This pilot study aimed to investigate the acceptability and effectiveness of a brief-guided internet-based cognitive behavioural therapy (iCBT) for people with emotional distress during the COVID-19 pandemic. Using a quasi-experimental research design with a 3-arm study, eligible participants were assigned to a group with student counsellors (n = 24), a group with counsellors (n = 23) or a non-active control group (n = 21). Participants received eight online modules and regular support via telephone counselling or video conferencing app (Zoom) from a counsellor or student counsellor during a 5-week intervention period, while the control group did not receive any intervention during the intervention period. An adherence rate of 85.10% was observed. The results of the 3 (group) × time (pre vs. post) repeated-measures analysis of covariance showed that the student counsellor and counsellor groups demonstrated significantly greater reductions in total emotional distress (partial η² = 0.10) and stress (partial η² = 0.14) than the control group. In addition, student counsellors and counsellors produced comparable intervention effects, and telephone counselling and Zoom support produced comparable intervention effects. This pilot study supports the acceptability and effectiveness of brief-guided iCBT for people with emotional distress during the COVID-19 pandemic.

The use of AI in digital mental healthcare promises to make treatments more effective, accessible, and scalable than ever before. At the same time, the use of AI opens a myriad of ethical concerns, including the lack of transparency, the risk of bias leading to increasing social inequalities, and the risk of responsibility gaps. This raises a crucial question: Can we rely on these systems to deliver care that is both ethical and effective? In attempts to regulate and ensure the safe usage of AI-powered tools, calls to trustworthy AI systems have become central. However, the use of terms such as “trust” and “trustworthiness” risks increasing anthropomorphization of AI systems, attaching human moral activities, such as trust, to artificial systems. In this article, we propose that terms such as “trustworthiness” be used with caution regarding AI and that when used, they should reflect an AI system's ability to consistently demonstrate measurable adherence to ethical principles, such as respect for human autonomy, nonmaleficence, fairness, and transparency. On this approach, trustworthy and ethical AI has the possibility of becoming a tangible goal rather than wishful thinking.

As e-health offerings rapidly expand, they are transforming and challenging traditional mental health care systems globally, presenting both promising opportunities and significant risks. This article critically examines the potential and pitfalls of integrating digital technologies into mental health care, particularly in the realms of diagnosis, prevention, and treatment. It explores current advancements and evidence-based practices, and provides a vision for how future technologies can evolve responsibly to meet mental health needs. The article concludes with the TEQUILA framework, addressing essential elements and challenges for fostering a beneficial and ethical future. A responsible future for digital mental health requires building Trust by ensuring data privacy, security, and transparency in AI-driven decisions, along with Evidence-based and robust regulatory oversight to maintain Quality. Usability, design, usability tailored to diverse needs, and ethical alignment with users' Interests will all be essential, while Liability and Accreditation standards will safeguard accountability in this evolving landscape.

Purpose: This study examined the efficacy of a 10-week internet-based cognitive behavioral therapy (iCBT) program “REST Online” for Hong Kong university students with mild to moderate levels of psychological distress. Method: A total of 206 Hong Kong university students were randomized into: (1) web-based and (2) app-based iCBT, and (3) waitlist control (WLC) groups. Results: Compared with the WLC group, the participants in the two iCBT groups showed a significant reduction in psychological distress, depression and anxiety symptoms, and negative thoughts and emotions, and significant increase in positive thoughts and emotions, with medium to large effect sizes. The positive effects were sustained at the 3-month follow-up test. No significant intervention effects were found between the two iCBT groups except for anxiety symptoms. Discussion: The findings were discussed in terms of program design and service delivery, and suggestions for delivering digital mental health service in Hong Kong universities were proposed. 

Researchers and clinicians are becoming increasingly aware of the importance of assessing positive functioning to inform clinical outcomes. This paper evaluates the Questionnaire on Well-Being (QWB, available for free https://doi.org/10.17605/OSF.IO/GSC3R), a clinically informed instrument that assesses subjective well-being, across two studies. Study One, consisting of treatment-seeking individuals in an assertiveness training sample (n = 495), explored the factorial structure of the QWB, assessed the four-week test-retest reliability, criterion-related validity, and identified a preliminary cutoff point for the QWB with clinical significance. Study Two, including participants from the general public (n = 1561), confirmed the factorial structure of the QWB and further evaluated criterion-related validity. The results provided support for a unidimensional structure for the QWB. Furthermore, the QWB exhibited excellent internal reliability (Cronbach’s alpha = 0.93 and 0.94 in Study One and Two, respectively), high test-retest reliability (ICC₃ = .50 at a four-week follow-up in Study One), and appropriate criterion-related validity demonstrating positive correlations with positive affect and negative correlations with psychopathology. Finally, a cutoff point on the QWB below 50 was associated with marked psychopathology. These findings provide preliminary support for the usage of the QWB in clinical and non-clinical settings, establishing the QWB as a reliable indicator of subjective well-being.

