More recent treatment approaches try to overcome some of the critical points of the RP approach, e. These treatment models also integrate the goal of teaching the offender coping skills in their treatment programs. But coping skills are now embedded in a much wider framework, considering not only skills enabling the offender to avoid re-offenses, but also skills to achieve positive goals which do not accord with a re-offense Yates et al.
In summary, most sex-offender treatment programs provide techniques to teach coping skills for high-risk situations and therefore can provide a theoretical basis for the development of meaningful virtual situations for the risk management of SOCs. It seems obvious, that some coping skills are essential before permitting SOCs to leave the controlled environment. For example, the skill to cope with high-risk situations, such as access to a potential victim seems to be essential for SOCs before leaving the controlled environment. These skills provide the ability for the child offender to stop the offense progression before an offense occurs.
Therefore, meaningful virtual risk situations should require the ability of the user to use coping skills.
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Furthermore, they should provide some trigger e. By doing so, VR based risk assessment provides for the first time the possibility to observe the behavior of SOCs in highly ecologic valid social situations: In contrast to the above mentioned risk assessment tools and intramural settings, VR allows the consideration of situational context factors, for example specific and for the individual SOC highly relevant cues and triggers, which can influence dynamic risk factors. In the current feasibility study, we tested for the first time the possibility to use VR for the behavioral monitoring of SOCs in risk situations.
The main aim of the developed VR tool was the assessment of the possible risk of SOCs to show inadequate behavior during unsupervised privileges approved for the first time. Thus, it should be possible to assess in a direct manner to which degree SOCs are able to perform coping strategies learned during therapy. It is important to note, that the method used in this study is not sufficient to assess the overall recidivism.
At the psychiatric clinic, where the forensic inpatients were recruited for the current study, one of the unsupervised privileges outside the secured ward consists of a walk outside of the controlled environment for shopping in a supermarket near the forensic hospital. Usually this walk is restricted to 1 hour. Thus, we developed risk situations that can occur while shopping in a supermarket. Furthermore, the treatment rationale of the psychiatric clinic follows the RP approach. Thus, all risk situations are constructed so that coping skills are required ability to avoid risk situations, ability to cope with unavoidable risk situations.
We hypothesize, that the monitoring of SOC's behavior in virtual risk situations can provide additional information, which are not accessible by risk assessment tools based on files or questionnaires or by observation of the inpatient's behavior during therapy. In more detail, we hypothesize that virtual risk situations allow the monitoring of the ability of SOCs to use coping skills. Furthermore, from a clinical perspective, one would assume, that SOCs, who have understood the basic rationale of the RP approach during therapy, would show avoidance behavior more frequently than healthy controls non-offender controls, NOC Laws et al.
The second aim of the study was to test, if forensic inpatients accept the usage of VR and if the designed VEs are able to induce a high degree of presence and co-presence in forensic inpatients. We hypothesize, that forensic inpatients show the same amount of presence and co-presence during the exposure to virtual risk situations as NOCs. NOCs were recruited by a notice posted on the campus, by social media groups and by posts in different online forums. All SOCs were recruited at a forensic-psychiatric hospital. Inclusion criteria for SOCs were at least one sexual assault against children documented in criminal records and no unsupervised hospital privileges outside the secured ward.
Exclusion criteria for SOCs were an acute psychotic episode, substance abuse during the previous month, or incapability or refusal to sign informed consent. They were on average hospitalized for 6. Child victims were on average 8. The current recidivism risk of the SOCs was assessed with two actuarial risk assessment tools, the Static and the Stable The Static Harris et al.
It consists of 10 items, which define risk factors that are not changeable over time static risk factors. The total score ranges from zero to 12, with higher scores indicating a greater risk of sexual recidivism. The Static provides good inter-rater reliability and validity Hanson and Morton-Bourgon, The SOCs demonstrated on the Static on average a score of 4. The Stable Hanson and Harris, b ; Matthes and Rettenberger, is an interview- and file-based tool for the assessment of dynamic risk factors for sex offender recidivism.
