Secondary Logo

Prevention of depression in first-year university students with high harm avoidance

Evaluation of the effects of group cognitive behavioral therapy at 1-year follow-up

Saigo, Tatsuo, MAa; Hayashida, Masaki, MD, PhDa,b,*; Tayama, Jun, PhDb,c; Ogawa, Sayaka, MAa,b; Bernick, Peter, MSWb,d; Takeoka, Atsushi, MD, PhDb; Shirabe, Susumu, MD, PhDa,b,d

Section Editor(s): Dang., Yong-hui

doi: 10.1097/MD.0000000000013009
Research Article: Observational Study

High harm avoidance (HA) scores on the temperament and character inventory appear to be a risk factor for depressive disorders and suicide. Since 2012, we have conducted group cognitive behavioral therapy (G-CBT) interventions for students at Nagasaki University with high HA and without depressive disorders, with the aim of preventing depression. Here, we report on the effects of the G-CBT at 1-year follow-up for the 2012 to 2015 period.

Forty-two participants with high HA were included in the final analysis. Outcomes were measured with the Beck Depression Inventory II, Manifest Anxiety Scale, 28-item General Health Questionnaire, and Brief Core Schema Scales at baseline, and at 6-month, and 1-year follow-ups.

Repeated-measures analyses of variance revealed a significant decrease in mean depressive symptom scores at the 6-month follow-up point; this decrease was maintained at 1 year. Improvements in cognitive schemas were also seen at 6 months and 1 year.

We observed improvements in cognitive schemas associated with depression as a result of the G-CBT intervention, with effects maintained at 1 year post-intervention. This intervention may be effective in positively modifying the cognitions of students with HA and preventing future depression.

aDepartment of Preventive Medicine, Graduate School of Biomedical Sciences

bCenter for Health and Community Medicine

cGraduate School of Education

dStudent Accessibility Office, Nagasaki University, Nagasaki, Japan.

Correspondence: Masaki Hayashida, Center for Health and Community Medicine, Nagasaki University, 1–14 Bunkyo-machi, Nagasaki, 852–8521, Japan (e-mail:

Abbreviations: BCSS = Brief Core Schema Scales, BDI-II = Beck Depression Inventory II, CBT = cognitive behavioral therapy, G-CBT = group cognitive behavioral therapy, GHQ-28 = 28-item General Health Questionnaire, HA = harm avoidance, M.I.N.I. = Mini International Neuropsychiatric Interview, MAS = Manifest Anxiety Scale, RCTs = randomized controlled trials, TCI = temperament and character inventory.

Data availability: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

This study was supported by the Center for Health and Community Medicine at Nagasaki University and the Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant Number 25460762) to MH.

The authors have no conflicts of interest to disclose.

The authors of this work have nothing to disclose.

This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Received December 20, 2017

Accepted October 5, 2018

Back to Top | Article Outline

1 Introduction

Psychiatric disorders can lead to prolonged absence or dropout among college students.[1] One such disorder is depression: major depressive disorder and depressive symptoms have clear effects on academic performance.[2] Individuals aged 15 to 21 years, especially university students, are at higher risk of a first episode of mental illness, with around 12% to 18% reporting a diagnosable mental disorder.[3,4] Although it is believed that there is a larger proportion of students with mild depression than with moderate or severe depression, mild depression is still considered a prodrome for major depressive disorder.[5] Furthermore, experiencing depressive symptoms leads not only to psychological distress, but may also lead to learning difficulties, interpersonal relationship problems, various dependency problems, and many other issues (e.g., increased medical expenses).[6,7] Additionally, major depressive disorder and depressive symptoms are risk factors for suicide among university students.[8,9] Thus, depression prevention interventions targeting college students can be considered very important.

Research using the temperament and character inventory (TCI),[10,11] based on Cloninger personality theory, has revealed temperament and character factors relevant to depression and suicide.[12] The TCI comprises 4 temperament dimensions: novelty seeking, harm avoidance, reward dependence, and persistence. It also consists of 3 character dimensions: self-directedness, cooperativeness, and self-transcendence. Cloninger theory posits that personality is comprised of “temperament,” which has a hereditary physiological basis, and “character,” which relates to one's self-concept and matures through insight and learning. Temperament relates to our heritable tendency towards self-insight in response to certain behaviors, which in turn promotes character development. Furthermore, character is transformed according to a series of flows, which in turn can lead to changes in temperament. In sum, Cloninger considered personality to be the result of mutual interaction between temperament and character.[13]

