Accepted Manuscript An online guided ACT intervention for enhancing the psychological wellbeing of university students: A randomized controlled clinical trial Panajiota Räsänen, Päivi Lappalainen, Joona Muotka, Asko Tolvanen, Raimo Lappalainen PII:

S0005-7967(16)30008-0

DOI:

10.1016/j.brat.2016.01.001

Reference:

BRT 2960

To appear in:

Behaviour Research and Therapy

Received Date: 29 June 2015 Revised Date:

7 January 2016

Accepted Date: 23 January 2016

Please cite this article as: Räsänen, P., Lappalainen, P., Muotka, J., Tolvanen, A., Lappalainen, R., An online guided ACT intervention for enhancing the psychological wellbeing of university students: A randomized controlled clinical trial, Behaviour Research and Therapy (2016), doi: 10.1016/ j.brat.2016.01.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Running head: AN ONLINE GUIDED ACT INTERVENTION FOR STUDENTS 1

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An online guided ACT intervention for enhancing the psychological wellbeing of university

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students: A randomized controlled clinical trial

Panajiota Räsänen, Päivi Lappalainen, Joona Muotka, Asko Tolvanen, Raimo Lappalainen

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Author Note

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Department of Psychology, University of Jyväskylä, Finland

Correspondence concerning this article should be addressed to:

Panajiota Räsänen, Department of Psychology, University of Jyväskylä, P. O. Box 35, FIN40014 University of Jyväskylä, Finland. Email: [email protected]

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Tel. +358 40 805 4518

AN ONLINE GUIDED ACT INTERVENTION FOR STUDENTS 2

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Stress, anxiety and depression are relatively common problems among university students. A online psychological intervention aimed at enhancing the wellbeing of university students

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could be an effective and practical alternative for meeting the needs of a university population. University students (N = 68; 85% female; 19–32 years old) were randomly

assigned to either a guided seven-week online Acceptance and Commitment Therapy (iACT) intervention or a waiting list control condition (WLC). A between-groups pre–post (iACT vs

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WLC) design with 12-month follow-up for the iACT participants was conducted. The

intervention participants were offered two face-to-face meetings, completed online exercises during a five-week period, and received personal weekly written feedback via the website

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from their randomly assigned, trained student coaches. Waitlist participants were offered the intervention program soon after the post measurements. Results in this small efficacy trial showed that the iACT participants had significantly higher gains in wellbeing (between group, d = 0.46), life satisfaction (d = 0.65), and mindfulness skills (d = 0.49). In addition, iACT participants’ self-reported stress (d = 0.54) and symptoms of depression (d = 0.69)

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were significantly reduced compared to the participants in the control group. These benefits were maintained over a 12-month follow-up period (within iACT group, d = 0.65-0.69, for primary measures). The results suggest that an online-based, coach-guided ACT program with blended face-to-face and online sessions could be an effective and well-accepted

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alternative for enhancing the wellbeing of university students. Keywords: acceptance and commitment therapy; online interventions; university students;

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wellbeing; stress; depression

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Introduction

The prevalence of psychological problems in university students, in both frequency and severity, has been on the rise (Gallagher, 2007; Benton, Robertson, Tseng, Newton &

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Benson, 2003; Kitzrow, 2003). In fact, psychological distress has been found to be significantly higher among university students than it is among the general population (Adlaf, Gliksman, Demers, & Newton-Taylor, 2001; Bayram & Bilgel, 2008; Cooke, Bewick,

Barkham, Bradley, & Audin, 2006; Stallman, 2010). University students may face an array of

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academic, interpersonal, financial, and cultural challenges (Beiter et al., 2015; Cooke et al., 2006; Pierceall & Keim, 2007; Vaez & Laflamme, 2008). At times, such challenges may go beyond students’ resources and capacity to effectively cope, leading to academic struggles, a

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decrease in the quality and satisfaction of life, and an increase in physical and mental health issues, including stress, anxiety, depression, sleep disturbances, eating disorders, and suicide (Hunt & Eisenberg, 2010; Kadison, & DiGeronimo, 2004; Lee, Olson, Locke, Michelson, & Odes, 2009). In fact, stress, anxiety, and depression are the most common problems experienced by the university population (Krumrie, Newton, & Kim, 2010; Regehr, Glancy,

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& Pitts, 2013). This prevalence indicates the importance of developing and establishing programs that address such problems (Beiter et al., 2015). In addition, certain attitudes and trends among the university population when seeking help need to be considered. Research shows that between 45% and 65% of university

