Background: Digital health literacy describes an individual’s ability to use digital information and tools to improve their own health. Understanding how digital health literacy varies across populations could help improve health equity. However, the determinants of digital health literacy have been scarcely evaluated. Objective: This study aims to assess the levels of digital health literacy in 4 countries (United Kingdom, Sweden, Italy, and Germany) and explore potential associations between digital health literacy and demographic characteristics and self-perceived health status. Methods: A cross-sectional online survey was disseminated to participants from the United Kingdom, Italy, Germany, and Sweden in December 2020. Digital health literacy was self-reported using the validated eHealth Literacy Scale (eHEALS; range: 0-40); low digital health literacy has been previously defined as an eHEALS score<26. Participant characteristics collected were sex, age group, ethnicity, country, and perceived overall health status. A multivariable linear regression analysis was performed to explore associations between these variables and digital health literacy. Results: A total of 6331 participants were included (51.7% female, n=3272). The mean eHEALS score was 29.2 (SD 6.8). Participant age, sex, health status, and country of residence were included in the final multivariable model. Compared to the 45- to 54-year age group, the 55 years and older age group had lower digital health literacy (β=–1.0; 95% CI –1.4 to –0.5; P<.001), while digital health literacy was higher in those aged 25-34 years (β=0.9; 95% CI 0.3-1.5; P=.002) and 35-44 years (β=0.6; 95% CI 0.1-1.2; P=.03). Better health status was associated with greater digital health literacy (β=0.3; 95% CI 0.2-0.4; P<.001). Compared to participants from Germany, those from the United Kingdom (β=2.1; 95% CI 1.7-2.5; P<.001) and Sweden (β=2.9; 95% CI 2.4-3.4; P<.001) had higher digital health literacy scores, while there was no difference with Italian participants (P=.399). Sex and ethnicity did not have any significant effect on digital health literacy. Conclusions: This study found significant variations in digital health literacy by age, health status, and country of residence. Targeted educational programs for vulnerable groups, particularly those of older age and poorer health status, are essential. Policies fostering accessible digital health solutions and mitigating health technology–related uncertainties for these populations are crucial for achieving optimal health outcomes.

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to directly locate the fovea. While traditional methods rely on explicitly extracting image features from the surrounding structures such as the optic disc and various vessels to infer the position of the fovea, deep learning based regression technique can implicitly model the relation between the fovea and other nearby anatomical structures to determine the location of the fovea in an end-to-end fashion. Although promising, using deep learning for fovea localisation also has many unsolved challenges. In this paper, we present a new end-to-end fovea localisation method based on a hierarchical coarse-to-fine deep regression neural network. The innovative features of the new method include a multi-scale feature fusion technique and a self-attention technique to exploit location, semantic, and contextual information in an integrated framework, a multi-field-of-view (multi-FOV) feature fusion technique for context-aware feature learning and a Gaussian-shift-cropping method for augmenting effective training data. We present extensive experimental results on two public databases and show that our new method achieved state-of-the-art performances. We also present a comprehensive ablation study and analysis to demonstrate the technical soundness and effectiveness of the overall framework and its various constituent components.

Concussions significantly impact sleep quality among adolescents. Despite increasing recognition of these effects, the complex relationship between adolescent concussions and sleep disturbances is still not fully understood and presents mixed findings. Here, we conducted a systematic review and meta-analysis to assess how concussions affect sleep-related symptoms in adolescents. We included papers presenting Pittsburgh Sleep Quality Index (PSQI) differences following concussion in high school and collegiate patients, with sleep measures recorded following concussion. Animal studies, research on participants with conditions other than concussion, non-English articles and papers failing to present PSQI data were excluded. We searched MEDLINE^®, Embase^®, CINAHL, Web of Science™, PsycINFO^®, Google Scholar, and Cochrane Central Register of Controlled Trials databases until 23 August 2024. In addition, we performed hand-searching of relevant reference lists and conference proceedings to identify further studies. Risk of bias was assessed using the Newcastle–Ottawa scale. In total, we considered 4477 studies, with nine meeting the inclusion criteria. Our analysis involved 796 participants, 340 of whom had experienced a concussion. Analysis was carried out using the meta and metafor packages in R (version 4.0.0). We showed a deterioration in sleep quality post-concussion, as evidenced by increased PSQI (standardised mean difference 0.84; 95% CI 0.53–1.16; p < 0.0001). Subgroup and quality assessments confirmed the consistency of these findings. Since poor sleep quality impacts daytime activities, we analysed the relationship between concussion and daytime dysfunction. We showed that concussion is associated with a significant worsening of the daytime dysfunction score by 0.55 (95% CI 0.24–0.70; p = 0.006). We conclude that concussions impair both sleep quality and daytime functioning in adolescents. Our research underscores the need for systematic inclusion of sleep quality assessments in post-concussion protocols and calls for targeted interventions to manage sleep disturbances post-concussion to mitigate their broader impacts on daily functioning.