Junhee Lee

Background: Digital therapeutics (DTx) are evidence-based software interventions with the potential to treat health conditions. However, uptake remains limited by low public awareness and overly complex patient education materials that exceed recommended readability levels. Large language models (LLMs) may simplify such content; however, their effects on users’ understanding have not been empirically demonstrated. Objective: This study aimed to examine whether LLM-based simplification of DTx explanatory materials enhances perceived understanding and subjective evaluations of readability, clarity, and comprehensibility compared with manufacturer-provided documents. Methods: We developed a simplification tool using the GPT-4o application programming interface (API), configured for deterministic outputs and guided by structured readability instructions. Original DTx explanatory materials about insomnia and nicotine dependence were obtained from manufacturers and transformed into simplified versions. Two randomized, between-subject online experiments were conducted (n=1000, with 500 participants in each experiment). Participants were stratified by age and sex and screened for relevance (Insomnia Severity Index ≥8 for the insomnia experiment and smoking ≥5 cigarettes per day for the nicotine dependence experiment). Within each experiment, participants were randomly assigned to review either the original or the LLM-simplified explanatory material. Perceived understanding was assessed before and after exposure. Postexposure evaluations of ease, clarity, and comprehensibility were also collected. Results: Repeated measures ANOVA revealed significant group×time interaction effects on perceived understanding in both experiments: insomnia ( F _1,498 =24.8; P <.001) and nicotine dependence ( F _1,498 =14.1; P <.001), with greater improvements observed in the LLM-simplified groups. Mann-Whitney U tests further showed that LLM-simplified explanations were rated as easier, clearer, and more comprehensible than the original versions in both experiments (all P <.05), with small to moderate effect sizes ( r =0.11-0.24). Conclusions: Compared with manufacturer-provided original materials, LLM-simplified DTx explanations led to greater improvements in perceived understanding and subjective evaluations of readability among lay audiences, even after a single exposure. This finding highlights the potential scalability of LLM-based simplification as a strategy to improve the perceived accessibility of health information for lay audiences. Integrating such tools into patient education may enhance how lay audiences perceive and engage with DTx, although further research using objective comprehension measures is needed to confirm these benefits.

BACKGROUND Digital therapeutics (DTx) are evidence-based software interventions with the potential to treat health conditions. However, uptake remains limited by low public awareness and overly complex patient education materials that exceed recommended readability levels. Large language models (LLMs) may simplify such content; however, their effect on actual comprehension has not been empirically demonstrated. OBJECTIVE To examine whether LLM-based simplification of DTx explanatory materials enhances public comprehension and subjective evaluations of readability, clarity, and comprehensibility compared with manufacturer-provided documents. METHODS We developed a simplification tool using the GPT-4o API, configured for deterministic outputs and guided by structured readability instructions. Original DTx explanatory materials about insomnia and nicotine dependence were obtained from manufacturers and transformed into simplified versions. Two randomized, between-subject online experiments were conducted (N = 1,000; 500 per condition). Participants were stratified by age and sex and screened for relevance (Insomnia Severity Index ≥8 for the insomnia experiment; smoking ≥5 cigarettes/day for the nicotine dependence experiment). Within each experiment, participants were randomly assigned to review either the original or the LLM-simplified explanation. Perceived understanding and post-exposure evaluations of ease, clarity, and comprehensibility were assessed pre- and post-exposure. RESULTS Repeated-measures analysis of variance revealed significant Group × Time interaction effects on perceived understanding in both experiments: insomnia (F₁,₄₉₈ = 24.8; P <.001) and nicotine dependence (F₁,₄₉₈ = 14.1; P < .001), with greater improvements in the LLM-simplified groups. Mann–Whitney U tests further showed that LLM-simplified explanations were rated as significantly easier, clearer, and more comprehensible than the original versions in both experiments (all P < .05). CONCLUSIONS Compared with manufacturer-provided original materials, LLM-simplified DTx explanations led to greater improvements in perceived understanding and subjective readability among lay audiences, even after a single exposure. This finding highlights the scalability of LLM-based simplification as a strategy to address health literacy barriers. Integrating such tools into patient education may enhance access to digital therapeutic information and support broader societal diffusion.

Patients with psychoses often show various symptoms and signs before their onset of illnesses including developmental delay during childhood and adolescence, cognitive impairments such as impaired attention, mood symptoms such as depressed mood or mood fluctuation, anxiety symptoms, sleep problems, and functional impairments such as decline in academic or social functions. This period can also show psychosis-related symptoms such as attenuated forms of psychotic symptoms or full psychotic symptoms with limited duration falling short of diagnostic criteria of psychoses. When an individual is prospectively thought to be in this “prodromal” state of psychoses, the individual is regarded to be at clinical high risk for psychosis (CHR). Naturally, some of the individuals at CHR experience a psychotic transition during their clinical course, but the others show much diverse clinical course including improvement of symptoms, remission of symptoms, emergence or persistence of non-psychotic co-morbid illnesses, and various alterations of functional level. It is reported that the majority of individuals at CHR do not experience psychotic conversion but show diverse clinical courses otherwise. Although the state of CHR has numerous aspects to investigate, this review will focus on various clinical courses of individuals at CHR, including psychotic transition and remission.

Junhee Lee, MD, PhD, Seung-Hwan Baek, PhD, Min-Kyung Jang, RN, BA, Hyeon-Hee Sim, MA, In Young Choi, PhD, Dai-Jin Kim, MD, PhD. Mood Emot 2024;22:63-8. https://doi.org/10.35986/me.2024.22.3.63

Objective Infertility, the inability to achieve pregnancy within a year despite normal attempts to conceive without contraception, causes psychosocial burden for individuals and couples. This review summarized the interrelationship between infertility and psychological stress and suggested various forms of psychological intervention for infertility.Methods The PubMed, Google Scholar, and Korean Studies Information Service System databases were searched for English- and Korean-language articles published from 1990 to 2024.Results Infertility leads to emotional distress from diagnosis to treatment. Also, psychological stress affects the trajectory of infertility. This distress may cause psychiatric illnesses, negatively affecting pregnancy. Psychotherapies, psychopharmacotherapies, and biological treatments can be used for the management of psychiatric illnesses in infertile patients. Digital therapeutics also have the potential to be a competitive treatment option.Conclusion Regular assessment and management of psychological stress in infertile couples are essential during the course of infertility treatment. Psychological intervention of infertile patients should be implemented according to a personalized plan that completely reflects the individual clinical characteristics.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) “OCD vs. healthy controls” (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) “unmedicated OCD vs. healthy controls” (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) “medicated OCD vs. unmedicated OCD” (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6–79.1 in adults; 35.9–63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.