthy sleep behaviours (Kelly
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SIROIS et al.
4
& Walton, 2021; Kroese et al., 2014; Li et al., 2020; Sirois, 2004, 2007, 2015; Sirois et al., 2003), as well
as greater engagement in unhealthy behaviours such as tobacco, cannabis and alcohol use (Johansson
et al., 2023). Trait procrastination has also been linked to a greater number of physical health problems
(Sirois et al., 2003) and physical symptoms (Johansson et al., 2023), poor self-rated health (Sirois, 2007)
and poor cardiovascular health (Sirois, 2015).
Previous tests of the mediating pathways of the procrastination–health model have found some
support for both routes. In a sample of undergraduate students, procrastination was associated with a
greater number of self-reported health problems; however, only stress and medical visits, but not health
behaviours, mediated the relationship between procrastination and health problems in separate path
models (Sirois et al., 2003). These findings were partially replicated in a sample of community adults,
with both stress and health behaviours explaining the link between procrastination and illness in nested
models, but only stress in the full model (Sirois, 2007).
Although this previous work provides some support for the procrastination–health model, several
methodological issues need to be considered. First, both studies were cross-sectional. This limits the
extent to which causal inferences can be made regarding the temporal precedence of trait procras-
tination in relation to health. A longitudinal test of the procrastination–health model is, therefore,
essential to provide more robust support for the suggested direction of relationships. Second, the
cross-sectional designs of these studies mean it is unclear whether the findings with respect to the
health behaviour pathway that was significant in the community sample but not in the student sample
are an artefact of the study design or a reflection of actual differences between students and adults in
their vulnerability to the detrimental short-term effects of procrastination on health behaviours. Exam-
ining these relationships prospectively over several assessments would help address this issue. Lastly,
the robust, non-trivial associations of trait procrastination with conscientiousness and neuroticism
(Van Eerde, 2003), two higher-order traits known to predict health outcomes (Bogg & Roberts, 2013;
Hampson et al., 2016), was not accounted for in previous tests of the procrastination–health model.
Assessing the contributions of conscientiousness and neuroticism when testing the procrastination–
health model is, therefore, crucial for improving our understanding of the implications of chronic
procrastination for health.
The current study
To address the limitations of previous research and obtain a temporal view of the procrastination–health
model, we tested whether chronic procrastination longitudinally predicted stress, health behaviours
and health problems using data collected with three repeated measures of the health-related variables
approximately 1 month apart. Given that stress and health behaviours are known to covary, with higher
stress linked to less practice of health-promoting behaviours (Rod et al., 2009; Steptoe et al., 1998), we
hypothesized that stress and health-promoting behaviours would be negatively associated across the three
time points. Consistent with previous investigations (Sirois, 2007; Sirois et al., 2003), we also hypothesized
that stress would be positively related to health problems, whereas health-promoting behaviours would be
negatively related to health problems at each time point.
Having tested the stability of the above relationships, we examined the total, direct and indirect effects
of trait procrastination on health-related outcomes over time, with the indirect effects posited as oper-
ating via stress and health behaviours. Specifically, we hypothesized that: 1) trait procrastination would
predict higher stress and less frequent health behaviours at each time point and 2) that stress and (less
frequent) health behaviours would predict subsequent health problems. Given 1) and 2), we expected that
stress and health behaviours would mediate the link between procrastination and health problems and
that stress would be the dominant mediational pathway. We also controlled for both conscientiousness
and neuroticism in the analyses with the expectation that the above hypotheses would hold after account-
ing for their contributions.
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PROCRASTINATION AND HEALTH OVER TIME 5
METHODS
Participants and procedure
Participants were recruited from the Carleton University Psychology Department participant pool which
included undergraduate students from departments across the university. The first survey (T1) was admin-
istered via paper during the winter at the beginning of the second academic term in 2002, the second
(T2) approximately 1 month later and the third (T3) during the last month of the second term. All T1
participants agreed to be contacted for the two follow-up studies. Participants received course credits for
their participation along with a chance to enter a draw to win one of three small cash prizes. The study
hypotheses were not pre-registered, but were based on extant theory, as the data were collected prior to
pre-registration becoming routine practice. No power analysis was conducted a priori; however, the aim
was to recruit as many students as possible initially as it was expected that there would be high attrition in
the two subsequent administrations.
