Secondary exercise addiction shows high comorbidity with eating and body image disorders. Despite its substantial impact on physical and mental health and daily functioning, evidence on effective therapeutic interventions remains limited. The aim of this scoping review was to identify and describe therapeutic interventions applied to adult individuals with secondary exercise addiction. This review followed the PRISMA Sc-R guidelines and covered the years 2002–2024. Ultimately, five studies were included (four randomized controlled trials and one quasi-experimental study). Three studies applied psychotherapeutic interventions based on cognitive-behavioral models (Cognitive Behavioral Therapy, Lifestyle, Exercise, Attitudes, and Relationships Program, Physical Exercise and Dietary Therapy), while two integrated physical or nutritional components. A secondary analysis published in 2024 based on the LEAP trial dataset was identified but not treated as an independent study to avoid duplication. EBSCOhost, Web of Science, PubMed, and Google Scholar were searched from January to May 2025 using terms related to exercise addiction, exercise abuse, psychotherapy, intervention, and treatment. English-language studies were eligible if they described an intervention with at least one treated group with pre- and post-test measures; the participants of the study were adult patients suffering from eating disorders and exercise addiction (the therapy programs involved one inpatient and four outpatient treatments) and therapeutic intervention was carried out with outcomes based on exercise addiction level data. Four out of five included studies reported improvements in variables related to compulsivity, although these did not always imply a reduction in the amount of exercise, indicating that qualitative changes may be more relevant. Longer interventions showed more consistent effects, but even brief treatments generated positive changes in non-clinical populations. The examination of the research revealed a gap in studies addressing interventions for those with secondary exercise addiction, especially highlighting the need for randomized controlled trials (RCTs) with proper randomization methods.
Real-world effectiveness of medication-assisted treatment and psychotherapy for opioid use disorder: a national multi–health care organization analysis
BackgroundHarm reduction strategies for opioid use disorder (OUD) emphasize pragmatic, evidence-based approaches that reduce overdose risk, relapse, and other adverse outcomes without requiring abstinence. Medication for opioid use disorder (MOUD) and structured psychotherapy represent core harm-reduction modalities, yet their real-world comparative effectiveness, alone and in combination, remains underexplored at scale.MethodsA retrospective cohort study was conducted using the TriNetX Research Network, comprising de-identified electronic health records from 112 U.S. health systems. 18,047 adults aged 18–45 were identified with a diagnosis of opioid dependence (ICD-10 F11.20) between 2016 and 2025. Subjects were assigned to eight mutually exclusive treatment cohorts: no treatment (Cohort 1); buprenorphine alone (Cohort 2); methadone alone (Cohort 3); psychotherapy alone (30 minutes (Cohort 4), 45 minutes (Cohort 5), or 60 minutes (Cohort 6)); buprenorphine + psychotherapy (Cohort 7); and methadone + psychotherapy (Cohort 8), with combination treatments defined within a ±30-day window. Cox proportional hazards models estimated adjusted hazard ratios (aHRs) for remission (F11.21, F11.11) within 12 months.ResultsBuprenorphine (aHR = 2.33; 95% CI: 1.85–2.94), methadone (aHR = 2.50; 95% CI: 2.05–3.04), and psychotherapy (30 min: aHR = 2.18; 45 min: aHR = 2.38) were each independently associated with significantly higher remission compared to no treatment. The combination of buprenorphine + psychotherapy yielded the strongest effect (aHR = 5.26; 95% CI: 2.68–10.32). Anxiety diagnoses and gabapentinoid prescriptions were positively associated with remission; benzodiazepine co-prescription was negatively associated.ConclusionsIn this first national-scale, multi–health-care-organization analysis, both pharmacologic and psychosocial harm-reduction interventions were independently associated with improved OUD remission, with additive benefit when integrated. These findings underscore the value of embedding comprehensive, multimodal harm-reduction services within routine care and support policies promoting equitable access to both MOUD and behavioral health supports across diverse health systems.
Human Ventral Tegmental Local Field Potentials in Treatment-Resistant Depression and Obsessive-Compulsive Disorder
The ventral tegmental area (VTA) is a key node within the limbic circuitry. Through dense dopaminergic, glutamatergic, and GABAergic projections, the VTA forms reciprocal loops with prefrontal and limbic cortices that are consistently implicated in major depressive disorder (MDD) and obsessive–compulsive disorder (OCD) (1,2). Decades of animal research have established the VTA as a central hub for motivational drive and reward prediction error signaling (3,4). Despite its presumed critical role in mental disorders, direct electrophysiological recordings from the human VTA have so far remained absent.
Predicting consequences of new hepatitis B vaccine recs
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Good morning. The other night I watched a shocking episode of “The Vampire Diaries.” A series of cursed, ghost-like hallucinations attempt to convince a teen vampire to end her own life using some disturbingly coercive, cogent arguments. Ultimately, the character is saved. And while this episode aired more than a decade ago, I was surprised by how many parallels there were to current debates about the risks of AI chatbots and people in mental health crises.
Opinion: Why do discussions about ‘brain health’ ignore mental illness?
Governments, industry, and philanthropies are investing in neuroscience at an unprecedented scale, and the ambition behind this impetus is a noble one: to reduce the growing burden of brain diseases and extend healthy cognitive life. We fully support this movement’s push for “brain health” to mirror successful frameworks established for cancer and heart health that prioritize early screening and aggressive preventive treatments, making it possible to act before irreversible damage sets in.
Even as this agenda gains momentum, however, a critical blind spot is emerging. As governments refocus their policies to tackle conditions like Alzheimer’s disease and other neurodegenerative disorders, mental illness is often being sidelined as a secondary concern rather than as a primary component of brain health. This artificial divide is a scientific and a strategic error.
STAT+: Google clinical director says AI can be a ‘bridge’ for people having a mental health crisis
As Google faces pressure to take greater accountability for the mental health impacts of its artificial intelligence products, the company’s clinical director Megan Jones Bell welcomed the challenge of making artificial intelligence helpful to people who come to its Gemini chatbot with a mental health crisis.
“It can seem sometimes like shutting something down is a way of preventing harm,” Jones Bell told STAT. “We believe that making our product experience safer and more helpful and strengthening that bridge to support it is the more effective path to support mental health for the most people.”
Google recently made updates to its Gemini app so that it more prominently features connections to crisis hotlines when it detects a person may be at risk of self harm. In conversations about mental health, the AI will frequently point people to outside resources — but the bot doesn’t disengage, reminding a user, for example, that “I’m here to listen.”
Multiannual Financial Framework 28-34: Time to prioritise Mental Health
Mental Health Europe reacts to the 2028-2034 Multiannual Financial Framework, which lacks consideration of mental health in the discussions about priorities, structures and processes.
The post Multiannual Financial Framework 28-34: Time to prioritise Mental Health appeared first on Mental Health Europe.
Comparing Acupuncture With Medication to Reduce Anxiety and Depression in China
Conditions: Anxiety Depression
Interventions: Drug: Selective serotonin reuptake inhibitor (SSRI); Other: Acupuncture
Sponsors: Duke Kunshan University; Kunshan Hospital of Traditional Chinese Medicine; Kunshan Mental Health Center; National Natural Science Foundation of China
Not yet recruiting
Interventions: Drug: Selective serotonin reuptake inhibitor (SSRI); Other: Acupuncture
Sponsors: Duke Kunshan University; Kunshan Hospital of Traditional Chinese Medicine; Kunshan Mental Health Center; National Natural Science Foundation of China
Not yet recruiting
Women’s Reproductive Choices and Mental Health During Pregnancy and After Birth in Wartime Ukraine
Conditions: Perinatal Mental Health; Reproductive Behavior; Women; Cohort Studies
Interventions: Diagnostic Test: Psychological Assessment; Diagnostic Test: Routine Clinical Data Collection
Sponsors: Taras Shevchenko National University of Kyiv
Not yet recruiting
Interventions: Diagnostic Test: Psychological Assessment; Diagnostic Test: Routine Clinical Data Collection
Sponsors: Taras Shevchenko National University of Kyiv
Not yet recruiting
Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study
Background: Depression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like the Patient Health Questionnaire-8 (PHQ-8) require disclosure and clinician oversight, limiting accessibility. Recent artificial intelligence–based approaches leverage multimodal behavioral cues (linguistic, acoustic, and visual) for automated depression detection but remain constrained by limited adaptability, scarce annotated data, weak emotional expression in real-world settings, and the high computational cost of deployment of socially assistive robots (SARs). Objective: This study introduces Depression Social Assistant Robot (DEPRESAR)-Fusion, a lightweight multimodal depression detection framework designed for natural interactions with emotion-aware SARs. The objective of this study was to enhance detection accuracy in everyday conversations while addressing the challenges of data scarcity, weak emotional cues, and computational efficiency. Methods: DEPRESAR-Fusion integrates acoustic, linguistic, and visual features with an emotion-aware response module powered by large language models to adapt conversational strategies dynamically. To stimulate richer emotional expression, participants were exposed to emotionally evocative videos before SAR interactions. To overcome data scarcity, we augmented training with (1) public depression-related social media corpora and (2) synthetic samples generated via large language models. The proposed multimodal fusion architecture was evaluated on benchmark clinical datasets for both binary depression classification and PHQ-8 regression tasks. Performance was compared against prior multimodal baselines using root mean square error, mean absolute error, and standard classification metrics. Results: Participants who viewed emotional stimuli before interacting with SARs exhibited significantly higher emotional expressiveness, leading to improved model performance. Regression tasks showed lower root mean square error and mean absolute error, while classification tasks achieved significantly higher accuracy than the nonstimulus condition. DEPRESAR-Fusion outperformed prior multimodal baselines across multiple benchmark datasets, achieving state-of-the-art performance in both binary classification and PHQ-8 regression. The system maintained a lightweight architecture suitable for real-time deployment on SARs. Conclusions: DEPRESAR-Fusion demonstrates that integrating emotion induction, data augmentation, and lightweight multimodal fusion can enable accurate and scalable depression detection in naturalistic SAR interactions. By bridging the gap between structured clinical assessments and everyday conversations, this approach highlights the potential of SAR-based systems as nonintrusive, artificial intelligence–driven tools for proactive mental health support.
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