The prevalence rates of refractive errors among children, adolescents, and adults in Germany

Sandra Jobke1, Erich Kasten2, Christian Vorwerk31Institute of Medical Psychology, 3Department of Ophthalmology, Otto-von Guericke-University of Magdeburg, Magdeburg, Germany; 2Institute of Medical Psychology, University Hospital Schleswig-Holstein, Luebeck, GermanyPurpose: The prevalence rates of myopia vary between 5% in Australian Aborigines to 84% in Hong Kong and Taiwan, 30% in Norwegian adults, and 49.5% in Swedish schoolchildren. The aim of this study was to determine the prevalence of refractive errors in German children, adolescents, and adults.Methods: The parents (aged 24–65 years) and their children (516 subjects aged 2–35 years) were asked to fill out a questionnaire about their refractive error and spectacle use. Emmetropia was defined as refractive status between +0.25D and –0.25D. Myopia was characterized as ≤−0.5D and hyperopia as ≥+0.5D. All information concerning refractive error were controlled by asking their opticians.Results: The prevalence rates of myopia differed significantly between all investigated age groups: it was 0% in children aged 2–6 years, 5.5% in children aged 7–11 years, 21.0% in adolescents (aged 12–17 years) and 41.3% in adults aged 18–35 years (Pearson’s Chi-square, p = 0.000). Furthermore, 9.8% of children aged 2–6 years were hyperopic, 6.4% of children aged 7–11 years, 3.7% of adolescents, and 2.9% of adults (p = 0.380). The prevalence of myopia in females (23.6%) was significantly higher than in males (14.6%, p = 0.018). The difference between the self-reported and the refractive error reported by their opticians was very small and was not significant (p = 0.850).Conclusion: In Germany, the prevalence of myopia seems to be somewhat lower than in Asia and Europe. There are few comparable studies concerning the prevalence rates of hyperopia.Keywords: Germany, hyperopia, incidence, myopia, prevalenc

Tailoring eHealth design to support the self-care needs of patients with cardiovascular diseases:a vignette survey experiment

Self-care support is a key cornerstone of treatment for patients with a cardiovascular disease. The success of any supportive intervention requires adaptation to the distinct needs of individuals. This requirement also applies to eHealth interventions. This study investigates how experts from multiple fields of science assess the potential success of different eHealth design strategies when matched to key self-care needs. An online vignette survey experiment was conducted. Nine vignettes representing different combinations of self-care needs (maintenance, monitoring, management) and eHealth persuasive design strategies (primary task support, dialogue support, social support) were evaluated. In total, 118 experts from 18 different countries participated in the survey. Their evaluations show primary task support as a promising design strategy across all self-care needs. In contrast, dialogue support and social support showed more promise for specific self-care needs. Above all, according to experts, the success of design strategies could be enhanced by (i) personalising the pacing of the intervention and (ii) tailoring the information to the literacy and culture of the person. Adding to that, self-care support should distinctly (iii) support the three self-care needs, be (iv) patient-centered, (v) support the collaboration with caregivers, and (vi) be aligned to the life goals and values of individuals

Personas for Better Targeted eHealth Technologies:User-Centered Design Approach

BACKGROUND: The full potential of eHealth technologies to support self-management and disease management for patients with chronic diseases is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes, and context of the prospective end users. To overcome such issues, the user-centered design practice of creating personas is widely accepted to ensure the fit between a technology and the target group or end users throughout all phases of development. OBJECTIVE: In this study, we integrate several approaches to persona development into the Persona Approach Twente to attain a more holistic and structured approach that aligns with the iterative process of eHealth development. METHODS: In 3 steps, a secondary analysis was carried out on different parts of the data set using the Partitioning Around Medoids clustering method. First, we used health-related electronic patient record data only. Second, we added person-related data that were gathered through interviews and questionnaires. Third, we added log data. RESULTS: In the first step, 2 clusters were found, with average silhouette widths of 0.12 and 0.27. In the second step, again 2 clusters were found, with average silhouette widths of 0.08 and 0.12. In the third step, 3 clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04. CONCLUSIONS: The Persona Approach Twente is applicable for mixed types of data and allows alignment of this user-centered design method to the iterative approach of eHealth development. A variety of characteristics can be used that stretches beyond (standardized) medical and demographic measurements. Challenges lie in data quality and fitness for (quantitative) clustering

Toward the Value Sensitive Design of eHealth Technologies to Support Self-management of Cardiovascular Diseases:Content Analysis

BACKGROUND: eHealth can revolutionize the way self-management support is offered to chronically ill individuals such as those with a cardiovascular disease (CVD). However, patients’ fluctuating motivation to actually perform self-management is an important factor for which to account. Tailoring and personalizing eHealth to fit with the values of individuals promises to be an effective motivational strategy. Nevertheless, how specific eHealth technologies and design features could potentially contribute to values of individuals with a CVD has not been explicitly studied before. OBJECTIVE: This study sought to connect a set of empirically validated, health-related values of individuals with a CVD with existing eHealth technologies and their design features. The study searched for potential connections between design features and values with the goal to advance knowledge about how eHealth technologies can actually be more meaningful and motivating for end users. METHODS: Undertaking a technical investigation that fits with the value sensitive design framework, a content analysis of existing eHealth technologies was conducted. We matched 11 empirically validated values of CVD patients with 70 design features from 10 eHealth technologies that were previously identified in a systematic review. The analysis consisted mainly of a deductive coding stage performed independently by 3 members of the study team. In addition, researchers and developers of 6 of the 10 reviewed technologies provided input about potential feature-value connections. RESULTS: In total, 98 connections were made between eHealth design features and patient values. This meant that some design features could contribute to multiple values. Importantly, some values were more often addressed than others. CVD patients’ values most often addressed were related to (1) having or maintaining a healthy lifestyle, (2) having an overview of personal health data, (3) having reliable information and advice, (4) having extrinsic motivators to accomplish goals or health-related activities, and (5) receiving personalized care. In contrast, values less often addressed concerned (6) perceiving low thresholds to access health care, (7) receiving social support, (8) preserving a sense of autonomy over life, and (9) not feeling fear, anxiety, or insecurity about health. Last, 2 largely unaddressed values were related to (10) having confidence and self-efficacy in the treatment or ability to achieve goals and (11) desiring to be seen as a person rather than a patient. CONCLUSIONS: Positively, existing eHealth technologies could be connected with CVD patients’ values, largely through design features that relate to educational support, self-monitoring support, behavior change support, feedback, and motivational incentives. Other design features such as reminders, prompts or cues, peer-based or expert-based human support, and general system personalization were also connected with values but in narrower ways. In future studies, the inferred feature-value connections must be validated with empirical data from individuals with a CVD or similar chronic conditions