Of the 407 participants recruited to complete the initial survey package, 401 returned at least partially
completed surveys at the second time point and 379 at the third time point. A total of 328 participants
had complete responses on all study variables at all three time points.
Measures
Measures of stress, health behaviours and health problems were included in the survey at all three time
points. A measure of trait procrastination and the Big Five personality factors was included only at Time
1. Other measures included but not analysed in the current study are listed in a supplemental file available
here: https://osf.io/nsvmd/?view_only=eab0a1d97aef4637842a66339a8f8291
Chronic/trait procrastination
Lay's General Procrastination scale (GPS; Lay, 1986) assessed stable global tendencies towards procras-
tination across a variety of tasks. This 20-item scale, consisting of 10 positively worded and 10 nega-
tively worded items, has been used previously to assess the relation of procrastination to health-related
behaviours and outcomes (Sirois, 2004, 2007). Items such as ‘In preparing for some deadlines, I often
waste time by doing other things’ and ‘I generally delay before starting work I have to do’ are scored on
a 5-point Likert-type response scale ranging from 1 (false of me) to 5 (true of me). Negatively worded items
were reverse scored before summing all items into a single score with high values indicating a higher
tendency to procrastinate. The GPS has demonstrated stability over a 10-year period (Steel, 2007), and
good internal consistency in both previous investigations (alpha = .082; Lay, 1986) and the current study
(alpha = .88).
Stress
The severity of common daily stressors occurring within the past month was assessed with an abbreviated
version of the Hassles Scale (Kanner et al., 1981). We used the abbreviated 70-item version by Lu (1991),
which is free from items related to psychological and somatic symptoms, and further removed the six
open-ended items. We also merged items with similar content (e.g. ‘Not enough money for clothing and
housing’.), and removed an item related to not having enough money for health care as it was not relevant
to the current sample. This resulted in a 60-item abbreviated version of the scale. For each of the three
assessments (T1, T2 and T3), participants indicated which of the 60 listed hassles (e.g. too many things to
do) occurred within the previous month, and then rated the severity of these hassles on a scale ranging
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SIROIS et al.
6
from 1 (Somewhat severe) to 3 (Extremely severe). If a hassle was not experienced, it was recorded as ‘0’. Cron-
bach's alphas for the current study ranged from .905 < alpha < .931 for Time 1 to Time 3.
Health behaviours
The Wellness Behaviour Inventory (WBI; Sirois, 2001) is a 12-item, theoretically derived measure of
how often people engage in common health-promoting behaviours organized across four conceptual
categories: healthy eating, regular exercise, sleep behaviour and stress management. Theory and research
indicate that these behaviours are conceptually clustered according to their behavioural consequences
(Flay et al., 2009; Lippke et al., 2012; Vickers et al., 1990), and therefore provide a reasonable index of
key health-promoting behaviours. The WBI mean is based on 10 of the 12 items and excludes two filler
items related to vitamin and supplement use. Items such as ‘I exercise for 20 continuous minutes or more,
to the point of perspiration’ and ‘I eat healthy, well-balanced meals’ are rated on a 5-point scale with
possible responses ranging from 1 (less than once a week or never) to 5 (every day of the week). After reverse
keying two items, a mean of all items is calculated with higher scores indicating more frequent perfor-
mance of health-promoting behaviours. The timeframe for Times 2 and 3 was ‘since the previous survey
administration’, and for the Time 1 administration, participants reported how often they practised the
given health behaviours in general over the preceding 3-month period. The WBI has demonstrated good
test–retest reliability over a 2-week period (rT1T2 = .89), and sensitivity to change among adults intending
to change their health behaviours over a 6-month period (Sirois et al., in preparation). A psychometric
meta-analysis of the coefficient alpha for the WBI across diverse samples found an overall average alpha
of .69 (k = 54, N = 14,517). The internal consistency of the WBI in the current study was acceptable,
ranging from .715 < alpha < .757 across Time 1 to Time 3.
Acute health problems
The number and type of acute health problems were assessed using the Brief Health History question-
naire (Sirois & Gick, 2002), which includes 13 acute physical health problems (e.g. colds, headaches and
digestive problems) plus an ‘other’ category with a space to list any other health problems not listed.
Participants report whether they experienced any of the listed health problems within a particular time-
frame. For the Time 1 administration, the timeframe was the last 6 months, and for Times 2 and 3, the
timeframe was the previous month. The sum of health problem scores formed the acute health problems
variable at each time point.
Control variables
In addition to the measures described above, at Time 1, we collected data on the potentially confound-
ing variables of sex (coded 0 = Male, 1 = Female) and age (in years), as well as the five factors of
personality. Participant age and sex were included as there is some evidence that chronic procrastination
scores are negatively related to age and are higher among younger males (Beutel et al., 2016; Ferrari &
Díaz-Morales, 2007).
The 44-item Big Five Factor Inventory (BFFI; John & Srivastava, 1999) assessed the Big Five person-
ality factors: openness, agreeableness, neuroticism, extroversion and conscientiousness. For the current
study, we were only interested in the effects of conscientiousness and neuroticism, as these are the factors
most strongly related to trait procrastination (Van Eerde, 2003). A list of characteristics is presented
after the statement ‘I see myself as someone who …’ and respondents rate how much they agree with
each characteristic on a 5-point Likert scale ranging from 1 (Disagree strongly) to 5 (Agree strongly). Higher
scores reflect greater identification with that particular personality factor. The BFFI has demonstrated
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PROCRASTINATION AND HEALTH OVER TIME 7
good internal consistency, with Cronbach's alpha coefficients ranging from .81 for conscientiousness
to .88 for extraversion (John & Srivastava, 1999). In the current study, both the Conscientiousness and
Neuroticism subscales demonstrated good internal consistency when collected at Time 1 (alpha = .803
and .820, respectively).
Statistical analyses
To test our hypothesized model, we used a cross-lagged path analysis mediation model (see Figure 1),
similar to that outlined by Cole and Maxwell (2003), but with our predictor variable (procrastination)
collected only at Time 1 given its trait status. While the optimal analytical procedure would have been
to have first run a confirmatory factor analysis (CFA) testing the measurement model for all our scales
across all time points, and then ‘extend’ this to a cross-lagged structural equation model (SEM) to test our
hypotheses using latent variables, the very large number of items across all scales and time points (288)
and hence the very large number of parameters to be estimated in such a CFA or SEM (approximately
950, depending on fixings) were incompatible with our sample size. Hence, having first checked the inter-
nal consistency reliabilities as described above, we calculated scale means (i.e. composite) scores for each
measure at each time point it was collected and tested our hypothesized cross-lagged model using these
observed variables. In addition to the hypothesized paths displayed in Figure 1, our control variables were
regressed upon the mediators (stress and health behaviours) and outcome (health problems) at each time
point, and these were also correlated with procrastination. Starting with an unrestricted model, we applied
a series of fixings to test the stability of relationships over time; specifically, first fixing within-variable
autoregressive paths equal across time for stress, health behaviours and health problems, then fixing the
two mediator-to-outcome paths (i.e. stress and health behaviours to health problems) equal across time.
If model fit was not significantly weakened, these fixings were retained. We then calculated the indirect
effects of procrastination on health problems via both mediators using bootstrapped confidence intervals
to assess their statistical significance (Hayes, 2013).
FIGURE 1 Cross-lagged mediation path analysis model testing the relationships of chronic procrastination to
health through stress and health behaviours over time. Time 1 variables were correlated with control variables (age, sex,
conscientiousness and neuroticism). Time 2 and Time 3 mediator and outcome variables were regressed upon control variables,
but these paths are omitted from the diagram above for presentational clarity.
