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Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Passive sensing data from smartphones and wearables may help improve the prediction of suicidal thoughts and behaviors (STB). In this systematic review, we explored the feasibility and predictive validity of passive sensing for STB. On June 24, 2024, we systematically searched Medline, Embase, Web of Science, PubMed, and PsycINFO. Studies were eligible if they investigated the association between STB and passive sensing, or the feasibility of passive sensing in this context. From 2107 unique records, we identified eleven prediction studies, ten feasibility studies, and seven protocols. Studies indicated generally lower model performance for passive compared to active data, with three out of four studies finding no incremental value. PROBAST ratings revealed major shortcomings in methodology and reporting. Studies suggested that passive sensing is feasible in high-risk populations. In conclusion, there is limited evidence on the predictive value of passive sensing for STB. We highlight important quality characteristics for future research., (© 2024. The Author(s).)

Background: Systemic sclerosis (scleroderma; SSc) is a rare autoimmune connective tissue disease. Functional impairment of hands is common. The Scleroderma Patient-centered Intervention Network (SPIN)-HAND trial compared effects of offering access to an online self-guided hand exercise program to usual care on hand function (primary) and functional health outcomes (secondary) in people with SSc with at least mild hand function limitations., Methods: The pragmatic, two-arm, parallel-group cohort multiple randomized controlled trial was embedded in the SPIN Cohort. Cohort participants with Cochin Hand Function Scale (CHFS) scores ≥ 3 and who indicated interest in using the SPIN-HAND Program were randomized (3:2 ratio) to an offer of program access or to usual care (targeted N = 586). The SPIN-HAND program consists of 4 modules that address (1) thumb flexibility and strength; (2) finger bending; (3) finger extension; and (4) wrist flexibility and strength. The primary outcome analysis compared CHFS scores 3 months post-randomization between participants offered versus not offered the program. Secondary outcomes were CHFS scores 6 months post-randomization and functional health outcomes (Patient-Reported Outcomes Measurement Information System profile version 2.0 domain scores) 3 and 6 months post-randomization., Results: In total, 466 participants were randomized to intervention offer (N = 280) or usual care (N = 186). Of 280 participants offered the intervention, 170 (61%) consented to access the program. Of these, 117 (69%) viewed at least one hand exercise instruction video and 77 (45%) logged into the program website at least 3 times. In intent-to-treat analyses, CHFS scores were 1.2 points lower (95% CI - 2.8 to 0.3) for intervention compared to usual care 3 months post-randomization and 0.1 points lower (95% CI - 1.8 to 1.6 points) 6 months post-randomization. There were no statistically significant differences in other outcomes., Conclusion: The offer to use the SPIN-HAND Program did not improve hand function. Low offer uptake, program access, and minimal usage among those who accessed the program limited our ability to determine if using the program would improve function. To improve engagement, the program could be tested in a group format or as a resource to support care provided by a physical or occupational therapist., Trial Registration: NCT03419208 . Registered on February 1, 2018., (© 2022. The Author(s).)

Background: The Scleroderma Patient-centered Intervention Network (SPIN) developed an online self-management program (SPIN-SELF) designed to improve disease-management self-efficacy in people with systemic sclerosis (SSc, or scleroderma). The aim of this study was to evaluate feasibility aspects for conducting a full-scale randomized controlled trial (RCT) of the SPIN-SELF Program., Methods: This feasibility trial was embedded in the SPIN Cohort and utilized the cohort multiple RCT design. In this design, at the time of cohort enrollment, cohort participants consent to be assessed for trial eligibility and randomized prior to being informed about the trial. Participants in the intervention arm are informed and provide consent, but not the control group. Forty English-speaking SPIN Cohort participants from Canada, the USA, or the UK with low disease-management self-efficacy (Self-Efficacy for Managing Chronic Disease Scale [SEMCD] score ≤ 7) who were interested in using an online self-management program were randomized (3:2 ratio) to be offered the SPIN-SELF Program or usual care for 3 months. Program usage was examined via automated usage logs. User satisfaction was assessed with semi-structured interviews. Trial personnel time requirements and implementation challenges were logged., Results: Of 40 SPIN Cohort participants randomized, 26 were allocated to SPIN-SELF and 14 to usual care. Automated eligibility and randomization procedures via the SPIN Cohort platform functioned properly, except that two participants with SEMCD scores > 7 (scores of 7.2 and 7.3, respectively) were included, which was caused by a system programming error that rounded SEMCD scores. Of 26 SPIN Cohort participants offered the SPIN-SELF Program, only 9 (35%) consented to use the program. Usage logs showed that use of the SPIN-SELF Program was low: 2 of 9 users (22%) logged into the program only once (median = 3), and 4 of 9 (44%) accessed none or only 1 of the 9 program's modules (median = 2)., Conclusions: The results of this study will lead to substantial changes for the planned full-scale RCT of the SPIN-SELF Program that we will incorporate into a planned additional feasibility trial with progression to a full-scale trial. These changes include transitioning to a conventional RCT design with pre-randomization consent and supplementing the online self-help with peer-facilitated videoconference-based groups to enhance engagement., Trial Registration: clinicaltrials.gov , NCT03914781 . Registered 16 April 2019., (© 2022. The Author(s).)

Background: Systemic sclerosis (scleroderma; SSc) is a rare autoimmune connective tissue disease. We completed an initial feasibility trial of an online self-administered version of the Scleroderma Patient-centered Intervention Network Self-Management (SPIN-SELF) Program using the cohort multiple randomized controlled trial (RCT) design. Due to low intervention offer uptake, we will conduct a new feasibility trial with progression to full-scale trial, using a two-arm parallel, partially nested RCT design. The SPIN-SELF Program has also been revised to include facilitator-led videoconference group sessions in addition to online material. We will test the group-based intervention delivery format, then evaluate the effect of the SPIN-SELF Program on disease management self-efficacy (primary) and patient activation, social appearance anxiety, and functional health outcomes (secondary)., Methods: This study is a feasibility trial with progression to full-scale RCT, pending meeting pre-defined criteria, of the SPIN-SELF Program. Participants will be recruited from the ongoing SPIN Cohort ( http://www.spinsclero.com/en/cohort ) and via social media and partner patient organizations. Eligible participants must have SSc and low to moderate disease management self-efficacy (Self-Efficacy for Managing Chronic Disease (SEMCD) Scale score ≤ 7.0). Participants will be randomized (1:1 allocation) to the group-based SPIN-SELF Program or usual care for 3 months. The primary outcome in the full-scale trial will be disease management self-efficacy based on SEMCD Scale scores at 3 months post-randomization. Secondary outcomes include SEMCD scores 6 months post-randomization plus patient activation, social appearance anxiety, and functional health outcomes at 3 and 6 months post-randomization. We will include 40 participants to assess feasibility. At the end of the feasibility portion, stoppage criteria will be used to determine if the trial procedures or SPIN-SELF Program need important modifications, thereby requiring a re-set for the full-scale trial. Otherwise, the full-scale RCT will proceed, and outcome data from the feasibility portion will be utilized in the full-scale trial. In the full-scale RCT, 524 participants will be recruited., Discussion: The SPIN-SELF Program may improve disease management self-efficacy, patient activation, social appearance anxiety, and functional health outcomes in people with SSc. SPIN works with partner patient organizations around the world to disseminate its programs free-of-charge., Trial Registration: ClinicalTrials.gov NCT04246528 . Registered on 27 January 2020., (© 2021. The Author(s).)

Background: Systemic sclerosis (SSc), or scleroderma, is a rare disease that often results in significant disruptions to activities of daily living and can negatively affect physical and psychological well-being. Because there is no known cure, SSc treatment focuses on reducing symptoms and disability and improving health-related quality of life (HRQoL). Self-management programs are known to increase self-efficacy for disease management in many chronic diseases. The Scleroderma Patient-centered Intervention Network (SPIN) developed a Web-based self-management program (SPIN self-management; SPIN-SELF) to increase self-efficacy for disease management and to improve HRQoL for patients with SSc., Objective: The proposed study aims to assess the feasibility of conducting a full-scale randomized controlled trial (RCT) of the SPIN-SELF program by evaluating the trial implementation processes, required resources and management, scientific aspects, and participant acceptability and usage of the SPIN-SELF program., Methods: The SPIN-SELF feasibility trial will be conducted via the SPIN Cohort. The SPIN Cohort was developed as a framework for embedded pragmatic trials using the cohort multiple RCT design. In total, 40 English-speaking SPIN Cohort participants with low disease management self-efficacy (Self-Efficacy for Managing Chronic Disease Scale score ≤7), who have indicated interest in using a Web-based self-management program, will be randomized with a 3:2 ratio into the SPIN-SELF program or usual care for 3 months. Feasibility outcomes include trial implementation processes, required resources and management, scientific aspects, and patient acceptability and usage of the SPIN-SELF program., Results: Enrollment of the 40 participants occurred between July 5, 2019, and July 27, 2019. By November 25, 2019, data collection of trial outcomes was completed. Data analysis is underway, and results are expected to be published in 2020., Conclusions: The SPIN-SELF program is a self-help tool that may improve disease-management self-efficacy and improve HRQoL in patients with SSc. The SPIN-SELF feasibility trial will ensure that trial methodology is robust, feasible, and consistent with trial participant expectations. The results will guide adjustments that need to be implemented before undertaking a full-scale RCT of the SPIN-SELF program., International Registered Report Identifier (irrid): DERR1-10.2196/16799., (©Marie-Eve Carrier, Linda Kwakkenbos, Warren R Nielson, Claire Fedoruk, Karen Nielsen, Katherine Milette, Janet Pope, Tracy Frech, Shadi Gholizadeh, Laura Hummers, Sindhu R Johnson, Pamela Piotrowski, Lisa Jewett, Jessica Gordon, Lorinda Chung, Dan Bilsker, Kimberly A Turner, Julie Cumin, Joep Welling, Catherine Fortune, Catarina Leite, Karen Gottesman, Maureen Sauve, Tatiana S Rodríguez-Reyna, Marie Hudson, Maggie Larche, Ward van Breda, Maria E Suarez-Almazor, Susan J Bartlett, Vanessa L. Malcarne, Maureen D. Mayes, Isabelle Boutron, Luc Mouthon, Fredrick Wigley, Brett D Thombs, SPIN Investigators. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 24.04.2020.)

Introduction: Sentiment analysis may be a useful technique to derive a user's emotional state from free text input, allowing for more empathic automated feedback in online cognitive behavioral therapy (iCBT) interventions for psychological disorders such as depression. As guided iCBT is considered more effective than unguided iCBT, such automated feedback may help close the gap between the two. The accuracy of automated sentiment analysis is domain dependent, and it is unclear how well the technology is applicable to iCBT. This paper presents an empirical study in which automated sentiment analysis by an algorithm for the Dutch language is validated against human judgment., Methods: A total of 493 iCBT user texts were evaluated on overall sentiment and the presence of five specific emotions by an algorithm, and by 52 psychology students who evaluated 75 randomly selected texts each, providing about eight human evaluations per text. Inter-rater agreement (IRR) between algorithm and humans, and humans among each other, was analyzed by calculating the intra-class correlation under a numerical interpretation of the data, and Cohen's kappa, and Krippendorff's alpha under a categorical interpretation., Results: All analyses indicated moderate agreement between the algorithm and average human judgment with respect to evaluating overall sentiment, and low agreement for the specific emotions. Somewhat surprisingly, the same was the case for the IRR among human judges, which means that the algorithm performed about as well as a randomly selected human judge. Thus, considering average human judgment as a benchmark for the applicability of automated sentiment analysis, the technique can be considered for practical application., Discussion/conclusion: The low human-human agreement on the presence of emotions may be due to the nature of the texts, it may simply be difficult for humans to agree on the presence of the selected emotions, or perhaps trained therapists would have reached more consensus. Future research may focus on validating the algorithm against a more solid benchmark, on applying the algorithm in an application in which empathic feedback is provided, for example, by an embodied conversational agent, or on improving the algorithm for the iCBT domain with a bottom-up machine learning approach.

Background: Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level., Objective: This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation., Methods: Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment., Results: Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%)., Conclusions: Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs., (©Vincent Bremer, Dennis Becker, Spyros Kolovos, Burkhardt Funk, Ward van Breda, Mark Hoogendoorn, Heleen Riper. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.08.2018.)

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

Recent developments in the field of sensor devices provide new possibilities to measure a variety of health related aspects in a precise and fine-grained manner. Subsequently, more empirical data will be generated than ever before. While this greatly improves the opportunities for creating accurate predictive models, other types of models besides the more traditional machine learning approaches can provide insights into temporal relationships in the data. Models that express temporal relationships between states in a mathematical manner are examples of such models. However, the evaluation methods traditionally used in the field of predictive modeling are not appropriate for those models, making it difficult to distinguish them in terms of validity. Appropriate assessment methodology is therefore necessary to drive the research of mathematical modeling forward. In this paper we investigate the applicability of such a formalized method. The method takes into account important model aspects, namely descriptive and predictive capability, parameter sensitivity and model complexity. As a case study the method is applied to a mathematical model in the domain of mental health, showing that the method generates useful insights into the behavior of the model., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation. [ABSTRACT FROM AUTHOR]

Mental health professionals have relied primarily on clinical evaluations to identify in vivo pathology. As a result, mental health is largely reactive rather than proactive. In an effort to proactively assessmood, we collected continuous neurophysiologic data for ambulatory individuals 8-10 h a day at 1 Hz for 3 weeks (N=24). Data were obtained using a commercial neuroscience platform (Immersion Neuroscience) that quantifies the neural value of social-emotional experiences. These data were related to self-reported mood and energy to assess their predictive accuracy. Statistical analyses quantified neurophysiologic troughs by the length and depth of social-emotional events with low values and neurophysiologic peaks as the complement. Participants in the study had an average of 2.25 (SD = 3.70, Min = 0, Max = 25) neurophysiologic troughs per day and 3.28 (SD = 3.97, Min = 0, Max = 25) peaks. The number of troughs and peaks predicted daily mood with 90% accuracy using least squares regressions and machine learning models. The analysis also showed that women were more prone to low mood compared to men. Our approach demonstrates that a simple count variable derived from a commercially-available platform is a viable way to assess low mood and low energy in populations vulnerable to mood disorders. In addition, peak Immersion events, which are mood-enhancing, may be an effective measure of thriving in adults. [ABSTRACT FROM AUTHOR]

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective. [ABSTRACT FROM AUTHOR]

Looking at the rapidity the social media has gained ascendancy in the society, coupled with considerable shortage of addressing the health of the social media users, there is a pressing need for employing mechanized systems to help identify individuals at risk. In this study, we investigated potential of people's social media language in order to predict their vulnerability towards the future episode of mental distress. This work aims to (a) explore the most frequent affective expressions used by online users which reflect their mental health condition and (b) develop predictive models to detect users with risk of psychological distress. In this paper, dominant sentiment extraction techniques were employed to quantify the affective expressions and classify and predict the incident of psychological distress. We trained a set of seven supervised machine learning classifiers on logs crowd-sourced from 2500 Indian Social Networking Sites (SNS) users and validated with 3149 tweets collected from Indian Twitter. We test the model on these two different SNS datasets with different scales and ground truth labeling method and discuss the relationship between key factors and mental health. Performance of classifiers is evaluated at all classification thresholds; accuracy, precision, recall, F1-score. and experimental results show a better traction of accuracies ranging from ~ 82 to ~ 99% as compared to the models of relevant existing studies. Thus, this paper presents a mechanized decision support system to detect users' susceptibility towards mental distress and provides several evidences that it can be utilized as an efficient tool to preserve the psychological health of the social media users. [ABSTRACT FROM AUTHOR]

