Background:The cut-off values of walking velocity and classification of functional mobility both have a role in clinical settings for assessing the walking function of stroke patients and setting rehabilitation goals and treatment plans.Objective:The present study investigated whether the cut-off values of the modified Rivermead Mobility Index (mRMI) and walking velocity accurately differentiated the walking ability of stroke patients according to the modified Functional Ambulation Category (mFAC).Methods:Eighty two chronic stroke patients were included in the study. The comfortable/maximum walking velocities and mRMI were used to measure the mobility outcomes of these patients. To compare the walking velocities and mRMI scores for each mFAC point, one-way analysis of variance and the post-hoc test using Scheffe’s method were performed. The patients were categorized according to gait ability into either mFAC=VII or mFAC ≤ VI group. The cut-off values for mRMI and walking velocities were calculated using a receiver-operating characteristic curve. The odds ratios of logistic regression analysis (Wald Forward) were analyzed to examine whether the cut-off values of walking velocity and mRMI can be utilized to differentiate functional walking levels.Results:Except for mFACs III and IV, maximum walking velocity differed between mFAC IV and mFAC V (p<0.01), between mFAC V and mFAC VI (p<0.001), and between mFAC VI and mFAC VII (p<0.05). The cut-off value of mRMI is >26.5 and the area under the curve is 0.87, respectively; the cut-off value for comfortable walking velocity is >0.77 m/s and the area under the curve is 0.92, respectively; also, the cut-off value for maximum walking velocity is >0.92 m/s and the area under the curve is 0.97, respectively. In the logistic regression analysis, the maximum walking velocity (>0.92 m/s, OR=22.027) and mRMI (>26.5 scores, OR=10.283) are able to distinguish mFAC=VII from mFAC ≤ VI.Conclusion:The cut-off values of maximum walking velocity and mRMI are recommended as useful outcome measures for assessing ambulation levels in chronic stroke patients during rehabilitation.

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.

This study investigated the feasibility and usability of a personalized mobile health (mHealth) app for self-management during the year following breast cancer surgery. Twenty-nine participants were instructed to use an app and smart band immediately after discharge. Only 18 completed the study. Their perceived necessity and satisfaction for main domains and app were assessed at 1, 2, 4, 6, 9, and 12 months. A self-reporting questionnaire assessed usability at 12 months. Consequently, retention rate as measures of feasibility showed a mean of 75.8%. Exercise and diet management were the most accessed app domains. Perceived necessity was higher than satisfaction. The mean usability score was 80.2. Most participants found the app useful and effective as a delivery for healthcare. Further, 94% of them were willing to pay for and recommend it. Thus, mHealth app can help breast cancer patients improve their healthy behaviors and healthcare further. This study provides insights for designing long-term randomized controlled trials using mHealth interventions.

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.

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