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students experiencing mental health problems do not seek professional help (Zivin, Eisenberg, Gollust, & Golberstein, 2009; Cooke et al., 2006; Eisenberg, Golberstein, &

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Gollust, 2007; urbisJHD, 2007) even though many universities in the world, particularly in the Nordic countries, offer free health and counseling services to students. Some of the reasons for not seeking professional help include the fear of stigma (McKinney, 2009), low perceived need for help, lack of time (Zivin et al., 2009; Hunt & Eisenberg, 2010), privacy issues (Gulliver et al., 2015; Hunt & Eisenberg, 2010), lack of knowledge of available services, and negative attitudes about the potential effectiveness of treatments (Eisenberg et al., 2007). People often choose informal support from family, friends, and interpersonal resources, such as self-help books and online sites (Ryan, Shochet, & Stallman, 2010). University students and young adults are the mostly likely group among the general population to use the Internet (Chiauzzi, 2008), and to seek health information online (Hanauer et al., 2004). In addition, even in the cases where students do access counseling

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services, the long waiting lists may become an obstacle for receiving help when needed. Overall, these attitudes amongst the university population need to be taken into account when providing mental health services and addressed accordingly. The increase in psychological problems among students is occurring, however, as university counseling budgets have been decreasing (Kitzrow, 2003; Terneus, 2006).

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Universities are challenged more than ever to provide services that are accessible as well as cost-effective and that meet the broad range of needs of student populations with both a

preventative and therapeutic scope. In recent years, online self-help interventions have been increasing due to easy accessibility, cost-effectiveness, and as alternatives with promising

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results for wellbeing and mental health issues, including anxiety, depression (e.g., Andersson & Cuijpers, 2009, Griffiths & Christensen, 2006; Spek et al., 2007; Richards & Richardson, 2012; Grist & Cavanagh, 2013), and stress (Zetterqvist, Maanmies, Ström, & Andersson,

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2003). There is a variety in the form of delivering internet-based interventions, with guided self-help interventions being reported in the literature as more effective than pure self-help (Gellatly et al., 2007; Johansson & Andersson, 2012; Newman, Szkodny, Llera, & Przeworski, 2011; Richards & Richardson, 2012; Spek et al., 2007). Some evidence even suggests that treatment effects are maintained over time (Carlbring, Bergman Nordgren,

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Furmark, & Andersson, 2009). In addition, pure self-help interventions seem to face problems with attrition (Christensen et al., 2009). Based on recent meta-analyses, guided selfhelp for anxiety and depression can have comparable results to face-to-face interventions (Cuijpers et al., 2010) as well as adequate or equal adherence (Van Ballegooijen et al., 2014).

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Moreover, in guided self-help, although the use of experts may lead to better results, there have been cases were other personnel could have just as promising results (Titov et al., 2010).

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A recent meta-analysis of twenty-four studies concluded that cognitive, behavioral, and mindfulness-based interventions have been effective in reducing psychological distress in the form of anxiety and/or depression in university students (Regehr, Glancy, & Pitts, 2012). One unified model that falls under the umbrella of such mindfulness and cognitive behavioral models (Twohig, 2012) is Acceptance and Commitment Therapy (ACT; Hayes, Strosahl, & Wilson, 2012). ACT is a transdiagnostic approach that focuses on increasing psychological flexibility by emphasizing acceptance, mindfulness, and values-based processes (e.g., Zettle, Rains, & Hayes, 2011; Arch et al., 2012). Support for the ACT model has been found for a wide range of psychological problems, including symptoms of depression (e.g., Lappalainen et al., 2014; Kohtala, Lappalainen, Savonen, Timo, & Tolvanen, 2015; Zettle, Rains, & Hayes, 2011), anxiety disorders, and somatic health problems (APA Div 12 SCP, 2012; A-

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Tjak et al., 2015; Smout et al., 2012; Ruiz, 2010). In the context of the university population, ACT has been applied, for example, as a self-help program with good results for reducing symptoms of stress, anxiety, and depression among Japanese international students attending an American university (Muto, Hayes, & Jeffcoat, 2011). It has also been applied in a class format (e.g., Christodoulou & Flaxman, 2012; Pistorello et al., 2012) and over the span of an

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academic semester (Boone & Manning, 2012). Furthermore, two studies have examined psychological wellness in first-year students (Danitz & Orsillo, 2014; Pistorello et al., 2012). Of these, Danitz and Orsillo reported a decrease in depression symptoms, higher acceptance scores and a non-significant reduction of stress and anxiety at a three-month follow up. In

