We used the SPIRIT reporting guidelines [28].
Design
The design is a single-blind, randomized controlled trial (RCT) comparing two trauma-focused cognitive-behavioural treatment (with and without positive memories’ module) for female survivors of IPV and a wait-list condition (superiority trial). A multigroup design (3 groups) will be used with repeated measures with five levels (pretreatment, posttreatment and follow-ups of 3, 6 and 12 months) for the experimental groups and two levels (pre and post) for the control group. Consequently, the three treatment groups are: (a) waiting list control group (WL); (b) cognitive-behavioural treatment (CBT); and (c) cognitive-behavioural treatment with a focus on positive memories (CBT-M +).
Assessors of treatment outcomes will be blinded to the experimental conditions. To avoid unplanned imbalances in the size of the three groups and a loss of statistical power, a block randomization procedure will be used.
Setting
Participants will be recruited in community settings, specifically through several women's and IPV support organizations, institutions, and services around Madrid (Spain). Women who attend these centres for attention because of IPV will be referred by professionals and assessed to establish the eligibility criteria.
Eligibility criteria
We adopt the WHO definition of IPV as “any behaviour within an intimate relationship that causes physical, psychological or sexual harm to those in the relationship” [29]. Participants are eligible if they (1) are women; (2) are over 18 years old; (3) are proficient in the use of the Spanish language; (4) are or have been victims of IPV; (5) have experienced IPV at least one month before the start of the trial; (6) present symptoms of PTSD according to DSM-5 criteria as determined by the Global Assessment of Posttraumatic Stress Scale 5 (EGEP-5; in Spanish: Evaluación Global de Estrés Postraumático 5; Crespo et al. [30]); and (7) have voluntarily agreed to take part in the study.
Participants will be excluded if they (1) show suicide risk as assessed by item 9 (scores> 1) of the Spanish version of the Beck Depression Inventory II - BDI-II [31]; (2) present conditions that could prevent compliance with the treatment (namely, abuse of alcohol or drugs, cognitive impairment or illiteracy in Spanish); (3) have been diagnosed or suffer from psychotic disorders; and (4) have received trauma-focused treatment within the past 6 months.
Sample size
To determine the target sample size, we considered the effect sizes found in the literature (specifically, the overall effect size - omnibus - calculated in the meta-analysis by Arroyo et al. [5]), which ranges from 0.71-1.33. Considering an alpha of 0.05 and a statistical power level of 0.80, a minimum group size of 31 was established. Taking a conservative estimate of the expected dropout rate of 30%, a sample size of 45 women per group will be the target, indicating a total of 135 participants across the 3 experimental conditions (CBT, CBT-M + and WL control group).
Interventions
The CBT-M + to be developed and evaluated in this project involves the incorporation of the focus on positive memories to the CBT focused on trauma for women victims of IPV previously developed by Labrador et al. [13, 14, 32]. In the experimental groups of this study, both treatments will be applied, that is, the original CBT and the CBT-M +. The two treatments will be applied in group format (6-8 women in each group) in 8 weekly sessions of 80-100 minutes in duration. Both treatments are detailed below, and the session-by-session application is summarized in Table 1.
Table 1
Session-by-session summary of the CBT and CBT-M+.
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)
CBT
As mentioned, the CBT to be used is based on the intervention protocol for women who have survived IPV with posttraumatic symptoms developed by the Labrador group and whose efficacy for the reduction of posttraumatic and associated symptoms has already been evaluated across various studies that have examined the following aspects: the effect of the order of the components (cognitive re-evaluation and exposure) [33]; the effect of the inclusion or exclusion of the exposure technique [34]; individual application vs. group [35, 36]; and the application to immigrant women [37].
Of the different versions that have been tested of the reference CBT, in this study, the version that includes the exposure technique will be used (compared to the version that replaces it with training in communication skills) and that uses the order Cognitive Re-evaluation + Exposure (versus the Exposure + Cognitive Re-evaluation option). In addition, given the objective of the study, an extended application of the exposure technique was included (to integrate it with the exposure to the memories of other types of events) following the guidelines of Foa et al. [38].
