Assessing the Effectiveness of Regression Therapy in Addressing a Broad Spectrum of Psychological and Physical Issues

Bé Groen¹, Ingrid Klooster² and Paul Hooijdonk³

¹ Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands

²Auora, Hardewijk, The Netherlands e-mail: [email protected]

³Paul Hooijdonk, Breda, The Netherlands, e-mail: [email protected]

 

Abstract

This study investigated the effectiveness of Regression Therapy (RT) in treating a wide spectrum of psychopathological and Medically Unexplained Symptoms/complaints (MUS). A single-blinded outcome trial was conducted with a large cohort of 169 subjects, who completed the validated Brief Symptom Index-53 (BSI-53) questionnaire before treatment and at two months and four months post-initiation. Fifteen experienced RT therapists delivered standardized therapy. Results showed a significant and substantial decrease in BSI-53 total scores, from 0.91 to 0.48 after four months of treatment, closely approaching the normative value for the Dutch population (0.42). The study yielded a large clinical effect size (Hedges’ g = $0.95$). Notably, a single session proved satisfactory for many participants. All nine BSI-53 domains exhibited a mean score decrease of 47%. Unbiased machine learning analysis accurately predicted the therapy’s positive effect from a few key BSI-53 questions, with individual therapist performance not being a significant predictor. These findings suggest that RT is a rapid and highly effective treatment for diverse psychological and physical symptoms, including MUS.

Keywords: Regression therapy, Brief Symptom Index-53, blinded trial, machine learning, medically unexplained symptoms.

Correspondence: Prof. Dr A.K. Groen, Amsterdam University Medical Centers, location AMC, Meibergdreef 9, room G1-147, 1105 AZ Amsterdam,
The Netherlands.

Email: [email protected]

Bé Groen¹, Ingrid Klooster² and Paul Hooijdonk³

 

¹ Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands

²Auora, Hardewijk, The Netherlands e-mail: [email protected]

³Paul Hooijdonk, Breda, The Netherlands, e-mail: [email protected]

 

Correspondence:

Prof Dr A.K. Groen

Amsterdam University Medical Centers, location AMC,

Meibergdreef 9, room G1-147, 1105 AZ Amsterdam, The Netherlands

Email: [email protected]

Declarations of interest: none

Background

In this study we evaluated the effectiveness of Regression Therapy (RT) to treat a broad spectrum of psychopathological and or Medical Unexplained Symptoms/complaints (MUS). Using an extensive validated questionnaire, we performed a single blinded outcome trial using a large cohort of clients

Methods

A total of 169 subjects filled out a BSI-53 questionnaire[1] before initiation of treatment as well as after two and four months of therapy. Fifteen established RT therapists conducted therapy according to a standardized protocol. Questionnaires were submitted anonymously to a commercial company that analysed the results and reported to an independent assessor.

Results

RT proved to be highly effective. The BSI-53 total score decreased from a value of 0.91 to 0.48 after four months of treatment. A score of 0.48 is close to the value of 0.42 measured in a representative sample of the Dutch population. For most study participants a single session was already satisfactory. Hedges g for this study was 0.95 indicating a large clinical effect. All nine domains of the BSI-53 questionnaire showed a similar decrease in score the mean decrease was 47%. Application of unbiased machine learning to all study parameters revealed that a positive effect of the therapy could be predicted from a few BSI-53 questions with high accuracy.

