Evidence map›Paper›PMID 37536278›Full record

SynthesisThe oncologist2023

Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review.

Madeleine Flaucher, Anastasiya Zakreuskaya, Michael Nissen, Alexander Mocker, Peter A Fasching, Matthias W Beckmann, Bjoern M Eskofier, Heike Leutheuser

Abstract readSystematic Review
In one paragraph

Synthesis in The oncologist, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Observational
  7. Article
  8. Article
  9. Review
  10. 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

8 authors.

Madeleine FlaucherDepartment Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0002-8086-1112
Anastasiya ZakreuskayaDepartment Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0003-3514-0110
Michael NissenDepartment Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0001-5472-132X
Alexander MockerDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Peter A FaschingDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0003-4885-8471
Matthias W BeckmannDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Bjoern M EskofierDepartment Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0002-0417-0336
Heike LeutheuserDepartment Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID 0000-0001-9931-2271

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is affecting millions of people worldwide. If not appropriately handled, the side effects of different modalities of cancer treatment can negatively impact patients' quality of life and cause treatment interruptions. In recent years, mobile health (mHealth) interventions have shown promising opportunities to support breast cancer care. Numerous studies implemented mobile health interventions aiming to support patients with breast cancer, for example, through physical activity promotion or educational content. Nonetheless, current literature reveals that real-world evidence for the actual benefits remains unclear. In this systematic review, we focus on analyzing the methodology used in recent studies to determine the effects of mHealth applications and wearable devices on the outcome of patients with breast cancer. We followed the PRISMA guideline for the selection, analysis, and reporting of relevant studies found in the databases of Medline, Scopus, Web of Science, and Cochrane Library. A total of 276 unique records were identified, and 20 studies met the inclusion criteria. Study quality was assessed with the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool for Quantitative Studies. While many of the studies used standardized questionnaires as patient-reported outcome measures, there was minimal use of objective measurements, such as activity sensors. Adoption, drop-out rates, and usage behavior of users of the mobile health intervention were often not reported. Future work should clearly define the focus and desired outcome of mHealth interventions and select outcome measures accordingly. Greater transparency facilitates the interpretation of results and conclusions about the real-world evidence of mobile health in breast cancer care.

Indexed as

Breast NeoplasmsMobile ApplicationsTelemedicineDelivery of Health CareFemaleHumansQuality of Lifedigital healthmHealthoncologypatient-reported outcome measureswearable sensors

Identifiers

PMID37536278
PMCPMC10546835

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.