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addition, a pilot study consisting of six two-hour group sessions using ACT showed

promising results in preventing the development of stress and burnout in nursing students (Frögéli et al., 2015). In an online format, ACT has focused on values training and goal

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setting (Chase, 2010) and as a prevention program for college students (Levin, Pistorello, Hayes, & Seely, 2014). However, while these studies have produced promising results in alleviating problems in the university population, there are several limitations. For example, the results show a reduction of gains over time (Pistorello et al., 2012), longer follow-up assessments have not been implemented (Danitz & Orsillo, 2014), and the absence of pre-test

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and control conditions have been reported (Boone & Manning, 2012). Overall, ACT could be a viable model for adaptation to the growing needs of universities and their students, given that this adaptation is not restricted by diagnostic presentations, severity of psychological problems, and student demographics (Hayes, Pistorello, & Levin, 2012; 2013). To date,

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however, there are only limited RCT studies based on ACT for university populations, often with some methodological limitations. This limited application increases the need to develop

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and implement programs that examine and support the efficacy and effectiveness of the model.

In view of the above, we developed an online, ACT-based program called The Student Compass designed to enhance the wellbeing of university students. The Student Life Compass was created based on our earlier experiences of both brief face-to-face (Lappalainen et al., 2007; Kohtala et al., 2015), and online ACT interventions (Lappalainen et al., 2014; Lappalainen et al., 2015). The purpose of this study was to evaluate the efficacy of the program, which was developed to provide students with two face-to-face meetings, tailored individual written feedback online, coping tools, and strategies based on the principles of ACT. More specifically, we were interested in investigating whether a coach supported ACTbased online intervention would increase university students’ wellbeing compared to that of

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students with no access to such intervention. Overall, this small efficacy trial tested the hypotheses that participating in the online ACT program would (a) increase the psychological wellbeing of students and (b) reduce the possible psychological distress and symptoms of stress, depression, and anxiety. We also expected that (c) the intervention would have a positive impact on psychological flexibility and mindfulness skills. In order to increase

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university students’ wellbeing and to ameliorate possible psychological problems in the future, it is important to develop and investigate models that could be effectively applied and used as an alternative or a complement to existing traditional forms of campus counseling

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services. This article describes and evaluates one such potential model.

Methods

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Ethics statement

The study protocol, interventions, participant information, and informed consent received the approval of the Ethics Committee of the University of Jyväskylä (JYU) and the regional ethical board of Central Finland Healthcare District’s Ethics Committee, under

Study design

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registration number (14U/2012). The program was offered free of charge to the participants.

This study was designed as a randomized controlled trial with two parallel groups and took place between September 2012 and May 2013. In the experimental condition,

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participants received free access to an online ACT intervention (iACT) called The Student Compass (in Finnish: Opiskelijan Kompassi). In the control waitlist condition (WLC),

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participants were placed on a waiting list, for seven weeks, before they were offered access to the Student Compass. Both groups were measured before the intervention started (premeasurement), and soon after the intervention (post-measurement, seven weeks after premeasurement). Participants were contacted twelve months after completing the program to fill out follow-up measures. The study was designed to compare the efficacy of the Student Compass program (iACT) to the waiting-list control condition (WLC) during the intervention period (from pre- to post-measurement). The maintenance of the impact of the intervention was studied in the iACT group only at the 12-month follow-up. Inclusion and Exclusion Criteria

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The following inclusion criteria were examined upon recruitment. Participants were required to be (a) enrolled students, (b) at least 18 years old, (c) have access to the Internet, (d) currently self-reporting as experiencing some form of psychological distress such as stress, a low mood, and/or anxiety, and e) have the willingness to commit to a free online program with two face-to-face meetings within a seven-week period. In addition, the

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following exclusion criteria were defined: (a) participating simultaneously in any psychological or pharmaceutical intervention or receiving psychological therapy that may intervene and have an effect on the results of this study, and (b) suicidal ideation. Participants who responded yes in the recruitment questionnaire about recently having thoughts of

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committing suicide were contacted by phone and were interviewed by the researchers.

