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Review

Digital Health Platforms for Breast Cancer Care: A Scoping Review

by
Elayna P. Kirsch
1,
Sameer A. Kunte
1,*,
Kevin A. Wu
1,
Samantha Kaplan
2,
E. Shelley Hwang
3,
Jennifer K. Plichta
3 and
Shivanand P. Lad
1
1
Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
2
Medical Center Library & Archives, Duke University School of Medicine, Durham, NC 27710, USA
3
Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(7), 1937; https://doi.org/10.3390/jcm13071937
Submission received: 28 February 2024 / Revised: 12 March 2024 / Accepted: 23 March 2024 / Published: 27 March 2024
(This article belongs to the Section Oncology)

Abstract

:
Breast cancer is a significant global health concern affecting millions of women each year. Digital health platforms are an easily accessible intervention that can improve patient care, though their efficacy in breast cancer care is unknown. This scoping review aims to provide an overview of existing research on the utilization of digital health platforms for breast cancer care and identify key trends and gaps in the literature. A comprehensive literature search was conducted across electronic databases, including Ovid MEDLINE, Elsevier EMBASE, and Elsevier Scopus databases. The search strategy incorporated keywords related to “digital health platforms”, “breast cancer care”, and associated terminologies. After screening for eligibility, a total of 25 articles were included in this scoping review. The identified studies comprised mobile applications and web-based interventions. These platforms demonstrated various functionalities, including patient education, symptom monitoring, treatment adherence, and psychosocial support. The findings indicate the potential of digital health platforms in improving breast cancer care and patients’ overall experiences. The positive impact on patient outcomes, including improved quality of life and reduced psychological distress, underscores the importance of incorporating digital health solutions into breast cancer management. Additional research is necessary to validate the effectiveness of these platforms in diverse patient populations and assess their impact on healthcare-resource utilization.

1. Introduction

Breast cancer remains the most prevalent and deadly malignancy in women worldwide with 2.26 million new cases and 684,000 deaths in 2020 [1]. The projected global burden is expected to be at 28.4 million cases in 2040 with 1 million deaths occurring annually [2,3]. This growing burden is expected to disproportionately impact less economically developed nations, necessitating the need for more affordable solutions to facilitate patient care [4].
The primary treatment options for breast cancer include surgery, chemotherapy, targeted therapy, endocrine therapy, and radiation [5]. The heterogeneity of breast cancers based on tumor burden, receptor expression, and genomic variability necessitates the coordination of multidisciplinary teams to personalize treatment to a patient’s disease. Receiving care from multiple teams in complex healthcare systems can be challenging for patients to navigate [6].
Patients undergoing treatment for breast cancer have high rates of emotional distress—studies show that at least 38% of patients suffer from depression and 32% of patients report anxiety [7,8]. While rates of emotional distress have been decreasing, likely due to improved psychosocial interventions, it is critical to find other methods of managing the psychological burden of breast cancer [9]. One potential intervention for reducing emotional distress in breast cancer patients is to augment patient education and engagement in their healthcare journey. Patients who are well informed and involved in decision making have lower depression scores and higher quality-of-life metrics [10].
Digital health is a relatively new mode of patient education and engagement that can help to disseminate evidence-based information, help guide decision making, and bolster self-efficacy [11,12]. These interventions are highly accessible to patients, as 70–80% of the world’s population owns a smartphone, and the cost of digital health platforms is often negligible to users [13]. There is a preponderance of digital health tools aimed at patient education in topics ranging from diabetes, electrophysiology, maternal health, and rheumatological disease [14,15,16,17]. The quality of digital health tools, however, is highly variable, with little guidance or oversight over these interventions [18]. The goal of this scoping review is to summarize the current literature on digital health interventions available for patients undergoing treatment for breast cancer, report their potential benefits to patient care, and identify new opportunities and applications in this space.

2. Materials and Methods

A literature search was conducted through Ovid MEDLINE, Elsevier EMBASE, and Elsevier Scopus databases on 14 March 2022, using the search strategies listed in Supplementary Tables S1–S3. Covidence was used for screening abstracts and full texts prior to data extraction. We screened a total of 5063 abstracts following PRIMSA guidelines. After a full-text review by two independent authors (EK and SK), 25 studies were included. We did not include review articles in the final extraction, as the intention was to identify primary studies, but we did review the references for additional citations, which did not yield new studies. The flow diagram of study selection is shown in Figure 1. Data were extracted from the final list of included studies. The two reviewers independently charted data and reviewed findings to come to a consensus about the study findings.
We included any studies describing the use of digital tools, specifically smartphone or web-based applications, in improving outcomes for breast cancer patients. We excluded strictly provider-facing tools, electronic healthcare messaging platforms, studies tracking website use, general health or fitness trackers, development and feasibility studies that did not report patient outcomes, or studies that did not pertain to the management of breast cancer.
Assessment of bias and quality was completed for all included studies. The Methodological Index for Nonrandomized Studies (MINORS) criteria was used for all cohort studies. MINORS consists of an 8-item checklist for non-comparative studies with each item being scored 0 (not reported), 1 (inadequately reported), or 2 (adequately reported). Overall scores range from 0–16. The Joanna Briggs Institute critical appraisal checklist was used to appraise bias for randomized control trials. The checklist contains 10 times, each scored as “Yes”, “No”, “NA” (not applicable), or “Not reported”.

3. Results

3.1. Cohort Studies

Five cohort studies were identified describing the development, feasibility, and implementation of digital health platforms in the care of breast cancer patients. The risk of bias as determined by the MINORS criteria was low, as all included studies scored at least 13 of 16 possible points (Table 1). Buscemi et al. piloted a smartphone application called My Guide, an application specifically designed for Hispanic breast cancer survivors (Table 2) [19]. The researchers found that both patient engagement and satisfaction were high after using the application. There was also a significant improvement in breast cancer knowledge after use of the application, but no difference in quality of life. Yu et al. report that, in a cohort of 4475 breast cancer patients undergoing multidisciplinary treatment (i.e., chemotherapy and radiation), the use of a smartphone application was associated with increased adherence to therapy [20]. A feasibility study by Ponder et al. investigated patient satisfaction and patient-reported outcomes after using a smartphone application called ManageMySurgery (MMS) [21]. MMS is an educational tool designed to help patients navigate the perioperative environment. The majority of the 33 study participants undergoing either a mastectomy or lumpectomy found the application useful, and there was a significant decrease in anxiety and depression after application use. Lin et al. studied a decision support aid for 11 women considering breast reconstruction surgery in Taiwan [22]. The application, called Pink Journey, provided information on various treatment options, encouraged patients to explore their values, and presented the options within the context of patients’ concerns. The app was found to help reduce decisional conflict, and the majority of study participants found the application useful. Lastly, Wyatt et al. studied the use of a web-based decision aid in 225 women newly diagnosed with breast cancer; the majority of patients found the application helpful and easy to use, and there was an increase in decision-making confidence, particularly among patients with low baseline confidence [23].

3.2. Randomized Control Trials (RCT)

3.2.1. Web-Based Platforms

There were six RCTs on web-based education tools. The risk of bias of web-based RCTs was low as determined by the Joanna Briggs Institute critical appraisal tool (Table 3). The item “Were participants blind to treatment assignment?” was deemed “NA” (not applicable) for all studies as blinding participants to the use of a digital health intervention is unfeasible. Admiraal et al. described the use of a web-based psychoeducational program, ENCOURAGE, in patients diagnosed with primary breast cancer and undergoing chemotherapy (Table 4) [24]. A total of 138 patients were enrolled in the RCT. While there was no difference between the subjects who used the web-based platform and those who did not, all groups reported an improvement in optimism, control, distress, and quality of life. Interestingly, patients who were clinically distressed at baseline had a greater improvement in optimism and control in the intervention group. Three studies described the use of the Comprehensive Health Enhancement Support System (CHESS), a web-based resource that integrates information, support, decision, and analysis tools for women recently diagnosed with breast cancer. An RCT of 257 patients found that patients who used CHESS had better social support, quality of life, and healthcare competence than patients with and without access to the internet [25]. In another RCT of 60 patients, CHESS increased participation in care among minority women and women with a lower formal education status, while also improving the wellbeing of women without private health insurance [26]. Kim et al. studied the use of CHESS along with a trained mentor who provided additional cancer information [27]. CHESS alone improved information competence, but the addition of a mentor improved emotional–social competence and emotional functioning. Ventura et al. conducted an RCT of 226 women with early-stage breast cancer scheduled for surgery and found no significant difference in anxiety and depression levels among patients who used a computer-based educational program [28]. On the contrary, Korkmaz et al. report the use of a web-based education platform for patients undergoing breast surgery with axillary lymph node dissection led to decreased anxiety and improved quality of life [29].
Five studies described the use of a web-based decision aid (Table 4). Manne et al. found that the use of B-Sure, an interactive decision aid that provided information on contralateral prophylactic mastectomy (CPM) and documented patient experiences, was associated with greater knowledge and increased clarity [30]. There were no differences in self-efficacy, perceived risk, worry, or motivations for proceeding with surgery. Another web-based decision aid regarding breast reconstruction after mastectomy, named BRAID, was studied in 55 participants. While knowledge level, satisfaction, preparation, and decisional conflict improved in all patients enrolled, there was no statistical difference between the control and BRAID groups [31]. Politi et al. investigated the use of BREASTChoice, also a web-based decision aid for breast reconstruction after mastectomy [32]. A total of 120 patients participated in the study; the intervention group had greater knowledge and confidence about reconstruction, but there were no differences in decisional conflict or quality of life between groups. Two studies explored BRECONDA, a web-based decision aid intended to help women with a hereditary risk of breast cancer make decisions regarding risk-reducing mastectomy. Both RCTs reported that the use of BRECONDA was associated with a decrease in decisional conflict, an increase in knowledge, and satisfaction with the information [33,34].

3.2.2. Smartphone-Application-Based Platforms

There were a small number of studies that focused on symptom monitoring and treatment adherence. The risk of bias of smartphone-application-based RCTs was low, as determined by the Joanna Briggs Institute critical appraisal tool (Table 5). The item “Were participants blind to treatment assignment?” was deemed “NA” (Not applicable) for all studies, as blinding participants to the use of a digital health intervention is unfeasible Zhu et al. studied a smartphone application developed in China, named Breast Cancer e-Support (BCS) (Table 6) [35]. BCS improved self-efficacy, symptoms, and quality of life in women with breast cancer three months after starting chemotherapy. A study based in Sweden on women undergoing neoadjuvant chemotherapy found that the use of a smartphone application, called Interkator, reduced physical symptoms and symptom distress, and improved emotional function. Interkator allowed patients to log their symptoms, communicate with healthcare professionals, and access educational resources about chemotherapy side effects [36]. A third study investigating a symptom-monitoring smartphone application, Msymptom, reported the application was associated with lower physical symptom scores and nausea/vomiting scores. Interestingly, patients in the control group had significantly higher sexual function and pleasure [37]. Lastly, a group based in Taiwan conducted an RCT on 112 women with recently diagnosed nonmetastatic breast cancer, randomized to either the breast cancer self-management support (BCSMS) mHealth mobile application or usual care. The mobile application group had higher quality-of-life scores three months after being introduced to the intervention [38].
Baik et al. completed an RCT among 80 Latina breast cancer survivors [39]. Participants were randomized between two applications: My Guide, aimed to improve quality of life and reduce symptoms, and My Health, aimed to promote healthy lifestyle behaviors. There was an improvement in overall wellbeing for low application users (less than 60 min per week) of My Guide, but increased social wellbeing among high application users of My Health. Yanez et al. also described the use of My Guide and My Health, but specifically studied symptom burden and quality of life [40]. Use of both My Guide and My Health were associated with decreased symptom burden and improved quality of life. Oswald et al. conducted a secondary analysis comparing My Guide and My Health in 78 Latina breast cancer survivors; patients using My Guide had a greater improvement in knowledge and a reduction in self-blame compared to those who used My Health [41].
Fang et al. conducted an RCT using the smartphone application Pink Journey, which was previously discussed [42]. Ninety-six women considering breast reconstruction were enrolled in the RCT. There were no differences in primary outcomes, including decisional conflict, regret, anxiety, or depression; however, there was a decrease in body-image distress for the intervention group compared to the controls. Foley et al. developed a smartphone application in Ireland to help deliver information to breast cancer patients in the perioperative period [43]. A total of 39 patients were enrolled in the RCT, which found that anxiety and depression were lower in the control group than in the group exposed to the application.
Table 5. Risk of bias and quality assessment for smartphone-application-based randomized control trials.
Table 5. Risk of bias and quality assessment for smartphone-application-based randomized control trials.
Joanna Briggs Institute Critical AppraisalBaik 2020 [39]Fang 2021 [42]Fjell 2020 [36]Foley 2016 [43]Hou 2020 [38]Oswald 2021 [41]Ozturk 2021 [37]Yanez 2019 [40]Zhu 2018 [35]
Was true randomization used for assignment of participants to treatment groups?YesYesYesYesYesYesYesYesYes
Was allocation to treatment groups concealed?YesYesYesYesYesYesYesYesYes
Were treatment groups similar at the baseline?YesYesYesYesYesYesYesYesYes
Were participants blind to treatment assignment?NANANANANANANANANA
Were those delivering treatment blind to treatment assignment?Not reportedNoNoNot reportedNoNot reportedNot reportedNot reportedYes
Were outcomes assessors blind to treatment assignment?YesYesYesYesYesYesYesYesYes
Were treatment groups treated identically other than the intervention of interest?YesYesYesYesYesYesYesYesYes
Was follow up complete and if not, were differences between groups in terms of their follow up adequately described and analyzed?YesYesYesYesYesYesYesYesYes
Were participants analyzed in the groups to which they were randomized?YesYesYesYesYesYesYesYesYes
Were outcomes measured in the same way for treatment groups?YesYesYesYesYesYesYesYesYes
Were outcomes measured in a reliable way?YesYesYesYesYesYesYesYesYes
Was appropriate statistical analysis used?YesYesYesYesYesYesYesYesYes
Was the trial design appropriate, and any deviations from the standard RCT design accounted for in the conduct and analysis of the trial?YesYesYesYesYesYesYesYesYes
Table 6. Randomized control trials using smartphone-application-based platforms.
Table 6. Randomized control trials using smartphone-application-based platforms.
PaperTitleInterventionCountryNumber of SubjectsKey Outcomes
Baik 2020 [39]Patterns of use of smartphone-based interventions among latina breast cancer survivors: Secondary analysis of a pilot randomized controlled trialMy Guide and My Health appsUSA80
  • MyGuide app was associated with improved well-being in participants with low use
  • MyHealth app was associated with increased social well-being in participants with high use
Fang 2021 [42]Long-Term Effectiveness of a Decision Support App (Pink Journey) for Women Considering Breast Reconstruction Surgery: Pilot Randomized Controlled TrialPink Journey appTaiwan96
  • Application resulted in decreased body image distress compared to control
Fjell 2020 [36]Reduced symptom burden with the support of an interactive app during neoadjuvant chemotherapy for breast cancer e A randomized controlled trialInteraktor appSweden150
  • Application reduced symptom burden, symptom distress, and improved emotional function
Foley 2016 [43]PATI: Patient accessed tailored information: A pilot study to evaluate the effect on preoperative breast cancer patients of information delivered via a mobile applicationunnamed appIreland39
  • Control was associated with lower anxiety and depression than the application
Hou 2020 [38]Quality of Life of Women After a First Diagnosis of Breast Cancer Using a Self-Management Support mHealth App in Taiwan: Randomized Controlled TrialBCSMS appTaiwan112
  • Application increased QoL scores
Oswald 2021 [41]Effects of smartphone interventions on cancer knowledge and coping among Latina breast cancer survivors: Secondary analysis of a pilot randomized controlled trialMyGuide and MyHealth appsUSA78
  • MyGuide app resulted in greater improvements to knowledge and reduction in self-blame than MyHealth app
Ozturk 2021 [37]The Effect of the Mobile Application-Based Symptom Monitoring Process on the Symptom Control and Quality of Life in Breast Cancer PatientsMsemptom appTurkey57
  • Application associated with lower physical symptom scores and nausea/vomiting scores
Yanez 2019 [40]Brief culturally informed smartphone interventions decrease breast cancer symptom burden among Latina breast cancer survivorsMyGuide and MyHealth appsUSA80
  • MyGuide and MyHealth apps were associated with decreased symptom burden and improved QoL
Zhu 2018 [35]Mobile Breast Cancer e-Support Program for Chinese Women With Breast Cancer Undergoing Chemotherapy (Part 2): Multicenter Randomized Controlled TrialBCS appChina114
  • Application improved self-efficacy, symptom burden, and QoL

