Internet-based cognitive-behavior therapy for procrastination: A randomized controlled trial.

Journal of Consulting and Clinical Psychology 2015, Vol. 83, No. 4, 808 – 824

© 2015 American Psychological Association 0022-006X/15/$12.00 http://dx.doi.org/10.1037/ccp0000023

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Internet-Based Cognitive⫺Behavior Therapy for Procrastination: A Randomized Controlled Trial Alexander Rozental

Erik Forsell and Andreas Svensson

Stockholm University

Linköping University

Gerhard Andersson

Per Carlbring

Linköping University and Karolinska Institutet, Stockholm, Sweden

Stockholm University

Objective: Procrastination can be a persistent behavior pattern associated with personal distress. However, research investigating different treatment interventions is scarce, and no randomized controlled trial has examined the efficacy of cognitive⫺behavior therapy (CBT). Meanwhile, Internet-based CBT has been found promising for several conditions, but has not yet been used for procrastination. Method: Participants (N ⫽ 150) were randomized to guided self-help, unguided self-help, and wait-list control. Outcome measures were administered before and after treatment, or weekly throughout the treatment period. They included the Pure Procrastination Scale, the Irrational Procrastination Scale, the Susceptibility to Temptation Scale, the Montgomery Åsberg Depression Rating Scale⫺Self-report version, the Generalized Anxiety Disorder Assessment, and the Quality of Life Inventory. The intention-to-treat principle was used for all statistical analyses. Results: Mixed-effects models revealed moderate betweengroups effect sizes comparing guided and unguided self-help with wait-list control; the Pure Procrastination Scale, Cohen’s d ⫽ 0.70, 95% confidence interval (CI) [0.29, 1.10], and d ⫽ 0.50, 95% CI [0.10, 0.90], and the Irrational Procrastination Scale, d ⫽ 0.81 95% CI [0.40, 1.22], and d ⫽ 0.69 95% CI [0.29, 1.09]. Clinically significant change was achieved among 31.3–40.0% for guided self-help, compared with 24.0 –36.0% for unguided self-help. Neither of the treatment conditions was found to be superior on any of the outcome measures, Fs(98, 65.17⫺72.55) ⬍ 1.70, p ⬎ .19. Conclusion: Internet-based CBT could be useful for managing self-reported difficulties due to procrastination, both with and without the guidance of a therapist.

What is the public health significance of this article? Procrastination is a common behavioral problem associated with personal distress and decreased well-being, but the lack of research on treatment interventions prevents the general public from receiving adequate care. The current study is one of the first to investigate the usefulness of cognitive⫺behavior therapy for procrastination, and to explore whether it can be delivered via the Internet. The results provide preliminary evidence for its efficacy, indicating that Internet-based cognitive⫺behavior therapy can be helpful for individuals suffering from self-reported difficulties due to procrastination.

Keywords: procrastination, Internet-based cognitive⫺behavior therapy, randomized controlled trial

This article was published Online First May 4, 2015. Alexander Rozental, Department of Psychology, Stockholm University; Erik Forsell and Andreas Svensson, Department of Behavioural Sciences and Learning, Linköping University; Gerhard Andersson, Department of Behavioural Sciences and Learning, Linköping University, and Division of Psychiatry, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Per Carlbring, Department of Psychology, Stockholm University. Supported by the Swedish Research Council (Grant 2011-38394-87877-7) and Linköping University. We thank Josefine Karlsson, Linda Malinen, Pernilla Fredell, and Smilla Färdig who helped out during the screening process and served as therapists throughout the treatment period; we also thank

Alexander Alasjö and George Vlaescu for their excellent webmaster services. In addition, we thank Piers Steel for allowing a Swedish translation and use of the Pure Procrastination Scale, the Irrational Procrastination Scale, and the Susceptibility to Temptation Scale. Conflict of interest: Treatment interventions used in the current study were based on a self-help book specifically developed for targeting problems related to procrastination; the book was released in Sweden after the completion of the current study (Rozental & Wennersten, 2014). Correspondence concerning this article should be addressed to Alexander Rozental, Division of Clinical Psychology, Department of Psychology, Stockholm University, Frescati Hagväg 8, Stockholm, Sweden 106 91. E-mail: [email protected] 808


INTERNET-BASED CBT FOR PROCRASTINATION

Postponing the tasks and assignments that need to be performed is a universal phenomenon shared by most individuals. Although sometimes experienced as stressful, delaying a given course of action is seldom associated with any major psychological suffering. However, for a considerable proportion of the population, deferring important commitments can become a persistent behavior pattern that interferes with their daily life and result in various negative consequences (Pychyl & Flett, 2012). Procrastination, defined as “to voluntarily delay an intended course of action despite expecting to be worse off for the delay” (Steel, 2007, p. 66), refers to a common self-regulatory failure that involves stalling the initiation or completion of important duties or responsibilities until the last minute, after a predetermined deadline, or indefinitely (Dryden, 2000). Even though procrastination has much in common with both difficulties in prioritizing and being self-assertive, procrastination requires an active choice between competing activities in which one is being avoided in favor of the other, and is often characterized by the preference for a more immediate reward or the escape from an aversive experience (Pychyl, Lee, Thibodeau, & Blunt, 2000). Procrastination is not only related to complications regarding the task at hand, but is also associated with personal distress and decreased well-being (Stead, Shanahan, & Neufeld, 2010). Severe and chronic procrastinators are at risk of developing and exacerbating physical disorders (e.g., delaying medical check-ups, adherence to a given rehabilitation program), and mental illnesses (e.g., fewer mental health promoting behaviors, increased levels of stress and anxiety) (Sirois, 2004, 2007). Procrastination is also linked to poorer performance in school and at work, particularly with regard to career and financial success, and is related to substantial financial worry (O’Donoghue & Rabin, 1999; Steel, Brothen, & Wambach, 2001; Tice & Baumeister, 1997). Procrastination has been proposed by Steel (2012) to be a growing self-regulatory failure, possibly related to increased societal demands for self-control, as well as greater availability of immediate gratification through the widespread use of modern information technology. According to the only available research on the prevalence of procrastination, approximately one fifth of the adult population (Day, Mensink, & O’Sullivan, 2000), and at least half of the university students (Harriott & Ferrari, 1996), describe themselves as experiencing significant difficulties initiating or completing tasks and assignments. These numbers are also reported to have increased over time among adults, from approximately 4 –5% in the 1970s to 20 –25% today (Steel, 2012). Even though this upsurge in self-reported procrastination does not necessarily correspond to a clinical condition, more and more individuals are assumed to have problems managing their everyday commitments because of severe and chronic procrastination (Pychyl & Flett, 2012). Despite this, research on procrastination has mainly involved the investigation of potential predictors, such as, individual differences, task characteristics, as well as sociodemographics (Steel, 2007), while paying less attention to treatment interventions that specifically target procrastination. Although some personality factors have been found to correlate with procrastination, including a high degree of impulsiveness (Specter & Ferrari, 2000), low conscientiousness (van Eerde, 2003), low selfcontrol (Tice & Baumeister, 1997), a high degree of neuroticism (Hettema, Neale, Myers, Prescott, & Kendler, 2006), and low

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self-regulation (Wolters, 2003), the results have been inconsistent and the clinical implications are not always clear (Steel, 2007). From a theoretical perspective, procrastination has been conceptualized as either a state or as a trait (Sirois, 2014). The state perspective highlights the interaction between environmental stimuli and procrastination, such as distractions, while the trait perspective emphasizes the stability of procrastination over time (van Eerde, 2000, 2003). According to Steel (2007), twin studies have indicated that procrastination is partly explained by genetic variables, in particular, impulsiveness, while the test–retest reliability of many self-report measures of procrastination is good over a 10-year period, providing evidence for the notion of procrastination as a trait. Hence, procrastination can be regarded as a persistent behavior pattern associated with a characteristic set of cognitions and behaviors that result in the voluntary delay of tasks and assignments (Sirois, 2014). In particular, dysfunctional beliefs, such as socially prescribed perfectionism, unrealistic expectations, and low self-esteem, have been put forward as possible explanations for procrastination, indicating that severe and chronic procrastinators engage in a type of negative thinking that resembles rumination (Pychyl & Flett, 2012; Stainton, Lay, & Flett, 2000). It has also been suggested that the difficulty performing instrumental behaviors that are in line with completing a given course of action can lead to self-blame and negative emotions, which, in turn, causes procrastination as a way of restoring positive mood (Tice, Bratslavsky, & Baumeister, 2001), consistent with so-called incompleteness theories of cognition (Gold & Wegner, 1995). In addition, procrastination can be explained using learning theory, in particular, operant conditioning, self-efficacy theory, as well as different theories of motivation (Rozental & Carlbring, 2014). Steel and König (2006) have, for instance, presented an integrated model of procrastination, the temporal motivational theory, which views procrastination-related difficulties as the result of four different variables: the value of an activity, the expectancy of achieving that value, the timing of that value, and the sensitivity to delay. Accordingly, the lack of value related to a given course of action is assumed to cause procrastination due to its effect on extrinsic or intrinsic motivation (Steel, 2007). However, value is also presumed to be closely related to self-efficacy, that is, the expectations to achieve an anticipated outcome (Bandura, 1977). Furthermore, the effect of different schedules of reinforcement is expected to complicate the initiation of tasks and assignments, because long-term goals are of a fixed interval schedule, while most distractions are of a variable ratio schedule (Stromer, McComas, & Rehfeldt, 2000). In addition, the sensitivity to delay is assumed to give rise to individual differences in procrastination because it affects the ability to defer an immediate gratification in order to complete a given course of action (Mazur, 2001). In sum, procrastination may be maintained by both cognitive and behavioral factors, warranting treatment interventions that target both dysfunctional beliefs and the variables influencing motivation (Steel & König, 2006). In terms of clinical trials examining the efficacy of treatment interventions for procrastination, most of the research has consisted of single-case designs (Dryden, 2012; Karas & Spada, 2009; Neenan, 2008), and the evaluation of different group therapies that are primarily intended for university students (van Essen, van den Heuvel, & Ossebaard, 2004; van Horebeek, Michielsen, Neyskens, & Depreeuw, 2004; Tuckman & Schouwenburg, 2004). Although


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informative from a clinical perspective, the lack of validated outcome measures as well as the absence of randomization obscures the results, making it complicated to explore the nature of the treatment outcome. However, treatment interventions that are often used in cognitive⫺behavior therapy (CBT) have been recognized as suitable for addressing problems of procrastination (Steel, 2007). Behavioral interventions that facilitate time management, increase automaticity, and decrease the number of distractions have been shown to improve self-regulation and avert procrastination (Mulry, Fleming, & Gottschalk, 1994). Likewise, establishing routines and using timetables and predetermined activities similar to those in behavioral activation for depression are particularly useful for preventing mental fatigue, creating normal diurnal rhythms, and enhancing overall performance (Digdon & Howell, 2008; Jacobson, Martell, & Dimidjian, 2001). Meanwhile, goal-setting techniques can help the individual set subgoals that are perceived as less burdensome than more long-term goals, while graded exposure can assist the individual in exposing and tolerating emotions that often lead to procrastination (Schraw, Wadkins, & Olafson, 2007). Rewards that are contingent on the performance of an intended response can also help increase extrinsic motivation (Eisenberger, Park, & Frank, 1976; Eisenberger, 1992). Similarly, value clarification might increase intrinsic motivation, which, in turn, may promote diligence (Rozental & Carlbring, 2014). Furthermore, cognitive interventions targeting dysfunctional beliefs that result in procrastination are also important, most notably in the case of perfectionism, fear of failure, and self-doubt (Lloyd, Schmidt, Khondoker, & Tchanturia, 2014; Pychyl & Flett, 2012). Cognitive restructuring can aid commitment to goal-directed behavior (McDermott, 2004), especially when accompanied by behavioral experiments that enable the individual to behave more adaptively in relation to the thoughts and emotions that often result in procrastination (Bennett-Levy, 2003). In addition, implementation intentions and mental contrasting have been shown to facilitate memory retrieval and instigate behavior change by generating memory cues and highlighting the steps that are necessary to attain a given outcome (Gollwitzer & Brandstätter, 1997; Oettingen & Mayer, 2002). Moreover, efficacy performance spirals may be useful in raising self-efficacy by completing commitments with increasing difficulty as well as providing corrective feedback (Lindsley, Brass, & Thomas, 1995). Because there are so few clinical trials examining the efficacy of treatment interventions for procrastination, further research is needed to determine their usefulness. In addition, no randomized controlled trials have previously been carried out, making it uncertain whether the treatment interventions that are often used in CBT are, in fact, helpful for managing severe and chronic procrastination. Moreover, individuals experiencing difficulties associated with deferring their everyday commitments might also suffer from different psychiatric disorders such as depression and anxiety (Pychyl & Flett, 2012). Prior research has yielded moderate correlations with worry, stress, and feelings of guilt, but no consistent relationship has been found with mood (Steel, 2007). As discussed by Klingsieck (2013), this may be due to the fact that self-report measures or a structured clinical interview assessing the occurrence and severity of psychiatric disorders are seldom used when investigating procrastination, and that most research has been performed on students and not clinical populations. Hence, although procrastination in itself does not constitute a psychiatric

disorder, it could be associated with personal distress and, in the long run, result in psychological suffering and psychiatric disorders (Rozental, Forsström, Nilsson, Rizzo, & Carlbring, 2014). Increasing the awareness of the etiology and maintenance of procrastination is therefore important, as is providing treatment interventions that have demonstrated efficacy, particularly because health care providers may not always recognize procrastination as a problem that can cause significant impairment, and because the knowledge of what treatment interventions to use might not be widespread. Meanwhile, prior research of delivering treatment interventions via the Internet, most notably CBT, has shown promising results for a number of psychiatric disorders, such as depression (Williams, Blackwell, Mackenzie, Holmes, & Andrews, 2013), pathological gambling (Carlbring & Smit, 2008), irritable bowel syndrome (Ljótsson et al., 2011), social anxiety disorder (Andrews, Davies, & Titov, 2011), tinnitus (Hesser et al., 2012), panic disorder (Carlbring et al., 2006), and insomnia (van Straten et al., 2014). Treatment interventions that are delivered via the Internet also offer many advantages in terms of greater cost-effectiveness, enhanced access to evidence-based care, increased opportunity to reach patients living in remote locations, as well as continuous symptom monitoring that tends to improve adherence (G. Andersson, Carlbring, Ljótsson, & Hedman, 2013). In addition, Internetbased CBT has been shown to generate positive results both with and without the provision of a therapist, that is, guided self-help and unguided self-help (Berger, Hämmerli, Gubser, Andersson, & Caspar, 2011), and there is some evidence suggesting that the format of delivering guided self-help does not seem to be related to treatment outcome, for instance, via a telephone or email (Lindner et al., 2014). Providing treatment interventions for procrastination via the Internet, in other words, could constitute a suitable alternative to regular health care if it is found to alleviate problems experienced by many individuals. The objective of the current study was thus to examine the efficacy of Internet-based CBT for procrastination in a randomized controlled trial, and to investigate whether it matters if participants receive guided self-help or unguided self-help in terms of treatment outcome. It is presumed that CBT delivered via the Internet will produce significant improvements in relation to self-reported difficulties of procrastination, compared with wait-list control, and that this also can affect levels of depression, anxiety, and well-being. Participants receiving guided self-help, involving weekly therapist feedback, are also predicted to fare better than those receiving unguided self-help, involving limited contact with the study supervisors, because the guidance from a therapist may help improve adherence and the completion of homework assignments inherent in the treatment interventions (Richards & Richardson, 2012).

Method Participants Participants were recruited through advertisements in the Swedish media, which consisted of reports about the current study on the radio, the Internet, and in several national newspapers. Individuals interested in participation completed an online screening process and sent a written informed consent form to the study supervisors. As described in the study protocol (Rozental & Car-



lbring, 2013), inclusion criteria were the following: Swedish residency, fluency in Swedish, a minimum age of 18 years, access to a computer with Internet access, and suffering from difficulties primarily related to procrastination. However, because procrastination is not considered a psychiatric disorder on its own, its occurrence and severity were determined using the primary outcome measure, the Irrational Procrastination Scale (IPS; Steel, 2010), in which participants scoring 32 points or more were considered eligible for the current study, distinguishing them as more chronic and severe cases of procrastination, that is, top 25% of the population (Steel, 2012). Exclusion criteria were: ongoing psychological treatment, and when taking psychotropic medication, the potential participant’s dosage had to be stabilized for at least three months prior to entering treatment. Participants with psychiatric disorders were not excluded as long as procrastination could be regarded as their principal problem, for example, mildto-moderate depression, obsessive– compulsive disorder, and social anxiety disorder. However, participants having other conditions deemed better cared for elsewhere were excluded, including bipolar disorder, schizophrenia, psychosis, attention-deficit/hyperactivity disorder, and severe misuse of alcohol or drugs, as indicted by the responses to open-ended questions used in the online screening process. In addition, participants were excluded if they were regarded as severely depressed, that is, scored more than 30 points on the Montgomery Åsberg Depression Rating Scale⫺Selfreport version (MADRS-S; Svanborg & Åsberg, 2001), or having suicidal ideation, that is, scored more than 3 points on the question regarding suicidality (Item 9).

Ethics and Clinical Trials Registry The current study received ethics approval from the Regional Ethical Board in Stockholm, Sweden (Dnr, 2013/974 –3175), and great attention was given to ensure that no participant was included while having another condition that could require more immediate attention. Deterioration was monitored by the study supervisors in case the condition of a participant worsened and required more specialized care. The number of participants having deteriorated from pre- to posttreatment assessment is presented in the results (Boettcher, Rozental, Andersson, & Carlbring, 2014), while other potential negative effects were investigated using open-ended questions (e.g., “Have you, during the course of treatment/being on a wait-list, experienced any unwanted events that you believe are related to treatment/having to wait for treatment, or have you encountered any unwanted effects that could be attributable to treatment/being on a wait-list? If you have experienced more than one event/effect, please state these separately below.”), distributed at posttreatment assessment or prior to entering treatment for participants on wait-list control (Rozental, Andersson, et al., 2014). For ethical reasons, participants in the wait-list control received unguided self-help after the first treatment period had ended. The current study was registered as a clinical trial on ClinicalTrials.gov (NCT01842945).

Procedure For the current study, a website was created with a presentation of the research project and its objectives (http://www.prokrastinera .se). Information on the screening process, inclusion and exclusion

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criteria, ethics, written informed consent, randomization procedure, treatment interventions, withdrawal, study supervisors, and therapists was also provided. Individuals interested in participating logged onto a secure online interface requiring registration and electronic identification (i.e., SSL certificates) to complete an automated and fully computerized online screening process that included all primary and secondary outcome measures, sociodemographics, as well as open-ended questions regarding ongoing psychological treatment, psychotropic medication, and the occurrence and severity of procrastination. When registering for the current study, participants also received an auto-generated identification code (e.g., 1234abcd), ensuring anonymity throughout the screening process, treatment period, and analysis of the results. Only reminders to log onto the secure online interface were sent to participants’ private email addresses, and all data being stored were encrypted to comply with the Swedish Personal Data Act (Datainspektionen, 1998). In addition, all participants were informed about a free online service for encrypting email correspondence (http://www.hushmail.com), and a randomly created 4-digit pin code was also delivered to participants’ mobile phones for logging onto the secure online interface. This involved a two-step verification, similar to systems used by many banks and government agencies (Bennett, Bennett, & Griffiths, 2010). All participants who completed the online screening process were reviewed by study supervisors and therapists to determine their eligibility for inclusion in the current study. A complete flowchart of the recruitment process and treatment period, as recommended by the CONSORT statement (Schultz, Altman, Moher, & the CONSORT Group, 2010), is provided in Figure 1. Any uncertainties concerning the inclusion of a participant were discussed collaboratively by study supervisors and therapists. Because procrastination is not considered a psychiatric disorder, use of diagnostic criteria (e.g., Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition: DSM-5; American Psychiatric Association, 2013) or a structured clinical interview (e.g., Structured Clinical Interview for DSM–IV; SCID; First, Spitzer, Gibbon, & Williams, 1996) was not applicable in the current study. Instead, the IPS was used to determine the occurrence and severity of procrastination, while the secondary outcome measures facilitated an investigation of possible comorbidity with psychiatric disorders. All participants that were excluded received a personal explanation why and information on where and how to seek treatment elsewhere, if deemed necessary. This included those participants who were regarded as eligible for the current study, but were randomly excluded because the maximum number of participants had been set at 150 due to financial and logistic reasons, that is, not being able to provide the number of therapists and time needed to attend more than 50 participants receiving guided self-help. Once enrolled, participants were randomly assigned to three conditions: guided self-help, unguided self-help, and wait-list control. The randomization procedures were performed by an individual external to the current study using a random number generator (http://www.random.org) to ensure complete randomness. The randomization procedures were implemented after inclusion of participants in order to conceal the allocation assignment until the commencement of treatment, and participants were allocated to one of the conditions in a 1:1 ratio using simple randomization. The subsequent treatment interventions, as well as communication

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Figure 1.

Flow chart of participants throughout the current study. IPS ⫽ Irrational Procrastination Scale.

with study supervisors and therapists, were conducted via the secure online interface. This included the completion of all primary and secondary outcome measures at the end of treatment, as well as weekly during the treatment period, thus minimizing the risk of data loss or data distortion (Thorndike et al., 2009). Openended questions about potential negative effects, completed modules, average time dedicated to treatment per module, and confidence in the treatment were also distributed. Reminders to log on and finish the posttreatment assessment were sent to participants by email, mobile phone text messages, and written letters during the 4 weeks after the treatment period had ended, similar to the procedures used in other treatments delivered by the Internet (Proudfoot et al., 2011).

Measures Primary outcome measures. Primary outcome measures assessed self-reported difficulties of procrastination using the Swedish versions of the Pure Procrastination Scale (PPS; Steel, 2010), the IPS (Steel, 2010), and the Susceptibility to Temptation Scale (STS; Steel, 2010). The PPS has 12 items measuring the severity of procrastination, with higher scores indicating greater difficulties with procrastination, and was developed to improve the validity of several existing procrastination scales (Steel, 2010). The PPS has previously shown convergent validity with other related measures, and the items have good internal consistency (Cronbach’s ␣ ⫽ .92; Steel, 2010); in the current study, alpha was .78. The IPS features nine items measuring the degree of irrational delay causing procrastination, with higher scores indicating more problems with procrastination. The IPS correlates with the PPS at r equal to .96,

enabling them to be used as parallel forms to share validation efforts, and the items have been shown to have a good internal consistency (␣ ⫽ .91; Steel, 2010); in the current study, alpha was .76. The STS features 11 items measuring the susceptibility to temptation and the degree of impulsivity, with higher scores indicating greater difficulties following through on tasks and assignments. The STS has been shown to correlate with both the PPS and the IPS at r equal to .69, and the items have been shown to have a good internal consistency (␣ ⫽ .89; Steel, 2010); in the current study, alpha was .87. For a complete review of the psychometric properties of the primary outcome measures, see Rozental, Forsell, et al. (2014). Participants completed all primary outcome measures during the screening process and at the end of treatment. In addition, the IPS was administered weekly throughout the treatment period, including participants in the wait-list control. Secondary outcome measures. Secondary outcome measures consisted of self-reported assessments of depression, anxiety, and quality of life, using the Swedish versions of the MADRS-S (Holländare, Andersson, & Engström, 2010; Svanborg & Åsberg, 2001), the seven-item Generalized Anxiety Disorder (GAD-7) assessment (Dear et al., 2011; Spitzer, Kroenke, Williams, & Löwe, 2006), and the Quality of Life Inventory (QOLI; Frisch, Cornell, Villanueva, & Retzlaff, 1992; Lindner, Andersson, Öst, & Carlbring, 2013). The MADRS-S is a self-report version of the MADRS and features nine items measuring changes in mood, anxiety, sleeping patterns, appetite, concentration, initiative, emotional engagement, pessimism, and attitude toward life, with higher scores indicating a higher level of depressive symptoms. The MADRS-S was developed to be particularly sensitive to



treatment effects and shows high correlations (r) ranging from .80⫺.94, between expert ratings and self-reports (Svanborg & Åsberg, 2001), and has been evaluated over the Internet with internal consistency similar to the paper version (Cronbach’s ␣ between .73 and .81), and a high correlation between the formats (r ⫽ .84; Holländare et al., 2010). For patients recruited from primary and psychiatric care, the mean score on the MADRS-S was 23.79 (SD ⫽ 7.98), which represents moderate depression (Holländare et al., 2010). The GAD-7, which features seven items for assessing anxiety and screening for generalized anxiety disorder, has yielded good internal consistency (Cronbach’s ␣ ⫽ .92), and a good factorial structure (69⫺81% of variance explained; Spitzer et al., 2006). The GAD-7 has been assessed over the Internet with good internal consistency (Cronbach’s ␣ ⫽ .79), and with large correlations to other related measures of anxiety and worry (r range, .68⫺.76; Dear et al., 2011). For patients recruited to a clinical trial and diagnosed with generalized anxiety disorder, the mean score on the GAD-7 ranged from 12.59⫺13.06 (SDrange ⫽ 3.96⫺3.93), which represents moderate difficulties of anxiety (Dear et al., 2011). The QOLI features 32 items on 16 areas of life rated by participants with regard to importance and satisfaction. The QOLI has been shown to have good internal consistency (Cronbach ␣ range, .77⫺.89), as well as 1 month test⫺retest reliability (r range, .80⫺.91; Frisch et al., 1992), and has been shown to have good internal consistency (Cronbach’s ␣ range, .71⫺.83) when administered over the Internet (Lindner et al., 2013). For patients from several clinical trials for depression and different anxiety disorders, the mean score on the QOLI ranged from ⫺0.21 to 0.64 (SDrange ⫽ 1.62⫺1.80; Lindner et al., 2013). Participants completed all secondary outcome measures during the screening process and at the end of treatment.

Treatment and Wait-List Control The current study consisted of two treatment conditions (guided and unguided self-help) where participants received texts and exercises over the Internet, which, in turn, was derived from a self-help book developed specifically for procrastination-related difficulties (Rozental & Wennersten, 2014). The treatment interventions target both behavioral and cognitive factors that have been proposed in the literature to maintain procrastination, for example, behavioral activation, graded exposure, behavioral experiments, cognitive restructuring, stimulus control, among others (cf., Rozental & Carlbring, 2014; Steel, 2007; van Eerde, 2003). The treatment interventions were divided into 10 modules that were delivered weekly to the participants: 1. An introduction to the current study and basic psychoeducation of CBT and procrastination. 2. Information on the etiology and maintenance of procrastination. 3. Psychoeducation on goal-setting techniques, avoidance behavior, and behavioral activation. 4. Theories of motivation and use of reward systems to facilitate learned industriousness.

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5. Presentation of ego-depletion, mental fatigue, and their relationship to procrastination. 6. The influence of distractions and using stimulus control to increase focused work. 7. Different ways of practicing self-assertiveness and becoming better at prioritizing. 8. The influence of dysfunctional beliefs and an introduction to performing behavioral experiments. 9. Exploration of personal values using value clarification and information on acceptance. 10. Information on the abstinence violation effect and the importance of relapse prevention. A more detailed description of the treatment content can be found in the study protocol (Rozental & Carlbring, 2013). Each module contained an average of 15 pages of text and visual material, and three exercises to be completed by the end of the week (in total, 166 pages), similar to the homework assignments in face-to-face treatments (Kazantzis & Dattilio, 2010). Participants were advised to download each module in PDF in order to print out the treatment content or use it without having to access the Internet. Participants receiving guided self-help sent their completed exercises to a therapist using the secure online interface and were given written feedback on their progress within 48 hr. However, if participants completed their readings and exercises before the end of the week, the next module was not released until the following week, and participants were advised to repeat their exercises to promote generalizability. Hence, the pace of the treatment was the same for all participants receiving guided self-help. In addition, the average time spent on feedback and administration per participant each week was 15 min, similar to other clinical trials of Internetbased CBT (G. Andersson et al., 2013). Participants receiving unguided self-help had to undertake the modules on their own, with one module being released each week with a short and generic description of the treatment content; they had limited contact with study supervisors. The average time spent on administration each week for all participants receiving unguided selfhelp was 5 min. Participants on wait-list control did not receive any modules during the waiting period, and were only sent reminders to complete the IPS each week. Two weeks prior to entering treatment, those participants were advised to complete all primary and secondary outcome measures, and were informed when and how the treatment was to be initiated. Therapists. Participants who were randomized to receive guided self-help were assigned one of six master’s degree⫺level students in clinical psychology, all but one having completed 1.5 years of basic clinical training in CBT. Therapists monitored treatment progress using weekly self-report measures provided on the IPS, distributed each new module each week, and gave written individualized feedback on the exercises being retrieved from participants. Feedback was similar to that of homework assignments in face-to-face treatments, that is, correcting any misunderstandings of the treatment rationale, reinforcing behavior change, promoting repeated exercises and time for reflection, or, alternatively, helping participant to adapt the level of difficulty to in-

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crease the sense of mastery. To ensure treatment integrity and to discuss any deviations in terms of average time spent on providing feedback and administration, 1 hr of weekly clinical supervision was provided to therapists by the study supervisors. Deterioration and withdrawal of participants receiving guided self-help were also monitored during clinical supervision. If a participant chose to end treatment prematurely, information on where and how to seek treatment elsewhere was given, when this was deemed necessary.


Statistical Analysis All statistical analyses were performed with IBM SPSS Statistics, Version 22. Power was calculated to determine adequate sample size in each condition to detect possible differences. However, because no previous effect sizes were available, power was established using effect sizes that are usually obtained in other clinical trials of Internet-based CBT, that is, approximately a between-groups effect size (Cohen’s d) of 0.70 when comparing guided self-help with wait-list control, and of 0.50 when comparing guided self-help with unguided self-help (G. Andersson et al., 2013). A power of 0.80 for two independent groups, using an alpha of .05 and a between-groups effect size (d) of 0.70, warranted a sample size of 36 in each condition. Similarly, a power of 0.80 for two independent groups, using an alpha of .05 and a betweengroups effect size (d) of 0.50, warranted a sample size of 64 in each condition. The maximum number of participants was, however, set at 150, with 50 participants in each condition, which resulted in a power of 0.70 when comparing guided self-help with unguided self-help. Two-sided independent t tests and Pearson ␹2 tests were used to detect possible differences between the conditions in terms of attrition and missing data, compliance and confidence in treatment, clinically significant change, negative effects, as well as reliable deterioration. All participants were included in the statistical analyses regardless of the number of completed modules, drop out, or loss to posttreatment assessment (i.e., the intention-to-treat principle; Hollis & Campbell, 1999). All primary and secondary outcome measures were analyzed with mixedeffects models (Gueorguieva & Krystal, 2004), including post hoc tests with Bonferroni correction for multiple comparisons. All differences between the three conditions (guided self-help, unguided self-help, and wait-list control) were investigated by modeling Time ⫻ Group interaction effects. Between-groups effect sizes (Cohen’s d) were calculated using the difference in means between conditions at posttreatment, while within-group effect sizes were calculated using the difference in means between preand posttreatment for each condition, in both cases employing the pooled standard deviation as the standardizer. Between-groups effect sizes (d) of 0.20, 0.50, and 0.80 are often used as indicators of small, moderate, and large effect sizes, but should also be interpreted within the context of the current study, for instance, by comparing an effect size to those obtained in other clinical trials (Cumming, 2014). Confidence intervals (CI) of 95% are provided for all statistical analyses, including effect sizes, and can be regarded as interval estimates of the true population parameter (Cumming & Finch, 2001). In accordance with Jacobson and Truax (1991), clinically significant change was used to determine the number of participants falling outside the range of the dysfunctional population in terms of their severity of procrastination. However, because none of the primary outcome measures have

previously been implemented in clinical trials, the mean of the normal population is unknown. Clinically significant change was thus defined as having a posttreatment score on one of the primary outcome measures that was 2 SD beyond the mean for the three conditions at pretreatment, in the direction of functionality (Jacobson & Truax, 1991). The Reliable Change Index (RCI) was also calculated for each primary outcome measure to determine whether participants had changed sufficiently enough to be regarded as a reliable change or reliable deterioration, and not just a consequence of measurement unreliability (Evans, Margison, & Barkham, 1998). A change exceeding 1.96 times the standard error of the measurement is deemed unlikely to occur more than 5% of the time due to unreliability alone (Jacobson, Follette, & Revenstorf, 1984). Thus, both criteria had to be achieved for a participant to be regarded as having achieved clinically significant change, while a negative change from pre- to posttreatment assessment that exceeded the RCI was seen as a reliable deterioration (Boettcher et al., 2014).

Results Enrollment A total of 704 individuals completed the computerized screening. Of these, 210 individuals were excluded for not fulfilling inclusion criteria, or for matching one or more exclusion criteria. Most individuals who were excluded did not provide enough information about their procrastination-related difficulties, complicating the assessment of their current problem, or had another condition that was deemed better cared for elsewhere. A few individuals were also currently receiving other psychological treatment, or had recently made changes in the dosage of their psychotropic medication. A total of 494 individuals were considered eligible for inclusion in the current study. However, because the maximum number of participants was set at 150, a randomization procedure had to be conducted, resulting in the exclusion of 344 participants. The remaining 150 participants were then randomly assigned to three conditions: guided self-help, unguided self-help, and wait-list control. A full description of the sociodemographics of participants who were enrolled can be found in Table 1. No differences between the conditions were detected in terms of age or severity of any of the primary or secondary outcome measures at pretreatment assessment, t(98) ⫽ ⫺0.09 to 1.91, p ⫽ .06⫺.92. Furthermore, no differences were obtained between the conditions with regard to sociodemographics, ␹2(1⫺4) ⫽ 0.00⫺5.60, p ⫽ 0.13⫺1.00, except for the variable cohabitant when comparing both guided self-help and unguided self-help with wait-list control, ␹2(1) ⫽ 4.24 and 5.19, p ⫽ .02 and 0.04. Hence, in relation to those participants receiving guided self-help or unguided self-help, participants in wait-list control were less likely to be living with someone else.

Attrition and Missing Data In general, the current study had a low confirmed attrition rate (n ⫽ 3), which was primarily related to being dissatisfied with the treatment interventions or receiving other psychological treatment. However, 44 participants did not complete their posttreatment


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Table 1 Sociodemographic Characteristics of Participants at Baseline


Baseline characteristic

Guided self-help (n ⫽ 50)

Unguided self-help (n ⫽ 50)

Wait-list control (n ⫽ 50)

Full sample (N ⫽ 150)

25 (50) 38.86 (9.55)

20 (40) 37.96 (10.78)

23 (46) 41.56 (9.9)

68 (45.3) 39.46 (10.14)

13 (26) 35 (70) 1 (2) 1 (1) 26 (52) 37 (74)

11 (22) 38 (76) 1 (2) 0 (0) 26 (52) 36 (72)

17 (34) 28 (56) 4 (8) 1 (2) 22 (44) 26 (52)

41 (27.3) 101 (67.3) 6 (4) 2 (1.3) 74 (49.3) 99 (66)

0 (0) 22 (44) 27 (54) 1 (2)

1 (2) 17 (34) 30 (60) 2 (4)

1 (2) 13 (26) 32 (64) 4 (8)

2 (1.3) 52 (34.7) 89 (59.3) 7 (4.7)

3 (6) 8 (16) 30 (60) 9 (18) 0 (0) 1 (2) 17 (34) 6 (12)

5 (10) 7 (14) 29 (58) 7 (14) 2 (4) 0 (0) 18 (36) 13 (26)

2 (4) 3 (6) 40 (80) 5 (10) 0 (0) 0 (0) 24 (48) 11 (22)

10 (6.7) 18 (12) 99 (66) 21 (14) 2 (1.3) 1 (0.6) 59 (39.3) 30 (20)

Gender, n (% female) Age, M (SD), years Marital status, n (%) Single Married/partner Divorced/widow Other Children, n (% yes) Cohabitant, n (% yes) Highest educational level, n (%) Middle school High school/college University Postgraduate employment, n (%) Unemployed Student Employed Self-employed Retired Sick leave, n (%) Previous psychological treatment, n (% yes) Previous psychotropic medication, n (% yes)

assessment, indicating that a considerable number of participants (29.3%) did not respond to either of the primary or secondary outcome measures, despite having been reminded on several occasions via email, mobile phone text messages, and written letters (guided self-help, n ⫽ 16; unguided self-help, n ⫽ 21; and wait-list control, n ⫽ 7). Differences in terms of noncompletion were obtained between guided self-help and wait-list control, ␹2(1) ⫽ 4.57, p ⫽ .03, as well as unguided self-help and wait-list control, ␹2(1) ⫽ 1.07, p ⫽ .00, but not between the two treatment conditions, ␹2(1) ⫽ 1.07, p ⫽ .30, suggesting a difference in noncompletion between participants receiving treatment and those on wait-list control.

Compliance and Confidence in Treatment In general, the mean number of completed modules were 7.15 (SD ⫽ 2.66), indicating that participants were able to finish two thirds of the modules during the treatment period (guided selfhelp: M ⫽ 6.85, SD ⫽ 2.7; unguided self-help: M ⫽ 7.64, (SD ⫽ 2.51), with no difference between treatment conditions, t(42) ⫽ 0.95, p ⫽ .35. In terms of the self-reported time dedicated to treatment, participants spent a mean of 2.52 hours (SD ⫽ 1.98) on each of the modules (guided self-help: M ⫽ 3.00, SD ⫽ 2.29; unguided self-help: M ⫽ 1.76, SD ⫽ .95), revealing a difference between treatment conditions, t(37.86) ⫽ ⫺2.43, p ⫽ .02, possibly indicating that participants receiving guided self-help spent more time on the modules. In addition, the average level of confidence in the treatment was rated on a scale ranging from 0 (not at all confident) to 10 (very confident), resulting in mean of 7.65 (SD ⫽ 2.76), (guided self-help: M ⫽ 7.14, SD ⫽ 2.46; unguided self-help: M ⫽ 6.35, SD ⫽ 3.19), with no difference detected between treatment conditions, t(41) ⫽ ⫺0.91, p ⫽ .37.

Primary and Secondary Outcome Measures Descriptive statistics of all primary and secondary outcome measures at pre- and posttreatment for the three conditions are provided in Table 2, and for the weekly assessments using the IPS in Table 3. The mixed-effects models revealed Time ⫻ Group interaction effects for the PPS, comparing guided self-help with wait-list control, F(98, 85.19) ⫽ 12.23, p ⫽ .00, and unguided self-help with wait-list control, F(98, 84.13) ⫽ 6.12, p ⫽ .02, as well as for the IPS, comparing guided self-help with wait-list control, F(98, 80.66) ⫽ 18.47, p ⫽ .00, and unguided self-help with wait-list control, F(98, 76.19) ⫽ 13.06, p ⫽ .00, indicating an improvement over time for the treatment conditions with regard to the two outcome measures of self-reported difficulties of procrastination. However, no Time ⫻ Group interaction effect was found for the STS, Fs(98, 85.35; 98, 78.99) ⫽ 1.69 and 0.99, p ⫽ .19 and 0.32. In terms of the secondary outcome measures, there was a Time ⫻ Group interaction effect for the MADRS-S, but only when comparing guided self-help with wait-list control, F(98, 83.96) ⫽ 4.33, p ⫽ .04, and not when comparing unguided self-help with wait-list control, F(98, 86.56) ⫽ 0.28, p ⫽ .59. None of the other secondary outcome measures had any Time ⫻ Group interaction effects, Fs(98, 84.43 to 98, 90.25) ⬍ 3.67, p ⬎ .06, suggesting that no improvement over time was detected for the treatment conditions with regard to susceptibility to temptation, anxiety, or quality of life. Taken together, the results showed that the treatment conditions had an effect on procrastination-related difficulties, as well as depression for those participants receiving guided selfhelp, but not on levels of susceptibility to temptation, anxiety, or well-being.

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Table 2 Estimated Means, Standard Deviations, and Sample Sizes for Each Measure by Condition Over Time Pre


Measure and condition Pure Procrastination Scale Guided self-help Unguided self-help Wait-list control Irrational Procrastination Scale Guided self-help Unguided self-help Wait-list control Susceptibility to Temptation Scale Guided self-help Unguided self-help Wait-list control Montgomery Åsberg Depression Rating Scale⫺Self-Report version Guided self-help Unguided self-help Wait-list control Generalized Anxiety Disorder Assessment (seven-item) Guided self-help Unguided self-help Wait-list control Quality of Life Inventory Guided self-help Unguided self-help Wait-list control Note.

Post (10 weeks)

M

SD

N

M

SD

n

49.16 49.98 47.90

5.41 5.41 5.41

50 50 50

38.80 40.36 44.67

8.86 9.19 8.07

35 29 43

38.46 38.56 38.42

3.16 3.16 3.16

50 50 50

30.59 31.19 35.47

6.38 6.75 5.74

35 29 43

41.46 42.52 40.62

6.86 6.86 6.86

50 50 50

35.12 35.59 37.33

8.25 8.64 7.58

35 29 43

14.58 15.04 15.24

6.37 6.37 6.37

50 50 50

10.69 13.02 13.85

8.09 8.46 7.48

35 29 43

8.02 8.52 7.44

5.34 5.34 5.34

50 50 50

5.15 6.02 6.31

5.33 5.55 4.96

35 29 43

.68 .82 .41

1.68 1.68 1.68

50 50 50

1.31 .99 .57

1.92 2.02 1.78

35 29 43

Weekly measures for the Irrational Procrastination Scale are presented in Table 3.

Within- and between-groups effect sizes using Cohen’s d together with their respective CIs are found in Table 4. Betweengroups effect sizes for the PPS were d ⫽ 0.70, 95% CI [0.29, 1.10], and d ⫽ 0.50, 95% CI [0.10, 0.90], comparing guided self-help and unguided self-help with wait-list control, while between-groups effect sizes for the IPS were d ⫽ 0.81, 95% CI [0.40, 1.22], and d ⫽ 0.69, 95% CI [0.29, 1.09]. Hence, the average effect sizes for the treatment conditions when compared with wait-list control ranged from moderate to large, and corresponded to a decrease in points equal to 0.50⫺0.81 SD on the PPS and the IPS. Betweengroups effect sizes for the other primary and secondary outcome measures ranged from d ⫽ 0.05, 95% CI [⫺0.34, 0.44], for the STS, to 0.41, 95% CI [0.01, 0.81], for the MADRS-S, albeit detecting a difference only when comparing guided self-help with wait-list control (p ⫽ .04). Within-group effect sizes varied from d ⫽ 0.09, 95% CI [⫺0.30, 0.48], for both unguided self-help and wait-list control on the QOLI, to 1.58, 95% CI [1.13, 2.03], for guided self-help on the IPS, however, no differences were observed (p ⬎ .05). Furthermore, as another measure of procrastination, the average delay in completing the posttreatment assessment was assessed as the number of days it took for participants to fill out the self-report measures from the date of distribution, resulting in a mean if 3.53 days (SD ⫽ 5.15), ranging from 0⫺27 days (guided self-help: M ⫽ 3.41 days, SD ⫽ 5.17; unguided self-help: M ⫽ 5.83 days, SD ⫽ 5.43; wait-list control: M ⫽ 2 days, SD ⫽ 4.46). When comparing treatment conditions, a difference between guided self-help and unguided self-help was detected, t(33) ⫽ 3.85, p ⫽ .00, as well as

among guided self-help, unguided self-help, and wait-list control, t(42) ⫽ 2.94⫺3.85, p ⫽ .00. Hence, on average, participants receiving guided self-help were faster in completing their posttreatment assessment than participants receiving unguided selfhelp, while participants on wait-list control were faster than both treatment conditions.

Guided Self-Help Versus Unguided Self-Help Guided self-help and unguided self-help were compared to detect Time ⫻ Group interaction effects between the treatment conditions, and not just between the treatment conditions and wait-list control. However, the mixed-effects models did not indicate any differences on the primary or secondary outcome measures, Fs(65.17⫺72.55) ⬍ 1.70, p ⬎ .20, and only indistinguishable to small between-groups effect sizes at posttreatment, ranging from d ⫽ 0.06, 95% CI [⫺0.33, 0.45], on the STS, to 0.28, 95% CI [⫺0.16, 0.67], on the MADRS-S (see Table 4). Hence, neither treatment condition was found to be superior to the other.

Clinically Significant Change Clinically significant change was determined by investigating the number of participants in each condition falling 2 SD below the pretreatment mean for the three conditions, in the direction of functionality, and if this change was deemed a reliable change according to the RCI (Evans, Margison, & Barkham, 1998; Jacobson & Truax, 1991). Thus, the following cutoffs were used on the

Negative Effects and Reliable Deterioration

IPS ⫽ Irrational Procrastination Scale.

35.92 3.02 50 35.55 3.64 47 35.60 4.36 47 35.22 4.29 45 35.35 4.27 46 35.27 4.91 44 34.85 5.52 40 35.03 5.85 39 34.87 4.98 39 34.76 5.61 37

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primary outcome measures: for the PPS, 38.13, RCI ⫽ 4.24; for the IPS, 32.182, RCI ⫽ 1.75; and for the STS, 27.796, RCI ⫽ 6.31. Subsequently, 31 of 150 (25.3%) achieved clinically significant change at posttreatment on the PPS (guided self-help: n ⫽ 18 [36%]; unguided self-help: n ⫽ 12 [24%]; wait-list control: n ⫽ 8 [16%]), compared with 47 participants (31.3%) on the IPS (guided self-help: n ⫽ 20 [40%]; unguided self-help: n ⫽ 18 [36%]; wait-list control: n ⫽ 9 [18%]), and 14 participants (9.3%) on the STS (guided self-help: n ⫽ 3 [6%]; unguided self-help: n ⫽ 5 [10%]; wait-list control: n ⫽ 6 [12%]). A difference was detected between guided self-help and wait-list control for the PPS, ␹2(1) ⫽ 5.19, p ⫽ .02, but not for any other comparison, ␹2(1) ⫽ 1 and 1.714, p ⫽ .19 and 0.32. In terms of the IPS, there was a difference between guided self-help and wait-list control, ␹2(1) ⫽ 5.88, p ⫽ .02, as well as between unguided self-help and wait-list control, ␹2(1) ⫽ 4.11, p ⫽ .04, but not between the treatment conditions, ␹2(1) ⫽ 0.17, p ⫽ .68. With regard to the STS, no differences were found between the conditions, ␹2(1) ⫽ 0.10⫺1.09, p ⫽ .30⫺.75. Thus, the results imply that clinically significant change may have been more prevalent among those participants receiving treatment than those on wait-list control, with the exception of the STS.

Note.

6.56 32 31.53 6.58 32 31.04 7.55 28 30.29 8.74 24

7.68 26 30.82 6.13 22 29.32 7.36 22 29.06 7.44 17 26.36 8.44 11 36.98 4.53 45 35.02 4.39 41 34.53 5.08 38 34.12 5.31 33 34.14 6.44 29 32

n

37.09 3.40 47 35.78 4.44 45 34.77 4.13 44 35.16 5.33 37 33.83 5.96 36 33.71 5.93 34 32

Guided selfhelp Unguided self-help Wait-list control

SD M SD SD

Week 8

M n SD

Week 7

M n SD

Week 6

M n SD

Week 5

M n SD

Week 4

M n SD

Week 3

M SD

n Week 2

SD

M Week 1

M Measure and condition

Table 3 Estimated Means, Standard Deviations, and Sample Sizes for Weekly Measures of the IPS by Condition During the Treatment Period


n

M

Week 9

n

Week 10

n


Open-ended questions probing for potential negative effects were distributed after the end of treatment to investigate the incidence of events or situations that were perceived as adverse or unwanted. The results indicated that 13 participants (8.6%) reported experiencing one negative effect during the treatment period or being on wait-list control, while one participant (0.6%) described having encountered two negative effects (guided selfhelp: n ⫽ 4 [8%]; unguided self-help: n ⫽ 1 [2%]; wait-list control: n ⫽ 8 [16%]). A difference was obtained comparing unguided self-help and wait-list control, ␹2(1) ⫽ 5.98, p ⫽ .01, indicating that negative effects may have been more prevalent for participants waiting for treatment. No other difference were detected, ␹2(1) ⫽ 1.52 and 1.89, p ⫽ .17 and 0.22. The average score on the degree to which participants perceived themselves to have been negatively affected by the event or situation was rated on a scale from 0⫺3 (“Did not affect me at all”; “Affected me somewhat negatively”; “Affected me quite negatively”; and “Affected me to a great extent”), resulting in a mean of 1.35 (SD ⫽ 1.05) at the time when the event or situation occurred, and 1.42 (SD ⫽ 1.15) at posttreatment, suggesting that the negative effects had a small-to-moderate impact on the participants, and that it remained relatively stable over time. In terms of reliable deterioration, six participants (4%) achieved reliable deterioration on the PPS, seven participants on the IPS (4.7%), and one participant on the STS (0.7%). No differences were obtained between the conditions on any of the primary outcome measures, ␹2(1) ⫽ 0.00⫺2.84, p ⫽ 0.09⫺1.00.

Discussion To our knowledge, the current study is the first randomized controlled trial examining the efficacy of CBT delivered via the Internet for procrastination, as well as investigating whether guidance by a therapist may render a more positive treatment outcome than unguided self-help. The results indicate that Internet-based

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Table 4 Within-Group Effect Sizes Comparing Means at Pre- and Posttreatment, and Between-Group Effect Sizes Comparing Means at Posttreatment, Presented as Cohen’s d [95% CI] for Each Outcome Measure Measure


PPS Within-group effect sizes Guided self-help Unguided self-help Wait-list control Between-group effect sizes Guided self-help vs. wait-list control Unguided self-help vs. wait-list control Guided self-help vs. unguided self-help

IPS

STS

1.43 [0.99, 1.87] 1.29 [0.86, 1.72] 0.47 [0.07, 0.87]

1.58 [1.13, 2.03] 1.41 [0.97, 1.85] 0.64 [0.24, 1.04]

0.70 [0.29, 1.10]ⴱⴱ

0.81 [0.40, 1.22]ⴱⴱⴱ 0.28 [⫺0.12, 0.67] 0.41 [0.01, 0.81]ⴱ

ⴱ

0.50 [0.10, 0.90]

ⴱⴱ

0.69 [0.29, 1.09]

0.17 [⫺0.22, 0.56] 0.09 [⫺0.30, 0.48]

0.84 [0.43, 1.25] 0.90 [0.49, 1.31] 0.46 [0.06, 0.86]

MADRS-S

0.22 [-0.17, 0.61]

GAD-7

QOLI

0.54 [0.14, 0.94] 0.54 [0.14, 0.94] 0.35 [-0.05, 0.74] 0.27 [⫺0.12, 0.66] 0.46 [0.06, 0.86] 0.09 [⫺0.30, 0.48] 0.20 [⫺0.19, 0.59] 0.22 [⫺0.17, 0.61] 0.09 [⫺0.30, 0.48] 0.23 [⫺0.16, 0.62] 0.40 [0.00, 0.80]

0.10 [⫺0.29, 0.49] 0.05 [⫺0.34, 0.44] 0.22 [⫺0.17, 0.61]

0.06 [⫺0.33, 0.45] 0.28 [⫺0.16, 0.67] 0.16 [⫺0.23, 0.55] 0.16 [⫺0.23, 0.55]

Note. CI ⫽ confidence interval; GAD-7 ⫽ Generalized Anxiety Disorder Assessment, seven-item version; IPS ⫽ Irrational Procrastination Scale; MADRS-S ⫽ Montgomery Åsberg Depression Rating Scale⫺Self-report version; PPS ⫽ Pure Procrastination Scale; QOLI ⫽ Quality of Life Inventory; STS ⫽ Susceptibility to Temptation Scale. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .00.

CBT produced substantial improvements in relation to selfreported difficulties of procrastination when comparing guided self-help and unguided self-help with wait-list control, yielding moderate-to-large between-groups effect sizes for the PPS (Cohen’s d range, 0.50⫺0.70), and the IPS (d range, 0.69⫺0.81), similar to the between-groups effect sizes obtained in clinical trials of Internet-based CBT for depression, panic disorder, social anxiety disorder, and generalized anxiety disorder (d range, 0.56⫺1.11), when compared with wait-list control (G. Andersson et al., 2013). Thus, both guided self-help and unguided self-help proved to be beneficial for participants in terms of self-reported difficulties of procrastination, indicating that treatment interventions often used in CBT might be useful in managing procrastination. Moreover, the current study demonstrated that the treatment intervention can be successfully delivered via the Internet. However, neither treatment conditions was found to be superior to the other, contradicting the prediction that participants receiving guided self-help would fare better than those assigned to unguided self-help. Furthermore, the treatment conditions did not differ in terms of attrition, number of completed modules, or confidence in the treatment, suggesting that the provision of weekly therapist feedback may not be essential to benefit from treatment. A difference was observed between the treatment conditions in relation to self-reported time dedicated to treatment, with a tendency to spend more time on each module for participants receiving guidance from a therapist. While this difference is interesting, follow-up data are required to explore whether this might have any impact on the long-term outcome. However, because participants receiving unguided self-help were able to have limited contact with the study supervisors during the online screening process, throughout the treatment period, and at posttreatment assessment, as well as having a predetermined deadline, they could be considered to have had some degree of guidance, making the distinction between treatment conditions less clear (Nordin, Carlbring, Cuijpers, & Andersson, 2010). According to Johansson and Andersson (2012), both limited contact with study supervisors and a predetermined deadline may have an impact on treatment outcome, warranting

cautious interpretations of the results when considering differences between treatment conditions. With regard to clinically significant change, a percentage of all participants (25.3% for the PPS and 31.3% for the IPS) fell outside the range of the dysfunctional population in terms of their severity of procrastination when calculating the RCI and using 2 SD in the direction of functionality as a cutoff. This is comparable to the number of participants achieving clinically significant change in some clinical trials of Internet-based CBT, for instance, 25% in an 8-week treatment for depression (Carlbring et al., 2013), and 29 – 44% in an 8-week treatment for tinnitus (Hesser et al., 2012), albeit less than others, for instance, 55% in an 15-week treatment for social anxiety disorder (Hedman et al., 2011) and 60% in a 10-week treatment for obsessive– compulsive disorder (E. Andersson et al., 2012). However, as mentioned by Jacobson and Truax (1991), using 2 SD as a cutoff, although useful in the absence of normative data, is limited, warranting careful interpretation of the results, particularly because a few participants in the wait-list control also achieved a clinically significant change after the end of the treatment period. Furthermore, due to the absence of test– retest reliability for the primary outcome measures, the RCI was calculated using Cronbach’s alpha as the reliability parameter, which may have inflated the number of participants considered to achieve clinically significant change (Evans, Margison, & Barkham, 1998). Further research could help establish accurate norms for clinical and nonclinical populations of procrastinators, which could be used to establish a more valid cutoff. The assumption that Internet-based CBT for procrastination could also affect levels of anxiety and well-being was not supported, and only when comparing guided self-help with wait-list control was there a difference in terms of depression, demonstrating that the treatment interventions either did not produce any clear improvements in these domains or only for those participants receiving guidance from a therapist. Between-groups effect sizes ranged from indistinguishable to moderate when compared with wait-list control, MADRS-S (d range, 0.10⫺0.41), GAD-7 (d range, 0.05⫺0.23), and QOLI (d range, 0.22⫺0.40), depending on



the treatment condition. This might be attributable to the fact that the treatment interventions focused specifically on problems related to procrastination, and thus did not explicitly target difficulties associated with depression, anxiety, or well-being. Moreover, pretreatment levels of depression and anxiety were low in relation to clinical cutoffs, indicating, on average, only mild forms of depression and anxiety. Subsequently, participants in the current study may not have been experiencing any major psychological suffering that was due to depression and anxiety prior to entering treatment, affecting the latitude for improvement. Whether this is due to fewer problems with procrastination than, for instance, individuals seeking treatment in an outpatient care setting is unclear. It could be argued that because participants in the current study consisted of self-referrals, their difficulties with procrastination may not have been large enough to develop into a psychiatric disorder, making participants less representative than those seen in everyday clinical practice. Alternatively, individuals suffering from severe and chronic procrastination may actively seek and receive treatment interventions that primarily target depression and anxiety rather than procrastination per se, resulting in a selection effect that could restrict the generalizability of results. However, the relationship between procrastination and psychiatric disorders is also inconclusive (Klingsieck, 2013), suggesting that procrastination might not be as detrimental for mental health as previously assumed in the literature. This could explain the low levels of depression and anxiety among participants in the current study, and should be regarded as a limitation when considering the clinical relevance of administering treatment interventions for procrastination. Further research is thus required to conclude whether procrastination in itself warrants the attention from clinicians, for instance, by performing a cluster analysis to examine possible subgroups of procrastinators. Furthermore, the lack of difference within and between the conditions in terms of improving quality of life corresponds to the findings of a recent meta-analysis by Hofmann, Wu, and Boettcher (2014), which demonstrated that face-to-face treatments of CBT may be more beneficial than Internet-based CBT when it comes to increasing well-being, illustrating a potential drawback of the treatment format. However, the results from the current study could also be explained by the fact that the treatment period might have been too short to increase the well-being of participants, suggesting that improvements might be more long term and related to the performance of more adaptive behaviors, warranting a closer examination of follow-up data to draw any conclusions about the relationship between the treatment interventions and quality of life. Also, neither treatment condition was associated with any improvement in terms of susceptibility to temptation, between-group effect sizes being intangible or small when compared with wait-list control (STS d range, 0.06⫺0.28). However, as noted by Steel (2010), susceptibility to temptation is assumed to be linked to impulsivity, which may reflect a trait that is particularly resistant to change. In terms of negative effects, a proportion of participants (8.6%) reported having experienced events or situations that were perceived as adverse or unwanted. Also, between 4% and 4.7% of participants achieved reliable deterioration on the PPS and IPS. This is in line with prior research that has suggested approximately 5–10% of all patients receiving psychological treatment encounter negative effects in terms of deterioration (Lambert, 2007). In addition, a recent clinical trial comparing Internet-based CBT with

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treatment as usual in a primary care setting found that 10% of patients receiving Internet-based CBT deteriorated (Kivi et al., 2014). However, to what extent other types of negative effects occur is unknown, warranting further investigations that also apply methods from qualitative research (Rozental, Andersson, et al., 2014). The results from the current study indicated that some participants experienced negative effects, as assessed by openended questions, and that these effects had a small-to-moderate impact, but their long-term influence needs to be investigated using follow-up data. Interestingly, negative effects were more prevalent among those participants on wait-list control, suggesting that having to wait for treatment could potentially be associated with a higher degree of negative effects. However, prior research has also found results pointing in the opposite direction (Elliott & Brown, 2002), warranting further research on possible differences in terms of negative effects and deterioration between wait-list control and treatment conditions. A closer examination using, for instance, qualitative content analysis might also prove to be useful in analyzing the characteristics of these negative effects.

Study Limitations The current study has several limitations that need to be considered. First, because procrastination is not considered a psychiatric disorder, ensuring that participants deemed eligible for inclusion were in fact severe and chronic procrastinators was complicated. Difficulties prioritizing and being self-assertive might have been the primary concern of some participants, thus requiring different treatment interventions than those that target problems of procrastination. Likewise, even though psychiatric disorders were not considered a reason for exclusion, it is possible that some participants may have had another condition that required other treatment interventions before addressing their procrastination-related difficulties, such as behavioral activation treatment for an ongoing depression, or graded exposure with response prevention for mild forms of obsessive– compulsive disorder. Determining the occurrence and severity of procrastination using the primary outcome measure (the IPS) was also found to be inadequate during the screening process because it did not distinguish more severe cases of procrastination. However, an alternative explanation might be that the IPS is unable to differentiate participants with different levels of severity from each other, or that the current study actually attracted participants with severe and chronic procrastination, hence, the large number of participants being eligible for the clinical trial. In either case, of the total number of participants completing the screening process (n ⫽ 704), only a small percentage (4.1%) did not meet the cutoff of IPS 32 points or more (n ⫽ 29), indicating that the IPS might not be sensitive enough to discriminate clinical from nonclinical populations of procrastination, or revealing a potential ceiling effect of the measure. Further research should therefore examine the usefulness of the IPS as well as other outcome measures for procrastination, and future clinical trials may also need to incorporate additional ways of determining improvement, such as the Clinical Global Impressions Scale (Busner & Targum, 2007). In addition, behavioral measures or measures of performance, such as duration of the delay, school grades, or frequency estimations, could also become valuable for ensuring that the treatment interventions have been beneficial (Krause & Freund, 2014). Similarly, use of a


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structured clinical interview, such as the SCID (First et al., 1996), in future clinical trials of procrastination might enhance understanding of the possible comorbidity with psychiatric disorders (Rozental, Forsström, et al., 2014). Second, although no differences were observed, small within-group effect sizes were found for participants in wait-list control (PPS, d ⫽ 0.47; IPS, d ⫽ 0.64; STS, d ⫽ 0.46), suggesting that some participants may have improved during the waiting period. This could also explain the number of participants on wait-list control achieving clinically significant posttreatment change (PPS, n ⫽ 8 [16%]; IPS, n ⫽ 9 [18%]; STS, n ⫽ 6 [12%]). However, such an effect might be attributable to natural fluctuations of their difficulties, regression to the mean, weekly assessments using the IPS, or anticipation, because participants on wait-list control were informed that they were to receive unguided self-help after the first treatment period had ended. Third, sociodemographic characteristics of participants included in the current study revealed a high degree of formal education and employment level, which might not represent the average population of severe and chronic procrastinators. More than half of participants (64%) had either a university degree (n ⫽ 89) or were engaged in postgraduate studies (n ⫽ 7), and a majority (80%) were either employed (n ⫽ 99) or self-employed (n ⫽ 21). However, this is in line with prior research, which has suggested that highly educated individuals are generally more inclined to seek help for mental health problems (Howard et al., 1996; Vessey & Howard, 1993), and that this also might be true for CBT delivered via the Internet (Arnberg, Linton, Hultcrantz, Heintz, & Jonsson, 2014). Furthermore, little is known about the characteristics of those individuals experiencing personal distress and decreased well-being due to procrastination, in part, because most research of procrastination is carried out on university students (van Eerde, 2003). According to Day, Mensink and O’Sullivan (2000) and Harriott and Ferrari (1996), self-reported problems of procrastination are widespread, but this does not necessarily correspond to a clinical condition. The sociodemographic characteristics of participants in the current study could therefore provide valuable and more comprehensive information about those individuals who regard themselves as having difficulties managing their everyday commitments, which in turn could be helpful to discriminate clinical from nonclinical populations and facilitate subgroup analyses of possible predictors of treatment outcome. Fourth, the percentage of participants (29.3%) that did not complete posttreatment assessment was high, and should be seen as a major limitation of the current study, particularly because there was a difference in response rates between the treatment conditions and wait-list control. When compared with other clinical trials of Internet-based CBT, this is also to be regarded as a relatively large number (G. Andersson et al., 2013), similar to 27% lost to follow-up for participants receiving guided self-help for depression (Vernmark et al., 2010). However, it is unknown whether this suggests that procrastinators are more likely to drop out or have lower response rates than nonprocrastinators. On the one hand, it could be argued that participants in the current study were self-referrals, thus being more motivated to complete the treatment interventions and the posttreatment assessment. On the other hand, it might also be assumed that those participants who did complete the posttreatment assessment were more motivated to change, and, therefore, more compliant. This may in turn affect the interpretation of the results, that is, drop out not occurring at

random, warranting some precaution when trying to generalize the findings. In either case, the considerable number of missing participants may be related to unchanged and ongoing difficulties of procrastination or the absence of a structured clinical interview, such as by telephone, which was not implemented during the screening process or at posttreatment assessment (cf., Boettcher, Berger, & Renneberg, 2012). Instead, participants were advised to complete primary and secondary outcome measures on their own using the secure online interface, which may have been forgotten or perceived as a lower priority after the treatment period. This might have contributed to a lower response rate and more delay in completing the posttreatment assessment for participants in the treatment conditions, while the higher response rate and less delay for participants in wait-list control could be explained by the fact that they were waiting to receive treatment and thus more motivated to fill it out in time. Interestingly, there was also a difference in delay between participants receiving guided self-help and unguided self-help, which may be related to the therapists urging them to complete the self-report measures in the last feedback given to participants at the end of the treatment period. In sum, precautionary actions in clinical trials of procrastination are warranted in the future, such as telephone reminders or face-to-face evaluation posttreatment (Boettcher, Renneberg, & Berger, 2013). In addition, use of a Smartphone application to monitor treatment progress and report homework assignments (Ly et al., 2014), increased feedback from a therapist (Bendelin et al., 2011), and a more interactive interface for delivering the treatment modules may improve response rates and adherence (G. Andersson et al., 2013), and should be investigated in subsequent clinical trials of procrastination.

Conclusion Despite certain limitations, the current study provided preliminary evidence for the efficacy of Internet-based CBT for procrastination. Hence, treatment interventions often used in CBT could be suitable for managing procrastination, producing substantial improvements in relation to self-reported difficulties of procrastination for participant receiving both guided self-help and unguided self-help when delivered via the Internet. Even though procrastination is not considered a psychiatric disorder, it is still a prevalent and debilitating condition that can cause personal distress, and, in turn, lead to increased levels of stress and anxiety (Sirois, 2004, 2007), decreased well-being (Stead, Shanahan, & Neufeld, 2010), and result in poorer performance in school and work (Steel, Brothen, & Wambach, 2001). The results of the current study might therefore help to increase awareness of the etiology and maintenance of procrastination, which, in turn, should not only benefit the growing number of individuals who regard themselves as severe and chronic procrastinators (Pychyl & Flett, 2012), but also help health care providers identify and target procrastinationrelated difficulties in the treatment of psychiatric disorders, such as, depression or mild forms of obsessive– compulsive disorder (Rozental & Carlbring, 2014). Using the Internet to disseminate CBT is also important because this might enhance the access to evidence-based care, particularly because procrastination may not always be recognized or understood by health care providers, and the availability of treatment interventions is scarce (Rozental, Forsström, et al., 2014). However, further research is required to


examine the long-term improvements of participants undergoing Internet-based CBT for procrastination, as well as to explore the components responsible for a positive treatment outcome.


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Received August 6, 2014 Revision received February 24, 2015 Accepted February 27, 2015 䡲

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