Hypnotherapy With Audiofiles for Children and Adolescents With Disorders of Gut-brain Interactions in Sweden -a Feasibility Study
Phase: NA
Conditions: Self-hypnosis, Functional Bowel Disorder, Functional Abdominal Pain Syndrome
Sponsor: Karolinska Institutet

Study protocol and statistical analysis plan 
Date: Apr 3, 2025 
 
Study title: 
Hypnotherapy With Audiofiles for Children and Adolescents With Disorders of Gut-
brain Interactions in Sweden -a Feasibility Study 
 
 

Scientific Summary 
Background: 
Functional gastrointestinal disorders (FGID) with abdominal pain, such as Irritable Bowel 
Syndrome (IBS), affect 10–25% of all children and adolescents in Sweden. These conditions 
are often associated with high school absenteeism, reduced quality of life, and frequent 
healthcare use. For children and adolescents with IBS, various psychosocial treatments are 
the only interventions that have shown good effect in scientific studies, while 
pharmacological treatments have had limited or no effect. In Sweden, the most commonly 
used and studied treatment for this patient group is exposure-based Cognitive Behavioral 
Therapy (CBT), which is also available as internet-delivered CBT (iCBT) in standard 
healthcare. However, iCBT requires therapist support from a psychologist, resulting in long 
wait times, and not all patients benefit. Therefore, additional evidence-based and preferably 
more accessible alternatives are in demand. 
Hypnotherapy involving gut-directed meditation and relaxation exercises has shown good 
treatment effects for FGID in children and adolescents in international studies, but this form 
of treatment is currently not available in Sweden. Studies have also shown that home-based 
hypnotherapy with self-guided exercises via audio files is as effective as individual 
hypnotherapy, making it a cost-effective treatment. Audio files allow treatment to begin 
immediately after diagnosis by a physician, avoiding long wait times like those for iCBT. If 
hypnotherapy proves to be as effective in Swedish standard care as in international studies, it 
would represent a major advancement for the large and suffering group of children and 
adolescents with FGID. 
Aim: 
In an international research collaboration, we intend to translate and adapt hypnotherapy 
audio files — proven highly effective in a Dutch context — to Swedish conditions so that 
they can be used to treat children and adolescents in Sweden. The feasibility study described 
in this project plan is required to evaluate effect sizes and the practical aspects of the 
treatment. Our experience from previous treatment studies is that the insights gained from 
this type of feasibility study provide the knowledge needed to conduct large and successful 
randomized controlled trials (RCTs) in the next phase — for example, comparing 
hypnotherapy and iCBT in terms of effectiveness, cost-efficiency, and which patients respond 
best to which treatment. The overarching goal of the project is to help children and 
adolescents with FGID by ultimately introducing a new treatment option into standard care. 
Feasibility Study Research Questions: 
1. Can children and adolescents with pain-dominant FGID experience reduced 
gastrointestinal symptoms through hypnotherapy with audio files? 
2. Can children and adolescents with pain-dominant FGID experience improved daily 
functioning through hypnotherapy with audio files? 
3. Can children and adolescents with pain-dominant FGID experience improved quality 
of life through hypnotherapy with audio files? 
4. What practical challenges may arise in patient recruitment, data collection, and the 
treatment itself (that need to be addressed before a larger RCT can be conducted)? 
5. Feasibility Study Method: 
A feasibility study of the hypnotherapy intervention without a control group to 
evaluate feasibility, acceptability, and effect sizes for the primary outcome measures. 

Children (N=20–30) of school age (8–17 years) with pain-dominant FGID (defined 
according to the Rome IV criteria) will be recruited from a pediatric outpatient clinic 
in Stockholm. Measurements of, among other things, gastrointestinal symptoms, 
functional level, and quality of life will be conducted using validated instruments 
before, during (every three weeks), and after treatment. 
6. Significance of the Feasibility Study: 
The feasibility study will provide estimates of effect sizes for the outcome measures 
(required for power analysis) and offer invaluable practical experience that will make 
it possible to later carry out a successful large-scale clinical trial. 
 
