Evidence map›Paper›PMID 31636917›Full record

ArticleDigital health

Re-thinking Digital Health: Data, Appisation and the (im)possibility of 'Opting out'.

Adi Kuntsman, Esperanza Miyake, Sam Martin

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. A scoping review of qualitative research in JAMIA: past contributions and opportunities for future work.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
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  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Adi KuntsmanManchester Metropolitan University, Manchester, UK.ORCID https://orcid.org/0000-0002-9970-9866
Esperanza MiyakeManchester Metropolitan University, Manchester, UK.ORCID https://orcid.org/0000-0001-5504-7648
Sam MartinManchester Metropolitan University, Manchester, UK.ORCID https://orcid.org/0000-0002-4466-8374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Presented as providing cost-, time- and labour- effective tools for the (self)management of health, health apps are often celebrated as beneficial to all. However, their negative effects - commodification of user data and infringement on privacy - are rarely addressed. This article focuses on one particularly troubling aspect: the difficulty of opting out of data sharing and aggregation during app use or after unsubscribing/uninstalling the app. Working in the context of the new European General Data Protection Regulation and its implementation in the UK health services, our analysis reveals the discrepancy between the information presented to users, and the apps' actual handling of user data. We also point to the fundamental tension in the digitisation of health, between the neoliberal model where both health and data concerns are viewed as an individual's responsibility, and the digital-capitalist model, which puts forward, and capitalises on, collective ('Big') data. Pulled between the 'biopolitics of the self' and the 'biopolitics of the population' (concepts coined by Btihaj Ajana), opting out of health datafication therefore cannot be resolved as a matter of individual right alone. The article offers two contributions. Methodologically, we present a toolkit for a multi-level assessment of apps from the perspective of opting out, which can be adapted and used in future research. Conceptually, the article brings together critical digital health scholarship with the perspective of data justice, offering a new approach to health apps, which focuses on

Indexed as

biopoliticsdataficationdata justicedata sharingGDPRHealth appsopting outprivacysurveillance capitalismtrust

Identifiers

PMID31636917
PMCPMC6785921

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.