Attitudes of German General Practitioners Toward eHealth Apps for Dementia Risk Reduction: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: eHealth interventions constitute a promising approach to disease prevention, particularly because of their ability to facilitate lifestyle changes. Although a rather recent development, eHealth interventions might be able to promote brain health and reduce dementia risk in older adults. OBJECTIVE: This study aimed to explore the perspective of general practitioners (GPs) on the potentials and barriers of eHealth interventions for brain health. Understanding the perspective of GPs allows us to identify chances and challenges for implementing eHealth apps for dementia risk reduction. METHODS: We conducted semistructured expert interviews with 9 GPs working in an outpatient setting in and near Leipzig, Germany. Data were fully transcribed and analyzed using a process model of qualitative content analysis with codes and categories being constructed inductively and deductively. RESULTS: We found generally favorable but balanced views of eHealth apps for brain health. Eight themes were identified and elaborated on in the data as follows: "addressing dementia," "knowledge about dementia," "need for information," "potential for prevention," "chances for apps for prevention," "development of apps for prevention," and "barriers of apps for prevention." GPs talked mostly about how and when to address dementia and the requirements for their use of eHealth apps for dementia prevention. GPs stated that they only addressed dementia once abnormalities were already present or less frequently when a patient or relative expressed a direct wish, while individual dementia risk or standardized diagnostic during routine check-ups were mentioned much less frequently. According to GPs, knowledge about dementia in patients was low; therefore, patients expressed little need for information on dementia risk factors and prevention in GP practices. Most patients wished for quick information regarding diagnostics, treatment options, and progression of the disease. GPs mentioned a lack of overview of the available eHealth apps and their content. They also expressed a fear of inducing health anxiety when talking to patients about risk factors and prevention. CONCLUSIONS: GPs want patients to receive relevant and individualized information. Prerequisites for the use of eHealth apps for dementia prevention were app characteristics related to design and content. GPs need to address dementia more routinely, assess relevant risk factors, and aid patients in a preventive role. Concerns were expressed over limited effectiveness, overwhelming patients, limited use in clinical practice, and only targeting patients with an already low risk of dementia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".