Background: Digital care has become an essential component of health care. Interventions for patients with cancer need to be effective and safe, and digital health interventions must adhere to the same requirements. Objective: The purpose of this study was to identify currently available digital health interventions developed and evaluated in randomized controlled trials (RCTs) targeting adult patients with cancer. Methods: A scoping review using the JBI methodology was conducted. The participants were adult patients with cancer, and the concept was digital health interventions. The context was open, and sources were limited to RCT effectiveness studies. The PubMed, CINAHL, Embase, Cochrane Library, Research Information Sharing Service, and KoreaMed databases were searched. Data were extracted and analyzed to achieve summarized results about the participants, types, functions, and outcomes of digital health interventions. Results: A total of 231 studies were reviewed. Digital health interventions were used mostly at home (187/231, 81%), and the web-based intervention was the most frequently used intervention modality (116/231, 50.2%). Interventions consisting of multiple functional components were most frequently identified (69/231, 29.9%), followed by those with the self-manage function (67/231, 29%). Web-based interventions targeting symptoms with the self-manage and multiple functions and web-based interventions to treat cognitive function and fear of cancer recurrence consistently achieved positive outcomes. More studies supported the positive effects of web-based interventions to inform decision-making and knowledge. The effectiveness of digital health interventions targeting anxiety, depression, distress, fatigue, health-related quality of life or quality of life, pain, physical activity, and sleep was subject to their type and function. A relatively small number of digital health interventions specifically targeted older adults (6/231, 2.6%) or patients with advanced or metastatic cancer (22/231, 9.5%). Conclusions: This scoping review summarized digital health interventions developed and evaluated in RCTs involving adult patients with cancer. Systematic reviews of the identified digital interventions are strongly recommended to integrate digital health interventions into clinical practice. The identified gaps in digital health interventions for cancer care need to be reflected in future digital health research.

Background: Because the recurrence rate of stroke increases 1 year after its initial occurrence, it is important to predict quality of life at this stage, although most functions are likely recovered. Objectives: We aimed to construct and verify a model of quality of life in patients 1 year post stroke by considering their characteristics at the time of the stroke based on the Wilson and Cleary model and previous literature. Methods: Participants comprised 288 patients who had experienced their first ischemic stroke and were enrolled in 3 regional stroke centers in South Korea. Data were analyzed using path analysis to identify a model of patients' quality of life 1 year post stroke. Results: Age, National Institutes of Health Stroke Scale score, the modified Rankin Scale score, and anxiety had a direct effect on quality of life, whereas type D personality and social support had an indirect effect. Type D personality, age, National Institutes of Health Stroke Scale score, social support, the modified Rankin Scale score, and anxiety explained 32.9% of the total variance in quality of life. Conclusions: When predicting quality of life in a patient 1 year after a stroke, it is important to consider variables such as type D personality, age, National Institutes of Health Stroke Scale score, social support, the modified Rankin Scale score, and anxiety at the time of the first stroke. Interventions to improve the quality of life of patients with stroke should consider these factors.