JMIR mHealth and uHealth

Prediabetes is a growing global health concern. Lifestyle modification is the cornerstone of management, yet scalable delivery strategies are needed. SMS text messaging is a promising mobile health approach for behavior change, but its effectiveness for metabolic outcomes in prediabetes remains uncertain.

Patients with head and neck cancer (HNC) frequently experience functional impairments and psychological distress following surgery or radiotherapy. While mobile health (mHealth) interventions are increasingly integrated into clinical care to support patient self-management and home-based recovery, evidence of their effectiveness in HNC remains inconsistent.

Digital behavioral interventions are increasingly used to support chronic disease self-management, yet many systems rely on predetermined content that limits personalization and sustained engagement. Large language models (LLMs) offer new opportunities to deliver conversational behavioral support. However, integrating LLMs into behavioral interventions requires careful architectural, methodological, and ethical planning, which may be challenging for researchers without formal training in artificial intelligence. This viewpoint provides a structured introduction to LLMs tailored to behavioral science. We describe foundational concepts in natural language processing and transformer-based architectures, outline the core components of LLM-based systems, including prompting strategies, context management, retrieval-augmented generation, and guardrails, and illustrate these principles through our experience integrating a proprietary LLM into a mobile self-management intervention for individuals with systemic sclerosis. Building on this case example, we propose a phased design workflow to guide early-stage development and responsible implementation, along with a decision framework to help researchers navigate scientific and logistical trade-offs between proprietary models and other alternatives. The considerations presented here are informed by formative implementation efforts and are intended to support early-stage design decisions for LLM-based behavioral interventions. As these interventions continue to evolve, rigorous evaluation and interdisciplinary collaboration will be important to ensure that these systems improve personalization and scalability while maintaining safety and scientific rigor.

Cataract surgery is the most frequently performed surgery worldwide, crucial for restoring sight in millions. The COVID-19 pandemic and an aging population have increased barriers to timely surgery. Missed preoperative instructions and poor adherence to postoperative care contribute to surgery cancellations, delays, and potential complications. Mobile digital health interventions could enhance adherence and reduce cancellations.

Wearable devices with real-time feedback (WRFs) provide increasing opportunities to enhance physical activity and improve rehabilitation through collecting and processing health-related data. Real-time feedback (RTF) from the device is expected to result in a more dynamic, coordinated, and synchronous rhythmic activity, defined as step-by-step movements mediated by the real-time heart rate feedback. However, age-specific characteristics in the user engagement with WRFs integrating real-time audio feedback have largely remained unexplored.

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Sedentary employees face increased chronic health risks due to physical inactivity, immobility, and unhealthy eating behavior. Although mobile health (mHealth) interventions show promise in improving lifestyle behaviors, their effectiveness in occupational settings remains underexplored. Building on previous workplace interventions, this study developed and evaluated a mobile-enabled web app, SIMPLE HEALTH, developed with Din-J Design Co, Ltd, integrating activity tracking, healthy eating, and behavioral support for sedentary employees.

Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with the risk of ischemic stroke, which can be reduced with anticoagulation therapy. Thus, early diagnosis of AF and AFL is essential. However, diagnosis may be challenging due to the paroxysmal and asymptomatic nature of these arrhythmias.

Cancer poses a significant threat to children’s health, and mobile health (mHealth) is emerging as a key tool for remote disease management, health education, and follow-up. However, evidence of its effectiveness remains limited.

In the current digital landscape, ensuring optimal usability is one of the most crucial factors determining the success of any mobile app. Questionnaire-based usability evaluations represent a highly prevalent methodology for this purpose. To date, questionnaires have been developed to assess the general system usability; however, there are hardly any questionnaires specifically designed to assess the usability of mobile health (mHealth) apps. The most widespread, the mHealth App Usability Questionnaire (MAUQ), has been developed in 4 versions according to the type of app (interactive or standalone) and the target user (patient or provider).

Respiratory dysfunction frequently occurs during the acute phase of stroke and is associated with reduced ventilatory capacity, respiratory muscle weakness, and increased pulmonary complications. However, delivering standardized respiratory training during hospitalization is often constrained by staffing and service continuity.

Anemia is a global health concern. It is disproportionately prevalent among pregnant women in low-resource regions, where iron deficiency is the leading cause. Given the multifactorial nature of anemia, a range of nutritional interventions is recommended. However, effective implementation is often hindered by limited health care access, poor adherence to supplementation, and gaps in nutrition knowledge and counseling. To address these challenges and optimize hemoglobin (Hb) levels among pregnant women, mobile health (mHealth)−based nutritional interventions offer a promising alternative.