Hugo Hesser

Emotion dysregulation is a potentially important process to target in eating disorder treatment. This study aimed to investigate whether emotion dysregulation may be improved in individuals with eating disorders using a brief online treatment designed specifically to increase emotion regulation skills. A replicated single-case experimental randomized AB-design was used to investigate the added effect of emotion regulation interventions (B phase), beyond the effects of eating disorder treatment focusing on normalization of eating and refrainment from compensatory behaviors (A phase). Twelve individuals with eating disorders were recruited from psychiatric outpatient services, and eight received an 8-week online treatment. The start of B phase in treatment (i.e. emotion regulation interventions) was randomized for each participant. Emotion dysregulation and negative affect were assessed every third day in both phases (20 assessments in total). Visual and baseline-corrected Tau analyses revealed short-term positive experimental effects on negative affect and emotion regulation for four participants, suggesting that emotion regulation interventions may be beneficial for some individuals. The study also demonstrated the feasibility and acceptability of delivering and evaluating treatment in this intensified online format, allowing for future detailed investigation of treatment processes and components at the intraindividual level.

Introduction: Mental health conditions are prevalent during adolescence, but access to early evidence-based treatments remains limited. Scalable, online, theory-based transdiagnostic interventions delivered in primary care have the potential to reduce this treatment gap, but the effectiveness remains unknown. The objective is to investigate whether an online transdiagnostic emotion regulation treatment for adolescents with mental health conditions is superior to an active control treatment. Methods and analysis: This single-blind, randomised clinical superiority trial will evaluate the effectiveness and cost-effectiveness of an online transdiagnostic emotion regulation treatment for adolescents with mental health conditions within primary care in Sweden. We aim to include 388 participants (adolescents aged 12–17 years with mental health conditions and their parents), recruited through primary care and self-referral. Patients will be randomised to either an online transdiagnostic emotion regulation treatment or an active control treatment. Participants will receive 6 weeks of therapist-guided online transdiagnostic emotion regulation treatment or an active control treatment consisting of 6 weeks of online supportive treatment. Parents will participate in parallel with their adolescents in both conditions and will receive an online parent course. The primary outcome, assessed by blinded assessors, will be clinical global symptom severity measured with the Clinical Global Impressions-Severity scale. Treatment effectiveness will be evaluated through blinded assessment and self-assessment at primary endpoint (immediately after treatment) and 3 months after treatment. In addition, a health economic evaluation will be conducted. The study will be undertaken between October 2023 and July 2026. Ethics and dissemination: The study has obtained ethical approval from the Swedish Ethical Review Authority. Findings will be disseminated in peer-reviewed publications and presented at scientific conferences. Trial registration number: NCT06067165 .

Aims: Low‐energy diet (LED) plus cognitive‐behavioural therapy (CBT) produces substantial short‐term weight loss in obesity, but long‐term maintenance remains difficult. We evaluated whether adding intragastric balloon (IGB) treatment to group‐based CBT improves weight maintenance after initial weight loss using LED. Materials and Methods: In this open‐label randomized controlled trial, adults aged 30–65 years with BMI 32.5–45 kg/m ² underwent a 6‐month run‐in period combining LED and group‐based CBT and were subsequently randomized to CBT and 1‐year IGB or CBT alone. The primary endpoint was weight change in percent between randomization (6 months from enrollment) and 18 months (from enrollment), compared to total body weight (TBW) at enrollment. Secondary outcomes included the proportion of participants achieving weight loss thresholds, metabolic parameters and health‐related quality of life (HRQoL). Results: Of 126 enrolled (mean 43.7 years; BMI 38.0 kg/m ² ), 107 were randomized 6 months after enrollment (IGB n = 51; CBT n = 56) and lost −18.7% (SE: 0.64) of TBW. At 18 months, the mean weight change from randomization was 5.2% with IGB versus 10.1% with CBT ( p < 0.001). A significantly greater proportion of IGB recipients achieved ≥ 5% (84.3% vs. 63.6%; p = 0.021) and ≥ 10% (70.6% vs. 45.5%; p = 0.013) weight loss after 18 months, compared to the CBT group. Improvements in metabolic parameters and HRQoL showed no between‐group differences. Conclusions: As an adjunct to group‐based CBT and after LED‐induced weight loss, IGB treatment provided a modest advantage in weight maintenance for up to 18 months, although safety concerns remain and long‐term studies are warranted.

Therapist-guided internet-based cognitive behavioural therapy (iCBT) has improved access to treatment for depression and anxiety, but scalability is limited by reliance on trained therapists. This feasibility trial evaluated a tailored digital self-help intervention for adults with depressive and anxiety symptoms, delivered with or without clinician guidance. In total, 124 participants in Sweden with at least mild depressive or anxiety symptoms were recruited through social media and randomized (1:1) to an eight-week program with (n = 63) or without (n = 61) clinician guidance. All participants completed telephone assessments before and after the intervention. The primary aim was to assess feasibility of both versions, while considering outcome advantages of guidance and reduced clinician time with self-help. Feasibility outcomes included adherence, credibility, satisfaction, and adverse events. The intervention was perceived as credible, with high satisfaction and engagement: most participants used the program weekly, and two thirds completed at least three of five modules. Outcomes were similar across groups, though participants with guidance reported higher satisfaction. Clinician time averaged 36 minutes for self-help participants and 66 minutes for those receiving guidance. Both groups showed large symptom reductions (Cohen's d = 1.05-1.10), supporting feasibility and motivating future trials comparing self-help and clinician-guided delivery.

Despite documented risks associated with excessive anger inhibition, few treatments specifically target maladaptive anger inhibition. Building on a previously evaluated treatment for general anger, this study evaluated the feasibility, acceptability, and preliminary efficacy of a brief (4-week), therapist-supported online emotion-regulation treatment for maladaptive anger inhibition. In total 38 participants (89.5% female; M-age = 43.8, SD = 14.1) with elevated levels of maladaptive anger inhibition were offered treatment, which was evaluated using a pooled interrupted time series design (ITS). Treatment outcomes included measures of anger inhibition, anger rumination, and anger assertion that were assessed during a baseline phase (4 weeks) and a treatment phase (4 weeks). Feasibility was supported by high retention and adherence, and minor adverse events. Moreover, participants reported high treatment credibility and satisfaction, indicating good acceptability of the treatment. Piecewise growth curve analysis for ITS, comparing baseline and treatment phases, revealed significant and large immediate effects on all outcomes: anger inhibition (d = 1.26), anger rumination (d = 0.80), and anger assertion (d = 0.79), with small additional gains observed at 3-month follow-up. Taken together, the findings suggest that this brief online emotion-regulation treatment for maladaptive anger inhibition is feasible, acceptable, potentially effective, warranting further investigation in an RCT. ClinicalTrials.gov identifier: NCT06697587.

INTRODUCTION: Irritable bowel syndrome (IBS) is a common and debilitating disorder. When dietary and pharmacological interventions are not satisfactory, psychological treatment may produce good results. However, the access to such treatment is scarce, and therefore, it is of importance to make use of technical solutions. In this study, we wanted to investigate the real-world effectiveness of an Internet-delivered exposure-based cognitive behavior therapy (ECBT) for IBS and to replicate an earlier finding regarding the working mechanism of the treatment. METHODS: A total of 309 consecutively recruited patients from the Internet Psychiatry Clinic in Stockholm received ECBT for 12 weeks. The patients' IBS symptoms, quality of life, avoidance behaviors, and gastrointestinal symptom-specific anxiety were monitored, and we used a bivariate cross-lagged panel model to investigate time-related change in symptoms and avoidance behaviors. RESULTS: IBS symptoms, measured with the Gastrointestinal Symptom Rating Scale for IBS, were reduced from 48.06 (SD = 11.26) before treatment to 33.06 (SD = 10.81) 6 months after treatment ( P < 0.001). The effect size (calculated by Cohen d ) was 1.30 (1.08–1.51). There was a significant ( P < 0.001) cross-lagged effect from reduction in avoidance behavior to reduction in symptoms but not in the reverse direction, indicating that the treatment effect is mediated by behavioral change. DISCUSSION: We conclude that ECBT is effective under real-world conditions, also when delivered through the Internet, and that an important treatment mechanism is the reduction of avoidance behaviors.

Individuals with social anxiety disorder (SAD) experience overall emotion regulation difficulties, but less is known about the long-term role of such difficulties in cognitive behavior therapy (CBT) for SAD. Forty-six patients with SAD receiving internet-delivered CBT, and matched healthy controls (HCs; n = 39), self-reported the Difficulties in Emotion Regulation Scale (DERS), Liebowitz Social Anxiety Scale (LSAS-SR), and participated in anticipatory speech anxiety behavioral experiments. Patients were measured at seven time points before, during and after CBT over a total period of 28 months, and HCs at two timepoints. Disaggregated growth curve models with a total of 263 observations were used, as well as intra-class correlation coefficients and regression models. Patients' LSAS-SR and DERS ratings were reliable (ICC = .83 and .75 respectively), and patients, relative to controls, showed larger difficulties in emotion regulation at pre-treatment (p < .001). During CBT, within-individual improvements in emotion regulation significantly predicted later LSAS-SR reductions (p = .041, pseudo-R² = 43%). Changes in emotion regulation may thus be important to monitor on an individual level and may be used to improve outcomes in future developments of internet-delivered CBT.

The present study combined a supervised machine learning framework with an unsupervised method, finite mixture modeling, to identify prognostically meaningful subgroups of diverse chronic pain patients undergoing interdisciplinary treatment. Questionnaire data collected at pre-treatment and 1-year follow up from 11,995 patients from the Swedish Quality Registry for Pain Rehabilitation were used. Indicators measuring pain characteristics, psychological aspects, and social functioning and general health status were used to form subgroups, and pain interference at follow-up was used for the selection and the performance evaluation of models. A nested cross-validation procedure was used for determining the number of classes (inner cross-validation) and the prediction accuracy of the selected model among unseen cases (outer cross-validation). A four-class solution was identified as the optimal model. Identified subgroups were separable on indicators, predictive of long-term outcomes, and related to background characteristics. Results are discussed in relation to previous clustering attempts of patients with diverse chronic pain conditions. Our analytical approach, as the first to combine mixture modeling with supervised, targeted learning, provides a promising framework that can be further extended and optimized for improving accurate prognosis in pain treatment and identifying clinically meaningful subgroups among chronic pain patients.