Consequently, the CBT applied here includes the following modules: (a) psychoeducation, providing the participants information about IPV and its emotional consequences; (b) exercises to control arousal by diaphragmatic breathing; (c) activity scheduling to improve mood; (d) specific techniques to improve self-esteem and re-evaluating biased cognitions; (e) exposure techniques in imagination focused on the memory of IPV situations (including hot-spot exposure); and (f) relapse prevention. In addition, to ensure equivalence between the two programs, the evocation of neutral images in the same spaces and times dedicated in the CBT-M + to the evocation of positive memories is introduced in the general scheme of the CBT.
The first session includes presentations and the establishment of the treatment rules in addition to some information about the violence and about the intervention format. The other sessions start with a brief “check in” and review of the homework, followed by some new elements (cognitive-behavioural skills) to discuss and practice. The specific content for each session is as follows: (1) psychoeducation (session 1); (2) exercises of diaphragmatic breathing (sessions 1–7); (3) activity scheduling (session 2); (4) techniques to improve self-esteem and cognitive re-evaluation (sessions 2–3); (5) exposure techniques (sessions 3–7); and (6) neutral memory evocation (sessions 4-7). Finally, session 8 is focused on relapse prevention. At the end of the first seven sessions, women receive written material that outlined the topics from the session, as well as exercises to be done as homework until the next session. Furthermore, evaluation instruments are applied to assess the satisfaction with the session and the state of the participants (OASIS and ODSIS). Additionally, once the session is over, the therapist completes the instruments for assessing her own satisfaction with the session and the adjustment to the treatment manual during it.
CBT-M +
This treatment incorporates a module focused on positive memories into the CBT. In its design, it was taken into account that, since the module would be integrated into the CBT, it would have to be brief, with a development between 1-3 sessions, in order not to prolong the program (something that could go in detriment of adherence). After a detailed review of positive memory intervention techniques [39], the broad-minded affective coping (BMAC) intervention proposed by Tarrier [40] was taken as a reference. This intervention focuses on experiencing positive memories through mental imagery exercises and on experiencing the positive emotions associated with these memories. In its application, participants retrieve an event associated with enjoyment, fun, and happiness. Thereafter, participants are taken through guided imagery of positive memories (engaging all the senses) and re-experiencing of the associated emotions. The original technique consists of one to three sessions, each lasting between 20 and 30 min. A 3-session face-to-face version of the technique was developed and initially tested in a sample of 31 female university students [41].
Although, as pointed by Contractor et al. [25], there is no evidence yet on whether it would be more pertinent to apply positive memories at the beginning and then move on to focus on the trauma, from a clinical point of view, and always taking as a reference the CBT in which the memory module is integrated, it was considered convenient to apply it in combination with the exposure technique, paying particular attention to the fact that positive memories do not become a resource for the escape/avoidance of the exposure itself. To correct this problem, the evocation of positive memories is proposed in an integrated manner with exposure to traumatic memories. Consequently, the application of the module of positive memories within the program is done in the same sessions as the presentation (i.e., 4-7). Moreover, it is proposed that the application in each session of the positive memories after the exposure could lead to a more positive affect after the sessions, in addition to favouring the control of their use as an escape strategy from the exposure. Regarding the content, positive memories not related to trauma are chosen to facilitate therapeutic work and be susceptible to obtaining a greater immediate effect.
For the application of both programs, two manuals have been developed, which in accordance with the guidelines of Carroll and Nuro [42] include the following: general description and reasoning of the treatment; conceptualization of the problem addressed; treatment objectives; specification and definition of the interventions to be included; specification of the content of the sessions; and general treatment format.
The therapist in charge of delivering the intervention is in possession of the certification currently required by legal standards for this task in Spain (i.e., official post-Grad clinical training, Master in General Health Psychology)."
Prior to the application of the treatments in the clinical trial and the drafting of the final version of the treatment manuals, a pilot study was conducted in which both treatment modalities were applied in two groups of women between April and June 2021.
Measures
The instruments include an initial characterization interview, and primary and secondary outcome measures; moreover, treatment implementation variables are also considered. The overview of the assessment is shown in Figure 1.
