Needs Expressed in Peer-to-Peer Web-Based Interactions Among People With Depression and Anxiety Disorders Hospitalized in a Mental Health Facility: Mixed Methods Study.

BACKGROUND: Hospitalization in psychiatric wards is a necessary step for many individuals experiencing severe mental health issues. However, being hospitalized can also be a stressful and unsettling experience. It is crucial to understand and address the various needs of hospitalized individuals with psychiatric disorders to promote their overall well-being and support their recovery. OBJECTIVE: Our objectives were to identify and describe individual needs related to mental hospitals through peer-to-peer interactions on Polish web-based forums among individuals with depression and anxiety disorders and to assess whether these needs were addressed by peers. METHODS: We conducted a search of web-based forums focused on depression and anxiety and selected samples of 160 and 176 posts, respectively, until we reached saturation. A mixed methods analysis that included an in-depth content analysis, the Pearson χ2 test, and φ coefficient was used to evaluate the posts. RESULTS: The most frequently identified needs were the same for depression and anxiety forums and involved informational (105/160, 65.6% and 169/393, 43%, respectively), social life (17/160, 10.6% and 90/393, 22.9%, respectively), and emotional (9/160, 5.6% and 66/393, 16.8%, respectively) needs. The results show that there is no difference in the expression of needs between the analyzed forums. The needs were directly (42/47, 89% vs 98/110, 89.1% of times for depression and anxiety, respectively) and not fully (27/47, 57% vs 86/110, 78.2% of times for depression and anxiety, respectively) addressed by forum users. In quantitative analysis, we found that depression-related forums had more posts about the need for informational support and rectification, the expression of anger, and seeking professional support. By contrast, anxiety-related forums had more posts about the need for emotional support; social life; and information concerning medications, hope, and motivation. The most common co-occurrence of expressed needs was between sharing own experience and the need for professional support, with a strong positive association. The qualitative analysis showed that users join web-based communities to discuss their fears and questions about psychiatric hospitals. The posts revealed 4 mental and emotional representations of psychiatric hospitals: the hospital as an unknown place, the ambivalence of presumptions and needs, the negative representation of psychiatric hospitals, and the people associated with psychiatric hospitals. The tone of the posts was mostly negative, with discussions revolving around negative stereotypes; traumatic experiences; and beliefs that increased anxiety, shock, and fright and deterred users from hospitalization. CONCLUSIONS: Our study demonstrates that web-based forums can provide a platform for individuals with depression and anxiety disorders to express a wide range of needs. Most needs were addressed by peers but not sufficiently. Mental health professionals can benefit from these findings by gaining insights into the unique needs and concerns of their patients, thus allowing for more effective treatment and support.

Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings.

Parkinson's disease (PD) is the 2nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson's disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in telemedicine.Clinical relevance- There is an urgent need to develop non invasive biomarker of Parkinson's disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases' symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.

Meeting the Unmet Needs of Individuals With Mental Disorders: Scoping Review on Peer-to-Peer Web-Based Interactions.

BACKGROUND: An increasing number of online support groups are providing advice and information on topics related to mental health. OBJECTIVE: This study aimed to investigate the needs that internet users meet through peer-to-peer interactions. METHODS: A search of 4 databases was performed until August 15, 2022. Qualitative or mixed methods (ie, qualitative and quantitative) studies investigating interactions among internet users with mental disorders were included. The φ coefficient was used and machine learning techniques were applied to investigate the associations between the type of mental disorders and web-based interactions linked to seeking help or support. RESULTS: Of the 13,098 identified records, 44 studies (analyzed in 54 study-disorder pairs) that assessed 82,091 users and 293,103 posts were included. The most frequent interactions were noted for people with eating disorders (14/54, 26%), depression (12/54, 22%), and psychoactive substance use disorders (9/54, 17%). We grouped interactions between users into 42 codes, with the empathy or compassion code being the most common (41/54, 76%). The most frequently coexisting codes were request for information and network (35 times; φ=0.5; P<.001). The algorithms that provided the best accuracy in classifying disorders by interactions were decision trees (44/54, 81%) and logistic regression (40/54, 74%). The included studies were of moderate quality. CONCLUSIONS: People with mental disorders mostly use the internet to seek support, find answers to their questions, and chat. The results of this analysis should be interpreted as a proof of concept. More data on web-based interactions among these people might help apply machine learning methods to develop a tool that might facilitate screening or even support mental health assessment.

Psychological Interventions and Bariatric Surgery among People with Clinically Severe Obesity-A Systematic Review with Bayesian Meta-Analysis.

AIM: To assess the effectiveness of perioperative psychological interventions provided to patients with clinically severe obesity undergoing bariatric surgery regarding weight loss, BMI, quality of life, and psychosocial health using the Bayesian approach. METHODS: We considered randomised trials that assessed the beneficial and harmful effects of perioperative psychological interventions in people with clinically severe obesity undergoing bariatric surgery. We searched four data sources from inception to 3 October 2021. The authors independently selected studies for inclusion, extracted data, and assessed the risk of bias. We conducted a meta-analysis using a Bayesian approach. PROSPERO: CRD42017077724. RESULTS: Of 13,355 identified records, we included nine studies (published in 27 papers with 1060 participants (365 males; 693 females, 2 people with missing data)). Perioperative psychological interventions may provide little or no benefit for BMI (the last reported follow-up: MD [95% credible intervals] = -0.58 [-1.32, 0.15]; BF01 = 0.65; 7 studies; very low certainty of evidence) and weight loss (the last reported follow-up: MD = -0.50 [-2.21, 0.77]; BF01 = 1.24, 9 studies, very low certainty of evidence). Regarding psychosocial outcomes, the direction of the effect was mainly inconsistent, and the certainty of the evidence was low to very low. CONCLUSIONS: Evidence is anecdotal according to Bayesian factors and uncertain whether perioperative psychological interventions may affect weight-related and psychosocial outcomes in people with clinically severe obesity undergoing bariatric surgery. As the results are ambiguous, we suggest conducting more high-quality studies in the field to estimate the true effect, its direction, and improve confidence in the body of evidence.

Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review.

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0-45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence- A Systematic Review.

Our review aimed to assess the current state and quality of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI), and emphasising cardiovascular (CV) signals. The quality of such datasets is essential to create replicable systems for future work to grow. We investigated nine sources up to 31 August 2020, using a developed search strategy, including studies considering the use of AI in AAER based on CV signals. Two independent reviewers performed the screening of identified records, full-text assessment, data extraction, and credibility. All discrepancies were resolved by discussion. We descriptively synthesised the results and assessed their credibility. The protocol was registered on the Open Science Framework (OSF) platform. Eighteen records out of 195 were selected from 4649 records, focusing on datasets containing CV signals for AAER. Included papers analysed and shared data of 812 participants aged 17 to 47. Electrocardiography was the most explored signal (83.33% of datasets). Authors utilised video stimulation most frequently (52.38% of experiments). Despite these results, much information was not reported by researchers. The quality of the analysed papers was mainly low. Researchers in the field should concentrate more on methodology.

Analysis and Use of the Emotional Context with Wearable Devices for Games and Intelligent Assistants.

In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors.