Cost-effectiveness analysis of mHealth applications for depression in Germany using a Markov cohort simulation.

Regulated mobile health applications are called digital health applications ("DiGA") in Germany. To qualify for reimbursement by statutory health insurance companies, DiGA have to prove positive care effects in scientific studies. Since the empirical exploration of DiGA cost-effectiveness remains largely uncharted, this study pioneers the methodology of cohort-based state-transition Markov models to evaluate DiGA for depression.

Negotiating pricing and payment terms for insurance covered mHealth apps: a qualitative content analysis and taxonomy development based on a German experience.

Background: Germany was the first country worldwide to offer mobile digital health applications (mHealth apps, "DiGA") on prescription with full cost coverage by statutory health insurances. Especially statutory health insurances criticize the current pricing and payment regulations in Germany due to "free and non-transparent" pricing in the first year and lack of cost use evidence. The study consists of two parts: The first part evaluates interests of digital health application providers and statutory health insurances in Germany to identify overlaps and divergences of interests.

Rating analysis and BERTopic modeling of consumer versus regulated mHealth app reviews in Germany.

Germany introduced prescription-based mobile health (mHealth) apps in October 2020, becoming the first country to offer them fully reimbursed by health insurance. These regulated apps, known as DiGAs, undergo a rigorous approval process similar to pharmaceuticals, including data protection measures and sometimes clinical trials. This study compares the user experience of DiGAs with non-prescription mHealth apps in Germany, analyzing both average app store ratings and written reviews.

Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study.

The study aims to (1) investigate current levels of patient acceptance of mHealth in Germany; (2) determine the influencing factors of patients' intention to use, and (3) test the influence of prescription and reimbursement status on patient acceptance. Online survey with 1349 participants, of which 1051 were complete and included for statistical analysis, from a broad cross-section of the German population, addressing both users of mobile health (mHealth) applications and people without prior experience. SEM modeling based on a combination of two theoretical frameworks: the extended Unified Theory of Acceptance and Use of Technology and Health Protective Behavior Theories were used to assess acceptance.