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Characterization interview
In the pretreatment assessment, an interview is conducted to collect data on sociodemographic characteristics (e.g., age, work situation, level of education), the history and characteristics of the abuse and its consequences (e.g., complaint, restraining order…), social and psychological support, and substance use. In the posttreatment assessment and in the follow-ups, an abbreviated version of the interview is conducted to assess the changes that have occurred in each of these areas since the last evaluation.
Primary outcomes
PTSD severity and diagnosis. The primary outcome is the change in the DSM-5 PTSD symptoms. These symptoms will be assessed with the Global Assessment of Posttraumatic Stress Scale 5 (EGEP-5; in Spanish: Evaluación Global de Estrés Postraumático - 5; Crespo et al. [30]), a self-reported measure that assesses the overall severity of PTSD symptoms and provides scores for the DSM-5 clusters for PTSD as well as PTSD diagnosis. The EGEP-5 shows high internal consistency for all severity symptom scales (ranging from 0.72 to 0.86) and for the total (0.91).
Secondary outcomes
Physical, psychological and sexual intimate partner abuse is measured using the Spanish version of the Conflict Tactics Scales version 2 (CTS2) [43] developed by Loinaz et al. [44]. It consists of 78 items that measure the degree to which the members of a couple are involved in physical or psychological attacks on each other, as well as the use of reasoning and negotiation to resolve conflicts. In the present study, only the part in which it is indicated that the partner has suffered each of these episodes will be used (39 items). The psychometric properties of both the original scale and the Spanish version are satisfactory.
Acceptability of intimate partner violence is measured using the Acceptability of Intimate Partner Violence Against Women short form scale (A-IPVAW-8, Martín-Fernández et al. [45]). The A-IPAW-8 is composed of 8 items in which respondents rate the acceptability of a set of men's behaviours towards their female partners. It has adequate internal consistency and a stable one-factor latent structure.
Victim-blaming attitudes is measured using Victim-blaming Attitudes in Cases of Intimate Partner Violence against Women Scale Short Form (VB-IPVAW, Martín-Fernández et al. [46]). This instrument is composed of five items assessing the tendency to blame female victims of IPV. It has been shown to have satisfactory psychometric properties.
Motivation to leave the abusive relationship is measured using an ad hoc generated questionnaire (MLAR) with 9 yes/no questions about the possibility of leaving the relationship in progressively graver situations (from “in case of questioning about the time you spend with family and friends” to “in case of danger or harm to your children”). Psychometric validation of this questionnaire is ongoing.
Posttraumatic cognitions are measured using the Brief Version of the Posttraumatic Cognitions Inventory (PTCI-9; Wells et al. [47]) with the Spanish version elaborated ad hoc taking the items of the translation by Blanco et al. [48]. The PTCI-9 is composed of 9 items across three subscales: Negative Cognitions About the Self, Negative Cognitions About the World, and Self-Blame. The PTCI-9 total and subscale scores showed strong internal consistencies.
Trauma centrality is measured using the Centrality of Event Scale (CES; Bernsten and Rubin [49]), which measures the extent to which a traumatic memory or event forms a central component of an individual's personal identity and a reference point for the attribution of meaning to other experiences. The reduced 7-item version will be used. Psychometric validation of the Spanish version of this scale is ongoing. The English version has been shown to have satisfactory psychometric properties.
Self-Concept and identity are measured using the Self-Concept and Identity Measure (SCIM; Kaufman et al. [50]). The SCIM is a 27-item, self-report measure developed to assess identity consolidation and clinically relevant identity disturbance. It has three subscales: identity consolidation, identity disturbance, and lack of identity. The English version has been shown to have satisfactory psychometric properties. Psychometric validation of the Spanish version of this tool is ongoing.
Positive and negative affect are measured using the Spanish version of the Positive and Negative Affect Schedule (PANAS; Watson, et al. [51]) developed by Sandín et al. [52]. The PANAS consists of two scales designed to measure PA and NA. Respondents are asked to read 20 words describing a series of feelings and emotions and indicate the extent to which they tend to feel them on a Likert scale. The psychometric properties of both scales of the PANAS are satisfactory.
Depressive symptoms are assessed with the Spanish version of the Beck Depression Inventory-II (BDI-II) [31] developed by Sanz et al. [53]. The BDI-II is the most widely used tool to measure depressive symptoms. It consists of 21 items that assess the global level of depression and the changes over time. The score ranges from 0–63 (higher scores indicate more severe symptoms). The Spanish version has been shown to have satisfactory psychometric properties.
