On Perception and Consciousness in HPPD:A Systematic Review

Hallucinogen-persisting perception disorder (HPPD) features as a diagnostic category in the DSM-5, ICD-11, and other major classifications, but our knowledge of the phenomenology of the perceptual symptoms involved and the changes in consciousness during the characteristic “flashbacks” is limited. We systematically evaluated original case reports and case series on HPPD to define its phenomenology, associated (psycho)pathology, and course. Our search of PubMed and Embase yielded 66 relevant publications that described 97 people who, together, experienced 64 unique symptoms of HPPD. Of these, 76% concerned symptoms characteristic of Alice in Wonderland syndrome, over 50% non-visual symptoms, and 38% perceptual symptoms not clearly linked to prior intoxication states. This is in contrast with the DSM-5 diagnostic criteria for HPPD. Even though less than half of the patients showed a protracted disease course of over a year, a third achieved remission. However, in patients with co-occurring depression (with or without anxiety) HPPD symptoms persisted longer and treatment outcomes were more often negative. Thus, unlike the acute stages of psychedelic drug intoxication, which may be accompanied by altered states of consciousness, HPPD is rather characterized by changes in the content of consciousness and an attentional shift from exogenous to endogenous phenomena. Since HPPD is a more encompassing nosological entity than suggested in the DSM-5, we recommend expanding its diagnostic criteria. In addition, we make recommendations for clinical practice and future research

Structured alcohol cessation support program versus current practice in acute alcoholic pancreatitis (PANDA):Study protocol for a multicentre cluster randomised controlled trial

Background/objectives: The most important risk factor for recurrent pancreatitis after an episode of acute alcoholic pancreatitis is continuation of alcohol use. Current guidelines do not recommend any specific treatment strategy regarding alcohol cessation. The PANDA trial investigates whether implementation of a structured alcohol cessation support program prevents pancreatitis recurrence after a first episode of acute alcoholic pancreatitis. Methods: PANDA is a nationwide cluster randomised superiority trial. Participating hospitals are randomised for the investigational management, consisting of a structured alcohol cessation support program, or current practice. Patients with a first episode of acute pancreatitis caused by harmful drinking (AUDIT score &gt;7 and &lt; 16 for men and &gt;6 and &lt; 14 for women) will be included. The primary endpoint is recurrence of acute pancreatitis. Secondary endpoints include cessation or reduction of alcohol use, other alcohol-related diseases, mortality, quality of life, quality-adjusted life years (QALYs) and costs. The follow-up period comprises one year after inclusion. Discussion: This is the first multicentre trial with a cluster randomised trial design to investigate whether a structured alcohol cessation support program reduces recurrent acute pancreatitis in patients after a first episode of acute alcoholic pancreatitis, as compared with current practice. Trial registration: Netherlands Trial Registry (NL8852). Prospectively registered.</p

Decision making as a predictor of first ecstasy use: a prospective study

Ecstasy (+/- 3,4-methylenedioxymethamphetamine) is a widely used recreational drug that may damage the serotonin system and may entail neuropsychological dysfunctions. Few studies investigated predictors for ecstasy use. Self-reported impulsivity does not predict the initiation of ecstasy use; the question is if neuropsychological indicators of impulsivity can predict first ecstasy use. This study tested the hypothesis that a neuropsychological indicator of impulsivity predicts initiation of ecstasy use. Decision-making strategy and decision-making reaction times were examined with the Iowa Gambling Task in 149 ecstasy-naive subjects. The performance of 59 subjects who initiated ecstasy use during a mean follow-up period of 18 months (range, 11-26) was compared with the performance of 90 subjects that remained ecstasy-naive. Significant differences in decision-making strategy between female future ecstasy users and female persistent ecstasy-naive subjects were found. In addition, the gap between decision-making reaction time after advantageous choices and reaction time after disadvantageous choices was smaller in future ecstasy users than in persistent ecstasy-naives. Decision-making strategy on a gambling task was predictive for future use of ecstasy in female subjects. Differences in decision-making time between future ecstasy users and persistent ecstasy-naives may point to lower punishment sensitivity or higher impulsivity in future ecstasy users. Because differences were small, the clinical relevance is questionabl

Therapeutic effect of psilocybin in addiction: A systematic review

Background: Psychedelic-assisted therapy [e.g., with lysergic acid diethylamide (LSD)] has shown promising results as treatment for substance use disorders (SUDs). Previous systematic reviews assessing the efficacy of psilocybin in SUDs only included clinical trials conducted in the last 25 years, but they may have missed clinical trials assessing the efficacy of psilocybin that were conducted before the 1980s, given much research has been done with psychedelics in the mid-20th century. In this systematic review, we specifically assessed the efficacy of psilocybin in patients with a SUD or non-substance-related disorder with no publication date restrictions in our search strategy. Methods: A systematic literature search was performed according to Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines from the earliest published manuscript up to September 2, 2022, in seven electronic databases, including clinical trials in patients with a SUD or non-substance-related disorder evaluating the efficacy of psilocybin. Results: A total of four studies (six articles, of which two articles were long-term follow-up results from the same trial) were included in this systematic review. Psilocybin-assisted therapy was administered to n = 151 patients in a dose ranging from 6 to 40 mg. Three studies focused on alcohol use disorder, and one study on tobacco use disorder. In a pilot study (n = 10), the percentage of heavy drinking days decreased significantly between baseline and weeks 5–12 (mean difference of 26.0, 95% CI = 8.7–43.2, p = 0.008). In another single-arm study (n = 31), 32% (10/31) became completely abstinent from alcohol (mean duration of follow-up 6 years). In a double-blind, placebo-controlled randomized controlled trial (RCT, n = 95), the percentage of heavy drinking days during the 32-week double-blind period was significantly lower for psilocybin compared to placebo (mean difference of 13.9, 95% CI = 3.0–24.7, p = 0.01). In a pilot study (n = 15), the 7-day point prevalence of smoking abstinence at 26 weeks was 80% (12/15), and at 52 weeks 67% (10/15). Conclusion: Only one RCT and three small clinical trials were identified assessing the efficacy of psilocybin combined with some form of psychotherapy in patients with alcohol and tobacco use disorder. All four clinical trials indicated a beneficial effect of psilocybin-assisted therapy on SUD symptoms. Larger RCTs in patients with SUDs need to evaluate whether psilocybin-assisted therapy is effective in patients with SUD