Introduction

Psychiatric disorders are prevalent worldwide and are associated with high rates of disease burden, including elevated rates of morbidity and mortality. In addition, there is a high rate of co-occurrence between psychiatric and medical disorders (Doherty and Gaughran, 2014). When psychiatric disorders co-occur with medical problems, not only are the medical symptoms more problematic, but the treatment of the medical condition is often more complicated, with lower levels of treatment adherence and higher levels of healthcare service utilization, with its associated costs. Therefore, increasing attention has been paid to the need for evidence-based pharmacological and psychotherapeutic interventions for a range of psychiatric disorders. A great number of different psychotherapy modalities have been developed. Most of these modalities have been shown more or less effective in clinical trials (Cook et al.,2017, Butler et al., 2006; Cook et al., 2017; Cuijpers et al., 2013; Hofmann et al., 2012). Regression therapy (RT) is one of these treatment modalities. It has been developed in the late 1970’s  (Cladder, 1983; Netherton M, 1978; Woolger, 1988). Research has shown that most personality disorders can be treated successfully with RT. Its effectiveness has been investigated in several scientific studies, but these did not reach a large readership (Trivedi, 2022; Van der Maesen, 1995; Van der Maesen, 1996). The aim of the present study was to investigate the effectiveness of RT in an open label trial with blinded outcome assessment. Although this setup minimizes therapist/client allegiance bias it is often criticized because of the lack of a control group. Since for psychotherapy trials of this kind a true placebo control is not feasible, the best option is an active control group. We have not succeeded in organizing this set up for the present study. A waiting list control group could have been an alternative but since such a control has the risk to overestimate the effect of the therapy (Laws et al., 2022; Patterson et al., 2016) we decided to carry out an uncontrolled trial.

 

Methods

Design: Open label trial with blinded outcome assessment.

Participants to the trial were recruited sequentially from clients of fifteen participating established regression therapists with a private practice.  All therapists were trained at one of the schools of regression therapy in the Netherlands and all therapists were member of the society for regression therapy in the Netherlands.

Inclusion criteria were wide and no formal DSM-5 diagnosis was established. Clients reported complaints of depression, general anxiety, medically unexplained somatic disorder, obsessive compulsive disorder, and generalized trauma/stress disorders.

Exclusion criteria; schizophrenia, bipolar disorders, heavy use of medication suppressing emotions.

The protocol started with an intake conversation by phone, carried out by the participating therapists. During this conversation clients were asked for consent to participate in the trial. When consent was given, they received the BSI-53 questionnaire from an anonymous specialized company (NetQ, Zeist, The Netherlands) by e-mail. Six custom made extra questions were added to the questionnaire (see Table 1). After participants filled out the form, it was returned to the company by email and the data were anonymously converted to an excel sheet which was exported to an independent outcome assessor. The therapists and clients remained blinded towards their own results. Two and four months after the start of the trial, participants received a new questionnaire by email that was again processed by NetQ. Participants received up to four gentle reminders when they failed to return the questionnaires. This research was conducted in accordance with the Helsinki Declaration as revised in 1989. All participants signed an informed consent form. Since the protocol was carried out without medical intervention and a single questionnaire was used, review of a Medical Ethical Review Board was not required.

Therapy procedure

After conducting an initial intake conversation to identify the client’s complaints and desired outcomes, clients were asked to explore their complaints on multiple levels: cognitive, emotional and somatic. By addressing these aspects comprehensively, clients entered a light trance-like state.  The therapist encourages the client to revisit the moments when these complaints arose through the induction: “Go back in time to a moment when you experience these (physical) feelings and thoughts exactly like this.”

During this therapeutic journey, the majority of clients find it relatively easy to delve into their past experiences and are guided through the process of reliving them. In many cases, these experiences trace back to early childhood; however, there are instances where clients describe situations that do not align with their present life circumstances. Regardless of the content, the therapist maintains a non-judgmental and non-suggestive stance and expertly navigates the client through these mostly traumatic experiences.

Throughout the session, the emotional intensity of these experiences is processed and discharged, allowing for healing and growth. The duration of these sessions typically ranges from 1.5 to 2 hours, providing ample time for thorough exploration and resolution.

Machine learning

The extreme gradient Boosting (XGBoost) algorithm was utilized to identify a panel of variables that best predicted whether RT improved the BSI-53 score of the participants. The same stability selection procedure was used in all simulations to ensure robustness of the results and prevent overfitting (Meinshausen et al., 2007). In total, twenty different subsets were made of the complete dataset. Within each random subset, random under sampling was performed for the 1^st generation to have equal group sizes. After under sampling, a fractional subset of the under sampled dataset was selected. The fraction was 0.5. Next, within each random subset, LeaveOneOut cross-validation was applied where the training set included all samples except for one, in which this one sample left out was included in the test set. Within the training set, the hyperparameters of the XGBoost model were found by performing a randomized search with a three-fold cross-validation, based on 80% of the training set and validated on the remaining 20%. The performance of the different models was estimated via an area under the curve (AUC) of the test dataset. This machine learning pipeline was implemented in python (v3.7.7), using the scikit-learn (v0.23.1) package.

