The field of digital health is rapidly evolving and expanding, powering a paradigm shift in evidence generation for clinical development and care delivery. New insights enabled by the continuous capture of multimodal data have the potential to drive a deeper understanding of patients’ daily life experience and more personalized medicines. Novel data science approaches and machine learning applications are pioneering the conversion of multimodal data on physical activity, sleep, vital signs, cognitive status as well as contextual information, into measures for symptoms and factors associated with health-related quality of life factors such as fatigue, stress, and depression.
Recent advances in multiple aspects of digital health and wellness are setting the basis for the development of better personalized treatment, incorporating digital and behavioral phenotyping with molecular endotyping.
Relevant aspects to these advances are:
• Cutting-edge biosensors and multi-sensor wearable devices
• Digital health solutions including remote clinical trials and digital therapeutics
• Computational approaches to multimodal health data with focus on developing novel outcomes, patient stratification or predictive models of disease progression
• Holistic solutions for personalized and precision medicine, e.g. endophenotyping of diseases.
Ultimately, by sharing methodologies, results and data, we aim to lower the remaining barriers to real world adoption of digital health solutions.
This research topic welcomes original contribution papers related to digital health sensing technology, solutions, and data science research on multimodal digital data to advance personalized medicine.
Relevant topics include, but are not limited to:
• Feasibility and exploratory work on novel multimodal digital data collection
• Application of machine learning to multimodal data to derive novel digital measures and predictive models
• Validation and evaluation of multimodal digital measures
• Qualification and acceptance of multimodal digital measures
• Application of multimodal digital measures to clinical development
• Application of multimodal digital measures to care delivery
• Mixed method research defining unmet need for novel multimodal digital measures
• Novel data science techniques, tools, systems, or applications for solving challenges in multimodal digital data collection, processing, and personalized clinical applications.
The field of digital health is rapidly evolving and expanding, powering a paradigm shift in evidence generation for clinical development and care delivery. New insights enabled by the continuous capture of multimodal data have the potential to drive a deeper understanding of patients’ daily life experience and more personalized medicines. Novel data science approaches and machine learning applications are pioneering the conversion of multimodal data on physical activity, sleep, vital signs, cognitive status as well as contextual information, into measures for symptoms and factors associated with health-related quality of life factors such as fatigue, stress, and depression.
Recent advances in multiple aspects of digital health and wellness are setting the basis for the development of better personalized treatment, incorporating digital and behavioral phenotyping with molecular endotyping.
Relevant aspects to these advances are:
• Cutting-edge biosensors and multi-sensor wearable devices
• Digital health solutions including remote clinical trials and digital therapeutics
• Computational approaches to multimodal health data with focus on developing novel outcomes, patient stratification or predictive models of disease progression
• Holistic solutions for personalized and precision medicine, e.g. endophenotyping of diseases.
Ultimately, by sharing methodologies, results and data, we aim to lower the remaining barriers to real world adoption of digital health solutions.
This research topic welcomes original contribution papers related to digital health sensing technology, solutions, and data science research on multimodal digital data to advance personalized medicine.
Relevant topics include, but are not limited to:
• Feasibility and exploratory work on novel multimodal digital data collection
• Application of machine learning to multimodal data to derive novel digital measures and predictive models
• Validation and evaluation of multimodal digital measures
• Qualification and acceptance of multimodal digital measures
• Application of multimodal digital measures to clinical development
• Application of multimodal digital measures to care delivery
• Mixed method research defining unmet need for novel multimodal digital measures
• Novel data science techniques, tools, systems, or applications for solving challenges in multimodal digital data collection, processing, and personalized clinical applications.