JMIR Mental Health

This article contends that the responsible artificial intelligence (AI) approach—which is the dominant ethics approach ruling most regulatory and ethical guidance—falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI’s impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new “therapeutic” area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools.

National suicide prevention strategies are general population-based approaches to prevent suicide by promoting help-seeking behaviours and implementing interventions. Crisis helplines are one of the suicide prevention resources available for public use where individuals experiencing a crisis can talk to a trained volunteer. Samaritans UK operates on a national scale, with a number of branches located in within each of the UK’s four countries or regions.

Adolescence and early adulthood are pivotal stages for the onset of mental health disorders and the development of health behaviors. Digital behavioral activation interventions, with or without coaching support, hold promise for addressing risk factors for both mental and physical health problems by offering scalable approaches to expand access to evidence-based mental health support.

The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored.

This paper reports on the growing issues experienced when conducting web-based–based research. Nongenuine participants, repeat responders, and misrepresentation are common issues in health research posing significant challenges to data integrity. A summary of existing data on the topic and the different impacts on studies is presented. Seven case studies experienced by different teams within our institutions are then reported, primarily focused on mental health research. Finally, strategies to combat these challenges are presented, including protocol development, transparent recruitment practices, and continuous data monitoring. These strategies and challenges impact the entire research cycle and need to be considered prior to, during, and post data collection. With a lack of current clear guidelines on this topic, this report attempts to highlight considerations to be taken to minimize the impact of such challenges on researchers, studies, and wider research. Researchers conducting web-based research must put mitigating strategies in place, and reporting on mitigation efforts should be mandatory in grant applications and publications to uphold the credibility of web-based research.

Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system’s decision-making.

Depression and anxiety have become increasingly prevalent across the globe. The rising need for treatment and the lack of clinicians has resulted in prolonged waiting times for patients to receive their first session. Responding to this gap, digital mental health interventions (DMHIs) have been found effective in treating depression and anxiety and are potentially promising pretreatments for patients who are awaiting face-to-face psychotherapy. Nevertheless, whether digital interventions effectively alleviate symptoms for patients on waiting lists for face-to-face psychotherapy remains unclear.

The demand for mental health (MH) services in the community continues to exceed supply. At the same time, technological developments make the use of artificial intelligence–empowered conversational agents (CAs) a real possibility to help fill this gap.

Every month, around 4,000 people complete an anonymous self-test for suicidal thoughts on the website of the Dutch suicide prevention helpline. Although 70% score high on the severity of suicidal thoughts, less than 10% navigate to the webpage about contacting the helpline.

Background: Depression is a major global public health issue that affecting the physical and mental well-being of hundreds of millions worldwide. However, an increasing number of potential depression sufferers often fail to receive timely diagnosis and effective treatment, which is progressively emerging as a major social health crisis. Objective: This paper aims to develop an online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo. Methods: Firstly, we collected approximately 527,333 posts publicly shared over one year from 1,600 individuals with depression and 1,600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical Transformer network for learning user-level semantic representations, which consists of three main components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder aims to learn semantic embeddings for each post, the post-level encoder further explores features of user post sequences, and the semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation for classification. Next, a classifier is used to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the posts’ content from individuals with and without depression using the Chinese LIWC. Results: We divided the original dataset into training, validation, and test sets. The training set consists of 1,000 individuals with depression and 1,000 individuals without depression. The validation and test sets each include 600 users, with 300 individuals with depression and 300 without depression. Our method achieved an accuracy of 84.62%, a precision of 84.43%, a recall of 84.50%, and an F1 score of 84.32% on the test set without applying sampling techniques. After applying our proposed retrieval-based sampling strategy, our method achieved an accuracy of 95.46%, a precision of 95.30%, a recall of 95.70%, and an F1 score of 95.43%. These results strongly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This provides new insights for large-scale depression detection through social media. Through language behavior analysis, we observed that individuals with depression are more likely to use negation words (the value of "swear" is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, we found that individuals with depression tend to use negative emotional vocabulary in their expressions (NegEmo: 0.022306, Anx: 0.003829, Anger: 0.004327, Sad: 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings towards life, themselves, or their surrounding environment. Conclusions: The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and non-invasive prediction of depression among online social media users.

Smartphone-delivered attentional bias modification training (ABMT) intervention has gained popularity as a remote solution for alleviating symptoms of mental health problems. However, the existing literature presents mixed results indicating both significant and insignificant effects of smartphone-delivered interventions.

Despite significant progress in our understanding of depression, prevalence rates have dramatically increased in recent years. Thus, there is an imperative need for more cost-effective and scalable mental health treatment options – including digital interventions that minimize therapist burden.