Extending the Privacy Calculus to the mHealth Domain: Survey Study on the Intention to Use mHealth Apps in Germany
Bibliographic record
Abstract
BACKGROUND: With the increasing digitalization of the health sector, more and more mobile health (mHealth) apps are coming to the market to continuously collect and process sensitive health data for the benefit of patients and providers. These technologies open up new opportunities to make the health care system more efficient and save costs but also pose potential threats such as loss of data or finances. OBJECTIVE: This study aims to present an empirical review and adaptation of the extended privacy calculus model to the mHealth domain and to understand what factors influence the intended usage of mHealth technologies. METHODS: A survey study was conducted to empirically validate our model, using a case vignette as cover story. Data were collected from 250 German participants and analyzed using a covariance-based structural equation model. RESULTS: The model explains R2=79.3% of the variance in intention to use. The 3 main factors (social norms, attitude to privacy, and perceived control over personal data) influenced the intention to use mHealth apps, albeit partially indirectly. The intention to use mHealth apps is driven by the perceived benefits of the technology, trust in the provider, and social norms. Privacy concerns have no bearing on the intention to use. The attitude to privacy has a large inhibiting effect on perceived benefits, as well as on trust in the provider. Perceived control over personal data clearly dispels privacy concerns and supports the relationship of trust between the user and the provider. CONCLUSIONS: Based on the privacy calculus, our domain-specific model explains the intention to use mHealth apps better than previous, more general models. The findings allow health care providers to improve their products and to increase usage by targeting specific user groups.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".