SEARCHSYSTEMATIC_REVIEWoncology, clinical trial

systematic_review·oncology, clinical trial, systematic review, public health·PMC10546835

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

The Oncologist · 8 authors, 8 centres

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TL;DR

This systematic review evaluated studies on mobile health applications for breast cancer care.

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This systematic review synthesized empirical studies examining the effectiveness of mobile health interventions for patients with breast cancer. The review notes that while mobile health technologies have potential to improve patient outcomes and reduce treatment burden, the evidence for their effectiveness remains inconsistent. The authors recommend future research should focus on specific app features and standardized outcome measures to improve comparability and develop clear scientific guidelines. Limitations include a limited number of searched databases and a short publication time span.

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systematic_review

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oncology

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ORIGINAL PAPER

PMC10546835

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

The Oncologist

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ABSTRACT

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. Keywords: digital health, mHealth, patient-reported outcome measures, wearable sensors, oncology

CITATION EXPORT

Madeleine Flaucher, Anastasiya Zakreuskaya, Michael Nissen et al.. Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review. The Oncologist. (2023). https://doi.org/10.1093/oncolo/oyad217

AUTHORS

8 · 8 CENTRES

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

1 Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
2 Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
3 Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
4 Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
5 Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
6 Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
7 Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
8 Department Artificial Intelligence in Biomedical Engineering (AIBE), Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany