Features of Cancer mHealth Apps and Evidence for Patient Preferences: Scoping Literature Review

**Background:** Cancer is increasingly managed as a chronic disease, and oral anticancer therapies have shifted treatment responsibility to patients outside clinical settings. Mobile health (mHealth) apps offer potential support for medication adherence, symptom tracking, and disease self-management. While prior reviews cataloged cancer mHealth apps, none specifically addressed patient preferences for app features. This scoping review aimed to describe the features and functions of mHealth apps designed for cancer self-management.

**Methods:** A scoping review methodology was adopted following Arksey and O'Malley, PRISMA-ScR, and Joanna Briggs Institute guidelines. Four databases (PubMed/MEDLINE, Embase, CINAHL, PsycINFO) were searched between February 1 and April 1, 2021. Citation/reference searches and gray literature searches were also conducted. Inclusion criteria covered manuscripts on patient preference, utilization, or utility studies for cancer self-management mHealth apps, including gray literature. Exclusion criteria included non-English manuscripts, pediatric studies, studies focused on prevention/diagnosis/palliative care/survivorship, and publications before 2010. Two independent reviewers screened titles/abstracts and full texts, with a third reviewer for arbitration. Data extracted included author, title, publication date, study design, sampling type, cancer type, treatment, age, features, availability/cost, design input, and patient preferences.

**Key Results:** The initial search identified 611 manuscripts; after removing duplicates, 522 underwent title/abstract review, 51 underwent full-text review, and 7 studies were included. Study designs included 5 descriptive studies (feasibility studies, usability tests), 1 experimental study, and 1 quasi-experimental study. Publication dates ranged from 2017 to 2021. Sample sizes ranged from 11 to 181 participants. Four apps targeted single cancer types (breast, gastrointestinal, oral, prostate), while others served diverse cancer populations. Mean participant age ranged from 52 to 57 years where reported. App features per study ranged from 2 to 11 (median 4). The most reported feature was a symptom/side effect tracker (6 of 7 studies). Other common features included reminders (4 studies), education (3 studies), health/fitness tracking (3 studies), and medication trackers (2 studies). Five features were unique to single apps: notes/questions, privacy/data use notices, personalized dosing schedules, vital sign tracking, and weight tracking. Two apps were free and publicly available; 2 were restricted to study participants; 3 did not specify availability. Three apps included patients and healthcare providers in design; 1 used IT/communications experts; 3 did not specify. Acceptability was measured in several ways: Birkhoff et al reported an overall usability score of 4.69/7 (higher among high school–educated patients at 6.38 vs graduate degree–educated at 3.87). Jacobs et al reported a usefulness score of 4.2/5. Wang et al reported significant increases from baseline to postintervention in intention to use (2.54 to 3.02), perceived usefulness (2.52 to 2.95), and perceived ease of use (2.32 to 3.01). Qualitative findings from Kongshaug et al noted patients valued reassurance about correct oral chemo treatment and used the app as a memory tool. Tran et al reported patients valued emotional/well-being support over symptom reporting and requested community support features.

**Clinical Implications:** Symptom tracking, education, and medication tracking are the most frequently offered features in cancer mHealth apps, but patient input into design is limited. The review identifies a critical need to systematically assess patient preferences for app features to improve engagement and self-management outcomes. Access barriers persist, with many apps restricted to specific institutions or removed after trials. Future research should use methods like discrete choice experiments to quantify patient preferences and inform app development that better meets patient needs for disease self-management.