Adapted Motivational Interviewing and Cognitive Behavioural Therapy for Food Addiction

Binge Eating Clinical Trial

— AMI+CBTforFA  

Official title:

Adapted Motivational Interviewing and Cognitive Behavioural Therapy for Food Addiction: A Randomized Controlled Trial

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04666831
• Other study ID #: REB 2020-517
• Status: Completed
• Phase: N/A
• Start date: March 7, 2021
• Est. completion date: January 21, 2023

Study information

• Verified date: November 2023
• Source: Toronto Metropolitan University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Food addiction is the concept that individuals can be "addicted" to foods, particularly highly processed foods. This concept has attracted growing research interest given rising obesity rates and the engineering of food products. Although food addiction is not a recognized mental disorder, individuals do identify as being addicted to foods and self-help organizations have existed since 1960 to purportedly treat it (i.e., through abstinence). However, little research has been conducted on how abstinence approaches work. Such methods may even be harmful given the risk of disordered eating. Currently, there are no empirically supported treatments for food addiction. However, evidence-based treatments do exist for addictions and eating disorders, such as motivational interviewing and cognitive behavioural therapy, which may prove beneficial for food addiction, given neural similarities between addictions and binge eating.

The current study proposes a randomized controlled trial using a four-session adapted motivational interviewing (AMI) and cognitive behavioural therapy (CBT) intervention for food addiction. This intervention combines the personalized assessment feedback and person-centred counseling of AMI with CBT skills for eating disorders, such as self-monitoring of food intake. The aim is to motivate participants to enact behavioural change, such as reduced and moderate consumption of processed foods. Outcome measures will assess food addiction and binge eating symptoms, self-reported consumption of processed foods, readiness for change, eating self-efficacy, and other constructs such as emotional eating. The intervention condition will be compared to a waitlist control group. Both groups will be assessed at pre- and postintervention periods, as well as over a 3-month follow-up period to assess maintenance effects. Based on a power analysis and previous effect sizes following AMI interventions for binge eating, a total sample size of n = 58 is needed. A total of 131 individuals will be recruited to account for previous exclusion and withdrawal rates. Participation is estimated to take place from March 2021 to March 2022. All intervention sessions will be conducted virtually over secure videoconferencing technology or telephone, expanding access to all adult community members across Ontario, Canada. Twenty randomly selected session tapes will be reviewed for MI adherence.

Description:

Background: Food addiction is the concept that individuals can be "addicted" to foods, particularly highly processed foods. This concept has attracted growing research interest given rising obesity rates and engineering of food products in industrialized countries. Food addiction is assessed using the validated Yale Food Addiction Scale (YFAS), which applies substance use disorder criteria from the most recent Diagnostic and Statistical Manual of Mental Disorders to the consumption of these types of foods. Prevalence estimates of food addiction range from 8-15% in two nationally representative samples in the U.S. and Germany. Although food addiction is not a currently recognized mental disorder, individuals do identify as being addicted to foods and self-help organizations such as Overeaters Anonymous have existed since 1960 to purportedly treat it (i.e., through abstinence). However, little research has been conducted on how abstinence approaches work and such methods may even be harmful for individuals with eating concerns, given the risk of disordered eating. Currently, there are no empirically supported treatments for food addiction. However, evidence-based treatments do exist for addictions and eating disorders, such as Adapted Motivational Interviewing (AMI) and Cognitive Behavioural Therapy (CBT), which may prove beneficial for food addiction, given neural similarities between substance addiction and binge eating, and the potential for high ambivalence. AMI is designed to allow clients to voice their own motivations for change and the use of AMI skills by therapists has been shown in meta-analyses to predict this type of change talk, which then predicts positive behavioural outcomes, Given that food addiction is also associated with internalized weight bias and lower eating self-efficacy, AMI techniques in fostering acceptance, highlighting client strengths, and providing psychoeducation may help to lower self-blame and bolster confidence to change one's eating habits.

Method: The current study proposes a randomized controlled trial using a four-session AMI and CBT intervention for food addiction. Due to COVID-19 limitations, all intervention sessions will be conducted virtually over secure videoconferencing technology or by telephone, expanding access to all adult community members across the province of Ontario in Canada. The intervention combines the personalized assessment feedback and person-centred counselling of AMI with CBT skills for eating disorders, such as self-monitoring of food intake and stimulus control. The aim is to motivate participants to enact behavioural change, such as moderate consumption of processed foods in a harm reduction approach. Twenty randomly selected session tapes will be reviewed by two trained coders to assess for MI adherence using the most commonly used MI fidelity measure. The intervention condition will be compared to a wait-list control (WLC) group. Both groups will be assessed at pre- and postintervention periods, as well as over a 3-month follow-up period to assess maintenance effects.

Hypotheses

Primary Hypotheses - Food Addiction and Binge Eating Frequency (H1-H3)

• H1: Compared to WLC, AMI will lead to a significantly greater reduction in food addiction symptoms (using the YFAS 2.0) at postintervention and up to 3 months postintervention.

• H2: Compared to WLC, AMI will lead to a significantly greater reduction in self-reported consumption of highly processed foods specified in the YFAS 2.0 (using the Canadian Diet History Questionnaire II) at postintervention and up to 3 months postintervention.

• H3: Compared to WLC, AMI will lead a to significantly greater reduction in number of binge eating episodes (using select Eating Disorder Examination Questionnaire questions) at postintervention and up to 3 months postintervention.

Secondary Hypotheses - Readiness for Change, Eating Self-Efficacy, and Weight Bias Internalization (H4-H6)

• H4: Compared to WLC, AMI will lead to a greater increase in motivation for changing one's food addiction symptoms (e.g., reducing consumption of highly processed foods; using Motivational Rulers) at postintervention.

• H5: Compared to WLC, AMI will lead to a significantly greater increase in eating self-efficacy (using the Weight Efficacy Lifestyle Questionnaire) at postintervention.

• H6: Compared to WLC, AMI will lead to a greater reduction in weight bias internalization (using the Modified Weight Bias Internalization Scale) at postintervention.

Secondary Hypotheses - Other Eating-Related Constructs (H7-H14)

It is hypothesized that AMI will lead to significantly greater reductions in other eating-related constructs compared to WLC at postintervention and up to 3 months postintervention, in terms of:

• H7: self-identified food addiction,

• H8: addiction-like eating behaviour (AEBS),

• H9: binge eating symptoms (Binge Eating Scale),

• H10: loss-of-control eating (Loss of Control over Eating Scale),

• H11: emotional eating (Emotional Eating Scale),

• H12: general appetite for palatable foods or hedonic hunger (Power of Food Scale),

• H13: cravings for specific highly processed foods (Food Craving Inventory),

• H14: Body Mass Index (BMI)

Tertiary Hypothesis - Working Alliance (H15-17)

Given that a collaborative partnership is key component of MI and that there is a robust positive association between working alliance and treatment outcomes, it is hypothesized that there will be positive associations between postintervention working alliance (using the Working Alliance Scale Short Form Revised) and postintervention motivation for change (H15), eating self-efficacy (H16), and weight bias internalization (H17).

Sample Size: Based on a power analysis and previous effect sizes following AMI interventions for binge eating (Cohen's d = 0.76), a total sample size of n = 58 is needed. Accounting for previous withdrawal rates and an inclusion rate of 44.6% in a similar study, a total of 131 individuals should be recruited. Recruitment is estimated to take place over 5 months beginning in March 2021. Given the 3-month follow-up, participation is estimated to end in March 2022.

Analyses: To determine whether both AMI and WLC groups are equivalent in terms of sample characteristics as a result of randomization, independent samples t tests will be conducted on baseline variables such as age, BMI, YFAS severity, and binge eating frequency. To determine whether sample characteristics differ between treatment completers and dropouts, independent samples t tests will be conducted on the same baseline variables and working alliance. Lastly, to determine if equal proportions dropped out of the AMI and WLC groups, a chi square test will be conducted.

