Online Journal of Public Health Informatics

Telemedicine and mobile health technologies increasingly rely on patient-generated health data collected outside traditional clinical settings. These data promise continuous monitoring, personalization, and early intervention, but in practice, they are often sparse, irregular, and incomplete because data availability depends on sustained patient participation. In this viewpoint, we argue that missingness in patient-generated telemedicine data should not be treated solely as a technical data quality problem. In many contexts, it may reflect patient adherence behavior and, therefore, carry behavioral or clinical meaning. We introduce the adherence-driven sparsity framework, an interpretive and reporting framework for reasoning about missingness in these data. The framework is grounded in 4 principles: patient adherence behavior is a central determinant of data availability, missingness patterns may carry clinically meaningful information, adherence heterogeneity creates differential representation in observed data, and analytical methods should be aligned with actual data characteristics rather than assumed data ideals. The adherence-driven sparsity framework does not propose a new algorithm or validated decision rule. Instead, it provides a structured workflow for preanalysis reasoning: identifying the data generation context; distinguishing adherence-driven from system-driven gaps; characterizing missingness across 6 reporting dimensions; assessing the plausibility of missing completely at random, missing at random, and missing not at random mechanisms; and aligning analytical choices with sparsity, irregularity, heterogeneity, and available evidence. The framework is intended for telemedicine researchers, clinical data scientists, and biostatisticians working with mobile health data. This viewpoint also cautions against the uncritical transfer of machine learning methods from dense, regularly sampled domains to patient-generated telemedicine data. In this setting, methodological sophistication alone is insufficient; researchers must first understand how the data were generated, why observations are missing, and whether missingness itself may be informative. Recognizing adherence-driven sparsity can support more transparent reporting, more defensible analytical decisions, and more clinically meaningful interpretation of patient-generated data.

Lack of knowledge about clinical trial (CT) research may perpetuate poor engagement of underrepresented populations. The rise of internet use may improve CT knowledge, and internet-based recruitment modalities might differentially benefit recruitment if internet users have higher baseline CT knowledge. We seek to bridge these gaps by assessing the relationship between internet use, access to health information, and CT participation.

Lymphoma is one of the most prevalent groups of cancers among people aged 15 to 29 years. Increasingly, patients with lymphoma and their caregivers use generative AI systems, supported by large language models (LLMs), to seek cancer-related online health information. Despite the need for plain and clear health information for clinical decision-making, there is a dearth of research on the readability and citation source classification of LLM-supported online health information.

TikTok has emerged as a major source of health information, yet concerns persist regarding content accuracy and monetization features. Gut health content is particularly vulnerable to misinformation.

The increasing use of social media as a source of health information has highlighted the need to understand the psychological factors influencing individuals’ intentions to seek health-related information online, particularly within the South African context.

The mental health of children, adolescents, and youths is a growing concern globally, with increasing prevalence of conditions and help-seeking behavior. Alongside traditional factors, digital determinants are now prominent influences on mental health. With digital platforms integral to young people’s lives, and problematic social media use rising, policymakers face the challenge of balancing the benefits and risks of young people’s digital technology use. Decisive action is hindered by inconsistent evidence on the impacts of social media and other technology use on mental health and well-being.

Eye diseases are prevalent conditions around the world. More than 2 billion individuals globally have visual impairment. However, reducing the worldwide burden of eye disorders requires increased public awareness of prevalent eye diseases.

Over the past decade, postpartum depression (PPD) diagnoses have significantly risen across all ethnic and racial groups. However, PPD remains underdiagnosed due in part to stigmatization and misunderstanding by the public. This can result in a reluctance among women to openly discuss their symptoms with clinicians. However, social media offers supportive communities and unique platforms for individuals to openly share the experiences and opinions that they may be less likely to disclose in person. Despite the availability of new Food and Drug Administration (FDA)–approved medical therapies for PPD over the past 7 years, research investigating comments about perceptions of recommendations for and barriers to treatment has been sparse.

Public reporting websites have become increasingly important for improving health care transparency and supporting informed decision-making. However, little empirical evidence exists regarding whether the readability of these websites adequately meets users’ information needs, particularly in the context of digital health and Finland’s ongoing social welfare and health care reform.

Rapid digitalization has led to the generation of vast volumes of health data across diverse sources, including public health surveillance, electronic health records from hospitals, primary care facilities, and telemedicine. However, it often remains fragmented across multiple domains and systems due to siloed data collection practices and a lack of interoperability standards, limiting its potential to generate actionable public health insights.

Telehealth use expanded rapidly during the COVID-19 pandemic, yet persistent digital inequities continue to shape who benefits from virtual and preventive health services. Although community-based digital inclusion programs aim to reduce barriers to access, less is known about how device reliability, broadband conditions, and perceptions of online health information influence health care use after access is provided.

Zero-dose (ZD) children—those who receive no routine vaccines—remain highly vulnerable, with global evidence showing that they are concentrated in marginalized and hard-to-reach settings. In Bangladesh, stagnant full-immunization coverage, population mobility, and inconsistencies in administrative data complicate accurate estimation of ZD and underimmunized children. Gaps in the completeness, timeliness, and accuracy of routine Expanded Programme on Immunization (EPI) information systems further limit the ability to identify and target these populations.