Suicidal risk assessment was based on the practical algorithm developed by Hirschfeld and Russel (1997). If, based on the interview, it was determined that participation in the study

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could pose a risk, potential participants were further instructed to contact other counseling services provided by the university. Furthermore, if they were using medication, participants had the responsibility to inform the research team of any dosage changes. Recruitment

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Participants were recruited primarily via an email sent through the JYU student unions’ mailing lists, an advertisement on the university’s website, and through printed posters, which were placed at several designated areas around the university campus. The content of the advertisement included brief information about the program, the inclusion criteria

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mentioned above, and contact information such as the program’s email, telephone number, and contact persons. If needed, participants were further contacted by phone to determine if the criteria were met. Prospective participants who expressed interest in the study received an

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email in which the purpose and a short outline of the study were described in more detail. They also received, in the form of an attachment, a screening questionnaire along with the Depression Anxiety Stress Scale (DASS-21; Lovibond & Lovibond, 1995). Randomization

After completing both the screening and DASS questionnaires at home along with their informed consent, participants who met the inclusion criteria were randomly assigned by an independent researcher to the experimental group (Student Compass) or to the waitlist control group. Block randomization was performed to ensure equal distribution of participants across two conditions: (a) gender and (b) severity of symptoms based on the DASS scales. The

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Depression Anxiety Stress Scale was selected because it represents most common aspects of psychological distress and mental health difficulties. Furthermore, each participant was randomly assigned to one of the 22 available coaches/counselors. Finally, all participants were notified by phone about the time they would receive the program and were invited to an initial meeting.

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Participant characteristics

Of the 117 participants who responded to the advertisement, 68 were included in the study. The reasons for exclusion are specified in the CONSORT flowchart (Figure1).

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Insert Figure 1 here: Flowchart

The mean age for the sample was 24.29 (SD = 3.28) and 58 participants were female

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(85.3%). No significant differences were observed between the groups (iACT, n = 33; WLC, n = 35) in the demographic data collected. More specific details of demographic and sample characteristics at baseline are provided in Table 1.

Insert Table 1 here: Demographics It is worth mentioning that according to the BDI-II, a majority (61.9%) of the

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participants reported at least mild depression symptoms (BDI ≥ 14): more precisely 7.4% (n = 5) reported severe (BDI ≥ 29), 22.1% (n = 15) moderate (BDI, 20–28), 32.4% (n = 22) mild (BDI, 14–19), and 38.1% (n = 26) few symptoms of depression (BDI 13, n = 22, 32.4%), moderate (BDI >19, n =

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15, 22.0%) or severe (BDI >28, n = 5, 7.4%) scores for depression symptoms. Such results are consistent with previous intervention studies in which participants experience elevated

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psychological distress and are in many cases comparable to the clinical population (e.g., Muto, Hayes, & Jeffcoat, 2011). The results also show that there was heterogeneity in symptomatology within this particular group. The intervention in this study had a particular effect on reducing symptoms of

depression (within group d = 1.12, for BDI-II at post-measurement). This is in accordance with previous studies that employ acceptance and mindfulness-based methods for university students (Pistorello et al., 2013). An encouraging finding in this study was that depression symptoms were maintained at the 12-month follow-up (within group d = 0.87 for BDI-II). These findings are promising, because depression is one of the most important barriers for university students’ psychological wellbeing, with weighted mean prevalence among university students of 30.6%, which is much higher than that of the general population

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(Ibrahim, Kelly, Adams, & Glazebrook, 2013). In comparison to a study by Levin, Pistorello, Hayes, & Seely (2014) using a online ACT-intervention for college students with a six-week follow-up instead of the 12-month follow-up in the current study, the within-group effect size values (d) for depression, anxiety, and stress (as measured by DASS) were comparable (d = .54–.64 vs. d = .81–.97, the current study vs. Levin et al., 2014). The post-measurement

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between-group d values for depression were also comparable (comparison with WLC: d = .55 vs. .40, the current study vs. Levin et al., 2014). These results indicate that it is possible to develop brief, cost-effective interventions to target psychological distress in the form of depression as well as anxiety and stress with lasting effects. The results also confirm a

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previous study with an 18-month follow-up that employed a similar methodology and a online model of delivery for depression with participants from the general population (Lappalainen et al., 2014).

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The within-group effect sizes of the iACT group from pre- to post-measurement (d = 0.42–1.12) and from pre-measurement to 12-month follow-up (d = 0.27-0.87) were mainly small to moderate. This could be due to the relatively low mean level of symptomatology at baseline allowing limited space for improvement. Alternatively, it could be argued that there is still space for improvement in the effectiveness of the iACT model applied in this study.