It consists of 13 items or dynamic risk factors, between zero and two. The total score ranges from zero to 26, with higher scores indicating a greater risk of sexual recidivism. The Stable provides a good reliability and validity Hanson and Morton-Bourgon, On average, the SOC group showed a score of All participants provided written informed consent before participating in the study. The HMD provides stereoscopic viewing by presenting a separate picture to each eye of the participant with a refresh-rate of at least 75 Hz, resulting in an effective resolution of x pixel per eye.
The MoCap system comprised an eight camera setup with an effective tracking space of 4. Overall, five virtual adult female characters, five virtual adult male characters, five virtual child female characters and five virtual child male characters were used in the study. All virtual characters were computer-generated realistic, fully rigged three-dimensional 3D models of clothed human beings see Supplementary S3. The textures incorporate different clothing styles and skins.
Especially the voices of the children were pre-processed in order to achieve a believable pitch. Two different VEs were modeled and designed: one simpler environment for the initial rating of the virtual characters see Supplementary S1 and Supplementary S2 Initial Rating and a virtual supermarket for the risk scenarios. The virtual supermarket had a virtual size of The supermarket was comprised of two sections: the entrance area and the product area. Both areas were divided by an automatic sliding door.
The entrance area consisted of some simple interior e. The product area consisted of 30 shelves, nine fridges, three cash-points and a vegetable section. The doors of the fridges could be opened and closed by the participant. Only after opening the doors, the products in the fridges were available. Overall, more than 1, products were modeled and implemented in the supermarket.
Interactions with the products or other objects were possible with the help of a red fixation point, the position of which was always in the middle of the screen. If the fixation point enters an interactive element, this element flashed up red and the participant could interact with the element with a specific button of the PPT Wand.
Furthermore, a shopping list was shown by a Head-up display HUD , which allowed the participant to control which products he already put into his virtual basket. The shopping list HUD could be switched on and off with a button. All interactions with virtual characters were implemented by speech and an additional HUD, which allowed the participant to choose one of the possible predefined answers. The participant had two possibilities to walk through the supermarket: Distances, which exceed the area of the lab, where handled by the joystick of the PPT Wand.
Distances within the lab area could be reached by foot. Thus, larger distances were reached with the joystick, small distances per pedes. Figure 1. The virtual supermarket. I Bird's eye view of the virtual supermarket. The supermarket comprised an entrance area A and a product area B. II Entrance area of the supermarket with the info screen. III Product area of the supermarket with the shopping list Head-up display. Red crosses symbolize products not yet in the basket; green check marks symbolize products that are already in the basket. The experiment was divided into two phases, which took place on two different dates see Figure 2.
In the first phase, an initial rating took place in order to identify the most attractive virtual child character and the most unattractive virtual adult character for each individual participant see Supplementary S1 and Supplementary S2 Initial Rating for further details. The main experiment phase had five parts: Tutorial and Training, a Baseline condition, and three virtual risk scenarios. Before each part, the participant was asked to fill in the pre-test of the Simulator Sickness Questionnaire Kennedy et al. Between the different parts of the main experiment phase, the participant has the opportunity to rest.
Figure 2. Experiment procedure. The experiment started with the initial rating. Goal of the initial rating was to identify the individual most unattractive virtual adult and most attractive virtual child character. In the main experiment phase, the participants learned the controlling of the virtual supermarket and the task in the tutorial and training.
Afterwards, the participant was exposed to the most attractive virtual child character consecutively in three risk scenarios. The risk scenarios differed from each other with regard to their difficulty to avoid the contact to the virtual child character. During the tutorial, the participant was instructed that his task is to buy five specific products chocolate, Coca-Cola, pasta, milk, and coffee in the virtual supermarket within 5 min.
At the beginning, the participants stood in the entrance area in front of the virtual screen. The investigator explained the controlling of the VE and asked the participant to test the two different options to walk through the supermarket per joystick or per pedes; see above. Then, the investigator guided the participant during his first virtual shopping trip. In order to ensure that all participants were at the same training level regarding the control of the VE, the participants trained in the VE until they were able to buy all five products within 5 min.
During this training phase, the participant received no help from the investigator. At the beginning of the baseline condition, the participants were told that the task to buy five products remained, but with no time restriction. They were further told, that a virtual character will make contact during the virtual shopping trip. Responses can be selected with the HUD. Based on the results of the initial rating, the virtual adult character with the shortest viewing time was used.
Until the participant reached the candy shelves in the middle of the virtual supermarket the participant had to buy chocolate , the virtual character walked through the supermarket at a predefined path. When the participant reached the candy shelves, the virtual character walked to the participant and asked, if the participant knew where he can find Coca-Cola bottles in the supermarket. During the interaction, the participant was not able to move. After the virtual character talked to the participant, the participant was able to choose among five different answers.
Depending on the chosen answer, the virtual character reacted in different ways see Supplementary S4. The baseline situation finished once the participant reached the entrance area. After finishing the baseline condition, the participant had to walk through three virtual risk scenarios in a fixed order. The task was the same as in the baseline condition. In contrast to the baseline condition, in the risk scenarios the participant was confronted with the most attractive virtual child character according to the results of the initial rating the virtual child character with the longest viewing time.
The same virtual child character was used in all three virtual risk scenarios. The virtual risk scenarios differed with regards to their difficulty to avoid a contact to the virtual child characters. In risk scenario one, the participant has the opportunity to avoid direct contact with the child at all. In risk scenario two and three, the participant was not able to avoid contact to the virtual child, but he could leave or not the situation immediately see Supplementary S5—S7. In each risk scenario, the virtual child character walked through the supermarket at a pre-defined path until the participant reached a specific trigger area of the supermarket risk scenario one and two: candy shelves; risk scenario three: cash-points.
The path of the virtual child character was defined in order to ensure, that the participant could see the child before he was forced to get in contact with the child. The Simulator Sickness Questionnaire Kennedy et al. Its symptoms are similar to those of motion-induced sickness, but originate from elements of the visual display and visuo-vestibular interaction Cobb et al. The questionnaire consists of 16 items based on a four-point Likert scale ranging from zero the symptom is not existent to three very severe symptom. The total score measures the overall severity of simulator sickness.
The Igroup Presence Questionnaire Schubert et al. It contains 14 items rated on a seven-point Likert scale ranging from zero to six.
The IPQ contains three sub-scales that measure different components of presence: 1 the Spatial Presence sub-scale is related to the sense of physically being in the VE, 2 the Involvement sub-scale is meant to evaluate the attention devoted to the VE, and 3 the Realness sub-scale evaluates the sense of reality attributed to the VE. The Social Presence Questionnaire Bailenson et al. Co-presence also reflects, if someone react to a virtual human as if it is a real human Bailenson et al.
The SPQ contains 10 items rated on a 7-point Likert scale ranging from zero strongly disagree to six strongly agree. One factor of the SPQ measures the perceived co-presence. The perceived co-presence reflects how a user has the feeling, that a virtual human was really there in the VE. Factor analysis identified four distinct factors [scene realism, audience behavior, audience appearance, and sound realism; Poeschl and Doering ]. Scene realism measures the naturalism of visual cues, colors, three-dimensionality, and realistic proportions of the VE.
Audience behavior measures the authenticity of postures, gestures, and facial expressions of virtual characters within the VE. The authenticity of virtual humans in general and the adequateness of the outfit of the virtual characters is subsumed under the factor audience appearance. Sound realism is a single item measure to describe the realism of the sound in general, e.
During each virtual scenario, the participant has to interact with a virtual character baseline scenario, risk scenario two, and risk scenario three or to decide how to react, upon seeing a child character at the candy shelves risk scenario one. All these interactions were provided in mixed modalities: the virtual character talked to the participant and the participant could choose predefined answers or behaviors, which were presented via a HUD.
The number of possible choices depended on the scenario and the choice the participant chose first see Supplementary S5—S7 for an overview of possible interaction sequences in the different scenarios. There were a maximum of two interaction levels for baseline scenario, risk scenario two, and risk scenario three. In risk scenario I, only one interaction level was provided.
All choices were categorized into approach behavior and avoidance behavior. Approach behavior was defined as every predefined answer in which the participant 1 could get in contact or tries to get in contact with a virtual character, 2 could touch or tries to touch the virtual character, 3 tries to extend the interaction sequence, or 4 tries to reduce the distance to the virtual character. Avoidance behavior was defined as every predefined answer, in which the participant 1 did not react to the virtual character or 2 tries to leave the situation.
Supplementary S3—S5 show the categorization of each predefined answer in approach or avoidance behavior. Additionally, in order to compare the answer behavior with coping strategies learned during therapy see the following section and Tables 2 , 3 , for some analyses see Data analyses for more details the approach behavior was further divided in approach behavior with physical contact or without physical contact. Table 2. Items of the therapist rating scale for virtual risk scenarios T-VRS. Table 3. The items of the patient rating scale for virtual risk situations P-VRS.
The Therapist Rating Scale for Virtual Risk Scenarios T-VRS was developed in order to assess coping strategies focused on during therapy by the therapist and the prediction of therapists with regards to the ability of the participant to perform learned coping strategies. Table 2 describes all items of the T-VRS in detail. It consists of two dichotomous items, which were explicitly linked to risk situations comparable to the virtual risk situations and asks for the prediction of the therapists with regards to the ability of the SOC to cope with risk situations.
Note that all therapists had walked through the virtual risk scenarios before filling in the T-VRS. The Patient Rating Scale for Virtual Risk Scenarios P-VRS was developed in order to assess the subject beliefs of SOCs about the correct behavior in risk situations comparable to the virtual risk scenarios and to assess coping strategies patients have learned during therapy. Table 3 describes all items of the P-VRS in detail. In order to evaluate the acceptance of the virtual risk scenarios between the two groups SOCs vs. In order to identify changes with regard to simulator sickness symptoms, a paired t -test pre vs.
Due to low sample size, the effect-size Cohen's d was used to interpret the results and not the p -value. The effect size Cramer' V was used to interpret the results due to the small sample size. Because of the small sample size, we decided not to perform further statistical analysis within the group of SOCs or NOCs with regard to their behavior.
Thus, only descriptive statistics are provided. In order to evaluate, if SOCs behavior was in concordance with their subject belief about correct behavior, the frequencies of congruent and not congruent behavior with the respective items of P-VRS were calculated. As shown in Table 4 , the reported subjective feeling of presence was at a high level in each experimental condition.
Thus, independent of the experimental condition there were if at all only small differences with regards to the feeling of presence between the participant groups. Table 4. As one can see, the subjective Copresence was at a medium level in all conditions and both participant groups. The statistical analysis of the factor Copresence revealed differences between the two participant groups with only small effect sizes in the baseline condition [ t 8.
A difference with a medium effect size was observed in scenario one [ t As shown in Table 4 , the reported severity of simulator sickness symptoms were at a low level after each experimental condition in both participant groups. Thus, one can assume that there was no significant increase of simulator sickness symptoms during the experimental conditions.
That holds true for both participant groups, except NOCs in scenario one. Bearing the maximum value of Audience Appearance could not be analyzed due to too many missing values. Welch tests for the factor Scene Realism revealed differences between the two groups in all experimental conditions with low to medium effect sizes [baseline: t The two groups differed with regards to the factor Audience Behavior at a medium effect size level in the baseline condition [ t In scenario one [ t 9.
At scenario two only a group difference of medium effect size emerged [ t Table 5. Table 6 shows the frequency of approach and avoidance behavior in the baseline condition. Table 6. Table 7. No healthy control participant showed avoidance behavior. Table 8. Table 9. Table In the current feasibility study, the usability of behavioral monitoring of SOCs in virtual risk situations for clinical risk management was evaluated. Three risk scenarios, which match possible real high-risk situations during a first unsupervised walk out of the secured ward to town, were developed. The participants had to buy specific products in a virtual supermarket, during which they were confronted with a virtual child.
The behavior of the participants was assessed by the participant's answers during the interaction with the virtual child. The predefined answer options were designed following the RP approach teaching SOCs coping skills to avoid risk situations or to cope with unavoidable risk situations. One aim of the study was to evaluate if virtual risk scenarios provide a high feeling of presence and co-presence, low symptoms of simulator sickness and high subjective feelings of realism for forensic inpatients who had sexually abused a child.
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Main aim of the study was to evaluate if the confrontation of SOCs with virtual risk situations can provide additional information for the decision about unsupervised privileges outside the secured ward of a forensic psychiatry beyond behavioral monitoring in controlled environments and traditional risk assessment tools.
The results with regards to presence, co-presence, realism, and simulator sickness showed that VEs are well suitable for forensic inpatients who sexually abused a child: Results demonstrated that differences between SOCs and NOCs with regards to their subjective feeling of presence are at a small effect size level. Thus, both groups had a high feeling of being in the VE. There was also only a difference at a small effect size level between the groups with regards to the subjective feeling of co-presence in all experimental conditions except scenario one. In scenario one, the groups differed by a medium effect size level.
The subjective realism of the virtual scenes as well as the realism of the behavior of virtual characters and the sound was high for both participant groups. Thus, the virtual scenarios were noticed as realistic with regards to three-dimensionality, proportions and colors. Furthermore, the postures, gestures, voices, and facial expressions of the virtual characters were regarded as authentic.
This shows that the virtual characters have been designed authentic and have been well animated, even for SOCs. The two groups differed in their subjective realism ratings, especially in the rating of the realism of the behavior of the virtual characters partly at a high effect size level. Furthermore, both groups didn't suffer from simulator sickness symptoms.
Simulator sickness is one of the main problems with high-immersive VEs and most important for the usability of VEs Kennedy et al. It was shown that simulator sickness can be reduced by using motion capturing instead of more traditional game controllers for navigation Llorach et al. During risk scenarios, both techniques were combined, which could explain the low frequency of simulator sickness symptoms.
In summary and facing the age difference between NOCs and SOCs as well as the differences with regards to the education level and the accessibility of e. It shows that the new VR technology seems to be well accepted by forensic inpatients, and seems to be able to provide a high ecological valid environment for forensic inpatients, also for elderly patients. It was hypothesized that behavioral monitoring of SOCs in virtual risk situations could provide information about the ability of SOCs to show adequate coping strategies in risk situations.
With some restriction, the results support this hypothesis: Firstly, results demonstrated that the approach-avoidance behavior of SOCs and NOCs only differed in the baseline scenario and risk scenario three at a high effect size level. In the risk situations one and two, NOCs showed approach behavior more frequently as SOCs the two groups differed by a medium effect size level. The scenarios were conceptualized as help situations and it is not surprising that NOCs choose to help the children.
In contrast, SOCs should have learned during the RP based treatment to avoid getting in touch with children. Thus, behavioral monitoring in virtual risk situations could provide information about the ability of SOCs to show adequate coping strategies. At first glance, SOCs chose inadequate coping behaviors in the majority of all cases.
In order to arrange the behavior of SOCs in the virtual risk situations correctly, it is necessary to consider what SOCs have learned during therapy, what therapists focused on during therapy, and what they consider the correct behavior is in such situations. Therefore, the congruence between the knowledge of the SOCs about correct behaviors in comparable situations and monitored behavior during virtual risk situations was analyzed. Thus, it seems that SOCs behaved half of the time in correspondence with the coping skills which therapists focused on.
Possibly, this is a result of low self-regulation abilities of the SOCs. Thus, behavioral monitoring of SOCs in virtual risk situations seems to provide information about the ability of SOCs to transfer the at a cognitive level learned coping strategies to the behavioral level. This information can be important for decisions about unsupervised privileges and for risk management in general. For example, Marques et al. This difference with regards to re-offense rates was more dominant in the group of high-risk sex offenders and most dominant in the group of child molesters.
Only 9. These results underline the importance to evaluate if coping skills are successfully learned during therapy before permission of unsupervised privileges. Therefore, behavioral monitoring of SOCs in virtual risk situations seems to provide necessary information for the decision about unsupervised privileges.
The difference between SOCs and NOCs in risk scenario I, in which a contact was avoidable, at a medium effect size level seems to be the result of not following the suggestions of the therapists for comparable situations. Also in scenario I, the most frequent discrepancy between behavior and during therapy focused coping skills occurred. One possible explanation for this result could be the existence of an order effect. The presentation order of the risk scenarios was fixed, beginning with risk scenario one and ending with risk scenario three.
Thus, it could be possible that SOCs did not recognize the situations as risk situations until they walked through the first situation. From a risk management point of view, it is important that SOCs recognize situations as risk situations as soon as they occur. The ability to recognize situations as risk situations is one important skill, which should be learned during RP based treatment Laws et al.
The lack of congruence between the behavior and the knowledge of the SOCs about correct behavior as well as the lack of congruence between the learned behavior and shown behavior during risk situations is striking. Moreover, there is an obvious mismatch between the learned behavior, SOCs stated and the focus of the therapy as reported by the therapists.
A possible explanation could be that SOCs answered these questions in a social desirable manner. It is well known that SOCs show tendencies to answer such questions in a social desirable manner O'Donohue et al. It is possible that SOCs indicated what they have heard during therapy, without being convinced that this is adequate behavior or without having internalized the learned coping strategies. One can further argue that sexual offenders against children with average intelligence may be able to adapt their behavior in the expected direction, since they know that their behavior is observed during immersion.
Possibly, the lack of congruence between the behavior and the knowledge of the SOCs about correct behavior is the result of intelligence deficits or emotional or introspective shortcomings within the studied sample of sexual offenders against children. These shortcomings may also be the reason for not succeeding in psychotherapy. Future studies have to consider this before VR can be used in clinical settings. Assuming, that therapists made their predictions based on risk assessment tools as well as behavioral monitoring in controlled environments, the confrontation of SOCs with virtual risk scenarios seems to provide at first glance no additional information to traditional risk management.
Nevertheless, for the first time, virtual risk scenarios enable the therapist to evaluate their predictions and the ability of SOCs to behave in correspondence to the coping strategies, which were focused on during therapy.
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This seems to be an important aspect: to be able to evaluate the therapeutic process and to be able to predict the outcome of the treatment. This was due to not applying adequate behavior as expected false positives; see Table For these cases, behavioral monitoring in virtual risk situations can provide essential new information useful for risk management decisions. It can also lead to re-evaluating the made predictions and the focus of therapy by the therapist.
Behavioral monitoring in virtual risk situations can only be useful when the behavior of SOCs in virtual risk situations reflects the behavior in real risk situations. However, VR studies in other contexts have already shown that behavior learned in VEs can be transferred to real situations. In a current meta-analyses, Morina et al. Furthermore, the performance of patients with VR treatment did not differ significantly from the performance of patients treated with exposure in vivo.
The authors concluded, that VR treatment seems to result in significant behavioral changes in real-life at least for patients suffering from specific phobias. In the already mentioned study by Greenwood et al. The number of correct products, the duration of the shopping trip and the number of aisles reached by the participant were measured during the real and the virtual shopping trip.
All three measures were significantly correlated between real live shopping trips and virtual shopping trips. Furthermore, VR measures were significant predictors for real life measures. These studies show exemplarily that behavioral monitoring in VEs seems to be correlated with real life behavior or can at least provide a significant improvement of the prediction accuracy of real life behavior.
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Despite these promising results, it has to be emphasized that these studies do not show that behavior in VEs necessarily reflect the behavior in daily life Greenwood et al. Thus, it is import that future studies test the predictive validity of SOC's behavior in VEs for their behavior in real life. London: Criminal Justice Joint Inspection.
Managing High Risk Sex Offenders in the Community: A Psychological Approach
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