Primary, Secondary, and Tertiary Outcomes: For the primary outcomes (i.e., YFAS symptoms, binge eating frequency, and consumption of highly processed foods), given the between-groups and repeated-measures mixed design, a 2 (group: WLC vs. AMI) x 4 (time: baseline, postintervention, and 1- and-, 3-month follow-up) mixed analysis of variance (ANOVA) will be conducted on SPSS statistical software. For the secondary outcomes (i.e., readiness for change, eating self-efficacy, and weight bias internalization), a 2 (group: WLC vs. AMI) x 2 (time: baseline, postintervention) mixed ANOVA will be used to compare the WLC and AMI groups from pre- to postintervention. For the other secondary eating-related outcomes, the same 2 (group) x 4 (time) mixed ANOVAs described above will be used to compare WLC and AMI groups across time. For the tertiary outcomes, to determine if there is a positive association between working alliance and readiness for change, eating self-efficacy, and weight bias internalization, two-tailed, bivariate, Pearson's correlation analyses will be performed. To explore the changes in readiness for change, eating self-efficacy, weight bias internalization, and working alliance from pre- to postintervention, paired samples t tests will be used for the AMI group for these four constructs. Prior to data analyses, data will be checked for bias and corrected as necessary. Any interactions from the ANOVAs will be followed up with planned contrasts, with the control group and baseline as the base categories for the between-groups and repeated-measures variables, respectively.

Assuming that there are no valid reasons to ignore missing data and to conduct complete case analysis (e.g., if less than 5% of data are missing), and assuming that data are missing at random, multiple imputation will be conducted on SPSS for the missing values, with at least 50 imputed datasets in order to reduce sampling variability in the imputation process. Results from complete case analyses and multiple imputation analyses will be compared for differences. To reduce bias of the imputation model, the model will include any variables that predict missing data. SPSS will automatically scan the data for a monotone pattern of missing values, and if such a pattern is present, a monotonic multiple imputation will be conducted. The default number of iterations per missing variable used will be 10 but at least 50 imputed datasets will be computed. If data are not assumed to be missing at random, sensitivity analyses will be performed for missing binary data.

Satisfaction Evaluation: Descriptive statistics will be obtained for the three quantitative satisfaction questions (e.g., an average score for how satisfied participants were with the research study). Qualitative responses from the open-ended questions will be analysed as per thematic analysis methodology.

Treatment Adherence: The minimum threshold of MI adherence will be based on the Motivational Interviewing Treatment Integrity Code (MITI) basic competence and proficiency thresholds for clinicians. Specifically, summary scores must fall in at least the "fair" scores (i.e., the Relational score must be 3.5/5, the Technical score must be 3/5, 40% of the reflections must be complex reflections, and the reflection-to-question ratio must be 1:1). If all four domains meet these thresholds, then the session will be rated as 100% fairly adherent, which will be the minimum goal. An average percentage of "fair" adherence across raters and tapes will be calculated to determine if sessions met this threshold. To determine interrater reliability for each summary score, a two-way mixed-effects model, using the mean of two raters (k = 2) and absolute agreement will be used. Intraclass correlation coefficients and their 95% confidence intervals will be reported from the SPSS output.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 94
• Est. completion date: January 21, 2023
• Est. primary completion date: January 21, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Meets criteria on the modified Yale Food Addiction Scale 2.0 for at least "Mild Food Addiction" (2 symptoms of food addiction and clinical significance)

• Fluent in English

• 18 years or older

• Have access to e-mail

• Have access to high speed internet and Zoom OR telephone

• Have private space to conduct remote therapy sessions

• Must live in the province of Ontario, Canada

Exclusion Criteria:

• Current active suicidality or recent psychiatric hospitalizations in the past 6 months

Related Conditions & MeSH terms

• Behavior, Addictive
• Binge Eating
• Binge-Eating Disorder
• Bulimia
• Food Addiction

Intervention

Behavioral:
• Adapted Motivational Interviewing (AMI) and Cognitive Behavioural Therapy (CBT)
The intervention combines AMI techniques as described by Miller and Rollnick (2013) in the third edition of their Motivational Interviewing book, as well as CBT techniques from the Tele-CBT protocol for bariatric surgery patients by Cassin et al. (2013).

Locations

Country	Name	City	State
Canada	Toronto Metropolitan University	Toronto	Ontario

Sponsors (5)

Lead Sponsor	Collaborator
Toronto Metropolitan University	BMS Canada Risk Services Ltd., Canadian Psychological Association, Council of Professional Associations of Psychology, The Jackman Foundation

Country where clinical trial is conducted

Canada, 

References & Publications (33)

American Psychiatric Association [APA]. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596

Burmeister JM, Hinman N, Koball A, Hoffmann DA, Carels RA. Food addiction in adults seeking weight loss treatment. Implications for psychosocial health and weight loss. Appetite. 2013 Jan;60(1):103-110. doi: 10.1016/j.appet.2012.09.013. Epub 2012 Sep 24. — View Citation

Cassin SE, Buchman DZ, Leung SE, Kantarovich K, Hawa A, Carter A, Sockalingam S. Ethical, Stigma, and Policy Implications of Food Addiction: A Scoping Review. Nutrients. 2019 Mar 27;11(4):710. doi: 10.3390/nu11040710. — View Citation

Cassin SE, von Ranson KM, Heng K, Brar J, Wojtowicz AE. Adapted motivational interviewing for women with binge eating disorder: a randomized controlled trial. Psychol Addict Behav. 2008 Sep;22(3):417-25. doi: 10.1037/0893-164X.22.3.417. — View Citation

Cassin, S. E., Sijercic, I., & Montemarano, V. (2020). Psychosocial interventions for food addiction: A systematic review. Current Addiction Reports, 7, 9-19. https://doi.org/10.1007/s40429-020-00295-y

Cassin, S. E., Sockalingam, S., Wnuk, S., Strimas, R., Royal, S., Hawa, R., & Parikh, S. V. (2013). Cognitive behavioral therapy for bariatric surgery patients: Preliminary evidence for feasibility, acceptability, and effectiveness. Cognitive and Behavioral Practice, 20(4), 529-543. https://doi.org/10.1016/j.cbpra.2012.10.002

de Jong M, Schoorl M, Hoek HW. Enhanced cognitive behavioural therapy for patients with eating disorders: a systematic review. Curr Opin Psychiatry. 2018 Nov;31(6):436-444. doi: 10.1097/YCO.0000000000000452. — View Citation

Dunn EC, Neighbors C, Larimer ME. Motivational enhancement therapy and self-help treatment for binge eaters. Psychol Addict Behav. 2006 Mar;20(1):44-52. doi: 10.1037/0893-164X.20.1.44. — View Citation

Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage Publications Ltd.

Fluckiger C, Del Re AC, Wampold BE, Horvath AO. The alliance in adult psychotherapy: A meta-analytic synthesis. Psychotherapy (Chic). 2018 Dec;55(4):316-340. doi: 10.1037/pst0000172. Epub 2018 May 24. — View Citation

Gearhardt AN, Corbin WR, Brownell KD. Development of the Yale Food Addiction Scale Version 2.0. Psychol Addict Behav. 2016 Feb;30(1):113-21. doi: 10.1037/adb0000136. — View Citation

Hauck C, Weiss A, Schulte EM, Meule A, Ellrott T. Prevalence of 'Food Addiction' as Measured with the Yale Food Addiction Scale 2.0 in a Representative German Sample and Its Association with Sex, Age and Weight Categories. Obes Facts. 2017;10(1):12-24. doi: 10.1159/000456013. Epub 2017 Feb 11. — View Citation

IBM Corp. (n.d.). Impute missing data values (multiple imputation). IBM Knowledge Center. https://www.ibm.com/support/knowledgecenter/en/SSLVMB_24.0.0/spss/mva/idh_idd_mi_variables.html

Ifland JR, Preuss HG, Marcus MT, Rourke KM, Taylor WC, Burau K, Jacobs WS, Kadish W, Manso G. Refined food addiction: a classic substance use disorder. Med Hypotheses. 2009 May;72(5):518-26. doi: 10.1016/j.mehy.2008.11.035. Epub 2009 Feb 14. — View Citation

Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol. 2017 Dec 6;17(1):162. doi: 10.1186/s12874-017-0442-1. — View Citation

Knowles L, Anokhina A, Serpell L. Motivational interventions in the eating disorders: what is the evidence? Int J Eat Disord. 2013 Mar;46(2):97-107. doi: 10.1002/eat.22053. Epub 2012 Sep 24. — View Citation

Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016 Jun;15(2):155-63. doi: 10.1016/j.jcm.2016.02.012. Epub 2016 Mar 31. Erratum In: J Chiropr Med. 2017 Dec;16(4):346. — View Citation

Kramer H. Kidney Disease and the Westernization and Industrialization of Food. Am J Kidney Dis. 2017 Jul;70(1):111-121. doi: 10.1053/j.ajkd.2016.11.012. Epub 2017 Jan 23. — View Citation

Magill M, Apodaca TR, Borsari B, Gaume J, Hoadley A, Gordon REF, Tonigan JS, Moyers T. A meta-analysis of motivational interviewing process: Technical, relational, and conditional process models of change. J Consult Clin Psychol. 2018 Feb;86(2):140-157. doi: 10.1037/ccp0000250. Epub 2017 Dec 21. — View Citation

Magill M, Gaume J, Apodaca TR, Walthers J, Mastroleo NR, Borsari B, Longabaugh R. The technical hypothesis of motivational interviewing: a meta-analysis of MI's key causal model. J Consult Clin Psychol. 2014 Dec;82(6):973-83. doi: 10.1037/a0036833. Epub 2014 May 19. — View Citation

Meadows A, Nolan LJ, Higgs S. Self-perceived food addiction: Prevalence, predictors, and prognosis. Appetite. 2017 Jul 1;114:282-298. doi: 10.1016/j.appet.2017.03.051. Epub 2017 Apr 3. — View Citation

Miller, W. R., & Rollnick, S. (2013). Applications of motivational interviewing. Motivational interviewing: Helping people change (3rd ed.). Guilford Press.

Moss, M. (2013, February 20). The extraordinary science of addictive junk food. New York Times Magazine. https://www.nytimes.com/2013/02/24/magazine/the-extraordinary-science-of-junk-food.html

Moyers TB, Rowell LN, Manuel JK, Ernst D, Houck JM. The Motivational Interviewing Treatment Integrity Code (MITI 4): Rationale, Preliminary Reliability and Validity. J Subst Abuse Treat. 2016 Jun;65:36-42. doi: 10.1016/j.jsat.2016.01.001. Epub 2016 Jan 13. — View Citation

Moyers, T. B., Manuel, J. K., & Ernst, D. (2015). Motivational Interviewing Treatment Integrity Coding Manual 4.2.1. Center on Alcoholism, Substance Abuse, & Addictions [Unpublished manual]. https://casaa.unm.edu/codinginst.html

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1-13. https://doi.org/10.1177/1609406917733847

Reid, J., O'Brien, K. S., Puhl, R., Hardman, C. A., & Carter, A. (2018). Food addiction and its potential links with weight stigma. Current Addiction Reports, 5(2), 192-201. https://doi.org/10.1007/s40429-018-0205-z

Russell-Mayhew S, von Ranson KM, Masson PC. How does overeaters anonymous help its members? A qualitative analysis. Eur Eat Disord Rev. 2010 Jan;18(1):33-42. doi: 10.1002/erv.966. — View Citation

Schulte EM, Gearhardt AN. Associations of Food Addiction in a Sample Recruited to Be Nationally Representative of the United States. Eur Eat Disord Rev. 2018 Mar;26(2):112-119. doi: 10.1002/erv.2575. Epub 2017 Dec 21. — View Citation

Schulte EM, Grilo CM, Gearhardt AN. Shared and unique mechanisms underlying binge eating disorder and addictive disorders. Clin Psychol Rev. 2016 Mar;44:125-139. doi: 10.1016/j.cpr.2016.02.001. Epub 2016 Feb 4. — View Citation

Smedslund G, Berg RC, Hammerstrom KT, Steiro A, Leiknes KA, Dahl HM, Karlsen K. Motivational interviewing for substance abuse. Cochrane Database Syst Rev. 2011 May 11;2011(5):CD008063. doi: 10.1002/14651858.CD008063.pub2. — View Citation

Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, Wood AM, Carpenter JR. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009 Jun 29;338:b2393. doi: 10.1136/bmj.b2393. — View Citation

Treasure J, Leslie M, Chami R, Fernandez-Aranda F. Are trans diagnostic models of eating disorders fit for purpose? A consideration of the evidence for food addiction. Eur Eat Disord Rev. 2018 Mar;26(2):83-91. doi: 10.1002/erv.2578. Epub 2018 Jan 17. — View Citation

* Note: There are 33 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Impulsivity	Measured by the Barratt Impulsiveness Scale, 15-Item Short Form. Minimum score = 15, Maximum score = 60. Higher values mean worse outcomes.	Baseline
Primary	Food Addiction Symptoms	Measured by the Yale Food Addiction Scale 2.0. Minimum score = 0 symptoms, Maximum score = 11 symptoms. Greater symptoms mean worse outcome.	Change from baseline to 3 months postintervention
Primary	Consumption of Highly Processed Foods	Measured by the Canadian Diet History Questionnaire II. Minimum score = 0. There is no maximum score, as this measures caloric consumption. Higher values mean worse outcome.	Change from baseline to 3 months postintervention
Primary	Binge Eating Frequency	Measured by select Eating Disorder Examination Questionnaire 6.0 questions. Minimum score = 0. There is no maximum as this measures binge eating frequency. Higher values mean worse outcome.	Change from baseline to 3 months postintervention
Secondary	Motivation to Change Eating	Measured by MI Motivational Rulers. Minimum score = 0, Maximum score = 30. Higher values mean better outcome.	Change from baseline and immediately postintervention
Secondary	Eating Self-Efficacy (confidence to resist the desire to eat in various situations) as assessed by the Weight Efficacy Lifestyle Questionnaire	Measured by the Weight Efficacy Lifestyle Questionnaire. Minimum score = 0, Maximum score = 180. Higher values mean better outcome.	Change from baseline and immediately postintervention
Secondary	Weight Bias Internalization	Measured by the Modified Weight Bias Internalization Scale. Minimum score = 11, Maximum score = 77. Higher values mean worse outcome.	Change from baseline and immediately postintervention
Secondary	Self-Identified Food Addiction	Measured by two yes/no questions related to self-perceived food addiction. Responses are yes/no (no minimum or maximum scores). Yes means worse outcome.	Change from baseline to 3 months postintervention
Secondary	Addiction-like Eating Behaviour	Measured by Addiction-like Eating Behaviour Scale. Minimum score = 15, Maximum score = 75. Higher values mean worse outcome.	Change from baseline to 3 months postintervention
Secondary	Binge Eating Symptoms	Measured by Binge Eating Scale. Minimum score = 0, Maximum score = 46. Higher values mean worse outcome.	Change from baseline to 3 months postintervention
Secondary	Loss of Control Eating	Measured by Loss of Control over Eating Scale. Minimum score = 7, Maximum score = 35. Higher values mean worse outcome.	Change from baseline to 3 months postintervention
Secondary	Emotional Eating	Measured by Emotional Eating Scale. Minimum score = 25, Maximum score = 125. Higher values mean worse outcomes.	Change from baseline to 3 months postintervention
Secondary	General Appetite for Palatable Foods or Hedonic Hunger	Measured by Power of Food Scale. Minimum score = 15, Maximum score = 75. Higher values mean worse outcomes.	Change from baseline to 3 months postintervention
Secondary	Cravings for Specific Highly Processed Foods	Measured by Food Craving Inventory. Minimum score = 28, Maximum score = 140. Higher values mean worse outcomes.	Change from baseline to 3 months postintervention
Secondary	Body Mass Index	Measured by (weight/height^2). There is no minimum or maximum BMI, as it measures weight and height. For the purposes of this study, higher BMI means worse outcome although this is very individual and is not necessarily true in every case.	Change from baseline to 3 months postintervention
Secondary	Working Alliance	Measured by Working Alliance Short Form Revised. Minimum score = 12, Maximum score = 60. Higher values mean better outcomes.	During the intervention (change from session 1 to session 4)