Current research on smartphone addiction has mainly focused on addiction at the device level. This motivated us to explore more specifically on app addiction. We investigate smartphone usage for college students using surveys, logged data, and interviews. Specifically, we adapted existing smartphone addiction assessment instruments to measure app addiction. The analysis of our data shows that social and communication apps are the top 2 most addictive categories among participants. Female and male participants show no significant difference in terms of smartphone addiction. However, female participants tend to report that they are addicted to more apps. The psychological factors associated with app addiction are different between app categories. For example, compared to communication apps, participants report that it is easier to withdraw from social apps, but more difficult to control time spent on them. Correlation analysis between app usage features and app addictiveness scores reveals that compulsive open times, usage duration, and regularity of usage are good indicators of app addiction, though response time to notifications has limited predictive power.

Background: The World Health Organization reports that >1 billion people worldwide experience mental disorders, with the prevalence of depression and anxiety among children and adolescents at 2.6% and 6.5%, respectively. However, commonly used clinical questionnaires such as the Hamilton Depression Rating Scale and the Beck Depression Inventory suffer from several problems, including the high degree of overlap of symptoms of depression with those of other psychiatric disorders and a lack of professional supervision during administration of the questionnaires, which often lead to inaccurate diagnoses. In the wake of the COVID-19 pandemic, the health care system is facing the dual challenges of a surge in patient numbers and the complexity of mental health issues. Artificial Intelligence (AI) technology has now been shown to have great promise in improving diagnostic accuracy, assisting clinical decision-making, and simplifying questionnaire development and data analysis. Objective: This review aimed to explore the current applications, potential benefits, and issues of AI in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. The global mental health burden remains severe. Methods: The review included peer-reviewed studies that applied AI technologies to medical, psychological, or physiological questionnaires and reported measurable outcomes; non–peer-reviewed, non-English/Chinese, ethically noncompliant, or AI-unrelated studies were excluded. Five databases (PubMed, Embase, Cochrane Library, Web of Science, and CNKI) were searched from inception through September 2024. Three independent reviewers conducted data extraction, quality appraisal using the Joanna Briggs Institute tools, and narrative synthesis of AI applications across questionnaire assessment, development, and prediction tasks. Results: Of 49,091 publications, a total of 14 (0.03%) studies met the inclusion criteria. AI technologies showed advantages in assessment, such as distinguishing myalgic encephalomyelitis or chronic fatigue syndrome from long COVID-19 with 92.18% accuracy. In questionnaire development, natural language processing using generative models such as ChatGPT was used to construct culturally competent scales. In terms of disease prediction, one study had an area under the curve of 0.790 for cataract surgery risk prediction. Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. Despite the positive findings, only 21% (3/14) of the studies had entered the clinical validation phase, whereas the remaining 79% (11/14) were still in the exploratory phase of research. Most of the studies (10/14, 71%) were rated as being of moderate methodological quality, with major limitations including lack of a control group, incomplete follow-up data, and inadequate validation systems. Conclusions: In summary, the integrated application of AI in medical questionnaires has significant potential to improve diagnostic efficiency, accelerate scale development, and promote early intervention. Future research should pay more attention to model interpretability, system compatibility, validation standardization, and ethical governance to effectively address key challenges such as data privacy, clinical integration, and transparency.

As a national intangible cultural heritage in China, traditional Miao batik has encountered obstacles in contemporary dissemination and design due to its reliance on manual craftsmanship and other reasons. Existing generative models are difficult to fully capture the complex semantic and stylistic attributes in Miao batik patterns, which limits their application in digital creativity. To address this issue, we construct the structured CMBP-9 dataset to facilitate semantic-aware image generation. Based on stable diffusion v1.5, Low-Rank Adaptation (LoRA) is used to effectively transfer the structure, sign, and texture features that are unique to the Miao people, and the Latent Consistency model (LCM) is integrated to improve the inference efficiency. In addition, a Style-Conditioned Linear Fusion (SCLF) strategy is proposed to dynamically adjust the fusion of LoRA and LCM outputs according to the semantic complexity of input prompts, thereby overcoming the limitation of static weighting in existing frameworks. Extensive quantitative evaluations using LPIPS, SSIM, PSNR, FID metrics, and human evaluations show that the proposed Batik-MPDM framework achieves superior performance in terms of style fidelity and generation efficiency compared to baseline methods.

This randomized controlled trial evaluated the efficacy ofAI-assisted drawing therapy for children with ADHD, comparing its impact on core symptoms and functional impairments against traditional drawing interventions while assessing engagement and therapeuticoutcomes. Forty-one children (ages 7-10) with ADHD were randomized into AI-assisted (n=19) or traditional drawing therapy (n=22) groups (Trial registered in ChiCTR: ChiCTR2400089640). The AI intervention employed a process that captured children's drawings, converted them into keywords, generated AI responses based on those keywords, and provided feedback to the children, while the control group received therapist-guided verbal feedback. SNAP-IV-26(Swanson, Nolan and Pelham-IV rating scales) and WFIRS-P(Weiss Functional Impairment Rating Scales-Parent) scales assessed at baseline (T1) and post-intervention (T2) at a 24-week interval, with data analyzed using independent t-tests (p<0.05). Both AI-assisted and traditional drawing therapies significantly reduced core ADHD symptoms. In the before-and-after comparison within the group, the AI group showed targeted improvements in attention (p = 0.039) and oppositional defiant behaviour (p = 0.025), while traditional therapy produced broader symptom reductions across all domains (p < 0.05). Between-group comparisons showed that the AI-assisted group achieved greater improvement in inattention (Δ = -1.09, p = 0.035), whereas the traditional drawing group improved more on oppositional defiant behavior (Δ=+1.706, p= 0.010) and the SNAP-IV total score (Δ=+4.777, p = 0.041). On the WFIRS-P, the AI group showed advantages for the Family domain (p = 0.041) and the total score (p = 0.047), while Self-Concept favored the traditional drawing group (p = 0.036). AI-assisted, drawing-based art therapy produced greater improvement in the pre-specified primary outcome (SNAP-IV inattention) and select WFIRS-P domains (e.g., Family, total score), whereas conventional drawing therapy yielded broader reductions in oppositional defiant symptoms and the overall SNAP-IV severity. Effects were modest and domain-specific; larger, multi-center trials are needed to confirm efficacy and identify which children benefit most.

Interior design, which integrates art and science, is vulnerable to infringements such as copying and tampering. The unique and often intricate nature of these designs makes them vulnerable to unauthorized replication and misuse, posing significant challenges for designers seeking to protect their intellectual property. To solve the above problems, we propose a deep learning-based zero-watermark copyright protection method. The method aims to embed undetectable and unique copyright information through image fusion technology without destroying the interior design image. Specifically, the method fuses the interior design and a watermark image through deep learning to generate a highly robust zero-watermark image. This study also proposes a zero-watermark verification network with U-Net to verify the validity of the watermark and extract the copyright information efficiently. This network can accurately restore watermark information from protected interior design images, thus effectively proving the copyright ownership of the work and the copyright ownership of the interior design. According to verification on an experimental dataset, the zero-watermark copyright protection method proposed in this study is robust against various image-oriented attacks. It avoids the problem of image quality loss that traditional watermarking techniques may cause. Therefore, this method can provide a strong means of copyright protection in the field of interior design.

Background: Caregivers in palliative care settings often face significant emotional and psychological burdens, yet their mental health needs remain under-addressed. Mind-body therapies (MBTs) have shown promise in clinical populations, but their effects on caregivers are understudied and poorly synthesized. Methods: This systematic review followed PRISMA 2020 guidelines and was registered with PROSPERO (CRD42024571202). A comprehensive search of PubMed, EMBASE, Web of Science, and Cochrane Library identified randomized controlled trials (RCTs) and quasi-experimental studies (QES) assessing MBTs for caregivers in palliative care. Data were extracted using the PICOS framework. Study quality was appraised using JBI tools, risk of bias was assessed via ROB2 and ROBINS-I, and evidence certainty was rated with GRADE. Results: 20 studies involving 1230 caregivers were included. Interventions encompassed 8 types of MBTs, with structured, therapist-guided formats demonstrating stronger effects than self-directed or digital approaches. Overall, MBTs showed short-term improvements in depression, anxiety, PTSD, distress, quality of life, and self-regulation. Informal and home hospice caregivers benefited most, while professional and spousal caregivers showed variable responsiveness. Methodological heterogeneity and limited follow-up constrained meta-analysis and generalizability. Conclusion: This review presents a comprehensive synthesis of existing evidence on MBTs targeting caregiver mental health in palliative care. It highlights the therapeutic potential of MBTs and identifies delivery modality and caregiver role as key moderators of intervention effectiveness. Furthermore, it contributes to the field by outlining practical implementation strategies for integrating MBTs into routine caregiver support, with an emphasis on contextual adaptation and policy-level facilitation. These findings support the development of scalable, person-centered interventions to meet the psychological needs of caregivers within palliative care systems.