Benefits and Barriers to mHealth in Hypertension Care: Qualitative Study With German Health Care Professionals
Bibliographic record
Abstract
BACKGROUND: Digital health technologies, particularly mobile health (mHealth) apps and wearable devices, have emerged as crucial assets in the battle against hypertension. By enabling lifestyle modifications, facilitating home blood pressure monitoring, and promoting treatment adherence, these technologies have significantly enhanced hypertension treatment. OBJECTIVE: This study aims to explore the perspectives of health care professionals (HCPs) regarding the perceived benefits and barriers associated with the integration of mHealth apps into routine hypertension care. Additionally, strategies for overcoming these barriers will be identified. METHODS: Through qualitative analysis via semistructured interviews, general practitioners (n=10), cardiologists (n=14), and nurses (n=3) were purposefully selected between October 2022 and March 2023. Verbatim transcripts were analyzed using qualitative content analysis. RESULTS: The results unveiled 3 overarching themes highlighting the benefits of mHealth apps in hypertension care from the perspective of HCPs. First, these technologies possess the potential to enhance patient safety by facilitating continuous monitoring and early detection of abnormalities. Second, they can empower patients, fostering autonomy in managing their health conditions, thereby promoting active participation in their care. Lastly, mHealth apps may provide valuable support to medical care by offering real-time data that aids in decision-making and treatment adjustments. Despite these benefits, the study identified several barriers hindering the seamless integration of mHealth apps into hypertension care. Challenges predominantly revolved around data management, communication contexts, daily routines, and system handling. HCPs underscored the necessity for structural and procedural modifications in their daily practices to effectively address these challenges. CONCLUSIONS: In conclusion, the effective usage of digital tools such as mHealth apps necessitates overcoming various obstacles. This entails meeting the information needs of both HCPs and patients, tackling interoperability issues to ensure seamless data exchange between different systems, clarifying uncertainties surrounding reimbursement policies, and establishing the specific clinical benefits of these technologies. Active engagement of users throughout the design and implementation phases is crucial for ensuring the usability and acceptance of mHealth apps. Moreover, enhancing knowledge accessibility through the provision of easily understandable information about mHealth apps is essential for eliminating barriers and fostering their widespread adoption in hypertension care. TRIAL REGISTRATION: German Clinical Trials Register DRKS00029761; https://drks.de/search/de/trial/DRKS00029761. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.3389/fcvm.2022.1089968.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".