Statistics

Data were presented as mean ± standard error of the mean (SE) unless otherwise stated. The Shapiro-Wilk test was applied to test whether the data was normally distributed. Statistical analyses were executed with IBM SPSS Statistics 25 (IBM, Armonk, New York, United States) and Graphpad Prism 9.02 (GraphPad Software Inc., La Jolla, California, United States).  When distribution was normal differences were tested with ANOVA or two-sided t-tests. Non-normal distributions were subjected to non-parametric tests. Graphs were created with GraphPad Prism 9.05. A p-value ≤ 0.05 was considered as statistically significant, p-values were adjusted for multiple testing with the Benjamini-Hochberg method.

Table 1 Additional questions to the BSI-53

1. Are Medical Unexplained Complaints (also) a reason to start regression therapy?
2. What score would you give the mentioned physical complaints at this moment? (score 1-9)
3. How many regression therapy sessions have you had from the first consultation until now?
4. What score would you give regression therapy?
5. Could you briefly explain what the sessions have meant for you?
6. Have you experienced a past-life regression?

Results

Figure 1 presents the flow scheme of the study.  At the start 259 persons participated in the trial; 211 females and 48 males; 208 subjects returned the first and second questionnaire; 169 (131/38, f/m) also returned the third questionnaire.

Figure 1. Flow scheme of trial

Fifteen experienced regression therapists (eleven female and four male) participated in the trial and performed therapy sessions at a session follow-up rate indicated by the clients. Four months after initiation of the therapy the trial was finished but clients could continue. Since there was no communication between participants and therapists about filling out the questionnaires, we have no information on the reasons for dropping out of the study. Fig 2. shows the effect on the score for BSI-Total for the cohort of 169 participants that completed the trial.

 

Figure 2. Effect of RT on BSI-53 Total score after two and four months of therapy

 

BSI-Tot dropped from 0.91±0.04 to 0.52±0.03, p<0.0001 at the two-month time point and then decreased further to 0.48±0.03, p=0.004 after 4 months. There was no significant difference between male and female participants. To be able to compare the results with other trials in the literature we calculated hedges g at time point 4 months. It amounted to 0.95 which is scored as a large effect size.

Fig.3 illustrates session counts; after two months of therapy, 70% of participants had partaken in a single session. The number of sessions increased at 4 months of therapy but still the number of participants with only one session was sizeable (34%). Notably, no discernible correlation surfaced between session count and therapy efficacy defined as delta BSI-Total (data not shown)

 

2 months                                                                        4 months           

Figure 3. Number of sessions after two and four months of therapy

 

 

Figure 4. Effectiveness of two and four months of RT on the nine different dimensions of the BSI-53.

53. A) Data for the first 5 dimensions of the BSI-53. B) Data for the remaining 4 dimensions. The lines in the figure represent the BSI score found in the normal Dutch population (n=200)(De Beurs and Zitman, 2006)

The BSI-53 questionnaire registers psychological and physical complaints in 9 dimensions. Fig. 4 shows the effect of the therapy on the different BSI-53 dimensions at T=2 and 4 months compared to the initial values at t=0. At t=0 considerable difference can be seen between the different dimensions. For instance, for Cognition the initial score was 1.27±0.06; Phobia scored much lower at 0.58±0.05. Interestingly, the decrease in score expressed as percentage was almost the same for all dimensions both at two and four months. At four months the mean decrease in score was 47.6% ± 3.2%. Clearly for all dimensions the values come close to the norms for the healthy Dutch population (De Beurs and Zitman, 2006). We asked the participants whether they visited other therapists before initiating regression therapy; 71 first obtained therapy from a “traditional” psychiatrist/psychologist. 31 of these subjects also sought help from another complementary therapist before starting RT. 50 went straight to a RT therapist.

Figure 5 shows that persons with a psychiatric/psychologic history show a significant higher BSI-53 total score at the onset of the therapy compared to participants that directly came to a RT therapist. Unsuspected and naïve to treatment participants reacted better to RT compared to the participants which had already undergone a therapy trajectory.