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SIROIS et al.
8
As a supplementary analysis to probe the causal direction of the stress and health behaviours rela-
tionship with health problems, we then added reverse causal paths from health problems to both stress
and health behaviours at the subsequent time point (i.e. from T1 health problems to T2 stress and health
behaviours, and from T2 health problems to T3 stress and health behaviours). We tested the difference
between the health problems to stress and the stress to health problems paths, and likewise for health
problems to health behaviours versus health behaviours to health problems.
Analyses were run using Mplus software v7.4 using full-information maximum-likelihood estimation
(FIML) to fit each model. This gave an analysis sample of 379 cases who had responded to all the exog-
enous variables and at least one outcome – however, as a robustness check, we also repeated the analysis
using maximum-likelihood estimation on a listwise deleted sample (328 cases who had completed all study
variables at all time points). Chi-square difference tests were used for model comparison; bootstrapped
confidence intervals were applied to assess indirect effects, with 10,000 bootstrap replications being
used (Hayes, 2013). Exact p values are reported below, along with confidence intervals and effect sizes.
Two-tailed tests were applied throughout. Hypotheses were not pre-registered as data collection occurred
prior to when pre-registration of research hypotheses was possible. Data files and all data analysis scripts
are available via this link: https://osf.io/nsvmd/?view_only=eab0a1d97aef4637842a66339a8f8291
RESULTS
Preliminary analyses
Of our analysis sample of 379 participants, the majority were female (67%) and in their first year of
study (81%). They ranged in age from 17 to 56 (median age = 19, mean age = 20.5; SD = 4.2), and the
majority reported their ethnicity as Caucasian (77%). Table 1 presents the descriptive statistics and inter-
correlations among trait procrastination and the study variables for these participants across the three
time points. When checking assumptions with respect to fitting our path analysis model, outcomes and
mediators had approximately symmetrical unimodal distributions at each time point, and there was no
evidence of non-linear relationships among predictors, mediator and outcomes. There was, likewise, no
evidence of multicollinearity among our predictor and mediator variables, with none of the correlations
between these distinct measures at the same time point exceeding .5 (see Table 1). The mean GPS score
(M = 2.76, S.D. = .65) was comparable to that reported in other research with undergraduate students
(e.g. M = 2.81, S.D. = .62; Blunt & Pychyl, 2000).
Hypothesis testing
The hypothesized cross-lagged mediation model (Figure 1) with all parameters free to vary overtime
gave a satisfactory fit to the data (model chi-square = 53.802 on 18df, RMSEA =.072, CFI = .982 and
SRMR = .039). This was not significantly weakened by first fixing autoregressive paths for each of stress,
health behaviours and health problems to be equal across time (i.e. for each of these variables the T1
to T2 path was equal to the T2 to T3 path; model chi-square = 59.132 on 21 df, change in chi-square
compared to the free model = 5.330 on 3 df, p = .149, RMSEA = .069, CFI = .981 and SRMR = .040).
Furthermore, fixing the paths from each mediator to the outcome to be equal across time (i.e. the T1
stress to T2 health problems path was equal to the T2 stress to T3 health problems path; and likewise for
the respective health behaviours to health problems paths) did not significantly weaken model fit (model
chi-square = 62.259 on 23 df, change in chi-square compared to the model with autoregressive paths only
fixed = 3.223 on 3 df, p = .200, RMSEA = .067, CFI = .980 and SRMR = .041), providing evidence for
the stationarity of these relationships. The path coefficients from this model are given in Table 2 and were
used with respect to testing our hypotheses.
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PROCRASTINATION AND HEALTH OVER TIME 9
TABLE 1 Descriptive statistics and intercorrelations among trait procrastination and the study variables across the three time points (343 < N < 379).
NMean SD 1 2 3 4 5 6 7 8 9 10 11 12 13
1 Gender (1 = Female, 0 = Male) 379 .673 .469 ---
2 Age (years) 379 20.549 4.241 −.011 ---
3 Conscientiousness 379 3.555 .650 .148 .109 ---
4 Neuroticism 379 2.958 .792 .227 −.011 −.094 ---
5 Trait procrastination 379 2.763 .647 −.099 −.078 −.572 .120 ---
6 Stress – time 1 379 .575 .336 .020 .006 −.148 .404 .274 ---
7 Health behaviours – time 1 379 3.164 .688 −.114 .082 .334 −.170 −.341 −.227 ---
8 Acute health problems – time 1 379 3.604 1.756 .118 .008 −.214 .157 .203 .277 −.256 ---
9 Stress – time 2 371 .666 .351 .095 −.011 −.189 .437 .313 .671 −.297 .377 ---
10 Health behaviours – time 2 378 3.151 .669 −.073 .049 .345 −.229 −.300 −.241 .754 −.240 −.306 ---
11 Acute health problems – time 2 373 2.552 1.697 .115 .003 −.293 .177 .225 .305 −.342 .565 .426 −.317 ---
12 Stress – time 3 356 .619 .361 .141 −.012 −.180 .434 .277 .693 −.288 .360 .838 −.300 .404 ---
13 Health behaviours – time 3 354 3.163 .701 −.039 .016 .289 −.165 −.328 −.211 .759 −.216 −.303 .815 −.308 −.329 ---
14 Acute health problems – time 3 343 2.300 1.493 .182 .009 −.154 .200 .233 .352 −.345 .510 .445 −.279 .681 .453 −.307
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SIROIS et al.
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TABLE 2 Path coefficients from the cross-lagged mediation model linking trait procrastination to health.
Outcome
Stress – time 2
Health behaviours –
time 2
Acute health
problems – time 2 Stress – time 3
Health behaviours –
time 3
Acute health problems – time 3
Direct effect
Indirect effect
via stress, t2
Indirect effect
via health
behaviours, t2 Total effect
Predictor
B95%CI B95%CI B95%CI B95%CI B95%CI B95%CI
ind’
eff ’
95%CI† ind’
eff ’
95%CI a
B95%CI
Gender (1 = Female,
0 = Male)
.046 −.011,
.102
.016 −.081, .113 .254 −.046,
.555
.058* .014, .101 .054 −.032, .141 .267* .020, .513 -- -- -- -- -- --
Age (years) .000 −.006,
.006
−.003 −.013, .007 .009 −.022,
.041
.000 −.005,
.004
−.006 −.015, .003 .002 −.023, .027 -- -- -- -- -- --
Conscientiousness −.013 −.061,
.034
.120* .037, .203 −.415** −.634,
−.195
−.019 −.056,
.018
−.074 −.149, .000 .291* .080, .501 -- -- -- -- -- --
Neuroticism .072** .037, .106 −.091* −.148,
−.034
.023 −.159,
.205
.020 −.008,
.049
.019 −.032, .070 −.030 −.182, .122 -- -- -- -- -- --
Trait procrastination .066* .017, .116 .008 −.076, .092 -- -- .000 −.039,
.039
−.092* −.168,
−.016
.231* .012, .450 .041* .004, .078 −.002 −.019,
.015
.270* .050, .290
Stress – time 1 .631** .572, .690 -- -- .615** .308, .922 .283** .210, .356 -- -- -- -- -- -- -- -- -- --
Health
behaviours – time 1
-- -- .654** .598, .709 −.203* −.343,
−.063
-- -- .294** .220, .367 -- -- -- -- -- -- -- --
Acute health
problems – time 1
-- -- -- -- .453** .393, .513 -- -- -- -- .118* .046, .190 -- -- -- -- -- --
Stress – time 2 -- -- -- -- -- -- .631** .572, .690 -- -- .615** .308, .922 -- -- -- -- -- --
Health
behaviours – time 2
-- -- -- -- -- -- -- -- .654** .598, .709 −.203* −.343,
−.063
-- -- -- -- -- --
Acute health
problems – time 2
-- -- -- -- -- -- -- -- -- -- .453** .393, .513 -- -- -- -- -- --
Total variance explained
in outcome by all
antecedents
52.3% 57.0% 38.7% 73.7% 71.7% 50.7%
N
= 379, ind’ eff ’ = indirect effect.
aBias-corrected bootstrapped 95% CI.
* p < .05 and ** p < .0005.
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PROCRASTINATION AND HEALTH OVER TIME 11
In support of our hypotheses regarding the linkages among the health variables, statistically signifi-
cant effects were found between trait procrastination and stress (B = .066, 95% CI = .017, .116, p = .009,
with trait procrastination (which was collected at T1) explaining 1.4% unique variance in T2 stress),
between stress and health problems (B = .615, 95% CI = .308, .922, p < .0005, with T2 stress explain-
ing 2.6% unique variance in T3 health problems) and between health behaviours and health problems
(B = −.203, 95% CI = −.348, −.063, p = .004, with T2 health behaviours explaining 1.0% unique variance
in T3 health problems).
The relationship between procrastination and health behaviours at T2 was not statistically significant
(B = .008, p = .847, with trait procrastination explaining just .1% unique variance in T2 health behav-
iours) – hence, unsurprisingly given this, there was no evidence that the indirect effect of procrastination
on health problems via health behaviours was non-zero (indirect effect = −.002, bootstrapped 95%
CI = −.019, .015). However, the indirect effect of procrastination on T3 health problems via T2 stress
was supported (indirect effect = .041, bootstrapped 95% CI = .004, .078), and comprised 15% of the
total effect of procrastination on health problems (B = .270, bootstrapped 95% CI = .050, .490).
Adding reverse causal paths from health problems to stress and to health behaviours significantly
improved model fit (model chi-square = 43.793 on 21 df, change in chi-square = 18.466 on 2 df, p < .0005,
CFI = .988, RMSEA = .054, SRMR = .027). However, of these paths, the health problems to health
behaviours path were non-significant (B = −.013, 95%CI = −.032, .005, p = .152, with T2 health prob-
lems explaining just .1% unique variance in T3 health behaviours). The health problems to stress path
(B = .022, 95%CI = .010, .033, p < .0005, with T2 health problems explaining 1.1% unique variance in
T3 stress), although significant, were significantly weaker than the stress to health problems path (test
of difference: difference = .584, 95%CI = .260, .912, p < .0005). This supports not only the existence of
effects between stress and health problems in both directions but also that the dominant effect occurs in
the direction hypothesized, that is, from stress to health problems.
When rerunning these analyses on the sample of 328 respondents who had completed all model
variables at all three time points, the results and conclusions mirrored those described above. The corre-
sponding Table S1 and Table S2 for this sample can be accessed in the online Supplementary Materials.
DISCUSSION
The current study aimed to address the limitations of previous research by providing a temporal test of
the procrastination–health model (Sirois et al., 2003). Consistent with our hypotheses, trait procrastination
was associated with higher perceived stress and less frequent practice of health-promoting behaviours.
However, only the stress-mediated pathway linked trait procrastination to health problems over time;
the indirect effect through health behaviours was not significant. Importantly, these findings held after
accounting for the contributions of conscientiousness and neuroticism, suggesting that trait procrastina-
tion has incremental value in predicting health problems via higher stress in relation to these higher-order
personality factors.
Our findings are generally consistent with previous cross-sectional investigations of the procrastination–
health model and provide the first longitudinal test of the ways in which chronic procrastination is linked
to short-term health outcomes. The two previous tests of the pathways linking trait procrastination to
health similarly found that stress was the key pathway linking procrastination to poor health (Sirois, 2007;
Sirois et al., 2003). In a sample of students, health behaviours did not mediate the procrastination–health
relationship (Sirois et al., 2003), and in replication with community adults, the health behaviour pathway
was only significant when the stress pathway was not included in the model (Sirois, 2007). Our findings
are also consistent with a 9-month longitudinal investigation of the effects of chronic procrastination on
health outcomes in a large sample of Swedish university students (Johansson et al., 2023). Procrastination
predicted higher stress, physical symptoms, poor sleep quality and physical inactivity, after accounting for
a number of potential confounds. However, key distinctions between the current investigation and the
Swedish study are that we tested the links between procrastination and a set of physical health problems
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SIROIS et al.
