Vertaile menetelmiä
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| Mobile Health Engagement Scale× | Digitaalisen terveydenhuollon hyväksyttävyysasteikko× | |
|---|---|---|
| Tieteenala | Terveysinformatiikka | Terveysinformatiikka |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 2017 | 1989 |
| Kehittäjä≠ | Oliver Perski, Anna Blandford, Robert West, Susan Michie | Fred D. Davis (Technology Acceptance Model); extended by Venkatesh et al. (Unified Theory of Acceptance and Use of Technology) |
| Tyyppi | Self-report questionnaire | Self-report questionnaire |
| Alkuperäislähde≠ | Perski, O., Blandford, A., West, R., & Michie, S. (2017). Conceptualising engagement with digital behaviour change interventions: a systematic review, meta-analysis and integrated framework. European Health Psychologist, 19(2), 519–552. link ↗ | Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. DOI ↗ |
| Rinnakkaisnimet | mHealth Engagement, Mobile Health Engagement | DHAS, Digital Health Acceptance |
| Liittyvät | 3 | 3 |
| Tiivistelmä≠ | The Mobile Health Engagement Scale measures the extent to which individuals engage with mobile health applications and digital behaviour change interventions. Developed through systematic review and meta-analysis by Perski and colleagues (2017), it captures both behavioural and psychological dimensions of engagement—frequency of use, depth of interaction, and subjective satisfaction—essential for understanding the effectiveness of mHealth interventions in real-world settings. | 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. |
| ScholarGateAineisto ↗ |
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