Project Description: 
The first part of the project (which this application concerns) consists of a feasibility study in 
which the applicability, preliminary effectiveness, and mechanisms of action of the 
hypnotherapy treatment will be evaluated in a smaller patient group. The goal is to recruit 
20–30 patients with a confirmed FGID diagnosis from the pediatric gastroenterology clinic at 
Sachsska Children’s Hospital and/or other hospital-based specialist clinics at Sachsska. These 
patients will undergo a 12-week treatment involving hypnotherapy self-exercises using audio 
files at least 5 times per week. Patients and their parents will complete self-report 
assessments on symptom severity, quality of life, school absenteeism, etc., using standardized 
questionnaires via an online platform every three weeks. A table listing the outcome 
measures is attached. 
All patients will be contacted by a gastroenterology-focused physician or nurse at the clinic 
or by phone (if referred from another unit) before inclusion to assess inclusion and exclusion 
criteria, inform the family about the study, and go over the treatment principles. 
Inclusion criteria: 
• 
Age 8–17 years 
• 
Confirmed diagnosis of functional dyspepsia, functional abdominal pain, or IBS 
according to Rome IV criteria (including evaluation for organic differential 
diagnoses) 
• 
Reading and writing proficiency in Swedish 
• 
Less than 40% school absenteeism 
• 
Stable dose (for over 1 month) if on psychopharmacological medication 
Exclusion criteria: 
• 
Non-functional medical condition 
• 
More than 40% school absenteeism 
• 
Ongoing psychotherapeutic treatment 
• 
Severe psychosocial or psychiatric problems requiring more intensive care than 
hypnotherapy 
These criteria will be assessed using questionnaires and individual interviews before 
treatment begins. 

Data Analysis: 
For the feasibility study, 20–30 patients will be recruited — the number is based on prior 
experience with similar pilot studies. Data will be collected every three weeks from the start 
to the end of treatment via a secure online service with identity verification. The same 
platform will be used to distribute treatment files and instructions to patients and their 
parents. Data will be stored in a secure database protected by two-factor authentication and 
access control. Once the data is no longer needed for the study, the key to the code will be 
destroyed (no later than 10 years after data collection). 
An improvement of >30% on the primary outcome is defined as clinically significant, based 
on recommendations from previous studies. Although data will be collected across multiple 
outcomes and time points, a preliminary mediation analysis can also be conducted to assess 
what drives potential improvements and in which phase of treatment this occurs. Statistical 
analyses will be conducted by a statistician with experience in similar studies and analyses. 
 
Data Analysis Plan: 
1 Methods 
The planned RCT shares many similarities with Bonnert et al. (2017) and Bonnert et al. (2014). 
1.1 Mixed models 
We let 𝒚𝑖=(𝑦𝑖1,...,𝑦𝑖𝑑) denote the response vector of 𝑑 repeated measurements for 
individual 𝑖=1,...,𝑛. For example, when modeling Peds-QL gastro the observed value 𝑦𝑖𝑗 in 
vector 𝒚𝑖 represents the observed value of Peds-QL gastro at week 𝑗 for patient 𝑖. In a GLMM, 
the conditional expected value of 𝑦𝑖𝑗 is given by 
𝑔[𝐸(𝑦𝑖𝑗|𝒖𝑖)]=𝒙𝑖𝑗𝜷+𝒛𝑖𝑗𝒖𝑖, 
where 𝑔 is the link function, 𝒙𝑖𝑗 and 𝒛𝑖𝑗 are vectors of observed values of explanatory variables, 
the parameters 𝛽 are the fixed effects, and 𝑢𝑖 are the random effects. The LMM is a special 
case of GLMM for which 𝑔 is the identity link function 
𝐸(𝑦𝑖𝑗|𝒖𝑖)=𝒙𝑖𝑗𝜷+𝒛𝑖𝑗𝒖𝑖. 
1.1.1 Model specification 
For each outcome variable with more than two measurements per patient (i.e., not only at week 
0 and at week 12), we estimate the pre-post treatment effect using an LMM fitted by restricted 
maximum likelihood (REML). The model can be written as 
𝑦𝑖𝑗=𝛽0+𝛽1WEEK𝑖𝑗+𝑢𝑖+𝜖𝑖𝑗, 
where 𝑦𝑖𝑗 represents the value of the outcome for patient 𝑖 at week 𝑗, 𝛽0 is the fixed 
intercept, 𝛽1 is the fixed slope coefficient that captures the average change of the outcome 
variable given one more week of treatment, and 𝑢𝑖 is the patient specific random intercept. By 
using random intercepts we allow patients to differ at baseline. For example, some patients 
might have much higher Peds-QL gastro than others at baseline. 
Due to the small sample size the decision was made to not include quadratic effects or random 
slopes as it cause convergence issues. However, normally these should be included if they 

