JMIR Formative Research

Mobile health (mHealth), the use of mobile technology in health care, is increasingly being used for mental health service delivery even in low- and middle-income countries to scale up treatment, and a variety of evidence supports their potential in different populations.

Attention is at the base of more complex cognitive processes, and its deficits can significantly impact safety and health. Attention can be impaired by neurodevelopmental and acquired disorders. One validated theoretical model to explain attention processes and their deficits is the hierarchical model of Sohlberg and Mateer. This model guides intervention development to improve attention following an acquired disorder. Another way to stimulate attention functions is to engage in the daily practice of mindfulness, a multicomponent concept that can be explained by the theoretical model of Baer and colleagues. Mobile apps offer great potential for practicing mindfulness daily as they can easily be used during daily routines, thus facilitating transfer. Laverdière and colleagues have developed such a mobile app called Focusing, which is aimed at attention training using mindfulness-inspired attentional exercises. However, this app has not been scientifically validated.

Maternal mental health disorders are prevalent, yet many individuals do not receive adequate support due to stigma, financial constraints, and limited access to care. Digital interventions, particularly chatbots, have the potential to provide scalable, low-cost support, but few are tailored specifically to the needs of perinatal individuals.

Youth and emerging adults with HIV (YWH) are less likely to engage in care and achieve viral suppression, compared with other age groups. YWH also have a high degree of self-efficacy and willingness to adopt novel care modalities, including mobile health (mHealth) interventions. Interventions to increase care engagement could aid YWH in overcoming structural and social barriers and leveraging youth assets to improve their health outcomes.

Graves' disease (GD) is an autoimmune thyroid disorder characterized by hyperthyroidism and autoantibodies. The COVID-19 pandemic has raised questions about its potential relationship with autoimmune diseases like GD.

Step count is used to quantify activity in individuals using accelerometers. However, challenges such as difficulty in detecting steps during slow or irregular gait patterns and the inability to apply this method to wheelchair users limit the broader utility of accelerometers. Alternative device-specific measures of physical activity exist, but their specificity limits cross-applicability between different device sensors. Moving standard deviation of acceleration (MSDA), obtained from truncal acceleration measurements, is proposed as another alternative variable to quantify physical activity in patients.

Wearable sensor bracelets have gained interest for their ability to detect symptomatic and presymptomatic infections through alterations in physiological indicators. Nevertheless, the use of these devices for public health surveillance among attendees of large-scale events such as hajj, the Islamic religious mass gathering held in Saudi Arabia, is currently in a nascent phase.

Digital mental health interventions may help increase access to psychological treatment for adolescents with anxiety disorders. However, many clinical evaluations of digital treatments report low adherence and engagement and high dropout rates, which remain challenges when the interventions are implemented in routine care. Involving intended end users in the development process through user-centered design methods may help maximize user engagement and establish the validity of interventions for implementation.

Therapy-accompanying mental health apps can play an important role in the psychotherapeutic treatment of adolescents. They can enhance adolescents’ engagement and autonomy, provide immediate support in critical situations, and positively influence the therapeutic working alliance. Nevertheless, mental health apps are rarely used by psychotherapists. Furthermore, due to the limited or nonexistent use of apps in psychotherapy, little is known about the actual barriers and drivers affecting their integration into psychotherapists’ daily routines. To better understand how mental health apps should be designed for practical use, it is essential to explore psychotherapists’ perspectives on key app features and characteristics, as well as the factors influencing their integration into clinical practice.

Popularized by ChatGPT, large language models (LLM) are poised to transform the scalability of clinical natural language processing (NLP) downstream tasks such as medical question answering (MQA) and automated data extraction from clinical narrative reports. However, the use of LLMs in the healthcare setting is limited by cost, computing power and concern for patient privacy. Specifically, as interest in LLM-based clinical applications grows, regulatory safeguards must be established to avoid exposure of patient data through the public domain. The use of open-source LLMs deployed behind institutional firewalls may ensure protection of private patient data. In this study we evaluate the extraction performance of a locally deployed LLM for automated MQA from surgical pathology reports.

HIV index case testing aims to identify people living with HIV and their contacts, engage them in HIV testing services, and link them to care. Index case testing implementation has faced challenges in Malawi due to limited counselling capacity among lay health care workers. Enhancing capacity through centralized face-to-face training is logistically complex and expensive. A decentralized blended learning approach to HCW capacity-building, combining synchronous face-to-face and asynchronous digital modalities, may be an acceptable way to address this challenge.

Maternal health research faces challenges in participant recruitment, retention, and data collection, particularly among underrepresented populations. Digital health platforms like PowerMom (Scripps Research) offer scalable solutions, enabling decentralized, real-world data collection. Using innovative recruitment and multimodal techniques, PowerMom engages diverse cohorts to gather longitudinal and episodic data during pregnancy and post partum.