Anxiety symptoms are assessed with the Spanish version of the Beck Anxiety Inventory (BAI) [54] developed by Sanz and Navarro [55]. The BAI is commonly used to measure the presence and severity of anxiety symptoms. It consists of 21 items; the score range is 0–63 (higher scores showing more severe symptoms). The internal consistency of the Spanish version is satisfactory.
Self-esteem level is measured using the Spanish version of Rosenberg's Self-Esteem Scale (RSS) [56] developed by Echeburúa and Corral [57]. It assesses women's levels of self-esteem, which is a person's feelings of self-satisfaction and self-acceptance. The Spanish version has been shown to have satisfactory psychometric properties.
Emotional dysregulation is measured by the Spanish version of the Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer [58]) developed by Hervás and Jódar [59]. This 36-item self-report measure with a 5-point Likert scale is used to assess individuals' typical levels of emotion dysregulation across six domains, and it also includes an overall score. The DERS has been shown to have good psychometric properties [58, 60].
The Difficulties in Emotion Regulation Scale – Positive (DERS-P; Weiss et al. [61]) is a 13-item self-report measure with a 5-point Likert scale that is used to measure clinically relevant difficulties in regulating positive emotions. The DERS-P was modelled after the original DERS [58] and comprises three specific domains as well as an overall score. It has demonstrated good psychometric properties [61]. Psychometric validation of the Spanish version of this scale is ongoing.
General health status is measured using the Spanish version of the 12 Item Short Form Health Survey Version 2 (SF-12 v2; Ware et al. [62]) developed by Alonso [63]. The SF-12 is one of the most widely used instruments to assess health-related quality of life grouped into two major dimensions: physical health and mental health. SF-12v2, in its original version, has shown internal consistency values of 0.88 for the physical health component and 0.82 for the mental health component [64].
Weekly evolution of anxious symptoms is measured using the Spanish version of the Overall Anxiety Severity and Impairment Scale (OASIS; Norman et al. [65]) developed by Osma et al. [66], at the end of each of the 8 sessions. It consists of five items and evaluates the frequency, intensity, and interference of anxious symptomatology in the person's life over the last week. The Spanish version has been shown to have good internal consistency.
Weekly evolution of depressive symptoms will be measured using the Spanish version of the Overall Depression Severity and Impairment Scale (ODSIS; Bentley et al. [67]) developed by Osma et al. [66], at the end of each of the 8 sessions. It consists of five items and evaluates the frequency, intensity, and interference of sadness/depression in the person's life over the last week. The Spanish version has been shown to have good internal consistency.
Treatment implementation
Features of the memories. The Autobiographical Memory Questionnaire (AMQ) [68] is used to determine the features of the memories (about maltreatment and about positive events) that are evoked in the exposure and the positive memories module, respectively. It consists of several questions concerning the processes involved in remembering an event. For this study, two versions were generated: one with 9 questions that collect information about the particular memory that has just been evoked, specifically, the way the person coped with the event and the integration and coherence of this memory in life history and personal identity; and another version with 12 items that adds three questions that characterize the event in general (when and how many times it happened and what it is like). AMQ has proven useful for measuring basic processes used in autobiographical memory construction and its association with posttraumatic adaptation [69]. Psychometric validation of the Spanish version of this tool is ongoing.
Satisfaction with each session is measured using an ad hoc generated questionnaire based on Marín et al. [70] at the end of each of the 8 sessions. It consists of four items that assess the following using a visual analogue 10-point scale: satisfaction with session; perceived usefulness of the session; mood and emotional state; and group cohesion.
Satisfaction with the overall treatment is measured using the Spanish version of the Client Satisfaction Questionnaire (CSQ-8; Larsen et al. [71] developed by Echeburúa and Corral [57]. This questionnaire assesses women’s satisfaction with the program and the treatment. The CSQ-8 has been shown to have high internal consistency and concurrent validity in mental health outpatient settings [72].
Participants' satisfaction with each program component and aspect is measured using an ad hoc generated questionnaire completed anonymously at the end of the last session. It consists of several visual analogue 10-point scales that assess satisfaction with each component of the program (i.e., breathing, self-esteem, pleasure activities, exposure to maltreatment, exposure to hot spots, exposure to positive/neutral memory, and homework). and with several issues related to its implementation (i.e., therapist, co-therapist, assessor, initial information session, reminder of the sessions, collective coffee, group environment, facilities, information provided by the centre and materials).
Additionally, in the final session, the participants complete a qualitative and anonymous evaluation of the different components and aspects of the program.
Therapists' satisfaction and adjustment to the treatment manual is measured using an ad hoc generated questionnaire complemented by the therapist at the end of each of the 8 sessions. It consists of six items with Likert scales ranging from 0-10 that assess satisfaction with the session, contents, participation and assimilation of contents, and adjustment to the protocol and the timing. Moreover, information about the real duration of the session and about possible incidences is recorded.
To provide a guide through which the therapist can verify the adherence of the session to the treatment protocol, a checklist is available for both intervention modalities. In this list, the 8 treatment sessions are outlined, with the different components of each session, the essential contents of each of them and the materials, questionnaires and records necessary for the correct application of the session.
Procedure
Once the participants are recruited through the collaborating centres, the members of the research team contact them, and an appointment is made to carry out the initial screening. Those participants who meet the inclusion criteria are informed of the project and the possibility of participating in it. If they agree to participate, informed consent will be signed, and an appointment will be made with them to carry out the initial evaluation. Those women who do not meet the inclusion criteria or who show their desire not to participate in the project are referred to the centres so that they can receive assistance.
The evaluation is carried out by trained psychologists who are blinded to the treatment modality. Once the initial evaluation is completed, the participants are assigned to a therapeutic group based on their time availability. The determination of the treatment modality of each group is determined randomly using the random number generator of the University of Granada (https://www.ugr.es/~jsalinas/Aleatorios.htm).
The application of the treatment is preceded by an informative-motivational session in which the participants are informed of the characteristics of the program and the presentation of the research team is made.
The treatment is developed in 8 weekly sessions, and thus, it lasts approximately 2 months. In the case of the participants assigned to the waiting list control group, a new evaluation is performed (with the same instruments as in the pretreatment evaluation) after 2 months (i.e., period equivalent to the duration of the treatments), and they are offered the possibility of participating in the treatment in case of acceptance of such participation. The therapists are also psychologists with clinical training who do not participate in the initial evaluation of the participants or in the subsequent evaluations and who are trained in the application of the different treatment modalities. In each treatment group, there are a therapist and a co-therapist in charge of assisting the therapist in carrying out the tasks during the session and in the delivery and collection of materials. The co-therapists are students with training in clinical psychology.
Once the treatment is finished, a new evaluation is carried out to be performed in the first 14 days after the last treatment session following the same guidelines as in the pretreatment evaluation and including in addition to the same battery applied in the pretreatment (except for the question identification), the questionnaire of satisfaction with the treatment. Likewise, follow-ups at 3, 6 and 12 months are performed.
Both the evaluators and the therapists and co-therapists are women to facilitate the therapeutic alliance.
All evaluation and treatment sessions are carried out in the reference centres of each participant, except in those cases in which they express their desire to do so in the Faculty of Psychology of the Complutense University of Madrid (something that is always offered as a possibility).
For the supervision of the therapy sessions to increase adherence to the protocols, one session is audio recorded in each intervention group (which is specified in the informed consent form). The determination of the session to be recorded in each intervention group is randomly assigned by a third person through the application of the online randomization program (https://www.augeweb.com/azar/).
Supervision is performed by an experienced senior clinical psychologist, who has been involved in the development of the currently tested program and the prior pilot study, as well as in the ongoing decision-making process regarding refinements to the protocol. He receives weekly information provided by the psychologist delivering the program, give guidance regarding adherence to the protocol and provide troubleshooting as needed. Communication between the supervisor and the delivering psychologist is done mostly through e-mails and, to a lesser extent, online supervision sessions, but more immediate exchanges through the telephone also occur when needed.
Treatment fidelity is assessed through the revision of the randomly recorded sessions (one for each group), with a focus on proper adherence to the times allotted for each treatment component in the session. When program delivery is been found to stray from the delivery-as-intended, proper feedback is provided, and follow-up contacts regarding this guidance occur. The development of a quantitative measure for treatment fidelity is currently underway.
Statistical analysis
For the graphic description of the flow of the participants throughout the protocol, the CONSORT diagram is used. The intention-to-treat (ITT) method will also be used. In the treatment of the missing values, it will be checked if the loss of data occurs completely at random using Little's MCAR test, and then, the estimation of the missing values will follow the maximum likelihood method through the EM algorithm [73].
As a step prior to determining the effectiveness of the intervention programs, the homogeneity of the groups will be analysed, taking into account all the variables evaluated and using c 2 tests and ANOVAs according to the type of variables.
To determine if the treatment is better than the control group, one-factor (type of intervention) ANOVAs and ANCOVAs will be performed for each of the scores in the posttreatment (or second pre measure for the control group on the waiting list) of the continuous treatment outcome variables, using in the ANCOVAs the value of the pretreatment evaluation as covariance. In the case of ANOVAs, the DSM or Games-Howel tests will be used for post hoc analyses depending on whether the assumption of equality of variances is met. In the case of significant differences in the ANCOVAs, the groups will be analysed in pairs using new ANCOVAs. For those variables that do not adjust to normality, the corresponding nonparametric tests will be applied (i.e., Kruskal–Wallis and Mann–Whitney U for pairwise comparisons). For the qualitative variables, c 2tests will be used.
To determine the changes over time, the two basic moments will be analysed first (i.e., the changes from pretreatment to posttreatment or the second premeasure for the control group). Repeated-measures ANOVAs will be performed in a mixed design with an intergroup factor with three levels (the two treatments plus the control group) and an intrasubject factor (pre and post measures). In those variables that do not adjust to normality, the corresponding nonparametric tests will be applied (i.e., Friedman). Additionally, in this case, for the qualitative variables, c 2 tests will be used.
In a second moment of the analysis of the changes over time, the changes between the pre, post and follow-ups will be analysed only for the two experimental groups. For this, an ANOVA will be applied for each of the outcome variables, including an intergroup factor (with two levels: CBT and CBT-M +) and an intrasubject or repeated measures factor, with five levels (i.e., pre, post, 3 months., 6 months and 12 months). In the application of these ANOVAs, the assumption of sphericity will be verified using the Mauchly test, and where appropriate, the Huynh-Feldt correction will be used. Likewise, ANCOVAs of one factor (type of treatment) will be performed for each outcome variable, using the value of the pretreatment evaluation as covariance.
To determine the differences between the two treatments, the intergroup differences and the interactions between time and type of treatment obtained in the different ANOVAs and ANCOVAs mentioned in the previous point will be addressed.
In all analyses, the Holm–Bonferroni correction will be used to adjust the level of significance for multiple comparisons.
To establish the clinical significance of the changes obtained, c 2 tests will be used considering the data relative to those variables that present a cut-off point capable of discriminating the clinical improvement of the participants. Likewise, the relative risk of presenting problems of one experimental group with respect to the other at different times of measurement will be calculated. In addition, the McNemar related proportions will be calculated to verify if the percentage of problems is the same or different in the pretreatment and in the different subsequent measurement moments, and the Jacobson-Truax method will be applied by calculating the index of reliable change. This analysis will be supplemented by the calculation of the effect size of each variable.
In the specific analysis of the reabuse or revictimization variables, it is expected that the scores show high asymmetry and kurtosis, so they will be recoded as dichotomous variables (presence vs. absence for each type of violence or abuse) in accordance with the procedure described by Johnson et al. [12].
To establish the effectiveness of the intervention program, adherence to treatment will be evaluated considering the percentage of dropouts in each group, the session in which dropouts occur, the number of sessions attended by the women who complete the treatment and the fulfilment of homework. For this, the two treatment groups will be compared in these variables through c 2 tests or, where appropriate, one-way ANOVA.
Finally, for the analysis of the variables related to therapeutic success, multiple regression (for the prediction of the values of quantitative variables) or logistic regression (for the prediction of therapeutic success vs. failure) will be applied. In addition, to the extent that each variable allows, moderation and mediation analyses will be performed with the PROCESS macro for SPSS using the procedure described by Hayes [74].