On Perception and Consciousness in HPPD: A Systematic Review

Hallucinogen-persisting perception disorder (HPPD) features as a diagnostic category in the DSM-5, ICD-11, and other major classifications, but our knowledge of the phenomenology of the perceptual symptoms involved and the changes in consciousness during the characteristic “flashbacks” is limited. We systematically evaluated original case reports and case series on HPPD to define its phenomenology, associated (psycho)pathology, and course. Our search of PubMed and Embase yielded 66 relevant publications that described 97 people who, together, experienced 64 unique symptoms of HPPD. Of these, 76% concerned symptoms characteristic of Alice in Wonderland syndrome, over 50% non-visual symptoms, and 38% perceptual symptoms not clearly linked to prior intoxication states. This is in contrast with the DSM-5 diagnostic criteria for HPPD. Even though less than half of the patients showed a protracted disease course of over a year, a third achieved remission. However, in patients with co-occurring depression (with or without anxiety) HPPD symptoms persisted longer and treatment outcomes were more often negative. Thus, unlike the acute stages of psychedelic drug intoxication, which may be accompanied by altered states of consciousness, HPPD is rather characterized by changes in the content of consciousness and an attentional shift from exogenous to endogenous phenomena. Since HPPD is a more encompassing nosological entity than suggested in the DSM-5, we recommend expanding its diagnostic criteria. In addition, we make recommendations for clinical practice and future research

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

BACKGROUND: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement. METHODS: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. RESULTS: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. DISCUSSION: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention

Development of a Wearable Biocueing App (Sense-IT) Among Forensic Psychiatric Outpatients With Aggressive Behavior: Design and Evaluation Study.

Background: The ability to regulate anger is often impaired in forensic psychiatric patients, frequently resulting in aggressive behavior. Although some treatment programs are partially successful in enhancing aggression regulation and reducing recidivism among specific subgroups, generalizable conclusions on the effectiveness of these interventions cannot be drawn to date. In forensic outpatient care, low treatment adherence and a predominant focus on cognitive control in most treatment programs may entail some of the factors impeding treatment. Technology-based interventions may address some of these treatment challenges. Objective: The aim of this study is to explore whether a new technology-based biocueing intervention, the Sense-IT app, can be a valuable addition to aggression regulation treatment programs in forensic outpatient care. The Sense-IT app, which provides the user with real-time physiological feedback and behavioral support, is developed to strengthen emotional awareness and facilitate real-life practice. In this study, we aim to develop and evaluate an updated version of the Sense-IT app that is suitable for forensic outpatients with aggressive behavior. Methods: First, we conducted a design study to assess the attitudes of forensic professionals and patients toward biocueing and to collect requirements for a biocueing app for this specific population. On the basis of this information, we developed an updated version of the Sense-IT app. In an evaluation study, 10 forensic outpatients used the app for 2 weeks. The app’s acceptability, usability, and clinical outcomes (aggression, anger, and recognition of bodily signals related to anger) were measured before and after the intervention using both quantitative and qualitative measures. Results: The design study revealed a cautiously positive attitude toward the use of biocueing as an addition to aggression regulation therapy. The evaluation study among forensic outpatients demonstrated moderate acceptability and adequate usability for the new version of the Sense-IT app. Exploratory analysis revealed a significant decrease in trait aggression postintervention; no significant changes were found in other anger-related clinical outcomes. To further increase acceptability and usability, a stable functioning app with self-adjustable settings, the use of smartwatches with a longer battery life, and the use of the patient’s own smartphone devices were recommended. Conclusions: This study, which is one of the first attempts to enroll and evaluate the real-life use of a biocueing intervention among forensic outpatients, emphasized the importance of involving both patients and therapists throughout the development and implementation process. In the future, experimental studies, including single-case experimental designs using ecological momentary assessment, should be performed to evaluate the effectiveness of the Sense-IT intervention on clinical outcomes. An open attitude toward new technology, allowing exploration of the potential benefits of the Sense-IT app case-by-case, and training of therapists in using the app are expected to facilitate its integration in therapy

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

Background: Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant’s goal achievement. Methods: We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy. Results: From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69–0.73) and (0.71 95%CI 0.67–0.76), respectively, followed by cannabis (0.67 95%CI 0.59–0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success. Discussion: Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention