Prescribing Experiences, Potentials, and Challenges of Digital Health Applications in the Field of Hormones and Metabolism: Cross-Sectional Survey Study of Health Care Providers in Germany
Bibliographic record
Abstract
BACKGROUND: In 2020, the global prevalence of overweight and obesity was approximately 42%. One of the most common associated conditions is type 2 diabetes mellitus, which had a global prevalence of around 10.5% in 2021. Digital health applications (DiHA), which can be prescribed as certified medical devices in Germany, have been shown to effectively support disease management in patients with overweight and diabetes mellitus. However, little is known about DiHA-prescribing behavior of health care providers (HCPs) specializing in hormones and metabolism or about potential barriers to prescribing these applications. OBJECTIVE: This study aimed to assess HCPs' experience with and willingness to prescribe DiHA in the field of hormones and metabolism. In addition, it sought to examine the patient-relevant health care effects that HCPs perceive as potentially achievable or have already observed with DiHA use, as well as the barriers they perceive to prescribing these applications. METHODS: An online questionnaire was developed based on preliminary studies and a literature review consisting of 86 items covering 6 key areas: experience and willingness to prescribe, health care effects, barriers, scientific evidence, digital affinity, and sociodemographics. The anonymous survey was distributed via the German Diabetes Association to 6035 HCPs in Germany between August 2 and October 9, 2024. Descriptive analyses, as well as correlation and regression analyses, were conducted. RESULTS: A total of 350 HCPs participated in the survey (response rate=5.8%). Although the low response rate may limit generalizability, the findings provide insights into prescribing behavior within this specialty. More than half (187/350, 53.4%) had never prescribed any of the 54 DiHA available at the time of the survey, with 47.6% (89/187) citing a lack of experience as the primary reason. Among those who had prescribed a DiHA (163/350, 46.6%), the majority (139/163, 85.3%) had prescribed 1 of the 8 DiHA available for obesity or diabetes mellitus. Looking ahead, 42.9% (149/348) of all surveyed HCPs stated that they were either very unlikely (83/348, 23.9%) or somewhat unlikely (66/348, 19%) to prescribe these DiHA in the next 12 months. The greatest perceived benefits of DiHA were improvements in self-management, health literacy, and adherence. The main barriers to prescribing DiHA in the field of hormones and metabolism included inadequate reimbursement for ancillary medical services, poor compatibility with existing practice software, and a lack of digital affinity or motivation among patients. CONCLUSIONS: DiHA have not yet been fully integrated into standard health care. To improve prescribing, we recommend integrating DiHA into medical guidelines, ensuring proper reimbursement, and involving HCPs in the pricing and health-economic evaluation of DiHA. The recommendations outlined should be considered to maximize DiHA's potential and improve HCPs' acceptance, providing valuable insights for health policy to enhance the integration, reimbursement, and use of DiHA.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".