High scores on the temperament dimension of harm avoidance (HA) have been shown to predict major depressive disorder.[12,14] HA relates to traits such as pessimism, worry, fear of uncertainty, shyness towards strangers, and tiring easily.[13] In particular, when compared with healthy individuals, persons with major depressive disorder or bipolar disorder, and who have made suicide attempts, have higher HA.[15–17] In Japan, high HA among college students has been reported as a risk factor for suicide.[18,19] Patients with depression and high HA also tend to have a longer disease duration and greater suicide risk compared with low HA patients with depression. Moreover, HA levels remain high even after pharmacotherapy.[20–22] Thus, high HA may be an important factor in identifying high-risk individuals for interventions targeting the prevention of depression and suicide.

According to guidelines on the treatment of depression, evidence-based psychotherapies, especially cognitive behavioral therapy (CBT), are treatment alternatives to antidepressants for mild depression.[23–26] According to a meta-analysis of randomized controlled trials (RCTs) for depression prevention, CBT resulted in a 14% reduction in depression prevalence (based on 19 studies).[27] In some RCTs, group CBT (G-CBT) has also been studied with university students.[28,29] Seligman et al[28] found that after G-CBT, students reported reduced mild depressive symptoms after 3 months, while Vázquez et al[29] found that both relaxation and G-CBT groups showed reductions in depressive and anxiety symptoms after 6 months. However, the G-CBT interventions in both of these RCTs targeted students with subthreshold depression, who represent a high-risk group due to the fact that they already have symptoms of depression. From the perspective of depression prevention, targeting university students without depressive symptoms may be more appropriate. Furthermore, no studies have yet investigated the effects of G-CBT at 1 year post-intervention in terms of changes in dysfunctional cognitions associated with depressive symptoms, despite such a study being necessary for the assessment of depression prevention.

In this context, we examined the effects of a G-CBT intervention on depression, at 1 year post-intervention, in individuals with high HA but no depressive symptoms at baseline. More importantly, we aimed to clarify how dysfunctional cognitions associated with depressive symptoms improved over 1 year as a result of G-CBT, which has not been studied in this population previously. It was our hope that these findings would provide baseline data for the development of a preventive depression intervention targeting university students. We hypothesized that G-CBT targeting high HA students would result in decreased or maintained depression inventory scores, and reduced dysfunctional cognitions.

Back to Top | Article Outline

2 Participants

We focused on students beginning their first year at university between 2012 and 2015. This study used the Japanese version of the revised TCI 140 (TCI-R140J; Kijima, personal communication) to identify students with high HA during the standard student health checks at the beginning of the school year, as HA has been reported to be a risk factor for onset of depression.[12] If HA score increases, the risk of developing depression may also increase.[14] An absolute “high HA cutoff” score has not yet been determined, but previous studies in Japan have reported that HA scores of university students with mood disorders[18] and those who completed suicide[19] were about 1 standard deviation (SD) higher than healthy control subjects. Based on these data, the present study targeted university students with HA scores that were equal to or greater than the overall mean HA score + 1 SD, as these students could be considered to be at higher risk for depression. The mean HA score of students entering in 2012 was 65.36 ± 12.20 (mean ± SD), and therefore “high HA students” were defined as those with an HA score of ≥77.

Next, all students with high HA were invited to participate in the G-CBT intervention during the mental health screening portion of the health check. Students who wanted to participate then went to a G-CBT briefing session, where study procedures were fully explained and written informed consent regarding study participation was obtained. In total, 86 students with high HA provided informed consent (Fig. 1). Participants were then assessed for a major depressive episode or some other affective disorder using the Mini International Neuropsychiatric Interview (M.I.N.I.).[30] Study exclusion criteria were as follows: a history of a major depressive episode or some other affective disorder, aged 23 years or older at baseline, being absent from half or more of all G-CBT sessions, dropping out during the follow-up period, and receiving any psychiatric treatment during the follow-up period.

Figure 1

Figure 1

Back to Top | Article Outline

3 Materials and methods

3.1 Intervention program

The G-CBT intervention program consisted of a 6-session program that was developed based on the Japanese treatment manual for therapists published by the Japanese Association for Cognitive Therapy ( The first session entailed education on the CBT theory of the relationship between negative automatic thoughts and psychological symptoms, as well as progressive muscle relaxation training. In the second to fourth sessions, participants were trained in cognitive restructuring using a 7-column technique aimed at changing their relationship to negative automatic thoughts. In the fifth session, participants were taught attribution theory, with a focus on how causal attribution leads to depressive symptoms and helplessness, and were then trained in causal attribution therapy. In the final session, participants engaged in assertiveness training to improve their social skills.

The G-CBT program was conducted once a week over a 6-week period. Groups comprised 4 to 8 participants. Sessions generally lasted 60 minutes, except for the first and final sessions, which were each 90 minutes. Each G-CBT session was run by 2 trained university counselors.

Back to Top | Article Outline

3.2 Outcome measures

Outcome measures were administered at baseline (T0), 6-month follow-up (T1), and 1-year follow-up (T2). The primary and secondary outcomes are described below.

Back to Top | Article Outline

3.3 Primary outcome

3.3.1 Beck Depression Inventory II

The Beck Depression Inventory II (BDI-II) is a self-administered questionnaire comprising 21 items that assess depression severity.[31] Each item is rated on a 4-point scale, and the range of total scores is 0 to 63. Cronbach α for the Japanese version has been reported to be 0.87.[32]

Back to Top | Article Outline

3.4 Secondary outcomes

3.4.1 Manifest Anxiety Scale

The Manifest Anxiety Scale (MAS) is a self-administered questionnaire to assess anxiety symptoms; the original version comprises 50 items derived from the Minnesota Multiphasic Personality Inventory.[33,34] The Japanese version, in contrast, comprises 65 items, including the original 50 items and 15 items from the L scale, a validity scale of the Minnesota Multiphasic Personality Inventory.[35] For the Japanese version the Cronbach α was 0.92.

Back to Top | Article Outline

3.4.2 Twenty-eight item General Health Questionnaire

The 28-item General Health Questionnaire (GHQ-28) is set of self-administered questionnaires that assess current physical and psychosocial problems.[36,37] The GHQ-28 comprises 4 subscales: somatic symptoms, anxiety and insomnia, social dysfunction, and depressive symptoms. The Cronbach α for the Japanese version of the GHQ-28 total score in the present study was 0.77.

Back to Top | Article Outline

3.4.3 Brief Core Schema Scales

The Brief Core Schema Scales (BCSS) is a set of self-administered questionnaires measuring positive and negative schemas about the self and others.[38] The BCSS contains 24 items in total, and comprises 4 subscales: positive self-schemas, negative self-schemas, positive other-schemas, and negative other-schemas. The scoring system for the Japanese version is the same as the original. The Cronbach α for the Japanese version was 0.92.[39] The BCSS was used to check for cognitive changes due to the G-CBT intervention. Participants completed the BCSS at baseline and at T1 and T2 follow-up points, but completion at T1 and T2 was voluntary.

Back to Top | Article Outline

3.5 Statistical analyses

All analyses were performed with SPSS 20.0 (SPSS, Inc., Chicago, IL). To determine the effect of the intervention, we conducted a repeated measures analysis of variance (ANOVA) with intervention time (T0, T1, and T2) for all outcome variables. Multiple comparisons between treatment periods were conducted using the Sidak post hoc test. We also calculated effect sizes of

and Cohen d. The effect sizes of

indicate simple ANOVA main effect, where values around 0.01, 0.06, and 0.14 are considered small, medium, and large, respectively.[40] The Cohen d effect size values of 0.20, 0.50, and 0.80 are generally considered small, medium, and large, respectively.[41] Furthermore, we conducted a Pearson correlation analysis with the T0 and T2 scores of the Beck Depression Inventory (BDI), Manifest Anxiety Scale (MAS), 28-item General Health Questionnaire (GHQ-28), and Brief Core Schema Scales (BCSS) variables to examine their effects on BDI-II. Finally, we conducted a propensity score matching analysis to exclude selection bias for students with high HA that participated in the G-CBT intervention and those who did not receive treatment. The covariates were age, sex, 4 dimensions of temperament, and 3 dimensions of character from the TCI-R140J. In the present study, statistical significance was set at P < .05.

Back to Top | Article Outline

3.5.1 Ethics

This study was carried out in accordance with the Declaration of Helsinki. Additionally, the protocol complied with the Ethical Guidelines for Epidemiological Research and Guidelines for Clinical Research developed by the Ministry of Health, Labor, and Welfare by acquiring informed consent from all participants and protecting participants’ personal information. This study was approved by the ethics committee of the Nagasaki University Graduate School of Biomedical Sciences (approval number: 10033193).

Back to Top | Article Outline

4 Results

First, a total of 17 participants were excluded after baseline screening: 6 participants (6.9%) were aged over 22 years, and 11 participants (12.8%) provided incomplete baseline questionnaires. Second, a total of 27 participants were excluded after the G-CBT intervention: 8 participants (9.3%) were absent from more than half of the G-CBT sessions, and 19 participants withdrew (22.0%). Consequently, we analyzed the data of 42 participants (21 men, 21 women; mean age 18.97 ± 0.81 years) with completed questionnaires at T2 (Fig. 1). There were no statistically significant differences in sex ratio or ages of participants (x 2 (3) = 2.905, P = .407).

Table 1 lists demographic data for study participants at T0. There were significant differences between men and women participants in novelty seeking (P = .002), self-directedness (P = .003), and self-transcendence (P = .015). BDI, MAS, GHQ-28, and BCSS scores did not differ significantly between men and women participants.

Table 1

Table 1

The results for the primary outcome are shown in Table 2. BDI-II scores showed a simple main effect (P = .015). Multiple comparison analysis showed significantly reduced scores at T1 compared with T0 (P = .025), while there was no difference between T0 and T2 (P = .086). Furthermore, BDI-II scores remained stable from T1 to T2 (P = .949).

Table 2

Table 2

Manifest anxiety showed a marginally significant influence (P = .070), but multiple comparison analysis showed no significant differences for T0 to T1 (P = .128), T0 to T2 (P = .150), and T1 to T2 (P = .985) (Table 2).

The effect of G-CBT on the GHQ-28 is shown in Table 2. First, for GHQ-28 total score a simple main effect was observed (P = .010). Multiple comparison analysis showed significantly reduced GHQ-28 scores at T1 compared with T0 (P = .002), while there was no significant difference between T0 and T2 (P = .231). However, GHQ-28 total score increased significantly from T1 to T2 (P < .001). Second, the somatic symptoms subscale showed a marginally significant simple main effect (P = .063), but multiple comparison analysis was non-significant for all time periods. Third, the anxiety and insomnia subscale showed a simple main effect (P = .010). Multiple comparison analysis showed significantly reduced scores at T1 compared with T0 (P = .005), but no significant differences at T0 to T2 (P = .739) and T1 to T2 (P = .061). Fourth, the social dysfunction subscale showed no simple main effect (P = .120). Finally, the depressive symptoms subscale also showed no simple main effects (P = .248).

The effect of G-CBT on core schemas is shown in Table 2. First, a simple main effect for positive self-image was observed (P < .001). Multiple comparison analysis showed significantly higher scores at T1 (P = .001) and T2 (P = .012) compared with T0, with no difference between T1 and T2 (P = .971). Second, a simple main effect was shown for negative self-image (P = .002). Multiple comparison analysis showed significantly lower scores at T1 compared with T0 (P = .009), but no differences from T0 to T2 (P = .081), and T1 to T2 (P = .378). Third, a simple main effect was shown for positive other image (P = .019), but multiple comparison analysis revealed no significant differences for T0 to T1 (P = .065), T0 to T2 (P = .072), and T1 to T2 (P = .988). Finally, no simple main effect was shown for negative other image (P = .456).

We verified the treatment effect of G-CBT on BDI-II. We conducted correlation analyses with the T0 and T2 scores of the BDI-II, MAS, GHQ-28, and BCSS (Table 3). BDI-II scores were positively correlated with the MAS (r = 0.56, P < .001), GHQ-28 total score (r = 0.76, P < .001), and all subscale scores. Additionally, BDI-II scores were positively correlated with the negative self-image (r = 0.47, P = .002) subscale of the BCSS. Furthermore, BDI-II scores were negatively correlated with the positive self (r = –0.32, P = .042) and other image (r = –0.42, P = .005) subscale of the BCSS.

Table 3

Table 3

The 42 participants who participated in the G-CBT intervention were compared with 1065 participants who had high HA but did not participate in G-CBT. Table 4 presents the matched data of the propensity score analysis results. All scores were non-significant.

