Distinguishing features in the assessment of mHealth apps
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: The renewed reliance experienced during the pandemic and the unprecedented injection of resources through recovery instruments can further boost the development of apps. Only robust evidence of the benefits of mHealth apps will persuade health-care professionals and beneficiaries to systematically deploy them. Regulatory bodies will need to question their current approaches by adopting comprehensive evaluation processes that adequately consider the specific features of mHealth apps.
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Economic evidence of digital solutions in primary care is rare.
“Economic evidence of digital solutions in primary care is rare, indicating the significance of this paper to drive more research into the true economic benefit digital solutions can hold in general practice.”
From A prospective observational real world feasibility study assessing the role of app-based remote patient monitoring in reducing primary care clinician workload during the COVID pandemic (2021) · cites this paper as [29]
The Article highlights (p. 4) say mHealth apps often lack adequate economic evidence. The paper does not establish that economic evidence is rare specifically for digital solutions in primary care; its discussion concerns mHealth apps more broadly. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- Tarricone, R., Petracca, F., Ciani, O., & Cucciniello, M.
- Affiliations
- Bocconi University (IT)
- University of Exeter (GB)
- University of Edinburgh (GB)
- Corresponding author
- Petracca, Francesco
- Venue
- Expert Review of Pharmacoeconomics & Outcomes Research
- Publisher
- Informa UK Limited
- Published
- 2021-03-04
- Volume
- 21
- Issue
- 4
- Pages
- 521-526
- ISSN
- 1473-7167 (ISSN-L)
- 1744-8379
- OpenAlex
- W3134899333
- Cited by
- 62
- References
- 39
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 17 · 2023: 12 · 2022: 9 · 2021: 9
- FWCI
- 13.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Expert Review of Pharmacoeconomics & Outcomes Research (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion, cc-by-nc-nd)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- Funders
- European Union’s Horizon (#779306)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- mobile health apps, mobile health, app evaluation, regulatory oversight, COVID-19 pandemic, digital health, health technology assessment
- MeSH terms
- COVID-19, Humans, Surveys and Questionnaires, Telemedicine, Evidence-Based Medicine, Pandemics, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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