Figure 5. Initial and four months BSI-Tot scores for subjects that were first treated by “traditional” psychiatrists/psychologists as compared to treatment naïve participants.**** p<0001,***p=0.004

 

We also asked the participants to answer additional questions to the BSI-53. These questions are given in Table 1. 83 participants reported presence of MUS with a wide variation in symptoms from tinnitus to chronic Lyme disease. This variation precluded analysing the effect of RT on specific disorders. Overall, the severity of MUS was scored 6.9 (range 2-10, scale 1-10) at baseline which improved to 5.3 (range 1-9) at 4 months. We verified how participants without MUS complaints scored in the somatic dimension in the BSI-53. As expected, they did not score significantly and hence showed no improvement. The answers to question 5 concerning the self-reported effect of the therapy were impressive. Supplemental Table 1 shows the results organized by main theme, increased insight and awareness is reported most followed by personal growth and acceptance. Fifty-seven of the participants reported past-life sessions. Interestingly, therapy in present or past-life showed no significant difference in effectiveness.

We investigated the relative importance of the different questions from the BSI-53 as well as influence of the various therapists and the extra questions (supplemental data) on the BSI-Total score by applying a machine learning protocol using an XGboost approach. The aim was to investigate whether the results of the therapy could be predicted by an unbiased analysis of the results. We analysed two data sets; the initial BSI-Total score at the start of the trial and the change in BSI-Total score at the end of the trial combined with participating therapists and added questions.  Interestingly analysis of the initial data set did not produce a significant result. Apparently, the severity of the complaints nor a combination did allow prediction of the success of the therapy. Analysis of the delta file, i.e. the difference between initial and final scores, did produce a statistically significant output.

Figure 6. Relative importance of various factors on prediction of the impact of RT therapy.

An XGBoost algorithm was employed to investigate which set of variables could predict a positive effect of RT. 

The positive effect of RT on the BSI-53-Total score could be predicted with a high accuracy of 87%.   The hierarchy of the influence of the most important parameters is listed in Figure 6. Inspection of the figure reveals that all features are derived from the BSI-53 questionnaire. The individual therapists do not appear in the top parameters that best predict a positive effect of the therapy. The top 4 BSI-53 questions are nr 20; being easily hurt; nr 18 no interest in anything; nr 48-paranoia and nr17; feeling blue.

 

Discussion

The major novel finding in this study is the high effectiveness of regression therapy to ameliorate a variety of psychopathological and MUS symptoms. Even after a single session of 2 hours duration 57 clients scored the BSI-53 questionnaire close to normal levels for the population in the Netherlands. We chose for using the BSI-53 questionnaire because it has been extensively validated both in the US and Europe (Adawi et al., 2019; Akhavan Abiri and Shairi, 2020; Derogatis, 1983; Müller et al., 2010). In the Netherlands the group of De Beurs extensively applied the questionnaire for both healthy and psychiatric patients (de Beurs et al., 2022). The average BSI-Total after 4 months of therapy in this study of 0.48 was close to the reported value of 0,42 for a random sample of the Dutch population (De Beurs and Zitman, 2006). We realize that psychotherapy modalities such as Cognitive Behavioural Therapy or EMDR show similar improvement of the BSI-53 score (Cuijpers et al., 2013). However, these results were reached after much longer treatment trajectories (Hofmann et al., 2012). The question arises why RT is so effective. Since in general the mechanism underlying the effect of psychotherapy is not known this important question cannot be answered yet. The unique selling point of regression therapy is the intensity by which clients process incidents that have led to part of their belief systems. RT seems very effective in addressing the root cause of an individual’s psychological or physical issues by accessing forgotten or suppressed memories that are present in the subconscious domain

A striking result in this trial was the fact that almost all dimensions in the BSI-53 reacted similarly on RT. This may not be surprising for dimensions such as depression and anxiety that show strong correlation. However, a similar phenomenon was observed for dimensions that did not show close correlation. We can speculate that an old traumatic experience can give rise to a myriad of phenotypes in later life, and as a consequence release of the emotional energy linked to the old trauma alleviates a variety of symptoms. Another striking result in this trial was the observation that a single session was for most clients enough to improve strongly. Yet we also observed that clients with a relatively high BSI-53 score at the onset of the therapy required more sessions to ameliorate symptoms (data not shown).  Interestingly, RT was also successful for clients with Medically Unexplained Symptoms (MUS). Notable outcomes were reported, such as the restoration of smell. However, due to the wide variability of symptoms, we were unable to identify distinct MUS themes. The MUS score decreased in accordance with the somatic dimension validated for the BSI-53. In the additional questions listed in Table 1, we inquired about the impact of the sessions on the clients. Given the broad range of issues among participants, the anonymous responses were diverse. The results are presented thematically in Supplemental Table 1. Insight in the mechanisms underlying the symptoms was reported most frequently. The diversity in the answers precluded precise characterization but close inspection of the answers yields some striking reports of trauma release and obliteration of all symptoms. In contrast 29 clients reported no or a minimal impact of the sessions indicating that RT was not the right approach for these clients. We also asked whether clients had past lives experiences during the therapy.  Sixty-seven clients acknowledged this, but the data did not show a significant difference in the improvement of BSI-total scores compared to present life therapy.

We applied an unbiased Machine Learning approach to answer the question which aspects of the procedure are most important for the therapeutic outcome. We included the participating therapists in the features of this procedure expecting that the quality of the individual therapists would play an important role. This did not surface in the results. Apparently, all these experienced therapists were able to convey an adequate level of trust (Wampold, 2015).The questions predicting the success of the therapy dealt mainly with resolving feelings of depression and importantly, being easily hurt. We conclude that application of unbiased machine learning technology may produce interesting mechanistic cues to identify factors important for characterizing success of psychotherapy. Clearly these results should be reproduced in independent studies.

This study has limitations. Our trial did not include a control group. Use of suitable control groups in trials aiming at measuring the effectiveness of psychotherapy is subject to controversy. For our trial a suitable control group would have been a waiting list group. Because of reported over estimation of clinical effects when including a waiting list cohort (Patterson et al., 2016, Laws et al., 2022) we decided against implementing this control group. We realize it is impossible to disentangle the influence of the individual therapist from the therapeutic technique used. We also realize that the sample of participants that has chosen for this specific therapy modality was only partly covered by health insurance payment which may have increased their motivation. Yet about half of the participants had undergone “traditional” therapy by a psychologist/psychiatrist without obtaining adequate results showing that there is a role for RT in psychotherapy.

Conclusion

This single-blinded outcome trial provides strong evidence for the high effectiveness of Regression Therapy (RT) in ameliorating a broad range of psychopathological and Medically Unexplained Symptoms (MUS). We observed a substantial reduction in BSI-53 total and domain-specific scores, reaching levels comparable to a healthy population within a remarkably short timeframe, often after just one session. The large clinical effect size (Hedges’ g = 0.95) further underscores its significant impact. Moreover, the application of unbiased machine learning revealed that the positive therapeutic outcome could be predicted with high accuracy from specific BSI-53 items, rather than being dependent on individual therapist variations, suggesting a robust and transferable therapeutic mechanism. While acknowledging the absence of a control group as a limitation, these findings advocate for RT as a potent, rapid, and broadly applicable psychotherapeutic modality for individuals experiencing diverse psychological and physical complaints.

Author contributions:

P.H., I.K. and A.K.G. conceptualized the study. P.H. and I.K were responsible for the inclusion of participants. P.H. and A.K.G: data curation: funding acquisition; A.K.G wrote the manuscript, with input from all the authors.

Financial support:

This study was supported by an unrestricted grant of the Netherlands Society of Regression Therapy NVRT). The NVRT was not involved in writing the manuscript nor the decision to submit.

Acknowledgements

We are indebted to Dr Ronald van der Maesen for critically reading the manuscript.
The authors are also indebted to the participating therapists and their clients.
This study was supported by an unrestricted grant from the Netherlands Society of Reincarnation and Regression Therapy.

 

References

Adawi, M., Zerbetto, R., Re, T.S., Bisharat, B., Mahamid, M., Amital, H., Del Puente, G., Bragazzi, N.L., 2019. Psychometric properties of the brief symptom   inventory in nomophobic subjects: Insights from preliminary confirmatory factor, exploratory factor, and clustering analyses in a sample of healthy Italian volunteers. Psychol Res Behav Manag 12.  https://doi.org/10.2147/PRBM.S173282

Akhavan Abiri, F., Shairi, M.R., 2020. Validity and Reliability of Symptom Checklist-90-Revised (SCL-90-R) and Brief Symptom Inventory-53 (BSI- 53). Clinical Psychology and Personality 17.

Butler, A.C., Chapman, J.E., Forman, E.M., Beck, A.T., 2006. The empirical status of cognitive-behavioral therapy: A review of meta-analyses. Clinical Psychology Review 26. https://doi.org/10.1016/j.cpr.2005.07.003

Cladder, H., 1983. Three years experience with reincarnation therapy. Tijdschrift voor Psychotherapie 9.

Cook, S.C., Schwartz, A.C., Kaslow, N.J., 2017. Evidence-Based Psychotherapy: Advantages and Challenges. Neurotherapeutics.             https://doi.org/10.1007/s13311-017-0549-4

Cuijpers, P., Berking, M., Andersson, G., Quigley, L., Kleiboer, A., Dobson, K.S.,  2013. A meta-analysis of cognitive-behavioural therapy for adult     depression, alone and in comparison with other treatments. Canadian Journal of Psychiatry. https://doi.org/10.1177/070674371305800702

De Beurs, E., Carlier, I., van Hemert, A., 2022. Psychopathology and health-related quality of life as patient-reported treatment outcomes: evaluation of concordance between the Brief Symptom Inventory (BSI) and the Short Form-36 (SF-36) in psychiatric outpatients. Quality of Life Research 31.  https://doi.org/10.1007/s11136-021-03019-5

De Beurs, E.D.E., Zitman, F.G., 2006. The Brief Symptom Inventory (BSI): Reliability and validity of a practical alternative to SCL-90. Maandblad Geestelijke Volksgezondheid 61.

Derogatis, L.R., 1983. The Brief Symptom Inventory: An Introductory Report. Psychol Med 13. https://doi.org/10.1017/S0033291700048017

Doherty, A.M., Gaughran, F., 2014. The interface of physical and mental health.  Soc Psychiatry Psychiatr Epidemiol. https://doi.org/10.1007/s00127-    014-0847-7

Hofmann, S.G., Asnaani, A., Vonk, I.J.J., Sawyer, A.T., Fang, A., 2012. The efficacy of CBT: a review of meta-analyses. Cognitive Therapy Research 36.     https://doi.org/10.1007/s10608-012-9476-1.

Laws, K.R., Pellegrini, L., Reid, J.E., Drummond, L.M., Fineberg, N.A., 2022. The Inflating Impact of Waiting-List Controls on Effect Size Estimates. Front Psychiatry 13. https://doi.org/10.3389/fpsyt.2022.877089

Meinshausen, N., Rocha, G., Yu, B., 2007. Discussion: A tale of three cousins: Lasso, L2boosting and dantzig. Annals of Statistics.   https://doi.org/10.1214/009053607000000460

Müller, J.M., Postert, C., Beyer, T., Furniss, T., Achtergarde, S., 2010. Comparison of     eleven short versions of the symptom checklist 90-Revised (SCL-90-R) for use in the assessment of general psychopathology. Journal of Psychopathological Behavior. Assess 32.                https://doi.org/10.1007/s10862-009-9141-5

Netherton M, 1978. Past Lives Therapy. William Morrow, 1978, New York.

Patterson, B., Boyle, M.H., Kivlenieks, M., Van Ameringen, M., 2016. The use of  waitlists as control conditions in anxiety disorders research. J Psychiatry   Res 83, 112–120. https://doi.org/10.1016/J.JPSYCHIRES.2016.08.015

Trivedi, G.T.R.G., 2022. Project completion report: regression therapy for      centralised anxiety and associated depression. International Journal of Regression Therapy 32, 1–10.

Van der Maesen, R., 1995. PLT for Gilles de la Tourette’s syndrome: a research study. International Journal of Regression Therapy 1–4.

Vander Maesen, R., 1996. Past-life therapy for people who hallucinate voices. International Journal of Regression Therapy 17, 1–5.

Wampold, B.E., 2015. How important are the common factors in psychotherapy?  An update. World Psychiatry 14, 270–277.      https://doi.org/10.1002/wps.20238

Weiss, B., 2012. Many Lives Many Masters. Simon and Shuster, New York.

 

Supplemental Table 1

Client Testimonials Summary

Responses to: “Could you briefly explain what the sessions have meant for you?”

1. Insight and Awareness

Many clients reported significant personal revelations:

• Gained clarity about deep-rooted patterns, emotions, and early life experiences
• Understood the origins of emotional and physical symptoms
• Discovered beliefs and behaviors formed in the past, allowing for conscious change
• Became aware of unconscious influences and developed better self-understanding
• Experienced connection to deeper sources of identity and purpose

2. Physical and Emotional Relief

Clients frequently described feeling lighter and more at peace:

• Relief from physical tension, chronic pain, and psychosomatic symptoms
• Reduction or disappearance of anxiety, sleep disturbances, and emotional distress
• Emotional release led to improved calmness, balance, and well-being
• Felt liberated from long-standing emotional baggage and energy blocks

3. Personal Growth and Acceptance

A strong theme was transformation in self-perception:

• Increased self-worth, confidence, and inner peace
• Acceptance of oneself and one’s emotional experiences
• Letting go of insecurities and developing more positive self-image
• Better ability to set boundaries and prioritize personal needs

4. Letting Go of Old Patterns

Clients highlighted significant inner shifts:

• Release of trauma-related behaviors and emotional patterns
• Disruption of negative cycles and unconscious burdens
• Letting go of inherited emotions or others’ feelings
• Reported lasting changes in coping mechanisms and decision-making

5. Healing Trauma and Processing Grief

The sessions were described as deeply therapeutic:

• Helped process and heal past traumas and loss
• Brought resolution in relationships with deceased loved ones
• Eased the weight of unresolved grief and generational pain
• Addressed and softened intense emotions like fear, sadness, and aggression

6. Mixed or Minimal Impact

A smaller group shared limited or unclear outcomes:

• Some found the sessions thought-provoking but not impactful
• Others reported feeling unsure or disappointed with results
• Several acknowledged needing more time, or that the therapy style wasn’t a fit

 

7. Uncertainty and Doubt

A few clients expressed confusion or ambiguity:

• Unclear about the effectiveness or meaning of insights
• Some felt disconnected from the process or unsure of personal impact

 

Author Profiles

Bé Groen is a professor of Systems Biology and Medicine with a long career in biomedical research, resulting in authorship of more than 400 peer-reviewed articles in biomedical journals. In addition to his scientific career, he was trained as a regression therapist at the SRN in the Netherlands. The dual specialty enabled him to coach the current study on the effectiveness of regression therapy.

Ingrid Klooster was trained as regression therapist at SRN in the Netherlands. In her therapeutic sessions she goes beyond aiding clients to heal childhood or past life traumas to transform their deeply rooted feelings of inadequacy into acceptance of being a beloved part of Oneness. Unconditional love and acceptance of all emotions in the therapy process as well as the equality of client and therapist are the basis of her work as regression therapist.

Paul Hooijdonk is a regression therapist trained at Tasso in the Netherlands. In addition, he works as organizational consultant, specializing in guiding individuals with ineffective communication patterns in the workplace. These patterns often stem from unconscious emotional blocks and early childhood experiences. In the study, his role was to coordinate the collaboration between the regression therapists who conducted the sessions, the organizations that sent the validated questionnaires to clients, and Bé Groen, who supervised and carried out the research at a scientific level.

[1] The Brief Symptom Inventory (BSI-53) is a 53-item self-report questionnaire used to assess psychological distress and psychopathology in adults and adolescents. It measures nine symptom dimensions: Somatization, Obsessive-compulsion, Interpersonal Sensitivity, Depression, Anxiety, Hostility, Phobic Anxiety, Paranoid Ideation, and Psychoticism, and also generates three global indices of distress: Global Severity Index (GSI), Positive Symptom Distress Index (PSDI), and Positive Symptom Total (PST). The BSI-53 is a shortened version of the Symptom Checklist-90-Revised (SCL-90-R).