Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes

The multiple uses of artificial intelligence in exercise programs: a narrative review

Background

Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the utilization of artificial intelligence in fostering physical activity, training, exercise, and health outcomes, addressing a significant gap in the comprehension of practical applications.

Methods

Only Randomized Controlled Trials (RCTs) published in English were included. Inclusion criteria: all RCTs that use artificial intelligence to program, supervise, manage, or assist physical activity, training, exercise, or health programs. Only studies published from January 1, 2014, were considered. Exclusion criteria: all the studies that used robot-assisted, robot-supported, or robotic training were excluded.

Results

A total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1,004 articles were screened by title and abstract. A total of 24 studies were identified, and finally, after a full-text review, 15 studies were identified as meeting all eligibility criteria for inclusion. The findings suggest that artificial intelligence holds promise in promoting physical activity across diverse populations, including children, adolescents, adults, older adult, and individuals with disabilities.

Conclusion

Our research found that artificial intelligence, machine learning and deep learning techniques were used: (a) as part of applications to generate automatic messages and be able to communicate with users; (b) as a predictive approach and for gesture and posture recognition; (c) as a control system; (d) as data collector; and (e) as a guided trainer.

From antidepressants and psychotherapy to oxytocin, vagus nerve stimulation, ketamine and psychedelics: how established and novel treatments can improve social functioning in major depression

Social cognitive deficits and social behavior impairments are common in major depressive disorder (MDD) and affect the quality of life and recovery of patients. This review summarizes the impact of standard and novel treatments on social functioning in MDD and highlights the potential of combining different approaches to enhance their effectiveness. Standard treatments, such as antidepressants, psychotherapies, and brain stimulation, have shown mixed results in improving social functioning, with some limitations and side effects. Newer treatments, such as intranasal oxytocin, mindfulness-based cognitive therapy, and psychedelic-assisted psychotherapy, have demonstrated positive effects on social cognition and behavior by modulating self-referential processing, empathy, and emotion regulation and through enhancement of neuroplasticity. Animal models have provided insights into the neurobiological mechanisms underlying these treatments, such as the role of neuroplasticity. Future research should explore the synergistic effects of combining different treatments and investigate the long-term outcomes and individual differences in response to these promising interventions.

Early Detection of Mental Health Crises through Artifical-Intelligence-Powered Social Media Analysis: A Prospective Observational Study

BACKGROUND

The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises.

METHODS

We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments.

RESULTS

The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%).

CONCLUSIONS

AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models.

Affective norms for 501 Chinese words from three emotional dimensions rated by depressive disorder patients

Introduction

Emotional words are often used as stimulus material to explore the cognitive and emotional characteristics of individuals with depressive disorder, while normal individuals mostly rate the scores of affective words. Given that individuals with depressive disorder exhibit a negative cognitive bias, it is possible that their depressive state could influence the ratings of affective words. To enhance the validity of the stimulus material, we specifically recruited patients with depression to provide these ratings.

Methods

This study provided subjective ratings for 501 Chinese affective norms, incorporating 167 negative words selected from depressive disorder patients' Sino Weibo blogs, and 167 neutral words and 167 positive words selected from the Chinese Affective Word System. The norms are based on the assessments made by 91 patients with depressive disorder and 92 normal individuals, by using the paper-and-pencil quiz on a 9-point scale.

Results

Regardless of the group, the results show high reliability and validity. We identified group differences in three dimensions: valence, arousal, and self-relevance: the depression group rated negative words higher, but positive and neutral words lower than the normal control group.

Conclusion

The emotional perception affected the individual's perception of words, to some extent, this database expanded the ratings and provided a reference for exploring norms for individuals with different emotional states.