12
rather than a single physical symptom (i.e. disabling pain), as well as the potential pathways that might
explain the link between procrastination and health in accordance with the procrastination–health model.
Despite these distinctions, overall, our findings generally align with this previous research and further
demonstrate that these associations and the linking pathways hold over time, even after accounting for
the contributions of related personality traits.
There are several factors that may explain the non-significant behavioural pathway from procras-
tination to health problems in the current study. Given the known reciprocal links between stress and
health behaviours (Rod et al., 2009), it may be that both routes are important in the long-term, but only
stress is key when assessing short-term effects of chronic procrastination. Accordingly, this finding may
be attributable to the short-term time scale of the current study and what is needed for the proposed
links between procrastination and health behaviours to accumulate and manifest as health problems. As
Andreou (2007) has noted, the intransitive preference structures that characterize procrastination are
based on small incremental differences in the potential negative outcome of failing to adhere to a health
behaviour; these accumulate over time and do not have an immediate health effect. Arguably, this is at the
heart of the problem in considering the behavioural effects of procrastination on health. For example,
in the case of someone who is overweight or obese, procrastinating on behaviours to reach a healthy
weight today will neither actually kill the individual nor make him or her noticeably sicker. However, the
cumulative effects of this delay are well established (World Health Organization, 2013). Understanding
the impact of chronic procrastination on health via the behavioural pathway will therefore likely require
investigation over much longer periods of time.
It is also possible that the health behaviour pathway was non-significant in the current study
because of the nature of the health problems that were assessed. Participants reported acute health
problems such as headaches, digestive issues, muscle pain and flus/colds. Health problems of this
nature are more likely to be affected in the short term by the experience of stress than by poor
eating habits or lack of regular exercise (e.g. Cohen et al., 2012). As noted above, a more complete
understanding of the contribution of chronic procrastination to health requires examining a wider
range of health issues, over longer periods of time, and thus is a key agenda for future research on
the health implications of chronic self-regulation failure. That said, examining the linkages between
procrastination and health on a micro-level scale, such as with daily diary methods, might also provide
important insights into the acute effects of chronic procrastination on stress and any associated and
more immediate health effects.
Limitations and future directions
The current findings should be considered in light of several limitations. Although we collected data
over three time points, this was a time-lagged cross-sectional study, making it difficult to confirm the
directionality of the relationships among the variables tested. Nonetheless, the order of the variables
tested was informed by theory, and the test of the reverse causality paths (i.e. from health problems
to stress and health behaviours) indicated they were weaker than those of the hypothesized pathways,
suggesting that the model may be a good approximation of the relationships between procrastination
and health. We only administered the measure of procrastination at Time 1, as it was expected that there
would be little change in this measure of a generalized tendency to procrastinate over a short period of
time. Indeed, previous research confirms that trait procrastination as measured by the General Procras-
tination Scale has excellent stability over a 10-year period (Steel, 2010). However, future research could
verify this by including this measure at all time points. As with any observational study, there is always the
possibility that other unknown factors linked to the predictor and outcome variables play a causal role
in the relationships observed. Despite this, the procrastination–health model is useful for providing a
glimpse of the possible mechanisms linking chronic procrastination to health and which cannot be easily
examined with a more controlled design.
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PROCRASTINATION AND HEALTH OVER TIME 13
In addition to the limitations noted previously regarding the short timeframe of the study and the
types of health problems assessed, the undergraduate sample was relatively young and healthy, and
this may also explain the lack of significant indirect effects of trait procrastination on health problems
over time via health behaviours. This sample was chosen given the high rates of chronic procrastina-
tion among university students and thus the relevance of understanding the implications of chronic
procrastination for this population. Given that the surveys were administered across the academic
term with the final assessment just before the exam period, it is possible that stress levels, especially
those due to procrastination, were heightened. Replicating the current findings longitudinally with a
more representative adult sample is needed to confirm the generalisability of these results and the
extent to which the behavioural route of the procrastination–health model contributes to the health
outcomes among people who chronically procrastinate. In addition, it would also be useful to test the
model with other measures of health behaviours, and ideally those measured with objective means
such as actimeters. Although the WBI provides an overall estimate of the frequency of a general set
of four health-promoting behaviours that are conceptually grouped (Lippke et al., 2012), it does not
provide a more granular assessment of individual behaviours. Future research on procrastination and
health would benefit from taking other approaches to assessing health behaviours and estimating the
frequencies of specific behaviours.
Nonetheless, the current study has addressed the limitations of previous research on the procrastination–
health model (Sirois, 2007; Sirois et al., 2003), and in doing so has highlighted new insights and areas for
future inquiry. The consistency of the links between procrastination and stress, health behaviours and
health problems found over the 3-month study period is a new and important finding that underscores
the contribution of chronic procrastination to poor health even among a sample consisting mainly of
young adults. Raising awareness about the health consequences of chronic procrastination and other
forms of chronic self-regulation failure among clinicians, academic counsellors and other stakeholders
could also lead to the implementation of targeted interventions to help address this problem. Indeed,
a meta-analysis of psychological interventions targeting procrastination behaviour suggests that cogni-
tive approaches can have small-to-moderate effects (Rozental et al., 2018). However, interventions that
address the dysfunctional beliefs and automatic thoughts that contribute to further stress and procrastina-
tion (Stainton et al., 2000) may be an effective approach for reducing both procrastination and any associ-
ated stress (Pychyl & Flett, 2012). In addition, finding ways to deal with the health-related by-products of
self-regulation failure, such as high stress and poor health behaviours, is also an important consideration.
To this end, strategies that improve coping may be beneficial given that trait procrastination is associated
with less use of adaptive coping strategies and greater use of maladaptive coping strategies (Sirois &
Kitner, 2015).
Evidence suggests that procrastination as a chronic problem is becoming increasingly prevalent in
North America, Europe and worldwide (Ferrari et al., 2005, 2009; Hen & Goroshit, 2018; Steel, 2007).
From a public health perspective, our findings suggest that the health risks from chronic procrastination
are an issue that may need to be addressed sooner, rather than later.
AUTHOR CONTRIBUTIONS
Christopher B. Stride: Data curation; formal analysis; visualization; writing – review and editing. Timo-
thy A. Pychyl: Conceptualization; methodology; resources; writing – review and editing. Fuschia M.
Sirois: Conceptulaization; methodology; data curation; investigation; writing – original draft; Writing –
review & editing; supervision; project administration.
[Correction added on 22 March 2023, after first online publication: Author Contribution has been
corrected in this version.]
CONFLICT OF INTEREST STATEMENT
All authors declare no conflict of interest.
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SIROIS et al.
14
OPEN RESEARCH BADGES
This article has earned an Open Data badge for making publicly available the digitally-shareable
data necessary to reproduce the reported results. The data is available at https://osf.io/
nsvmd/?view_only=eab0a1d97aef4637842a66339a8f8291.
DATA AVAILABILITY STATEMENT
The data, analyses and a list of materials used for this research are available on the Open Science Frame-
work website: https://osf.io/nsvmd/?view_only=eab0a1d97aef4637842a66339a8f8291.
ORCID
Fuschia M. Sirois https://orcid.org/0000-0002-0927-277X
Christopher B. Stride https://orcid.org/0000-0001-9960-2869
Timothy A. Pychyl https://orcid.org/0000-0003-2851-1438
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SUPPORTING INFORMATION
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How to cite this article: Sirois, F. M., Stride, C. B., & Pychyl, T. A. (2023). Procrastination and
health: A longitudinal test of the roles of stress and health behaviours. British Journal of Health
Psychology, 00, 1–16. https://doi.org/10.1111/bjhp.12658
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