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More attention needs to be devoted in the future to identifying the critical active components of effective programs and predictors of successful outcomes for treatment-based programs as well as prevention programs for university students. It is worth noting that the attrition rate in this study was fairly low. More specifically,

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only 9.1% of the participants in this study (n = 3) discontinued the program. We have observed similar low drop-out rates (2.6%) in our earlier study (Lappalainen et al., 2014),

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upon which the protocol of this current study was based. It is common for guided online interventions to have high dropout rates (Melville, Casey, & Kavanagh, 2010; Day, McGrath, & Wojtowicz, 2013). For example, Wojtowicz, Day and McGrath (2013) reported that 56% of participants discontinued a cognitive-behavior therapy-oriented online self-help program for university students experiencing mild to moderate anxiety, depression, and stress. Perhaps in our study, the face-to-face support along with the tailored weekly contact online was a type of support that may have significantly contributed to having so many participants complete the program and also participate in the 12-month follow up (79%, n = 26). This result may indicate that this type of support, where a coach is present and provides immediate (within 48 hours) written feedback as to what the participant has practiced and how to proceed next, may be engaging enough for participants to follow through with the program and make them more

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likely to complete the program. It is also possible that the type of support provided in this program could be one of the critical components of the intervention. This needs to be investigated more closely in future studies. In addition, participants in this intervention were initially highly motivated to change, which may also have been a contributing factor to the low attrition rate. This, in line with the findings of a study that investigated predictors of

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adherence (Farrer, Griffiths, Christensen, Mackinnon, & Batterham, 2014) and in which there were indications that younger participants with high motivation and higher educational level—as in this study—may adhere better to interventions. This low attrition rate is

promising, because it has been reported earlier that only about half of the university students

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who participate in face-to-face therapy complete it as endorsed by their counselor (Lucas, 2012).

There were several limitations that need to be noted and may affect the generalizability

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of the results. While the participants enrolled in a variety of major programs at the university and the online program was advertised equally to both male and female students, the majority of participants in this study were female (85%). This appears to be an inherent problem in many online as well as face-to-face interventions. Advertising strategies that appeal to male university students may be a necessary focus in future studies. Furthermore, while this study

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included a fairly heterogeneous student sample, the size of the sample was relatively small with participants that volunteered and were highly motivated to change. Limited recruitment time may have had an effect on the amount of participation, since the intervention had to be delivered within one semester. These observations need to be taken into consideration when

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drawing conclusions from the study. In addition, the study included a waiting-list control but not an active comparable condition. Even though a waiting-list control group allows the

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control, for example, of effects of repeated measurements, social attention, and hope, the presence of an additional comparable condition could have strengthened the study. Moreover, at the time of the study, it was not possible to produce reliable login data through the university’s online management platform, which is, of course, a severe limitation for a online program. Such login data would have provided valuable additional information on the selfreported data. In addition, in this study inexperienced psychology students provided the coach with support. These results can be generalized to interventions provided by inexperienced counselors or similar personnel. The student coaches had received brief training on the ACT methodology, had limited or no previous client experience, and were not accustomed to using an online platform. Coaches therefore had an array of new knowledge and skills to adapt to

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and familiarize themselves with in a limited timeframe. This may in some way explain the extended weekly time used per client (on average 45 minutes) despite the initial instructions to limit the amount of time used for each client (to 15–20 minutes). It could be predicted that the time for the online feedback would decline after completing an increased number of online-interventions. Based on previous findings, Andersson and colleagues (2009) indicated

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that it could be adequate for online interventions to spend 100 minutes for client feedback during the course of a 10-week program. However, there is also support for the view that increasing the clinicians’ and or coaches’ contact time does not necessarily result in

significantly better treatment gains (Vernmark et al., 2010). Furthermore, in a recent

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systematic review (Baumeister et al., 2014) of online interventions, the level of the coaches’ qualifications did not determine the treatment efficacy. According to Baumeister (2014), coaches’ high motivation and more time to prepare for each client may be some of the

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reasons that explain such finding. Andersson (2010) suggests that the supportive role of the counselor and/or e-coach in online interventions may require fewer skills than in interventions in face-to-face settings. In future studies, it is important to determine the amount and the type of feedback needed to be given to clients that would be simultaneously time and cost-effective as well as meaningful in promoting positive outcomes. It would also

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be of interest to investigate in more depth what kinds of coaching skills played a role in the overall effectiveness of the program. In any case, the results from this study are encouraging for the use of psychology students to provide support and are also in line with previous studies that had employed psychology students as coaches (Andersson & Carlbring, 2010;

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Kohtala et al., 2015: Lappalainen et al., 2014). In addition, similar positive results using comparable psychology students as coaches applying CBT in face-to-face interventions have

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been reported by Hiltunen et al. (2013) and Öst (2012). An online intervention can be used as a stand-alone intervention or be integrated with other mental health and wellbeing services of universities as well as with teaching. However, it could be challenging to integrate online interventions with other services. Integration often requires change in current practices, and there could be resistance or skepticism of the usefulness of online approaches with limited personal support. On the other hand, it is possible that guided internet-based interventions could provide concrete tools to university students to cope with possible psychological challenges during their studies as well as later in life. Technology-based guided interventions also have the possibility to reach a wide spectrum of young people, who might have otherwise not been seeking any help, and it can be empowering in that students themselves, with limited guidance, can actively participate in

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improving their own wellbeing and health. At the moment, the online-based intervention described in this study is a part of a wider cluster of student wellbeing services offered at the University of Jyväskylä. The program is available to all 15,000 students at the university as a self-help offering and twice a year it is also available in coach-supported formats. The intervention presented in the current study presents an example of an effective,

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easily accessible alternative service that could be provided to students along with more conventional services (e.g., face-to-face student counseling and psychotherapy) or as a precounseling tool for students who might already experience more severe psychiatric issues. This study indicated that acceptance, mindfulness, and value-based guided web programs

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could be an effective tool when offered by peer students as an alternative to a wider range of services available for university students. Further research is needed to identify and clarify the different forms of delivering such online interventions, including the amount and type of

impact in delivering effective results.

Competing interests

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human support, the intervention’s length, and the components that may have a stronger

Author’s contributions

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The authors declare that they have no competing interests.

PR, PL, and RL contributed to the design of the online-based intervention. PR, PL, and RL

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contributed to the design of this trial. PR supervised the students who provided the coaching for the participants, recruited the participants, and collected the data. PR, PL, JM, and AT

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drafted the manuscript. PR and RL contributed to the further writing of the manuscript. All authors read and approved the final manuscript. Acknowledgments

The study was funded by the strategic funds of the University of Jyväskylä’s rector. The authors would like to thank all the participants who helped in realizing this study: the coaches for their devotion and support to the participants during the intervention as well as the students and project secretary who assisted with recruitment, data collection, and organization. We would also like to thank Professor John Nietfield and Mary Chassandra for their valuable comments.

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Theme and aim

Examples of exercises and activities

1.Introductory face-to-face session

Baseline measurements. Semi-structured interview aiming at mapping the participant’s current situation, problems, and level of functionality Coach’s role: Interview the participant and perform a functional analytic clinical case formulation Brief orientation to the online intervention Values. Clarifying one’s own values. Difference between values and goals. Coach’s role: Provide written feedback to the client on their values, encourage them on their progress, provide further recommendations and personalized exercises, guide them in the next week’s theme (2)

3. Online session: Taking action

Taking values-based action and concrete steps towards them. Examining possible obstacles to taking action based on values. Coach’s role: In addition to (2), focus on possible obstacles to taking concrete action

4. Online session: Being present

Contact with the present moment. Learning how to be mindful in the here and now. Coach’s main role: In addition to (2): If needed, point out the importance of mindfulness and its practical application in daily life

5. Online session: Watching one’s thinking

Cognitive defusion. Taking an observer’s perspective towards one’s own thoughts and feelings. Weakening of language control. Coach’s role: In addition to (2): focus on the client’s thinking patterns and their usability/functionality

6. Online session: Awareness and Acceptance

Developing awareness of the self-as-context. Acceptance of thoughts, feelings, memories as they are, changing what can be changed through action Coach’s role: In addition to (2): provide further guidance on the concepts of acceptance and awareness, prepare for face-to-face meeting Wrapping up the intervention. Relapse prevention. Post-measurements. Coach’s role: Evaluate, on the basis of a semi-structured interview, the client’s situation and wrap up the intervention. Provide further referral, if needed.

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Optional modules

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2. Online session: Clarifying values

7. Concluding face-toface session

Functional analytic clinical case formulation (FACCM) diagram The values of a good life (worksheet)

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Core modules /per week

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Table 2. Structure and content of the iACT intervention: modules, themes, and coach’s role

Introduction/ Psychoeducation

Basic information on stress, anxiety, depression, and the problem with controlling. How to face obstacles.

Relaxation

Deep breathing and muscle relaxation exercises

Half-a-year of living (exercise) Two kids in a car (metaphor) Video on values ACT weekly diary (AWD) Weekly wellbeing exercise: Clarifying and reflecting on one’s values Passengers on a bus (metaphor) Video on values and goals, AWD Weekly wellbeing exercise: Defining goals and committing to take values-based action Mindful breathing, eating, sitting Video on being present, AWD Weekly wellbeing exercise: Being mindful in daily activities. Taking action according to values. Observer (exercise) Video on noticing and naming thoughts, AWD Weekly wellbeing exercise: You are not the same as your thoughts (exercises). Taking action according to values. Continuing with mindfulness activities. A stone on the beach (exercise) Video on expansion and self-awareness, AWD Weekly wellbeing exercise: What I would need to accept (exercise). Taking action according to values. Continuing with mindfulness activities. 3 things to continue practicing (card) Review of FACCM and values

e.g., progressive muscle relaxation

ACCEPTED MANUSCRIPT Table 3. Mean scores, standard deviations and effect sizes of all measures at pre- and post-measurement between treatment (iACT) and waiting-list control (WLC) group

Post M (SD)

Group

MHC-SF

iACT

37.21 (11.94)

44.81 (14.02)

46.89 (15.08)

WLC

39.88 (13.04)

41.80 (13.57)

-

iACT

21.54 (5.11)

17.70 (5.75)

17.13 (5.11)

WLC

21.54 (4.38)

20.25 (5.12)

iACT

51.18 (14.01)

63.36 (15.19)

WLC

56.05 (16.20)

58.37 (14.75)

iACT

50.54 (17.92)

62.72 (15.33)

WLC

53.68 (18.13)

54.51 (17.32)

iACT

16.81 (7.54)

8.88 (6.84)

WLC

15.51 (7.66)

12.85 (5.72)

iACT

16.78 (9.51)

6.55 (4.97)

WLC

15.82 (9.80)

10.94 (7.44)

iACT

8.60 (6.39)

5.79 (5.42)

WLC

7.28 (6.52)

5.74 (4.94)

DASS Depression

DASS Anxiety

iACT

15.93 (8.58)

11.22 (8.04)

WLC

16.57 (7.40)

13.28 (7.40)

iACT

42.48 (9.77)

47.52 (9.80)

WLC

41.60 (10.43)

45.51 (9.04)

iACT

121.54 (17.35)

131.83(16.47)

WLC

121.02 (17.92)

122.62 (16.62)

iACT

54.72 (9.42)

59.69 (9.95)

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DASS Stress

AAQ-II

FFMQ

OLQ-13

61.58 (19.13)

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BDI-II

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VAS Self-esteem

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VAS Satisfaction to life

Pre, Post, Fup Change** Wald test (df = 2) p value

5.674 p = .006

23.00 p = .0000

-2.559 p = .0281

20.725 p = .0000

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SCALE

PSS

Pre–Post Change* Estimate (df = 1) p value

12-month Follow-up M (SD)

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Pre M (SD)

62.59 (20.24)

10.08 (7.71)

9.77 (8.37)

5.27 (3.74)

11.75 (6.97)

49.27 (10.96)

9.785 p = .000

10.559 p = .000 -5.032 p = .0030

24.22 p = .0000

27.297 p = .0000 36.291 p = .0000

-3.476 p = .0749

32.948 p = .0000

-1.167 p = .4146

9.410 p = .0090

-1.399 p = .4163

15.739 p = .0004

0.974 p = .6720

10.851 p = .0044

133.49 (17.93)

8.200 p = .0075

57.87 (12.15)

4.694 p = .005

WLC 54.60 (9.21) 54.65 (8.62) *investigates whether groups change differently from Pre to Post **investigates whether the iACT group changes significantly over time (Pre, Post, Follow-up)

14.321 p = .0008

16.43 p = .0003

ACCEPTED MANUSCRIPT Table 4. Effect sizes between-group (Cohen’s d corrected) at post-measurement and effect sizes within-group (d) at post and 12-month follow-up Between PrePost (Corrected Cohen’s dcorr)

Within iAct Pre–Post

Within control Pre– Post

Within iACT Pre–Followup

MHC-SF

.46**

.61***

.14

.65***

PSS

.54*

.76***

.27

VAS Satisfaction to life

.65**

.82***

.14

VAS Self-confidence

.63***

.72***

BDI-II

.69**

DASS Depression

.55**

DASS Anxiety

.20

DASS Stress AAQ-II

OLQ-13

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.04

.69*** .63**

.66***

.39**

.87***

1.10***

53***

.64***

.42*

.26

.60***

.18

56**

.44**

.54**

.11

.51*

.40**

.63**

.49**

.62***

.09

.62***

.52***

.00

.27

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1.12***

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FFMQ

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Measure

.53**

Note. * = p < .05, ** = p < .01, *** = p < .001.

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Table 1. Participant characteristics Baseline characteristics

All (N = 68)

Age M (SD)

24.29 (3.28)

iACT (n = 33) 24.61 (3.33)

Gender N (%) Female Male

58 (85.3) 10 (14.7)

28 (84.8) 5 (15.2)

30 (85.7) 5 (14.3)

Relationship status N (%) Single In a relationship Married/Registered Others*

33 (48.5) 23 (32.4) 9 (13.2) 3 (4.4)

14 (42.4) 11 (33.3) 6 (18.2) 2 (6.1)

19 (54.3) 12 (34.3) 3 (8.6) 1 (2.8)

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24 (3.25)

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Faculty N (%) Humanities Mathematics & Science Social Sciences Education Information Technology Business & Economics Sport & Health Sciences

WLC (n = 35)

9 (27.3) 3 (9.1)

14 (40) 10 (28.6)

10 (14.7) 9 (13.2) 6 (8.8)

9 (27.3) 5 (15.2) 3 (9.1)

1 (2.9) 4 (11.4) 3 (8.6)

4 (5.9)

2 (6.1)

2 (5.7)

3 (4.4)

2 (6.1)

1 (2.9)

2 (2.9) 25 (36.8) 41 (60.3)

1 (3) 15 (45.5) 17 (51.5)

1 (2.9) 10 (28.6) 24 (68.6)

Previous Psychological Counseling N (%) **

42 (61.7)

21 (63.6)

21 (60)

Earlier diagnosis N (%)

9 (13.2)

5 (15.1)

4 (11.4)

Medication N (%)***

9 (13.2)

3 (9.1)

6 (17.1)

AC C

EP

Employment N (%) Full-time Part-time Not employed

TE D

23 (33.8) 13 (19.1)

Motivation to change M (SD) ****

7.96 (1.45)

7.93 (1.61)

*The group Others included those on sick leave, homemakers, or others choosing this option ** Previous counseling including visit at a psychologist, psychiatrist and/or nurse ***Use of medication for mental health problems **** Motivation to change, Scale 1–10, 1 = not ready to change, 10 = highly motivated to change

7.97 (1.29)

ACCEPTED MANUSCRIPT

Assessed for eligibility

Randomized (n = 68)

Allocation

Allocated to wait-list control group (n = 35)

M AN U

Allocated to iACT intervention group (n = 33):

SC

RI PT

(N = 117)

Enrollment

Figure 1 Participants recruited through the university website, mailing lists, and posters

Excluded (n = 49) Reason: Not meeting inclusion criteria: • Ongoing psychological treatment (n = 8; 16.3%) Reason: Other: • Could not be reached / did not respond (n = 24; 48.9%) • Busy schedule (n = 9; 18.3%) • Did not return screening questionnaires before set date for randomization (n = 7; 14.2%) • Not willing to have face-to-face meetings (n =

Pre-measurement Participated in pre-measurement (n = 33)

Participated in pre-measurement (n = 35)

TE D

Post-measurement: 7 weeks Completed post-measurement (n = 29; 88%), Analysed (n = 33; 100%) Withdrew: Did not provide post-measurement data (n = 4; 12%), included in calculation:

AC C

• •

Discontinued after Module 3 (n = 2), reasons: a) busy schedule, b) no reason given Discontinued after Module 4 (N=1), reason: busy schedule Completed program, did not participate in post-measurement n = 1), reason: feeling better

EP

•

Analysed (n = 35; 100%)

Follow-up: 12-month

Participated in 12-month follow-up (n = 26; 78.8%), Analysed (n = 33; 100%)

Figure 1. Participant flow

Participated in postmeasurement (n = 35; 100%)

No WLC group during follow-up

ACCEPTED MANUSCRIPT Highlights

•

A controlled clinical trial of an online guided intervention based on Acceptance and Commitment Therapy focusing on enhancing the wellbeing of university students. Significantly higher gains in well-being, life satisfaction, mindfulness skills, selfesteem were observed in the intervention group (iACT) compared to the waitlist control group (WLC).

RI PT

•

Self-reported stress and symptoms of depression were significantly reduced in the

• •

iACT group compared to WLC. Treatment gains were maintained at a 12-month follow-up. The intervention was well-accepted by the participants.

AC C

EP

TE D

M AN U

SC

•