Digital Health Acceptance Scale
Digital Health Acceptance Scale (DHAS) · Also known as: DHAS, Digital Health Acceptance
The Digital Health Acceptance Scale measures the extent to which patients and providers perceive digital health technologies as useful, easy to use, and worth adopting. Grounded in Davis's Technology Acceptance Model (TAM) and extended by Venkatesh and colleagues through the Unified Theory of Acceptance and Use of Technology (UTAUT), the scale captures both intrinsic factors (usefulness, ease of use, subjective norms) and contextual factors (facilitating conditions, effort expectancy) that predict technology adoption and sustained use in healthcare settings.
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When to use it
Assess acceptance early in digital health implementation—before major rollout—to identify barriers and optimize introduction strategies. Use acceptance assessment to compare competing platforms or tools; select the one with highest acceptance among target users. Measure acceptance changes over time as users gain experience; early low acceptance may improve with proficiency. Use in implementation science research to understand which contextual factors drive adoption in real-world healthcare settings.
Strengths & limitations
- Grounded in robust theoretical framework: TAM and UTAUT have decades of validation in information technology and healthcare; strong empirical support for predictive validity.
- Modular design: Four distinct subscales enable diagnosis of specific barriers, guiding targeted interventions.
- Predictive power: Acceptance scores correlate with actual use behaviour, clinical outcomes, and return on investment in digital health technology.
- Generalizable across technologies: Framework applies to patient portals, mHealth apps, wearables, AI decision-support tools, and other health technologies.
- Pre-adoption bias: Acceptance measured before extended use may not reflect long-term satisfaction; the 'honeymoon period' effect inflates initial acceptance.
- Self-report limitations: Individuals may report high acceptance to please researchers or developers, especially if they perceive their feedback will influence tool design.
- Context dependency: Acceptance differs substantially by healthcare setting (primary care versus specialist); rural versus urban settings; resource-rich versus resource-limited contexts.
- Does not measure actual usability: High acceptance does not guarantee the technology is genuinely usable; qualitative and usability testing complementary to acceptance measures.
Frequently asked
Can acceptance be measured before patients or providers have used the technology?
Yes, but with caveats. Pre-use acceptance ('intention to use') is often measured after demonstration, brief trial, or viewing prototypes. Pre-use acceptance scores are less accurate predictors than post-use; actual experience reveals usability issues not apparent in demonstrations. Use pre-use acceptance for early-stage concept testing; measure post-use acceptance for adoption prediction.
How much does acceptance need to improve to predict technology adoption?
An increase from <50 to ≥60 (10-point gain on 0–100 scale) can shift technology from likely rejection to possible adoption, depending on baseline. However, contextual factors (competitive alternatives, regulatory mandates, organizational push) strongly influence adoption regardless of acceptance score.
Should acceptance be measured the same way for patients and clinicians?
Core domains are universal, but emphasis differs. For patients, perceived usefulness for health outcomes and ease of use dominate. For clinicians, perceived usefulness for clinical decision-making and workflow integration are critical; facilitating conditions (IT support, integration with EHR) strongly predict adoption.
How long does acceptance typically take to stabilize?
Initial acceptance (days 1–7) may be high (novelty effect) or low (learning curve frustration). Stable acceptance typically emerges after 4–8 weeks of regular use. For longitudinal studies, measure acceptance at baseline, 2 weeks, 4 weeks, 8 weeks, and 3 months to track trajectory.
Sources
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. DOI: 10.2307/249008 ↗
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. DOI: 10.2307/30036540 ↗
How to cite this page
ScholarGate. (2026, June 3). Digital Health Acceptance Scale (DHAS). ScholarGate. https://scholargate.app/en/health-informatics/digital-health-acceptance-scale
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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