TY - JOUR AU - Patel, Smit AU - Goldsack, C. Jennifer AU - Cordovano, Grace AU - Downing, Andrea AU - Fields, K. Karen AU - Geoghegan, Cindy AU - Grewal, Upinder AU - Nieva, Jorge AU - Patel, Nikunj AU - Rollison, E. Dana AU - Sah, Archana AU - Said, Maya AU - Van De Keere, Isabel AU - Way, Amanda AU - Wolff-Hughes, L. Dana AU - Wood, A. William AU - Robinson, J. Edmondo PY - 2023/1/4 TI - Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care JO - J Med Internet Res SP - e43404 VL - 25 KW - digital health KW - innovation KW - oncology KW - cancer care KW - cancer KW - patient journey KW - digital transformation KW - digital divide KW - health care delivery KW - service delivery KW - equity KW - patient-reported outcome KW - PROM KW - biomarker KW - digital innovation UR - https://www.jmir.org/2023/1/e43404 UR - http://dx.doi.org/10.2196/43404 UR - http://www.ncbi.nlm.nih.gov/pubmed/36598811 ID - info:doi/10.2196/43404 ER - TY - JOUR AU - Clay, Ieuan AU - Cormack, Francesca AU - Fedor, Szymon AU - Foschini, Luca AU - Gentile, Giovanni AU - van Hoof, Chris AU - Kumar, Priya AU - Lipsmeier, Florian AU - Sano, Akane AU - Smarr, Benjamin AU - Vandendriessche, Benjamin AU - De Luca, Valeria PY - 2022/5/26 TI - Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint JO - J Med Internet Res SP - e35951 VL - 24 IS - 5 KW - digital measures KW - quality of life KW - machine learning KW - digital health KW - digital product KW - digital wellness KW - digital therapeutics KW - digital therapy KW - multimodal technology KW - drug development KW - care delivery KW - data integration UR - https://www.jmir.org/2022/5/e35951 UR - http://dx.doi.org/10.2196/35951 UR - http://www.ncbi.nlm.nih.gov/pubmed/35617003 ID - info:doi/10.2196/35951 ER - TY - JOUR AU - Clay, Ieuan AU - Angelopoulos, Christian AU - Bailey, Lord Anne AU - Blocker, Aaron AU - Carini, Simona AU - Carvajal, Rodrigo AU - Drummond, David AU - McManus, F. Kimberly AU - Oakley-Girvan, Ingrid AU - Patel, B. Krupal AU - Szepietowski, Phillip AU - Goldsack, C. Jennifer PY - 2021/11/9 TI - Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices JO - J Med Internet Res SP - e34493 VL - 23 IS - 11 KW - digital measures KW - data integration KW - patient centricity KW - utility UR - https://www.jmir.org/2021/11/e34493 UR - http://dx.doi.org/10.2196/34493 UR - http://www.ncbi.nlm.nih.gov/pubmed/34751656 ID - info:doi/10.2196/34493 ER - TY - JOUR AU - Shandhi, Hasan Md Mobashir AU - Goldsack, C. Jennifer AU - Ryan, Kyle AU - Bennion, Alexandra AU - Kotla, V. Aditya AU - Feng, Alina AU - Jiang, Yihang AU - Wang, Ke Will AU - Hurst, Tina AU - Patena, John AU - Carini, Simona AU - Chung, Jeanne AU - Dunn, Jessilyn PY - 2021/9/15 TI - Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review JO - J Med Internet Res SP - e29875 VL - 23 IS - 9 KW - digital clinical measures KW - academic research KW - funding KW - biosensor KW - digital measures KW - digital health KW - health outcomes N2 - Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized. Objective: We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research. Methods: We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types. Results: The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded. Conclusions: Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust. UR - https://www.jmir.org/2021/9/e29875 UR - http://dx.doi.org/10.2196/29875 UR - http://www.ncbi.nlm.nih.gov/pubmed/34524089 ID - info:doi/10.2196/29875 ER - TY - JOUR AU - Bent, Brinnae AU - Sim, Ida AU - Dunn, P. Jessilyn PY - 2021/2/3 TI - Digital Medicine Community Perspectives and Challenges: Survey Study JO - JMIR Mhealth Uhealth SP - e24570 VL - 9 IS - 2 KW - digital medicine KW - digital health KW - interoperability KW - mHealth KW - wearables KW - sensors N2 - Background: The field of digital medicine has seen rapid growth over the past decade. With this unfettered growth, challenges surrounding interoperability have emerged as a critical barrier to translating digital medicine into practice. In order to understand how to mitigate challenges in digital medicine research and practice, this community must understand the landscape of digital medicine professionals, which digital medicine tools are being used and how, and user perspectives on current challenges in the field of digital medicine. Objective: The primary objective of this study is to provide information to the digital medicine community that is working to establish frameworks and best practices for interoperability in digital medicine. We sought to learn about the background of digital medicine professionals and determine which sensors and file types are being used most commonly in digital medicine research. We also sought to understand perspectives on digital medicine interoperability. Methods: We used a web-based survey to query a total of 56 digital medicine professionals from May 1, 2020, to July 10, 2020, on their educational and work experience, the sensors, file types, and toolkits they use professionally, and their perspectives on interoperability in digital medicine. Results: We determined that the digital medicine community comes from diverse educational backgrounds and uses a variety of sensors and file types. Sensors measuring physical activity and the cardiovascular system are the most frequently used, and smartphones continue to be the dominant source of digital health information collection in the digital medicine community. We show that there is not a general consensus on file types in digital medicine, and data are currently handled in multiple ways. There is consensus that interoperability is a critical impediment in digital medicine, with 93% (52) of survey respondents in agreement. However, only 36% (20) of respondents currently use tools for interoperability in digital medicine. We identified three key interoperability needs to be met: integration with electronic health records, implementation of standard data schemas, and standard and verifiable methods for digital medicine research. We show that digital medicine professionals are eager to adopt new tools to solve interoperability problems, and we suggest tools to support digital medicine interoperability. Conclusions: Understanding the digital medicine community, the sensors and file types they use, and their perspectives on interoperability will enable the development and implementation of solutions that fill critical interoperability gaps in digital medicine. The challenges to interoperability outlined by this study will drive the next steps in creating an interoperable digital medicine community. Establishing best practices to address these challenges and employing platforms for digital medicine interoperability will be essential to furthering the field of digital medicine. UR - http://mhealth.jmir.org/2021/2/e24570/ UR - http://dx.doi.org/10.2196/24570 UR - http://www.ncbi.nlm.nih.gov/pubmed/33533721 ID - info:doi/10.2196/24570 ER -