Background Although many mobile health (mHealth) apps have evolved as support tools for self-management of breast cancer, limited studies have developed a comprehensive app and described the algorithms for personalized rehabilitation throughout the breast cancer care continuum. Objective This study aimed to develop a comprehensive mobile app and to describe an algorithm that adjusts personalized content to facilitate self-management throughout the breast cancer care continuum. Methods The development process of the modular mHealth app included the following 4 steps: (1) organizing expert teams, (2) defining evidence-based fundamental content and modules, (3) classifying user information for algorithms to personalize the content, and (4) creating the app platform and connectivity to digital health care devices. Results We developed a modular mHealth app service, which took 18 months, including a review of related literature and guidelines and the development of the app and connectivity to digital health care devices. A total of 11 functionalities were defined in the app with weekly analysis. The user information classification was formulated for personalized rehabilitation according to 5 key criteria: general user information, breast operation type, lymph node surgery type, chemotherapy and hormonal therapy use, and change in treatment after surgery. The main modules for personalized content included a self-monitoring screen, personalized health information, personalized exercise, and diet management. Conclusions The strength of this study was the development of a comprehensive mHealth app and algorithms to adjust content based on user medical information for personalized rehabilitation during the breast cancer care continuum.

BACKGROUND Although many mobile health (mHealth) apps as a support tool for self-management of breast cancer have evolved, limited studies have developed a comprehensive app and described the algorithms for personalized rehabilitation in the breast cancer care continuum. OBJECTIVE This study aimed to develop a comprehensive mobile app and an algorithm that adjust personalized content to facilitate self-management throughout the breast cancer care continuum. METHODS The development process of the modular app included the following six steps: (1) organizing expert teams, (2) defining evidence-based fundamental content and modules, (3) classifying user cases according to surgery type and treatment, (4) describing algorithms of the key modules for personalized content, (5) constructing the app platform and connectivity to digital health care devices, and (6) conducting a user evaluation of the key modules using needs and satisfaction questionnaires after app testing. RESULTS The expert teams developed algorithms and the mHealth app to dynamically adjust the content for individual patients throughout the breast cancer care continuum. The review of the related literature, and guidelines and the development of the app took 18 months. User evaluation outcomes were positive, with needs and satisfaction questionnaire scores of 4.4 and 4.1 of 5, respectively. The ranking results demonstrated that personalized exercise was the most needed feature with the highest satisfaction score. CONCLUSIONS The strength of this study was the development of a comprehensive mHealth app and algorithms for adjusting content based on user medical information for personalized rehabilitation during the breast cancer care continuum and obtaining positive user evaluation results.

BACKGROUND Exercise is predicted to have a positive effect among hepatocellular carcinoma (HCC) patients. However, these patients are hesitant to start and build up an exercise program for one major reason: the vague fear of developing hepatic decompensation, a potentially fatal condition that can lead to death. Integrating mobile health (mHealth) with individualized exercise programs could be a possible option for promoting physical capacity among HCC patients. OBJECTIVE The aim of this study was to evaluate the efficacy and safety of rehabilitation exercises, which have been individually prescribed via an mHealth app, on physical fitness, body composition, biochemical profile, and quality of life among HCC patients. METHODS A total of 37 HCC patients were enrolled in a 12-week course with an mHealth app program targeted to HCC patients. The wearable wristband device Neofit (Partron Co) was provided to participants, and recorded daily physical data, such as the number of steps, calorie expenditure, exercise time, and heart rate. Each participant was given an individualized rehabilitation exercise program that was prescribed and adjusted at the 6-week midintervention period based on the assessment results. At baseline, 6-week, and 12-week sessions, participants’ physical fitness levels (ie, 6-minute walk test, grip strength test, and 30-second chair stand test) were measured. Physical activity levels, as measured by the International Physical Activity Questionnaire-Short Form (IPAQ-SF); body composition (ie, body mass index, body fat percentage, and muscle mass); biochemical profiles; and quality of life, as measured by the European Organization for Research and Treatment of Cancer Quality-of-Life Questionnaire C30, were assessed at baseline and at the end point. At the 6-week midpoint, exercise intensity was individually adjusted. RESULTS Of the 37 patients, 31 (84%) completed the 12-week intervention. Grip strength improved significantly after 12 weeks of the intervention. The 30-second chair stand test and the 6-minute walk test showed significant improvement from 0 to 6 weeks, from 0 to 12 weeks, and from 6 to 12 weeks. Muscle mass and the IPAQ-SF score increased significantly after 12 weeks of the intervention without biochemical deterioration. CONCLUSIONS Following 12 weeks of mHealth care, including an individually prescribed rehabilitation exercise program, we saw significant improvements in physical fitness, body composition, and physical activity without any complication or biochemical deterioration among compensated HCC patients who had completed therapy.