Major Depressive Disorder (MDD) is the leading cause of disability worldwide, affecting individuals’ functioning in various life areas. Prolonged residual functional impairment is one of the risk factors for recurrence. Moreover, symptoms severity and accompanying functional disabilities negatively impact quality of life (QoL) and personal well-being, affecting the recovery process of people with MDD and their reintegration into daily life. Therefore, restoring functional abilities is no less important than reducing symptoms.

Recent changes in mental health policy have led to an expansion of client-oriented community-based services, focusing on preventing health problems and promoting QoL and well-being. A significant change can also be seen in depression evaluation and treatment, moving from traditional face-to-face therapy to hybrid care settings that incorporate remote or home-based treatments and assessments of everyday life.

While traditional assessments of symptoms and behavior often rely on questionnaires and interviews, they frequently miss the dynamic changes in daily functioning experienced by people with MDD. Clinicians primarily rely on patients’ retrospective reports regarding mood, affective state, thoughts, and behavior. However, understanding and gaining insight into day-to-day experiences requires addressing dynamic processes and changes that occur over time, rather than in a single time point. Hence, ecological momentary assessment (EMA) is a powerful and effective technique for assessing moment-to-moment function patterns in daily life.

Advancements in technology have enabled the use of computer-assisted methodology and real-time monitoring EMAs. The methodological advantages, including the circumvention of retrospective bias and increased longitudinal and ecological validity, have facilitated the widespread use of EMA in mood disorder clinical practice. Nevertheless, addressing everyday functioning using EMA remains limited in clinical research and practice.

Utilizing EMA can enhance our understanding of human experience, leading to human-centered research, design practice, and mental health care. It has the potential to reveal real everyday functioning and reflect the activities and contexts chosen and experienced by people with MDD. Addressing each patient’s unique functional profile can facilitate personalized interventions, supporting the recovery process and improving QoL.

This presentation will review the benefits of EMA in the field of mood traits, and especially EMA monitoring for daily function. Additionally, it will present recent studies using EMA and discuss advancements and clinical applications.

None Declared

Psychopathology in adolescents is influenced by genetic factors, hormonal changes, individual vulnerabilities, and coping skills. Telepsychiatry has proven effective in improving access to mental health services.

To implement a Remote Primary Care and Psychiatry Model (MAP/PSI) to facilitate early diagnosis and timely treatment of depressive symptoms in youths from the Municipality of Ciudad Fernández, San Luis Potosí, Mexico, using an implementation science approach

A prospective study was conducted with 38 patients evaluated in child psychiatry, using the PHQ-9 (Patient Health Questionnaire) and GAD-7 (Generalized Anxiety Disorder) scales. Non-parametric statistical tests were applied

The sample included patients aged 15 to 25. Diagnoses included 8 (20%) with generalized anxiety, 8 (20%) with mild depression, 15 (35%) with moderate depression, and 9 (25%) with severe depression, who were referred to the general hospital due to suicidal ideation. 60% of patients were female and 40% male. The mean age was 20 years ±3, with mean scores on the PHQ-9 of 16 ±7 and on the GAD-7 of 13 ±6, reduced in the final consultation to 8 ±6 and 7 ±6, respectively. Increased symptom frequency was observed in females (p < 0.044) and older age correlated with higher initial PHQ-9 scores (p < 0.034), with no correlation to generalized anxiety (p < 0.021). No relationship was found between the duration of symptoms and improvement

The MAP-PSI model facilitated early detection and treatment of depressive and anxiety symptoms in youths, preventing progression to severe depression and its complications.

None Declared

Cognitive impairments often hinder daily functioning in people with severe mental illness (SMI). Cognitive Adaptation Training (CAT) is an effective psychosocial intervention that reduces this impact. However, barriers such as the two-day training and lack of neuropsychological expertise in some professionals hinder CAT implementation.

To make CAT more accessible to healthcare professionals and relatives of people with SMI by developing a web application (House-CAT) that guides users through the intervention.

For the development, User Centered Design (UCD) is used: a design process where close cooperation with future users (healthcare professionals, relatives, service users) is important to make sure that their needs are met. Although UCD is circular and iterative, three phases can be distinguished: analysis, design, and evaluation. In the analysis phase, users’ needs are identified and translated into design criteria. A testable prototype of House-CAT is created (design phase), followed by implementation and evaluation on efficiency, acceptability, and user-friendliness (evaluation phase).

Three focus groups were conducted in the analysis phase: CAT-experienced professionals (n=5); professionals unfamiliar with CAT (n=4); and relatives (n=5). Further, individual meetings with family members (n=8), and professionals (n=6) were conducted. Design criteria included the app’s ability to (1) support with setting up individual goals; (2) find personalized strategies or tools; (3) improve communication between professionals; (4) encourage cooperation between professionals, relatives, and service users; (5) be available in hybrid form; (6) use simple language. The design- and evaluation phase are currently in process: the web app and results from the evaluation phase will be presented at the conference.

House-CAT should support users in creating individual goals and personalized strategies, stimulate contact between professionals, relatives and service users, and be simple to use.

None Declared

The Lancet Commission on dementia prevention, intervention and care and the WHO Guidelines on risk reduction of cognitive decline and dementia provide evidence-based recommendations on lifestyle behaviours and interventions to delay or prevent cognitive decline and dementia. Due to demographic change and because new developments in pharmacotherapy are not suitable for all patients, there is a great need for non-pharmacological interventions. The MEMODIO app was developed to provide a multi-domain therapy for users with Mild Cognitive Impairment (MCI) and dementia. A screenshot of the app is shown in Image 1.

The aim of this study was the exploratory evaluation of whether an app-based therapy can improve cognitive function in patients with MCI and mild dementia. For the interim analysis as of October 2024, the results of the MCI study arm are reported (n=42).

One hundred forty patients with confirmed diagnosis of MCI [Montreal Cognitive Assessment (MoCA) score 21-25] or Mild Dementia (MoCA score 14-20) were randomized to an intervention (IG) or standard of care (SoC) group. IG patients received SoC plus the MEMODIO app, providing cognitive and physical exercises, as well as psychoeducation on a brain-healthy diet and risk factors for cognitive decline, for 12 weeks. MoCA, Amsterdam Instrumental Activity of Daily Living Questionnaire - Short version (A-IADL-Q-SV) Dementia-Related Quality of Life (DEMQOL) as well as Physical Activity Questionnaire (PAQ 50+) were collected at baseline and study end.

In the MCI group, the mean age was 71 years, 20 out of 42 patients were female. Preliminary outcomes are shown in Images 2 and 3.

The preliminary analysis on MCI patients (mean age: 71 y ±9,49 SD, 48% were female) showed statistically significant improvements in MoCA in the IG (-0.84±3.5 SoC vs. 1.96±2.7 IG, p=0.006). Quality of life, physical activity and activities of daily living at the time point of the interim evaluation were not significantly different from baseline, but the activity level measured by PAQ showed a non-significant improvement in the SoC group.

Image:

Image 2:

Image 3:

In this RCT, a significant improvement in cognition was shown in MCI-patients using the MEMODIO app compared to those receiving SoC alone. Further analyses are ongoing.

G. Nelles Grant / Research support from: PI of MEMODIO@APP_CARE, T. Steinmann Employee of: memodio GmbH, D. Stein Shareolder of: memodio GmbH, V. Weil Employee of: memodio GmbH, A. Bicu: None Declared, C. Polidori: None Declared

Depressive and anxiety disorders have a high prevalence and pose a significant socioeconomic burden. To reduce this, the importance of early intervention through the screening of high-risk groups has been highlighted by many researchers and clinicians. However, due to the heterogeneity of symptoms and comorbidity with other mental disorders, it is challenging to identify high-risk groups of depression and anxiety disorders early on. To address this issue, we developed App-based neuropsychological tasks for screening high-risk groups and classifying subtypes of depression and anxiety disorders through services in the metaverse.

As a preliminary step, we have developed four app-based neuropsychological tasks for the early screening and subtype classification of individuals at high risk for depression and anxiety disorders, and examined the reliability and validity of the tasks.

Subjects were 30 adults with mean age of 32.93(male=8, female=22). After obtaining written agreement to participate the study, smartphones with the app-based neuropsychological tasks were provided to them, and then smartphone app tasks as well as digitized self-report scales related to depression and anxiety were performed. To analyze the reliability of the tasks, Cronbach’s alpha was calculated. And correlation analyses were conducted between the scores of each task (ex; number of correct response, reaction time, number of skipping the task level) and the scores of the self-questionnaires to verify validity of the tasks. The overview of the tasks developed in this study is as follows: The catching raccoon task investigates the tendency to abandon performance in complex working memory task situations. The soccer task measures vitality and planning abilities. The risk-taking decision-making task examines the tendency to take risks. In the verbal memory task with mood induction, we investigate whether attention bias towards negative emotions, such as depression, affect the memory functions after inducing negative emotions through audiovisual stimuli.

The range of Cronbach’s alpha values for the task was between .71 and .92, showing that the tasks are reliable. Additionally, three out of the four tasks showed statistically significant correlations with the scores of depression or anxiety measured by self-report rating scales, confirming the validity of the tasks.

Despite being conducted with a normal population, these results verified the reliability and validity of the tasks, suggesting the potential usefulness of the tasks for early intervention through the screening of high-risk groups with depression and anxiety disorders.

None Declared

The treatment landscape for mental health has transformed significantly with the emergence of digital platforms, which offer a promising public health solution by expanding access to mental health services. Despite their potential, further research is needed to evaluate both the usability and clinical effectiveness of these online interventions.

To investigate the usage patterns of an online mental health app and the effectiveness of chat-based emotional support (ES) in reducing psychological distress and achieving therapeutic goals.

The analysis included data from 3,751 users of ifeel app onboarded in 2023, 58% of whom were female and 42% male. Two main modalities were studied: chat-based therapy,( N= 3,170) and video therapy,( N=1,942), with some overlap as users could engage in both modalities. Key metrics for analysis included session frequency, session timing, session duration, and user demographics. The study also explored chat-based ES therapy by evaluating 113 individuals at two points in time: after one week of therapy, and after three weeks. Psychological distress levels were measured using the Kessler Psychological Distress Scale (K6), and goal attainment was measured through self-reports over the two time points.

Usage data showed a strong preference for chat-based therapy, with 85% of users engaging in chat therapy compared to 52% who used video therapy. Younger users, particularly those aged 25-34, were the most active in engaging with chat-based therapy. On average, chat therapy users send between 14 to 17 messages per month, whereas video therapy users take 2 sessions per month. These results underscore the higher frequency of engagement in chat therapy, particularly among younger demographics. In terms of clinical effectiveness, psychological distress was reduced significantly at both one week and three weeks of therapy. After one week of treatment, participants showed a significant reduction in distress symptoms (F(1) = 5.10, p = .03*) this was evident also after three weeks (F(1) = 24.25, p = .000***). Goal attainment showed substantial progress over time, with significant improvements in both groups (F(1) = 5.03, p = .03*; F(1) = 31.61, p = .000*), indicating that users were making meaningful strides toward their therapeutic goals.

This study underscores the usage pattern and clear preference for chat-based therapy, particularly among younger users. Chat-based emotional support therapy was found effective in reducing psychological distress, suggesting that online platforms, especially those offering chat-based therapy, are both accessible and clinically effective. Further research should focus on refining these services to maximize clinical outcomes, adapt to diverse user demographics, and ensure long-term engagement.

None Declared

Sleep problems are very prevalent among university students, affecting their mood, energy levels, daily functioning, and quality of life. Irregular sleep-wake patterns contribute to the disruption of their circadian rhythms. Cognitive behavioural therapy for insomnia (CBT-I) has been proven effective in adults, but research in university students is still limited.

The aim of this study is to investigate the effectiveness of a guided digital self-help intervention targeting sleep and the biological clock in university students to improve sleep and mental health outcomes.

We conducted a two-arm randomized controlled trial in nine Dutch Universities. We included 196 university students (Bachelor, Master, and PhD) with self-reported insomnia symptoms (Insomnia Severity Index ≥ 10) and randomly assigned them (1:1) to receive the 5-week ‘i-Sleep & BioClock’ intervention or online psychoeducation. The intervention is based on CBT-I with specific emphasis on the biological clock. It consists of 5 weekly online modules and is guided by online coaches. The primary outcome is insomnia severity. Secondary outcomes are depression, anxiety, daily functioning, academic performance, quality of life, and sleep & light exposure diary outcomes. Outcomes were measured at baseline, mid-treatment (3 weeks), post-treatment (6 weeks after baseline), and at 18 weeks follow-up. Data will be analyzed with intention-to-treat analysis using linear mixed models. The study is registered at ClinicalTrials.gov (NCT06023693).

Recruitment started on Nov 1st, 2023 and ended on Sept 5th, 2024. Data collection is currently ongoing and will be finalized in January 2025. We hypothesize that the guided digital self-help intervention will reduce insomnia severity and improve mental health outcomes in students (results will be presented).

Findings from this randomized controlled trial will contribute to the growing body of knowledge on digital sleep interventions and their potential impact on mental health. If the intervention proves effective, we aim to disseminate the intervention widely in higher education to benefit a broader student population.

None Declared

Recently, CBT-based digital therapy has been developed and used for the treatment of various psychiatric disorders, including insomnia, depression, anxiety and panic disorders, and alcohol/drug addiction. In the United States, the first game-based digital therapy for ADHD has also received FDA approval and is being used for the treatment of children and adolescents with ADHD.

We conducted a randomized controlled study to examine the effectiveness of a digital therapeutic (model named ‘ADAM-101’) for children with ADHD in Korea, which was developed by Dragonfly GF Co., Ltd.”

Participants are 18 children with ADHD, aged 7 to 13 years, who are visiting the Department of Child and Adolescent Psychiatry at Seoul National University Children’s Hospital in Seoul, Korea. ADHD children with an IQ of 70 or above, who are currently taking stimulants and do not have other pediatric psychiatric disorders such as depression, anxiety disorders, tic disorders, ASD, were included in the study. They were randomly assigned to either the combined treatment group (medication + digital therapy, n = 9) or the medication-only group (n = 9). The digital therapy program was conducted using a tablet PC for 25 minutes a day, 5 days a week, for 4 weeks. Before starting the study, permission was obtained from the Institutional Review Board of Seoul National University Hospital. As a primary outcome measure, the Korean version of the Continuous Performance Test (KAT) was administered individually to the ADHD children by child clinical psychologists to assess inattention, impulsivity, and processing speed, after obtaining written agreement to participate in the study. Additionally, the Korean version f the ADHD Rating Scale-5 (K-ARS-5) was administered to the parents of the ADHD children.

We have not yet completed the study. Currently, out of the 18 ADHD children, 8 have completed the training and both pre- and post-assessments. All training and evaluations are expected to be completed by early October, and an analysis to verify the effectiveness of the digital therapeutic will be conducted in mid-October. Since this was not a double-blind study, we observed that, based on some children’s CPT and K-ARS-5 results, children in the combined treatment group tended to show a reduction in omission and commission errors on the CPT compared to those in the medication-only group. Additionally, there was a trend towards a reduction in inattention and hyperactivity-impulsivity scores on the K-ARS-5 in the combined treatment group.

Despite being conducted with a small sample, these results suggest the potential efficacy of the digital therapeutic (model named ‘ADAM-101’) for Korean ADHD children, indicating its potential clinical usefulness as an adjunctive treatment tool for ADHD children

None Declared

Access to timely mental health care in the Netherlands has become increasingly challenging, with waiting times steadily increasing in recent years. Extended waiting times for treatment in mental health care can be harmful due to increased severity of symptoms, poorer treatment outcomes and a reduced quality of life. There is growing interest in the potential of unguided e-health interventions to provide support during these waiting periods without overburdening healthcare professionals

The main objective of this study is to test the clinical effectiveness and cost-effectiveness of a personalized unguided e-health intervention during the waiting list period.

A Randomized Controlled Trial (RCT) with two trial arms will be conducted: the intervention condition and the treatment as usual waiting list condition. Adult outpatients awaiting an intake at a several specialized mental health care institutions will be included in the study. Both trial arms will include repeated measures during the waiting period. The intervention arm will receive online access to a selection of existing e-health modules. Personalization will be achieved by employing both innovative and traditional methods to identify symptoms that are most influential for each individual participant. A Dynamic Time Warping analysis based on a three-week ecological momentary assessment (EMA) will be used to determine which specific symptoms are most responsible for maintaining the patient’s overall complaints (4). This analysis will be used to provide tailored recommendations of e-health modules. The study’s outcomes will focus on symptom severity, cost-effectiveness, quality of life, digital phenotyping and patient satisfaction during the waiting period.

At this stage, no results are available yet as the study is still being conducted.

No conclusions can be drawn as the study is still being conducted.

None Declared

Artificial intelligence (AI) language models are increasingly accessible tools that offer potential support in mental health care. Despite their promise in revolutionizing mental health care through symptom assessment and treatment suggestions, concerns about their validity, accuracy, ethical considerations, and risk management persist. This study evaluates the clinical reasoning capabilities of two leading AI language models in assessing a clinical case vignette of Major Depressive Disorder (MDD).

To evaluate the diagnostic accuracy, risk assessment proficiency, and quality of treatment recommendations provided by ChatGPT and Claude when applied to a standardised clinical vignette of a case of MDD.

A clinical vignette describing a 50-year-old male patient exhibiting symptoms consistent with MDD was presented to both ChatGPT 4o and Claude 3.5 Sonnet. The patient had significant cardiac disease, leading to unemployment, social withdrawal, and passive suicidal ideation. Both AI models were asked five identical questions regarding: (1) diagnosis, (2) severity assessment, (3) first-line treatment recommendations, (4) optimal antidepressant selection, and (5) suicide risk evaluation. Two psychiatrists independently reviewed the responses for accuracy, comprehensiveness, and alignment with established guidelines and evidence-based treatment for depression with comorbid cardiac disease.

Both AI models correctly diagnosed MDD and accurately recognized the severity of the case due to the presence of suicidal ideation and significant functional impairment. Both offered comprehensive treatment recommendations, including pharmacotherapy and psychotherapy, and specifically suggested Sertraline as the antidepressant of choice due to its favourable cardiac safety profile. Both models assessed the patient as having a moderate to high suicide risk and provided a reasonably thorough analysis of risk and protective factors. However, limitations were noted in their ability to incorporate individualized patient nuances and psychosocial factors fully.

ChatGPT 4o and Claude 3.5 Sonnet demonstrated significant capabilities in clinical reasoning, providing diagnoses and treatment recommendations that align with best clinical practices. Their responses were largely accurate and comprehensive, indicating potential utility as supportive tools for healthcare professionals. AI models may assist non-specialists in preliminary assessment and management but are not substitutes for professional psychiatric evaluation. Caution is advised in relying on AI for clinical decision making, and further refinement is necessary to enhance their ability to integrate patients-centered care and adherence to ethical guidelines, to mitigate risks associated with self-diagnosis and inappropriate treatment.

None Declared

This study represents the first effort in North Macedonia to examine the contextual attributes that may influence the effectiveness and acceptability of a novel digital intervention, DIALOG+, within the mental health care system. The intervention aims to enhance mental health outcomes through a structured approach, but its success depends on understanding the specific characteristics of the local health context.

The primary objective of this research is to identify the key contextual attributes within the mental health care system of North Macedonia that are relevant to the successful implementation of DIALOG+. This includes examining factors that could impact both the effectiveness of the intervention and its acceptance by various stakeholders, including patients, clinicians, carers, and policymakers.

Data for this study were drawn from a variety of sources, including the National Mental Health Strategy 2018-2025, relevant documents from the World Health Organization, and other action plans. In addition, interviews were conducted with key stakeholders—patients, carers, clinicians, and policymakers—to gather perspectives on the anticipated introduction of DIALOG+ and assess the readiness of the mental health centers for its implementation. The data were subsequently mapped to a framework developed by the Ottawa Implementation Group, which outlines 14 key contextual attributes influencing health interventions.

The findings were categorized into two subgroups, identifying both facilitators and barriers to the implementation of DIALOG+ in North Macedonia’s mental health system. The intervention’s characteristics as a broadly applicable psychosocial tool align well with modern approaches to psychosocial rehabilitation, particularly for individuals diagnosed with psychosis.

DIALOG+ presents a valuable tool for mental health professionals in North Macedonia, offering structured support for monitoring patient progress and achieving institutional objectives. The intervention has the potential to facilitate patients’ reintegration into society, enhance their independence, and enable them to reach their full potential in the pursuit of a healthy and functional life.

None Declared

The widespread use of mobile devices, particularly among young people, extends beyond entertainment to education and professional purposes. However, excessive smartphone use has led to health issues such as headaches, poor concentration, sleep problems, and anxiety. A condition called nomophobia, or the fear of being without a mobile phone, has emerged, though it’s not officially recognized as a psychiatric disorder. In adults, 20% experience mild nomophobia, 50% moderate, and 20% severe symptoms, which can be measured using the Nomophobia Questionnaire (NMP-Q). Smartphone overuse is also linked to psychopathological issues like insomnia and anxiety.

The aim of this study is to evaluate the impact of nomophobia in insomnia and anxiety.

Non-systematic review of the literature regarding nomophobia and anxiety and insomnia. The research was carried out through the PubMed® database, using the terms “nomophobia”, “nomophobia and anxiety” and “nomophobia and insomnia”.

The included studies highlight that nomophobia is associated with higher anxiety levels in most individuals as well as a substantial correlation between nomophobia symptoms and insomnia.

Nomophobia is increasing due to technological advancements and widespread access. Overuse of mobile phones is linked to psychopathologic symptoms, like anxiety and insomnia. Raising awareness and helping young adults manage their phone use is essential for promoting health and well-being as digital technologies become an integral part of daily life.

None Declared

Autism Spectrum Disorder is a neurodevelopmental condition characterized by deficits in social communication, restricted and repetitive patterns of behavior. In recent years, there has been a growing interest in using virtual reality technologies for social skills training in individuals with ASD. Virtual reality offers the opportunity to create immersive and interactive virtual environments where participants can practice and enhance their social skills in a safe and controlled setting.

To analyze the quality of using virtual reality as a treatment method for patients with autism spectrum disorder to improve their social skills.

Through a descriptive systematic review, studies in Spanish and English from the years 2021 to 2024 were analyzed, selected from the databases PubMed, Scielo, and Lilacs, using the keywords: “Virtual reality”; “Autism spectrum disorder”; “Social skills”. Articles that did not address virtual reality as the main training method, were off-topic, or did not present results from this method were excluded.

Children and Social Skills

Programs like FaceMe use gamified environments to teach facial expressions, while tools like GameBook use interactive scenarios and augmented reality to reinforce these skills. VR’s ability to track and analyze eye movement patterns offers insights into social behavior. Programs like “Virtual Farm” and VR games such as Zentastic have demonstrated positive effects, with children engaging in various virtual social scenarios. Programs like VRESS use immersive scenarios to teach social skills that provide real-time feedback on behavior can help individuals develop self-awareness and adjust their interactions.

Adults and Social Skills

Adults with autism face challenges in non-verbal communication, understanding social cues, and forming relationships, which can lead to social isolation. VR offers simulations for practicing social interactions, enhancing communication, empathy, and conflict resolution. Programs like VRESS use immersive scenarios to teach social skills, and tools that provide real-time feedback on behavior can help individuals develop self-awareness and adjust their interactions.

The development of virtual reality systems for social skills training in individuals with autism spectrum disorders represents an innovative and promising approach to assisting in the development of these skills in both children and adults with autism. Virtual reality offers a more engaging and effective training experience, allowing users to practice social skills in a controlled environment tailored to their individual needs. Despite the promising results, there are still challenges to be faced, such as methodological issues and the need for awareness and training of health and education professionals.

None Declared

Artificial intelligence (AI) can potentially enhance healthcare professionals’ understanding of certain disorders, facilitating improved diagnosis, treatment, and prevention. Exploring potential psychological factors that can possibly influence healthcare professionals’ attitudes towards AI in their work is crucial to assist successful adoption and utilization of these technologies.

The possible role of burnout, perceived distress, and factors related to work circumstances on willingness to use AI were explored in this investigation.

Attitudes towards artificial intelligence, perceived distress and factors related to work were assessed by using an online questionnaire. Participants (86 % women, Mage = 46.9 years, SD = 11.3) were healthcare professionals recruited from Hungarian hospitals and healthcare institutions.

Linear regression analysis indicated that most participants (58%) were open to using AI in their work. Significant predictors of use were job satisfaction, work performance, and administrative workload. Higher burnout levels and perceived distress were not associated with attitudes towards AI.

The present findings suggested that work-related environmental factors may have a greater predictive power in explaining the propensity to use AI in healthcare than individual psychological factors. However, the explanatory power of these factors in AI use was modest (7.5%), suggesting that future research should investigate further possible predictors of attitudes towards AI such as social factors.

None Declared

Recently, there has been growing interest in leveraging large language models (LLMs) in psychiatry and counseling. Specifically, there is a need to develop LLM-based programs that generate psychodynamic assessments, helping individuals gain self-insight and evaluate the quality of such services. However, research in this area remains limited.

This pilot study aims to evaluate quality, risk of hallucination, and client satisfaction with psychodynamic psychological reports generated by GPT-4.

The reports consisted of five components: psychodynamic formulation, psychopathology, parental influence, defense mechanisms, and client strengths. Participants experiencing distress from recurring interpersonal issues were recruited for the study, which followed three stages: 1) GPT-4 generated tailored questions for participants to infer psychodynamic formulations, then used the responses to create psychological reports. 2) Seven psychiatry professors from various university hospitals assessed the reports for quality and hallucination risk, comparing GPT-4-generated reports with expert-inferred reports. 3) Participants evaluated their satisfaction with the psychological reports. All assessments were conducted using self-report questionnaires based on a Likert scale developed for this study.

Ten participants were recruited for the study, with an average age of 32 years. The median response indicated that the quality across all five components of the psychological report aligned closely with expert evaluations. The risk of hallucination was assessed to be minimal, ranging from unlikely to minor. In the satisfaction evaluation, over 90% of participants agreed or strongly agreed that the report was clear, insightful, credible, useful, satisfying, and recommendable.

These findings suggest that artificial intelligence may have the potential to provide expert-level psychodynamic interpretations with minimum face-to-face interaction.

None Declared

People with a severe mental illness (SMI) have a higher prevalence of obesity than the general population. Weight gain is one of the main adverse side effects of antipsychotic treatment, leading to health complications, higher mortality and low treatment adherence in people with SMI. E-Health programs are increasingly used for weight loss.

