personalized prediction

Background Diabetes frequently co-occurs with depression, anxiety, and diabetes-related distress, and psychological status directly influences glycaemic control and the risk of complications. Traditional mental health assessment relies on subjective scales. With the growth of digital health, digital phenotyping offers a new paradigm for real-time, objective mental health monitoring and prediction. However, the pathways through which these technologies perform monitoring and prediction remain unclear. Objective This scoping review aimed to systematically map the application pathways of digital phenotyping for mental health monitoring and prediction in people with diabetes, and to use machine learning-assisted text mining to analyse the main research themes and hotspots, thereby informing the future development of individualised risk prediction models and monitoring systems. Methods A scoping review search was conducted in six English databases (PubMed, Embase, CINAHL, Web of Science, Scopus, and the Cochrane Library) and four Chinese databases (CNKI, Wanfang, VIP, and China Biomedical Literature Database[CBM]), covering studies published from database inception to 10 September 2026. The search strategy was built around terms related to diabetes, digital phenotyping, and mental health. Machine learning-assisted literature mining was used to identify thematic patterns in the included studies. The study selection process and overall workflow followed the PRISMA extension for scoping reviews (PRISMA-ScR). Results A total of 2,968 records were retrieved. After screening, 10 studies were included. The evidence indicated that digital phenotyping mainly enables real-time monitoring of mental health in people with diabetes through signals such as continuous glucose monitoring (CGM), passive smartphone sensing, voice, and ecological momentary assessment (EMA). Machine learning methods were widely used for risk prediction modelling, but most studies built models at the group level, with only a few adopting n -of-1 analysis or subgroup prediction to achieve individualised risk prediction. External validation of models was generally lacking, and no intervention studies were identified. Conclusions Preliminary evidence suggests that digital phenotyping has exploratory potential for mental health monitoring and prediction in people with diabetes, but research that genuinely achieves individualised risk prediction remains limited. Closed-loop monitoring-intervention design and multicentre external validation are the main directions for future research. Systematic Review Registration https://osf. io/6wsaj/ .

Mobile GUI Agents powered by multimodal large language models (MLLMs) show promise in human-computer intelligence. However, current research primarily focuses on reactive task execution while lacking a comprehensive “understanding-prediction-execution” process for user intentions, which are the core requirements of active agents. In this paper, we propose the Act2Intention framework that builds an active mobile agent by integrating understanding, predicting user intentions, and executing decisions. Firstly, we constructed the Act2Intention Bench through collection and validated generation, comprising 72,511 intentions and over 700,000 actions across 52 apps, thereby establishing the first benchmark for evaluating proactive agents via continuous “intention-actions” trajectories. We further develop the Act2Intention Agent, achieving proactive services through Proactive-oriented Intention Understanding, Personalized Proactive Intention Prediction, and Experience-guided Intention Execution. Experimental results show that supervised fine-tuning on Act2Intention Bench yields absolute improvements of +32.0 Acc-S, +10.25 Acc-S, and +6.9 SSR points over non-fine-tuned counterparts under the same agent framework for intention understanding, prediction, and execution, respectively. This success underscores the necessity and value of the Act2Intention Bench, which establishes a standardized platform for developing and evaluating proactive agents and consequently paves the way for research on intention-driven human-computer interaction. To facilitate further research, we open-source our code and Act2Intention Bench at: npuNancy/Act2Intention.

عامل‌های رابط کاربری موبایل مبتنی بر مدل‌های زبانی بزرگ چندوجهی (MLLMs) نویدبخش پیشرفت در هوش انسان-رایانه هستند. با این حال، پژوهش‌های کنونی عمدتاً بر اجرای واکنشی وظایف تمرکز دارند و فاقد فرایند جامع «درک-پیش‌بینی-اجرا» برای نیت‌های کاربر هستند که از الزامات اصلی عامل‌های فعال به‌شمار می‌رود. در این مقاله، چارچوب Act2Intention را پیشنهاد می‌کنیم که با ادغام درک، پیش‌بینی نیت کاربر و اجرای تصمیمات، یک عامل فعال موبایل ایجاد می‌کند. نخست، Act2Intention Bench را از طریق گردآوری و تولید اعتبارسنجی‌شده ایجاد کردیم که شامل ۷۲٬۵۱۱ نیت و بیش از ۷۰۰٬۰۰۰ کنش در ۵۲ برنامه است و بدین ترتیب نخستین معیار برای ارزیابی عامل‌های پیش‌فعال از طریق مسیرهای پیوسته «نیت-کنش» را پایه‌گذاری کردیم. همچنین عامل Act2Intention را توسعه دادیم که خدمات پیش‌فعال را از طریق «درک نیت مبتنی بر پیش‌فعالیت»، «پیش‌بینی نیت شخصی‌سازی‌شده» و «اجرای نیت هدایت‌شده با تجربه» ارائه می‌دهد. نتایج تجربی نشان می‌دهد که تنظیم نظارت‌شده بر روی Act2Intention Bench بهبود مطلق +۳۲٫۰ Acc-S، +۱۰٫۲۵ Acc-S و +۶٫۹ SSR را نسبت به نسخه‌های بدون تنظیم در همان چارچوب عامل، به‌ترتیب در درک، پیش‌بینی و اجرای نیت به همراه دارد. این موفقیت بر ضرورت و ارزش Act2Intention Bench تأکید می‌کند که بستری استاندارد برای توسعه و ارزیابی عامل‌های پیش‌فعال فراهم می‌آورد و مسیر پژوهش در تعامل انسان-رایانه مبتنی بر نیت را هموار می‌سازد. به‌منظور تسهیل پژوهش‌های بیشتر، کد و Act2Intention Bench به‌صورت متن‌باز در npuNancy/Act2Intention ارائه شده است.