Digital Health Literacy of the Population in Germany and Its Association With Physical Health, Mental Health, Life Satisfaction, and Health Behaviors: Nationally Representative Survey Study
Bibliographic record
Abstract
BACKGROUND: Digital health literacy, also known as eHealth literacy, describes the ability to seek, find, understand, and apply health information from the internet to address health problems. The World Health Organization calls for actions to improve digital health literacy. To develop target group-specific digital health literacy interventions, it is necessary to know the digital health literacy of the general population and relevant subgroups. OBJECTIVE: This study aims to representatively assess the digital health literacy of the population in Germany and relevant subgroups. The results are meant to facilitate the development of target group-specific digital health literacy interventions. Additionally, this study further explores the associations between digital health literacy and physical health, mental health, life satisfaction, and diverse health behaviors. METHODS: Study participants were drawn from a representative panel of the German-speaking population with internet access. To further increase the representativeness of the sample, survey weights were calculated using an iterative proportional fitting procedure. Participants answered a series of questionnaires regarding their digital health literacy, physical health, mental health, life satisfaction, and diverse health behaviors. Two-sided independent sample t tests were conducted to determine the significant differences between societal subgroups. Pearson correlation coefficients were calculated to explore the correlates of digital health literacy. RESULTS: Digital health literacy is unevenly distributed within German society. The results of this study suggest that people with a low level of formal education and people with a low social status would benefit from digital health literacy interventions that address their competencies in the domains of information seeking and information appraisal. Furthermore, the results suggest that older people would likely benefit from digital health literacy interventions that address their competencies in the domains of information seeking and also information appraisal. Regarding sex, this study suggests that men might benefit from digital health literacy interventions that specifically address their competencies in the domain of information seeking. Furthermore, digital health literacy is weakly positively correlated with physical health, mental health, life satisfaction, exercise routines, fruit consumption, and vegetable consumption. CONCLUSIONS: Overall, the results of this study demonstrate that digital health literacy is associated with diverse health outcomes and behaviors. Furthermore, the results provide a starting point for the development of target group-specific digital health literacy interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".