Table 4

Table 4

Back to Top | Article Outline

5 Discussion

Our results indicated that G-CBT for students with high HA, but without depressive symptoms, was effective in reducing BDI-II scores at 1 year post-intervention. BDI-II scores declined from T0 to T1, and maintained this improvement from T1 to T2. In terms of secondary outcomes, the MAS score was unchanged from T0 to T2. The GHQ-28 total score declined from T0 to T1, but this improvement was not maintained at T2. The GHQ-28 subscales did not change from T0 to T2, except for the anxiety and insomnia subscales. Scores for these subscales declined from T0 to T1, and maintained this improvement from T1 to T2. This may be explained as noted below.

First, the G-CBT intervention likely made it easier for participants to control their automatic thoughts, which coincides with Beck cognitive model of depression.[42] Negative automatic thoughts can give rise to negative emotions and maladaptive behaviors, while cognitive reconstructing—a key aspect of G-CBT—can be used to reduce these negative automatic thoughts. Previous findings have indicated that reducing negative automatic thoughts results in an improvement in depressive symptoms.[42,43]

Second, our results may be due to the improvement in BCSS scores. Specifically, we observed increases in positive self-schema scores at T2 and a maintained reduction in negative self-schema scores at T1 to T2. According to Beck cognitive model of depression,[42] maladaptive schemas are considered deeper-level constructs compared with automatic thoughts, whereby changes to schemas influence automatic thoughts, which in turn would lead to improvements in psychological symptoms (e.g., depression and anxiety). The fact that the G-CBT intervention influenced schemas even after 1 year may explain the sustained reduction in depressive symptoms.

Third, the results of the correlation analyses between the T0 and T2 treatment changes in the BDI-II, MAS, GHQ-28, and BCSS revealed that positive and negative self-schemas were associated with BDI-II scores. This suggests that self-schemas work to maintain depressive symptoms among university students. Furthermore, because the G-CBT intervention led to improvements in positive self-schemas, negative self-schemas, and depression scores, it is likely that it promoted depression prevention.

It is notable that none of our participants developed depressive disorders and were rather young, meaning that they experienced no degradation in cognitive functioning. This suggests that participants had relatively good cognitive flexibility, which may in turn suggest the possibility of cognitive plasticity. Thus, our results suggest that implementing G-CBT at an early stage may contribute to depression prevention in university students.

Finally, we must mention some of the limitations of this study. First, this was a single-arm study without a control group, and it did not use randomization. Therefore, it was not possible to adequately compare effects of improvements that may have been due to G-CBT. Second, we did not consider the effects of sex or age. Third, the intervention was conducted at a single facility at a regional university in Japan. Fourth, the intervention comprised a total of 6 sessions, which was fewer than the 8 sessions employed by Seligman et al[28] and Vázquez et al.[29] Finally, this study had a high attrition rate (39.1%). For that reason, we conducted propensity score matching analysis to examine differences in characteristics between the 42 participants who completed the study and 42 other students with high HA, and found no significant differences in sex, age, and TCI scores between the 2 groups. Additionally, submission of the BCSS was voluntary, and this may have increased attrition.

In future studies it will be important to recruit a control group for comparison purposes. Furthermore, because a university education typically spans 4 years, individuals will experience a variety of stressful experiences during that time. Therefore, it is necessary to investigate longer-term changes in the future.

Back to Top | Article Outline

6 Conclusion

To summarize, this G-CBT intervention led to decreases in depression scores compared with baseline, and these improvements were maintained at 1 year post-intervention. The G-CBT program also led to changes in schemas associated with depressive symptoms, which may have contributed to depression prevention.

Back to Top | Article Outline


The authors would like to express their gratitude to the participants of the present study.

Back to Top | Article Outline

Author contributions

Conceptualization: Tatsuo Saigo, Masaki Hayashida, Sayaka Ogawa, Peter Bernick, Atushi Takeoka, Susumu Shirabe.

Data curation: Tatsuo Saigo, Masaki Hayashida, Jun Tayama, Sayaka Ogawa, Peter Bernick.

Formal analysis: Tatsuo Saigo.

Funding acquisition: Masaki Hayashida.

Investigation: Tatsuo Saigo, Masaki Hayashida, Jun Tayama, Sayaka Ogawa, Peter Bernick.

Methodology: Tatsuo Saigo, Masaki Hayashida, Jun Tayama, Peter Bernick, Atushi Takeoka, Susumu Shirabe.

Project administration: Tatsuo Saigo, Masaki Hayashida, Jun Tayama, Sayaka Ogawa, Peter Bernick, Atushi Takeoka, Susumu Shirabe.

Resources: Tatsuo Saigo, Masaki Hayashida, Jun Tayama, Susumu Shirabe.

Supervision: Masaki Hayashida, Jun Tayama.

Visualization: Tatsuo Saigo.

Writing – original draft: Tatsuo Saigo.

Writing – review & editing: Masaki Hayashida, Jun Tayama, Peter Bernick, Atushi Takeoka, Susumu Shirabe.

Back to Top | Article Outline


[1]. Richardson M, Abraham C, Bond R. Psychological correlates of university students’ academic performance: a systematic review and meta-analysis. Psychol Bull 2012;138:353–87.
[2]. Hysenbegasi A, Hass SL, Rowland CR. The impact of depression on the academic productivity of university students. J Ment Health Policy Econ 2005;8:145–51.
[3]. Eisenberg D, Gollust SE, Golberstein SE, et al. Prevalence and correlates of depression, anxiety and suicidality among university students. Am J Orthopsychiatry 2007;77:534–42.
[4]. Mowbray CT, Megivern D, Mandiberg JM, et al. Campus mental health services: recommendations for change. Am J Orthopsychiatry 2006;76:226–37.
[5]. Cuijpers P, Smit F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatry Scand 2004;109:325–31.
[6]. Andrews B, Wilding JM. The relation of depression and anxiety to life-stress and achievement in students. Br J Psychol 2004;95:509–21.
[7]. Mackenzie S, Wiegel JR, Mundt M, et al. Depression and suicide ideation among students accessing campus health care. Am J Orthopsychiatry 2011;81:101–7.
[8]. Kisch J, Leino EV, Silverman MM. Aspects of suicidal behavior, depression, and treatment in college students: results from the Spring 2000 National College Health Assessment Survey. Suicide Life Threat Behav 2005;35:3–13.
[9]. Gollust SE, Eisenberg D, Golberstein E. Prevalence and correlates of self-injury among university students. J Am Coll Health 2008;56:491–8.
[10]. Cloninger CR, Przybeck TR, Svrakic DM, et al. The Temperament and Character Inventory (TCI): A Guide to its Development and Use. St. Louis, MO: Center for Psychobiology and Personality, Washington University; 1994.
[11]. Cloninger CR, Svrakic DM, Przybeck TR. A psychobiological model of temperament and character. Arch Gen Psychiatry 1993;50:975–90.
[12]. Cloninger CR, Svrakic DM, Przybeck TR. Can personality assessment predict future depression? A twelve-month follow-up of 631 subjects. J Affect Disord 2006;92:35–44.
[13]. Kijima N, Tanaka E, Suzuki N, et al. Reliability and validity of the Japanese version of the temperament and character inventory. Psychol Rep 2000;86:1050–8.
[14]. Kampman O, Poutanen O. Can onset and recovery in depression be predicted by temperament? A systematic review and meta-analysis. J Affect Disord 2011;135:20–7.
[15]. Zaninotto L, Souery D, Calati R, et al. Temperament and character profiles in bipolar I, bipolar II and major depressive disorder: impact over illness course, comorbidity pattern and psychopathological features of depression. J Affect Disord 2015;184:51–9.
[16]. Zaninotto L, Solmi M, Toffanin T, et al. A meta-analysis of temperament and character dimensions in patients with mood disorders: comparison to healthy controls and unaffected siblings. J Affect Disord 2016;194:84–97.
[17]. Calati R, Giegling I, Rujescu D, et al. Temperament and character of suicide attempters. J Psychiatr Res 2008;42:938–45.
[18]. Mitsui N, Asakura S, Inoue T, et al. Temperament and character profiles of Japanese university student suicide completers. Compr Psychiatry 2013;54:556–61.
[19]. Mitsui N, Asakura S, Shimizu Y, et al. Temperament and character profiles of Japanese university students with depressive episodes and ideas of suicide or self-harm: a PHQ-9 screening study. Compr Psychiatry 2013;54:1215–21.
[20]. Nishioka G, Yashima H, Kiuchi Y, et al. Prediction and structural equation model of sertraline treatment response in Japanese patients with major depressive disorder. Hum Psychopharmacol 2013;28:576–85.
[21]. Asano T, Baba H, Kawano R, et al. Temperament and character as predictors of recurrence in remitted patients with major depression: a 4-year prospective follow-up study. Psychiatry Res 2015;225:322–5.
[22]. Teraishi T, Hori H, Sasayama D, et al. Personality in remitted major depressive disorder with single and recurrent episodes assessed with the Temperament and Character Inventory. Psychiatry Clin Neurosci 2015;69:3–11.
[23]. Japanese Society of Mood Disorders. Japanese Society of Mood Disorders, Treatment Guideline II Depression (DSM-5)/Major Depressive Disorder, 2016. 2016. Available at: Accessed December 18, 2018 (in Japanese).
[24]. Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines Team for DepressionAustralian and New Zealand clinical practice guidelines for the treatment of depression. Aust N Z J Psychiatry 2004;38:389–407.
[25]. National Collaborating Centre for Mental Health (UK)Depression: The Treatment and Management of Depression in Adults (Updated Edition). Leicester, UK: British Psychological Society; 2010.
[26]. Bauer M, Pfennig A, Severus E, et al. World Federation of Societies of Biological Psychiatry. Task Force on Unipolar Depressive Disorders. World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for biological treatment of unipolar depressive disorders, part 1: update 2013 on the acute and continuation treatment of unipolar depressive disorders. World J Biol Psychiatry 2013;14:334–85.
[27]. van Zoonen K, Buntrock C, Ebert DD, et al. Preventing the onset of major depressive disorder: a meta-analytic review of psychological interventions. Int J Epidemiol 2015;43:318–29.
[28]. Seligman M, Schulman P, Tryon MA. Group prevention of depression and anxiety symptoms. Behav Res Ther 2007;45:1111–6.
[29]. Vázquez FL, Torres A, Blanco V, et al. Comparison of relaxation training with a cognitive-behavioural intervention for indicated prevention of depression in university students: a randomized controlled trial. J Psychiatr Res 2012;46:1456–63.
[30]. Otsubo T, Tanaka K, Koda R, et al. Reliability and validity of Japanese version of the Mini-International Neuropsychiatric Interview. Psychiatry Clin Neurosci 2005;59:517–26.
[31]. Beck AT, Steer RA, Ball R, et al. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996;67:588–97.
[32]. Kojima M, Furukawa TA, Takahashi H, et al. Cross-cultural validation of the Beck Depression Inventory-II in Japan. Psychiatry Res 2002;110:291–9.
[33]. Taylor JA. A personality scale of manifest anxiety. J Abnorm Psychol 1953;48:285–90.
[34]. Taylor JA. Drive theory and manifest anxiety. Psychol Bull 1956;53:303–20.
[35]. Taylor JA, Abe M, Takaishi N. The Japanese version of the Minnesota Multiphasic Personality Inventory (MMPI) and the Manifest Anxiety Scale (MAS) Manual. 1968;Kyoto: Sankyo-bo, (In Japanese).
[36]. Iwata N, Saito K. The factor structure of the 28-item General Health Questionnaire when used in Japanese early adolescents and adult employees: age- and cross-cultural comparisons. Eur Arch Psychiatry Clin Neurosci 1992;242:172–8.
[37]. Goldberg DP, Hillier VF. A scaled version of the General Health Questionnaire. Psychol Med 1979;9:139–45.
[38]. Fowler D, Freeman D, Smith B, et al. The Brief Core Schema Scales (BCSS): psychometric properties and associations with paranoia and grandiosity in non-clinical and psychosis samples. Psychol Med 2006;36:749–59.
[39]. Yamauchi T, Sudo A, Tanno Y. Reliability and validity of the Japanese version of the Brief Core Schema Scales. Jpn J Psychol 2008;79:498–505.
[40]. Rovai AP, Baker JD, Ponton MK. Social Science Research Design And Statistics: A Practitioner's Guide to Research Methods and IBM SPSS. Chesapeake, VA: Watertree Press LLC; 2014.
[41]. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed.Hillsdale, NJ: L. Erlbaum Associates; 1988.
[42]. Beck AT, Rush AJ, Shaw BF, et al. Cognitive Therapy of Depression: A Treatment Manual. New York, NY: Guilford Press; 1979.
[43]. Beck R, Perkins TS. Cognitive content-specificity for anxiety and depression: a meta-analysis. Cogn Ther Res 2001;25:651–63.

depression; group cognitive behavioral therapy; harm avoidance; high-risk approach; temperament and character inventory

Copyright © 2018 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.