This scoping review aimed to identify and synthesize the literature related to patient-generated health data (PGHD) among older adults with cancer in home setting. Of the 1,090 articles extracted through six databases searches, 53 were selected. Studies were published from 2007 to 2022 and the types of devices to generate PGHD included research-grade and consumer-grade wearable devices. PGHD was assessed for physical activity, vital signs, and sleep. PGHD utilization was categorized: 1) identification, monitoring, review, and analysis (100%); 2) feedback and information report (32.1%); 3) motivation (26.4%); and 4) education and coaching (17.0%). Our study reveals that various PGHDs from older adults with cancer are mainly collected passively, with limited use for interaction with healthcare providers. These results may provide valuable insights for healthcare providers into potential PGHD applications in geriatric cancer care.

A As health technology advances, this study aims to develop an innovative nutritional intake management system that integrates artificial intelligence technology and social media software to achieve precise analysis of patient-generated data and comprehensive management in continuous care. Our system is built on the Line Bot platform, allowing users to easily and intuitively obtain detailed analyses of their individual nutritional intake by reporting dietary information. While users report their dietary habits through the Line Bot, our AI model conducts real-time analysis of nutrient intake, providing personalized nutritional recommendations. This instantaneous feedback not only enhances user engagement in nutritional management but also aids in establishing healthy habits. Additionally, through integration with social media software, our system facilitates information sharing and community support among users, promoting the exchange of nutritional knowledge and mutual assistance. This study further explores the specific needs of patients with chronic diseases, collecting individual data on chronic conditions and total nutritional intake. Based on the nutritional intake guidelines proposed by the Health Promotion Administration in Taiwan, more precise nutritional management recommendations are provided to meet the unique health needs of each patient. This study introduces a comprehensive, patient-generated data-based approach for precision nutrition management in continuous care. By integrating artificial intelligence, social media software, and data analysis, our system not only offers effective tools for monitoring and managing patients\' nutritional intake but also fosters interaction and support among patients, driving the implementation of continuous care practices.

OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.
METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors\' experience; and (4) validation of the models by a 26-member steering committee.
CONCLUSIONS: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices.
CONCLUSIONS: Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

Mogamulizumab is a humanized antibody targeting CC chemokine receptor 4 (CCR4). This post-marketing surveillance was conducted in Japan as a regulatory requirement from 2014 to 2020 to ensure the safety and effectiveness of mogamulizumab in patients with relapsed or refractory (r/r) CCR4-positive peripheral T-cell lymphoma (PTCL) or r/r cutaneous T-cell lymphoma (CTCL). Safety and effectiveness data were collected for up to 31 weeks after treatment initiation. A total of 142 patients were registered; safety was evaluated in 136 patients. The median number of doses was 8.0 (range, 1-18). The main reasons for treatment termination were insufficient response (22.1%) and adverse events (13.2%). The frequency of any grade adverse drug reaction was 57.4%, including skin disorders (26.5%), infections and immune system disorders (16.2%), and infusion-related reactions (13.2%). Graft-versus-host disease, grade 2, developed in one of two patients who underwent allogeneic-hematopoietic stem cell transplantation after receiving mogamulizumab. Effectiveness was evaluated in 131 patients (103 with PTCL; 28 with CTCL). The best overall response rate was 45.8% (PTCL, 47.6%; CTCL, 39.3%). At week 31, the survival rate was 69.0% (95% confidence interval, 59.8%-76.5%) [PTCL, 64.4% (54.0%-73.0%); CTCL, 90.5% (67.0%-97.5%)]. Safety and effectiveness were comparable between patients <70 and ≥ 70 years old and between those with relapsed and refractory disease. The safety and effectiveness of mogamulizumab for PTCL and CTCL in the real world were comparable with the data reported in previous clinical trials. Clinical Trial Registration.

BACKGROUND: Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients\' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.
OBJECTIVE: This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.
METHODS: A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.
RESULTS: The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients\' devices. PGHD about patients\' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.
CONCLUSIONS: PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.
UNASSIGNED: RR2-10.2196/39389.

BACKGROUND: The increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology.
OBJECTIVE: In this scoping review, we aimed to (1) identify the different areas of women\'s health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women\'s adoption and use of connected health devices.
METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting.
RESULTS: We included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women\'s use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior.
CONCLUSIONS: Leading up to the COVID-19 pandemic, the adoption of digital health tools to address women\'s health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women\'s health research and highlights opportunities for digital technology development in other women\'s health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women\'s personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women\'s health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result.
UNASSIGNED: RR2-10.2196/26110.

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Building an integrated data model that includes not only clinical data but also personal health records has become increasingly important. We aimed to build a big data healthcare platform by developing a common data model that can be utilized in the healthcare field. To this end, we acquired health data from various communities to establish community care digital healthcare service models. Further, to improve personal health data interoperability, we ensured conformance to international standards, namely, the Systemized Nomenclature of Medicine Clinical Terms (SNOMED-CT) and transmission standards, namely, Health Level 7 Fast Healthcare Interoperability Resource (HL7 FHIR). Furthermore, FHIR resource profiling was designed to transmit and receive data, following the HL7 FHIR R4 guidelines.

Quality of life (QoL) is affected by environmental influences and varies between patients. A combined measurement through Patient Reported Outcomes (PROs) and Patient Generated Data (PGD) may enhance the detection of QoL impairments by a longitudinal survey. Leveraging different approaches of QoL measurement techniques, the challenge is to combine data in a standardized, interoperable way. We developed an app (Lion-App) to semantically annotate data from sensor systems as well as PROs to be merged in an overall analysis of QoL. A FHIR implementation guide was defined for a standardized assessment. To access sensor data the interfaces of Apple Health or Google Fit are used instead of integrating various provider directly into the system. Since QoL cannot be collected exclusively via sensor values, a combination of PROs and PGD is necessary. PGD enable a progression of QoL which offers more insight into personal limitations whereas PROs give insight about personal burden. The use of FHIR enables structured exchange of data while personalized analyses might improve therapy and outcome.

People with diabetes-related foot ulcers (DFUs) need to perform self-care consistently over many months to promote healing and to mitigate risks of hospitalisation and amputation. However, during that time, improvement in their DFU can be hard to detect. Hence, there is a need for an accessible method to self-monitor DFUs at home. We developed a new mobile phone app, \"MyFootCare\", to self-monitor DFU healing progression from photos of the foot. The aim of this study is to evaluate the engagement and perceived value of MyFootCare for people with a plantar DFU over 3 months\' duration. Data are collected through app log data and semi-structured interviews (weeks 0, 3, and 12) and analysed through descriptive statistics and thematic analysis. Ten out of 12 participants perceive MyFootCare as valuable to monitor progress and to reflect on events that affected self-care, and seven participants see it as potentially valuable to enhance consultations. Three app engagement patterns emerge: continuous, temporary, and failed engagement. These patterns highlight enablers for self-monitoring (such as having MyFootCare installed on the participant\'s phone) and barriers (such as usability issues and lack of healing progress). We conclude that while many people with DFUs perceive app-based self-monitoring as valuable, actual engagement can be achieved for some but not for all people because of various facilitators and barriers. Further research should target improving usability, accuracy and sharing with healthcare professionals and test clinical outcomes when using the app.