Objective: This study investigated the effectiveness of guided internet-based cognitive behavioral therapy (iCBT) for people with mental health problems. Methods: Participants (n = 57) recruited from collaborating mental health and counseling centers in Hong Kong and Shenzhen were randomly assigned to a control group receiving standardized educational information about COVID-19 or an intervention group receiving the guided iCBT with eight online self-study modules and weekly telephone counseling by a social worker and a counselor in addition to the standardized educational information. Results: A dropout rate of 27.58% was recorded in the intervention group. The results of the 2 (group) × 2 (time) repeated measures analysis of variance with modified intention-to-treat analysis showed that the intervention group had a significantly greater reduction in perceived stress compared to the control group. Conclusion: The study results support the effectiveness of guided iCBT in reducing perceived stress.

Background and Aims: The digitalization of gambling provides unprecedented opportunities for early identification of problem gambling, a well-recognized public health issue. This study aimed to advance current practices by employing advanced machine learning techniques to predict problem gambling behaviors and assess the temporal stability of these predictions. Methods: We analyzed player account data from a major Swedish online gambling provider, covering a 4.5-year period. Feature engineering was applied to capture gambling behavior dynamics. We trained machine learning models, XGBoost, to classify players into low-risk and higher-risk categories. Temporal stability was evaluated by progressively truncating the training dataset at various time points (30, 60, and 90 days) and assessing model performance across truncations. Results: The models demonstrated considerable predictive accuracy and temporal stability. Key features such as loss-chasing behavior and net balance trend consistently contributed to accurate predictions across all truncation periods. The model's performance evaluated on a separate holdout set, measured by metrics like F1 score and ROC AUC, remained robust, with no significant decline observed even with reduced data, supporting the feasibility of early and reliable detection. Discussion and Conclusions: These findings indicate that machine learning can reliably predict problem gambling behaviors over time, offering a scalable alternative to traditional methods. Temporal stability highlights their potential for real-time application in gambling operators' Duty of Care. Consequently, advanced techniques could strengthen early identification and intervention strategies, potentially improving public health outcomes by preventing the escalation of harmful behaviors.

Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training. Baseline depression and anxiety symptoms were found to be the most influential factor, with individuals with more severe symptoms showing the greatest improvement. The number of training sessions was also important, with more sessions yielding greater benefits. Cognitive trainings were associated with higher predicted improvement than control conditions, with attention and interpretation bias modification showing the most promise. Despite the limitations of heterogeneous datasets, this investigation highlights the value of large-scale comprehensive analyses in guiding the development of personalized training interventions.

Introduction: Little is known about what pretreatment patient characteristics the outcome of Cognitive Therapy (CT) and Behavioral Therapy (BT) for insomnia disorder depends on. Identifying for whom treatment is most useful is essential for treatment optimization and personalized care. Therefore, this investigation aimed to examine both theory-driven constructs and insomnia-associated clinical variables as potential predictors and moderators of outcomes in CT and BT.

Materials and methods: One hundred forty-five participants diagnosed with insomnia disorder were randomly assigned to 10 weekly internet-delivered modules of CT or BT, along with 15 min of weekly telephone support. General clinical predictors and theory-driven moderators (cognitive and behavioral processes) assessed in a previous randomized controlled trial were analyzed using multiple linear regression, with insomnia severity as the outcome.

Results: Bedtime variability and early morning awakening interacted with treatment and indicated that lower bedtime variability and early morning awakening were associated with a higher effect for CT, whereas the opposite was true for BT. Wake time after sleep onset, insomnia severity index, and sleep efficiency emerged as predictors, indicating prognostic value for treatment outcome.

Conclusions: In addition to identifying three insomnia-associated variables as predictors of outcome across both treatments, this trial showed that CT and BT could be differentially effective based on patient insomnia heterogeneity at baseline. The differential moderator findings are in line with the theoretical models of CT and BT and might clinically implicate the ability to match therapy to patient features to optimize outcomes.

An intimate partner relationship is one of the most significant life goals for humans. Romantic relationships can promote healthy behavior and buffer against the development of psychiatric disorders. However, reliable and valid measures of relationship satisfaction are lacking. The Valentine scale is a freely available brief measure of relationship satisfaction (https://osf.io/fb72s), intended to provide an easily interpretable index of relationship satisfaction. Across two studies, we evaluated the reliability, validity, and factor structure of the Valentine scale. Study One (n = 851) explored the factor structure of the Valentine scale, assessed its test–retest reliability, and criterion-related validity. Study Two (n = 527) confirmed the factor structure of the Valentine scale, explored its measurement invariance, and further evaluated criterion-related validity. The results supported a unidimensional structure of the Valentine scale. Furthermore, the Valentine scale exhibited good internal reliability (Cronbach’s alpha = .75 and .81 in Study One and Two, respectively), high test–retest reliability (ICC₃ = .80 at a two-week follow-up in Study One), and appropriate criterion-related validity demonstrating positive correlations with other measures of relationship satisfaction and positive affect, as well as and negative correlations with measures of psychopathology. Together, these findings provide good support for the usage of the Valentine scale to quantify relationship satisfaction.