4. Discussion

This review identified numerous studies discussing many of the digital health platforms available for patients undergoing breast cancer treatment. The majority of studies suggest that digital health platforms can enhance the management of breast cancer and improve the quality of life for patients. Digital health platforms have been shown to improve patient outcomes and quality of life by increasing patient education, encouraging patient involvement, and reducing illness anxiety [44,45]. Given the prevalence of breast cancer worldwide, digital health platforms have the potential to make a positive impact on a significant number of patients.
Importantly, a handful of studies identified in this review suggest that digital health platforms may be most useful in patients who are more distressed at baseline, are of minority status, have lower levels of education, or lack insurance [24,26]. Social determinants of health are known to be associated with morbidity and mortality in breast cancer patients. Goel et al. report that a lower neighborhood socioeconomic status is associated with a shorter breast cancer-specific survival, even after controlling for individual sociodemographic, access to care, comorbidities, tumor characteristics, and the National Comprehensive Cancer Network (NCCN) treatment paradigms [46]. Furthermore, non-Hispanic Black patients and Hispanic patients are more likely to undergo mastectomies and receive delayed treatment than non-Hispanic White patients [47]. Digital health platforms can provide important education to patients who may have difficulty accessing care otherwise.
Studies identified in this review also suggest digital health platforms improve the overall symptom burden from breast cancer treatment. One study specifically found that treatment adherence was improved by patients using their digital health platform, an association likely mediated by a decrease in symptom burden [20]. The oncologic literature suggests patients who routinely report symptoms through digital health platforms have a lower symptom burden due to the increased awareness of serious adverse reactions and earlier detection by patients and providers [48]. Furthermore, by providing education to patients, expectations are more easily managed, and there is a reduction in patient visits to emergency departments [49].
While the majority of studies ascertained in this review revolve around adjuvant treatment for breast cancer, a handful specifically targeted patients in the perioperative period. Digital health platforms that were designed as decision-making aids were overall shown to improve education and reduce decisional conflicts among patients [23,30,32,33,34]. However, one study reported that the use of a digital health platform as an educational tool for patients prior to surgery increased anxiety and depression scores [43]. This could potentially be explained by the idea of information overload. A surplus of information is seen as overwhelming to patients, and conflicting information regarding treatment options leads to difficulty in decision making [50]. Nonetheless, material on digital health platforms should be curated by expert physicians to ensure accurate and relevant information, and providers should be prepared to discuss information acquired through digital health platforms and the broader web with their patients.
The efficacy of digital health platforms in patients from resource-limited backgrounds suggests that this intervention will be invaluable in addressing the disproportionate increase in breast cancer cases that resource-limited nations are facing [4]. Digital health is highly accessible given the ubiquity of smartphones as well as the minimal cost of digital health applications to users [13]. Expanding access to digital health globally requires further consideration than simply increasing patient access to technology. Support will be required from governmental and private industry groups to help develop reliable access to electricity and the Internet [51]. Additionally, technological illiteracy poses one of the largest barriers preventing digital health uptake in low-income countries [52]. Institutional-level interventions will be needed to address these barriers to aid in the growth of digital health. Finally, it is crucial that digital health platforms integrate culturally relevant information for the populations they seek to serve [53]. Considering local values and customs in the creation of digital health interventions will help to better serve patients globally.
The primary limitations of this study are common to scoping reviews. A meta-analysis or study appraisal was not conducted, given the differences in intervention type and patient populations across the studies identified. Nevertheless, this scoping review was conducted through a rigorous and standardized process. A limitation to investigating the efficacy of digital health platforms is the lack of a formal regulatory process overseeing the development of such tools. However, the majority of studies had institutional protocols for the content validity of the digital health platform. Lastly, it is difficult to extrapolate the results of each study to all patients undergoing breast cancer treatment, given the differences in demographics and stage of cancer diagnosis between the study populations.

5. Conclusions

We present a scoping review analyzing the landscape of digital health platforms for patients undergoing breast cancer treatment. There are a select number of web-based and smartphone digital health tools that have been developed globally to assist patients with breast cancer. Overall, digital health platforms aid in patient education, are associated with higher quality of life, lower levels of anxiety and depression, a decrease in overall symptom burden, and can assist in important decision-making regarding treatment options. Further research should validate the use of digital health platforms in a broader patient population and investigate the impact of digital health on healthcare-resource utilization in breast cancer management.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm13071937/s1, Table S1: Search Strategy Report from Ovid Medline. Table S2: Search Strategy Report from Embase. Table S3: Search Strategy Report from Scopus. Table S4: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. Reference [54] are cited in the supplementary materials.

Author Contributions

Conceptualization, S.P.L., E.S.H. and J.K.P.; methodology, S.K.; study acquisition, S.K.; study review, E.P.K. and S.A.K.; writing—original draft preparation, E.P.K., S.A.K. and K.A.W.; writing—review and editing, E.P.K., S.A.K. and K.A.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article or Supplementary Materials.

Conflicts of Interest

S.P.L has equity in Higgs Boson Health. The other authors declare no conflicts of interest.

