Mobile Apps and Quality of Life in Patients With Breast Cancer and Survivors: Systematic Literature Review

Background Side effects of breast cancer treatment may persist long into survivorship, reducing quality of life (QOL) in patients with breast cancer and survivors. There is growing evidence for the use of digital health technologies, such as mobile apps, to support self-management, decrease symptom burden, and improve QOL in patients with cancer. However, an updated overview of the effects of mobile apps on QOL and well-being in patients with breast cancer and survivors is needed. Objective The aim of this review was to provide an overview of breast cancer–specific, mobile app–driven lifestyle or behavioral interventions in patient care through to survivorship and their impact on QOL and mental well-being. Methods A systematic search of PubMed, Scopus, and Web of Science was conducted to identify relevant studies. The inclusion criteria were limited to original studies involving a trial of a mobile app–driven lifestyle or behavioral intervention for patients with breast cancer or survivors and using QOL or well-being measures. The results of the studies that met the inclusion criterion were then synthesized in text and table format. The quality of the evidence was assessed with the Cochrane risk-of-bias tool. Results A total of 17 studies with the number of participants ranging from 23 to 356 met the inclusion criterion. Of the 17 reviewed studies, 7 (41%) delivered an app-only intervention, and 10 (59%) combined an app with additional supporting materials, such as SMS text messaging, telecoaching, wearables, or printed materials. Among the 17 reviewed studies, 6 (35%) focused on aiding patients with breast cancer during the active treatment phase (excluding ongoing hormone therapy), whereas the remaining 11 (65%) focused on survivorship. The majority of the studies (14/17, 82%) observed some positive effects on QOL or well-being measures. Conclusions The results of the review indicate that mobile apps are a promising avenue for improving QOL and well-being in breast cancer care. Positive effects were observed in patients undergoing active treatment in all reviewed studies, but effects were less clear after chemotherapy and in long-term survivors. Although lifestyle and behavioral digital interventions are still being developed, and further research should still be pursued, the available data suggest that current mobile health apps aid patients with breast cancer and survivors.

Selection process 8 Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.
p 4-5 (selection of studies Data collection process 9 Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process. p 5 (selection of studies + data extraction) Data items 10a List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect.
p 5 (selection of studies) 10b List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information.
p 5 (selection of studies) Study risk of bias assessment 11 Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.
p 5 (quality assessmen t) Effect measures 12 Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results. p 5 (selection of studies) Synthesis methods 13a Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).

Ite m # Checklist item
Location where item is reported of studies) 13b Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.

NA
13c Describe any methods used to tabulate or visually display results of individual studies and syntheses. NA 13d Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used.

NA
13e Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression). NA 13f Describe any sensitivity analyses conducted to assess robustness of the synthesized results. NA

Reporting bias assessment
14 Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases). NA

Certainty assessment
15 Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome. NA

Study selection
16a Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.
p 5-6 (study selection + figure 1) 16b Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded. p 5-6 (study selection) For more information, visit: http://www.prisma-statement.org/