improve the goodness of fit of the model (based on i.a. the distribution of residuals, likelihood 
ratio tests, or the Akaike information criterion). A model that includes both quadratic effects and 
random slopes can be written as 
𝑦𝑖𝑗=𝛽0+𝛽1WEEK𝑖𝑗+𝛽2WEEK2𝑖𝑗+𝑢0𝑖+𝑢1𝑖WEEK𝑖𝑗+𝜖𝑖𝑗=𝛽0+𝑢0𝑖+(𝛽1+𝑢1𝑖)WEEK𝑖𝑗+𝛽2WEEK2
𝑖𝑗+𝜖𝑖𝑗. 
By including a quadratic effect for weeks of treatment we account for nonlinear trends. For 
example, it might be that the effect of the treatment is decreasing over time. Furthermore, by 
including random slopes we allow for the weekly effect of the treatment to differ between 
patients. 
For parent rated outcomes, we should also consider using a 3-level mixed model were we 
cluster the data by both parent and adolescent. 
1.1.2 LMM estimate of Cohen’s d 
To obtain a standardized pre-post treatment effect (Cohen’s 𝑑), the LMM estimate of the 12-
week treatment effect is divided (standardized) by the standard deviation at baseline. That is, 
the LMM estimate of Cohen’s 𝑑 is defined as 
𝑑=12×𝛽1𝑆𝐷𝑤𝑒𝑒𝑘=0. 
The confidence interval for Cohen’s 𝑑 is based on the LMM estimated standard errors (SE), that 
is 
𝑆𝐸𝑑=12×𝑆𝐸𝛽2𝑆𝐷𝑤𝑒𝑒𝑘=0. 
Standard errors can also be computed using bootstrap. This is particularity useful in case of a 
complex model for which Cohen’s 𝑑 is estimated using more than one parameter For example, 
if the model includes a quadratic treatment effect, then Cohen’s 𝑑 is estimated as follows 
𝑑=12×𝛽1+122×𝛽2𝑆𝐷𝑤𝑒𝑒𝑘=0. 
The standard error of Cohen’s 𝑑 with a quadratic treatment effect can also be calculated using 
the formula 𝑉𝑎𝑟(𝑎𝑋+𝑏𝑌)=𝑎2𝑉𝑎𝑟(𝑋)+𝑏2𝑉𝑎𝑟(𝑌)+2𝑎𝑏𝐶𝑜𝑣(𝑋,𝑌), where 𝑋=𝛽1 and 𝑌=𝛽2. 
However, since multicollinearity can be a problem in polynomial regression which can lead to 
inflated standard errors, the variable for weeks of treatment should first be centralized. 
1.2 Cohen’s d for paired data 
Since the outcome variables for depression (CDI-S), anxiety (SCAS-S-C), quality of life of 
parents, and school absence only have two measurements per patient at week 0 and week 12, 
the decision was made to estimate the effect size for these variables using Cohen’s 
adjusted 𝑑 for paired data together with Hedges’ small-sample bias correction, see Borenstein 
et al. (2009) . It is important to note that as this estimator requires paired data only patients with 
an observed value at both week 0 and at week 12 can be included. 
We let (𝑦𝑖,𝑤𝑒𝑒𝑘=0,𝑦𝑖,𝑤𝑒𝑒𝑘=12) denote a pair of observed values for some outcome variable 𝑌 at 
week 0 and 12 for patient 𝑖 among 𝑛 pairs with 𝑑𝑖=𝑦𝑖,𝑤𝑒𝑒𝑘=0−𝑦𝑖,𝑤𝑒𝑒𝑘=12. Then Cohen’s 𝑑 is 
defined as 
𝑑=𝑌¯𝑑𝑖𝑓𝑓𝑆𝑤𝑖𝑡ℎ𝑖𝑛=𝑌¯𝑑𝑖𝑓𝑓𝑆𝑑𝑖𝑓𝑓/2(1−𝑟)‾‾‾‾‾‾‾‾√ 
where 𝑌𝑑𝑖𝑓𝑓 and 𝑆𝑑𝑖𝑓𝑓 is the mean and standard deviation of 𝑑𝑖, and 𝑟 is the estimated 
correlation between weeks 0 and 12. The variance of Cohen’s 𝑑 is given by 
𝑉𝑑=(1𝑛+𝑑22𝑛)2(1−𝑟). 
To get the Hedges’ corrected 𝑑, denoted 𝑔, we use the correction factor 𝐽 as follows 
𝑔=𝐽×𝑑𝑉𝑔=𝐽2×𝑉𝑑 
where 

𝐽=1−34𝑑𝑓−1 
with the degrees of freedom 𝑑𝑓=2𝑛−2. 
1.3 Paired T-test 
The paired 𝑇-test can be used to test the null hypothesis that the pre-post mean difference is 
equal to zero. The test requires paired data and shares strong similarities with Cohen’s 
adjusted 𝑑 for paired data. Letting 𝑌¯𝑑𝑖𝑓𝑓 denote the mean difference and 𝑆𝑑𝑖𝑓𝑓 denote the 
standard deviation of the difference, the test statistic is defined as 
𝑡=𝑌¯𝑑𝑖𝑓𝑓𝑆𝑑𝑖𝑓𝑓/𝑛√. 
1.4 Unpaired Two-sample T-test 
To compare the effect of the hypnotherapy treatment between two age groups at week 12 we 
can perform a two-sample T-test assuming unequal variances. The test statistic is calculated as 
𝑡=𝑦¯1−𝑦¯2𝑠21𝑛1+𝑠22𝑛2‾‾‾‾‾‾‾√ 
where 𝑦¯1 and 𝑦¯2 represent the mean values, 𝑠21 and 𝑠22 represent the estimated variances, 
and 𝑛1 and 𝑛2 the sample sizes for age group 1 and 2 at week 12. Note that the T-test only 
uses data at week 12. Another option is to include an interaction term between age group and 
weeks of treatment in the mixed model in order to compare the pre-post treatment effect 
between the groups. 
Seeing that our sample size is very small, we should avoid making to many tests as it could be 
viewed as P-value hacking. 
1.5 Imputation of missing values 
The pilot study includes 32 patients at baseline (week 0). Of these patients, there are 21 patients 
with complete follow-up (no missing data), 25 patients with no missing data at week 12, and 2 
patients with no data after week 0. Data is rarely missing completely at random (MCAR). It is 
more likely to assume that there are systematic differences between the non-participating 
patients (i.e. the patients who at some point decided to drop out) and the participating patients, 
which could bias the results. For example, some patients might have discontinued the treatment 
due to insufficient treatment effect. If that is the case, there is a risk that the estimated effect of 
the treatment could be overestimated if the missing values are not handled correctly. For this 
reason, a sensitivity analysis was made, comparing the results of using no imputation to the 
results of using Last Observation Carried Forward (LOCF). A drawback of LOCF is that it 
underestimates the variability in the data, which can lead to too narrow confidence intervals. 
Another alternative is to use multiple imputation, which account for the uncertainty of estimating 
the missing data. However, if the observed data cannot account for the systematic differences, 
that is if the data is missing not at random (MNAR), multiple imputation may still lead to biased 
results. 
1.6 Limitations 
No control group: It is important to note that since we do not have a control group to compare 
with, the risk of receiving biased results is high, especially due to the phenomenon known as 
regression toward the mean. That is, many of the patients who were in poor health at baseline 
(week 0) would probably have felt better at week 12 even without treatment. 
No analysis of outliers: No analysis of outliers has been performed. 

No comparison of models: Due to the small sample size, the decision was made to only fit an 
LMM with patient specific random intercepts. However, a larger sample size would enable us to 
us more complex models that can provide better fit to the data. In a more complex model we 
can for example include quadratic effects, random slopes, or interaction terms. For parent rated 
outcomes, we should also consider using a 3-level mixed model were we cluster the data by 
both parent and adolescent. 
Modeling discrete variables as continuous variables: The outcome variable for pain 
frequency represents the number of days during a week that the individual felt pain, which 
means that it is a discrete variable with a minimum value of zero and a maximum value of seven. 
In this analysis we will model pain frequency as if it was a continuous variable using LMM for 
ease of interpretation. However, count data is usually modeled using Poisson regression.