Advanced Mental Health Care

Gamified mHealth applications are increasingly used to promote healthy behavior change, yet low participant engagement remains a challenge. Personalized content has shown promise in increasing engagement, however, creating personalized content is a time-consuming task. Generative Artificial Intelligence models, particularly Large Language Models, offer a potential solution with their ability to quickly generate relevant content. In this study, we aim to evaluate the impact of LLM-generated content on participant engagement in health interventions using gamified mHealth applications. A total of 73 students and staff members of a university participated in a health intervention and were assigned into groups receiving either LLM-generated goals or predetermined goals. Engagement and perceived intrinsic motivation were measured and compared between the two groups. The results show no significant difference in engagement or perceived intrinsic motivation between participants receiving LLM-generated goals and those receiving predetermined goals. LLM-generated goals did not significantly negatively impact participant engagement and could therefore possibly offer a time-efficient approach to scalable content generation for mHealth applications.

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation method increasingly used to treat psychiatric disorders, primarily depression. Initial studies suggest that rTMS may help to treat addictions, but evaluation in multicenter randomized controlled trials (RCTs) is needed. We conducted a multicenter double-blind RCT in 262 chronic smokers meeting DSM-5 criteria for tobacco use disorder, who had made at least one prior failed attempt to quit, with 68% having made at least three failed attempts. They received three weeks of dailybilat-eral active or sham rTMS to the lateral prefrontal and insular cortices, followed by once weekly rTMS for three weeks. Each rTMS session was administered following a cue-induced craving procedure, and participants were monitored for a total of six weeks. Those in abstinence were monitored for additional 12 weeks. The primary outcome measure was the four-week continuous quit rate (CQR) until Week 18 in the intent-to-treat efficacy set, as determined by daily smoking diaries and verified by urine cotinine measures. The trial was registered at ClinicalTrials.gov (NCT02126124). In the intent-to-treat analysis set (N=234), the CQR until Week 18 was 19.4% following active and 8.7% following sham rTMS (X2 =5.655, p=0.017). Among completers (N=169), the CQR until Week 18 was 28.0% and 11.7%, respectively (X2 =7.219, p=0.007). The reduction in cigarette consumption and craving was significantly greater in the active than the sham group as early as two weeks into treatment. This study establishes a safe treatment protocol that promotes smoking cessation by stimulating relevant brain circuits. It represents the first large multicenter RCT of brain stimulation in addiction medicine, and has led to the first clearance by the US Food and Drug Administration for rTMS as an aid insmok-ing cessation for adults.

The efficacy of deep repetitive transcranial magnetic stimulation (dTMS) for obsessive compulsive disorder (OCD) was recently confirmed in a Food and Drug Administration-regulated, multicenter, sham-controlled study. In this study, patients who failed pharmacotherapy underwent individually tailored provocations just prior to each stimulation session, in the attempt to activate the relevant circuitry and making it labile to change. The procedure that was developed reliably evoked moderate intensity symptoms, making it effective on the one hand and mild enough to allow the patient to continue with the dTMS session on the other. This methodology article describes in a detailed step wise fashion how to evaluate the patient’s specific symptoms and design the individualized provocations. Additionally, the article explains how to instruct relevant personnel to administer the provocations, gauge their efficacy, and overcome possible obstacles. This method, apart from its ongoing role in the clinical treatment of OCD by dTMS, may be used for provocation of symptoms in basic studies [e.g., imaging with Electroencephalogram (EEG) or Functional magnetic resonance imaging fMRI] as well as other treatments.

Challenges intrinsic to the accurate diagnosis of endometriosis contribute to an extended delay between the onset of symptoms and clinical confirmation. Intraoperative visualization, preferably with histologic verification, is considered by many professional organizations to be the gold standard by which endometriosis is diagnosed. Clinical diagnosis of symptomatic endometriosis via patient history, physical examination, and noninvasive tests, though more easily executed, is generally viewed as less accurate than surgical diagnosis. Technological advances and increased understanding of the pathophysiology of endometriosis warrant continuing reevaluation of the standard method for diagnosing symptomatic disease. A review of the published literature was therefore performed with the goal of comparing the accuracy of clinical diagnostic measures with that of surgical diagnosis. The current body of evidence suggests that clinical diagnosis of symptomatic endometriosis is more reliable than previously recognized and that surgical diagnosis has limitations that could be underappreciated. Regardless of the methodology used, women with suspected symptomatic endometriosis would be well served by a diagnostic paradigm that is reliable, conveys minimal risk of under- or over-diagnosis, lessens the time from symptom development to diagnosis, and guides the appropriate use of medical and surgical management strategies.