References

  1. Lukasiewicz, S.; Czeczelewski, M.; Forma, A.; Baj, J.; Sitarz, R.; Stanislawek, A. Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review. Cancers 2021, 13, 4287. [Google Scholar] [CrossRef] [PubMed]
  2. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  3. Arnold, M.; Morgan, E.; Rumgay, H.; Mafra, A.; Singh, D.; Laversanne, M.; Vignat, J.; Gralow, J.R.; Cardoso, F.; Siesling, S.; et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast 2022, 66, 15–23. [Google Scholar] [CrossRef] [PubMed]
  4. Li, N.; Deng, Y.; Zhou, L.; Tian, T.; Yang, S.; Wu, Y.; Zheng, Y.; Zhai, Z.; Hao, Q.; Song, D.; et al. Global burden of breast cancer and attributable risk factors in 195 countries and territories, from 1990 to 2017: Results from the Global Burden of Disease Study 2017. J. Hematol. Oncol. 2019, 12, 140. [Google Scholar] [CrossRef] [PubMed]
  5. McDonald, E.S.; Clark, A.S.; Tchou, J.; Zhang, P.; Freedman, G.M. Clinical Diagnosis and Management of Breast Cancer. J. Nucl. Med. 2016, 57 (Suppl. S1), 9S–16S. [Google Scholar] [CrossRef] [PubMed]
  6. Harbeck, N.; Gnant, M. Breast cancer. Lancet 2017, 389, 1134–1150. [Google Scholar] [CrossRef] [PubMed]
  7. Tsaras, K.; Papathanasiou, I.V.; Mitsi, D.; Veneti, A.; Kelesi, M.; Zyga, S.; Fradelos, E.C. Assessment of Depression and Anxiety in Breast Cancer Patients: Prevalence and Associated Factors. Asian Pac. J. Cancer Prev. 2018, 19, 1661–1669. [Google Scholar] [PubMed]
  8. Park, E.M.; Gelber, S.; Rosenberg, S.M.; Seah, D.S.E.; Schapira, L.; Come, S.E.; Partridge, A.H. Anxiety and Depression in Young Women with Metastatic Breast Cancer: A Cross-Sectional Study. Psychosomatics 2018, 59, 251–258. [Google Scholar] [CrossRef]
  9. Mokhtari-Hessari, P.; Montazeri, A. Health-related quality of life in breast cancer patients: Review of reviews from 2008 to 2018. Health Qual. Life Outcomes 2020, 18, 338. [Google Scholar] [CrossRef]
  10. Vogel, B.A.; Leonhart, R.; Helmes, A.W. Communication matters: The impact of communication and participation in decision making on breast cancer patients’ depression and quality of life. Patient Educ. Couns. 2009, 77, 391–397. [Google Scholar] [CrossRef]
  11. Lopez-Olivo, M.A.; Suarez-Almazor, M.E. Digital Patient Education and Decision Aids. Rheum. Dis. Clin. N. Am. 2019, 45, 245–256. [Google Scholar] [CrossRef]
  12. Conard, S. Best practices in digital health literacy. Int. J. Cardiol. 2019, 292, 277–279. [Google Scholar] [CrossRef] [PubMed]
  13. Kao, C.K.; Liebovitz, D.M. Consumer Mobile Health Apps: Current State, Barriers, and Future Directions. PM R 2017, 9, S106–S115. [Google Scholar] [CrossRef]
  14. Pal, K.; Dack, C.; Ross, J.; Michie, S.; May, C.; Stevenson, F.; Farmer, A.; Yardley, L.; Barnard, M.; Murray, E. Digital Health Interventions for Adults with Type 2 Diabetes: Qualitative Study of Patient Perspectives on Diabetes Self-Management Education and Support. J. Med. Internet Res. 2018, 20, e40. [Google Scholar] [CrossRef]
  15. Schnitman, G.; Wang, T.; Kundu, S.; Turkdogan, S.; Gotlieb, R.; How, J.; Gotlieb, W. The role of digital patient education in maternal health: A systematic review. Patient Educ. Couns. 2022, 105, 586–593. [Google Scholar] [CrossRef]
  16. Mariani, M.V.; Pierucci, N.; Forleo, G.B.; Schiavone, M.; Bernardini, A.; Gasperetti, A.; Mitacchione, G.; Mei, M.; Giunta, G.; Piro, A.; et al. The Feasibility, Effectiveness and Acceptance of Virtual Visits as Compared to in-Person Visits among Clinical Electrophysiology Patients during the COVID-19 Pandemic. J. Clin. Med. 2023, 12, 620. [Google Scholar] [CrossRef] [PubMed]
  17. Kataria, S.; Ravindran, V. Digital health: A new dimension in rheumatology patient care. Rheumatol. Int. 2018, 38, 1949–1957. [Google Scholar] [CrossRef] [PubMed]
  18. Jandoo, T. Who guidance for digital health: What it means for researchers. Digit. Health 2020, 6, 2055207619898984. [Google Scholar] [CrossRef]
  19. Buscemi, J.; Buitrago, D.; Iacobelli, F.; Penedo, F.; Maciel, C.; Guitleman, J.; Balakrishnan, A.; Corden, M.; Adler, R.F.; Bouchard, L.C.; et al. Feasibility of a Smartphone-based pilot intervention for Hispanic breast cancer survivors: A Brief Report. Transl. Behav. Med. 2019, 9, 638–645. [Google Scholar] [CrossRef]
  20. Yu, J.; Wu, J.; Huang, O.; Chen, X.; Shen, K. A Smartphone-Based App to Improve Adjuvant Treatment Adherence to Multidisciplinary Decisions in Patients with Early-Stage Breast Cancer: Observational Study. J. Med. Internet Res. 2021, 23, e27576. [Google Scholar] [CrossRef]
  21. Ponder, M.; Venkatraman, V.; Charalambous, L.; Ansah-Yeboah, A.A.; Adil, S.M.; Antezana, L.A.; Dharmapurikar, R.; Gellad, Z.F.; Lad, S.P.; Hwang, E.S.; et al. Mobile Health Application for Patients Undergoing Breast Cancer Surgery: Feasibility Study. JCO Oncol. Pract. 2021, 17, e1344–e1353. [Google Scholar] [CrossRef] [PubMed]
  22. Lin, P.J.; Fang, S.Y.; Kuo, Y.L. Development and Usability Testing of a Decision Support App for Women Considering Breast Reconstruction Surgery. J. Cancer Educ. 2021, 36, 160–167. [Google Scholar] [CrossRef] [PubMed]
  23. Wyatt, K.D.; Jenkins, S.M.; Plevak, M.F.; Venegas Pont, M.R.; Pruthi, S. A personalized, web-based breast cancer decision making application: A pre-post survey. BMC Med. Inform. Decis. Mak. 2019, 19, 196. [Google Scholar] [CrossRef] [PubMed]
  24. Admiraal, J.M.; van der Velden, A.W.G.; Geerling, J.I.; Burgerhof, J.G.M.; Bouma, G.; Walenkamp, A.M.E.; de Vries, E.G.E.; Schroder, C.P.; Reyners, A.K.L. Web-Based Tailored Psychoeducation for Breast Cancer Patients at the Onset of the Survivorship Phase: A Multicenter Randomized Controlled Trial. J. Pain. Symptom Manag. 2017, 54, 466–475. [Google Scholar] [CrossRef] [PubMed]
  25. Gustafson, D.H.; Hawkins, R.; McTavish, F.; Pingree, S.; Chen, W.C.; Volrathongchai, K.; Stengle, W.; Stewart, J.A.; Serlin, R.C. Internet-Based Interactive Support for Cancer Patients: Are Integrated Systems Better? J. Commun. 2008, 58, 238–257. [Google Scholar] [CrossRef]
  26. Gustafson, D.H.; Hawkins, R.; Pingree, S.; McTavish, F.; Arora, N.K.; Mendenhall, J.; Cella, D.F.; Serlin, R.C.; Apantaku, F.M.; Stewart, J.; et al. Effect of computer support on younger women with breast cancer. J. Gen. Intern. Med. 2001, 16, 435–445. [Google Scholar] [CrossRef]
  27. Kim, S.C.; Hawkins, R.P.; Shah, D.V.; Gustafson, D.H.; Baker, T.B. Understanding how e-health interventions meet psychosocial needs of breast cancer patients: The pathways of influence on quality of life and cancer concerns. Psychooncology 2020, 29, 1704–1712. [Google Scholar] [CrossRef]
  28. Ventura, F.; Sawatzky, R.; Ohlen, J.; Karlsson, P.; Koinberg, I. Challenges of evaluating a computer-based educational programme for women diagnosed with early-stage breast cancer: A randomised controlled trial. Eur. J. Cancer Care 2017, 26, e12534. [Google Scholar] [CrossRef]
  29. Korkmaz, S.; Iyigun, E.; Tastan, S. An Evaluation of the Influence of Web-Based Patient Education on the Anxiety and Life Quality of Patients Who Have Undergone Mammaplasty: A Randomized Controlled Study. J. Cancer Educ. 2020, 35, 912–922. [Google Scholar] [CrossRef]
  30. Manne, S.L.; Smith, B.L.; Frederick, S.; Mitarotondo, A.; Kashy, D.A.; Kirstein, L.J. B-sure: A randomized pilot trial of an interactive web-based decision support aid versus usual care in average-risk breast cancer patients considering contralateral prophylactic mastectomy. Transl. Behav. Med. 2020, 10, 355–363. [Google Scholar] [CrossRef]
  31. Manne, S.L.; Topham, N.; D’Agostino, T.A.; Myers Virtue, S.; Kirstein, L.; Brill, K.; Manning, C.; Grana, G.; Schwartz, M.D.; Ohman-Strickland, P. Acceptability and pilot efficacy trial of a web-based breast reconstruction decision support aid for women considering mastectomy. Psychooncology 2016, 25, 1424–1433. [Google Scholar] [CrossRef] [PubMed]
  32. Politi, M.C.; Lee, C.N.; Philpott-Streiff, S.E.; Foraker, R.E.; Olsen, M.A.; Merrill, C.; Tao, Y.; Myckatyn, T.M. A Randomized Controlled Trial Evaluating the BREASTChoice Tool for Personalized Decision Support about Breast Reconstruction after mastectomy. Ann. Surg. 2020, 271, 230–237. [Google Scholar] [CrossRef]
  33. Sherman, K.A.; Kilby, C.J.; Shaw, L.K.; Winch, C.; Kirk, J.; Tucker, K.; Elder, E. Facilitating decision-making in women undergoing genetic testing for hereditary breast cancer: BRECONDA randomized controlled trial results. Breast 2017, 36, 79–85. [Google Scholar] [CrossRef]
  34. Sherman, K.A.; Shaw, L.E.; Winch, C.J.; Harcourt, D.; Boyages, J.; Cameron, L.D.; Brown, P.; Lam, T.; Elder, E.; French, J.; et al. Reducing Decisional Conflict and Enhancing Satisfaction with Information among Women Considering Breast Reconstruction Following Mastectomy: Results from the Breconda Randomized Controlled Trial. Plast. Reconstr. Surg. 2016, 138, 592e–602e. [Google Scholar] [CrossRef] [PubMed]
  35. Zhu, J.; Ebert, L.; Guo, D.; Yang, S.; Han, Q.; Chan, S.W. Mobile Breast Cancer e-Support Program for Chinese Women with Breast Cancer Undergoing Chemotherapy (Part 1): Qualitative Study of Women’s Perceptions. JMIR Mhealth Uhealth 2018, 6, e85. [Google Scholar] [CrossRef] [PubMed]
  36. Fjell, M.; Langius-Eklof, A.; Nilsson, M.; Wengstrom, Y.; Sundberg, K. Reduced symptom burden with the support of an interactive app during neoadjuvant chemotherapy for breast cancer—A randomized controlled trial. Breast 2020, 51, 85–93. [Google Scholar] [CrossRef]
  37. Ozturk, E.S.; Kutluturkan, S. The Effect of the Mobile Application-Based Symptom Monitoring Process on the Symptom Control and Quality of Life in Breast Cancer Patients. Semin. Oncol. Nurs. 2021, 37, 151161. [Google Scholar] [CrossRef]
  38. Hou, I.C.; Lin, H.Y.; Shen, S.H.; Chang, K.J.; Tai, H.C.; Tsai, A.J.; Dykes, P.C. Quality of Life of Women after a First Diagnosis of Breast Cancer Using a Self-Management Support mHealth App in Taiwan: Randomized Controlled Trial. JMIR Mhealth Uhealth 2020, 8, e17084. [Google Scholar] [CrossRef]
  39. Baik, S.H.; Oswald, L.B.; Buscemi, J.; Buitrago, D.; Iacobelli, F.; Perez-Tamayo, A.; Guitelman, J.; Penedo, F.J.; Yanez, B. Patterns of Use of Smartphone-Based Interventions among Latina Breast Cancer Survivors: Secondary Analysis of a Pilot Randomized Controlled Trial. JMIR Cancer 2020, 6, e17538. [Google Scholar] [CrossRef]
  40. Yanez, B.; Oswald, L.B.; Baik, S.H.; Buitrago, D.; Iacobelli, F.; Perez-Tamayo, A.; Guitelman, J.; Penedo, F.J.; Buscemi, J. Brief culturally informed smartphone interventions decrease breast cancer symptom burden among Latina breast cancer survivors. Psychooncology 2020, 29, 195–203. [Google Scholar] [CrossRef]
  41. Oswald, L.B.; Baik, S.H.; Buscemi, J.; Buitrago, D.; Iacobelli, F.; Guitelman, J.; Penedo, F.J.; Yanez, B. Effects of smartphone interventions on cancer knowledge and coping among Latina breast cancer survivors: Secondary analysis of a pilot randomized controlled trial. J. Psychosoc. Oncol. 2022, 40, 695–707. [Google Scholar] [CrossRef] [PubMed]
  42. Fang, S.Y.; Lin, P.J.; Kuo, Y.L. Long-Term Effectiveness of a Decision Support App (Pink Journey) for Women Considering Breast Reconstruction Surgery: Pilot Randomized Controlled Trial. JMIR Mhealth Uhealth 2021, 9, e31092. [Google Scholar] [CrossRef] [PubMed]
  43. Foley, N.M.; O’Connell, E.P.; Lehane, E.A.; Livingstone, V.; Maher, B.; Kaimkhani, S.; Cil, T.; Relihan, N.; Bennett, M.W.; Redmond, H.P.; et al. Pati: Patient accessed tailored information: A pilot study to evaluate the effect on preoperative breast cancer patients of information delivered via a mobile application. Breast 2016, 30, 54–58. [Google Scholar] [CrossRef] [PubMed]
  44. Wilmer, M.T.; Anderson, K.; Reynolds, M. Correlates of Quality of Life in Anxiety Disorders: Review of Recent Research. Curr. Psychiatry Rep. 2021, 23, 77. [Google Scholar] [CrossRef] [PubMed]
  45. Lee, J.A.; Choi, M.; Lee, S.A.; Jiang, N. Effective behavioral intervention strategies using mobile health applications for chronic disease management: A systematic review. BMC Med. Inform. Decis. Mak. 2018, 18, 12. [Google Scholar] [CrossRef] [PubMed]
  46. Goel, N.; Hernandez, A.; Thompson, C.; Choi, S.; Westrick, A.; Stoler, J.; Antoni, M.H.; Rojas, K.; Kesmodel, S.; Figueroa, M.E.; et al. Neighborhood Disadvantage and Breast Cancer-Specific Survival. JAMA Netw. Open 2023, 6, e238908. [Google Scholar] [CrossRef] [PubMed]
  47. Fwelo, P.; Nwosu, K.O.S.; Adekunle, T.E.; Afolayan, O.; Ahaiwe, O.; Ojaruega, A.A.; Nagesh, V.K.; Bangolo, A. Racial/ethnic and socioeconomic differences in breast cancer surgery performed and delayed treatment: Mediating impact on mortality. Breast Cancer Res. Treat. 2023, 199, 511–531. [Google Scholar] [CrossRef] [PubMed]
  48. Tolstrup, L.K.; Pappot, H.; Bastholt, L.; Moller, S.; Dieperink, K.B. Impact of patient-reported outcomes on symptom monitoring during treatment with checkpoint inhibitors: Health-related quality of life among melanoma patients in a randomized controlled trial. J. Patient Rep. Outcomes 2022, 6, 8. [Google Scholar] [CrossRef] [PubMed]
  49. Venkatraman, V.; Kirsch, E.P.; Luo, E.; Kunte, S.; Ponder, M.; Gellad, Z.F.; Liu, B.; Lee, H.J.; Jung, S.H.; Haglund, M.M.; et al. Outcomes with a Mobile Digital Health Platform for Patients Undergoing Spine Surgery: Retrospective Analysis. JMIR Perioper. Med. 2022, 5, e38690. [Google Scholar] [CrossRef]
  50. Klerings, I.; Weinhandl, A.S.; Thaler, K.J. Information overload in healthcare: Too much of a good thing? Z. Evid. Fortbild. Qual. Gesundhwes. 2015, 109, 285–290. [Google Scholar] [CrossRef]
  51. Kabore, S.S.; Ngangue, P.; Soubeiga, D.; Barro, A.; Pilabre, A.H.; Bationo, N.; Pafadnam, Y.; Drabo, K.M.; Hien, H.; Savadogo, G.B.L. Barriers and facilitators for the sustainability of digital health interventions in low and middle-income countries: A systematic review. Front. Digit. Health 2022, 4, 1014375. [Google Scholar] [CrossRef] [PubMed]
  52. Xiong, S.; Lu, H.; Peoples, N.; Duman, E.K.; Najarro, A.; Ni, Z.; Gong, E.; Yin, R.; Ostbye, T.; Palileo-Villanueva, L.M.; et al. Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries. NPJ Digit. Med. 2023, 6, 12. [Google Scholar] [CrossRef] [PubMed]
  53. Labrique, A.B.; Wadhwani, C.; Williams, K.A.; Lamptey, P.; Hesp, C.; Luk, R.; Aerts, A. Best practices in scaling digital health in low and middle income countries. Global. Health 2018, 14, 103. [Google Scholar] [CrossRef] [PubMed]
  54. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Flow diagram of study selection.
Figure 1. Flow diagram of study selection.
Jcm 13 01937 g001
Table 1. Risk of bias and quality assessment for cohort studies.
Table 1. Risk of bias and quality assessment for cohort studies.
MINORSBuscemi 2019 [19]Lin 2021 [22]Ponder 2021 [21]Wyatt 2017 [23]Yu 2021 [20]
A clearly stated aim22222
Inclusion of consecutive patients22222
Prospective collection of data22222
Endpoints appropriate to the aim of the study22222
Unbiased assessment of study endpoints22222
Follow-up period appropriate to the aim of the study21222
Loss to follow up less than 5%02200
Prospective calculation of study size11111
Total1314151313
Table 2. Cohort studies for digital health interventions.
Table 2. Cohort studies for digital health interventions.
PaperTitleInterventionCountryNumber of SubjectsKey Outcomes
Buscemi 2019 [19]Feasibility of a Smartphone-based pilot intervention for Hispanic breast cancer survivors: a brief reportMy Guide appUSA25
  • Application had high patient engagement and satisfaction
  • Application significantly improved breast cancer knowledge
Lin 2021 [22]Development and Usability Testing of a Decision Support App for Women Considering Breast Reconstruction SurgeryPink Journey appTaiwan11
  • Majority found the application useful
  • Application reduced decision conflict
Ponder 2021 [21]Mobile Health Application for Patients Undergoing Breast Cancer Surgery: Feasibility StudyMMS appUSA33
  • Majority found the application useful
  • Application associated with significant decrease in anxiety and depression
Wyatt 2017 [23]A personalized, web-based breast cancer decision making application: a pre-post surveyunnamed appUSA255
  • Majority found the application helpful and easy to use
  • Application associated with an increase in decision making confidence
Yu 2021 [20]A Smartphone-Based App to Improve Adjuvant Treatment Adherence to Multidisciplinary Decisions in Patients With Early-Stage Breast Cancer: Observational Studyfull course management system appChina4475
  • Application associated with increased therapy adherence
Table 3. Risk of bias and quality assessment for web-based randomized control trials.
Table 3. Risk of bias and quality assessment for web-based randomized control trials.
Joanna Briggs Institute Critical AppraisalAdmiraal 2017 [24]Gustafson 2008 [25]Gustafson 2001 [26]Kim 2020 [27]Korkmanz 2020 [29]Manne 2020 [30]Manne 2016 [31]Politi 2020 [32]Sherman 2017 [33]Sherman 2016 [34]Ventura 2016 [28]
Was true randomization used for assignment of participants to treatment groups?YesYesYesYesYesYesYesYesYesYesYes
Was allocation to treatment groups concealed?YesYesYesYesYesYesYesYesYesYesYes
Were treatment groups similar at the baseline?YesNot reportedYesYesYesNot reportedYesYesYesYesYes
Were participants blind to treatment assignment?NANANANANANANANANANANA
Were those delivering treatment blind to treatment assignment?NoNot reportedNot reportedNot reportedNot reportedNoNot reportedNot reportedYesYesNot reported
Were outcomes assessors blind to treatment assignment?YesYesYesYesYesYesYesYesYesYesYes
Were treatment groups treated identically other than the intervention of interest?NoYesYesYesYesYesYesYesYesYesYes
Was follow up complete and if not, were differences between groups in terms of their follow up adequately described and analyzed?YesYesYesNot reportedYesYesYesYesYesYesYes
Were participants analyzed in the groups to which they were randomized?YesYesYesYesYesYesYesYesYesYesYes
Were outcomes measured in the same way for treatment groups?YesYesYesYesYesYesYesYesYesYesYes
Were outcomes measured in a reliable way?YesYesYesYesYesYesYesYesYesYesYes
Was appropriate statistical analysis used?YesYesYesYesYesYesYesYesYesYesYes
Was the trial design appropriate, and any deviations from the standard RCT design accounted for in the conduct and analysis of the trial?YesYesYesYesYesYesYesYesYesYesYes
Table 4. Randomized control trials using web-based interventions.
Table 4. Randomized control trials using web-based interventions.
PaperTitleInterventionCountryNumber of SubjectsKey Outcomes
Admiraal 2017 [24]Web-Based Tailored Psychoeducation for Breast Cancer Patients at the Onset of the Survivorship Phase: A Multicenter Randomized Controlled TrialENCOURAGE websiteNetherlands138
  • No significant difference between website and control on optimism, control, distress, or QoL
  • Website led to greater improvements in optimism in participants who were clinically distressed at baseline
Gustafson 2008 [25]Internet-Based Interactive Support for Cancer Patients: Are Integrated Systems Better?CHESS websiteUSA257
  • Website group had better social support, QoL, health-care competence
Gustafson 2001 [26]Effect of Computer Support on Younger Women with Breast CancerCHESS websiteUSA60
  • Website led to increased participation among minority women and women with low educations status
  • Website improved wellbeing for women without private insurance
Kim 2020 [27]Understanding how e-health interventions meet psychosocial needs of breast cancer patients: The pathways of influence on quality of life and cancer concernsCHESS websiteUSA326
  • Website alone improved information competence
  • Website with trained cancer information mentor improved emotional-social competence and emotional functioning
Korkmanz 2020 [29]An Evaluation of the Influence of Web-Based Patient Education on the Anxiety and Life Quality of Patients Who Have Undergone Mammaplasty: a Randomized Controlled StudyBilinclihasta websiteTurkey75
  • Website resulted in decreased anxiety and increased QoL compared to control
Manne 2019 [30]B-Sure: a randomized pilot trial of an interactive web-based decision support aid versus usual care in average-risk breast cancer patients considering contralateral prophylactic mastectomyB-sure websiteUSA93
  • Decision aid resulted in greater knowledge and increased clarity
Manne 2015 [31]Acceptability and pilot efficacy trial of a web-based breast reconstruction decision support aid for women considering mastectomyBRAID websiteUSA55
  • There was no difference between decision aid group and control group in improvements to knowledge, satisfaction, preparation, and decisional conflict
Politi 2020 [32]A Randomized Controlled Trial Evaluating the BREASTChoice Tool for Personalized Decision Support About Breast Reconstruction After MastectomyBREASTChoice websiteUSA376
  • Decision aid led to greater knowledge and confidence than the control
Sherman 2017 [33]Facilitating decision-making in women undergoing genetic testing for hereditary breast cancer: BRECONDA randomized controlled trial resultsBRECONDA websiteAustralia64
  • Decision aid was associated with decrease in decision conflict and increase in knowledge and satisfaction
Sherman 2016 [34]Reducing Decisional Conflict and Enhancing Satisfaction with Information among Women Considering Breast Reconstruction following Mastectomy: Results from the BRECONDA Randomized Controlled TrialBRECONDA websiteAustralia222
  • Decision aid was associated with decrease in decision conflict and increase in knowledge and satisfaction
Ventura 2016 [28]Challenges of evaluating a computer-based educational programme for women diagnosed with early-stage breast cancer: a randomised controlled trialSIRI websiteSweden226
  • Intervention had no significant effect on self-efficacy, health participation, anxiety, or depression
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Kirsch, E.P.; Kunte, S.A.; Wu, K.A.; Kaplan, S.; Hwang, E.S.; Plichta, J.K.; Lad, S.P. Digital Health Platforms for Breast Cancer Care: A Scoping Review. J. Clin. Med. 2024, 13, 1937. https://doi.org/10.3390/jcm13071937

AMA Style

Kirsch EP, Kunte SA, Wu KA, Kaplan S, Hwang ES, Plichta JK, Lad SP. Digital Health Platforms for Breast Cancer Care: A Scoping Review. Journal of Clinical Medicine. 2024; 13(7):1937. https://doi.org/10.3390/jcm13071937

Chicago/Turabian Style

Kirsch, Elayna P., Sameer A. Kunte, Kevin A. Wu, Samantha Kaplan, E. Shelley Hwang, Jennifer K. Plichta, and Shivanand P. Lad. 2024. "Digital Health Platforms for Breast Cancer Care: A Scoping Review" Journal of Clinical Medicine 13, no. 7: 1937. https://doi.org/10.3390/jcm13071937

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