This systematic review aims to analyze target groups and the inclusion and exclusion criteria in evaluation studies of such programs, with a focus on the exclusion of individuals with SMI and the resulting gap in healthcare.

The systematic search consisted of studies in English using the databases PUBMED, PsychInfo and Cochrane Database, published between January 1, 2003 - February 29, 2024. Exclusion criteria were studies with a focus on programs other than weight loss or combined interventions. Further, we used the search terms: web-based, app* OR artificial intelligence* OR software OR online OR machine learning* OR digital* OR Internet AND weight loss OR body mass index OR weight reduction* OR body weight OR waist circumference OR obesity* OR BMI OR weight. The database search identified 4560 records, of those 633 duplicates were removed. The remaining 3927 records were further screened and 3584 were excluded, 343 full-text articles were assessed for eligibility. 117 full text articles were excluded with reasons and finally 226 studies were included in qualitative synthesis.

Preliminary results showed that app recommendations were reported in only 44 out of 226 studies. Additionally, analysis of target groups for recommended apps indicated that most were designed for individuals with diabetes, closely followed by apps targeting maternity, hypertension or cardiovascular diseases and corporate health management. Notably, none of the apps were specifically designed for people with a mental illness. Moreover, a comparison of the frequency of exclusion criteria for mental vs. somatic vs. chronic illnesses in general resulted in: 77:54:105. The terminology used for exclusion specifically for people with a mental illness was often general (e.g. “mental impairments”, “any psychological problem”, “unstable emotional condition”).

The results point to deficits in the area of app recommendation and in sample selection. Specifically, people with a mental illness were most frequently excluded using vague terminology. Furthermore, there is an enormous lack of such programs for people with SMI. Knowledge about such gaps is crucial in order to avoid inadequate care for vulnerable groups. Excluding people with SMI from the development and subsequent evaluation of such programs may not adequately address their challenges and may lead to additional frustration experienced by this group due to inadequate app recommendations. The results suggest a potential stigmatization.

N. Khorikian-Ghazari: None Declared, A. Hasan Consultant of: Rovi, Recordati, Otsuka, Lundbeck, AbbVie, Teva and Janssen-Cilag, Speakers bureau of: Janssen-Cilag, Otsuka, Recordati, Rovi, Boerhinger-Ingelheim and Lundbeck, J. Grimmer: None Declared, I. Papazova: None Declared

In the digital era, online dating platforms have emerged as a prominent means for establishing social relationships. While these platforms facilitate connections, they also introduce a distinct form of external validation through likes, matches, and compliments. Despite their popularity, researchs on the psychological effects of this validation on users are limited.

This study aimed to reveal the profile of dating platforms users and assess whether these platforms foster a dependence on external validation and affect operator’s self-esteem

A cross-sectional study was shared via social media and online forums. In addition to socio demographic and work related variables, it comprised an evaluation of dependency on external validation through a series of targeted questions, and explored its relationship with the use of dating platform. The Rosenberg Self-Esteem Scale was used to assess participants’ self-esteem, with a score below 31 indicating low self-esteem.

The study included 55 participants aged between 19 and 40 years, with a mean age of 26.27. The gender distribution was nearly equal, with 49.1% male (n=27) and 50.9% female(n=28). Most participants (54.54%) were single, and 40% reported using dating apps primarily for casual dating and entertainment.

Among applications users, 46.7% engaged with the platforms several times a week, while 20% used them daily. Furthermore, 53.7% of users plan to continue using them.

Regarding external validation, 64.4% reported that receiving validation motivated them to use the applications more frequently, while 56.2% indicated they would adjust their behavior based on feedbacks. Additionally, 47.1% felt influenced by compliments, and 58.9% reported that criticism and rejection affected them negatively.

According to the Rosenberg Self-Esteem Scale, 33.3% of users exhibited low self-esteem and 28.5% of users reported that dating applications had a negative impact on their self-esteem. Notably, no significant correlation was found between dating applications’ usage and self-esteem (p = 0.53 > 0.05).

This study highlights a reliance on external validation among online dating users, suggesting that these platforms may encourage behaviors driven by the need for approval. This underscores the importance of further research into the psychological effects of such dependence.

None Declared

Blended therapy (BT) is defined as the use of digital tools combined with traditional face-to-face therapy. Despite growing evidence concerning BT in outpatient settings, there is not much data on BT in inpatient care. To our knowledge, this is one of the first studies to investigate the implementation of BT in the routine care of psychiatric inpatients.

Our objective was to identify factors potential barriers and facilitating factors influencing the implementation of BT in psychiatric inpatient settings.

We used a retrospective, exploratory qualitative research design and developed a semi-structured interview to collect our data. This enabled us to gain insights into the experiences of healthcare staff and patients. Participants were asked to reflect on the following themes we had pre-defined: resources, support, integration and sequential BT.

Therapists cited work overload with inadequate time adjustments and lack of ongoing training to sustain the use of BT in the long term as common barriers. A key finding of this study from the therapists’ perspective was also the importance of a clearly defined concept and setting for successful implementation. Patients valued the availability of satisfactory instructions for use, integration by therapists and a flexible setting. There were mixed opinions on sequential BT, with therapists mentioning in particular a lack of resources for patient care after discharge.

In summary, the results show that therapists reported more barriers in contrast to patients’ perceptions. This highlights that while understanding the patient’s perspective is important, healthcare professionals play the central role in implementing and maintaining BT.

None Declared

The gap between staff resources and population needs for psychiatric treatment and support, as well as the geographical gap between services delivered in rural areas and in cities is a major challenge for the Danish society as well as globally.

The current project will investigate video consultations (VC) in the context of developing a full-scale home-based mental health service for users with severe mental illness (SMI) necessitating multidisciplinary and municipal support. Digital patient reported outcome (PRO) monitoring can strengthen shared decision making in consultations, and is frequently applied for users with SMI, but it is not regularly combined with VC.

TELMA (Telemedical Monitoring Application) is a national IT system that makes it possible to monitor a patients’ health status in their own home. TELMA includes several questionnaires and the option to connect telemedicine devices that can send vital data such as sleep quality or heart rate measurements directly to the app using Bluetooth. A number of affiliated, cross-sectoral teams who monitors the patient’s health status can view the patient’s responses and data. By mapping the needs and preferences of patients and staff along with an initial trial of the application of TELMA-PSYK for home-based treatment in psychiatry, this study will pave the way for the development of fully digital, person-centered, sector integrating home-based treatment.

This study aims to explore barriers and opportunities to home-based treatment for users with severe mental illness as seen by stakeholders, staff, management and users in mental health services and municipalities.

TELMA-PSYK will be examined in a small-scale feasibility study.

Twenty users with SMI and their regional and municipal staff will be invited to participate in the study. A qualitative study based on phenomenological enquiry will capture the respondents’ experiences of VC and their concerns for and opportunities of full-scale home-based mental health service with PRO recording.

Qualitative data will be interviews examining the users’ experience of three months’ home-based service provision, and the corresponding staffs’ experience of the user’s treatment course and the intervention. Qualitative data will be analyzed with reflexive thematic analysis.

It is expected results from the feasibility study will be ready by March 2025.

It is expected conclusions from the feasibility study will be ready by March 2025.

None Declared

The econsulta is the telematic coordination system between hospital care and primary care. In this way, it allows an agile and fluid coordination, improving the coordination and the care process of the patient.

With the data collected and analyzed, a descriptive study of the functioning of the econsulta as a coordination tool between Mental Health and Primary Care is carried out.

The data collected corresponds to health area 6 of the Community of Madrid, which consists of 18 primary health centers for a Mental Health Center. The econsultations carried out between January 2022 and August 2024 are analyzed.

The data collected are broken down into patient diagnoses, coded according to the ICD-10 classification system, and the attitude to be taken in each of them.

A total of 1489 econsults performed in the period between January 2022 and August 2024 were obtained. The majority diagnoses are:

• - Organic mental disorders, including symptomatic.

• - Mental and behavioral disorders due to the use of multiple drugs or other psychotropic substances.

• - Schizophrenia, schizotypal disorder and delusional disorders.

• - Mood (affective) disorders.

• - Mood disorders

• - Neurotic disorders, secondary to stressful situations and somatoform disorders.

• - Eating behavior disorders.

• - Personality and behavioral disorders in adults.

• - Mental retardation.

• - ADHD

Thanks to the econsult, 692 patients have been resolved, without requiring referral to Mental Health, through direct indications and recommendations on treatments prescribed in Primary Care. Likewise, 61 of them avoided referral only to obtain a visa for the usual treatment. On the other hand, 378 patients required direct referral to Mental Health and 54 to the CAID. In addition, 229 of the patients who were already under follow-up were consulted about their treatment.

After analyzing the data, Primary Care made adequate use of the system, given that only 40 of the consultations were not appropriate.

After analyzing the data, the econsultation is a fast and effective coordination system that increases the efficiency of the patient care system.

None Declared