Background: Linguistic Inquiry and Word Count (LIWC) are known as indicators of vocabulary in texts. Specifically, with the help of algorithms and computational processes, it can analyze elements such as emotions, cognitions, attitudes, vocabulary, language style and social communication in texts, and the lack of a Persian version was one of the most important reasons for conducting this research. Aims: The purpose of this research was to develop and examine the psychometric features of the Persian Linguistic Inquiry and Word Count in categories of Affect and Cognitive Processes. Methods: The present research method was descriptive and correlational. The method of developing categories of cognitive processes and affect consists of several stages. The development of the dictionary started with the initial translation of the words of the LIWC-22, and then, a huge corpus of Persian texts was analyzed to maximize the coverage of words of each category in the Persian language using text analysis methods. In the next step, the words were evaluated by psychologist judges, and then linguists determined the lemma of the approved words in order to distinguish different forms of the word. After completing these steps, in order to check the reliability of the dictionaries of cognitive processes and affect and their subcategories, Cronbach's alpha and KuderRichardson 20 were calculated, and then to check the external validity, the equivalence of the Persian Linguistic Inquiry and Word Count (P-LIWC) with the original English version of LIWC-22 was analyzed. Results: The results showed that all categories of emotions and cognitive processes in the P-LIWC had a significant correlation with the final English version of LIWC-22 (p<0.01) and this tool has good validity and reliability. Conclusion: It can be concluded that this software is used in a wide range of research fields and analysis of Persian texts and also can be used in the analysis of the texts and the treatment session of the people who refer to the psychological clinics. [ABSTRACT FROM AUTHOR]

According to several global burdens of disease reports, mental health issues are a leading cause of disease burden. A worrying trend is the increasing contribution of college students to this pool of mental health woes. Anxiety, depression, stress, and suicidal thoughts are dominant emerging issues. This population cohort's demographic and socio-economic significance has made this an urgent social imperative for everyone. Of late, digital technologies, namely Artificial Intelligence (AI) & Machine Learning (ML)-based models, have gained prominence in resolving mental health issues of young people. This paper employs bibliometric analysis in a nascent attempt to document the research on applying AI & ML-based models in treating mental issues among students. The finding suggests an urgent need to align with the United Nations Sustainable Development Goal 3, "Health and Well-Being", and create awareness at the individual, family, society, university, and country levels and the world at large. China, the USA, and Korea contributed the maximum number of articles. Among the universities, Carnegie Mellon University, the University of Southern California, and Osaka University contributed the most. Evident collaborations between countries and universities are also visible. The top sources include Frontiers in Psychology, the International Journal of Environmental Research & Public Health, and Scientific Reports. The determinations of this study can provide valuable direction toward integrating AI/ML in mental health research. [ABSTRACT FROM AUTHOR]

The perception of sound greatly impacts users' emotional states, expectations, affective relationships with products, and purchase decisions. Consequently, assessing the perceived quality of sounds through jury testing is crucial in product design. However, the subjective nature of jurors' responses may limit the accuracy and reliability of jury test outcomes. This research explores the utility of facial expression analysis in jury testing to enhance response reliability and mitigate subjectivity. Some quantitative indicators allow the research hypothesis to be validated, such as the correlation between jurors' emotional responses and valence values, the accuracy of jury tests, and the disparities between jurors' questionnaire responses and the emotions measured by FER (facial expression recognition). Specifically, analysis of attention levels during different statuses reveals a discernible decrease in attention levels, with 70 percent of jurors exhibiting reduced attention levels in the 'distracted' state and 62 percent in the 'heavy-eyed' state. On the other hand, regression analysis shows that the correlation between jurors' valence and their choices in the jury test increases when considering the data where the jurors are attentive. The correlation highlights the potential of facial expression analysis as a reliable tool for assessing juror engagement. The findings suggest that integrating facial expression recognition can enhance the accuracy of jury testing in product design by providing a more dependable assessment of user responses and deeper insights into participants' reactions to auditory stimuli. [ABSTRACT FROM AUTHOR]

Background: Clinicians collect session therapy notes within patient session records. Session records contain valuable information about patients' treatment progress. Sentiment analysis is a tool to extract emotional tones and states from text input and could be used to evaluate patients' sentiment during treatment over time. This preliminary study aims to investigate the validity of automated sentiment analysis on session patient records within an eating disorder (ED) treatment context against the performance of human raters. Methods: A total of 460 patient session records from eight participants diagnosed with an ED were evaluated on their overall sentiment by an automated sentiment analysis and two human raters separately. The inter-rater agreement (IRR) between the automated analysis and human raters and IRR among the human raters was analyzed by calculating the intra-class correlation (ICC) under a continuous interpretation and weighted Cohen's kappa under a categorical interpretation. Furthermore, differences regarding positive and negative matches between the human raters and the automated analysis were examined in closer detail. Results: The ICC showed a moderate automated-human agreement (ICC = 0.55), and the weighted Cohen's kappa showed a fair automated-human (k = 0.29) and substantial human-human agreement (k = 0.68) for the evaluation of overall sentiment. Furthermore, the automated analysis lacked words specific to an ED context. Discussion/conclusion: The automated sentiment analysis performed worse in discerning sentiment from session patient records compared to human raters and cannot be used within practice in its current state if the benchmark is considered adequate enough. Nevertheless, the automated sentiment analysis does show potential in extracting sentiment from session records. The automated analysis should be further developed by including context-specific ED words, and a more solid benchmark, such as patients' own mood, should be established to compare the performance of the automated analysis to. [ABSTRACT FROM AUTHOR]

Background: Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them. Methods: Literature and expert opinions were integrated to formulate relevant guiding principles. Results: Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it. Conclusions: The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field. [ABSTRACT FROM AUTHOR]

In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapted treatment delivery and improve outcomes. This study seeks to develop a state-of-the-art deep-learning framework for predicting clinical outcomes in internet-delivered Cognitive Behavioural Therapy (iCBT) by leveraging large-scale, high-dimensional time-series data of client-reported mental health symptoms and platform interaction data. We use de-identified data from 45,876 clients on SilverCloud Health, a digital platform for the psychological treatment of depression and anxiety. We train deep recurrent neural network (RNN) models to predict whether a client will show reliable improvement by the end of treatment using clinical measures, interaction data with the iCBT program, or both. Outcomes are based on total improvement in symptoms of depression (Patient Health Questionnaire-9, PHQ-9) and anxiety (Generalized Anxiety Disorder-7, GAD-7), as reported within the iCBT program. Using internal and external datasets, we compare the proposed models against several benchmarks and rigorously evaluate them according to their predictive accuracy, sensitivity, specificity and AUROC over treatment. Our proposed RNN models consistently predict reliable improvement in PHQ-9 and GAD-7, using past clinical measures alone, with above 87% accuracy and 0.89 AUROC after three or more review periods, outperforming all benchmark models. Additional evaluations demonstrate the robustness of the achieved models across (i) different health services; (ii) geographic locations; (iii) iCBT programs, and (iv) client severity subgroups. Results demonstrate the robust performance of dynamic prediction models that can yield clinically helpful prognostic information ready for implementation within iCBT systems to support timely decision-making and treatment adjustments by iCBT clinical supporters towards improved client outcomes. [ABSTRACT FROM AUTHOR]

Stochastic distribution methods were used to construct patient flow simulation sub-models such as patient inflow, length of stay (LoS), cost of treatment (CoT) and clinical pathways (CPs). However, the patient inflow rate demonstrates seasonality, trend, and variation due to natural and human-made factors. LoS, CoT and CPs are determined by social-demographics factors, clinical and laboratory test results, resource availability and healthcare structure. For this reason, patient flow simulation models developed using stochastic methods have limitations including uncertainty, not recognising patient heterogeneity, and not representing personalised and value-based healthcare. This, in turn, results in a low acceptance level and implementation of solutions suggested by patient flow simulation models. On the other hand, machine learning becomes effective in predicting patient inflow, LoS, CoT, and CPs. This paper, therefore, describes why coupling machine learning with patient flow simulation is important, proposes a conceptual architecture for machine learning integrated patient flow simulation and demonstrates its implementation with examples. [ABSTRACT FROM AUTHOR]

