Journal of Medical Internet Research

Although efficacious psychotherapies exist, a limited number of mental health care providers and significant demand make their accessibility a fundamental problem. Clinical researchers, funders, and investors alike have converged on self-help digital mental health interventions (self-help DMHIs) as a low-cost, low-burden, and broadly scalable solution to the global mental health burden. Consequently, exorbitant financial and time-based resources have been invested in developing, testing, and disseminating these interventions. However, the public’s assumed desirability for self-help DMHIs by experts has largely proceeded without question. This commentary critically evaluates whether self-help DMHIs can, and will, reach their purported potential as a solution to the public burden of mental illness, with an emphasis on evaluating their real-world desirability. Our review finds that self-help DMHIs are often perceived as less desirable and credible than in-person treatments, with lower usage rates and, perhaps accordingly, clinical trials testing self-help DMHIs suffering from widespread recruitment challenges. We highlight two fundamental challenges that may be interfering with the desirability of, and engagement in, self-help DMHIs: (1) difficulty competing with technology companies that have advantages in resources, marketing, and user experience design (but may not be delivering evidence-based interventions) and (2) difficulty retaining (vs initially attracting) users. We discuss a range of potential solutions, including highlighting self-help DMHIs in public mental health awareness campaigns; public education about evidence-based interventions that can guide consumers to appropriate self-help DMHI selection; increased financial and expert support to clinical researchers for marketing, design, and user experience in self-help DMHI development; increased involvement of stakeholders in the design of self-help DMHIs; and investing in more research on ways to improve retention (versus initial engagement). We suggest that, through these efforts, self-help DMHIs may fully realize their promise for reducing the global burden of mental illness.

Adaptive systems serve to personalize interventions or training based on the user’s needs and performance. The adaptation techniques rely on an underlying engine responsible for processing incoming data and generating tailored responses. Adaptive virtual reality (VR) systems have proven to be efficient in data monitoring and manipulation, as well as in their ability to transfer learning outcomes to the real world. In recent years, there has been significant interest in applying these systems to improve deficits associated with autism spectrum disorder (ASD). This is driven by the heterogeneity of symptoms among the population affected, highlighting the need for early customized interventions that target each individual’s specific symptom configuration.

Several treatment-related challenges exist for gambling disorder, in particular at-scale dissemination in health care settings.

While perceptions of electronic labeling (e-labeling) in developed countries have been generally positive, existing data primarily come from studies involving hospital pharmacists, community pharmacy customers who may not be frequent medication users, and individuals receiving COVID-19 vaccines.

Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT.

Although COVID-19 is no longer a global health emergency, it remains pervasive in Singapore, a city-state situated in Southeast Asia, with periodic waves of infection. In addition to disease management, strong communication strategies are critical in the government’s response to the pandemic to keep the public updated and equip them in protecting themselves.

Data dashboards have become more widely used for the public communication of health-related data, including in maternal health.

Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed.

The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on “cold or flu” calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization.

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Specialized studies have shown that smartphone-based social interaction data are predictors of depressive and anxiety symptoms. Moreover, at times during the COVID-19 pandemic, social interaction took place primarily remotely. To appropriately test these objective data for their added value for epidemiological research during the pandemic, it is necessary to include established predictors.

Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment.