Association Between the Use of a Mobile Health Strategy App and Biological Changes in Breast Cancer Survivors: Prospective Pre-Post Study

Mapping the Evidence on the Impact of mHealth Interventions on Patient-Reported Outcomes in Patients With Breast Cancer: A Systematic Review

PURPOSE

To comprehensively synthesize the existing evidence concerning mHealth interventions for patients with breast cancer (BC).

DESIGN

On July 30, 2023, we searched PubMed, PsycINFO, and Google Scholar for articles using the following inclusion criteria: evaluation of mHealth interventions in patients with cancer, at least 30 participants with BC, randomized control trials or prospective pre-post studies, determinants of health (patient-reported outcomes [PROs] and quality of life [QoL]) as primary outcomes, interventions lasting at least 8 weeks, publication after January 2015. Publications were excluded if they evaluated telehealth or used web-based software for desktop devices only. The quality of the included studies was analyzed with the Cochrane Collaboration Risk of Bias Tool and the Methodological Index for Non-Randomized Studies.

RESULTS

We included 30 studies (20 focused on BC), encompassing 5,691 patients with cancer (median 113, IQR, 135.5). Among these, 3,606 had BC (median 99, IQR, 75). All studies contained multiple interventions, including physical activity, tailored information for self-management of the disease, and symptom tracker. Interventions showed better results on self-efficacy (3/3), QoL (10/14), and physical activity (5/7). Lifestyle programs (3/3), expert consulting (4/4), and tailored information (10/11) yielded the best results. Apps with interactive support had a higher rate of positive findings, while interventions targeted to survivors showed worse results. mHealth tools were not available to the public in most of the studies (17/30).

CONCLUSION

mHealth interventions yielded heterogeneous results on different outcomes. Identifying lack of evidence on clinical scenarios (eg, patients undergoing systemic therapy other than chemotherapy) could aid in refining strategic planning for forthcoming research endeavors within this field.

A systematic review of eHealth technologies for breast cancer supportive care

Breast cancer places a substantial burden on patient physical and mental wellbeing, and the delivery of high-quality supportive care is essential at all stages of the disease. Given the increased uptake of technology in recent years, eHealth interventions may be a convenient and accessible method for supportive care. Within this context, we conducted a systematic review to describe and quantify the use of eHealth for breast cancer supportive care. We searched MEDLINE, EMBASE, and CINAHL databases for primary research studies published from 2016 to 2021 (present) that assessed the effects of eHealth interventions on adult patients with breast cancer. We explored the effects of the interventions on patient symptoms, lifestyle, satisfaction, and barriers, as well as factors related to feasibility and implementation. The risk of bias of each study was also assessed. Findings were presented according to stage of cancer care. We identified 43 relevant studies capturing n = 6,285 patients (30 randomized controlled trials and 13 non-randomized interventional studies); 5 evaluated patients who were newly diagnosed, 16 evaluated patients undergoing active treatment, and 22 evaluated patients in post-treatment follow-up. A total of 19 studies used mobile apps, 18 used online patient portals, 5 used text messaging, and 1 used both a patient portal and text messaging. We found that patients were broadly satisfied with the eHealth interventions; however, findings were less consistent for symptom and lifestyle-related outcomes. Eight studies were judged as high risk of bias. There was substantial between-study heterogeneity, which made it challenging to discern consistent trends. Overall, future research should continue to explore the use of eHealth for breast cancer supportive care, with a focus on improving patient symptoms.

Behavior Change Effectiveness Using Nutrition Apps in People With Chronic Diseases: Scoping Review

BACKGROUND

Cardiovascular disease, cancer, diabetes mellitus, and obesity are common chronic diseases, and their prevalence is reaching an epidemic level worldwide. As the impact of chronic diseases continues to increase, finding strategies to improve care, access to care, and patient empowerment becomes increasingly essential. Health care providers use mobile health (mHealth) to access clinical information, collaborate with care teams, communicate over long distances with patients, and facilitate real-time monitoring and interventions. However, these apps focus on improving general health care concerns, with limited apps focusing on specific chronic diseases and the nutrition involved in the disease state. Hence, available evidence on the effectiveness of mHealth apps toward behavior change to improve chronic disease outcomes is limited.

OBJECTIVE

The objective of this scoping review was to provide an overview of behavior change effectiveness using mHealth nutrition interventions in people with chronic diseases (ie, cardiovascular disease, diabetes mellitus, cancer, and obesity). We further evaluated the behavior change techniques and theories or models used for behavior change, if any.

METHODS

A scoping review was conducted through a systematic literature search in the MEDLINE, EBSCO, PubMed, ScienceDirect, and Scopus databases. Studies were excluded from the review if they did not involve an app or nutrition intervention, were written in a language other than English, were duplicates from other database searches, or were literature reviews. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, the systematic review process included 4 steps: identification of records through the database search, screening of duplicate and excluded records, eligibility assessment of full-text records, and final analysis of included records.

RESULTS

In total, 46 studies comprising 256,430 patients were included. There was diversity in the chronic disease state, study design, number of participants, in-app features, behavior change techniques, and behavior models used in the studies. In addition, our review found that less than half (19/46, 41%) of the studies based their nutrition apps on a behavioral theory or its constructs. Of the 46 studies, 11 (24%) measured maintenance of health behavior change, of which 7 (64%) sustained behavior change for approximately 6 to 12 months and 4 (36%) showed a decline in behavior change or discontinued app use.

CONCLUSIONS

The results suggest that mHealth apps involving nutrition can significantly improve health outcomes in people with chronic diseases. Tailoring nutrition apps to specific populations is recommended for effective behavior change and improvement of health outcomes. In addition, some studies (7/46, 15%) showed sustained health behavior change, and some (4/46, 9%) showed a decline in the use of nutrition apps. These results indicate a need for further investigation on the sustainability of the health behavior change effectiveness of disease-specific nutrition apps.

Mobile health applications for the care of patients with breast cancer: A scoping review

Objectives

In recent years, the use of mobile health applications (mHealth apps) to deliver care for patients with breast cancer has increased exponentially. This study aimed to summarize the available evidence on developing mHealth apps to care for patients with breast cancer and identify the need for systematic efforts.

Methods

A scoping review was performed according to Arksey and O'Malley's framework, aiming to identify eligible research studies in PubMed, CINAHL, and Web of Science between January 2010 and December 2020. All identified studies were screened, extracted, and analyzed independently by two reviewers.

Results

A total of 676 studies were retrieved, and eight eligible studies were finally included. Four themes emerged: the involvement of patients and health professionals in the phases of design and development, patients' preferences, the characteristics of patients, and the motivators to use mHealth apps. The results indicated promising prospects for using mHealth apps to care for patients with breast cancer and identified the need for systematic efforts to develop and validate relevant apps.

Conclusions

The attributes of patient characteristics, needs, and patient-reported outcomes data are vital components for developing mHealth apps for patients with breast cancer. Additionally, collaborative efforts, including patients, nurses, and other significant health professionals, should develop mHealth apps for breast cancer care. Additional research focusing on the design and development of mHealth apps for patients with breast cancer is warranted.

A review of AI and Data Science support for cancer management

INTRODUCTION

Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.

OBJECTIVE

Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.

METHODS

We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient's state from it and deliver coaching/behavior change interventions.

RESULTS

Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.

CONCLUSION

Development of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.