Traditional teaching approaches in research methods courses demotivate students, hindering their ability to learn and apply research concepts in practical settings. Instructors who are devoted to employing active learning pedagogy in teaching research courses have observed favorable outcomes. However, a dearth of literature has focused on students' perceptions and evaluation of active learning pedagogy. This study explores how students perceive active learning approaches via in-person and writtenresponse interviews. Eighteen participants volunteered and self-selected one of the interview forms. In addition to exploring students' perspectives on active learning pedagogy, we conducted a comparison of those interviewing modes. Our analysis revealed that regardless of interview type, participants provided similar results. In-person interviews generated more information segments; however, the segments from written-response interviews provided more informative meaning. Combining all data, we developed three themes and 29 codes about students' perceptions of active learning pedagogy. We found that students valued self-exploration as well as interactive activities that promoted deep learning and knowledge transfer. Based on the results, we provide practical recommendations for how instructors can design active learning-based research courses to facilitate students in active and self-regulated learning. [ABSTRACT FROM AUTHOR]

Background: Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta-analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions., Methods: PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random-effects meta-analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior., Results: Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta-analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects., Conclusions: This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed., (© 2024 The Authors. Alcohol, Clinical and Experimental Research published by Wiley Periodicals LLC on behalf of Research Society on Alcohol.)

Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called "general" or "human-like" AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy. [ABSTRACT FROM AUTHOR]

Interpersonal transgressions, subsequent apologies, and offered (or withheld) forgiveness hold important consequences for both perpetrators and victims. Research has focused largely on the perceptions of victims and processes that promote forgiveness in relation to transgressions of low severity. In order to extend this domain of inquiry we examined the emotional substrates that facilitate and constrain apologies for severe transgressions (i.e., murder). We collected data on the final statements from incarcerated persons on death row and applied a sentiment analysis to obtain estimates of the emotions expressed in them (i.e., anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). We manually coded each statement to indicate whether it exhibited some form of apology and compared the emotions expressed in apologetic statements versus non-apologetic statements. Results indicated that overall, final statements reflected high levels of joy and trust. Similar to studies of less severe transgressions, we found that 33.50% of statements contained some form of apology. Our comparative analyses revealed that apologetic statements reflected significantly greater sadness and less anger. In regression and subsequent dominance analyses we found that sadness and anger were the most important emotions to apologizing. We also found that anger moderated the association between sadness and the likelihood of apologizing such that as anger increased the effect of sadness decreased. Taken together these findings suggest that apologies for severe transgressions involve a delicate balance between sadness and anger rather than either emotion in isolation. [ABSTRACT FROM AUTHOR]

Short texts generated by individuals in online environments can provide social and behavioral scientists with rich insights into these individuals' internal states. Trained manual coders can reliably interpret expressions of such internal states in text. However, manual coding imposes restrictions on the number of texts that can be analyzed, limiting our ability to extract insights from large-scale textual data. We evaluate the performance of several automatic text analysis methods in approximating trained human coders' evaluations across four coding tasks encompassing expressions of motives, norms, emotions, and stances. Our findings suggest that commonly used dictionaries, although performing well in identifying infrequent categories, generate false positives too frequently compared to other methods. We show that large language models trained on manually coded data yield the highest performance across all case studies. However, there are also instances where simpler methods show almost equal performance. Additionally, we evaluate the effectiveness of cutting-edge generative language models like GPT-4 in coding texts for internal states with the help of short instructions (so-called zero-shot classification). While promising, these models fall short of the performance of models trained on manually analyzed data. We discuss the strengths and weaknesses of various models and explore the trade-offs between model complexity and performance in different applications. Our work informs social and behavioral scientists of the challenges associated with text mining of large textual datasets, while providing best-practice recommendations., (© 2024. The Author(s).)

Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. Objective: This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. Methods: A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. Results: A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment--18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). Conclusions: These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a vastly established domain for social media monitoring, feedback insights, and commercial or political campaigns. It allows us to gain an overview of the wider public opinion on certain topics. Nowadays, different social media platforms play a crucial role in web-based sentiment analysis and emotion detection from distinct perspectives. Likewise, WhatsApp is probably the most popular messaging app, allowing users to send messages, images, audio, or videos. However, it is still highly under-explored for any type of linguistic synthesis and analysis. Like many other groups of people, students use WhatsApp for various purposes, even more since the last two years of the pandemic phase, for instance, class communication, study group communication, etc. In this paper, we present a novel approach to analyze the sentiments and emotions of students in valence, arousal, and dominance space by classifying the messages from their WhatsApp group chat. The emotional dimensions of valence, arousal and dominance (VAD) can derive a person's interest (attraction), level of activation, and perceived level of control for a particular situation from textual communication. We propose a vanilla SVM model fused with a language classifier to calculate each message's sentiment ratings. Finally, using the SVM classifier, we classify the sentiment ratings concerning the degree of the VAD scale. The data were analyzed using a qualitative content analysis method. The results of the study in the form of cumulative sentiment scale and sentiment clustering in VAD space reveal that the students' WhatsApp groups were mostly used for sharing information, exchanging ideas, and discussing issues, with mostly neutral to positive sentiment viewpoints for the provided topics of discussions. [ABSTRACT FROM AUTHOR]

Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection. [ABSTRACT FROM AUTHOR]

The aim of this proof-of-concept study is to develop a predictive model based on deep learning algorithms to predict working alliances after the first therapeutic session and to provide a basis for clinical decisions. Using a sample of 325 patients and 32 psychotherapists from three university counseling centers, a deep learning algorithm known as fully connected neural networks (FCNNs) was adopted to construct data-driven predictive models. The performance differences between the model including only patient indicators and the model including both patient and therapist indicators were compared. The optimal model was further tested in a general hospital sample of 85 patients and 8 therapists. The model incorporating both patient indicators and therapist-level indicators (R²: 0.30 ± 0.02) performed better than the model incorporating only patient indicators (R²: 0.11 ± 0.02). The performance of this model decreased when being transferred to the independent general hospital sample, but still retained some predictive value (R² = 0.11). This study showed that the inclusion of therapist-level indicators can improve the performance of a predictive model in predicting working alliances. This model could assist clinical decisions on choosing psychotherapists for patients and may also initiate new possibilities for future research. [ABSTRACT FROM AUTHOR]

Objective: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches., Method: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores., Results: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types., Conclusion: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area., (© 2023 The Authors. Clinical Psychology & Psychotherapy published by John Wiley & Sons Ltd.)

The purpose of business sentiment analysis is to determine the emotions or attitudes expressed toward the company, products, services, personnel, or events. Text analysis are the simplest and most developed types of sentiment analysis so far. The textbased business sentiment analysis still has some unresolved challenges. For example, the machine learning algorithms are unable to recognize double meanings, jokes and allusions. The regional differences between language and non-native speech structures cannot be explained. To solve this problem, an undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract. To improve the accuracy of sentiment classification, this work proposes a robust news mining-based business sentiment analysis framework, called BuSeD. It contains two main stages: (1) news collection and preprocessing, and (2) feature extraction and sentiment classification. In the first stage, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In the second stage, topics in each article is extracted by using traditional topic extraction tools. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conduct comprehensive experiments to evaluate the performance of BuSeD for sentiment classification. Compared with four classical classification algorithms, the proposed CNN-based classification model of BuSeD achieves the highest F1 scores. We also present a quantitative trading application based on sentiment analysis to validate BuSeD, which indicates that the news-based business sentiment analysis has high economic application value. [ABSTRACT FROM AUTHOR]

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively. [ABSTRACT FROM AUTHOR]

We carried out a case study with a group of female victims of gender violence (n = 39) with the objective of evaluating a blended psychotherapeutic intervention. The results show that blended therapies with mobile text messages combined with face-to-face group therapies significantly improved the health of these women. Mood states and the symptoms of depression were measured with a PHQ-9 personal health questionnaire and evolved positively. In the group of women (n = 39) the scores improved from an initial 13.5 (SD = 7.2, range 3–34) to 6.0 at the end of the period of messages (SD = 5.2, range 0–18), which was a significant difference (t (39) = 2.02; p = 0.000). Most of the women stated that the messages had helped them improve their mood (91.6%) and their general health (83.3%), which made them feel more connected to their social environment (80.6%). We observed that adherence to medication for each woman improved. With mixed therapies and mobile phones, social service professionals can incorporate technology into daily practices and offer personalized attention and daily counseling to victims of gender-based violence. [ABSTRACT FROM AUTHOR]