Evidence map›Paper›PMID 36812525›Full record

ArticlePLOS digital health2022

Effectiveness of a tailored web app on sun protection intentions and its implications for skin cancer prevention: A randomized controlled trial.

Vasileios Nittas, Margot Mütsch, Tobias Frey, Julia Braun, Milo A Puhan

Abstract read
In one paragraph

Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

5 authors.

Vasileios NittasEpidemiology, Biostatistics and Prevention Institute, University of Zurich, CH-8001 Zurich, Switzerland.ORCID https://orcid.org/0000-0002-6685-8275
Margot MütschEpidemiology, Biostatistics and Prevention Institute, University of Zurich, CH-8001 Zurich, Switzerland.ORCID https://orcid.org/0000-0003-0620-5376
Tobias FreyDepartment of Communication and Media Research, University of Zurich, CH-8050 Zurich, Switzerland.ORCID https://orcid.org/0000-0002-9190-8633
Julia BraunEpidemiology, Biostatistics and Prevention Institute, University of Zurich, CH-8001 Zurich, Switzerland.ORCID https://orcid.org/0000-0001-8359-6177
Milo A PuhanEpidemiology, Biostatistics and Prevention Institute, University of Zurich, CH-8001 Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Skin cancers related to sunexposure are rising globally, yet largely preventable. Digital solutions enable individually tailored prevention and may play a crucial role in reducing disease burden. We developed SUNsitive, a theory-guided web app to facilitate sun protection and skin cancer prevention. The app collected relevant information through a questionnaire and provided tailored feedback on personal risk, adequate sun protection, skin cancer prevention, and overall skin health. SUNsitive's effect on sun protection intentions and a set of secondary outcomes was evaluated with a two-arm randomized controlled trial (n = 244). At 2 weeks post-intervention, we did not find any statistical evidence for the intervention's effect on the primary outcome or any of the secondary outcomes. However, both groups reported improved intentions to sun protect compared to their baseline values. Furthermore, our process outcomes suggest that approaching sun protection and skin cancer prevention with a digital tailored "questionnaire-feedback" format is feasible, well-perceived, and well accepted. Trial registration: Protocol registration: ISRCTN registry (ISRCTN10581468).

Identifiers

PMID36812525
PMCPMC9931317

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.